Daily Archives: January 13, 2025

Abstract-This article delves into the concept of transcultural adaptations in visual arts, a practice that involves blending diverse cultural traditions, aesthetics, and ideas to create innovative and boundary-defying works. From historical exchanges along trade routes to the digital innovations of the contemporary era, transcultural art reflects the dynamic interplay of cultures across time. While celebrating creativity and hybridity, it also navigates critical ethical questions surrounding cultural sensitivity, power dynamics, and authenticity. By examining its historical roots, modern practices, and societal impact, this piece highlights the role of transcultural art in fostering dialogue, empathy, and inclusivity, ultimately enriching the global artistic landscape.

Abstract-Polyvinyl alcohol (PVA) nanocomposites reinforced with titanium dioxide (TiO2) nanoparticles have garnered significant attention due to their unique properties and potential applications. In this study, we investigated the impact of TiO2 incorporation on the structural characteristics and thermal stability of PVA-matrix-based nanocomposites. The PVA polymer nanocomposite films were prepared using a solution casting method. The structural studies of the prepared films were characterized via X-ray diffraction (XRD), transmission electron microscopy (TEM). Moreover, the thermal properties of the prepared films were characterized by DSC, TGA and DTA. The addition of TiO2 nanoparticles induces structural changes in the PVA matrix. TEM studies showed that a PVA polymer surrounds TiO2 in its entirety. The PVA-TiO2 nanostructure is the same as the structure of a core-shell nanostructure. TiO2-doped PVA nanocomposites exhibited improved thermal stability. Thermogravimetric analysis of the nanocomposite films demonstrated enhanced resistance to thermal degradation. DSC analysis of the PVA-TiO2 nanocomposite films revealed that the glass transition temperature (Tg) and melting temperature (Tm) were 141°C and 265°C, respectively, for the 8 wt.% TiO2-incorporated PVA-TiO2 nanocomposites. The TGA and DTA studies of these nanocomposites revealed that their degradation behavior follows a four-step process. In comparison to those of pure PVA, these composites exhibit a sluggish decomposition rate, suggesting that the better thermal stability of these composites can be attributed to the better interaction among the -OH functional groups of PVA and TiO2 nanoparticles. These nanocomposites hold promise for various applications, including coatings, sensors, and optoelectronic devices. The combined effects of structural reinforcement and thermal stability make these materials attractive for engineering applications.

Abstract-This study aimed to investigate the potential of durian husk, durian leaf litter, and banana pseudo stem as bio-leather. The bio leather was made from durian husk, durian leaf litter and banana pseudo stem. The bio leather made from these materials were tested in terms of its thickness, elongation and tensile strength. Also, as comparison the synthetic leather was tested according to its thickness, elongation and compression strength. The tests were performed at TERMS Concrete and Materials Testing Laboratory, Inc. Data were analyzed using mean and Mann Whitney U test. Results showed that, bio leather can be used to make light weight wallets since it only requires less thickness, low percentage of elongation and low tensile strength. For synthetic leather, can be used to make bags since the values of the indicators are high. The bio leather and synthetic leather do not significantly differ in terms of thickness, elongation and tensile strength; therefore, bio leather can be a good substitute for synthetic leather in making valuable items with high economic value.

Abstract-This article explores the intersection of reception theory and the value of adaptation in literature, visual arts, music, and theater. Reception theory, pioneered by Hans Robert Jauss, shifts the focus from the creator to the audience, emphasizing the evolving cultural and personal contexts that shape how stories are interpreted. Adaptations serve as transformative dialogues between the original work, its reimagining, and contemporary audiences, ensuring stories remain relevant across time and space. Examples such as Jean Rhys’s Wide Sargasso Sea, Alfred Hitchcock’s Rebecca, and Lin-Manuel Miranda’s Hamilton illustrate how adaptations reframe narratives to address new perspectives, cultural dynamics, and societal values. This article highlights how the reinterpretation of familiar tales enriches their meaning, engages diverse audiences, and underscores the timeless power of storytelling. By applying reception theory, the article demonstrates that the true value of adaptations lies in their ability to connect, challenge, and inspire audiences across generations.

Abstract-This study aimed to develop a prototype of an automated smart plant watering system for urban home gardening, focusing on the reliability and functionality of its monitoring, notification, and water dispensing features. The system incorporated components such as the Arduino Uno R4 WiFi, DHT22 sensor, soil moisture and water level sensors, a raindrop sensor, and a submersible pump to address urban gardening challenges. The study evaluated the accuracy of the system’s sensors, the real- time data display, and SMS notifications, as well as the precision of water dispensing based on soil moisture levels. Results indicated high reliability, with most sensors achieving accuracy rates between 90% and 100%. The soil moisture sensor provided consistent readings, while the raindrop and water level sensors performed with near- perfect accuracy, enabling precise environmental monitoring. Notification features, including the LCD display and SMS alerts, were effective, with minimal delays in SMS reception. The water dispensing system demonstrated precision, adjusting water volume according to soil moisture levels, achieving an average water conservation effectiveness of 85% or higher. Additionally, a weak negative correlation between soil moisture and water dispensed highlighted the system’s responsiveness to environmental conditions. In conclusion, the prototype proved effective in monitoring and responding to soil conditions while minimizing water usage, making it a viable solution for urban home gardening. Future work could explore IoT-based enhancements for improved real-time monitoring, remote control, and data logging to further optimize system functionality.

Abstract-Social networking platforms attract millions of users globally. The interactions of these users with sites like Twitter and Facebook have a significant effect, often bringing about negative consequences in everyday life. Major social networking sites have become prime targets for spammers who disseminate vast amounts of irrelevant and harmful information. For instance, Twitter has emerged as one of the most extensively used platforms, leading to an overwhelming influx of spam. Fake accounts distribute unwanted tweets to promote services or websites, impacting genuine users and causing disruption in resource utilization. Additionally, the likelihood of spreading misinformation through counterfeit identities has grown, resulting in the circulation of harmful content. Lately, research has increasingly focused on detecting spammers and identifying fake accounts on Twitter within the realm of modern online social networks (OSNs). This paper examines various methods employed to identify spammers on Twitter.

Abstract-Multimodal emotion recognition is essential for advancing human-computer interactions and enabling applications like mental health monitoring and social robotics. This study focuses on utilizing text, audio, and motion data from the IEMOCAP dataset to develop independent models that capture unique emotional cues from each modality. The audio model employs a hybrid architecture combining Convolutional Neural Networks (CNN), Multi-Head Attention, and Gated Recurrent Units (GRU), achieving an accuracy of 81%. The text model leverages a CNN-based approach inspired by Temporal Convolutional Networks (TCN), achieving 94% accuracy. For motion data, a Spatio-Temporal Graph Convolutional Network (ST-GCN) was implemented, achieving 63% accuracy. A score-level fusion strategy integrates these models, improving the overall recognition performance. Evaluations using metrics like accuracy, precision, and recall demonstrate how multimodal approaches can provide a more accurate and reliable emotion recognition system by combining complementary information from diverse data types.

Abstract-Youth expect religion to create meaning in life. These expectations play a significant role in practised religion and significant changes in the religious landscape. Participation in places of worship continues to decline. Organised religion might be facing a shifting landscape but this does not mean people are shunning religion. The interactions between religious institutions and an individuals’ perspectives of religion are investigated to reveal the transformation of religious practices in Singapore, from the lens of globalization and migration. Kierkegaard’s Theory of Existentialism is used along with non-probability purposive sampling with an objective to explore how globalisation has affected religious practices of Christian youth in Singapore and to investigate how migration has affected religious practices among Christian youth in Singapore. The key informants are six young Singaporean Christian adults between 25 and 30. Qualitative approach, semi-structured interviews, open-ended questions, in-depth interviews and thematic analysis is used.

Abstract-Adaptive reuse is the best eco-friendly design strategy to repurpose existing building forms, stepping towards sustainability and a better environment. This type of revitalization is not restricted to buildings of historic significance but is also a smart strategy adopted in the case of archaic buildings. Customizing and reusing the existing built form not only saves money and profit but also a large amount of reduction in energy consumption and environmental impacts. Preservation, Rehabilitation, Restoration, and Reconstruction are major methods in bringing Adaptive reuse and Customization efficiently. Reusing the older vacant buildings for other purposes forms a very important outlook of any urban regeneration scheme and the adaptation process suggests opting for new technologies and design concepts that will support the older built to acclimate successfully to contemporary requirements without destroying the existing urban form. Adopting the adaptive reuse approach for the redevelopment of older vacant buildings provides added benefits to the regeneration of an urban area in a sustainable way, by transforming these buildings into usable and accessible units and providing a new sense of access to the public. While a large amount of historically built structures are being demolished and reconstructed. Adaptive reuse and customization could retain the built environment to the functions and needs and also maintain the historical facts and cultural factors.”

Abstract-This paper discusses the identification of faults in a Francis turbine using vibration-based condition monitoring with Minitab and MATLAB. The vibration signals are analyzed to detect faults in the motor components of the turbine. Statistical analysis uses Minitab to identify trends, while MATLAB carries out advanced signal processing, including Fast Fourier Transform (FFT) and wavelet analysis, to extract fault features. The combined approach effectively diagnoses issues like misalignment and bearing defects, showing its value in predictive maintenance for improved turbine performance.

Abstract-This research focuses on the design and development of an innovative seed sowing machine aimed at improving agricultural productivity through precision and efficiency. Traditional sowing methods, which are either manual or use basic machinery, face challenges like inconsistent seed spacing, high labor requirements, and frequent blockages in seed dispensing tubes. These issues lead to reduced crop yields, increased operational costs, and significant seed wastage.The proposed seed sowing machine addresses these limitations by integrating automated seed dispensing, consistent depth control, and a blockage detection system using sensors. This machine is designed to place seeds uniformly at a specific depth and spacing, enhancing germination rates and ensuring even crop growth. Testing results demonstrate improved accuracy in seed placement and reduced downtime, showing a potential to save up to 40% of labor compared to traditional methods.Overall, this seed sowing machine offers a cost-effective and efficient solution for small to medium-scale farmers, enabling more sustainable and productive farming. This research lays the groundwork for future advancements in automated agricultural machinery, contributing to the broader goal of technological innovation in agriculture.

Abstract-This paper discusses the aerodynamic performance of oblique wing configuration in transonic and supersonic flight regimes. By using CFD tools within the ANSYS, the research has explored the function of oblique wing towards wave drag reduction and efficiency enhancement. Pivot angle variations of 0°, 30°, 45°, and 60° were used in asymmetrically designed wing and analysed at Mach 0.9 and 1.2. Some critical parameters include CL, CD, and pressure distribution, through which the design attains optimum operating characteristics. Some results reveal wave drag at specific pivot angles with the oblique wing. As the wave drags show least values for specific pivot angles (30°: Mach 0.9; 45°: Mach 1.2), these result in great applicability in improved aerodynamic efficiency and adaptability in varied conditions of flight towards the further developments of high-speed aircraft technology.

Abstract-This research paper investigates the multifaceted landscape of Amazon Simple Storage Service (Amazon S3), a pivotal component of cloud infrastructure provided by Amazon Web Services (AWS). By synthesizing findings from academic papers, industry reports, and case studies, it explores the fundamental features, security considerations, best practices, and real-world applications of Amazon S3. The analysis underscores the imperative of configuring S3 buckets meticulously to mitigate security risks, citing numerous instances of misconfigurations leading to data breaches. Through an in-depth examination of security best practices advocated by experts, including access control policies (ACPs), encryption mechanisms, and monitoring protocols. Additionally, it evaluates the scalability, reliability, and versatility of Amazon S3, positioning it as an indispensable asset for enterprises across various sectors. By leveraging insights from diverse sources, this research paper offers a comprehensive understanding of Amazon S3’s capabilities and advantages, providing actionable recommendations for optimizing its usage while safeguarding data integrity and confidentiality.

Abstract-Prostate cancer is a major cause of morbidity and mortality among men worldwide. While traditional screening methods, such as Prostate Specific Antigen (PSA) testing and Digital Rectal Examiniations (DRE), have facilitated early detection, they face limitations, including false results and difficulty distinguishing aggressive from non-aggressive cancers. Recent advancements in urine-based testing offer a non-invasive, accurate alternative that improves diagnostic precision and reduces unnecessary biopsies. These tests analyze genetic and RNA biomarkers, providing personalized risk scores to guide biopsy decisions without requiring a DRE. They also address cultural barriers to screening and promote higher participation rates, particularly in underserved populations. Urine-based tests have the potential to optimize healthcare resources, reduce costs, and improve public health outcomes through early detection and intervention. However, equitable access, patient education, and data privacy protections remain critical considerations. As these tests become more widely available, they may transform prostate cancer screening and care.

Abstract-The information relates to the use of substances that are harmful to one’s social, physical, mental, and emotional well-being, such as alcohol, opioids, tobacco, and some addictive medications like baclofen. This will have a complete impact on health. If used recreationally and developed into a habit of reliance. Substance misuse should be managed in its early stages since it cannot be stopped once it has developed into a habit. It can be challenging to stop using drugs if one has become habituated to doing so, and occasionally it can result in potentially fatal situations. When prescribed, some medications, such as opioids and non-opioid medications should be taken. However, longer periods of time should not be spent consuming them. And shouldn’t be stopped abruptly. Tapering the doses will help to progressively discontinue the consumption. In the event that consumption is abruptly stopped, coma or death may result. Substance abuse may influence vital organs over time, changing typical vital values over time. Because of reliance, each organ in the body will sustain harm through a variety of means.

Abstract-The evolving dynamics of modern workplaces underscore the growing importance of flexible work arrangements in addressing the diverse needs of today’s workforce. This research delves into the necessity and impact of flexible workplaces, aiming to understand their prevalence, motivational drivers, and implications across various demographics, including students, professionals, and part-time employees. The study adopts a multi-faceted approach to examine patterns of flexibility, encompassing remote work, hybrid models, and flexible working hours, while evaluating their role in enhancing productivity and promoting a better work-life balance. A key focus is placed on identifying the motivational factors that lead individuals to prefer flexible arrangements, such as improved productivity, reduced commuting time, educational commitments, and family responsibilities. The study also assesses the challenges encountered, including time management difficulties, communication barriers, technical issues, and social isolation. By exploring these dimensions, it seeks to illuminate how flexible work environments can both empower individuals and pose unique obstacles that require organizational attention. In addition to individual experiences, the research evaluates organizational support systems and infrastructure, such as the provision of digital tools, internet allowances, structured guidelines, mental health initiatives, and workspace accommodations. These mechanisms are analyzed to understand their effectiveness in creating a conducive environment for flexible working. The study further examines how flexible work arrangements influence productivity across different contexts and demographic groups, offering valuable insights into their broader organizational and societal implications. The findings provide actionable recommendations for organizations aiming to implement or improve flexible work policies. These include fostering a culture of inclusivity, investing in digital infrastructure, offering targeted training programs, and creating clear guidelines to support employees effectively. Ultimately, the research highlights the transformative potential of flexible work arrangements in building resilient, adaptive, and employee-centric organizations capable of thriving in a rapidly changing work environment.

Abstract-Agricultural equipment booking can be a challenging task for rural farmers due to language barriers and the complexity of existing digital platforms. This study is based on the concept of equipment rental. The E-commerce website has been improved as part of this project to bridge the gap between the farmer and the vendor on a lease basis. Only the user has access to the main programme after going through the login procedure; only the user may pick and book resources. This paper is jam-packed with information about the products. Farmers will benefit from this paper. The main goal of this website is to manage a variety of agricultural machinery, including Harvester, JCB, Tractor, Pickup, Rotor, and other agricultural machinery. End users will find the proposed system simple to use. As a result, we created a single website. We are attempting to provide the farmer or user with a solution that allows them to rent the goods by the hour.

Abstract-Quantum computing is a new technology capable of solving problems that traditional/normal computers cannot handle. It is based on principles like superposition, entanglement, and interference to process information in ways that are not possible in classical computing. Unlike traditional computers that rely on bits as units of information, quantum computers use qubits, which can exist in multiple states simultaneously. This unique ability of qubits enables the quantum machines to perform computations at speeds that cannot be attainable by classical/normal systems. This new technology has the potential to transform all industries by addressing challenges in optimization, simulation, and data processing. For instance, quantum algorithms can simulate complex molecular interactions, leading to faster drug discovery in the pharmaceutical industry. Similarly, in logistics, quantum computers can optimize supply chains and reduce energy consumption, supporting more sustainable practices. Despite its promise, quantum computing also faces hurdles such as high costs, limited accessibility, and the need for stable operating environments.

Abstract-Trolling, defined as repeated and intentional online harassment, has become a significant issue for online content creators, affecting their mental health and creativity. This study aims to explore the role of trolling on psychological well-being and creative processes of content creators, addressing the gap in existing literature. Utilizing a qualitative exploratory design, semi-structured interviews were conducted with online content creators aged 18-45 who have experienced trolling. Participants were recruited through social media platforms using purposive and snowball sampling techniques. Data were analyzed thematically to identify patterns related to coping strategies, emotional impact, influence on content, long-term effects, and the role of platform support. The findings revealed that trolling contributes to heightened anxiety, self- censorship, and decreased motivation to produce creative content. Creators also reported dissatisfaction with the current support systems on social media platforms, highlighting a need for better moderation policies. This research underscores the importance of developing more effective support systems and mental health resources for content creators, along with stronger platform policies to combat trolling. The study contributes to a deeper understanding of the dual impact of trolling on mental health and creativity, paving the way for further research on supportive interventions in digital spaces.

