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The cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constraints such as PHEV scheduling and battery life assessment. Pollutant reduction, however, is rarely highlighted as a primary goal. An incentive-based demand response (IBDR) is introduced in the proposed work to close this gap and promote load curtailment during peak hours. IBDR policy rewards participant customers with incentives for load curtailment which in turn lowers emissions and generation costs. Furthermore, a trade-off approach ensures both environmental and economic sustainability by striking a balance between cost reduction and emission reduction. Considering the fact in view that the 30–40% of the microgrid customers are willing to participate in the IBDR program, six different scenarios that have been analysed, each of which involves various levels of grid participation and different approaches to pricing in the electricity market. These scenarios also include the implementation of demand response programmes. Differential evolution algorithm was used as the optimization tool for the study. The results achieved for all the scenarios demonstrate the suitability and effectiveness of implementing the suggested IBDR strategy in terms of cost savings. According to numerical results reported, the generating cost decreased by 10–13% with the inclusion of IBDR. Additionally, a 6–8% reduction in peak and 4–5% improvement in load factor was also realised as a positive impact of the IBDR policy. The weighted economic emission dispatch algorithm offered a balanced solution that considered both the minimum generation cost and emissions for various load models in the microgrid system. [ABSTRACT FROM AUTHOR]

We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. The latter have been shown to be promising in violence detection. The aim of our architecture is to harness the combined strengths of the two known methods for improved accuracy. First, we leverage the pre-trained weights of MobileNetV2 and ResNet50V2 to initialize the network. Next, we fine-tune the network by training it on a dataset of labeled surveillance videos, with a focus on optimizing the fusion process between the two architectures. Experimental results demonstrate a significant improvement in accuracy compared with individual models. MobileNetV2 achieves an accuracy of 90%, while ResNet50V2 achieves a 94% accuracy in violence detection tasks. SynthSecureNet achieves an accuracy of 99.22%, surpassing the performance of individual models. The integration of MobileNetV2 and ResNet50V2 in SynthSecureNet offers a comprehensive solution that addresses the limitations of the existing architectures, paving the way for more effective surveillance and crime prevention strategies. [ABSTRACT FROM AUTHOR]

The urban landscape of Kinshasa, Democratic Republic of Congo, faces significant mobility challenges, primarily stemming from rapid urbanization, overpopulation, and outdated infrastructure. These challenges necessitate the exploration of modern smart mobility concepts to improve traffic flow, road safety, and sustainability. This study investigates the potential of solutions such as Mobility-as-a-Service, car sharing, micro-mobility, Vehicle-as-a-Service, and electric vehicles in addressing these challenges. Through a comparative analysis of global implementations, this research identifies key success factors and barriers that inform the feasibility of integrating these solutions into Kinshasa's unique socio-political and infrastructural context. The study presents a conceptual framework, supported by stakeholder analysis, for adapting these solutions locally. A detailed feasibility analysis considers technological, economic, social, environmental, and regulatory factors, offering a clear roadmap for implementation. Drawing on lessons from cities facing similar urban mobility challenges, the paper concludes with actionable recommendations and insights for policymakers and urban planners in Kinshasa. This research not only highlights the viability of smart mobility solutions in Kinshasa but also contributes to the broader discourse on sustainable urban development in rapidly growing cities. While smart mobility studies have largely focused on cities with developed infrastructure, there is a gap in understanding how these solutions apply to cities like Kinshasa with different infrastructural and socio-political contexts. Previous research has often overlooked the challenges of integrating smart mobility in rapidly urbanizing cities with underdeveloped transportation systems and financial constraints. This study fills that gap by offering a feasibility analysis tailored to Kinshasa, assessing smart mobility solutions for its traffic congestion and road safety issues. The smart mobility solutions studied—Mobility-as-a-Service (MaaS), car sharing, electric vehicles (EVs), and micro-mobility—were chosen for their ability to address Kinshasa's key mobility challenges. MaaS reduces reliance on private vehicles, easing congestion and improving public transport. Car sharing offers affordable alternatives to vehicle ownership, essential in a city with income inequality. EVs align with sustainability goals by reducing emissions, while micro-mobility (bikes and e-scooters) improves last-mile connectivity, addressing public transit gaps. These solutions are adaptable to Kinshasa's context and offer scalable, sustainable improvements for urban mobility. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1. [ABSTRACT FROM AUTHOR]

