Nowadays, fossil fuels, such as petrol, diesel, and coal, are being replaced by non-conventional energy resources (NCERs) because NCERs are free from CO2 emissions, and their availability is surplus in the environment. Because of these major advantages, NCERs are the main focus for researchers. The hydro energy-based power system (HEPS) is one of the important NCERs. The modelling of HEPS is complicated by their higher-order nature. Consequently, the literature is progressively proposing reduction approaches. This study proposes a novel model order reduction approach to approximate the higher-order HEPS, based on the particle swarm optimisation (PSO) algorithm and the factor division technique (FDT). The PSO algorithm is used to estimate the denominator polynomial, while the FDT approach is used to generate the numerator polynomial of the desired reduced order model (ROM). A sixth-order HEPS model is considered as a test case to demonstrate the superiority of the suggested approach. The outcomes are contrasted with established techniques found in the literature.
Efficient patient room allocation is a critical challenge in healthcare management due to increasing patient demand, limited hospital resources, and the need for improved operational efficiency. Cloud-based scheduling and resource allocation techniques have been increasingly adopted in healthcare workflows because they provide scalable computing resources dynamic workload management with service level guarantees. Motivated by these advantages, this study proposes a hybrid multi-object optimization framework that integrates the Non-dominated Sorting Genetic Algorithm III (NSGA-III) with an Artificial Bee Colony (ABC) based hybrid (ABC-NSGA-III) local search strategy for healthcare room scheduling. The proposed model simultaneously optimizes four key objectives: minimization of hard constraint violations caused by overlapping patient room assignments, reduction of average waiting time and balancing of room occupancy load to ensure efficient and fair resource utilization. Experimental evaluation across multiple datasets demonstrates that the hybrid ABC-NSGA-III framework achieves the highest overall performance score of 30.62%, outperforming Harris Hawks Optimization 25.06%, NSGA-III 24.70%, Artificial Bee Colony 16.83% and Marine Predators Algorithm 27%. The consistent top ranking performance confirms the robustness and effectiveness of framework provide a scalable efficient solution for healthcare room scheduling leading to improved resource utilization balance workload and enhanced operational efficiency.
This study presents the Neural Network-based Context-Aware Routing (NNCAR) protocol for Opportunistic Networks (OppNETs), designed to overcome routing inefficiencies in existing approaches. NNCAR integrates multi-layer neural networks with adaptive buffer management, leveraging context parameters-delivery predictability, node speed, and closeness-for optimized forwarding. Simulations in the Opportunistic Network Environment (ONE) confirm that NNCAR significantly improves performance across varying network conditions. With larger buffer sizes, NNCAR reduces hop count by 7%-26%, buffer-time by 12%-55%, and latency by up to 21% compared to Cognitive Routing Protocol for OppNETs (CRPO), K-nearest Routing (KR), Epidemic, and ProPHET. As node density increases, delivery probability improves by 43.2%, while buffer-time and latency decrease by 58.4% and 17.8%, respectively. Statistical regression analysis reveals that buffer size and node density are the most influential factors in enhancing delivery probability. Pareto analysis further indicates buffer size as the dominant parameter across all metrics except hop count. Overall, NNCAR demonstrates superior adaptability and efficiency in dense and high-traffic OppNET environments.
A hybrid energy storage system (HESS) is developed with a single capacitor-based multi-input DC-DC converter (SCMIC) for EV drive, employing an advanced power management scheme (APMS). This work integrates two sources, a battery and an ultracapacitor (UC), of unequal voltages, through the SCMIC. The proposed APMS is designed with a fuzzy logic controller (FLC) to decide the power sharing among the sources, depending on their state-of-charge (SOC), and the instantaneous load demand. Additionally, the frequency separation technique is realized to ensure the high-frequency power delivery by the UC only, causing an improvement in battery life. The bidirectional operation of the SCMIC is demonstrated by implementing regenerative braking of the motor drive. The proposed HESS with the developed control scheme is found to be capable of (a) power sharing between the battery and UC, (b) simultaneously regulating output voltage and UC current, with (i) an urban drive cycle for EV, (ii) variation in motor load, (iii) a high-frequency load, resembling an urban driving cycle, (iv) change in reference output voltage level, (v) regenerative braking operation, and (vi) transition between discharging to charging modes of operations. The experimental results from a scaled prototype successfully validate the claims and efficacy of the control scheme for the SCMIC.
Electric vehicles (EVs) are increasingly recognized as an environmentally sustainable transportation option that supports reduced emissions and improved energy utilization. However, integrating EV charging infrastructure into existing power distribution networks presents technical challenges because EV chargers employ power electronic converters that behave as non-linear loads. This study investigates the influence of EV charging on a low-voltage three-phase distribution network (LV3PDN), focusing on key power quality indicators such as voltage deviation, current distortion, and total harmonic distortion (THD) at the point of common coupling (PCC). A three-phase LV network based on a 25-kVA, 415 V/415 V, 1:1 isolation transformer is modelled in the MATLAB Simulink environment, incorporating representative residential and commercial load conditions. To represent EV charging behaviour, a two-wheeler onboard charger model is adopted, selected because its input current harmonic profile lies between those reported for commonly used electric two-wheelers operating in Wardha city, Maharashtra, India. The analysis evaluates three operating conditions: EV charging location along the feeder, battery state of charge (SoC), and DC-DC converter operation mode (average and switched). Simulation results show that EV integration increases feeder current by about 50% and raises THD; distortion grows downstream, while voltage THD stays below 0.1%.