Abstract-Optimizing AI-driven decision support systems necessitates a careful balance between efficiency, accuracy, and ethical considerations. Efficiency involves ensuring that the system processes data swiftly and provides timely insights. Accuracy emphasizes the need for reliable and precise outputs to inform decision-making. Ethical considerations are paramount, addressing potential biases in data and algorithms to ensure fair and just outcomes. Transparency in the decision-making process fosters trust and accountability. By integrating these factors, AI-driven decision support systems can enhance decision-making processes while upholding ethical standards and maintaining user trust.

Abstract-The primary vectors for the spread of diseases of concern like malaria, dengue, are mosquito species, particularly Aedes and Culex. There has been an exponential use and increased reliance by smaller groups, not specific to municipal and govt health bodies, but by housing societies and private pest control companies who rely on the acceptability of Thermal Fogging, as one of the key control or management factors of mosquito in urban setup. The professional pest control agencies are forced to adopt the use of Thermal Fogging even if there are ULV based options as there is fairly low awareness and poor visible changes at municipal level where adoption of ULV is not seen for more immediate adoption at social self help groups and with housing societies or with professional pest management companies. The urgent need to induct for a more less pollutant carrier, like BIODIESEL in thermal fogging and use of Plant Extract based Larvicides in water sources having significantly low toxicity will help build a more sustainable and low toxic mosquito management program, helping the human society at large to be truly living healthy through the means of Integrated Mosquito Management. In stark contrast where the use of polluting carriers like diesel leaves more lasting environmental damage affecting many more lives, than saving a few.

Abstract-Student engagement would be one of the core elements that improve educational outcomes in the classroom. A gamification framework with machine learning would increase participation and personalize the experience of learning. The framework, through mechanics such as points, badges, leader boards, and challenges, encourages the participants to engage in the learning experience and enjoy it. However, machine learning facilitates adaptive learning by adapting the content based on the individual’s performance metrics. Student sentiment analysis helps to identify which students are in need of support, and predictive analytics would be used for identifying the students that may require more support. Real-time analytics allows teachers to keep track of student progression as well as classroom trends in real time. The system was designed to improve engagement and increase efficiency in the learning process. It considers the fact that competition can get unhealthy at times as well.

Abstract-The “Daily Diary Writer with Sentiment Analysis” project is a full-stack web application focused on enhancing personal well-being through sentiment analysis. Users can write daily diaries journals, and the system uses natural language processing to analyze the emotions expressed in their entries. This helps users reflect on their emotional patterns over time. Voice input allows for hands-free journaling, making the process more convenient and accessible. The project promotes self-reflection and emotional well-being through detailed sentiment insights.

Abstract-This paper presents the development and implementation of an automation bot designed for efficient data extraction and processing tasks. The bot automates the process of accessing a website, downloading an input Excel file, and extracting account numbers from the file. It then compares the last four digits of each account number with the digits in the names of zip files available on the website. Upon finding a match, the bot downloads the corresponding zip file, extracts its contents, and processes the required data from the unzipped text file, subsequently loading this data into the specific account number’s field. This automation bot significantly enhances data handling efficiency, reduces manual errors, and streamlines the data management process.

Abstract-Global warming is impacting our communities, health, and wildlife, while noise pollution negatively affects both physical and mental well-being. This study examined durian husk and cogon grass fibers as sustainable materials for fiberboard production, focusing on their moisture resistance, thermal insulation, and soundproofing properties. These natural fibers outperformed traditional fiberboards. In sound absorption tests, durian fibers achieved 64.017 Hz, cogon fibers measured 67.600 Hz, and combined fibers recorded 62.617 Hz, compared to 83.033 Hz for the control group. Regarding thermal performance, durian fiberboards exhibited temperatures of 36.25°C and 36.95°C, while cogon fiberboards measured 37.90°C and 39.00°C. The commercial insulator consistently registered temperatures of 45.05°C and 46.05°C. Both durian and cogon fiberboards demonstrated 0% water absorption after 24 hours, in stark contrast to traditional fiberboard, which absorbed 200% more. This research underscores the potential of durian husk and cogon grass fibers as superior, eco-friendly alternatives for construction, effectively addressing noise and heat challenges in tropical regions.

Abstract-Drones are becoming indispensable tools in various critical sectors of India, such as agriculture, disaster management, land surveys, mining, and infrastructure mapping. Their ability to access remote, hazardous, or hard-to-reach areas makes them invaluable for tasks, such as crop monitoring, search and rescue, and real-time data collection. However, the effectiveness of drones in these mission-critical applications is often limited by their battery life and the need for frequent recharging, particularly in environments where human access is difficult or impossible. This project addresses this challenge by developing a wireless power transfer (WPT) system for drone charging. The system converted a standard 230V supply into a low-voltage DC output, which was then wirelessly transferred via a high-frequency (100kHz) inverter and coil setup. This WPT system is particularly suited for use in remote or inaccessible locations, where minimizing downtime is critical.

Abstract-Human-centered artificial intelligence (HCAI) emphasizes designing AI systems that prioritize human values, ethics, and usability, fostering trust and responsible adoption. This research explores the advancement of HCAI by addressing key challenges such as improving explainability, integrating ethical considerations, and optimizing real-world applications across diverse sectors. By investigating state-of- the-art methods for interpretable machine learning, the study aims to enhance user understanding and transparency in AI decision-making. It further examines frameworks for embedding ethical principles, including fairness, accountability, and privacy, into AI system design. Additionally, the research evaluates case studies from healthcare, education, and autonomous systems to illustrate the transformative potential of HCAI. This study underscores the need for interdisciplinary collaboration and innovation to ensure AI technologies align with human values and societal goals, paving the way for more inclusive and sustainable AI solutions.

Abstract-One of the important and tedious tasks in agricultural practices is the detection of disease on crops. It requires time as well as skilled labor. This paper proposes a smart and efficient technique for the detection of crop disease which uses computer vision and machine learning techniques. Every year India loses a significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, a computer-aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, CNN was proposed to detect plant disease. It has three processing steps namely feature extraction, downsizing image, and classification. In CNN, the convolutional layer extracts the feature from the plant image. It helps to give personalized recommendations to farmers based on soil features, temperature, and humidity.

Abstract-This research introduces two innovative methods to convert a standard 16-control dental chair into an IoT-enabled dental chair at a minimal cost of under 2,000 INR. The first method involves directly interfacing the chair’s control wires with a 16-channel relay and an ESP32 microcontroller, enabling remote operation through the Blynk IoT platform. The second method leverages signal analysis by identifying the dental chair PCB’s communication lines, capturing control signals with a logic analyzer, and replicating them via the ESP32 for seamless functionality. Both approaches offer global control with minimal delay (<10ms) and enhance operational efficiency by enabling preemptive actions, such as heating water or cleaning the spit bowl remotely. This study provides a scalable, low-cost solution for modernizing dental chairs, ensuring ease of use and adaptability for dental clinics worldwide.

Abstract-Facial expression detection has emerged as a transformative technology with applications in numerous fields such as healthcare, security, and entertainment. The proposed system aims to enhance user engagement by dynamically tailoring playlists based on the user’s emotional state. The proposed Emotion Recognition provides a foundation for further exploration and development of intelligent systems that adapt to users’ emotional states, fostering more immersive and personalized interactions in the realm of digital entertainment.

Abstract-This paper aims to provide an in-depth understanding of A.I., its historical development, and its transformative influence on modern civilisation. We will discuss significant concepts, evolving technologies under influence, technological advancements, and ethical and unethical A.I..

Abstract-Mechanical engineers dream up and design amazing machines and technologies that improve people’s lives in all kinds of ways. From airplanes and cars to robots and renewable energy systems, mechanical engineers have shaped our modern world. New technologies are opening up incredible opportunities for innovation. Read on to learn about the exciting changes & future trends in mechanical engineering and how you can prepare for it!

Abstract-Unauthorized access remains a critical threat to network security, as attackers can exploit vulnerable systems to obtain sensitive data or disrupt services. This paper introduces Honeypot Sentinel, a proactive intrusion detection tool designed to flag unauthorized access attempts by monitoring and verifying usernames and IP addresses. Honeypot Sentinel uses a MongoDB database for logging, enabling the system to record details of unauthorized access attempts efficiently. Upon detecting any access attempts outside the predetermined criteria, Honeypot Sentinel triggers alerts, allowing system administrators to promptly address potential threats. This approach provides network security teams with real-time data, helping them respond effectively to unauthorized access incidents.

Abstract-Microglia, the principal immune cells within the central nervous system (CNS), are essential for maintaining neuronal homeostasis. Nonetheless, the chronic activation of microglia has been associated with the development of neurodegenerative diseases, notably Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). This review investigates the mechanisms underlying microglial activation, the dual functions of microglia in neuroprotection and neurotoxicity, and the implications for therapeutic strategies. By examining contemporary research, we aim to clarify the molecular pathways that link microglial activation to the progression of these diseases and identify potential approaches for modulating microglial responses to alleviate neurodegeneration.

Abstract-This journal discuss in detail on a suggested medication reminder device that will be made for senior citizens based on their problems. This study’s background is explained in the report, and its primary goal is to guarantee that the medication reminder device will be resolving issues that older people have. The problems that have been discovered are mostly focused at the elderly and are meant to address the problems that they encounter on a daily basis, particularly with regard to medication use. In order to design a better device, the study will also examine similar implemented devices and systems to determine the advantages and disadvantages of other pertinent devices and systems. This portable and economical system would be helpful to every age group also.

Abstract-The green infrastructure has some other benefits besides flood risk reduction. Those benefits mainly covers conservation of water, energy, and improvement in air quality and much more. The materials used are the essential components of the green infrastructure. The proper design accompanied by the material properties provides the accountability of the mechanical strength of the infrastructure. Hence, the eco-friendly materials are given more priority for green infrastructure. The price is considered primarily when either selected or r4elated material is compared for the similar purpose. Except the social and environmental costs, the cost of the building element conveys only the cost of transportation and manufacturing. Therefore, the sustainable development of the nation relies on the proper choice of the construction materials having least burden on the environment. Moreover, the result of mixture of blue, green and grey infrastructure is likely the best adaptation strategy as these comply with each other. The grey infrastructure reduces the flooding risk while the green infrastructure has its own multiple benefits which is not offered by the grey infrastructure. The paper focusses on the contribution of the sustainable building material in order to reduce the impact of environmental degradation which could help in identification of the strategies that are highly effective in improving the urban flood risk management.

Abstract-Heart attacks remain a critical global health issue, necessitating accurate predictive models to identify at- risk individuals and support preventive care. This project, titled “Heart Attack Risk Assessment Using Deep Learning with Feature Optimization,” applies deep learning techniques to assess the likelihood of a heart attack. The study utilizes a Fully Connected Neural Network (FCNN) model enhanced by feature optimization methods, ensuring that the most relevant predictors are prioritized. Additionally, the project incorporates risk visualization, enabling clear and actionable insights for early detection and management of heart attack risks.

Abstract-This research examines the impact of artificial intelligence (AI) and machine learning (ML) on improving human resource (HR) decision-making procedures, with an emphasis on the years from 2015 to 2025. Employing a bibliometric approach, the study uncovers trends, obstacles, and prospects related to artificial intelligence and machine learning usage in human resource management. The results depicted in Figure 1 (“Document by Year”) indicate a marked rise in research activity after 2020, emphasizing an increasing interest in the incorporation of AI and ML in HR practices. Figure 2 (“Document by Area”) illustrates that computer science (45%) and business studies (30%) lead in research contributions, highlighting the technical and strategic aspects of these technologies. The geographic analysis shown in Figure 5 (“Document by Country”) reveals that 40% of the studies come from the United States, while European and Asian nations account for 30% and 20%, respectively. Institutional contributions, shown in Figure 7 (“Document by Affiliation”), indicate that 60% of research originates from academic institutions, while corporate research centers account for 25%. Figures 3 and 4 underscore the variety of sources and funding, showing a balance between academic integrity and practical uses, with government funding representing 50%. The research highlights the revolutionary impact of Artificial intelligence and Machine learning in human resource management, especially concerning talent acquisition, employee engagement, and workforce management. Nevertheless, ethical issues, biases in algorithms, and privacy threats present significant challenges. By combining technological advancements with ethical guidance, as illustrated by the trends shown in the figures, organizations can develop adaptable, inclusive, and effective HR systems that meet the changing needs of the workforce.

Abstract-Innovation in robotic arm control has been sparked by the development of Arduino-based technology, which provides both experts and enthusiasts with an affordable and user-friendly platform. The creation of robot arm control with an Arduino controller is presented in this work. The project entails integrating sensors and Arduino microcontrollers to provide dynamic and accurate control over a robotic arm. Four servo motors—which rotate left, right, front, and back—control the suggested robot. The study lays the groundwork for future developments in this emerging topic by discussing the difficulties faced during the development process and offering solutions. The demonstrated robotic arm control system has the potential to increase access to robotics education and promote automation innovation due to Arduino’s broad availability and low cost.

Abstract-Ultrasound is simply sound whose frequencies are too high to be heard by the mortal observance, that’s to say the frequencies are above c 20 kHz. At the top end of the scale, ultrasound is used at frequentness up to several GHz. The main end of this system is to descry object that will be ahead of ultrasonic transducer. Utmost ultrasonic detectors are grounded on the principle of measuring the propagation time of sound between send and admit (propinquity switch). The hedge principle determines the distance from the detector to the glass (retro-reflective detector) or to an object (through- ray detector) in the measuring range. Ultrasonic detectors are grounded on the measured propagation time of the ultrasonic signal. They emit high- frequency sound swells which reflect on an object. The objects to be detected may be solid, liquid, grainy or in greasepaint form. It sends an ultrasonic palpitation out at 40 kHz which travels through the air and if there is a handicap or object, it will bounce back to the detector. By calculating the trip time and the speed of sound, the distance can be calculated. Ultrasonic detectors are a great result for the discovery of clear objects.

Abstract-Cloud Computing with field of emergence with frameworks with intelligent platform based on cloud which is designed with “reliability , availability “ with key deliverables with a specific and specialized platforms which are designed on methods for computing technology and service streamlined with pattern – self based analysis for Multimedia Management with Enterprise on Digital Platforms for reviewing Data Agents for digital Background for computing level of architecture format with a self-developing ecosystem for pattern recognition and texture delivering format.

Abstract-Light fidelity (Li-Fi) technology is a wireless communication system that utilizes visible light spectrum to transmit data with high speed and secure manner compared to the traditional Wireless Fidelity (Wi-Fi) architecture. In this paper a smartphone is used in Li-Fi communication system. The aim of this proposed approach is to maximize the bit rate with high accuracy by using the flashlight of built-in smartphone camera as a source to send data and detect the effect of using a built-in smartphone ambient light sensor and external light detector sensors that is connected to Arduino UNO circuit to receive data. Four practical experiments were conducted to discover which light sensor accomplish higher data bit rate and tested the system performance under changing the distance between transmitter and receiver. The evaluation results demonstrated that the data bit rate is better with the proposed research than the others, where it reached more than 100 bps with accuracy 100%.

Abstract-Robots that harvest cotton have become a viable way to alleviate labor shortages and boost production efficiency. In order to detect, navigate, and gather cotton bolls in the field, these robots use cutting-edge technologies. Cotton boll detection relies heavily on deep learning and machine vision methods. An innovative weed identification model that distinguished weeds from cotton seedlings with a map of 98.43% was developed using the CBAM module, the BiFPN structure, and the bilinear interpolation technique (Fan et al., 2023). The chromatic aberration approach showed great sensitivity and specificity with a 91.05% identification rate for cotton boll detection in natural lighting (Singh et al., 2021). GNSS and optical detection techniques are combined in cotton harvesting robot navigation systems. While boll position estimation demonstrated great precision with an R2 value of 99% when stationary and 95% when moving, a pixel-based method for cotton row detection obtained 92.3% accuracy (Fue, Li, et al., 2020). These developments in navigation and identification aid in the creation of accurate and productive cotton harvesting robots. In conclusion, there is a lot of promise for automating cotton harvesting through the combination of sophisticated detection algorithms, navigation systems, and robotic manipulation techniques. But there are also issues with adapting these technologies to different crop kinds and field conditions, which calls for more study and advancement in this subject.

Abstract-This study examines the effects of interrupting versus continuing beta-blocker therapy in post-myocardial infarction patients. The ABYSS trial, a multicenter noninferiority study, found that interrupting beta-blocker therapy did not offer any advantages over continuation in reducing major cardiovascular events or improving quality of life. The interruption group experienced a slight increase in hospitalizations for coronary-related conditions. These findings challenge existing guidelines recommending beta-blocker discontinuation after one year for certain patients. The trial highlights the necessity for additional research to clarify the role of beta-blockers in modern post-MI care, especially for patients with preserved left ventricular function.

Abstract-This study investigated School Management Committee roles and the academic performance of pupils in selected government aided primary schools in Bulambuli Town Council, Bulambuli District. The study sought to; examine the relationship between the supervisory role of School Management Committees (SMCs) and academic Performance of pupils; examine the relationship between the supervisory role of School Management Committees (SMCs) and academic Performance of pupils; and examine the effect of the consultative role of School Management Committees (SMCs) on academic Performance of pupils. The study adopted a cross-sectional research design. Both simple random sampling and purposive sampling techniques were used to select the sample of respondents. The researcher studied a sample of 82 participants who included teachers, SMC members from selected government-aided primary schools, officials from DEO’s office and CCTs of selected schools in Bulambuli TC. Questionnaires and key informant interviews were used for data collection. Quantitative data from questionnaire was analyzed for both descriptive and inferential statistics using SPSS and Excel while qualitative data was analyzed thematically. The findings of this study were that; SMCs have significant influence on academic performance of pupils. The Pearson correlation coefficient shows that there is a significant positive relationship between the administrative role of SMCs and academic performance of pupils, r = 0.729, p = 0.000; supervisory role of SMCs influences the academic performance of pupils, r = 0.689, p = 0.000; and consultative role of SMCs has a significant positive relationship with academic performance of pupils, r = 0.648, p = 0.000. From the findings of this study, the researcher recommended that the SMCs should work with school administration to provide support such as academic intervention programs to struggling pupils so as to improve the academic performance of pupils; tighten regular monitoring and assessment of pupils’ progress to identify areas of improvement; and work closely with parents and other stake holders to support pupils’ learning. By involving the broader school community in academic initiatives, school will create a network of support that might help pupils thrive academically.