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO2-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. [ABSTRACT FROM AUTHOR]

Globally, the integration of electric vehicles (EVs) in the transportation sector represents a significant step towards achieving environmental decarbonization. This shift also introduces a new demand for electric power within the utility grid network. This study focuses on the design and development of a grid-connected renewable energy system tailored to meet the EV load demands in Taif, Kingdom of Saudi Arabia (KSA). The integration of renewable energy sources, specifically solar photovoltaic (SPV) and wind turbines (WT), is explored within the context of economic feasibility and system reliability. Key considerations include optimizing the system to efficiently handle the fluctuating demands of EV charging while minimizing reliance on conventional grid power. Economic analyses and reliability assessments are conducted to evaluate the feasibility and performance of the proposed renewable energy system. This article discusses the technical sizing of hybrid systems, energy reduction, and net present cost for the selected location. A rigorous sensitivity analysis is performed to determine the impact of major variables such as inflation rate, real discount rate, solar irradiation, and Lack of Power Supply Probability (LPSP) on system performance. The results demonstrate that the Pufferfish Optimization Algorithm (PFO) significantly outperforms other metaheuristic algorithms documented in the literature, as well as the HOMER software. The study found that the grid-connected renewable energy system is the best option for operating EV charging stations at the selected location. The findings underscore the potential for sustainable energy solutions in urban environments like Taif, highlighting the importance of integrating renewable energy technologies to meet growing energy demands with enhanced economic efficiency and system reliability. This initiative seeks to pave the way for a greener and more resilient energy infrastructure, aligning with global efforts towards sustainable development and clean transportation solutions. [ABSTRACT FROM AUTHOR]

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows' food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method's performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system's generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO2, SO2, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. [ABSTRACT FROM AUTHOR]

Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class's data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning technique is analyzed to address the challenges of feature redundancy and data imbalance, providing robustness in the classification process. To overcome these problems and lessen their impact on the fault classification performance, authors carefully integrate effective feature selection with ensemble learning models. Forward selection demonstrates that a significant area under the receiver operating curve (ROC) can be attributed to only a small subset of features. The Greedy forward selection (GFS) technique outperformed Pearson's correlation method when evaluating feature selection techniques on the datasets. Ensemble learners, such as random forests (RF) and the proposed average probability ensemble (APE), demonstrate greater resistance to the impact of weak features when compared to weighted support vector machines (W-SVMs) and extreme learning machines (ELM). Furthermore, in the case of the NASA and Java datasets, the enhanced average probability ensemble model, which incorporates the Greedy forward selection technique with the average probability ensemble model, achieved remarkably high accuracy for the area under the ROC. It approached a value of 1.0, indicating exceptional performance. This review emphasizes the importance of meticulously selecting attributes in a software dataset to accurately classify damaged components. In addition, the suggested ensemble learning model successfully addressed the aforementioned problems with software data and produced outstanding classification performance. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is a common issue in power transformers that can lead to catastrophic failures if left undetected. Time reversal (TR) is a well-known technique in signal processing that can reconstruct signals by reversing the direction of time. The paper investigates the use of time reversal and the integration of time reversal with convolution neural networks (CNNs) for diagnosing PD in power transformers. We compare the performance of these techniques on a dataset of PD signals collected from power transformers. We propose a novel method of using time reversal as a pre-processing step to improve the accuracy of CNNs on noisy or distorted signals. Our experimental results demonstrate that this approach can significantly enhance the performance of CNNs on various datasets, including speech, audio, and image datasets. This paper provides a novel approach to signal processing and demonstrates the potential of time reversal as a pre-processing step in CNNs. [ABSTRACT FROM AUTHOR]