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This research paper delves into the crucial role of materials management in construction projects, categorizing it into five key areas: measurement and specification, procurement, delivery, administration, and utilization. The study, focusing on qualitative and quantitative aspects, particularly explores factors contributing to project delays. The literature review establishes materials management as critical, involving planning, quality control, procurement, and timely delivery. Given the significant expense of building materials, strategic management is vital for cost reduction. The research identifies and categorizes key factors responsible for delays in construction projects, with a focus on the Indian context. A survey employing the Relative Importance Index (RII) reveals limited supplier availability, low IT utilization, and delays in material receipt as primary contributors to material management delays. The paper concludes with practical recommendations such as diversifying supplier bases and improving technology integration and communication, contributing nuanced insights for practitioners and researchers in the ongoing effort to optimize materials management and minimize delays in construction projects.

The global energy landscape is undergoing a significant transformation, driven by the dual imperatives of innovation and regulation. This paper reviews the work in the areas of sustainable energy transitions, focusing on the intricate balance between demand-side innovations and governance, as seen in Germany’s energy transition journey. The study further delves into the European context, highlighting the emergence of Renewable Energy Communities (RECs) and the challenges and opportunities they present in achieving a just and democratic energy transition. The paper also introduces the World Economic Forum’s Energy Transitions Index (ETI), a comprehensive tool that tracks the performance of energy systems at the country level, offering insights into the macroeconomic, institutional, social, and geopolitical considerations that shape energy transitions globally. Collectively, these areas of study underscore the importance of a holistic approach to energy transition, one that harmonizes technological advancements with policy frameworks and societal needs.

Electricity market reforms, pivotal in shaping the energy landscapes of both developed and emerging economies, have garnered significant attention in academic and policy circles. This review, drawing from existing research, seeks to understand the nuanced implications of these reforms, particularly on small producers who are integral to the energy matrix. By synthesising insights from seminal works on the subject, we aim to elucidate the multifaceted consequences of electricity market reforms. Our exploration reveals that while reforms can potentially streamline operations and optimise price-cost margins, their tangible impact is moulded by diverse factors, from fuel costs and technological shifts to market structures. The intricate relationship between regulatory accounting, tariff dynamics, and the overarching goals of market regulators emerges as a recurrent theme. As the global energy paradigm undergoes transformation, this review underscores the need for a holistic understanding of market reforms, ensuring a balanced and sustainable energy future for all players in the sector.

This review explores Peer-to-Peer (P2P) Energy Trading Platforms in Local Energy Markets, drawing insights from three key studies. These markets, using P2P trading, efficiently distribute electricity within communities. Research assesses P2P trading’s impact in a Norwegian neighbourhood, comparing it to scenarios without local markets. It also examines integrating PV systems, batteries, and electric vehicles on grid operations. Findings reveal minimal grid impacts with PVs alone, but adding batteries increases voltage fluctuations and losses. However, P2P trading benefits end-users with cost savings and supports Distribution System Operator operations. The paper surveys global P2P energy trading projects, emphasising communication and control networks within local Microgrids. It discusses the transition from passive consumers to prosumers in power networks and introduces the concept of a federated power plant, combining virtual power plants and P2P transactions among prosumers to address challenges and unlock additional value. This review fills research gaps, shedding light on P2P energy trading’s multifaceted aspects in local markets and its transformative potential for the energy sector.

This paper provides a comprehensive review of dynamic control strategies for FACTS (Flexible AC Transmission Systems) devices, aligning with the “Dynamic Control Strategies for FACTS Devices in Modern Power Grids.” It addresses the critical role of FACTS in managing modern power grids, focusing on their ability to enhance power quality, stabilize voltage, and improve energy efficiency. As power systems evolve to incorporate renewable and distributed energy sources, the challenges of ensuring reliable and stable grid operations become increasingly complex. The review discusses how FACTS technology and dynamic control strategies are vital components in addressing these challenges. By regulating voltage profiles and power system stability, FACTS devices contribute to the efficient integration of renewable energy sources, aligning with the overarching theme of the review article. In essence, this review sets the stage for a deeper exploration of the dynamic control strategies that underpin FACTS devices’ contributions to the modern power grid’s reliability and efficiency, emphasizing their relevance and significance in the evolving energy landscape.