Abstract-An electric locomotive power supply device is responsible for providing electrical power to the traction motors that drive the locomotive. These systems often use alternating current (AC) or direct current (DC) to power the motors, and they can operate using either overhead catenary systems or third-rail power supplies. Simulation of a locomotive power supply device involves analyzing its electrical and mechanical performance, power quality and efficiency. The power electric device which works under condition of high power and heavy load, suffer from faults frequently. The main circuit of the device is a kind of single-phase full bridge half controlled rectifier circuit. Harmonics are higher-frequency components that distort the waveform of current or voltage. In locomotive power supply systems, harmonics are typically introduced by non- linear loads, such as the power electronic devices (inverters, converters) used in these systems. Harmonics can cause several issues, including increased losses, power quality .

Abstract-The focus of this project is to make a secure and transparent voting system at college level. Even though paper based voting was a traditional approach and is being used for centuries, but still as we are facing the challenges during the overall voting process which includes, security risk, lack of transparency, human errors and privacy concerns. So, to overcome this limitations and vulnerabilities we came up with the idea of a blockchain based voting system. It is in high demand due to the immutability, transparency and decentralized solutions. The objective of this paper is to incorporate blockchain technology to construct a secure, tamper-proof elections at college level. It will also help developers to build and deploy smart contracts. The use of smart contracts guarantees the accuracy and provides fast voting result and makes counting procedures protected against fraudulent actions. This technology supports peer-to-peer decentralized network in which all the transactions are stored in blocks. To sum up, the proposed system will shorter the time for voting process while offering security and authentication and it also dwindles the expenses as there is no need to print ballots.

Abstract-This work is concerned with the machining of Glass Fiber Reinforced Plastics (GFRP), with a primary interest in how surface roughness is affected by cutting tool materials. GFRP has found a niche in aerospace, automotive, and marine applications, and has obtained recognition for its ratio of strength to weight, resistance to corrosion, and thermal stability. Because of the peculiar structure of E-glass fibers in epoxy resin matrix systems, specific problems concerning the use of this material arise, such as delamination, fiber pull out, and wear of tools, thus requiring tailored machining techniques. The experiment compared performance between cut rods using high-speed steel (HSS) and high-carbon steel (HCS) while machining a GFRP rod (30 mm diameter, 240 mm length) using a lathe. Surface roughness parameters are Ra, Rz, Rt, Rpk, all measured by a Talysurf device. The results showed that HSS tools led to smoother surfaces and greater accuracy but that HCS tools were more economically viable for less rigorous jobs. These results touch upon the need for tool selection based on application and give some insight into further pros in terms of coatings, monitoring systems, and further sustainable machining approaches.

Abstract-This paper presents a Fuzzy Logic-based Maximum Power Point Tracking (MPPT) algorithm for Solar Photovoltaic (PV) systems to enhance energy efficiency. The proposed approach adapts to fluctuating solar irradiance and temperature by utilizing rule-based logic, eliminating the need for precise mathematical models. Unlike traditional methods, the fuzzy logic controller provides fast and accurate responses, minimizing power loss and improving performance. It continuously adjusts the duty cycle of a DC-DC converter to maintain operation at the peak power point. MATLAB/Simulink simulations show faster tracking, reduced oscillations, and higher energy harvest compared to Perturb and Observe (P&O) and Incremental Conductance (IncCond) methods. This robust solution maximizes PV output, advancing the feasibility of solar energy as a renewable source.

Abstract-Photovoltaic power generation system implements an effective utilization of solar energy, but has very low conversion efficiency. The major problem in solar photovoltaic system is to maintain the DC output power from the panel as constant. Irradiation and temperature are the two factors, which will change the output power of the panel. A boost converter is utilized as a DC-DC converter.The simulation includes the modeling of a solar panel, and a power conversion unit such as a DC-AC inverter. Key parameters such as irradiance, temperature, and shading effects are considered in the analysis to assess their impact on the power output and overall system efficiency. The results highlight the dynamic behavior of the system under different operating conditions. The MATLAB/Simulink environment is utilized to evaluate the system’s performance, and a comparison is made between the theoretical and simulated values. It is obtained by using MATLAB Simulink Model.The aim is to effectively track the maximum power points considering the fluctuations in solar irradiation and temperature.

Abstract-Nutritional deficiencies are common worldwide and can also lead to weak immunity, weak stamina, metabolism and decreased bone health. To address these challenges, relying solely on diet may be insufficient, as most individuals do not consume nutritionally balanced diets. Nutritional supplements can help in achieving optimal, balanced nutrition while preventing nutritional deficiencies. This multicentre, double-blind, randomized, parallel-group phase 3 clinical trial evaluated the efficacy and safety of a nutritional supplement from British Life Sciences, Pvt. Ltd, BSURE Sugar-Free (Dutch Chocolate Flavour) against a multivitamin powder (Zooversandhaus Jung, Germany) in patients with nutritional deficiencies, weak immunity, low stamina, compromised bone health, and weak metabolism. Over three months, 231 participants were recruited, with 200 completing the study. Results demonstrated that BSURE achieved 97% and 98% efficacy in improving weak immunity and stamina, respectively, and showed comparable safety and tolerability to the control product. These findings support the use of the product for nutritional support in adults.

Abstract-This is a presentation of the development of “Magma Estate Agility” web application. The application is designed with the use of HTML, CSS, and JavaScript for improving on estate management and aims at processes like property listing, tenant management, and sending in requests for maintenance. This paper describes the methodologies used, the technologies employed, and the results realized from the implementation of the project. From the findings, it shows that incorporating these technologies into the estate management process raises efficiency and user-friendliness in the processes.

Abstract-An electric locomotive power supply device is responsible for providing electrical power to the traction motors that drive the locomotive. These systems often use alternating current (AC) or direct current (DC) to power the motors, and they can operate using either overhead catenary systems or third-rail power supplies. Simulation of a locomotive power supply device involves analyzing its electrical and mechanical performance, power quality and efficiency. The power electric device which works under condition of high power and heavy load, suffer from faults frequently. The main circuit of the device is a kind of single-phase full bridge half controlled rectifier circuit. Harmonics are higher-frequency components that distort the waveform of current or voltage. In locomotive power supply systems, harmonics are typically introduced by non- linear loads, such as the power electronic devices (inverters, converters) used in these systems. Harmonics can cause several issues, including increased losses, power quality.

Abstract-An electric locomotive power supply device is responsible for providing electrical power to the traction motors that drive the locomotive. These systems often use alternating current (AC) or direct current (DC) to power the motors, and they can operate using either overhead catenary systems or third-rail power supplies. Simulation of a locomotive power supply device involves analyzing its electrical and mechanical performance, power quality and efficiency. The power electric device which works under condition of high power and heavy load, suffer from faults frequently. The main circuit of the device is a kind of single-phase full bridge half controlled rectifier circuit. Harmonics are higher-frequency components that distort the waveform of current or voltage. In locomotive power supply systems, harmonics are typically introduced by non- linear loads, such as the power electronic devices (inverters, converters) used in these systems. Harmonics can cause several issues, including increased losses, power quality.

Abstract-The CORDIC (Coordinate Rotation Digital Computer) algorithm is a versatile and efficient iterative method for computing a wide range of mathematical functions, including trigonometric, hyperbolic, exponential, logarithmic, and square root functions. Central to the CORDIC approach is its ability to perform vector rotations in a polar coordinate system, effectively transforming coordinates through a series of predefined angles. These methods eliminates the need for complex multiplications by utilizing simple shift and add operations, making it particularly well-suited for hardware implementations in resource-constrained environments, such as digital signal processors (DSPs) and field-programmable gate arrays (FPGAs).The algorithm operates in several modes, including rotation mode and vectoring mode, allowing it to adapt to various computational requirements. Each iteration reduces the angle by a fixed amount, using pre-computed arctangent values to guide the rotations. The convergence of the algorithm depends on the number of iterations, with higher iterations yielding greater accuracy. CORDIC’s unique architecture supports parallel processing, enabling simultaneous calculations of multiple functions, further enhancing its efficiency. Evaluation of trigonometric functions such as sine, cosine and tan has been obtained using MATLAB. This abstract encapsulates the fundamental principles, operational modes, computational advantages, and diverse applications of the CORDIC algorithm, underscoring its significance in modern digital computation and system design.

Abstract-Automated traffic monitoring solutions have become necessary due to the difficulties of manual monitoring systems and the exponential growth in vehicular traffic. The new Advanced Vehicle Detection System described in this work uses sophisticated computer vision algorithms to identify, recognize, and log vehicle data in real time. Utilizing OpenCV, CNN (Convolutional Neural Network), YOLO (You Only Look Once), and OCR (Optical Character Recognition) technologies, the suggested system detects automobiles and records license plate information. In addition, the system gives law enforcement, traffic management, and institutional surveillance a reliable and scalable approach by automating the entry and exit timestamp logging process. Mostly we are developed for the college buses which has been include arrival and Departure time with an owner details and also the vehicle claim the insurance or not, These also updated the count of vehicle that are recognized by the entry and the exit time. Experimental results demonstrate the system’s high precision and efficiency, ensuring its practical applicability in real- world scenarios. This practical and efficient system is an excellent example of how technology can address real- world challenges in monitoring and managing vehicles.

Abstract-This project presents a simulation-based study of a Photovoltaic (PV) panel driving a 3-phase induction motor using MATLAB Simulink. The model is developed to explore the feasibility of utilizing solar energy to power electric motors, which are essential in various industrial and agricultural applications. The PV panel generates DC power from sunlight, which is then converted into 3-phase AC power using a 3-phase inverter. The AC power is used to drive the induction motor, which converts electrical energy into mechanical energy to operate loads such as pumps and machinery. The MATLAB/Simulink environment is used to model and simulate the behavior of the entire system, including the PV panel, inverter, and induction motor. The simulation allows for real-time monitoring of key parameters such as power output, rotor speed, electromagnetic torque, and current in the motor’s stator and rotor. This enables performance optimization and ensures the system operates efficiently under varying irradiance and temperature conditions.

Abstract-The Himalayas are referred to as the “Third Pole” and contain the largest concentration of glaciers outside of the Arctic and Antarctic. The glaciers are, therefore, an important source of water for these river systems including the Indus, Ganga, and Brahmaputra that support nearly 15% of India’s population. It has been observed during recent decades that the glaciers have retreated, and snow cover reduced significantly because of climate change, and this has resulted in the formation of lakes. In this study, the focus is on Uttarakhand in the Central Himalayas, assessing the relationship between climate change, snow cover, glacial lakes, and associated geo-hazards. There is a focus on key climate trends, snow cover dynamics, glacial lake expansion, and geo-hazards such as Glacier Lake Outburst Floods (GLOFs) using long-term satellite imagery, numerical models, and ground-based observations. The findings show that high-altitude areas are warming at 0.6 °C/decade, with declining rainfall trends, widespread reductions in the extent of snow cover, and deposition of potentially hazardous glacial lakes. Effective mitigation, long-term monitoring, and community-based approaches are necessary to minimize the risks to the environment and socio-economic sectors.

Abstract-The Social media’s ubiquitous impact on contemporary life has unquestionably changed communication, connection, and information sharing, but underlying its glitzy exterior is a more sinister reality with significant ramifications for both societal well-being and personal mental well- being. This abstract examines how social media negatively impacts mental health, emphasizing problems like body dysmorphia, loneliness, anxiety, and depression that have become more prevalent as virtual platforms have grown in popularity. Feelings of inadequacy and loneliness are made worse by the continual push to produce idealized versions of oneself and the addictive nature of social media. Emotional well-being is further undermined by the culture of comparison, cyberbullying, and reality distortion promoted by algorithm- driven material. At the societal level, an over dependence on social media has led to a disintegration of interpersonal relationships, promoting divisiveness, echo chambers, and the dissemination of false information. Two of the main signs of this digital age are the decline in in-person interactions and people’s shortening attention spans. This abstract examines how social media is eroding the basis of genuine relationships and shared societal ideals while also enabling virtual interactions.

Abstract-Research in modern science based on primordial particles those constitute the observable world. It is highly interesting to cover the vast field of materialistic world which lead to the scientists to investigate the building stone of the object that occurs naturally. Without proper knowledge of constituents of observable objects research on high energy physics will never yield satisfactory result. The aim of this paper is to intricate the right direction of investigation.

Abstract-Efficient power extraction from photovoltaic (PV) systems is critical in optimizing energy utilization for renewable applications. This project explores a Neural Network (NN)-based MPPT technique implemented in MATLAB/Simulink, designed to dynamically predict and track the maximum power point (MPP) of a solar PV system with battery storage. The NN-based MPPT is trained on a dataset encompassing various environmental conditions (irradiance, temperature, PV voltage, and current) to accurately predict the optimal duty cycle for the DC- DC converter, thereby maximizing power transfer from the PV system to the battery. A Simulink model incorporating a PV array, DC-DC converter, and the NN-based MPPT controller was developed, allowing for simulation and performance assessment under diverse scenarios. This work underscores the viability of intelligent MPPT solutions for advancing solar energy efficiency and sustainability.

Abstract-A serious environmental issue that has an impact on ecosystems, wildlife, and human health is plastic pollution. Since plastic production has increased to almost 368 million tons per year, plastics are found in both terrestrial and marine habitats. Although plastic materials are useful for many purposes, their durability also adds to their persistence in nature, which frequently harms the environment. Millions of marine species consume or become entangled in plastic waste, which can cause harm or even death, making marine life especially vulnerable. Microplastics, which are tiny plastic particles smaller than five millimeters, have also gotten into food chains, affected biodiversity, and endangered human health by contaminating water and shellfish. Plastic pollution has major socioeconomic repercussions since it impacts public health, tourism, and fisheries. Animal health and biodiversity are severely harmed by plastic pollution, which also poses a serious threat to ecosystems and animals. Ingestion, entanglement, or accumulation in food chains are all possible outcomes of the millions of tons of plastic waste that enter rivers, oceans, and terrestrial habitats every year. Fish, marine mammals, seabirds, and other marine species are especially at risk. Malnutrition, internal damage, and obstructions can result from consuming plastic waste, which significantly lowers survival rates. Several governmental efforts are being implemented to reduce plastic production and consumption, encourage recycling and biodegradable alternatives, and increase public knowledge of sustainable practices to reduce plastic pollution. To properly solve this complex issue, however, a comprehensive strategy including individuals, businesses, and governments is required.

Abstract-AThe rapid advancements in data science have revolutionized the healthcare industry, offering tools to enhance decision-making and optimize patient care. This paper focuses on the application of predictive analytics and machine learning models in healthcare, demonstrating how these technologies can forecast outcomes, identify patterns in patient data, and improve operational efficiency. By leveraging large-scale patient datasets, this research aligns with ethical practices and sustainability goals, ensuring equitable and impactful healthcare solutions. The results underscore the potential of data science in transforming healthcare delivery and promoting evidence-based decision-making.

Abstract-The most popular renewable energy technology is Hybrid Power System consisting of wind and solar energy sources because the system is reliable and complimentary in nature. Wind / PV Hybrid system is commonly used in Distributed Generation (DG). This project proposes a new solution for improved voltage stability with quality power output. In this system voltage output from Wind Energy Conversion System(WECS) and Photo Voltaic Panels are given to separate DC-DC converters are independently controlled and connected to a common DC bus and from there it is inverted. In the proposed controller the voltage stability is obtained with a PI controller. The implementation of the proposed method is done by using MATLAB Simulink platform. The performance of the suggested coordinate control system is analyzed by comparing the computer simulation results with and with out using controllers and it shows that the proposed system is more efficient.

Abstract-This paper presents a comprehensive simulation and performance analysis of the WAP-7 electric locomotive, a cornerstone of Indian Railways’ passenger traffic. The WAP-7, with its robust design and advanced propulsion system, has been operational since its introduction in 2000, demonstrating remarkable versatility and efficiency in hauling heavy passenger trains. The WAP-7 electric locomotive has been the workhorse of Indian Railways’ passenger fleet for over two decades, with its robust design and propulsion system Utilizing MATLAB for simulation, we modeled the locomotive’s propulsion dynamics by incorporating critical parameters such as thrust, weight, drag coefficient, and braking forces.This also examines the evolution of the WAP-7’s propulsion system, the simulation provides insights into the impact of track conditions on WAP-7 performance.

Abstract-The aim of this study is to Identification of hazards in various machineries in the steel industry and solution based on the hierarchy of controls. This will minimize the occupational health hazards of the workers. Hazard Identification and Risk Assessment (HIRA) is a process that involves examining what could cause harm to people in black bar to bright bar process workplace and evaluating whether the necessary precautions are in place. The goal is to ensure that no one becomes ill or gets hurt. Based on the risk assessment tool will Identify hazards, assess exposure, evaluate potential risks, and take precautions and to ensure that your workplace is safe and then decide what type of control measures shall be taken to control the employees are protected from harm by using the risk matrix to assist with the process.

