Coordinates: 31°23′45″N 75°32′09″E / 31.3958746°N 75.5358439°E / 31.3958746; 75.5358439Dr. B. R. Ambedkar National Institute of Technology Jalandhar (NIT Jalandhar or NITJ), formerly Regional Engineering College Jalandhar, is a public engineering university located in Jalandhar, Punjab, India. It has been declared as an Institute of National Importance under the Ministry of Human Resoure Development, Govt of India. It is one of the 31 National Institutes of Technology of India. It was founded as a joint venture between the governments of Punjab and India, originally under the name Regional Engineering College, Jalandhar, Punjab, India (RECJ). RECJ and now NITJ was established in 1989 by the joint venture of Govt of India and Govt of Punjab on the Land of Village Bidhipur and Village Suranussi of District Jalandhar (Punjab) on the Grand Trunk Road bye-pass.
Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent years for their potential applications in surveillance, monitoring, search and rescue, and mapping. However, efficient and optimal path planning remains a key challenge for UAV navigation. This survey article reviews various UAV path planning algorithms, encompassing Sampling-Based techniques, Potential Field methods, Bio-Inspired algorithms, and Artificial Intelligence-based approaches. We explore key factors affecting path planning, including environmental constraints, objectives, and uncertainties. We explore vital factors affecting path planning, including environmental constraints, objectives, and uncertainties. A comparative analysis of these techniques focuses on their strengths, weaknesses, and applicability in different UAV scenarios, including heuristic, mathematical, Bio-Inspired, and machine-learning methods. Critical parameters like path length, flight time, number of UAVs and targets, environmental dynamics, obstacle management, algorithmic approaches, real-time execution, and collision avoidance are examined. This survey aims to inform researchers, practitioners, and engineers in UAV path planning, offering insights into these techniques' challenges, limitations, and future research directions. By presenting a comprehensive overview of state-of-the-art methods and trends, our survey provides a clear understanding of the diverse path-planning strategies, their merits and demerits, and highlights key research challenges and unresolved issues in the field.
This study compares two numerical modeling approaches, Eulerian-Eulerian and Eulerian-Lagrangian, to model air/water mist jet impingement on a flat plate at constant heat flux. A parametric analysis investigates the effects of air Reynolds number (4,500-10,000), mist loading fraction (0.5%-1.2%), and non-dimensional nozzle-to-plate distance (20-40) on flow and heat transfer characteristics. The Eulerian-Lagrangian approach showed significant inaccuracies, with heat transfer coefficient errors up to 35% compared to experimental results. The results of the present investigation, predicted by the Eulerian-Eulerian technique, have a maximum error of 8% and are well within the experimental uncertainty. Heat transfer increases with Reynolds number and loading fraction, with maximum increases of 78.28% and 22.96%, respectively. Heat transfer decreases with nozzle-to-plate distance, with a maximum decrease of 44.5%.
In recent years, the increasing connectivity in the Internet of Vehicles (IoV) has raised critical concerns regarding secure authentication and intrusion detection. The proposed methodology presents a Blockchain-based IoV Authentication Model integrated with a Deep Learning-based authorized participant detection to address these challenges. Leveraging the decentralized and tamper-proof nature of blockchain technology, the system assigns each vehicle a unique cryptographic identity, securely recorded on the blockchain to prevent unauthorized access and identity spoofing. It ensures a reliable and immutable authentication process across the network. To further enhance the security infrastructure, a deep learning-based authorized participant detection is embedded within the blockchain storage framework to detect unauthorized users in real time. The authorized participant detection is built using Lightweight Deep Dense Recurrent Model (LDDRM), which balances computational efficiency and detection accuracy. The performance of the LDDRM is further improved through the integration of an Adaptive Grouper Moray Eel (AGrME) Optimization Algorithm, which fine-tunes the model’s loss function for optimal learning and threat detection. The combined approach strengthens the IoV ecosystem by providing robust, intelligent, and scalable security solutions. In addition, the proposed framework is designed to support large-scale vehicular networks with low authentication latency and reduced computational overhead through the use of lightweight deep learning inference and consortium blockchain-based consensus.
Mixed-criticality (MC) systems must guarantee safety for high-criticality tasks under pessimistic assumptions while still delivering useful service from low-criticality tasks. The traditional Vestal approach does this by discarding all low criticality tasks after a mode switch, which is easier to certify but lacks functionality. The imprecise mixed criticality (IMC) approach alleviates this problem by dividing each low criticality task into mandatory and optional components, thus allowing graceful degradation. Although the IMC approach has been analyzed thoroughly for priority-driven scheduling algorithms such as EDF-VD, its combination with time-triggered (TT) scheduling has not been investigated, although there is a great industrial need for deterministic and certifiable execution. This paper proposes the first TT scheduling algorithm (TTA) for IMC systems. Our method constructs two static tables that guarantee all deadlines of high-criticality and mandatory low-criticality jobs under worst-case execution, while opportunistically accommodating optional work. We present correctness proofs, schedulability conditions, and a comprehensive evaluation on synthetic workloads. Results demonstrate that the proposed algorithm preserves determinism, achieves graceful degradation, and significantly outperforms EDF-IMC and existing baseline algorithms in terms of schedulability and robustness.
Graphdiyne is a recently discovered two-dimensional carbon allotrope composed of mixed sp and sp2 hybridized carbon, exhibiting intrinsic semiconducting behaviour with a tunable direct bandgap. Unlike graphene, it offers distinct advantages such as chemical tunability, inherent porosity, and high charge-carrier mobility. This review summarizes the current state of graphdiyne research, including its synthesis, structure, electronic properties, and functionalization strategies. Recent progress in scalable fabrication methods, particularly bottom-up chemical synthesis and chemical vapour deposition, has enabled the production of high-quality films and nanosheets. Its unique hybridized carbon framework provides excellent mechanical strength and thermal stability, essential for reliable device performance. Graphdiyne has shown strong potential in energy applications, serving as an efficient electrode material for lithium- and sodium-ion batteries and supercapacitors, with high capacity, excellent rate capability, and improved cycling stability. Additionally, its catalytic activity supports key energy-conversion reactions such as hydrogen evolution and oxygen reduction. Owing to its large surface area and tunable electronic structure, graphdiyne is also highly effective for gas, chemical, and biomolecular sensing. Despite its promise, challenges remain in achieving large-area, defect-free synthesis and ensuring long-term stability, which must be addressed to fully realize its technological potential.
