Agni College of Technology

A Wireless Sensor Network (WSN) refers to a decentralized network architecture where sensor nodes, administrative units, and sink nodes work together to observe conditions and gather data efficiently. Due to the rapid progress in wireless communication, WSNs have gained significant popularity across various industries, as they overcome the limitations of existing techniques in collecting and monitoring data in harsh environments. The rapid growth in data transmission has made security a critical concern in WSN. The prevailing methods used to analyze network traffic data exhibit poor detection accuracy during intrusion attacks. This paper proposes a novel hybrid framework named Snow Leopard Beetle Optimization_Convolutional eXtreme Gradient Boosting (SLepBO_ ConvXGB) for intrusion detection in WSN, which introduces a unique integration mechanism, combining the global exploration capability of Snow Leopard Optimization Algorithm (SLOA) with the local exploitation strength of Dung Beetle Optimization (DBO) to achieve faster and more stable convergence in routing decisions. The adaptive synergy between SLOA and DBO ensures optimized routing with reduced energy consumption and minimal packet delay, outperforming conventional routing approaches. Routing in the simulated WSN is achieved using Snow Leopard Beetle Optimization (SLepBO), taking multiple objective fitness parameters into account. Next, the input data undergoes normalization using the Tanh estimator, after which feature fusion is performed through a Deep Neural Network (DNN) with Jeffreys similarity to improve classification performance. Subsequently, intrusion detection is carried out using ConvXGB, with its performance optimized through SLepBO. Further, the SLepBO for routing achieved distance, residual energy, and delay of 0.100 J, 81.346 m, and 0.718 ms, whereas the SLepBO_ConvXGB for intrusion detection obtained 92.777

A series of Ba₂CaZn₂Si₆O₁₇ (BCZSO) silicate phosphors, emitting a broad spectrum of visible light including deep blue, blue, green, and red, were successfully synthesized by doping with Pb2+, Bi3+, Tb3+, and Eu3+ ions via a conventional solid-state reaction method. Powder X-ray diffraction (PXRD) analysis confirmed the formation of a single-phase orthorhombic structure, while scanning electron microscopy (SEM) revealed irregular morphologies indicative of typical high-temperature synthesis. Photoluminescence (PL) and excitation spectra demonstrated distinct emission features for each dopant, with optimal doping concentrations found to be 7 mol

Polyvinyl alcohol (PVA)-based hybrid composites reinforced with Pycnandra acuminata stem-derived NiO nanoparticles and lychee shell-derived biochar were developed to investigate their mechanical, dielectric, and electromagnetic interference (EMI) shielding performance. The study uniquely integrates biomass-derived magnetic and conductive fillers with silane (3-APTMS) surface modification to enhance interfacial compatibility. Composites with varying filler loadings were fabricated, and the influence of dispersion and interface engineering on structure–property relationships was systematically evaluated. Silane-treated composites exhibited significantly improved mechanical properties, with the PSB2 composition (1.5 vol.

The pursuit of high-performance, flexible energy storage devices has driven the exploration of bimetallic metal-organic frameworks that can simultaneously enhance electrochemical activity and mechanical durability. In the present study, a high performing flexible solid-state supercapacitor is demonstrated based on a bimetallic CuMn-BDC metal-organic framework electrode, where the synergistic redox interaction between Cu and Mn ions plays a central role in boosting charge storage kinetics. Unlike conventional single-metal MOF electrodes, the CuMn-BDC architecture facilitates rapid electron transfer and increased electroactive sites, leading to significantly improved energy-power characteristics. Coupled with a PVA-KOH@Super P gel electrolyte, the assembled device delivers a high energy density of 19.33 W h kg- 1 at a power density of 4000 W kg- 1, outperforming many previously reported flexible MOF-based supercapacitors. The device further maintains stable electrochemical performance under repeated bending at 0 degrees, 90 degrees, and 360 degrees, demonstrating excellent mechanical resilience. These findings underscore the innovation of bimetallic MOF design in enabling high-energy, mechanically robust flexible supercapacitors, advancing their prospects for wearable energy storage systems.

This paper proposes a Random Forest-based intelligent energy management strategy for electric vehicle participation in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation under dynamic grid conditions. The study is motivated by the need for smart charging schemes that can reduce peak demand stress while maintaining battery state of charge (SOC) within safe operating limits, as highlighted by prior V2G and smart charging research. The proposed framework uses supervised learning trained on rule-based labels derived from SOC, demand, and electricity-price inputs to determine one of three actions: charging, discharging, or idle. In the closed-loop simulation, the EV battery is modeled as a 50 kWh storage unit with 5 kW bidirectional power capability over 100 time steps. Results show that the controller maintained SOC between 40% and 90%, with an average SOC of 59.3% and final SOC of 60.0%, while executing 27 G2V intervals and 26 V2G intervals. The trained model achieved 100% classification accuracy on the generated supervisory dataset, indicating that the learner successfully captured the underlying dispatch logic. Feature analysis further shows that grid demand and SOC dominate the decision process, which is consistent with the technical foundations of V2G support and demand-responsive charging. The paper demonstrates that a lightweight machine-learning controller can provide an interpretable baseline for future realtime EV energy management systems integrated with renewable energy, user preferences, and battery aging models.