Abstract-The COVID-19 pandemic dramatically accelerated the adoption of online learning methods in education. This abstract explores the various teaching methods employed during this period, including synchronous and asynchronous learning, flipped classrooms, and project-based learning. It also discusses the challenges and opportunities presented by these methods, such as the digital divide, student engagement, and assessment strategies. The abstract concludes by emphasizing the need for ongoing research and innovation in online teaching methods to ensure effective and equitable education for all students.

Abstract-This project aims to design and develop a conversational AI chat bot that can engage in basic conversations with users, providing helpful responses to frequently asked questions. Leveraging natural language processing (NLP) and machine learning algorithms, the chat bot will be integrated with a messaging platform to demonstrate its capabilities. The project’s objective is to create a functional chat bot that can understand user inputs, recognize intents, and generate appropriate responses.

Abstract-The study aimed to develop a prototype of an automated canal waste collection system. Specifically, it sought to determine the ultrasonic sensor’s capability to detect waste, servo motor’s spin to collect waste, system’s ability to update the serial monitor when the bin is full, average amount of time taken to complete a waste collection cycle, and average amount of waste detected and collected by the system for a certain period. The prototype used Arduino Uno R3, HC-SR04 ultrasonic sensors, MG995 servo motor, SG90 servo motor, jumper wires, breadboard, and a powerbank. The data were analyzed using frequency distribution, percentage, mean and Mann Whitney U-test. The results showed that the automated canal waste collection system prototype was 100% successful across three indicators; the prototype takes an average time of 14.6 seconds to complete a waste collection cycle; it detects an average amount of 7.50 wastes and collects an average amount of 7.20 wastes.  The amount of waste detected and collected does not significantly differ. This means that the prototype can collect a significant amount’ of detected wastes in the canal without human intervention. With these, the automated canal waste collection system has the potential in its functionality and consistency. Additionally, the device needs an IoT-based notification system for real-time monitoring.

Abstract-Plasma Physics, the study of charged particles and fluids interacting with electromagnetic fields, is increasingly gaining interest in the field of space propulsion. Traditional chemical rockets have limitations that restrict long-distance space travel, whereas plasma-based propulsion system promise higher efficiency and greater fuel academy. This paper explores the principles behind the plasma propulsion and examines the most recent advancement that bring this futuristic technology closer to practical applications. Additionally, it investigates the ongoing challenges, such as power requirements, fuel sources, and magnetic confinement, and how overcoming these challenges could open new frontiers for deep-space explorations.

Abstract-Photovoltaic power generation system implements an effective utilization of solar energy, but has very low conversion efficiency. The major problem in solar photovoltaic system is to maintain the DC output power from the panel as constant. Irradiation and temperature are the two factors, which will change the output power of the panel. In this article it is shown that for charging lead acid batteries from solar panel, MPPT can be achieved by perturb and observe algorithm. MPPT is used in photovoltaic systems to regulate the photovoltaic array output. A buck converter is utilized as a DC-DC converter for the charge controller. It is used to match the impedance of solar panel and battery to deliver maximum power. Voltage and current from the solar panel is sensed and duty cycle of gating signal is varied accordingly by the algorithm to attain maximum power transfer. It is obtained by using MATLAB Simulink Model.

Abstract-The Smart rides is robotic car. The rise of smart transportation solutions is revolutionizing urban mobility, and the concept of “Smart Rides” is at the forefront of this transformation. Smart Rides integrate emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) with transportation systems to provide efficient, safe, and sustainable travel experiences. This review paper explores the development, implementation, and challenges of Smart Rides, focusing on key components like real-time data processing, autonomous vehicles, ride-sharing services, and predictive analytics. We analyse various smart transportation initiatives across global cities, highlighting their impact on reducing congestion, enhancing energy efficiency, and improving accessibility for diverse populations. The paper also examines the role of smart infrastructure, including sensors and communication networks, in enabling seamless mobility. Additionally, the environmental and social implications of Smart Rides are discussed, with an emphasis on sustainability and equity. Challenges related to data privacy, cybersecurity, and regulatory frameworks are also addressed, proposing solutions for overcoming these barriers. By providing a comprehensive overview of the current state of Smart Rides and future trends, this review aims to guide policymakers, engineers, and researchers in shaping the next generation of intelligent transportation systems.

Abstract-Wind power generation has emerged as a crucial component of renewable energy systems, offering a sustainable and environmentally friendly alternative to fossil fuels. However, the integration of wind energy into the grid requires efficient power conversion mechanisms due to the variable nature of wind speed and the need for compatibility with existing infrastructure. A typical wind power system involves the conversion of mechanical energy into alternating current (AC) power using a generator, which is driven by wind turbines. This project focuses on the design and implementation of an AC to DC converter in wind power generation systems. The AC to DC converter plays a vital role in transforming the variable frequency AC output of wind turbines into a stable DC voltage. In this process, the generated AC power is first converted into direct current (DC) using power electronics, which enables efficient integration with batteries or facilitates smooth conversion. This conversion process is essential for stabilizing power output, minimizing losses, and ensuring the efficient transmission of energy over long distances. Advanced AC to DC converters and control systems enhance the reliability, efficiency, and scalability of wind power systems, making them a vital component in modern renewable energy infrastructures. This is implemented in the MATLAB/SIMULINLK.

Abstract-A well-designed main line is the backbone of any center pivot irrigation system. Ensuring it’s optimally sized and configured helps in achieving uniform water distribution, preventing pressure variations that could affect sprinkler performance. By using analytical methods such as hydraulic modeling, and optimization techniques, one can fine-tune pipe sizes, pump capacities, and valve configurations to ensure maximum efficiency. As you mentioned, AI-driven modeling could play a crucial role in real-time monitoring, helping predict system behavior under varying conditions. With real-time data, adjustments could be made on-the-fly to optimize water usage and reduce waste, even accounting for changing weather patterns or soil moisture levels.

Abstract-Population growth is a multifaceted and evolving issue that has captivated the attention of demographers, ecologists, and policymakers for many years. The swift increase in the global population carries significant consequences for resource management, environmental sustainability, and socioeconomic development. This study seeks to enhance the current body of knowledge on population growth by creating and comparing mathematical models that reflect the fundamental dynamics of this phenomenon. In particular, this research examines the exponential and logistic models of population growth, which are commonly utilized in the fields of demography and ecology. The exponential model posits that population growth is influenced by a constant birth rate and death rate, whereas the logistic model considers the environmental carrying capacity and the effects of resource constraints on population growth. Employing a mix of analytical and numerical techniques, this study evaluates the advantages and drawbacks of each model in forecasting population growth across various scenarios. The findings underscore the necessity of accounting for environmental carrying capacity and resource limitations when modeling population growth, and illustrate how mathematical models can guide policy and decision making in areas such as demography, ecology, and resource management. The implications of this study are significant for our comprehension of population growth and its effects on both the environment and society. The outcomes can be leveraged to create more precise and realistic population models, which can aid in policy and decision-making at local, national, and global scales. Additionally, this research showcases the potential of mathematical modeling to deepen our understanding of intricate social and environmental issues, emphasizing the need for further exploration in this area.

Abstract-This project presents the design, simulation, and performance analysis of a single-phase grid-connected solar photovoltaic (PV) inverter using MATLAB /SIMULINK. The primary objective is to develop an efficient and reliable inverter system that ensures maximum power extraction from the solar PV array and seamless integration with the grid. The main elements of the PV control structure are: a maximum power point tracker (MPPT) algorithm using the incremental conductance method: a synchronization method using the phase-locked-loop (PLL), based on delay: the input power control using the DC voltage controller and power feed-forward and the grid current controller implemented in two different ways, using the classical proportional integral (PI) and the novel proportional resonant (PR) controllers. The control strategy was tested experimentally on 2kW PV inverter.

Abstract-Distributed computing is a flexible, savvy, and proven conveyance stage for conveying corporate or purchaser IT administrations by means of the Internet. Distributed computing, then again, represents an extra danger on the grounds that basic administrations are regularly moved to an outsider, making information security and protection more hard to ensure, help with information and administration accessibility, just as show consistence. Distributed computing utilizes an assortment of advancements (SOA, virtualization, Web 2.0), and it acquires their security concerns, which we inspect here by distinguishing the most widely recognized shortcomings in these frameworks and among the most regularly referred to risks in the Cloud Computing writing and its environmental factors, just as to identify and associate shortcomings and dangers to possible cures.

Abstract-This project aims to develop a prototype that could potentially assist dog owners in managing feeding schedules more efficiently and inclusively. The functionality of the prototype was assessed based on the aptness of the object detection using an ultrasonic sensor, the accuracy of its real-time clock, the precision of its servo motor for kibble dispensing, the audibility of its voice recorder, the visibility of the neon signage for aging dogs, the braille integration for visually impaired owners, and its overall system reliability. The automated dog feeder was built using an Arduino Uno R3, an HC-SR04 ultrasonic sensor, RTC Module DS3231, PCB matrix, jumper wires, SG90 servo motor, LCD I2C screen, 12V AC adapter, MP3 player, speaker, acrylic, PVC elbow, and some recycled materials such as wood and PVC pipe. The data analysis was based on user feedback and system performance parameters. The results indicated that the prototype accurately dispensed kibble upon detection of the dog’s presence, tracked and scheduled feeding times, and got favorable feedback on its overall functionality, usability, and inclusivity. The given results imply that the automated dog feeder has strong potential to assist diverse dog owners in maintaining regular feeding schedules, making it a more practical solution for busy homes.

Abstract-Online payment fraud detection is a critical area of research and development in the realm of financial security. With the rise of e-commerce and digital transactions, ensuring the integrity and safety of online payments has become paramount, This abstract explores various methodologies and techniques employed in the detection and prevention of fraud in online payment systems. The detection of online payment fraud involves the use of advanced machine learning algorithms, anomaly detection techniques, and behavioral analytics. These methods analyze transactional data in real-time to identify suspicious patterns or anomalies that deviate from normal user behavior or transaction patterns. Additionally, the integration of artificial intelligence (Al) and deep learning models has enhanced the accuracy and efficiency of fraud detection systems by enabling them to adapt and learn from new fraud patterns continuously. Moreover, the abstract discusses the challenges associated with online payment fraud detection, including the balance between security and user experience, the need for real-time decision-making, and the evolving nature of fraudulent tactics employed by cybercriminals. Furthermore, it highlights the importance of collaboration between financial institutions, payment service providers, and cybersecurity experts in combating fraud effectively. In conclusion, effective online payment fraud detection is crucial for maintaining consumer trust, safeguarding financial transactions, and mitigating potential financial losses for businesses. Continued advancements in technology and methodologies will play a pivotal role in strengthening fraud prevention strategies and adapting to emerging threats in the digital payment landscape.

Abstract-The Biomatrix Beat Sensor, developed by Siemens Healthineers, represents a significant leap forward in cardiac and respiratory MRI. By eliminating the need for traditional electrocardiogram (ECG) electrodes and respiratory belts, this contactless technology leverages electromagnetic navigation (EMN) and the Pilot Tone (PT) concept to provide real-time, artifact-free synchronization of cardiac and respiratory motion. This review explores the limitations of conventional methods, the working principles of the Biomatrix Beat Sensor, its clinical applications, and its potential to transform patient care in MRI.

Abstract-This project presents a versatile antenna design suitable for various wireless communication platforms, including cognitive radio (CR) communication, 5G, and Wireless Local Area Network (WLAN) applications. A two-port Vivaldi antenna is designed using an FR4 substrate material with a dielectric constant of 4.4 and dimensions of 45.12 mm × 57.94 mm × 1.6 mm. This design is suitable for communication within a cognitive radio architecture. The antenna operates across multiple frequency bands, including the n79 band (4.4 GHz to 5 GHz) for 5G networks via port 1, and the 2.4 GHz band for Wi-Fi and Bluetooth communication via port 2. It achieves a return loss below -10 dB and a VSWR below 1.5 across the n79 band for 5G communication, and the 2.4 GHz band for WLAN applications.

Abstract-The increasing reliance on digital platforms for electoral processes in Uganda has brought to light significant cybersecurity issues that the Uganda Electoral Commission faces and this has made it imperative for the UEC to prioritize the strengthening of its cybersecurity. The Uganda Electoral Commission relies solely on traditional password-based authentication but hacking technologies have become more advanced and diversified. As a result, for security and authentication organizations are unable to rely on user ID and password-based authentication. (Hong, 2011). This single- factor authentication has been found to be vulnerable to attacks like malware, brute force, dictionary attacks, shoulder surfing, replay and phishing attacks etc. Much as passwords can easily be memorized and users at no cost are able to use them in their daily lives, these can be forgotten especially if the users have to log into multiple systems. (Mohammadreza Hazhirpasand Barkadehi, 2018). The UEC users log in to each of the systems independently which could lead to password fatigue. Cyberattacks can lead to unauthorized access to sensitive data, and data breaches and can therefore undermine the entire electoral process if the integrity of electoral data is compromised. This can lead to public distrust which could pose a threat to national stability. Therefore, by integrating MFA the UEC protects its sensitive electoral data and ensure secure access to its applications. Single sign-on is the ability for a user to authenticate once and access other protected resources where he has permissionwithout logging re- authentication. This system not only secures sensitive data but streamlines user experience through Single Sign-on (SSO) enabling users to log on once and gain access to multiple applications seamlessly.

Abstract-The rise of autonomous ships allows for great opportunities in the search for greater efficiency, cost-effectiveness, and environmental sustainability in maritime operations. Safety, though, has always been a major concern, particularly with the risks of collision within increasingly congested lanes. This paper reviews the literature on how artificial intelligence is being used to transform safety standards, including, in particular, autonomous shipping, for a collision avoidance system. We examined how AI-driven methodologies such as machine learning, path-planning algorithms, predictive analytics, and decision-support systems should be integrated to advance minimal human intervention in the development of navigational decision-making processes. Sensor technologies such as radar, LiDAR, sonar, and satellite imagery are analysed for situational awareness, real-time risk assessment, and dynamic adaptation to the maritime environment. The paper discusses the use of sensor technologies, for example, radar, LiDAR, sonar, and satellite imagery, in support of situational awareness, real-time risk assessment, and dynamic adaptation to the maritime environment. Further, it shows a number of regulatory challenges, ethical considerations, and urgent international standardization issues that the development and integration of AI technologies may have for maritime industries.

Abstract-The per capita of power in India is insufficient compared to other developed countries in the world. Hence, the only way is the optimal utilization of available energy sources but the difference between production and consumption of electrical energy, during summer is very high, due to large utilization of cooling machines like Air conditioner, Air coolers in such case a software industry, like BPO call center or any office with large server and many systems need to have a 24 hours working Air conditioner. This leads to huge power consumption. Conservative measures need to be initiated and implement to decrease this gap to restrain this situation the concert of DSM has begun in power system planning and management. Therefore this paper included Fuzzy logic applied to Ac which results to calculate the actual hourly turn off period and reduction in energy consumption. By the optimal consumption of electrical power results increase saving by reducing the electricity bill and reduce the over load on live grid during peak hours and also calculate the cost of savings and playback period for the return of investment. In this paper, solar energy is used to run air conditioner. The cost of saving and playback period is calculated by considering only photo voltaic (PV) and photo voltaic with fuzzy controller, Results proved that usage of PV with fuzzy controller has better annual savings and lower pay back period compared with only considering PV.

Abstract-Composite materials have emerged as a critical area of research and development, rapidly gaining importance as structural materials. Among polymer applications, composite materials are poised for significant advancements. Aluminum matrix composites (AMCs) are particularly favored in automotive and aerospace industries due to their exceptional mechanical properties, such as a high strength-to-weight ratio, superior wear resistance, increased stiffness, enhanced fatigue resistance, controlled thermal expansion, and stability at elevated temperatures. Stir casting is widely recognized as an efficient and cost-effective method for AMC fabrication. This study investigates the mechanical behavior of composites made from pure aluminum reinforced with copper, fabricated using the stir casting method. The composites were produced with reinforcement levels of 0%, 2%, 4%, and 6%. Results indicate that the inclusion of copper particles significantly enhanced the hardness, tensile strength, and wear resistance of the composites, though an increase in copper content resulted in decreased density. These findings highlight the potential of copper as a reinforcement material for aluminum-based metal matrix composites, offering valuable insights for diverse engineering applications.

Abstract-The objective of this research is to develop an Arduino-based rainfall and flood monitoring system with real-time alert notification to address the challenges faced by the affected residents of the outlying areas of the Davao region. It focuses on using Arduino technology for the areas affected by floods that can be easily monitored with an SMS alert notification and a buzzer system. This research employed an experimental approach, starting with the design and assembly of the prototype, followed by sensor accuracy testing and data collection over multiple trials. The findings revealed that the prototype accurately measured water level according to three categories: caution, warning, and danger; this category also achieves a 100% success rate in sending SMS alerts and providing timely warnings to the buzzer during moderate and critical rainfall events. The data logged on a microSD card confirmed the system’s consistent performance in tracking environmental conditions. In conclusion, the prototype reliably shows a rainfall and flood monitoring solution that ensures real-time alerts to the affected communities, significantly contributing to disaster preparedness and response of the local communities. Some of the suggestions for future upgrades are further testing under a variety of conditions, integrating the system with IoT platforms to manage data better, programs across the community to understand and develop the most effective response mechanisms, and system expansion for a greater spatial coverage by having multiple sensors along with a monitoring station network.

