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Digitalisation and Green Strategies: A Systematic Review of the Textile, Apparel and Fashion Industries

Circular Economy and Sustainability 2025年 1-53页

作者： Orisadare, Emmanuel Ayo Achukwu, Okechukwu Emmanuel Ogunyemi, Abiodun Afolayan Adedeji, Deborah Oluwayemisi Diyaolu, Idowu Jamiu Ugwu, Eunice Ifenyinwa Oluwatope, Ayodeji Oludola Bakare, Kudirat Oyebisi Awoyelu, Iyabo Olukemi Department of Information and Communication Technology University of Ilesa Osun State Ilesa Nigeria School of Computing Engineering and Technology Sir Ian Wood Building Robert Gordon University Garthdee Road Aberdeen AB10 7GJ United Kingdom School of Digital Technologies Tallinn University Narva mnt 29 Tallinn 10120 Estonia Department of Clothing and Textile Design Faculty of Sciences University of Ilesa Ilesa Nigeria Department of Family Nutrition and Consumer Sciences Obafemi Awolowo University Osun State Ile-Ife Nigeria Department of Home Economics and Hospitality Management Education Faculty of Vocational and Technical Education University of Nigeria Enugu State Nsukka Nigeria Department of Computer Science and Engineering Obafemi Awolowo University Osun State Ile-Ife Nigeria

The textile, apparel, and fashion (TAF) industries are vital to national economies, providing employment and fostering global economic development. On the other hand, TAF industries contribute to approximately 20% of global pollution. The rise of Industry 4.0 (I4.0) technologies—including the Internet of Things (IoT), Artificial Intelligence (AI), and robotics—aims to enhance production efficiency and resource management. However, this transition brings several challenges, such as potential disruptions from automation, concerns regarding energy efficiency, and escalating sustainability demands. In response, Industry 5.0 (I5.0) emphasises the integration of human-centric and sustainable practices, encouraging collaboration between humans and machines. While some existing studies highlight the potential of these technologies as enablers of the circular economy (CE), they often fall short of comprehensively linking these technologies to specific operational and strategic elements of CE. Furthermore, they do not sufficiently address how these digital tools fit within a broader sustainability framework in the context of CE principles. This review study aims to explore how digital transformation through I4.0 and I5.0 technologies can enhance circular economy practices in the TAF industries while alleviating the environmental and social challenges associated with industrial growth. The study examined 42 peer-reviewed studies from 2013 to 2023, employing a descriptive and theoretical literature review methodology to analyse the strategies, impacts, and challenges of digitalisation and green transition in the TAF industries. Findings reveal a significant disparity between firms in the Global North, which focus on technology advancement, and those in the Global South, facing implementation barriers. The study advocates for an approach that contextualises the integration of I4.0 and I5.0 technologies, ensuring that sustainable production practices align with regional needs. ©

关键词： Circular economy Digital transformation Green strategies Industry 4.0 Industry 5.0 Textile, apparel and fashion industries

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An Exploratory Investigation into Sustaining Cybersecurity Protection Through the Implementation of SETA

An Exploratory Investigation into Sustaining Cybersecurity P...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Abdullah Mahfuzul Huda Mohammed Brayyich Noor Hanoon Haroon Abdul Azeem Khan Rajan Verma University Institute of Computing Chandigarh University Punjab India College of Computing and Informatics Saudi Electronic University Riyadh KSA College of Technical Engineering National University of Science & Technology Dhi Qar Iraq Department of Computer Technical Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Faculty of Islamic Technology Negara Brunei Darussalam Division of Research and Development Lovely Professional University Phagwara KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Most modern businesses run their competitive operations online. Cybersecurity is crucial to commercial operations. No cybersecurity defensive solution is universal. Standard security strategy involves using hardware and software to counter known security threats that may become stronger over time. Attackers and security professionals fight constantly. A security approach based primarily on tools is untenable. However, some firms skip employee awareness training, resulting in biased cybersecurity breach decisions. This study objectively analyzes key factors that help enterprises and organizations deploy Security Education, Training, and Awareness (SETA) to find ways to maintain such protection. A dataset of UK corporate cybersecurity breach surveys is used to perform performance testing on eight supervised learning models. The entire research uses the model's built characteristics that are excellent at recognizing SETA-adopting enterprises and organizations. The trial shows that awareness is vital when organizations use SETA. Many firms don't know how to safeguard employees from cybersecurity risks when they use online products and services, such as external web services.

