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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.

The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strategy enables quicker execution times and enhanced performance in general. Parallel and distributed computing are essential for high-performance applications like scientific simulations, data analysis, and artificial intelligence since they frequently call for significant computational resources. High-performance apps are able to effectively handle computationally demanding tasks thanks in large part to parallel and distributed computing. This article offers a thorough review of the theories, methods, difficulties, and developments in parallel and distributed computing for high-performance applications. Researchers and practitioners may fully utilize the potential of parallel and distributed computing to open up new vistas in computational science and engineering by comprehending the underlying concepts and utilizing the most recent breakthroughs.

Bioinformatics is a fast evolving field that combines biology, computer science, and statistics to analyze and comprehend enormous volumes of biological data. As a result of the introduction of high-throughput technologies like next-generation sequencing and mass spectrometry, genomic and proteomics research has generated enormous volumes of data, necessitating the development of computational tools to process and extract useful insights from these datasets. This presentation presents a survey of computational approaches in bioinformatics with a particular emphasis on their application to genomics and proteomics. The study of the entire genome is a topic covered in the discipline of genomics, which also includes genome annotation, assembly, and comparative genomics. Proteomics focuses on the investigation of proteins, including their identification, quantification, structural analysis, and functional characterization. Consequently, the importance of the area of bioinformatics has increased.

The globe has adopted the cloud computing environment, which organizes data and manages space for data storage, processing, and access. This technical development has brought up questions regarding data security and privacy in cloud computing environments, though. The purpose of this abstract is to offer a thorough review of the issues, solutions, and future developments related to data privacy and security in cloud computing. Keeping data private and secure while it is being processed and stored in outside data centres is the main difficulty in cloud computing systems. The abstract discusses the dangers of insider threats, data breaches, and illegal access to sensitive information. It digs further into the legal and compliance criteria that businesses must follow in order to protect user data in the cloud. In result, data privacy and security in cloud computing environments remain critical concerns for organizations and individuals alike. In the survey the overview of how to use cloud storage globally and its challenges, solution and future innovation is well explained. It underscores the importance of robust encryption, access controls, user awareness, and emerging technologies in safeguarding data in the cloud. By addressing these concerns, organizations can leverage the power of cloud computing while maintaining the confidentiality, integrity, and availability of their data.

Complex optimization issues may now be solved using computational intelligence (CI), which has shown to be a powerful and diverse discipline. Traditional optimization approaches frequently struggle to offer efficient and effective solutions because real-world situations are becoming more complicated. Evolutionary algorithms, neural networks, fuzzy systems, and swarm intelligence are just a few examples of the many methods that fall under the umbrella of computational intelligence and are inspired by both natural and artificial intelligence. This abstract examines how computational intelligence techniques are used to solve complicated optimization issues, highlighting their benefits, drawbacks, and most recent developments. In this, computational intelligence techniques provide a potent and adaptable solution for resolving challenging optimization issues. They are highly adapted for dealing with the non-linear connections, uncertainties, and multi-objective situations that arise in real-world problems. The limits of computational intelligence have recently been pushed by recent developments in hybrid techniques and metaheuristics, even if obstacles in algorithm design and parameter tuning still exist. Computational intelligence is anticipated to play an increasingly significant role in tackling complicated optimization issues and fostering innovation across a variety of disciplines as technology continues to advance.

Cryptography, optimization, simulation, and machine learning are just a few of the industries that might be completely transformed by quantum computing. This abstract gives a thorough introduction to quantum computing with an emphasis on its algorithms, architectures, and applications. In conclusion, this abstract offers an in-depth analysis of quantum computing, including its algorithms, structures, and applications. It highlights the revolutionary potential of quantum computing in tackling difficult issues that are beyond the scope of conventional computers, laying the groundwork for further research and understanding of this quickly developing topic.

SRAM (Static Random Access Memory) is one of the type of memory which holds the data bit without periodic refreshment. Compared with DRAM (Dynamic Random Access Memory) which requires periodic refreshment of data bit stored in it. Unlike Dynamic RAM, Static RAM uses a flip-flop circuit to store each data bit, whereas Dynamic RAM uses a capacitor to store the data bit. But capacitor has tendency of losing charge which requires periodic refreshment. Thus SRAM perform better and have more stability than DRAM especially in idle state. In this work, we analysed the performance of the SRAM cell which are built with different field effect transistors and calculated the Write and Read delays, PDP (Power Delay Product) and Static Noise Margin (SNM) for all types of transistors. SRAM cell which is based on the CNT technology with optimized parameters of CNT density, CNT diameter and CNTFET flat band voltage has the better performance and stability compared with other device technologies. Optimized CNTFET SRAM cell compared with the MOSFET based SRAM the write and read delays are improved by 85.8% and 94.3% respectively. All the simulations have been carried out using HSPICE tool for 32nm technology node.