Abstract-The sacred syllable “Aum” (or “Om”) holds profound significance in Hinduism, Buddhism, Jainism, and other spiritual traditions. It is revered as the primordial sound of the universe, symbolizing the essence of ultimate reality, consciousness, and the interconnectedness of all existence. Explores the multifaceted dimensions of Aum, bridging its spiritual symbolism with modern scientific and technological paradigms, particularly in the realm of Artificial Intelligence (AI). By examining Aum’s representation of creation, preservation, and destruction, as well as its vibrational resonance with Earth’s natural frequencies and cosmic phenomena, Highlights the potential for harmonizing AI development with ethical principles, sustainability, and human well-being. Furthermore, it delves into the applications of Aum-inspired concepts in data science, neural networks, quantum computing, and AI-driven meditation tools, offering a holistic perspective on the convergence of ancient wisdom and cutting-edge technology.

Abstract-The efficiency of recycling systems is crucial for promoting sustainability and reducing environmental impact. However, contamination in recycling streams remains a significant challenge, often leading to decreased recycling effectiveness and increased operational costs. This paper investigates the potential of machine learning (ML) to optimize sorting systems in recycling plants, aiming to minimize contamination and improve material recovery rates. We explore the application of various ML algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), and Random Forests, for automating the detection and classification of contaminants in waste streams. By leveraging sensor data, image recognition, and real-time decision-making, our approach enhances sorting accuracy, reduces human error, and supports the efficient separation of recyclable materials. Experimental results from simulations and real-world case studies demonstrate that ML-driven sorting systems can achieve higher contamination reduction and sorting efficiency compared to traditional methods. This study highlights the promising role of machine learning in transforming recycling processes and proposes future directions for integrating AI technologies in waste management to create more sustainable and effective recycling solutions.

Abstract-Fingerprint matching is a crucial task in forensic science, where the accurate and reliable identification of individuals is essential for criminal investigations. Traditional fingerprint matching algorithms often struggle with challenges such as occlusion, distortion, and partial prints. In this study, we propose a novel approach that leverages Hypergraph Neural Networks (HGNNs) to enhance the robustness and accuracy of fingerprint matching in forensic applications. By modeling fingerprint features as hypergraphs, we capture higher-order relationships between minutiae points and their spatial configurations, enabling more effective matching despite partial or degraded fingerprints. The HGNN framework integrates both local and global feature information, improving the system’s ability to recognize subtle and complex patterns in fingerprint data. Extensive experiments on benchmark fingerprint datasets demonstrate that our approach outperforms conventional methods in terms of matching accuracy and robustness to noise. The proposed HGNN-based model provides a promising solution for advancing forensic fingerprint identification systems, offering improved performance under challenging real-world conditions.

Abstract-Urbanization and population growth have led to an exponential increase in vehicles, exacerbating parking-related challenges. Efficient parking management systems have become imperative to mitigate congestion, reduce fuel consumption, and minimize environmental impact. This paper reviews the integration of Internet of Things (IoT) technologies, computer vision, and Bluetooth Low Energy (BLE)-based indoor positioning systems for developing an efficient parking management system in urban areas. The proposed system is divided into three core modules: prediction of parking availability, real-time parking detection, and indoor navigation to guide users. This review evaluates existing approaches, highlights technological advancements, and discusses potential challenges in developing a proof of concept for the Indian context, emphasizing the cost- efficiency of the system.

Abstract-Inflammatory Bowel Disease (IBD) is a long-term condition that presents as Ulcerative Colitis (UC), or Crohn’s Disease (CD) based on its manifestations. It is characterized by inflammation in the small intestine and colon, impacting millions of individuals globally. The development of IBD is influenced by genetic, environmental, and immunological factors. Various pro-inflammatory agents such as TNF-α, IL-1β, IL-6, IL-12, TGF-β, INF-γ, COX-2, and increased reactive oxygen species contribute to significant intestinal damage. Typical symptoms of IBD include fever, abdominal pain, vomiting, diarrhea, weight loss, blood in the stool, and an elevated risk of colon cancer. Changes in colonic motility linked to IBD can worsen discomfort and diarrhea. Prostaglandins, particularly elevated in IBD patients, may modulate these alterations. The enzyme Cyclooxygenase-2, responsible for producing prostaglandins, is targeted in IBD treatment. The role of PGE2 in the pathogenesis of IBD is intricate; while it can have anti-inflammatory effects by inhibiting pro-inflammatory cytokines, it can also act pro-inflammatory in IBD. Dysregulation of PGE2 production in IBD can lead to excess levels in inflamed gut tissue, perpetuating chronic inflammation by attracting immune cells, increasing blood vessel permeability, and causing tissue damage. The context-dependent role of PGE2 in IBD warrants further research for a comprehensive understanding. Modulating PGE2 levels or its signaling pathways may provide potential therapeutic options for managing IBD. This review specifically examines the involvement of Cyclooxygenases and coxibs in treating IBD.

Abstract-The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) technologies has led to the development of increasingly sophisticated algorithms and models. In particular, these advancements have been pivotal in the domain of flower classification and recognition, aiming to identify and categorize the vast array of species of flowers present on our planet. This review delves into the convergence of AI and ML within the realm of flower classification, a domain that greatly benefits from the advancements in computer vision. As a sub-field of AI, computer vision plays a crucial role in extracting intricate features from floral specimens and subsequently utilizing classification algorithms to accurately label and categorize them. This literature review offers a meticulous and comprehensive exploration of the existing body of knowledge, aiming to elucidate the various methodologies and approaches employed in the taxonomic categorization of floral specimens. It encompasses an extensive survey of scholarly works, research papers, and innovative techniques that contribute to the advancement of flower identification systems. The review addresses diverse strategies, including but not limited to deep learning architectures, neural networks, feature extraction methodologies, and optimization techniques used in the classification of flowers. By synthesizing and critically analyzing the existing literature, this review aims to provide insights into the state-of-the-art techniques and emerging trends in the field of flower classification and recognition using AI and ML. This paper holds several benefits to the society such as: agriculture, environment conservation, education and tourism.

Abstract-Parental involvement is crucial in the cognitive and socioemotional development of student, and during the pandemic, parents played a vital role in shaping their student’s educational success. This study examines the relationship between parental involvement and the academic performance of Bachelor of Technology and Livelihood Education (BTLED) students at the University of Science and Technology of Southern Philippines. The study aims to determine the level of parental involvement and its correlation with the academic performance (GPA) of the students, specifically exploring the relationship between these two variables. A quantitative correlational research design was employed to assess how parental involvement correlates with academic performance. The study was conducted at the University of Science and Technology of Southern Philippines, Cagayan de Oro City, with a sample of 133 third year BTLED students. A two-part questionnaire was used to gather demographic data, parental involvement levels, and students’ GPA. Data were analyzed using descriptive statistics (mean, frequency, percentage) and Spearman’s rank correlation to determine the relationship between parental involvement and academic performance. The findings revealed that while parental involvement was generally high, the relationship with academic performance was weak and negative. Despite a high level of parental engagement, there was no significant correlation between involvement and GPA. The conclusion of the study indicates that while parental involvement positively influences student motivation, it did not significantly impact academic performance. Other factors, such as student self-motivation and program structure, likely play a more influential role. The study recommends that parents maintain active communication and structure in their student’s academic progress and that teachers and policymakers focus on strategies to enhance student self-motivation and independent learning.

Abstract-Electronic devices and circuits form the backbone of modern technological advancements, driving innovation across a multitude of industries. From consumer electronics such as smartphones and wearables to complex systems in aerospace, telecommunications, and healthcare, the applications of electronic circuits are vast and diverse. These circuits enable functionality, automation, and communication, making them an integral part of everyday life. This article explores the fundamentals of electronic devices, including key components like semiconductors, diodes, transistors, and capacitors, and delves into the operation and application of essential circuits such as rectifiers, amplifiers, and oscillators. It also covers digital electronics, providing insights into logic gates, flip-flops, microprocessors, and the interface between analog and digital systems. Furthermore, the paper examines the role of power electronics in energy management, renewable energy solutions, and industrial automation. Communication circuits, including RF systems, modulation techniques, and wireless communication, are also discussed, along with their crucial role in enabling modern-day connectivity. Advanced topics such as integrated circuits, VLSI, embedded systems, and emerging trends like IoT, AI, and quantum electronics are presented to highlight the trajectory of innovation in the field. Finally, this article concludes with a reflection on the impact of electronic devices and circuits on contemporary life and their future potential in shaping technological progress.

Abstract-Our work proposes a classification architecture based on deep learning techniques, particularly convolutional and recurrent neural networks, for the classification of tomato diseases from digital images. More specifically, the objective is to classify leaves infected by a disease using supervised learning on a pre-labeled image dataset from PlantVillage. One of the main challenges of using deep learning, however, is the need for a very large amount of annotated data, which is not always available. Therefore, the objective of our study is to develop a specific hybrid architecture, CNN-LSTM (Convolutional Neural Networks – Long Short-Term Memory), capable of leveraging small (frugal) and relatively imbalanced datasets. To assess the relevance of this approach, we propose to compare it with deep learning algorithms frequently described in the literature. The proposed model achieved better classification performance in terms of validation Accuracy of 94,16%,

Abstract-Landslides are significant natural disasters that pose threats to human life and the environment, particularly in hilly regions. This study contributes to the understanding of landslide dynamics by providing localized geotechnical data and stability analyses. After the landslide in 2023, this study looks into the geotechnical conditions and stability factors that led to landslides in the Fudale, Gamo Zone, Southern Ethiopia. The research paper aims to analyze the soil characteristics contributing to landslide occurrences and to assess slope stability using the Finite Element Method (FEM) through Plaxis 2D software. Ten soil samples were collected from various depths, and laboratory tests were conducted to determine their index and engineering properties. Test results indicate that the predominant soil types are fine-grained, comprising significant percentages of clay and silt, which are particularly susceptible to saturation and subsequent landslides. The analysis identified rainfall, slope geometry, soil permeability, and groundwater conditions as critical factors influencing slope stability. The computed factor of safety (FOS) for natural conditions was found to be 0.972, indicating an unstable slope.

Abstract-Mental illnesses are a serious concern in India where every seventh person suffers from mental health problems[1,5]—with women more affected than men. While the burden of perinatal mental illnesses grows, India lacks exclusive policies to address it. Although postpartum depression or blues are restricted to the period of six weeks post-delivery, the roots of this condition are traced right from pre-pregnancy through the antenatal period to the period of one year post-delivery. We took up a study amongst postpartum mothers about their self-assessment of this condition, their awareness and their strategies to combat postpartum anxiety and reinforce the importance of psychological well-being as a part of routine assessment during antenatal period, fortified in the postpartum phase.

Abstract-The pharmaceutical industry, precision and efficiency of tablet manufacturing are required to meet quality standards. In this project, the production process is being modernized by incorporating information and communication technology (IoT) into the production line. The machine performs auto-loading of all important steps including material feeding, compression and ejection and also IoT-powered sensors track parameters such as compression force, tablet weight, and humidity. In real-time, data is data is sent to a cloud-based server, enabling remote monitoring and predictive maintenance. This system guarantees of quality tablet, reduces downtime, and improves efficiency. The resulting machine is scalable and intuitive to use, making it suitable for both small- and large-scale production and brings Smart Manufacturing into the pharmaceutical industry.

Abstract-The worldwide economy relies vigorously upon horticulture, yet ordinary cultivating rehearses remember disadvantages like flightiness for the climate, ineffectual asset the board, and an absence of ongoing independent direction. The information driven brilliant cultivating application introduced in this examination advances farming administration by joining Enormous Information, Computerized reasoning (man-made intelligence), and Web of Things (IoT) sensors. The framework involves OpenCV for plant illness finding, TensorFlow and K-Closest Neighbors (KNN) for crop observing, and Choice Tree calculations for crop suggestion. Besides, LLaMA-fueled “Vigro Bot,” a chatbot, offers ranchers constant exhortation. The proposed procedure supports practical cultivating techniques, increments efficiency, and lessens asset squander.

Abstract-High-rise buildings are a hallmark of modern urban development, offering solutions to space constraints and population density. However, the maintenance of these structures presents unique challenges due to their complexity, height, and the diverse systems they encompass. This paper explores the critical aspects of maintaining high-rise buildings, including structural integrity, mechanical and electrical systems, façade maintenance, and safety protocols. It also discusses the role of technology, such as Building Information Modeling (BIM) and Internet of Things (IoT), in enhancing maintenance practices. The paper concludes with recommendations for best practices and future research directions to ensure the longevity and safety of high-rise buildings.

Abstract-There was much hype and importance of the Year 2000 as known as Y2K Problem and all the legacy application Software needs to changed and incorporated with This and now we talk about IOT – Internet of Things that is Network of Physical Objects that can be connected and share data within themselves. So these changes are like Paradigm changes and it impacted a lot in our daily life, this article will talk about more About on this in details.

Abstract-This project focuses on improving the cart’s utility by integrating advanced loading and storage features. A cylindrical roller mechanism is introduced to simplify the process of loading and unloading items, reducing the need for manual effort and improving efficiency. The inclusion of two spacious and organized compartments provides ample storage space, ensuring the safe and secure transportation of stationary items. Attention is given to the ergonomic design of these compartments to facilitate easy access and optimal space utilization. Additionally, this stage involves analysing the structural stability of the cart to ensure it can handle varying weights without compromising performance. By enhancing its functional capabilities, this phase ensures the cart is tailored to meet the material handling needs of a busy campus environment.

Abstract-This paper will design an Automatic Chakali Making Machine based on Programmable Logic Controller (PLC) technology to automate the traditional chakali making process. Automation is a major concern in contemporary food industries to overcome the limitations of quality control, production rate, shortage of manpower and profitability. The suggested system combines mechanical, electrical and control elements to execute primary operations such as dough extrusion, shaping, cutting and frying with high accuracy and efficiency. The process starts with a dough feeder, which transports the dough to an extruder, where PLC controls the extrusion process to deliver regular shape and size. Uniformity is achieved by a synchronized cutting system and the shaped chakalis are transported to a frying unit by a conveyor system, where PLC automation controls temperature and oil levels to deliver uniform cooking. The system also features real-time monitoring to deliver safety and efficiency. With increasing demand for food industry automation, manufacturers are continuously upgrading equipment to meet consumer demands, deliver hygiene standards and boost profitability. By minimizing manual intervention, delivering optimal utilization of ingredients and product uniformity, this automated system not only increases productivity and food safety but also enables small to medium-scale businesses to boost production on a large scale in an efficient manner. This project is intended to revolutionize chakali manufacturing by introducing automation, enhancing raw material traceability and delivering consistency in mass production.

Abstract-The Hostel Management System (HMS) is an automated solution designed to streamline hostel operations, including student registration, room allocation, mess management, and attendance tracking. This system enhances efficiency, reduces manual workload, and ensures data security and accessibility. This paper presents an overview of the proposed system, its architecture, implementation, advantages, and future scope.For room allocation, Genetic Algorithm is used which allocates room to the students as per their preferences. Also, the web application consists of a generation of barcodes which can be used by the students to scan it while leaving/entering hostel premises. And the same can be used in mess also. Students will get endorsement notices in their mails which informs guardians about their ward’s presence in the hostel and their curricula using this model just in one touch. The student can raise leave requests as well as raise cleaning issues to the warden. The warden can monitor the student records and daily roll call list. The fee details and the due of the student can also be verified using this QR database management and inquiry method.

Abstract-Drip irrigation is an advanced water delivery system designed to provide efficient irrigation by delivering water directly to the root zone of plants. 1This method involves a network of pipes, tubing, and emitters, ensuring that water is distributed evenly and precisely, minimizing water wastage. Compared to traditional irrigation methods, drip irrigation significantly reduces water consumption by preventing evaporation and runoff. Additionally, it promotes healthier plant growth by providing consistent moisture levels and reducing the risk of overwatering. This system is particularly beneficial for water-scarce regions and sustainable agriculture, offering advantages such as improved crop yields, reduced weed growth, and the efficient use of fertilizers. With its ability to optimize water usage and promote environmental sustainability, drip irrigation is a highly effective and cost-efficient solution for modern farming and gardening practices.

Abstract-This study examines the impact of key macroeconomic indicators—Consumer Price Index for All Urban Consumers (CPI-U) and Federal Reserve Rate (Fed Rate)—on the performance of Bitcoin (BTC), Gold (XAUUSD), and the S&P500. Through regression analysis, the research provides a comparative perspective on traditional and emerging asset classes (Wu, 2022). The findings indicate that inflation plays a dominant role in influencing asset prices, with the strongest effects observed in equities and Gold. Bitcoin, despite its perception as a digital hedge, exhibits moderate sensitivity to inflation alongside high volatility driven by speculative and external factors. The Fed Rate has a weaker influence on all three assets, particularly Bitcoin, suggesting that monetary policy alone does not dictate cryptocurrency price movements (Pinchuk, 2021). The study underscores the importance of inflation in shaping investment strategies, especially for traditional assets, while highlighting Bitcoin’s speculative nature. The research also introduces a model framework that can be adapted to assess various asset classes against different macroeconomic indicators. Future work should explore advanced analytical techniques and a broader set of variables to enhance market insights.

Abstract-Wastewater treatment is a crucial process for managing and purifying water contaminated by domestic, industrial, and commercial activities before it is safely discharged or reused. The treatment process involves multiple stages, including preliminary, primary, secondary, and tertiary treatment, each designed to remove solids, organic matter, harmful microorganisms, and chemical pollutants. 1Advanced techniques such as biological treatment, filtration, and disinfection further enhance water quality. Proper sludge management ensures the safe disposal or reuse of byproducts. Wastewater treatment plays a vital role in protecting public health, preserving ecosystems, and promoting sustainable water use. With growing concerns about water scarcity and pollution, innovative and efficient wastewater treatment technologies are increasingly essential for environmental sustainability and resource conservation.

