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Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact the performance of the electrical network in terms of power losses, voltage deviations and transformer overloads. This paper presents a two-stage multi-agent-based scheme for the coordinated charging scheduling of EVs. The first stage uses particle swarm optimization (PSO) at the distribution network operator (DNO) level to determine the optimal power allocation among the participating EV aggregator agents to minimize power losses and voltage deviations, whereas the second stage at the EV aggregator agents level employs a genetic algorithm (GA) to align the charging activities to achieve customers’ charging satisfaction in terms of minimum charging cost and waiting time. The proposed method is implemented on the IEEE-33 bus network connected with low-voltage nodes. The coordinated charging plan is executed with the time of use (ToU) and real-time pricing (RTP) schemes, considering EVs’ random arrival and departure with two penetration levels. The simulations show promising results in terms of network performance and overall customer charging satisfaction.

The investigation aims to explore the food choice behavior of tourists concerning traditional Kashmiri food. The Extended Theory of Planned Behaviour (ETPB) has been applied that explains human behavior from the perspective of the attitude-behavior-experience relationship. An instrument was developed based on previous literature and was tested for a pilot study to check its validity and reliability. A descriptive research design and a positivist approach were applied to the present investigation. A sample of 387 valid questionnaires was analyzed for the present study. The purposive sampling technique was used to collect the data from tourists consuming traditional Kashmiri traditional food. Collected data was analyzed through SPSS-20 and Smart-PLS 4.0 software. The investigation results revealed that the Extended Theory of Planned Behaviour provided feasible explanations for tourists' traditional food consumption behavior. Except for the Esthetic Experience, all other variables (i.e. Attitude, Curiosity, Education, Perceived Behavioural Control, Perceived Usefulness, and Subjective norms) have a significant positive relation with Behavioural Intention, and Behavioural Intention has a significant positive association with Actual Behaviour. Given the lack of research examining tourist food consumption, this study provided evidence that one of the effective ways to understand tourists' traditional food buying behavior is from the perspectives of both attitude-behavior relationships and experience.

The ever-increasing electricity demand, its dependency on fossil fuels, and the consequent environmental degradation are major concerns of this era. The worldwide domination of fossil fuels in bulk electricity generation is rapidly increasing the emissions of CO2 and other environmentally dangerous gases that are contributing to climate change. The economic and emission dispatch are two important problems in thermal power generation whose combination produces a complex highly constrained nonlinear optimization problem known as combined economic and emission dispatch. The optimization of combined economic and emission dispatch aims to allocate the generation of committed units to minimize fuel cost and emissions, simultaneously while honoring all equality and inequality constraints. Therefore, in this article, we investigate a solution of the combined economic and emission dispatch problem using quantum particle swarm optimization and its two modified versions, that is, enhanced quantum particle swarm optimization and quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor. The enhanced quantum particle swarm optimization algorithm achieves particles’ diversification at early stages and shows good performance in local search at later stages. The quantum particle swarm optimization integrated with weighted mean personal best and adaptive local attractor boosts search performance of quantum particle swarm optimization and attains better global optimality. The suggested methods are employed to achieve solution for the combined economic and emission dispatch in four distinct systems, encompassing two scenarios with 6 units each, one with a 10-unit configuration, and another with an 11-unit setup. A comparative analysis with methodologies documented in existing literature reveals that the proposed approach outperforms others, demonstrating superior computational performance and robust efficiency.

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers’ appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification.

This meta-analysis evaluated the efficacy and safety of nemolizumab in treating prurigo nodularis through a systematic review of randomized controlled trials. A comprehensive literature search was conducted across multiple databases, including PubMed, Embase, Cochrane Library, and Web of Science, identifying relevant studies until January 5th 2025. Four randomized controlled trials involving 859 participants were included in the final analysis. The primary outcomes assessed were itching response measured by the Worst Itch Numeric Rating Scale (WI-NRS) and Investigator's Global Assessment (IGA) success. Nemolizumab demonstrated significant improvement in itch response compared to control, with a risk ratio of 3.52 (95% CI: 2.48 to 5.02, p < 0.00001) and low heterogeneity (I² = 28%). Similarly, IGA success rates were notably higher in the nemolizumab group, with a risk ratio of 4.40 (95% CI: 2.86 to 6.75, p < 0.00001) and low heterogeneity (I² = 11%). While adverse events were slightly more frequent in the nemolizumab group, the difference was not statistically significant (RR: 1.11, 95% CI: 0.99 to 1.24). The analysis was limited by the small number of included trials, relatively short follow-up periods, and lack of subgroup analysis. Despite these limitations, the findings suggest that nemolizumab is an effective and well-tolerated treatment for prurigo nodularis. Further research with longer follow-up periods and larger, more diverse patient populations is recommended to establish the long-term efficacy and safety profile of nemolizumab in treating this condition.