Due to recent developments, the POSIT number system, winch has been planned as a successor for numbers that are expressed in IEEE floating-point, which are in the focus of advances in arithmetic. Although this format claims to deliver more precise outcomes with the same bit width as ordinary floating point, the duration of the operation fluctuation during posit field identification poses a hardware design problem. The POSIT-based MAC Unit is created using Verilog HDL in this study, and the designed architecture is evaluated for good operation before being implemented on an FPGA using Xilinx Vivado.

Multiple valued logic (MVL) can represent an exponentially higher number of data/information compared to the binary logic for the same number of logic bits. Compared to the conventional devices, the emerging device technologies such as Graphene Nano Ribbon Field Effect Transistor (GNRFET) and carbon nanotube field effect transistor (CNTFET) appears to be very promising for designing MVL logic gates and arithmetic circuits due to some exceptional electrical properties such as the ability to control the threshold voltage. This variation of the threshold voltage is one of the prescribed techniques to achieve multiple voltage levels to implement the MVL circuit.This work presents a 4-input ternary adder using carbon nanotube field effect transistor (CNTFET). Many researchers have been done work on implementation of ternary adders and multipliers. But no one has done the comparison of this proposed ternary adder with different types of nano transistors. Hence this work has been proposed a design of low power and high speed 4-input adder which will be useful for designing of fast ternary multipliers. All the proposed designs have been simulated using emerging device such as CNTFET at 32nm technology node. From the simulations, we have calculated the power consumptions of the proposed designs, carry propagation delay and power delay product for the CNTFET circuits. It has been observed that CNTFET based proposed logic circuits given a better performance than the conventional logical circuits.

This work presents comparison of ternary combinational digital circuits that reduce energy consumption in low-power VLSI (Very Large Scale Integration) design. CNTFET and GNRFET-based ternary half adder (THA) and multiplier (TMUL) circuits has been designed using ternary unary operator circuits at 32nm technology node and implement two power supplies Vdd and Vdd/2 without using any ternary decoders, basic logic gates, or encoders to minimize the number of used transistors and improve the energy efficiency. The effect of CNTFET and GNRFET parametric variation with threshold voltage on performance metrics namely delay and power has been analyzed. Dependence of threshold voltage on the geometry of carbon nanotube and graphene nanoribbon makes it feasible to be used for ternary logic design. It is analyzed that CNTFET based circuits are energy efficient than the GNRFET- based circuits. It is also concluded that the CNTFET-based circuitshas less power-delay product (PDP) when compared to GNRFET- based circuits. CNTFET-based THA is 23.5% more efficient than GNRFET-based THA and CNTFET-based Tmul is97.8% more efficient than GNRFET-based Tmul.All the digital circuits have been simulated using HSPICE tool.

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.)

Background: The degree of atrophy and fatty infiltration of rotator cuff muscle belly is a key predictor for cuff repairability. Traditionally, Goutallier grading of fatty infiltration is assessed at sagittal scapular Y-view. Massive rotator cuff tears are associated with tendon retraction and medial retraction of cuff musculature, resulting in medialization of the muscle bulk. Thus, standard Y-view can misrepresent the region of interest and may misguide clinicians when assessing repairability. It is hypothesized that by assessing the muscle belly with multiple medial sagittal magnetic resonance imaging (MRI) sections at the medial scapular body, the Medial Scapular Body-Goutallier Classification (MSB-GC) will improve reliability and repeatability, giving a more representative approximation to the degree of fatty infiltration, as compared with the original Y-view., Methods: Fatty infiltration of the rotator cuff muscles were classified based on the Goutallier grade (0-4) at 3 defined sections: section 1, original Y-view; section 2, level of suprascapular notch; and section 3, 3 cm medial to the suprascapular notch on MRI scans. Six subspecialist fellowship-trained shoulder surgeons and 3 musculoskeletal radiologists independently evaluated deidentified MRI scans of included patients., Results: Of 80 scans, 78% (n = 62) were massive cuff tears involving the supraspinatus, infraspinatus, and subscapularis tendons. Interobserver reliability (consistency between observers) for Goutallier grade was excellent for all 3 predefined sections (range: 0.87-0.95). Intraobserver reliability (repeatability) for Goutallier grade was excellent for all 3 sections and 4 rotator cuff muscles (range: 0.83-0.97). There was a moderate to strong positive correlation of Goutallier grades between sections 1 and 3 and between sections 2 and 3 and these were statistically significant (P < .001). There was a reduction in the severity of fatty infiltration on the Goutallier classification from sections 1 to 3 across all muscles: 42.5% of both supraspinatus and infraspinatus were downgraded by 1, 20% of supraspinatus and 3.8% of infraspinatus were downgraded by 2, and 2.5% of supraspinatus were downgraded by 3., Conclusion: This study found that applying the Goutallier classification to more medial MRI sections (MSB-GC) resulted in assignment of lower grades for all rotator cuff muscles. Additionally, this method demonstrated excellent test-retest reliability and repeatability. Inclusion of a more medial view or whole scapula on MRI, especially in advanced levels of tear retraction, could be more reliable and representative for assessment of the degree of fatty infiltration within the muscle bulk that could help predict tear repairability and therefore improve clinical decision making, which should be studied further in clinical studies., (Copyright © 2024 The Author(s). Published by Elsevier Inc. 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.)

Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.

Medical image classification poses significant challenges in real-world scenarios. One major obstacle is the scarcity of labelled training data, which hampers the performance of image-classification algorithms and generalisation. Gathering sufficient labelled data is often difficult and time-consuming in the medical domain, but deep learning (DL) has shown remarkable performance, although it typically requires a large amount of labelled data to achieve optimal results. Transfer learning (TL) has played a pivotal role in reducing the time, cost, and need for a large number of labelled images. This paper presents a novel TL approach that aims to overcome the limitations and disadvantages of TL that are characteristic of an ImageNet dataset, which belongs to a different domain. Our proposed TL approach involves training DL models on numerous medical images that are similar to the target dataset. These models were then fine-tuned using a small set of annotated medical images to leverage the knowledge gained from the pre-training phase. We specifically focused on medical X-ray imaging scenarios that involve the humerus and wrist from the musculoskeletal radiographs (MURA) dataset. Both of these tasks face significant challenges regarding accurate classification. The models trained with the proposed TL were used to extract features and were subsequently fused to train several machine learning (ML) classifiers. We combined these diverse features to represent various relevant characteristics in a comprehensive way. Through extensive evaluation, our proposed TL and feature-fusion approach using ML classifiers achieved remarkable results. For the classification of the humerus, we achieved an accuracy of 87.85%, an F1-score of 87.63%, and a Cohen's Kappa coefficient of 75.69%. For wrist classification, our approach achieved an accuracy of 85.58%, an F1-score of 82.70%, and a Cohen's Kappa coefficient of 70.46%. The results demonstrated that the models trained using our proposed TL approach outperformed those trained with ImageNet TL. We employed visualisation techniques to further validate these findings, including a gradient-based class activation heat map (Grad-CAM) and locally interpretable model-independent explanations (LIME). These visualisation tools provided additional evidence to support the superior accuracy of models trained with our proposed TL approach compared to those trained with ImageNet TL. Furthermore, our proposed TL approach exhibited greater robustness in various experiments compared to ImageNet TL. Importantly, the proposed TL approach and the feature-fusion technique are not limited to specific tasks. They can be applied to various medical image applications, thus extending their utility and potential impact. To demonstrate the concept of reusability, a computed tomography (CT) case was adopted. The results obtained from the proposed method showed improvements.

The main challenge in adopting deep learning models is limited data for training, which can lead to poor generalization and a high risk of overfitting, particularly when detecting forearm abnormalities in X-ray images. Transfer learning from ImageNet is commonly used to address these issues. However, this technique is ineffective for grayscale medical imaging because of a mismatch between the learned features. To mitigate this issue, we propose a domain adaptation deep TL approach that involves training six pre-trained ImageNet models on a large number of X-ray images from various body parts, then fine-tuning the models on a target dataset of forearm X-ray images. Furthermore, the feature fusion technique combines the extracted features with deep neural models to train machine learning classifiers. Gradient-based class activation heat map (Grad CAM) was used to verify the accuracy of our results. This method allows us to see which parts of an image the model uses to make its classification decisions. The statically results and Grad CAM have shown that the proposed TL approach is able to alleviate the domain mismatch problem and is more accurate in their decision-making compared to models that were trained using the ImageNet TL technique, achieving an accuracy of 90.7%, an F1-score of 90.6%, and a Cohen's kappa of 81.3%. These results indicate that the proposed approach effectively improved the performance of the employed models individually and with the fusion technique. It helped to reduce the domain mismatch between the source of TL and the target task.

