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-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-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-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-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-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 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-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-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 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.