The realm of energy storage devices is witnessing a paradigm shift, driven by the exploration of novel materials. This review delves into the transformative potential of unconventional materials in enhancing the performance and versatility of energy storage systems. We discuss the potential of a hierarchical carbon material derived from E. coli cells, graphene oxide, and iron ion, underscoring its green credentials and promising attributes. Further, we highlight the transformative impact of paper as a material platform, tracing its evolution from a historical medium to a contemporary game-changer in microfluidic devices and microelectronics. The integration of electronics on paper reveals the potential of paper-based devices in diagnostics, MEMS, and energy storage. Collectively, these innovations illuminate the vast potential of avant-garde materials in revolutionising energy storage solutions, bridging the gap between cost-effectiveness and high performance. This review aims to provide a comprehensive overview of these groundbreaking innovations, fostering a deeper understanding of the future trajectory of energy storage research.

The current paper discusses about the work made on the study to develop a highly adaptable and versatile stadium, departing from traditional single-purpose designs, with a focus on maximizing utility. With a substantial gross seating capacity of 61424, the stadium is intended to host various sports competitions and large public gatherings. The planning process relies on Computer-Aided Design (CAD) and Building Information Modelling (BIM) software to ensure precise detailing in structural aspects. Advanced engineering software enhances structural analysis, allowing for comprehensive investigations into dynamic load responses. The design's reliability is ensured through a combination of manual calculations and engineering software outcomes.

We now understand that, in the general construction of structures, the selection of reinforcing materials plays a critical role in guaranteeing structural durability and integrity. The comparative study of fibre reinforced polymer (CFRP) bars and steel, two widely utilized materials, is explored in this abstract with an emphasis on the differences in their load-deflection properties and ductility behaviours. Because of its exceptional ductility, steel is commonly used in constructions that are situated in seismically active areas. On the other hand, CFRP bars have certain benefits, such resistance to corrosion and less weight, even if they are less ductile. The trade-offs between various materials are revealed by the study of load-deflection qualities, highlighting the significance of choosing the best choice in light of particular building constraints and needs.

The escalating use of plastic products alongside rising ambient temperatures has intensified concerns regarding their performance under diverse thermal conditions. This paper delves into the intricate relationship between plastic materials, ambient temperature fluctuations, and resulting stresses. Various grades of plastic materials are subjected to distinct ambient temperatures to elucidate stress generation—a pivotal aspect in plastic product design. Leveraging the finite element method, a comprehensive analysis is conducted to design and assess a plastic product under specified loads and ambient temperatures. The study encompasses the evaluation of equivalent stresses, normal and shear stresses, and deformations. Ultimately, the research culminates in the development and analysis of a thermally stable plastic product, offering valuable insights for robust design practices amidst evolving environmental conditions.

As the healthcare landscape embraces the revolutionary power of digitalization, improving inventory systems has become a top priority. Hospital asset tracking primarily focuses on monitoring various assets within a healthcare facility. These assets encompass human resources, medical equipment, vehicles, and other essential resources crucial for seamless hospital operations. Tracking assets in a hospital setting can range from monitoring individual medical devices to overseeing entire fleets of equipment and staff resources. The central objective is to maintain real-time records of asset status, and location to optimize productivity while minimizing costs. This project tracks the assets within the hospital using RFID sensors and outside the hospital using GPS. Radio frequency identification system (RFID) is an automatic technology and is used to identify objects, and record data through radio waves. Hospital asset management and tracking has become one of the main priorities in healthcare as it saves hospitals both money and time. Hospitals are under constant pressure to persistently track their medical equipment and supplies to make sure that they are properly utilized. This enhances financial performance and ensures the delivery of high-quality patient care. These are the advanced approaches to improve hospital asset management through real-time tracking and data analysis.