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97-100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91-0.92 and an accuracy range of 88-92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% ( n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% ( n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% ( n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% ( n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 A. S. Albahri et al.)

The current methods of classifying plant disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share the same features but with different densities. The manual labelling of such samples demands exhaustive labour work that may contain errors and corrupt the training phase. Furthermore, the labelling and the annotation consider the dominant disease and neglect the minor disease, leading to misclassification. This paper proposes a fully automated leaf disease diagnosis framework that extracts the region of interest based on a modified colour process, according to which syndrome is self-clustered using an extended Gaussian kernel density estimation and the probability of the nearest shared neighbourhood. Each group of symptoms is presented to the classifier independently. The objective is to cluster symptoms using a nonparametric method, decrease the classification error, and reduce the need for a large-scale dataset to train the classifier. To evaluate the efficiency of the proposed framework, coffee leaf datasets were selected to assess the framework performance due to a wide variety of feature demonstrations at different levels of infections. Several kernels with their appropriate bandwidth selector were compared. The best probabilities were achieved by the proposed extended Gaussian kernel, which connects the neighbouring lesions in one symptom cluster, where there is no need for any influencing set that guides toward the correct cluster. Clusters are presented with an equal priority to a ResNet50 classifier, so misclassification is reduced with an accuracy of up to 98%.

Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS. However, its basic structure relies on the usage of traditional static machine learning models that receive onetime training, limiting its ability to handle CD. To address this issue, we propose an extended variant of the GPC using three main components. First, we replace existing classifiers with alternatives: online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM). Second, we add two new components to the GPC, specifically, a data balancing and a classifier update. Third, the coordination between the sub-models produces three novel variants of the GPC: GPC-KOS for KA-OSELM; GPC-FOS for FA-OSELM; and GPC-OS for OSELM. This article presents the first data stream-based classification framework that provides novel strategies for handling CD variants. The experimental results demonstrate that both GPC-KOS and GPC-FOS outperform the traditional GPC and other state-of-the-art methods, and the transfer learning and memory features contribute to the effective handling of most types of CD. Moreover, the application of our incremental variants on real-world datasets (KDD Cup '99, CICIDS-2017, CSE-CIC-IDS-2018, and ISCX '12) demonstrate improved performance (GPC-FOS in connection with CSE-CIC-IDS-2018 and CICIDS-2017; GPC-KOS in connection with ISCX2012 and KDD Cup '99), with maximum accuracy rates of 100% and 98% by GPC-KOS and GPC-FOS, respectively. Additionally, our GPC variants do not show superior performance in handling blip drift.

Background : The fight against COVID-19 appears to extend beyond screening and treatment of acute diseases to its medium- and long-term health consequences. Little is known about the epidemiology and the determinants of developing post-COVID-19 conditions (PCCs) among children. The aims of this study were to explore and determine the prevalence of PCCs among three age groups (children and adolescents, adults, and the elderly), and study the predictors of participants' return to their pre-COVID-19 health status among COVID-19 patients at least four weeks after they got sick, from February to 15 July 2022. Methods : This comparison survey study targeted 12,121 COVID-19 patients who fulfilled the selection criteria from the national register system and received a virtual assessment from the Medical Consultation Call Center (937), which was conducted by a well-trained family physician using a validated, well-structured assessment tool. The collected data were coded and analyzed using appropriate tests. Results: Out of the 12,121 recovered COVID-19 patients who received the virtual assessment calls, only 5909 (48.8%) agreed and completed the assessment. The majority of participants (4973, or 84.2%) reported no PCCs. The most common PCCs among young people were a cough, dyspnea, fatigue, and loss of appetite or weight loss, while among the elderly they were a cough, dyspnea, fatigue, stomachaches, poor concentration, sleep disturbance, and recurrent fever. Most post-COVID-19 cases require nothing more than reassurance and health education as only 384 (6.5%) required referral to primary health care centers (PHCCs.) The severity of COVID-19 infection, age group, sex, vaccination status, and body mass index were significant predictors for returning to the pre-infection health status and the required referral was significantly related to many factors. Conclusions: The comparison of children, adults, and the elderly with regard to the acute and post-COVID-19 conditions in Saudi Arabia in terms of the clinical health assessment and the required management plans showed significant differences.

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k -means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k -means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.

An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis.

Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets., Competing Interests: The authors declare that they have no competing interests., (© 2021 Alzubaidi et al.)

Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes-either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2021.)

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.