This paper proposes a decentralized multiagent system (MAS) approach to solve the service restoration problem considering the uncertainty of load demand and renewable distributed generators (RDGs). Service restoration is formulated as a multi-objective optimization problem and solved by implementing controlled DG islanding using the proposed MAS approach. First, the uncertainty of load demand and RDG generation is forecast by generating scenarios using Monte Carlo simulations. Next, the expected nodes to be restored in an island are determined using a heuristic rule-based technique. Finally, the maximum likelihood of the expected island ranges to be restored are estimated using the maximum likelihood estimation method to assist the utility in decision making. Extensive case studies are conducted on 38 bus and 119 bus distribution system to show the efficacy of the proposed restoration strategy. The use of electric vehicles and battery energy storage as flexible sources of energy to alleviate the uncertainties in the system is also highlighted.

The field of optical nanoscopy, a paradigm referring to the recent cutting-edge developments aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical microscopy, has gained recent prominence & traction in the 21st century. Numerous optical implementations allowing for a new frontier in traditional confocal laser scanning fluorescence microscopy to be explored (termed super-resolution fluorescence microscopy) have been realized through the development of techniques such as stimulated emission and depletion (STED) microscopy, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), amongst others. Nonetheless, it would be apt to mention at this juncture that optical nanoscopy has been explored since the mid-late 20th century, through several computational techniques such as deblurring and deconvolution algorithms. In this review, we take a step back in the field, evaluating the various in silico methods used to achieve optical nanoscopy today, ranging from traditional deconvolution algorithms (such as the Nearest Neighbors algorithm) to the latest developments in the field of computational nanoscopy, founded on artificial intelligence (AI). An insight is provided into some of the commercial applications of AI-based super-resolution imaging, prior to delving into the potentially promising future implications of computational nanoscopy. This is facilitated by recent advancements in the field of AI, deep learning (DL) and convolutional neural network (CNN) architectures, coupled with the growing size of data sources and rapid improvements in computing hardware, such as multi-core CPUs & GPUs, low-latency RAM and hard-drive capacities.

Electric Vehicles (EV) have the potential to completely transform the transportation industry, reduce carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes car, truck, and bus. With this a charging discharging scheme has been established using Real Time Pricing for each hour for a 20‐min block. The Voltage Stability Index (VSI) for the system is calculated for the system and the strongest bus is identified for locating the EVs to charge/discharge. Augmenting this, in the present work, the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed in each feeder, considering the VSI and Penetration Level (PL) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE‐33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

This study presents a novel predictive framework for estimating electric vehicle (EV) charging demand in smart cities, contributing to the advancement of data-driven infrastructure planning through behavioural and spatial data analysis. Motivated by the accelerating regional demand accompanying EV adoption, this work introduces HCB-Net: a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Extreme Gradient Boosting (XGBoost) for robust regression. The framework is trained on user-level survey data from two demographically distinct UK regions, the West Midlands and the North East, incorporating user demographics, commute distance, charging frequency, and home/public charging preferences. HCB-Net achieved superior predictive performance, with a Root Mean Squared Error (RMSE) of 0.1490 and an $R^2$ score of 0.3996. Compared to the best-performing traditional model (Linear Regression, $R^2=0.3520$), HCB-Net improved predictive accuracy by 13.5% in terms of $R^2$, and outperformed other deep learning models such as LSTM ($R^2=-0.3756$) and GRU ($R^2=-0.6276$), which failed to capture spatial patterns effectively. The hybrid model also reduced RMSE by approximately 23% compared to the standalone CNN (RMSE = 0.1666). While the moderate $R^2$ indicates scope for further refinement, these results demonstrate that meaningful and interpretable demand forecasts can be generated from survey-based behavioural data, even in the absence of high-resolution temporal inputs. The model contributes a lightweight and scalable forecasting tool suitable for early-stage smart city planning in contexts where telemetry data are limited, thereby advancing the practical capabilities of EV infrastructure forecasting.