Category Archives: Uncategorized

Authors: Sanjana Sunilkumar Dubey, Dr Vipin Kumar

Abstract: Education of school and college students on climate change is highly important in influencing mitigation and adaptation behaviours in the long term especially on the climate prone countries like India. This research is a survey-based evaluation of climate change awareness, risk perception, self efficacy, and pro environmental behavioural intention among Indian students, with a special interest in the variations of these variables according to the urban and rural geographical location, the type of school, and the exposure to climate education programmes. The questionnaire comprised a structured questionnaire that was delivered through a stratified sampling design to the participants that were secondary school students (Classes 912) and first-year undergraduate programmes. The measure consisted of climate knowledge, perceived risk, self-efficacy, behavioural intention, and information sources on climate. The analysis of data was done using descriptive statistics, group comparison, and multiple regression modelling to determine the predictors of behavioural intention toward climate action. Using an exemplary sample size (N = 600), the findings show that, although students will exhibit average knowledge of climate as a whole, there exist significant disparities in knowledge of health-related climatic effects and locally applicable strategies of adaptation. Students in urban areas always claim more knowledge and perception of risk of the climate than rural students due to the information availability and access to education. The results also indicate that perceived risk and self-efficacy have a stronger effect on behavioural intention than knowledge does. Being members of eco-clubs and having undergone climate-focused school-based climate modules are both substantially linked with intentions to participate in climate-positive behaviours.

Published by: vikaspatanker

Authors: M.Selvam

Abstract: Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. Rapid urbanization and the continuous growth of vehicle population have placed immense pressure on existing traffic management systems. Conventional traffic surveillance methods, such as fixed cameras, loop detectors, and manual monitoring, often suffer from limited coverage, high infrastructure costs, and lack of real-time adaptability. Therefore, this project proposes a drone-based traffic surveillance system operates through the coordinated functioning of power, sensing, control, communication, and actuation modules. The system is powered by a 3.7V Li-ion/Li-Po battery, which supplies energy to all onboard components through a battery protection and charging circuit to ensure safe and stable operation. The flight controller acts as the central processing unit, receiving real-time data from sensors such as the gyroscope and accelerometer to maintain flight stability, orientation, and balance. Front and bottom cameras capture aerial and ground-level traffic footage, which can be switched using the camera switching module depending on surveillance requirements. The optical flow sensor assists in position holding and low-altitude navigation. User commands are transmitted via a 2.4 GHz transmitter and receiver, enabling remote control and mission updates. Based on sensor inputs and control commands, the flight controller generates appropriate signals to the motor driver, which regulates the speed of the coreless DC motors for precise manoeuvring. Additionally, the LED lighting module enhances visibility during low-light or night-time operations. Through this integrated workflow, the drone efficiently captures real-time traffic data while maintaining stable and controlled flight.

Published by: vikaspatanker

Authors: Ms.Walunj P.D, Ms.Sangme S.R, Ms.Suryawanshi P.P, Ms.Hanumante K.B, Ms. Upase M.S

Abstract: This paper presents the design and implementation of an OTP (One-Time Password) based door lock system using Arduino. The system enhances security by allowing access only after successful OTP verification. The OTP is generated and transmitted to the authorized user via a GSM module. The proposed system is low-cost, reliable, and suitable for homes, offices, and restricted areas. Experimental results show that the system provides improved security compared to traditional lock systems. Security of residential and commercial premises is a major concern in today’s world. Conventional locking systems such as mechanical keys and password-based locks are vulnerable to theft, duplication, and unauthorized access. To overcome these limitations, this project presents an OTP (One-Time Password) based door lock system that provides enhanced security and flexibility. The proposed system generates a unique, time-limited OTP for every access request, which is sent to the authorized user’s registered mobile number through a GSM module. The user must enter the received OTP using a keypad or mobile interface to unlock the door. A microcontroller controls the verification process and activates a relay to operate the electronic door lock. Since the OTP is valid for only one use and for a short duration, the chances of unauthorized entry are significantly reduced. The system is simple, cost-effective, and suitable for homes, offices, and restricted areas, offering a reliable solution for modern security needs.

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Authors: Sushovan Chandra, Barsha Maity, Swagatam Biswas, Angshuman Ghosh, Angshuman Ghosh

Abstract: Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, influencing nearly every sector of society. From healthcare and education to finance and governance, AI-driven systems are reshaping how decisions are made and services are delivered. Despite its growing adoption, public awareness and understanding of AI remain limited, particularly regarding its risks and ethical challenges. This research paper examines the positive and negative impacts of AI, highlights key ethical and social concerns, and emphasizes the importance of awareness, regulation, and responsible implementation. The study aims to provide a balanced perspective on AI, encouraging informed usage that maximizes benefits while minimizing harm.

