Architectural design holds a pivotal role in shaping the development of university campuses. This study critically examines the extent to which the architectural plans of four campuses correspond with the conceptual framework of an educational campus. The evaluation of design quality is crucial, as it significantly influences both academic and non-academic environments, thereby contributing to the overall enhancement of higher education quality. Adopting a mixed methods research (MMR) approach, the initial phase employs the Analytic Hierarchy Process (AHP) to establish the Priority Design Principles for Educational Campuses. Subsequently, the quantitative phase focuses on formulating a decision-making model to assess the quality of the educational atmosphere through the application of a fuzzy logic system. The findings of this research reveal that the top three design criteria, identified as having the highest priority, serve as the basis for developing a comprehensive model to evaluate campus designs grounded in the educational campus paradigm. These three key criteria functions as benchmarks for determining whether a campus’s architectural design satisfies the essential standards of an educational campus. An empirical assessment conducted across four university campuses indicates that only the campus design of Malikussaleh University falls short of meeting the educational campus design criteria. In contrast, the remaining three universities have successfully adhered to the principles underpinning the educational campus concept.
Architecture, Architectural engineering. Structural engineering of buildings
Distance relays (DRs) have long been considered one of the most reliable protection schemes for transmission lines (TLs), providing primary and backup protection. However, the increasing complexity of transmission networks, the integration of large-scale renewable energy and dynamic operating conditions present significant challenges to their effectiveness. Traditional DRs protection logic often struggles when networks deviate from predefined configurations, potentially leading to protection failures. Numerous solutions have been proposed in the literature to address individual challenges in distance relaying for the protection of TLs. However, there are only a few review papers which comprehensively cover these issues across multiple domains of problems encountered in modern power system networks. This paper thoroughly reviews the technical, topological, and operational challenges that impact DRs performance, as reported in the literature. It systematically categorizes protection issues and their corresponding solutions available in the literature into twelve key areas: power swings, voltage instability, load encroachment, parallel lines, multi-terminal lines, flexible alternating current transmission systems (FACTS) devices, series compensation, high voltage direct current (HVDC) lines, renewable energy integration, microgrids, high-impedance faults, and cyberattacks. Additionally, the paper identifies emerging research opportunities to tackle the increasing complexity of modern power systems, aiming to foster the development of more robust and adaptive distance protection schemes for TLs.
We propose a comprehensive Electric Vehicle (EV) routing algorithm to find the optimal set of intermediate charging stations (CSs) present between a given source and destination. Each intermediate charging station is selected to maximize efficiency by considering three crucial parameters: distance to reach the destination from the selected CS, waiting time at the CS, and energy consumed to reach the selected CS along the route. Unlike existing algorithms, that focus solely on energy or distance, this algorithm integrates all three factors to generate an efficient path. Machine Learning (ML) is employed to predict vehicle range using data provided by the user, ensuring that the selected route avoids the risk of battery depletion midway. This predicted range is then used to determine CSs that can be reached from current location. Furthermore, the algorithm utilizes Breadth-First Search (BFS) to identify CS nodes with the least cost, enhancing routing accuracy. The cost of reaching each CS node is calculated using Neuro-Fuzzy Logic, which effectively handles uncertain inputs, which is common in EV routing scenarios. Comparative analysis against a recently proposed route planning algorithm (EV-RPA) reveals superior performance of the proposed approach, particularly as the number of CSs increases. It excels in all three aspects: distance covered, waiting time, and energy consumed, highlighting its effectiveness in optimizing EV routing.
In the realm of healthcare, an imperative necessity for all, institutions are increasingly recognizing the advantages of adopting lean strategies to enhance performance. Lean implementation in healthcare can lead to significant improvements in efficiency, patient care, and overall institutional performance. This paper aims to assess the readiness levels for implementing lean practices in healthcare institutes in Bangladesh, employing a fuzzy logic approach. The construction of a conceptual model is grounded in literature review and expert opinions, incorporating critical enablers, criteria, and attributes identified from extensive research. Factors measured include leadership commitment, workforce capability, operational processes, technological infrastructure, and organizational culture, each pivotal in determining readiness for lean implementation. The fuzzy logic approach is particularly useful in this context due to its ability to handle uncertainty and imprecision, which are common in complex environments like healthcare. This methodology not only provides a clear picture of current capabilities but also highlights specific areas that need enhancement, paving the way for more targeted and effective lean interventions. Data sourced from consultations with experts in three prominent hospitals in Bangladesh forms the basis of the analysis, enabling a detailed examination of readiness levels. The model's application of fuzzy logic facilitates a comprehensive assessment, revealing 12 critical attributes across the hospitals that require attention. Interestingly, the evaluation identifies varying levels of readiness, with two hospitals demonstrating moderate readiness and one showing a lower level. This conceptual approach has significant potential to assist top management in healthcare companies by providing a structured framework to prioritize crucial areas for improvement. By accurately assessing readiness levels and pinpointing weaker aspects before implementing lean strategies, this study aims to transform the healthcare industry. Ultimately, its implementation has the potential to enhance organizational performance and elevate standards in patient care, contributing to improved healthcare delivery in Bangladesh and beyond.