This systematic review and meta-analysis evaluated the efficacy and safety of vitamin E supplementation in adults with metabolic dysfunction-associated steatohepatitis (MASH), formerly known as nonalcoholic steatohepatitis (NASH). A comprehensive search of PubMed, Cochrane Library, Embase, and Scopus databases was conducted from inception to May 25, 2025, identifying randomized controlled trials comparing vitamin E versus placebo in MASH patients. After screening 752 records, three high-quality randomized controlled trials were included in the final analysis. The pooled analysis demonstrated that vitamin E significantly reduced serum alanine aminotransferase levels compared to placebo (mean difference (MD): -12.27, 95% confidence interval (CI): -16.66 to -7.89) and aspartate aminotransferase levels (MD: -7.08; 95% CI: -14.93 to 0.76). Vitamin E was associated with significantly higher odds of fibrosis improvement (odds ratio (OR): 1.96, 95% CI: 1.25-3.09) with no heterogeneity observed across studies. However, MASH resolution showed no statistically significant difference between groups (OR: 1.71, 95% CI: 0.69-4.27) with substantial heterogeneity, though sensitivity analysis excluding one study revealed a significant benefit. The studies varied in vitamin E dosing from 300 to 800 mg daily, with two conducted in the United States and one in China. These findings suggest that vitamin E supplementation provides biochemical and histological benefits in MASH patients, particularly in reducing liver enzyme levels and improving fibrosis. However, the limited number of trials and varying outcome definitions highlight the need for larger, standardized multinational studies to establish optimal dosing recommendations and long-term safety profiles.

Sodium-glucose co-transporter-2 (SGLT2) inhibitors have demonstrated significant cardiovascular benefits in patients with type 2 diabetes. However, head-to-head comparisons between dapagliflozin and empagliflozin, two widely prescribed SGLT2 inhibitors, remain limited. This meta-analysis aimed to directly compare the cardiovascular outcomes of these agents in patients with type 2 diabetes. We conducted a comprehensive literature search across multiple databases and included eight retrospective studies enrolling 280,617 patients (158,352 receiving empagliflozin and 122,265 receiving dapagliflozin). The primary outcome was major adverse cardiovascular events (MACE), with secondary outcomes including all-cause mortality, myocardial infarction, and stroke. Our pooled analysis revealed no significant difference in MACE risk between empagliflozin and dapagliflozin (RR: 1.04; 95% CI: 0.96 to 1.13). Similarly, no significant differences were observed for all-cause mortality (RR: 1.05; 95% CI: 0.96 to 1.15), myocardial infarction (RR: 1.04; 95% CI: 0.94 to 1.16), or stroke (RR: 1.00; 95% CI: 0.91 to 1.09). Subgroup analyses by gender, atherosclerotic cardiovascular disease, and chronic kidney disease status showed consistent results. However, in patients with heart failure, a trend toward reduced MACE risk was observed with empagliflozin (RR: 0.90; 95% CI: 0.82 to 1.00). Despite pharmacokinetic differences between these agents, our findings suggest comparable cardiovascular outcomes in patients with type 2 diabetes, with potentially enhanced benefits of empagliflozin in those with heart failure. However, due to lack of studies, this finding should be interpreted with caution. These results provide valuable insights for clinical decision-making when selecting SGLT2 inhibitors for cardiovascular risk reduction in diabetic patients. Further prospective studies are warranted to confirm these findings and explore potential mechanistic differences between these agents.

This systematic review and meta-analysis compared the efficacy of continuous positive airway pressure (CPAP) and high-flow nasal cannula (HFNC) therapy in children with acute bronchiolitis. A comprehensive literature search across multiple electronic databases identified six randomized controlled trials for inclusion. The primary outcomes assessed were treatment failure, the need for invasive mechanical ventilation, and length of hospital stay. Pooled analysis revealed no significant difference between CPAP and HFNC in the risk of requiring invasive mechanical ventilation (RR 0.94, 95% CI: 0.60-1.46) with minimal heterogeneity across studies. Treatment failure was higher in the HFNC group than in CPAP, but this difference was not statistically significant (RR 1.20, 95% CI: 0.63-2.27), though heterogeneity was substantial (I²=70%). Sensitivity analysis after removing one study showed a significantly higher risk of treatment failure with HFNC (RR 1.67, 95% CI: 1.07-2.61) with reduced heterogeneity. Length of hospital stay was comparable between both interventions (MD 0.57, 95% CI: -0.16-1.31). Both respiratory support strategies effectively reduce respiratory effort in moderate to severe bronchiolitis through different mechanisms, such as CPAP, which provides consistent positive end-expiratory pressure, and HFNC through enhanced minute ventilation and nasopharyngeal dead space reduction. Despite some limitations, including small sample sizes and inability to conduct subgroup analyses due to lack of individual patient data, this meta-analysis suggests HFNC may serve as a viable alternative to CPAP, particularly in resource-limited settings, showing comparable outcomes for critical endpoints while potentially offering practical advantages in administration and patient comfort.

The coronary sinus reducer (CSR), a minimally invasive device, has emerged as a promising alternative for improving myocardial perfusion in these patients. This meta-analysis evaluated the effectiveness of CSR implantation in patients with refractory angina. A comprehensive search of PubMed, EMBASE, and Web of Science databases identified 10 relevant studies with a pooled sample size of 799 patients. The analysis focused on changes in the Canadian Cardiovascular Society (CCS) classification score, Seattle Angina Questionnaire (SAQ) score, and six-minute walk distance (6MWD) from baseline to follow-up. Results showed significant improvements across all measured outcomes. CCS scores decreased significantly post-CSR implantation, indicating reduced angina severity. SAQ scores improved across all domains, including physical limitation, anginal stability, anginal frequency, treatment satisfaction, and quality of life, suggesting enhanced overall well-being. The 6MWD also increased significantly, reflecting improved functional capacity. These findings highlight CSR's potential as an effective treatment option for patients with refractory angina who have exhausted traditional therapies. CSR implantation appears to alleviate angina symptoms, improve quality of life, and enhance exercise tolerance. Future research should prioritize larger, multi-center randomized controlled trials to validate these findings. Long-term follow-up studies are needed to assess sustained benefits and potential risks.