Abstract-Carbon dioxide (CO₂) is a sustainable, abundant, and non-toxic carbon feedstock, offering immense potential in organic synthesis. However, its thermodynamic stability and low reactivity necessitate innovative activation strategies. Recent advances have demonstrated CO₂’s utility in various transformations, including carboxylation, cycloaddition, hydrogenation, and carbonylation reactions. These processes enable the production of valuable compounds such as carboxylic acids, carbonates, carbamates, and heterocycles, often using transition metal catalysts, organocatalysts, or electrochemical methods. 1Catalytic systems such as metal complexes, N-heterocyclic carbenes, and metal-organic frameworks have been instrumental in overcoming the inherent challenges of CO₂ activation. Additionally, emerging approaches like electrocatalysis and photocatalysis provide sustainable pathways for CO₂ reduction and incorporation into organic frameworks. By converting a greenhouse gas into valuable products, CO₂ utilization not only addresses environmental concerns but also advances green chemistry. Ongoing efforts focus on improving reaction efficiency, selectivity, and scalability, paving the way for industrial applications and contributing to a circular carbon economy.

Abstract-Rainwater harvesting (RWH) is a sustainable method of collecting and storing rainwater for various uses, reducing dependence on conventional water sources. This paper discusses the potential for rainwater harvesting in buildings by calculating the amount of water that can be collected based on rooftop area, annual rainfall, and runoff efficiency. 1 The standard formula for estimating rainwater harvesting potential is outlined, considering key factors such as surface type and climatic conditions. Additionally, the benefits of RWH—including groundwater recharge, flood prevention, and cost savings—are highlighted. The study emphasizes the importance of designing efficient storage and filtration systems to maximize usability. Implementing RWH in urban and rural settings can contribute to water conservation and sustainability, making it a crucial component of modern water management strategies.

Abstract-Muslim-run media outlets influence India’s online conversation by highlighting underrepresented voices, fighting false information, and encouraging open discussions. In India, the mainstream media is often accused of misleading or ignoring Muslim viewpoints. As a result, independent digital platforms created by and for Muslims have become important for sharing their stories, supporting their rights, and shaping their narratives. Independent internet platforms like TwoCircles.net and Maktoob Media are crucial spaces for representation, advocacy, and grassroots storytelling in India, while mainstream media have been condemned for reinforcing negative stereotypes and marginalizing Muslim voices. This study emphasizes the importance of editorial independence in unbiased reporting and helps us understand how independent Muslim media work in India’s changing digital ecosystem and how they democratize media representation and promote public equity.

Abstract-In the era of advanced artificial intelligence and machine learning, collaborative deep learning has emerged as a powerful approach to leverage distributed data and computational resources. However, a significant challenge that persists is ensuring the generalizability of models developed in collaborative environments. This project addresses the generalizability challenge in collaborative deep learning by proposing a novel framework that integrates advanced techniques in model training and validation. Deep learning models typically require data to be collected at a centralized location to learn effective representations, which introduces several issues such as communication costs and risks to data privacy. These issues are particularly critical in the case of clinical data, where patient privacy is paramount. In such contexts, distributed machine learning offers a viable solution where various data-holding sites can locally train a mutually agreed-upon model and share their knowledge. Federated learning (FL) facilitates this process using a client-server framework. Clients in the FL environment are independent small edge devices that retain their data locally, while the server acts as a central site that aggregates and distributes the knowledge learned by each client to others. The server receives locally trained weights from all participating clients, aggregates them, and then transfers the aggregated weights back to all clients before the next training round begins. This iterative process continues until the server achieves the desired accuracy. FL thus enables multiple clients to collaboratively train a shared global model without sharing their local data, preserving data privacy and addressing issues of limited data availability. However, FL faces challenges such as high communication costs for transferring weights, statistical data heterogeneity among clients, and the single point of failure of the server. Client heterogeneity arises mainly due to differences in data distribution among clients and their respective computational power. This project targets statistical data heterogeneity in the FL environment and proposes a simple yet effective attention-based approach to address this issue. Specifically, in the proposed setting, each client sends a mean representation to the centralized server along with the trained model’s weights. A similarity matrix is computed based on the similarity score of each client’s mean representation from every other participating client. This similarity matrix determines the weightage of each client’s model in the aggregated model. The centralized server computes the attention vector for each client using this similarity matrix and then broadcasts this attention vector to all clients. This attention mechanism is implemented both on the centralized server and the participating clients. We consider FedAvg, FedProx, and FedMomentum as baselines for comparison, and our proposed approach outperforms all of them. For statistical heterogeneity, we perform extensive experiments on FOOD101 and CIFAR10, demonstrating that our approachperforms well even with highly skewed data. To address the single point of failure issue in FL, we propose an efficient version of swarm learning. We demonstrate the effectiveness of context- aware swarm learning through experiments on the HAM10000 and ISIC Skin Lesion 2019 datasets. Additionally, to mitigate the high communication costs in FL, we propose BAFL (Federated Learning for Base Ablation), which introduces a fine-tuning approach to leverage the feature extraction ability of layers at different depths of deep neural networks. We evaluate the proposed approach using VGG-16 and ResNet-50 models on datasets including WBC, FOOD-101, and CIFAR-10, achieving up to two orders of magnitude reduction in total communication cost compared to conventional federated learning.

Abstract-Ransomware attacks have escalated, posing significant threats to organizations by encrypting critical data and demanding ransoms. Traditional security measures often fall short against sophisticated ransomware variants. This paper explores the deployment of hardware-based honeypot files utilizing Server Message Block (SMB) traps as a proactive defense mechanism. By integrating deceptive SMB shares at the hardware level, organizations can detect, analyze, and mitigate ransomware activities more effectively.

Abstract-The design and development of a drone for spraying pesticides on agricultural lands have been described in this paper. The drone developed is a quadcopter integrated with a spraying mechanism. A quadcopter can be described as a mechanical device that can hover using propellers fitted into it is four arms. Hovering is achieved using one set of clockwise spinning propellers and another set of counter- clockwise spinning propellers that generate the thrust required to facilitate the taking off and hovering process. The agricultural industry contributes heavily to India’s GDP, thus making it one of the chief sources of revenue. It is the foundation of India’s economy and contributes to approximately one-fourth of its gross domestic product. It is inevitable that fertilizers and pesticides will be used to increase crop yields. However, few health-related problems can arise due to prolonged exposure to such chemicals during manual spraying. A few examples include mild skin irritation to congenital disabilities, changes in genetics, falling into a coma, or even death in severe cases. Drones have been used extensively in agriculture over the past few years. This paper describes the components required for the successful design and development of a quadcopter that can be utilized for spraying fertilizer on agricultural lands. The quadcopter is equipped with a container carrying a Direct Current water pump fitted with a pipe and nozzle arrangement. The liquid passes and is controlled using the instructions that the user provides the controller.

Abstract-This paper examines the pivotal role of artificial intelligence (AI) in transforming business practices, tracing its evolution from foundational research and innovation to practical market deployment. As AI technologies rapidly advance, they are reshaping industries by enhancing productivity, enabling data-driven decision-making, and fostering the development of intelligent products and services. The study highlights the dual nature of AI’s impact, addressing both the opportunities it presents for economic growth and innovation, as well as the challenges and ethical considerations it raises for various stakeholders, including businesses, consumers, and policymakers. Through an analysis of key research breakthroughs and their implications for entrepreneurial activities, the paper identifies trends in AI start-ups and their contributions to the market. Ultimately, this research aims to provide a comprehensive understanding of how AI is not only revolutionizing business operations but also influencing the broader economic landscape, thereby offering valuable insights for practitioners and researchers alike. In recent years, the emergence of a multitude of intelligent products and services has sparked widespread interest in artificial intelligence (AI) and its commercial viability, raising critical questions about whether this trend represents genuine transformation or mere hype. This paper investigates the extensive implications of AI, exploring both its positive and negative impacts on governments, communities, companies, and individuals. By examining the journey of AI from research and innovation to market deployment, the study highlights significant academic achievements and innovations in the field, as well as their influence on entrepreneurial activities and the global market landscape. Additionally, the paper identifies key factors driving the advancement of AI technologies. To further explore entrepreneurial engagement with AI, two lists of the top 100 AI start-ups are analyzed. The findings aim to enhance understanding of AI innovations and their broader impact on businesses and society, ultimately providing insights into how AI can transform business operations and contribute to the global economy.

Abstract-This paper introduces Vishwanath’s Law of Dynamic Mass-Energy Redistribution, which proposes a novel framework to understand the adaptive behavior of mass in non-inertial reference frames. Traditional mass-energy equivalence fails to incorporate mass fluctuations due to high internal energy shifts and entropy variations. Using advanced tensor calculus and Lagrangian mechanics, we derive a modified mass-energy relationship. Applications in missile propulsion, quantum mechanics, and astrophysical anomalies are explored, providing new insights into mass-energy interactions.

Abstract-The growing demand for internships among students has highlighted the need for an effective platform that connects students and teachers in a more organized manner. Currently, many colleges rely on WhatsApp groups to share internship opportunities, which often leads to confusion, missed messages, and a cluttered experience. This research proposes the development of an Internship Portal application designed to address these challenges by providing a dedicated space for students and teachers to manage internship postings and applications efficiently. The proposed Internship Portal aims to create a user-friendly application that allows students to browse available internships, apply directly, and keep track of their applications. Teachers will have the ability to post internship opportunities tailored to their students’ courses, ensuring that all relevant information is shared in an easily accessible format. By consolidating internship postings in one platform, we hope to eliminate the chaos of multiple messages in WhatsApp groups and create a streamlined process for both students and teachers. Another important aspect of this project is the focus on user experience. The application will feature a simple and intuitive interface that is easy to navigate, ensuring that both students and teachers can use the platform without difficulty. This is particularly important for students who may not be technologically savvy and need a straightforward solution to access internship information. By prioritizing user experience, we aim to encourage more students to engage with the platform and take advantage of the internship opportunities available to them.

Abstract-Electricity usage is expanding at an exponential rate. This research recommends making use of human locomotion energy, which, despite being extractable, is largely wasted. This research presents an energy storage concept that employs human movement, skipping, and running as energy. The piezoelectric sensors are used in this innovative footstep power production system. The piezo sensors are positioned below the platform to generate a voltage from footstep. The sensors are arranged in such a way that maximum output voltage is generated, which is then sent to our monitoring circuitry. This energy is then stored in the batteries and can be used whenever it is convenient. A model like this is near suitable for India, which has a large pedestrian people. This method of generating charge and storing it for later use encourages an environmentally responsible approach to energy creation and the development of clean green energy.

Abstract-This research paper conducts a comparative analysis of the old and new tax regimes for the financial year 2023-2024 in order to evaluate their impact on individual taxpayers, businesses, and government revenue. The study compares the main differences in tax slabs, deductions, and overall tax burden at different income levels. Further, it covers the compliance burden and administrative efficiency of both regimes, analysing how they affect taxpayer behaviour and economic decision making. This research will apply a combination of both qualitative and quantitative methodologies. The financial impact of each regime for different taxpayer groups is analysed by comparing tax liabilities under different income brackets, showing which regime provides more benefits for each group of taxpayers. Interviews and surveys with tax professionals and salaried people reveal information related to preferences, challenges, and practical implications associated with each regime. The study further analyses broader macroeconomic indicators, such as revenue generation, disposable income, and investment trends, in order to find out the broader economic implications of the tax reforms. The research results find that the old tax regime remains beneficial for those with significant investments that result in savings under the deduction sections: 80C, 80D, and HRA. The old regime is likable by high-income earners and those with complicated financial structures because it saves on taxes. On the other hand, middle-income earners and those without substantial investments prefer the new tax regime since it reduces complexity in tax filing and compliance. The new regime may also involve an increase in disposable income, which may fire up consumer spending, although it is less clear what the effect will be on long-term savings and investment patterns. This, therefore, implies that both regimes have their respective advantages and limitations, and the optimal choice would depend on an individual’s financial situation and tax saving strategy. Policymakers must continue to refine tax structures for better revenue generation and taxpayer convenience, ensuring economic stability. This detailed comparative assessment will help taxpayers make informed financial decisions and contribute to the ongoing discourse on tax policy improvements in India.

Abstract-In the present study shows that global hemophilia research from 2018 to 2024, examining publication trends, authorship, collaboration, and citation impact. The study analysed that a steadily increase in research output, with 2024 being the most productive year (1,333 publications, 15.61%), followed by 2023 (1,279 publications, 14.98%). Articles (5,537 records) and reviews (1,339 records) are the dominant communication channels, while collaborative research is prevalent, with most papers having more than six authors (3,021). Most productive authors are Hermans, C. (165 papers) and Peyvandi, F. (146 papers), with European institutions leading contributions, alongside notable input from Japan. The United States is the leading contributor (2,414 papers, 28.27%), followed by the United Kingdom (9.72%) and Italy (9.67%), with China, Japan, and India also playing significant roles. Highly cited studies focus on immune checkpoint inhibitors, gene therapy, and RNA-based therapeutics, with the most cited article by Brahmer, Julie R., et al. (2018) having 2,761 citations. The study highlights the increasing global collaboration and evolving research priorities in hemophilia, emphasizing innovations in gene therapy and personalized medicine.

Abstract-Authenticity plays a crucial role in shaping consumer purchase decisions, influencing brand perception, trust, and overall satisfaction. In an era where consumers have access to vast information and numerous product choices, authenticity has emerged as a key differentiator for brands. This study explores the impact of authenticity on consumer behaviour, examining factors such as brand transparency, product originality, ethical practices, and emotional connection. By analysing consumer preferences and decision-making patterns, the research highlights how perceived authenticity fosters brand loyalty and drives purchasing intent. The findings suggest that businesses that prioritize authenticity in their branding, communication, and product offerings gain a competitive edge in the market. This study provides valuable insights for marketers and brand strategists aiming to build long-term consumer relationships based on trust and credibility.

Abstract-The increasing demand for energy-efficient computing systems has propelled the research and development of low-power Very Large Scale Integration (VLSI) circuits, particularly in high-performance computing (HPC) applications. This paper explores a variety of design and optimization techniques aimed at minimizing power dissipation while maintaining high performance levels. We analyze key methodologies including Dynamic Voltage and Frequency Scaling (DVFS), multi-threshold voltage design, and power gating strategies that contribute to significant energy savings in VLSI architectures. The integration of these low-power techniques is crucial in responding to the rigorous challenges posed by growing transistor densities and the resultant heat dissipation concerns in modern computing systems. Furthermore, this research addresses the intersection of circuit-level optimizations with architectural design choices, offering insights into effective power management across various operational states. Through a comprehensive review of recent advances and case studies in low-power VLSI design, this paper underscores the critical importance of these innovations in meeting the evolving energy efficiency requirements of high-performance computing platforms, ensuring sustainability and cost-effectiveness in future technological landscapes.

Abstract-Network connection for systems with purpose of exchanging with collection of Mobile communication system with ground operating surface for allowing mobile devices with telecommunication network for transmitting data with use of underground devices While the research paper focuses on Self – evolving method for featuring Time – series analysis with use of magnetic field of lines for self-adaptive signaling recombining and readvancing patterns for distribution and maintaining automated Rekeying Technology for Wireless Communication system . Intelligent Ecosystem Networking with the use of Cloud or Hybrid Cloud environments with the future of wireless communication network involves solutions for users, applications and devices involving identity management with securing adaptive access, identifying governance and user experience with use of self – evolving patterns for allowing mobile communication while transmitting network through all medium. The Main objective of the paper is Readvancing patterns for self-adaptive signaling following approach for distribution patterns.

Abstract-Feeding the fish at a very specified schedule has proven to be a really complicated task for the aquaculture farmers. This study aimed to develop an automated fish feeding system for nursing ponds. A functionality test was used in the conduct of the study. The automated fish feeding system used Arduino IDE to code the features such as delivering feeds, time interval, and the servo motors spin. Based on the results of the study, the automated fish feeding system had successfully passed the overall functionality test on its feeding mechanism in terms of delivering feeds, time interval, the servo motors spin and the system programming. Furthermore, the result showed that the actual masses of feeds dispensed on each aquarium had no significant difference compared to masses of feeds set on the device. The automated fish feeding system has the potential to greatly benefit aquaculture farmers by ensuring consistent and precise feeding schedules, reducing human intervention, optimizing feed usage, and promoting healthier fish growth, ultimately improving productivity and profitability.

Abstract-Facial emotion detection is an emerging field that leverages artificial intelligence (AI), machine learning, and computer vision to recognize and interpret human emotions based on facial expressions. This study explores the effectiveness of deep learning models, such as Convolutional Neural Networks (CNNs), in identifying emotions like happiness, sadness, anger, fear, surprise, and neutrality. The system’s applications span healthcare, marketing, security, and human-computer interaction. However, challenges such as cultural variability in expressions, mixed emotions, and privacy concerns necessitate further improvements. This research aims to enhance facial emotion detection by addressing accuracy, ethical considerations, and real-world implementation. The purpose of this is to make a study on recent work on automatic facial emotion recognition In deep learning. There are many different techniques for recognizing human emotion.

Abstract-A focus on synthesizing different types of two-dimensional Carbon nanostructures using Methane and Argon without catalyst has been conducted in Radio Frequency Plasma Enhanced Chemical Vapor Deposition. This study reports the variation in growth density, size and morphological characteristics of Carbon nanostructures by varying the gas compositions. Field Emission Scanning Electron Microcopy (FE-SEM) and Atomic Force Microscopy (AFM) studies shows the high percentage of Methane gas in the composition is directly proportional to the density and inversely proportional to the size of the nanostructure. We report that the concentration of Methane usually offers more carbon species or driving force for the growth of the two-dimensional carbon nanostructures. This process enables to increase the density and decreases the size of the nanostructures. The results of Raman spectroscopy show the typical carbon features at 1321,1571 and 2639cm-1 respectively. The intensity ratio of these two peaks ID/IG increases with increase in the Methane gas percentage in the composition indicates the nanocrystalline nature of two-dimensional carbon nanostructures with a large number of defects.