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Authors: Shani Singh

Abstract: Additive manufacturing (AM) has evolved from a rapid prototyping technique into a key production technology for complex, high-performance components in aerospace, automotive, biomedical, and energy sectors. However, the mechanical reliability of AM parts remains highly sensitive to process parameters, thermal history, and defect formation mechanisms. Traditional empirical and physics-based models struggle to describe the nonlinear and multidimensional interactions that arise in AM processes, limiting their ability to predict strength and structural integrity. Machine learning (ML) has emerged as a powerful alternative, capable of learning complex process–property relationships directly from data and providing accurate predictions for tensile strength, porosity, surface roughness, hardness, and dimensional accuracy. This review synthesizes recent advances in ML applications across polymer-, metal-, and ceramic-based AM technologies, focusing on process parameter analysis, mechanical strength prediction, defect monitoring, and parameter optimization. The discussion highlights commonly used ML algorithms, sensor integration strategies, and hybrid optimization approaches, and identifies key research gaps related to dataset scarcity, model generalization, interpretability, and cross-platform reproducibility. Finally, the review outlines future directions, including digital twins, physics-informed ML, and reinforcement learning, to enable autonomous, industrial-grade intelligent additive manufacturing systems.

DOI: https://doi.org/10.5281/zenodo.18494934

 

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Authors: Sampang, Althea Mae A., Halik, Iederf Sean B, Labadan, David Andrew, Canada, Kevin Raymart C

Abstract: The researchers aimed to develop a multi-sensor automated system for mosquito detection and environmental surveillance, Mosquitect, having a purpose of offering support in the early assessment of dengue risks in areas prone to mosquito presence. MosquiTect is designed with the use of an Arduino UNO R4 Microcontroller that aids in tracking wingbeat frequencies, temperature, humidity, and visual-based detection of mosquitoes through a camera module. The data analysis is performed by utilizing percentages and means. During the 14-day testing, the findings show that MosquiTect had a 97.64% of success rate in terms of detecting wingbeat frequencies and gender identification signals; temperature and humidity provided a 100% success rate in monitoring environmental parameters; and a 77.07% success rate in terms of visual-detection of mosquitoes. The result shows that MosquiTect holds high relevance in the facilitation of preventive steps against dengue, especially in tropical areas. MosquiTect also possesses strong practicality for aiding governmental departments in forming preventive measures for dengue. This cultivates improvements in the capability of optical detection and image recognition, energy efficiency, environmental surveillance, and predictive modelling for the population of mosquitoes and their potential dengue outbreaks by the public health agencies.

DOI: https://doi.org/10.5281/zenodo.18494907

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Authors: Shani Singh

Abstract: Injection molding remains the dominant process for high-volume plastic production, but its strong sensitivity to process parameters, material behaviour, and cooling efficiency makes traditional trial-and-error optimization costly and slow. Moldex3D, a dedicated 3D CAE package for polymer processing, enables detailed simulation of filling, packing, cooling, and warpage, allowing defects such as short shot, weld lines, air traps, sink marks, voids, and deformation to be predicted before tool manufacture. By combining non-Newtonian flow models, temperature-dependent material data, and realistic mold and cooling layouts, Moldex3D helps designers and process engineers optimize gate locations, runner balance, packing profiles, and conventional or conformal cooling channel designs. This review consolidates recent Moldex3D-based research across automotive, consumer, electronic, and medical applications, with emphasis on flow and shrinkage analysis, warpage prediction in fibre-reinforced parts, and use in multi-cavity and thin-walled molds. Advanced and hybrid workflows are also examined, including integration with CAD/CAE platforms, topology and DOE-based optimization, and transfer of fibre orientation and residual stress fields into structural FEA. Key limitations are identified in material modeling (PVT, viscosity, fibre orientation), mesh and computation cost for full 3D models, and incomplete coupling to structural durability analysis and real machine behaviour. Finally, the review highlights future opportunities for AI-assisted optimization, cloud-based simulation, and digital twin integration, positioning Moldex3D as a core enabler of simulation-driven, intelligent injection molding.

