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Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies.

This investigation is centrally focused on the comprehensive evolution and enhancement of FOODIEBOT(shortened name for food delivery robot, an adaptive service automaton with a wide range of functionalities. Its capabilities encompass sophisticated image processing methods, seamlessly integrated via mobile applications (APP) and web interfaces, tailored specifically for intricate object manipulation in dining hall settings. During its developmental phase, the precise calibration of PID controller coefficients emerged as an essential requirement. The model underwent meticulous scrutiny through detailed simulations using MATLAB software. Following this phase, its operational efficiency navigating through circular, elliptical, spiral, and octagonal trajectories underwent rigorous examination, utilizing optimization methodologies like Beetle Antennae Search (BAS) Algorithm, Particle Swarm Optimization (PSO), Pelican Optimization Algorithm (POA), and Equilibrium Optimizer (EO). The exposition emphasizes the diverse dispersion of optimized coefficients within each algorithmic framework. The pinnacle of this effort involved a comprehensive evaluation of pathway performance, amalgamating insights from each optimization paradigm. The discussion extensively delineates both simulated and real-time performance metrics of the robot, validating the accuracy and reliability of simulation in deriving PID controller values. In the comprehensive evaluation of methodologies and the robotic system’s effectiveness, the BAS technique excels in operational efficiency. This method consistently outperforms its counterparts in execution time, primarily due to its meticulous optimization of particle count. The comparative analysis across various trajectories reveals intriguing insights. The EO approach showcases outstanding accuracy in Path 1, while the POA method achieves optimal precision in Path 3. Impressively, the BAS technique demonstrates unparalleled accuracy in Path 4. Furthermore, in terms of solution optimization, the BAS method consistently displays the shortest execution times across all traversed pathways. When examining maximum velocity along these routes, the PSO method excels in Paths 1, 3, and 4, consistently achieving the highest speeds. Notably, Path 2 uniquely displays the peak velocity attained by the POA method. This article presents comprehensive insights into the constituent elements of the robotic system’s design. The inquiry delves into the intricate nuances of optimization methodologies, elucidating their profound impact on the service automaton’s performance across diverse orientations. The pragmatic implications underscore the critical role of temporal considerations in the judicious selection of these methodologies. The observed congruence between simulated and practical performance serves as a definitive validation, affirming the precision of simulation computations and the subsequent derivation of PID controller values.

In this research study, we address the critical task of identifying and classifying non-functional requirements (NFRs) in software development. NFRs, described in the software requirements specification (SRS) document, offer a comprehensive system view and are closely aligned with software design and architecture. However, they are often overlooked compared to functional requirements, leading to potential issues such as rework, increased maintenance efforts, and inefficient resource utilization, impacting project cost and budget. To streamline software development, we propose a novel approach based on transfer learning methods to automate NFR identification and classification, aiming to reduce development time and resource consumption, ultimately leading to improved efficiency. We evaluate multiple state-of-the-art transfer learning models, including XLNet, BERT, Distil BERT, Distil Roberta, Electra-base, and Electra-small, for this purpose. Among them, XLNet demonstrates exceptional performance, achieving an impressive value of 0.91489 for Accuracy, Precision, Recall, and F1 Score. This research highlights the importance of considering non-functional requirements (NFRs) in software development and the negative consequences of neglecting them. It also emphasizes the benefits of using the XLNet tool to automate the identification and classification of NFRs. By using XLNet, we aim to make software development easier, optimize resource usage, and improve the overall quality of software systems.