关键词： Training Surveys Radio frequency Analytical models Web services Standards organizations Information security Computer security Protection Testing

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Non-Invasive Device for Hearing Rehabilitation

Non-Invasive Device for Hearing Rehabilitation

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E-Health and Bioengineering Conference (EHB)

作者： Daniela Goldan Mihai Aron Teofil Ilie Ursache Gladiola Petroiu Cristian Rotariu Dept. of Biomedical Sciences Faculty of Medical Bioengineering Grigore T. Popa University of Medicine and Pharmacy of Iasi Iași Romania Dept. of Automatic Control and Applied Informatics Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University Iași Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Hearing loss impacts over 5% of the world's population, a percent rising due to aging populations, noise exposure, and chronic health conditions. To address this, we developed a non-invasive hearing device designed for individuals with hearing challenges that limit the use of hearing aids. The proposed device integrates a set of Bluetooth headphones with a smartphone, that process the ambient sound in real time through a software application and delivers to the hear impaired patient via bone conduction, bypassing traditional auditory pathways. Laboratory tests confirmed that the proposed device effectively replicates auditory perception. The developed device offers an accessible and user-friendly alternative to patients who cannot use traditional hearing aids.

关键词： Irrigation Noise Auditory system Ear Receivers Hearing aids Propagation losses Real-time systems Software Testing

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Cardiac Condition Detection Model Using Alexnet and Time-Frequency Representation of ECG Signals

Cardiac Condition Detection Model Using Alexnet and Time-Fre...

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E-Health and Bioengineering Conference (EHB)

作者： Mihai Aron Teofil Ilie Ursache Gladiola Petroiu Cristian Rotariu Dept. of Biomedical Sciences Grigore T. Popa University of Medicine and Pharmacy of Iasi Faculty of Medical Bioengineering Iaşi Romania Dept. of Automatic Control and Applied Informatics Gheorghe Asachi Technical University Faculty of Automatic Control and Computer Engineering Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Cardiac conditions are one of the leading causes of death worldwide. To address this, we designed a model that can detect cardiac conditions from a patient's electrocardiogram (ECG). This will convert the 1D ECG signal into a 2D scalogram using wavelet transforms with Morlet wavelets. With the help of a network based on the AlexNet convolutional neural network, it distinguishes between a normal ECG signal and one indicative of arrhythmia or congestive heart failure. The resulting model performs well, achieving a classification accuracy of 92.20% and providing a robust approach for detecting cardiac conditions.

关键词： Wavelet transforms Training Time-frequency analysis Accuracy Biological system modeling Electrocardiography Predictive models Congestive heart failure Convolutional neural networks Biomedical engineering

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Boosting Cybersecurity Effectiveness through Machine Learning for Proactive Detection and Mitigation of New Threats

Boosting Cybersecurity Effectiveness through Machine Learnin...

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International Conference on Advances in Information Technology (ICAIT)

作者： N. Duraimutharasan N.Venkatesvara Rao N. Poongavanam K.V. Kanimozhi S.P. Manikandan Faculty of Advanced Computing Sciences AMET UNIVERSITY Chennai Tamil Nadu India Department of Computer Science and Engineering J.N.N.INSTITUTE OF ENGINEERING Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India School of Engineering and Technology CMR UNIVERSITY Bengaluru Karnataka

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Cybersecurity still remains an all-time issue before the technological age as the enterprises continue to deal with the evolving nature of the threats arising every day. Traditional cybersecurity approaches are becoming deficient in the situation of constantly improving, swiftly mutating threats, which leads to the revision and refocus of the current approaches to the preventative reactivity type. For that, machine learning (ML) has come into the arena as a strong tool for improving cybersecurity capacity through the.. p roactive” recognition and the “mitigation” of unknown threats. The focus of this paper is the function of ML in cyber security. Here is considered anomaly detection, malware classification, threat intelligence analysis, and security form adversarial attacks. We conduct literature search, detail a novel research methodology embracing data acquisition, preprocessing, ML model training, and deployment. We present experiments versus conventional security approaches from different ML domains used confirming the superiority in cybersecurity of the presented models. Moreover, resistance to chicanery such as adversarial attacks and the models understandability are discussed. Next, research vector and innovation directions in the ML cybersecurity are highlighted. As a whole, this study provides the example of how ML can strengthen previous alliance strategies to cybersecurity and prevent the risks associated with interchanging cyber threats.

关键词： Deep learning Heuristic algorithms Prevention and mitigation Malware Data models Real-time systems Computer crime Anomaly detection Monitoring Resilience

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Recognition of Colon Neoplasm using Contrast-Aware Capsule Network

Recognition of Colon Neoplasm using Contrast-Aware Capsule N...

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International Conference on Adaptive Science & Technology (ICAST)

作者： Mavis Serwaa Yeboah Patrick Kwabena Mensah Adebayo Felix Adekoya Mighty Abra Ayidzoe Department of Computer Science and Informatics University of Energy and Natural Resources Sunyani Ghana Department of Computer Science Sunyani Technical University Sunyani Ghana Faculty of Computing Engineering and Mathematical Sciences Catholic University of Ghana Sunyani Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Colon neoplasms are the second leading cause of mortality worldwide, and despite advances in endoscopy, even experienced endoscopists miss 22-28% of cases. Intelligent systems can bridge this gap, but current approaches face significant challenges. For instance, Convolutional Neural Networks (CNNs) excel in image recognition but require large datasets, which are often scarce in healthcare. On the other hand, Capsule Networks, suitable for smaller datasets, struggle with complex images. To address these limitations, we propose a novel contrast-aware Capsule Network for colon neoplasm recognition. By incorporating a novel contrast-aware feature transformation algorithm, our proposed model enhances extracted features and focuses on processing relevant features, achieving remarkable recognition accuracies of 96.70%, 93.68%, and 99.99% on PolyGen, ETIS-Larib, and CVC-ClinicDB datasets, respectively. Our approach outperforms state-of-the-art Capsule Network algorithms and offers a promising solution to support endoscopists in accurate colon neoplasm detection, reducing the burden of image analysis and improving patient outcomes.