Abstract-Performance measurement is essential for improving decision-making, aligning project outcomes with stakeholder’s objectives, and driving future improvements. In the context of municipal construction projects, construction sector in Nigeria in general, and Delta State in particular faces persistent challenges, including with delays, cost overruns, and failure in operational performance and stakeholders dissatisfaction these challenges underscore the need for comprehensive system that incorporates Critical Success Factors (CSF), Performance Measures (PMs), and Success Metrics to ensure project efficiency and stakeholder’s satisfaction. Hence, the goal of this research is to develop framework tailored to Delta State municipal construction sector. It identifies challenges; explore best practices from developed countries, and leverages input from key stakeholders. Data collected through surveys and analyzed using statistical tools, including mean analysis and ANOVA, informed the framework’s development. Findings reveal that significant gaps in performance measurement practices in the study area, highlighting the absence of a holistic approach to managing municipal construction projects. The proposed framework will address these gaps by offering a structured, stakeholder-focused approach to project evaluation. This research contributes to improving the effectiveness of municipal projects and offers a foundation for future studies on performance measurements in developing countries.

Abstract-Lunar mineral mapping is crucial for understanding the Moon’s formation, geological evolution, and resource potential. This review paper examines the significant contributions of the Chandrayaan-2 mission to this field. Prior to Chandrayaan-2, missions like Clementine and Chandrayaan-1 provided foundational mineralogical data, revealing the Moon’s diverse composition dominated by minerals such as plagioclase feldspar, pyroxenes, and olivine, with regional variations reflecting magmatic differentiation and impact processes. Chandrayaan-2, equipped with advanced instruments including the Imaging Infrared Spectrometer (IIRS), significantly enhanced lunar mineral mapping capabilities. This review synthesizes key findings from Chandrayaan-2, highlighting its high-resolution spectral and spatial data that have refined our understanding of mineral distribution across the lunar surface. Methodologies employed encompass sophisticated spectral unmixing and analysis techniques applied to IIRS data, enabling the identification and mapping of subtle mineralogical variations, including hydration features and the composition of lunar geological units. The improved mineral maps generated by Chandrayaan-2 have profound implications for future lunar exploration, resource utilization strategies, and a more nuanced comprehension of planetary formation processes within our solar system. This paper underscores the enduring legacy of Chandrayaan- 2 in advancing lunar science.

Abstract-Steganography is a technique of hiding secret information within an innocuous carrier such as text, image, audio or video. Its purpose is to conceal the existence of the message and to prevent detection by an eavesdropper. Steganography has gained popularity as a means of secure communication due to its ability to hide the message in plain sight. This paper provides an overview of the concept of steganography, its history, and its applications. It also discusses different types of steganographic techniques, such as least significant bit (LSB) embedding and transform domain techniques. The paper then examines the importance and limitations of steganography, such as the security and legal compliance, and the vulnerability to attacks. Finally, the paper explores some of the emerging trends in steganography research, the difference between steganography and cryptography and real-life examples. Overall, this paper provides a comprehensive understanding of steganography, its applications, advantages, and future directions.

Abstract-communication, collaboration, and the sharing of ideas. It can also help students develop critical thinking and digital literacy skills. The prevalence of social media in modern society has raised questions about its implications for educational contexts. This study aims to investigate whether social media usage contributes positively to classroom engagement or serves as a distraction. The primary objectives are to analyze patterns of usage, identify benefits and drawbacks, and propose methods for effective integration. A quantitative-descriptive research design was utilized to investigate social media engagement levels and academic performance among elementary pupils at Eugenio A. Abunda Sr. Elementary School during the 2024-2025 school year. This research tries to conduct an investigation by paying attention to and considering the Profile of Respondents According to Reading Level which consists of Reading Level which consists of Frustration, Instructional, Independent. In this study, the data was divided into various categories of respondents. This aligns with Sivakumar (2020), who characterized social media engagement as an obsessive fixation with social media and an insatiable desire to access or utilize it. The notion of social media engagement as a condition of reliance leading to excessive use and difficulties in abstaining resonates with the observed moderate cognitive engagement among respondents.

Abstract-This study investigates the effectiveness of integrating Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) for the diagnosis of breast cancer through mammographic image analysis. Recognizing breast cancer as a leading cause of mortality among women, early and accurate detection is crucial for better treatment outcomes. The research focuses on the development of a hybrid model that leverages the strengths of both SVM for classification and CNN for feature extraction. The model is tested on a comprehensive dataset of mammographic images, employing advanced preprocessing techniques to enhance image quality and reduce noise. It meticulously compares the performance metrics, such as accuracy, sensitivity, and specificity, of the proposed hybrid approach against traditional methods. Initial findings indicate the hybrid model outperforms individual classifiers in terms of diagnostic accuracy, showcasing its potential application in clinical settings for effective breast cancer screening. This research not only contributes to understanding the anatomical nuances in imaging but also emphasizes the importance of machine learning in medical diagnostics, paving the way for enhanced early detection strategies.

Abstract-The quality of work life (QWL) is a critical aspect of employee satisfaction, productivity, and overall well-being. In the education sector, where women constitute a significant portion of the workforce, understanding the factors that influence their QWL is essential for fostering a conductive work environment. This research paper aims to explore the various factors affecting the QWL of women employees in the education sector, including work- life balance, job satisfaction, organizational support, career development opportunities, and workplace culture. The study employs a mixed- methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive data. The findings reveal that work-life balance, organizational support, and career development opportunities are the most significant factors influencing QWL. The paper concludes with recommendations for educational institutions to enhance the QWL of women employees, thereby improving their overall job satisfaction and productivity.

Abstract-Industrial pollution is a major global issue affecting human health, the environment, and economic development. This paper explores the pervasive nature of industrial pollution, particularly its impact on developing nations, and presents an analysis of its sources, types, health, and environmental consequences. The paper highlights the significant challenges posed by industrial pollutants, such as heavy metals, particulate matter, and chemical discharges, that affect air, water, and soil quality. It further emphasizes the importance of technological innovations, stringent regulations, and international cooperation in mitigating industrial pollution. Ultimately, a transition toward sustainable production and consumption is crucial to addressing this global crisis and ensuring a more equitable future for all nations.

Abstract-The Student Management System (SMS) is a revolutionary web-based system aimed at simplifying the management of student-related information with unparalleled ease. As a central repository for schools, SMS makes it easy to manage student records, attendance, grades, and academic progress.With the frontend designed based on HTML, CSS, and JavaScript, the system provides an interactive and responsive user interface that fascinates its users. The strong backend, developed in Java, proficiently handles business logic and handles API requests, while MySQL provides secure and organized data storage.This study explores the system’s architecture, development process, security measures, and performance criteria. Through extensive testing, the system has established its outstanding capability to handle vast student datasets with the utmost level of security and scalability. Amidst an environment where efficiency and reliability are most valued, the SMS is an anchor of contemporary educational management.

Abstract-Unsupervised learning algorithms play a crucial role in discovering hidden patterns and structures within the data This paper delves into two prominent clustering approaches K-means and Hierarchical clustering. Evaluating their performance, strengths and weakness and their methodology and their process. The results highlight the strength of Hierarchical clustering in identifying complex clusters and k-means in handling well separated clusters. This study provides the insights for choosing the suitable algorithm for specific clustering tasks.

Abstract-Saussurea costus (قسط, ہندی عود) is a medicinal herb widely used in traditional medicine, including Ayurveda, Unani, and Chinese medicine, for its therapeutic effects on respiratory and digestive ailments. This study aims to evaluate the efficacy and safety of Saussurea costus in a cohort of 50 patients suffering from chronic respiratory or digestive conditions. The study employs a randomized clinical trial (RCT) approach, analyzing symptomatic relief, biochemical markers, and side effects over a 12-week treatment period. The findings suggest significant improvements in patient conditions with minimal side effects, supporting the continued use and potential integration of Saussurea costus in modern therapeutic applications.

Abstract-In modern society, electronic and online voting systems are emerging as significant advancements in electoral technology, offering the potential to reduce organizational costs and increase voter turnout. Electronic Voting Machines (EVMs) have already revolutionized the electoral process by improving voter participation and enhancing the speed and accuracy of elections compared to traditional methods like paper ballots, punch card voting, and optical scan systems. These conventional approaches often face challenges such as fraud, voter manipulation, inaccuracies, and inefficiencies. Similarly, online voting systems promise to further streamline elections by eliminating the need for physical infrastructure, enabling voters to cast their votes from any location with internet access. However, despite their advantages, online voting solutions are met with caution due to vulnerabilities to cybersecurity threats. Risks such as Man-in-the-Middle (MitM) attacks, Denial-of-Service (DoS) attacks, and malware injection jeopardize the integrity and reliability of elections, highlighting the need for more secure and robust solutions. Blockchain technology offers a transformative approach to modernizing voting systems by providing a decentralized, transparent, and tamper-proof framework. Its decentralized architecture eliminates single points of failure, ensuring higher levels of security and reliability. This paper explores how blockchain technology addresses the limitations of conventional voting systems, including EVMs and online voting systems, by leveraging its inherent characteristics—speed, accuracy, immutability, and transparency. By distributing control across a network of nodes, blockchain-based voting systems enhance the integrity, accessibility, and trustworthiness of elections. Furthermore, the paper examines the potential of blockchain to modernize the existing voting framework, significantly improving the efficiency and trust in electoral processes while safeguarding democratic values in the digital age.

Abstract-In India nearly about 70 percentage of people are depending on agriculture. Numerous operations are performed in the agricultural field like seed sowing, grass cutting, ploughing etc. The present methods of seed sowing, pesticide spraying and grass cutting are difficult. The equipment’s used for above actions are expensive and inconvenient to handle. So the agricultural system in India should be encouraged by developing a system which will reduce the man power and time. This work aims to design, develop and design of the robot which can sow the seeds, cut the grass and spray the pesticides, this whole system is powered by solar energy. The designed robot gets energy from solar panel and is operated using Bluetooth/Android App which sends the signals to the robot for required mechanisms and movement of the robot. This increases the efficiency of seed sowing, pesticide spraying and grass cutting and also reduces the problem encountered in manual planting.

Abstract-In order to provide a structured and interactive learning environment, a Learning Management System (LMS) is essential to modern education and training. This project entails designing and developing a feature-rich LMS using Django for backend development and Tailwind CSS for a responsive and user-friendly interface. The system offers a centralized dashboard for administrators, instructors, and students, integrating essential functionalities like secure user authentication, dynamic course enrollment, and real-time attendance tracking. The platform enhances the learning experience by enabling personalized learning pathways, robust assessment tools, automated certification, and role- based access control for administrators, instructors, and students. The LMS is built with scalability and seamless third- party integrations, including payment gateways and video conferencing solutions, supporting both self-paced and instructor-led learning models. This LMS solution is intended to transform online learning by creating an efficient and captivating digital learning ecosystem. By emphasizing usability, security, and efficiency, this project seeks to improve educational outcomes, automate administrative workflows, and increase learner engagement. Django integration guarantees a stable and scalable backend, while Tailwind CSS offers an aesthetically pleasing and highly responsive design.

Abstract-Developing a GenAI-enabled AR platform for museums to offer personalized, interactive experiences, enhancing visitor engagement and educational value, thereby preserving and promoting the heritage and culture of the nation. The system proposes the development of a novel GenAI-enabled Augmented Reality (AR) platform tailored for museums, aimed at delivering personalized and interactive experiences to visitors. Leveraging Unity Vuforia Area Target/Image Target technology, C# API, GenAI, langchain, and the OpenAI API, the platform seeks to revolutionize traditional museum visits by offering enhanced engagement and educational value. While existing solutions such as audio guides, mobile apps, and interactive displays have improved visitor experiences, they often lack interactivity and personalization. The proposed platform addresses these limitations by employing Generative AI to power a virtual assistant that delivers detailed information about exhibits and aids in navigation. Accessible via a cross- platform AR application on web, Android, and iOS devices, the solution promises to create a more immersive and enriching museum experience, ultimately contributing to the preservation and promotion of cultural heritage.

Abstract-In this paper, a generalized matrix inverse-based unknown input observer (UIO) has been developed for a benchmark 3-DOF helicopter system to manage unknown, time-varying nonlinear dynamics and disturbances. The goal is to ensure the helicopter accurately follows the specified elevation and pitch references. To achieve this, we introduce a novel, simplified UIO to estimate the combined unknown dynamics, which are subsequently incorporated into the control design as a compensator. By introducing an auxiliary system, an invariant manifold is derived and utilized in the UIO design. The full-order observer, constructed using the g-inverse, is expanded and implemented to achieve this purpose. This new estimator requires setting only a single scalar and achieves exponential convergence. Consequently, the proposed control design utilizing the estimator can achieve precise output tracking. This control method is implemented on a benchmark 3-DOF helicopter, and its efficacy is validated through simulations and results.

Abstract-The paper presents a solution to the issue of manually filtering out resumes from a large number of applications on the internet. The system uses Natural Language Processing to extract relevant information from unstructured resumes, creating a summarised form of each application. This simplifies the screening process and allows recruiters to analyze each resume in less time better. After the text mining process, the solution employs a vectorization model and uses cosine similarity to match each resume with the job description. The calculated ranking scores can then be used to determine the best-fitting candidates for a specific job opening. This approach addresses the challenges of manual filtering and fairness in resume screening, ensuring that the right candidates are selected for the job.

Abstract-Big data is a combination of structured, semi structured and unstructured data. Also we discuss about intrusion detection system. The introduction of Big Data into the field of information technology has made the process of managing and analyzing data a great deal more difficult. It is essential to take everything into consideration, including aspects such as volume, diversity, pace, importance, and complexity. The processing of enormous amounts of data is simplified with the use of clustering. When dealing with unstructured data, this is a very useful skill to have. It is possible to offer a wide range of computer services, such as servers, storage, databases, and networking, in addition to analytics and intelligence, at a cheaper cost by using cloud computing, which makes use of the Internet as its delivery route. This makes it feasible to give a variety of cloud services at a reduced cost. The protection of such vast quantities of data is the primary challenge.

Abstract-In the past few decades urbanization has seen an exponential growth. This gave rise to idea of ‘Smart Cities. The ‘Water level management system using GSM technology’ Model introduces a cutting-edge approach to tackle the escalating challenges of urban water management. In response to the burgeoning urbanization and burgeoning fresh water usage, conventional water management systems have proven insufficient. This model harnesses hardware and software technology to create an intelligent and efficient water management system. The core of the system comprises an array of electronic sensors strategically positioned in water storage tanks, continuously monitoring water level in real-time. These sensors communicate with a centralized server through a network, providing live updates on water level on one or more devices. The proposed model includes deployment of prototype of actual model which helps to reduces the water wastage.

Abstract-The following research work captures the development of Travel Tourism Planner Application – An integrated system for effective trip planning. It provides an LBS feature which enhances the pace of planning, personalization and creating efficiency in traveling among the users. Form for trip planning. The application integrates location-based services, which empower users to effectively plan, customize, and optimize their travel experiences. The frontend of our application is developed in HTML, CSS, and JavaScript; however, to enable us to develop our application for various platforms and still be compatible, we use a platform called React. The backend uses Node.js and Express.js to facilitate communication with external APIs like Sky Scanner, Booking.com, & Google Maps, to offer live data. Developed with an Agile approach, this application is well optimized for user experience, secure and performance oriented. Measures of user security comprising of Auth 2.0 for user’s authenticate and SSL/TLS for protection of users’ data have been established. In addition to that, there is the use of features like lazy loading and code mini fication for improvement of the performance. In regard to this, this paper shall give an account of the development process of the system alongside the various difficulties faced and measures put in place to contain the. It seeks to focus on tool design and development processes that lead to a credible and effective travel planner for today’s travelers.

Abstract-The study focuses on the development and analysis of polyvinyl alcohol (PVA) nanocomposites incorporating silver (Ag) nanoparticles, aimed at improving the performance characteristics of footwear insoles. The thesis abstract presents a comprehensive overview of research dedicated to the formulation and evaluation of polyvinyl alcohol (PVA) nanocomposites infused with silver (Ag) nanoparticles, with the primary objective of enhancing the functional properties of insoles used in footwear. The introduction outlines the significance of integrating nanotechnology into material science, particularly in the context of footwear applications, where comfort and durability are paramount. The materials and methods section details the synthesis of PVA nanocomposites, the incorporation of Ag nanoparticles, and the various analytical techniques employed to assess their performance characteristics. The conclusion summarizes the findings, highlighting the potential of these nanocomposites to significantly improve the quality and longevity of footwear insoles, thereby contributing to advancements in the field of wearable technology.

Abstract-This research focuses on using machine learning and data analysis tools to determine the possibility of a heart disease problem in an individual. A predictive model for Heart diseases using XGBoost was developed using features from blood sample tests and habitual factors. Several other models were used to validate the efficiency of the result from the XGBoost model. The performance of the model was then evaluated and finally a web application with an intuitive user interface was developed to serve the model for public use. XGBoost model is under a family of extreme gradient boosting models – which are known for remarkable results. Hence, it was used in this project as a classification tool. With an accuracy of over 90%, XGBoost was able to successfully classify the result, other models fell short within ranges of -2 to -20%; therefore, even further justifying the use of XGBoost. A web application was then hosted allowing medical practitioners and public users, run their features and get results on the possibility of a heart disease problem. In conclusion, the model was sufficient enough to yield possibilities of a heart disease problem which is in that regard, successful. Albeit, future work would be needed on further making variations on the interface – mobile, desktop e.t.c. making such solutions more accessible, and also including more important fields – especially habitual factors like sleep schedule etc.