DOI: https://doi.org/10.5281/zenodo.18494341

 

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Authors: A. Priyadharsini

Abstract: The rapid adoption of artificial intelligence in the healthcare sector has led to an increased demand for high-quality medical datasets. However, the sensitive nature of patient information and the strict regulatory requirements surrounding healthcare data often restrict institutions from sharing data with external entities. Federated Medical Learning (FedMed) presents a promising solution by enabling multiple healthcare institutions to collaboratively train deep learning models without exposing raw patient data. This paper proposes a robust FedMed framework that integrates federated averaging, secure aggregation, and advanced privacy-preserving techniques to ensure confidentiality while maintaining high model performance. Experiments conducted using medical imaging datasets demonstrate that the FedMed model achieves accuracy levels comparable to centralized deep learning approaches, while significantly reducing privacy risks. The findings highlight the potential of FedMed to enable scalable, secure, and efficient AI-driven healthcare applications across diverse medical environments.

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Authors: Hachimenum Nyebuchi Amadi, Biobele A. Wokoma, Barineka Richard Zarakpege, Richeal Chinaeche Ijeoma

Abstract: Transformer differential protection can experience false trips and mis-coordination, especially during external feeder faults and magnetizing inrush conditions. These malfunctions can compromise supply continuity, reduce system reliability, and put unnecessary stress on critical substation equipment. This research examines the reliability of the existing differential protection scheme at Corner Stone Substation and develops an enhanced adaptive configuration aimed at mitigating false operations while ensuring secure and selective fault clearance. To establish a performance baseline, historical relay event records from 2024 to 2025 were analyzed. A detailed MATLAB/Simulink model of the 15MVA, 33/11kV transformer protection system was created. The baseline protection scheme was tested under internal faults, external feeder faults, and transformer energization conditions. Subsequently, an improved protection configuration that integrates adaptive directional logic was implemented and validated through comparative simulations. The study found that the existing differential protection at Corner Stone Substation was reliable during internal faults, operating within 100 to 120 milliseconds. However, it was prone to false tripping during transformer energization, which produced an inrush current of approximately 6000 A with significant second harmonic distortion. Additionally, mis-coordination occurred during external feeder faults exceeding 7kA, with trip times ranging from 60 to 100 milliseconds. By integrating adaptive directional logic, the new scheme achieved secure restraint during external faults while maintaining rapid isolation of internal faults in less than 120 milliseconds. MATLAB simulations confirmed that the improved configuration enhanced selectivity, minimized false operations, and ensured reliable coordination between transformer and feeder protections. The findings indicate that adaptive directional differential protection improves selectivity, reduces false operations, and ensures robust coordination between transformer and feeder protections. This advancement contributes to enhancing protection strategies for modern substations and has potential applications for mitigating relay misoperations in other high-voltage grid systems.

DOI: https://doi.org/10.5281/zenodo.18493328

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Authors: Olamilekan Emmanuel Solomon, Hachimenum Nyebuchi Amadi, Barididum P. Biragbara, Richeal Chinaeche Ijeoma

Abstract: This study aims to analyze the impact of integrating a 100MW solar photovoltaic (PV) system into the Port Harcourt 132kV transmission network, specifically to assess its effects on grid performance and stability. The increasing incorporation of renewable energy, especially solar PV, presents operational challenges such as voltage fluctuations, reactive power imbalances, harmonic distortion, and frequency instability. If left unmanaged, these issues can lead to transformer overloading, grid congestion, and increased system losses. To address these challenges, we conducted load flow, voltage stability, and harmonic analyses using the Electrical Transient Analysis Program (ETAP) to model the existing network and evaluate the integration of the 100 MW solar PV systems alongside a battery energy storage system (BESS). The simulation results indicated that prior to integration, critical buses (T1A = 89%, T2A = 89.1%, T3A = 90.8%) and transformers (T1A = 112.8%, T2A = 111.8%, T3A = 91.7%) were operating beyond acceptable limits. After integration, the bus voltages improved to T1A = 96.06%, T2A = 96.11%, and T3A = 97.36%. Additionally, transformer loading decreased to T1A = 71%, T2A = 70%, and T3A = 46.8%, while total network losses significantly reduced from 6086.8 kW + j32740.7 kvar to 1093.172 kW + j9392.581 kvar. These findings demonstrate that the coordinated integration of solar PV and BESS can enhance voltage stability, reduce system losses, and minimize transformer stress. The study recommends supportive policy frameworks to encourage large-scale solar PV integration with energy storage, representing a sustainable approach to improving grid reliability and advancing Nigeria’s transition to renewable energy.

DOI: https://doi.org/10.5281/zenodo.18493122

 

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