Kalinga University

With India having the second-largest road network globally (5,603,293 km), following the United States, the country is positioned to lead in transforming smart roads. The rapid advancement of Internet of Things (IoT) technology has garnered significant attention for its applications in road infrastructure monitoring. This study systematically examines the challenges in adopting smart road sensing technology for infrastructure monitoring and maintenance in Bilaspur city of Chhattisgarh. The study used a mixed-methods approach, combining both primary data collection through surveys and interviews and secondary data collection through a deep literature survey. Currently, in Bilaspur city, the monitoring and maintenance of road infrastructure is done through traditional methods, which include: visual distress survey, manual pothole patching, crack sealing, cleaning the drainage system, and periodic resurfacing. The traditional system of monitoring and maintenance of road infrastructure in Bilaspur city is associated with limitations. The limitations of traditional methods for monitoring and maintenance of road infrastructure in Bilaspur city can be significantly mitigated through the integration of smart road sensing technologies, which shift maintenance paradigms from reactive to proactive strategies by enabling early detection and quantification of pavement distress. While interviewing the related authorities and through a previous literature survey, the author found that the adoption of smart road sensing technologies for monitoring and maintenance of road infrastructure in Bilaspur city faces a number of challenges. The results reveal that the key challenges faced in Bilaspur city for adopting smart road sensing technology include: a). Technical challenges (sensor accuracy and reliability, sensor failure under prolonged use, data fusion and interpretation, real-time processing and latency, power consumption and durability, and environmental and weather challenges), b). Economic challenges (high initial costs, return on investment concerns, ongoing maintenance and upgrade costs, infrastructure damage during installation), and c). Regulatory and standardisation challenges (lack of standardisation, privacy and security concerns, and regulatory approval and compliance). There is a need to address these challenges before the implementation of the smart road sensing system in Bilaspur city of Chhattisgarh. Future studies should focus on the development of more durable and energy-efficient sensor technologies, integration of 5G and edge computing for improved real-time processing.

Chhattisgarh is one of the forest rich state of India, with nearly 44% of its geographical area under forest cover. The present investigation focuses on the ethnomedicinal plant diversity of the Uttar Bastar region, particularly the Paralkot area of Kanker district, which forms part of a continuous forest landscape adjoining the Abujhmadh forests. The region represents an ecologically rich yet comparatively unexplored zone of Central India. Ethnomedicinal information was gathered through field surveys, interaction with local tribe communities and interviews with traditional healers. The survey led to the documentation of 50 medicinal plant species belonging to 29 families. Fabaceae showed maximum representation among the recorded families, while herbs constituted the dominant life form. Leaves were identified as the most frequently utilized plant part in traditional remedies. The documented species are commonly used for treating ailments such as fever, dysentery, jaundice, snake bite, kidney disorders, skin infection, digestive problems and their common diseases. Due to limited accessibility to modern healthcare facilities, local communities continue to rely significantly on plant based traditional medicine for primary healthcare needs. The study emphasizes the importance of systematic documentation, conservation and sustainable utilization of ethnomedicinal plant resources.

Gram-negative bacterium Pseudomonas aeruginosa causes most opportunistic infections and chronic illnesses such as cystic fibrosis, urinary tract infections, and burns. During invasion, P. aeruginosa releases exopolysaccharides, matrix proteins, and DNA, causing persistent infections. These substances encircle bacterial cells to produce biofilms. P. aeruginosa biofilms produce multiple drug resistance, making single antibiotic therapies difficult. The development of anti-biofilm pharmaceuticals is imperative. Quorum-sensing inhibitors, antimicrobial peptides, photodynamic therapy, and bacteriophage therapy provide novel therapeutics for P. aeruginosa biofilms. This paper succinctly outlines the genesis of P. aeruginosa biofilms and evaluates anti-biofilm treatment technology to suggest novel approaches for treating biofilm infections. Non-antibiotic methods like as quorum sensing inhibition, nanoparticles, and phage therapy show promise. Clinical application encounters challenges related to cost, adverse effects, and safety concerns. Research, clinical development, and investigations into host-pathogen interactions are essential to combat P. aeruginosa . Antibiotic overutilization and slow antibacterial advancement are concerns. Innovative platforms are required to evaluate and cultivate new therapies for refractory diseases, particularly multi-drug-resistant bacteria. Progress in research methodology and technology has uncovered molecular, mechanistic, dynamic, and comprehensive insights into diseases. P. aeruginosa biophysical characteristics, virulence factors, behaviours, host defence mechanisms, and invasive regulators is presented. This review article discusses the novel research and development of innovative therapeutics for clinically relevant pathogen. • Pseudomonas aeruginosa biofilms cause multiple drug resistance, reducing the effectiveness of single antibiotic treatments.. • Quorum-sensing inhibitors, antimicrobial peptides, photodynamic and bacteriophage therapies offer new therapeutics for P. aeruginosa biofilms. • Antibiotic overutilization and slow antibacterial advancement are concerns. • Progress in research methods and technology reveal molecular, mechanistic and dynamic, insights into diseases.

The increasing need for Heating, Ventilation, and Air-Conditioning (HVAC) systems, which serve airways of developing, alongside their significant global power use and role in the proliferation of microbial contaminants and illnesses, has compelled researchers, sectors, and legislators to prioritize the enhancement of HVAC system environmental sustainability. Comprehending and evaluating diverse factors associated with the long-term sustainability of current HVAC systems, which function as the respiratory systems, is essential for delivering healthy, environmentally friendly, and cost-effective solutions for different green building types. The most significant potential for increasing the environmental impact of HVAC systems arises during the design of new green buildings and the renovation of current ones. The considerable prevalence of older HVAC systems worldwide underscores required for upgrading. An effort has been undertaken to compile all significant elements influencing the choices to identify the optimal HVAC system design among the many prospects for sustainable enhancement.