Abstract-Manufacturing green nanoparticles is a safe, secure, and promising technique. In the current study, piper dravidii leaves extract was used to prepare iron oxide nanoparticles (Fe2O3-NPs) and cobalt oxide nanoparticles (CoO-NPs). UV-visible spectroscopy, scanning electron microscopy (SEM), dynamic light scattering (DLS), vibrating sample magnetometer (VSM), and differential scanning calorimetry (DSC) were used to evaluate the produced Fe2O3-NPs and CoO-NPs. The surface plasmon resonance effect was used to validate the synthesis of FeONPs. FeONPs have an average particle size of about 163.5 nm, a polydispersity index of 0.091, and a zeta potential of -13.8 mV, according to dynamic light scattering (DLS). At 176.91°C, differential scanning calorimetry (DSC) revealed an endothermic peak. With a magnetization value of 3.483 emu/g at ambient temperature, iron nanoparticles were shown to have superparamagnetic characteristics by the Vibrating Sample Magnetometer (VSM) examination, suggesting that they might be used in a magnetically targeted medication delivery system. It has been shown that this biosynthetic method is economical, environmentally benign, and has a lot of potential for use in biomedical research.

Abstract-This paper presents a solution to the inefficiencies of traditional manual attendance systems by proposing a face recognition-based attendance system. project aims to enhance the manual attendance process by using a mobile platform and face recognition technology. The design consists of three main modules: inputting attendance information, signing in with facial recognition, and maintaining attendance records. It begins by explaining the principles of face detection and classification, followed by an analysis of how to build a face recognition classifier. The system is then implemented on an Android platform, allowing for practical use in workplaces.

Abstract-The gravity model to the blood supply chain is a conceptual framework that seeks to explain and optimize the distribution of blood products within a healthcare system. It is useful when identifying suitable geographical location within arrange. It is also used to find location that minimizes the cost of transporting raw material from the supplier and finished goods to the markets served. This model also assumes that the transportation cost grows linearly with the quantity shipped. All distances are calculated as the geometric distance between two points on the plane. Using a numerical example, the applicability of the proposed network is analyzed.

Abstract-A rapid shift towards sustainability and clean energy is evident in this decade. The fervent adoption of EVs is acting as a catalyst for the same. Technologies such as smart power grids, communication, V2G, and integration systems render market growth—EVs as mobile power systems can serve as a potential market in the coming years. The paper focuses on the current scenario’s innovative grid technologies, VGI, and the literature on ML algorithms that aid in optimizing the integration configurations. The paper proposes a popular ML model in various bright grid areas that make VGI feasible.

Abstract-EVs offer technology and a smooth driving experience while reducing tailpipe emissions. EV adoption has been increasing both by volume and market share. Batteries and Battery technology constitute vital components of smooth functioning. However, battery degeneration and practical management issues remain significant challenges in the EV industry. Evolving Deep learning and machine learning approaches are being applied to solve these challenges. The current study focuses on using deep learning approaches to battery health management and explores the role of neural networks in predicting battery health.

Abstract-The Market is on the rise now due to the heavy adoption of clean energy and the availability of flexible options for all target consumers. The EV market is gaining a grip in the automotive industry due to new innovations around battery technologies. It reviews the market trends, challenges and strengths of the ICE and EV powertrain in the current global market. The paper focuses on Powertrain performance and its relationship with battery optimization. The Tesla 3 Long Range Model is studied and analysed to find out the impact of battery performance on Power train performance.

Abstract-A Multiple Input Multiple output (MIMO)-fed circular slot antenna with wide tunable dual band-notched function and frequency reconfigurable characteristic is designed, and its performance is verified experimentally for high-frequency millimeter-waveband (MWB) communication 6G application s. The dual band-notched function is achieved by using an Ring-shapedresonator inserted the circular ring radiation patch and by etching a parallel stub loaded resonator in the MIMO transmission line. There are a wide range of approaches that have been advanced in the literature for adding reconfiguration to metamaterial devices all the way from the RF through the optical regimes, but some techniques are useful only for certain wavelength bands. A tunable range of almost one octave can be achieved if the R-SRR is loaded in its center with a slot. Furthermore, it has been demonstrated that a reconfigurable device can be achieved if a pair of shunt connected slots are introduced across the slots of the host MIMO. This feature, in conjunction with the tunability of a loaded R-SRR, has been used to achieve a reconfigurable and tunable structure. Finally, in order to demonstrate the potential 6G application of the proposed structure, a MWB antenna design with tunable notch band for Future 6G Communications. The design methodology has been validated through electromagnetic simulations.

Abstract-Fly ash and granulated blast furnace slag (GBFS) are major by-products of thermal and steel plants, respectively. These materials often cause disposal problems and environmental pollution. Detailed laboratory investigations were carried out on cement stabilized fly ash-(GBFS) mixes in order to find out its suitability for road embankments, and for base and sub-base courses of highway pavements. Proctor compaction test, unconfined compressive strength (UCS) test and California Bearing Ratio (CBR) test were conducted on cement stabilized fly ash-GBFS mixes as per the Indian Standard Code of Practice. Cement content in the mix was varied from 0% to 8% at 2% intervals, whereas the slag content was varied as 0%, 10%, 20%, 30% and 40%. Test results show that an increase of either cement or GBFS content in the mixture, results in increase of maximum dry density (MDD) and decrease of optimum moisture content (OMC) of the compacted mixture. The MDD of the cement stabilized fly ash-GBFS mixture is comparably lower than that of similarly graded natural inorganic soil of sand to silt size. This is advantageous in constructing lightweight embankments over soft, compressible soils. An increase in percentage of cement in the fly ash-GBFS mix increases enormously the CBR value. Also an increase of the amount of GBFS in the fly ash sample with fixed cement content improves the CBR value of the stabilized mix. In the present study, the maximum CBR value of compacted fly ash-GBFS-cement (52:40:8) mixture obtained was 105%, indicating its suitability for use in base and sub-base courses in highway pavements with proper combinations of raw materials.

Abstract-Transportation plays a key role in the development of an area, but it happens only when the transportation is safe, rapid, comfortable and economy. A road is considered safe when only a few, or no accidents occur. Road and its surroundings, road users and vehicles are the elements contributing to road accidents. Pedestrians, bicyclists and two-wheeler motorized riders are the vulnerable road users. The loss of human life due to accident is to be avoided. Road safety audit (RSA) is a formal procedure for assessing accident potential and safety performance in the provision of new road schemes and schemes for the improvement and maintenance of existing roads. These Audit studies or analysis give scope for the reduction of accidents and helps us to provide safe, self-explaining and forgiving roads. By this we can save the precious human life as well as the nation’s economy. The selected for this study is part of 4 Laning of Khalghat – MP/ Maharashtra Border Section of NH-52 (Old NH-3). Knowledge of accidents that have occurred on roads helps us to improve the design of the roads or to influence the behavior of road users, so that similar accidents do not occur again. Literature review will be done for the safe movement of the Road safety audit and will check the merits and demerits of the techniques used previously.

Abstract-Physical adversities such as illness, obesity, accidents and psychological adversities such as emotional, social and play hazards and family and relationship adversities or personality threats like formation of unfavourable self-concept can lead to low self-esteem and low emotional intelligence. Adversity quotient is a score that measures the ability of a person to deal with setbacks, challenges, and problems. Focus on adversity quotient, as it is positively correlated with emotional intelligence. Focus on self-awareness, self-regulation and empathy. Today’s youths are tomorrow citizens.

Abstract-Disease prediction with machine learning is one of the areas that is very rapidly developing with the help of historical medical data to find the patterns and diagnose early symptoms of diseases, hence predicting the diseases. This study covers a wide range of machine learning algorithms, from traditional methods like Naïve Bayes, K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) to more advanced techniques such as Random Forest and deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Real-world medical datasets have been applied to train and evaluate these models; those contain medical histories of the patient, life habits, genetic conditions, and findings from diagnostic tests. Accuracy, precision, recall, and F1 score metrics measure the efficiency of each algorithm in predicting diseases. Experimental results show that deep learning algorithms, specifically CNN and RNN and hybrid models perform much more accurately than traditional machine learning techniques. This superiority is especially observed in complex and unstructured data, such as medical images; deep learning models happen to very effectively extract difficult and intricate features and patterns. Traditional algorithms used are best suited for structured data but are incompetent in handling the complexity and variability that characterize most of the medical datasets. The paper further emphasizes gathering heterogenous data sources, like genetic information, lifestyle, and other contributing factors, to enhance predictive accuracy.

Abstract-Purpose: The goal of this study is to ascertain how well-informed Indian hospital staff members are regarding emotional intelligence (EI) principles, how well EI skills are implemented in various work environments, and which EI competency areas require staff development. Design/Methodology/Approach: A sample of 715 Indian hospital employees participated in the study, which employed a quantitative research approach. Being under the leadership of the HODL, the sample was kept stratified to ensure job title and division diversity. Using surveys standardized by the EQ-MAP organizations, research participants were assessed on their EI knowledge and practice. Findings: This study was conducted, in which 51.6% of participants answered they understood their emotions and influenced the performance of a professional setting “quite well” or “extremely well.” Similarly, it was 57.1% successful in sensing and deciphering the emotions of his or her colleagues. Furthermore, 59.4% reported being able to properly manage their emotions, whereas 58.7% could effectively communicate their thoughts and feelings. Conclusion: The study explains levels of understanding and application in practical settings of EI among hospital employees. It further highlights the important role of focused interventions for the development of EI competencies that can enhance workplace dynamics and the quality of care for patients. Originality/Value: Hospital employees’ understanding of and use of Emotional Intelligence (EI) underscores the inclusion of a new study in hospitals, greatly expanding the corpus of information on medical care and improving the standard of care that patients receive. The study gives insights for targeted interventions towards improving workplace dynamics and patient care quality, thereby highlighting a vital area of focus in healthcare management.

Abstract-Enterprise Resource Planning (ERP) systems have become essential tools for organizations seeking to integrate and streamline business functions such as finance, human resources, sales, and manufacturing. However, ERP implementation remains a complex, multi-phase process characterized by both technical and organizational challenges. This study systematically reviews the critical success factors (CSFs) that influence successful ERP implementations, drawing insights from extensive literature and case studies. Key factors identified include effective change management, robust data management, strong management commitment, comprehensive project planning, proactive risk assessment, and strategic vendor partnerships. These elements play a pivotal role in addressing challenges such as resistance to change, system integration issues, and process reengineering complexities. By focusing on these CSFs, organizations can enhance operational efficiency, improve decision-making, and ensure a positive return on investment. This review provides valuable guidance for practitioners and scholars, offering a consolidated perspective on achieving successful ERP deployment in today’s competitive business landscape.

Abstract-In this project, a hand-operated hydraulic Metal Sheet cutter machine has been fabricated. A hydraulic jack is used as the hydraulic component here. The project was started to minimize the effort required in shearing metal sheets of various thicknesses compared to that required when using a simple hand-operated mechanical sheet cutter. The cutting of metal sheets is an essential process in various industries, but conventional cutting machines are often expensive, energy-intensive, and cumbersome to operate. This thesis presents the design and fabrication of a cost-effective, hand-operated hydraulic metal sheet cutter aimed at providing a simple yet efficient solution for small-scale industries and workshops. The device operates using a hydraulic mechanism, eliminating the need for electrical power, and can be manually operated with minimal physical effort. The cutter is designed to handle a range of metal sheet thicknesses, offering versatility while maintaining precision and durability. The design focused on optimizing the cutting force and mechanism to achieve high cutting efficiency with reduced human exertion. The project encompasses the entire development process, including the design calculations, material selection, and fabrication techniques. Performance tests were conducted to assess the functionality and efficiency of the cutter under various conditions. The results demonstrate that the hand-operated hydraulic cutter can effectively cut metal sheets with minimal deformation and high accuracy, making it a practical tool for small workshops or environments with limited resources. This study concludes that the developed system is not only economical and environmentally friendly but also provides an innovative alternative to conventional electrically powered cutting machines. Further optimization could potentially enhance its applications across various industries.

Abstract-In hospitals, maintaining safe anesthesia levels during long surgeries is vital. Manual administration poses risks, as overdosing may be fatal, and underdoing could cause the patient to wake mid-surgery. This project proposes an automated, microcontroller- based anesthesia injector that precisely delivers doses using a syringe infusion pump. The anesthetist sets the dosage in milliliters per hour based on sensor feedback monitoring patient vitals. The microcontroller adjusts a DC motor to control the infusion pump accurately, ensuring steady anesthesia throughout the procedure. This automation reduces manual dependency and enhances patient safety. The system also incorporates safety mechanisms, including alarms and fail-safe operations. In case of anomalies such as syringe blockages, motor malfunctions, or irregular patient vitals, the system triggers an alert to notify the anesthetist immediately. Additionally, the microcontroller stores real-time data, which can be accessed later for review and analysis, contributing to improved surgical procedures and patient outcomes. By integrating advanced monitoring and control features, this solution ensures precision, reliability, and adaptability in critical medical environments.

Abstract-The increasing penetration of renewable energy sources and the rise in non-linear loads in microgrids have led to the growing concern of harmonic distortion in power systems. Harmonics can deteriorate power quality, affect system performance, and damage sensitive equipment. Shunt Active Power Filters (SAPFs) are commonly used to mitigate harmonic distortion. However, conventional SAPF methods face challenges in dynamic microgrid environments, especially when dealing with changing loads and renewable energy variations. This paper presents an AI-enhanced SAPF approach for minimizing harmonic distortion in microgrids. By integrating machine learning and optimization algorithms, the proposed approach provides real-time harmonic detection and compensation, adapts to fluctuating conditions, and improves power quality. Simulation results demonstrate the effectiveness of the AI-based method in reducing harmonic distortion, enhancing system performance, and optimizing computational efficiency compared to traditional approaches.

Abstract-The quality of education reflects the quality of life. This quality is not only confined to a particular dimension, but it also has an expansive connotation. In the words of Bernard (1999), the rights of all the children to survival, protection, development, and participation are at the centre of the discourse, which covers all aspects of the school and its surrounding community. This means that the focus is on learning to strengthen the capacities of children to act progressively on their own through acquiring relevant knowledge, valuable skills and appropriate attitudes; this builds up a safety network permeated by a sense of security and healthy interaction. Primarily, the present paper focuses on the quality enhancement and skill development by incorporating community-centered indigenous knowledge within the school’s curriculum. In this paper, the researchers made a humble attempt to explore the components of indigenous knowledge within the community network space and thereby suggest the inclusion of community-based indigenous knowledge for the objective of an inclusive school curriculum through skill-development techniques and community-participative indigenous knowledge. An attempt has been made to determine why community-based knowledge is crucial for various kinds of risk management, which is situational knowledge, and is highly pertinent for shaping survival strategies within the community. Overall, researchers perceive that it is also helpful for re-constructing and re-orienting our ongoing education system because it has built-in cultural support and cultural value with ancient spiritual essence. With the help of document analysis and analysis of primary and secondary data the researchers sought to reveal that the indigenous knowledge cum community knowledge has many important aspects in respect of educational goals and it also helps to improve the educational instructional strategy. Even such community-centric knowledge is essential for the individual perspective, because it celebrates the diversity of learning. This study is an avenue for policymakers, educators, and activists associated with quality education to advance the vocational system in school education.

Abstract-Even in the past few decades, cyberattacks have grown tremendously in number as well as quality. Consequently, creating a cyber-resilient mechanism is significant. Traditional security measures cannot prevent data breaches during cyberattacks. Cybercriminals have devised new and sophisticated methods and high-end gadgets in their hacking and data breaching capabilities. As both sides resort to Artificial Intelligence (AI) technologies to bring smart models to prevention systems from attacks in cyberspace, it is now feasible to rely on these emerging technologies, themselves able to rapidly adapt to such situations, as core cornerstones in the field of cybersecurity. The AI-based techniques provide the best cyber defense tool, efficient and powerful enough to discover malware attacks, network intrusion case, spam and phishing emails, breaches of data, and many more, and issue alerts when security incidents happen. In this paper, we evaluate how AI is impacting cybersecurity and summarize relevant research toward understanding the benefits of AI in cybersecurity.

Abstract-An ANN is an information- processing paradigm inspired by the way natural nervous systems similar as the brain process information. The crucial element of this paradigm is the unique structure of the information- processing system. It consists of multitudinous largely connected processing rudiments( neurons) wanting to work with each other to break particular problems. ANNs learn by exemplifications like humans. It’s through learning that an ANN is set to work on a particular operation sphere, say, for case, pattern recognition or bracket of data. Learning in natural systems refers to change in the synaptic connections being between the neurons. The same holds for ANNs. This paper gives an overview of artificial neural networks, their working, and training. It mentions the operation and advantages of AANN.

Abstract-The banking industry is undergoing a significant transformation with the integration of automation technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and advanced data analytics. Automation streamlines banking operations by reducing manual intervention, increasing efficiency, and minimizing errors. AI-powered chatbots enhance customer service with instant support, while automated fraud detection systems strengthen security and compliance. Additionally, automation improves regulatory adherence by facilitating real-time monitoring and reporting, ensuring transparency and risk mitigation. The implementation of automation also leads to cost savings, operational scalability, and seamless digital banking experiences. As the industry moves towards fully automated banking ecosystems and blockchain integration, automation is set to redefine the financial landscape, making banking more accessible, secure, and customer-centric.