In the current study, the investigation of heat transfer and fluid flow Characteristics of Pure water when pass through a double tube heat exchanger (DTHX). this investigation has been conducted across various Reynolds Number to gain insights into their performance also conducted a computational fluid dynamics (CFD) simulation using the ANSYS-FLUENT 22 R1 software. Result obtained was validated by comparing to empirical correlation data found in the existing literature. The investigation considered various operating variable as Reynolds Number and temperature across the inner, and outer tubes. Specifically, the Reynolds Number of a range of 2500 to 5500 at 333 K, and 2500 at 303 K for the respective tubes. Key findings are that friction factor is increase by 6.38% as compared to correlation (Blasius) in existing literature. And Nusselt number (Nu) increase by 40.84% as compared to correlation at the Reynolds Number (Re) of 2500. The heat transfer coefficients (hi) were increased by 8.30% as compared to existing literature.

Automobiles and motorcycles that run on conventional fuels are currently not the greatest for transportation because to the rising awareness of pollution and the energy shortage concerns. There is a demand for a less expensive and more effective mode of transportation because the cost of petroleum goods is soaring right now. Additionally, it is becoming more and more crucial to conserve energy in order to assess the issue of leftover fuel depletion. Even large-scale manufacturing and industrial operations restrict their employees' use of transportation within the premises in order to reduce the risk of air pollution. Research on environmentally friendly transport has increased to fulfil these demands. Technology for electrically powered vehicles is a step in the right direction. One such environmentally friendly vehicle that aids employees in covering large distances inside of their organisations is the monowheel. A monowheel is a single-track, one wheeled vehicle that looks a lot like a unicycle. The rider sits inside the wheel as opposed to being above it like on a unicycle. The wheel is a ring that is often propelled by smaller wheels contacting its inner rim. The smaller wheels are powered by an electric motor and a battery to turn. Changes to the frame's design and the addition of solar panels as a backup power source are proposed. Then, the car can also be powered by solar energy. Software from CATIA and ANSYS, respectively, are used for the design and analysis of the inner and exterior fames.

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields., Competing Interests: The authors declare no conflicts of interests. The company was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Alzubaidi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

SARS-CoV-2 caused the pandemic of the rapidly evolving COVID-19. As of December 6, 2023, there were 765,152,854 COVID-19-recovering cases. Long-term consequences known as "long COVID" and "post-COVID-19 conditions" (PCCs) or "post-acute COVID-19 syndrome" are being reported more frequently in a subset of recovering patients. Systemic, neuropsychiatric, cardio-respiratory, and gastrointestinal symptoms are the most prevalent. The management of PCCs poses unique challenges due to the lack of official guidelines and the complex nature of the illness. This abstract highlights key principles derived from recent reviews and expert recommendations to provide healthcare professionals with a comprehensive approach to manage post-COVID-19 patients. Preventive medicine plays a crucial role in managing PCCs. While no specific medications are available for treatment, preventive measures such as COVID-19 vaccination, adherence to precautionary measures, regular consultations with medical professionals, monitoring symptoms and progress, and seeking information on symptom management are essential to assist patients in their recovery and improve their quality of life. Medical management requires transparent goal-setting and collaborative decision-making based on the patient's symptoms, comorbidities, and treatment objectives. Treatment plans for post-COVID-19 patients should focus on patient education, using registries and calendars to track symptoms and triggers, providing support and reassurance, and offering holistic support through peer networks and supportive psychotherapy techniques. Symptomatic and rehabilitative care, including well-established symptom management techniques, physical rehabilitation programs, and addressing mental health and well-being, are vital components of post-COVID-19 management. Lifestyle factors such as stress reduction, nutrition, and sleep should be incorporated into managing underlying medical conditions in post-COVID-19 patients. Regular follow-up visits and referrals to specialists are recommended to monitor the patient's progress and address specific organ system involvement or additional care needs. In summary, for the effective management of PCCs, a holistic approach should include preventive measures, patient education, supportive psychotherapy, symptomatic and rehabilitative care, medical management, counseling on lifestyle elements, and appropriate follow-up plans. However, it is crucial to stay updated with evolving guidelines and recommendations from healthcare authorities to provide the most effective and evidence-based care to post-COVID-19 patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Assiri et al.)