Recent developments in wireless sensor networks (WSNs) have led to significant advances in energy-efficient routing protocols through the integration of deep learning and bio-inspired optimization algorithms. WSNs face critical challenges due to limited battery capacity, computational constraints, and dynamic network topology. This review article provides a comprehensive comparative analysis of state-of-the-art approaches for energy optimization in WSNs, focusing on clustering and routing protocols. We examine three major publications that propose hybrid frameworks combining deep learning architectures (CapsNet, ResNet-50) with metaheuristic optimization algorithms (ETSA, SOPSO, ACO). The analysis categorizes these protocols based on network architecture, energy models, optimization strategies, and performance metrics. We compare their effectiveness in extending network lifetime, reducing energy consumption, improving packet delivery ratio, and minimizing transmission delay. The comparative study demonstrates that hybrid deep learning-swarm intelligence approaches achieve 18-38% improvement in network lifetime, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2} \boldsymbol{-} \mathbf{1 4 \%}$</tex> reduction in energy consumption, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 5} \boldsymbol{-} \mathbf{2 4 \%}$</tex> lower delay compared to traditional protocols. This article serves as a comprehensive reference for researchers and practitioners working on nextgeneration energy-efficient WSN deployments.

Intelligent and ubiquitous systems like smart surveillance systems, autonomous service robots, and crowd-oriented urban infrastructure, among others, require the proper human trajectory forecasting. Nevertheless, most of the current learning-based methods only primarily investigate the social behaviour of agents but disregard the physical restrictions of the environment, and the physically infeasible predictions of the obstacles, walls, or non-walkable areas. These limitations are addressed by this paper via an environment-constrained graph neural network (EC-GNN) framework, in human trajectory forecasting under dynamic crowd behaviour, offered in the paper. The suggested method will represent pedestrian interactions in the form of a dynamic graph where agents take the role of nodes and social influences of the agents is represented by adaptive edge relationships whereas scene-conscious constraints by using structured map and obstacle embedding. The projected socially compliant and physically realistic agent-environment trajectory forecasts of the framework are provided by combining the agent-agent interaction properties and agent-environment representations. The large-scale experiments on the benchmark datasets in crowd trajectory prove that the proposed EC-GNN shows a way to achieve great improvements in the Average Displacement Error (ADE) and Final Displacement Error (FDE) and the rates of collisions and obstacle violations in the face of different crowd densities and the complex layouts of the scene. Thesefindings confirm the fact that the ability to solve problems concerning the environment constraints into the graph-based learning is effective to have robust and scalable human trajectory forecasts in intelligent environments.

Cross-sectional adaptability, accuracy, and robustness of embedded and edge computing applications have been facilitated by the growing use of artificial intelligence (AI) in signal processing. However, the real implementation of AI-enhanced signal processing pipes on resource-constrained environments is still a major challenge because there are strict constraints on power consumption, silicon area, memory bandwidth, and real-time latency. The paper has offered a proposal of a set of hardware effective VLSI design methodology used to design AI-enhanced signal processing pipelines specifically designed to run on low-power, resource-limited embedded systems. The suggested approach assumes an algorithm-hardware co-design architecture which narrowly wraps traditional signal preprocessing phases with a slim AI inference device as part of a profoundly-pipelined and parallel VLSI design. In order to realize high hardware efficiency, it uses multiple hardware considerations such as fixed-point arithmetic, quantization-aware model design of AI models, dataflow pipelining that is optimized, and buffering strategies that have low memory usage. The entire architecture is derived on an FPGA platform and tested on post-implementation conditions in order to provide realistic performance evaluation. It has been experimentally shown that the proposed architecture provides significant gains in throughput and energy efficiency over traditional non-optimized and non-AI baseline architectures with competitive inference accuracy. These findings affirm the tight design of AI inference through a hardware-optimized signal processing pipeline to highly decrease the latency and resource overhead. All in all, the suggested VLSI architecture offers a relevant scalable solution to making real-time AI-assisted signal processing possible in resource-constrained embedded and edge systems.

The emergence of drug-resistant pathogens, particularly multidrug-resistant (MDR) bacteria, continues to evolve rapidly and remains a global health threat. Because of their lengthy timeframes and exorbitant development costs, traditional methods of drug discovery have not worked. The application of bioinformatics and artificial intelligence (AI) to drug design could change that. AIs, especially machine learning (ML) and deep learning (DL) techniques, can sift through enormous databases to uncover new drug targets, predict and assess molecular interactions, and refine leads. Leveraging bioinformatics with AIs offers an opportunity to fast-track MDR pathogen drug candidate discovery. Recently, AI's capacity to improve various drug discovery processes, notably target discovery, molecular docking, and drug efficacy and toxicity testing, has been documented. This paper describes advancements in computational tools for drug design in bioinformatics to illustrate AI's value. In addition, the paper assesses the time and cost of drug development and the challenges of data, algorithm training, and ethics in clinical trials. The integration of artificial intelligence with bioinformatics will most likely expedite the discovery of novel therapeutic agents. This combination will provide a strong response to the worldwide challenges posed by MDR pathogens.