High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments’ challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

The integrated Electronic Control Unit (ECU) plays a pivotal role in optimizing energy efficiency within electric vehicles (EVs) by coordinating various subsystems, including the Vehicle Control Unit (VCU), Electrical Power Steering (EPS), Electronic Stability Control (ESC), and Body Control Unit (BCU). Our comprehensive review deeply explores the various aspects of integrated ECUs and their sub-disciplines, emphasizing development approaches and algorithms specifically designed for mobility energy efficiency. The study shows the effect of various control units on energy efficiency by carefully examining case studies and real-world applications. Employing a critical assessment, the paper examines the advantages and disadvantages of various control systems utilized in electric vehicles (EVs), providing insight into their effectiveness in various situations. The presented study is typically related to the relationship between ECUs and their sub-branches through an integrated approach. By comparing current control approaches, the study offers a deep knowledge of the role of these units in improving vehicle performance, stability, and overall control. Furthermore, we evaluate the strengths and limitations of integrated ECU algorithms in the context of EV control by comparing them in a fair amount of detail. We identify important research gaps by integrating knowledge of multiple control areas in EV. The research effort will give researchers a path forward and make it possible to pinpoint the prospective dimensions that need further exploration in advancing the area of integrated ECUs in EVs.

This survey critically examines the integration of energy management systems within smart residential buildings, serving as key nodes in the smart city network. It systematically maps out the intricate relationships between smart grid technologies, energy storage capabilities, infrastructure development, and their confluence in residential settings. From the evolution of power generation methods, incorporating both traditional and renewable sources, to the cutting-edge progress in energy-efficient transport systems, we assess their cumulative impact on the smart urban environment. While our approach is rooted in theoretical exploration rather than mathematical modeling, we provide a comprehensive review of the prevailing frameworks and methodologies that drive energy management in smart urban ecosystems. We also discuss the implications of these systems on urban sustainability and the critical importance of integrating various energy domains to facilitate effective energy governance. By bringing together a diverse array of scholarly insights, our paper aspires to enhance the understanding of energy interdependencies in smart cities and to catalyze the development of innovative, sustainable policies and practices that will define the future of urban energy management. Through this expanded perspective, we underscore the necessity of cross-disciplinary research and the adoption of holistic strategies to optimize energy usage, reduce carbon footprints, and promote resilient urban living in the era of smart cities.

The scientific community has raised increasing apprehensions over the transparency and interpretability of machine learning models employed in various domains, particularly in the field of materials science. The intrinsic intricacy of these models frequently results in their characterization as “black boxes”, which poses a difficulty in emphasizing the significance of producing lucid and readily understandable model outputs. In addition, the assessment of model performance requires careful deliberation of several essential factors. The objective of this study is to utilize a deep learning framework called TabNet to predict lead zirconate titanate (PZT) ceramics’ dielectric constant property by employing their components and processes. By recognizing the crucial importance of predicting PZT properties, this research seeks to enhance the comprehension of the results generated by the model and gain insights into the association between the model and predictor variables using various input parameters. To achieve this, we undertake a thorough analysis with Shapley additive explanations (SHAP). In order to enhance the reliability of the prediction model, a variety of cross-validation procedures are utilized. The study demonstrates that the TabNet model significantly outperforms traditional machine learning models in predicting ceramic characteristics of PZT components, achieving a mean squared error (MSE) of 0.047 and a mean absolute error (MAE) of 0.042. Key contributing factors, such as d33, tangent loss, and chemical formula, are identified using SHAP plots, highlighting their importance in predictive analysis. Interestingly, process time is less effective in predicting the dielectric constant. This research holds considerable potential for advancing materials discovery and predictive systems in PZT ceramics, offering deep insights into the roles of various parameters.