关键词： Uncertainty Accuracy Neural networks Estimation Training data Mortality Medical services Feature extraction Colon Neoplasms

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Temperature Characteristics of the Conductivity of Human Cerebrospinal Fluid at 10 Hz-100 MHz 10th

Temperature Characteristics of the Conductivity of Human Cer...

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10th Frontier Academic Forum of Electrical engineering, FAFEE 2022

作者： Wang, Weice Guo, Jie Li, Weichen Bai, Siyuan Li, Kun Wang, Yu Xu, Canhua Shi, Xuetao Department of Biomedical Engineering Air Force Medical University Xi'an710032 China Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception Xi'an710032 China Institute of Medical Research Northwestern Polytechnical University Xi'an710072 China School of Life Sciences Northwest University Xi'an710127 China Faculty of Electrical and Control Engineering Liaoning Technical University Huludao125000 China

ISBN: (纸本)9789819934034

The temperature characteristics of the conductivity of the cerebrospinal fluid (CSF) are important in the fields of biological effect research, and electromagnetic protection of electromagnetic fields. In this study, the temperature control tank was used for temperature control (17-37 ℃), and the four/two electrode data integration method was used to measure its dielectric parameters (10 Hz-100 MHz). The results showed that the conductivity didn't not change significantly with frequency and the temperature coefficient of conductivity of cerebrospinal fluid remained basically unchanged in the whole frequency range, being 1.591-1.612%/℃. The temperature coefficient could lay a foundation for the calculation of brain specific absorption rate, the study of frequency and temperature dependence of brain dielectric properties. © Beijing Paike Culture Commu. Co., Ltd. 2023.

关键词： Cerebrospinal fluid

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Educational Process of Employees Supported by Augmented Reality

Educational Process of Employees Supported by Augmented Real...

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International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： L. Knapčíková A. Behúnová M. Martiček R. Tauberová D. Strametz M. Abdul-Hadi Department of Industrial Engineering and Informatics Faculty of Manufacturing Technologies with a seat in Prešov Technical University of Košice Prešov Slovak republic Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Technical University of Košice Slovak republic TU Graz Faculty of Mechanical Engineering and Economic Sciences Institute of Production Engineering Austria Pro2Future GmbH. Graz Austria

ISBN: (数字)9798331527716

ISBN: (纸本)9798331527723

Augmented reality technology is currently experiencing a significant surge. The potential of AR is immense, and while it may not be as ubiquitous as smartphones, it is rapidly gaining ground. The past decade has seen a remarkable expansion of AR applications, with many industries already leveraging its capabilities. The use of AR is not limited to a few areas but is expanding across various fields, from science to marketing to art. This adaptability across diverse fields reassures us that AR is not just a passing trend but a technology with wide-ranging applications. However, in most of these industries, AR is often only at a theoretical level in development. The use of augmented reality is increasingly important in the production process. With rapid digital transformation and the introduction of Industry 4.0, the company's leadership must find new ways to optimise its processes and increase production efficiency.

关键词： Industries Visualization Accuracy Refining Production Time measurement Reliability Surges Augmented reality Smart phones

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Discrete Modes Faults Diagnosis for Hybrid Dynamical Systems Using Petri Nets 21

Discrete Modes Faults Diagnosis for Hybrid Dynamical Systems...

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21st IEEE International Conference on sciences and Techniques of Automatic control and Computer engineering, STA 2022

作者： Arichi, Fayssal Cherki, Brahim Djemai, Mohamed Engin, Seref Naci Ecole Supérieure en Sciences Appliquées Tlemcen Algeria Algeria LAMIH CNRS Umr 8201 UVHC ValenciennesF-59313 France QUARTZ Laboratory Cergy95000 France Yildiz Technical University Faculty of Electrical and Electronics Engineering Department of Control and Automation Engineering Istanbul Turkey

ISBN: (纸本)9781665482615

This paper investigates the problem of diagnosing discrete faults for a class of hybrid dynamical systems, where the evolution of the discrete state is led by a set of discrete modes governed by a partially observed Petri net which means that we can know the activation of some discrete modes and the firing of some switching conditions. The considered class may have several modes evolving simultaneously. The modes can be associated with normal or faulty operating conditions. Therefore, the knowledge of the current mode represents the coeur part of the proposed diagnosis approach. We present at first an asymptotic observer to detect both the activation of discrete modes and the switching conditions. Then, we propose an algorithm to diagnose faults using the estimated discrete states provided by the observer. Simulation results show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Petri nets

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