Digital Technology Enabled Education for Sustainable Development in South Africa: A Case Study of a University of Technology

Part 5: ICT Curriculum and EducationInternational audienceSouth Africa is confronted with various social development challenges, and the higher education sector is well-positioned to address these challenges by developing and implementing appropriate solutions. Furthermore, as technology becomes more prominent in the global development agenda, its adoption in higher education holds great promise for advancing education by overcoming some traditional barriers and transforming pedagogical practices and learning outcomes. This study seeks to understand how the integration digital technologies with teaching and learning activities can facilitate education for sustainable development (ESD) in the context of a university of technology to enhance students’ learning outcomes and competencies needed in today’s job market. The research design adopted is a qualitative case study approach. Semi-structured interviews were conducted with purposively selected participants at a university of technology. The data analysis was guided by the Unified theory of acceptance and use technology (UTAUT) as a theoretical lens to identify patterns and interpretations of digital technology enabled ESD. Data analysis suggests that facilitating conditions such as educator training, adequate policies and strategies, and reliable digital infrastructure are important determinants of the behavioral intentions to use digital technology to facilitate ESD. Furthermore, social factors such as digital divide can impede adequate use of technology in ESD. The study contributes towards understanding factors that enable effective integration of digital technologies to facilitate ESD in higher education

An Overview of Cloud-Based Services for Smart Production Plants

Part 6: Advances in Production Management SystemsInternational audienceCloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective

A Simulation Study for Integrating Library Material Handling with Autonomous Mobile Robots

Part 7: IntralogisticsInternational audienceLibraries have served customer, so-called patrons, as a cultural and educational arena for many decades. Although there is a trace of attempts for incorporating advantageous technologies into the library environment, efficient operation of the library is still an underinvestigated topic which deserves scholarly efforts in the realm of material handling. Thus, this study seeks to contribute to the improvement of the library material handling by taking advantage of mobile robot solutions. A case study at the Trondheim Public Library of Norway is primary employed in order to capture the material flow of books within a library and to study the challenges of material handling in this context. The literature review assists in exploring the recent technologies that have contributed to the material handling. In pursuit of improving the moving processes, a simulation analysis is then considered to examine the utilization of autonomous mobile robot (AMR) under two layout configurations: centralized and decentralized. The simulation analysis is accomplished under the influence of two automation strategy, consisting semi-automated and fully-automated. Given the impact of two layout configurations, four scenarios were examined throughout the simulation, and the results demonstrate that an exhaustive technological upgrade of the material handling in the library does not necessarily yield the optimal performance. In this regard, the semi-automated approach in conjunction with the decentralized layout configuration demonstrated the highest performance with respect to two key performance indicators (KPIs), namely time in the machine and time for moving/emptying the bins

A Dynamic Fit-Out Scheduling Framework for Digital Twin-Enabled Modular Integrated Construction

Part 3: Digital Twin Concepts in Production and ServicesInternational audienceModular Integrated Construction (MiC) is a novel construction method for its approved space-saving and time-saving for the construction industry. Fit-out construction, as the last essential process of MiC module assembly, significantly affects the delivery of MiC modules for its complexity and flexibility in operations. Emerging technologies, such as the Internet-of-Things (IoT) and Digital Twin (DT), capture real-time fit-out operation data and worker information, which reshape the fit-out job scheduling problem. This study proposes a dynamic fit-out scheduling framework by integrating job scheduling and worker assignment for DT-enabled MiC. First, this study defines the workflow of fit-out operations and the fit-out scheduling problem in MiC. The new fit-out scheduling problem considers job scheduling and worker assignment simultaneously. Second, this work designs a DT-enabled framework for cyber-physical operational synchronisation in MiC regarding workers, operations and jobs. Third, this work implements a dynamic fit-out scheduling method based on Deep Reinforcement Learning. This work intends to conduct experimental analysis using real-life case data. This work aims to provide a real-time data-driven solution for fit-out scheduling problems in MiC

A Digital Twin Framework for Flexible Manufacturing System

Part 3: Digital Twin Concepts in Production and ServicesInternational audienceIn the era of digitalization and automatization, several technologies and manufacturing paradigms emerged and became popular and attracted the interest of both researchers and industrials. Flexible Manufacturing System that is one of these paradigms is a production system able to switch between tasks easily making it adaptable to varying production needs. Furthermore, the concept of Digital Twin—a virtual counterpart of a tangible object—emerged and gained widespread popularity across diverse domains. Despite the focus of several research studies to develop these concepts, there is still a lack of work on an integrated and comprehensive framework that encompass a Digital Twin for Flexible Manufacturing System with a focus on smart manufacturing. Thus, this paper provides an attempt to propose a detailed Digital Twin framework for Flexible Manufacturing System with a focus on smart manufacturing and outlining various components and information. This is done by integrating different paradigms namely Machine learning; Simulation based Optimization, and Acquisition technologies. The proposed framework is composed of Physical part, Virtual part, and Stakeholders

Assembly Line Design for Industrialized Electrolyser Production

Part 1: Modelling Supply Chain and Production SystemsInternational audienceThe electrolyser industry is under rapid development, where manufacturing companies seek opportunities to develop industrialized solutions for electrolyser production. However, electrolyser production implies major logistical challenges related to for instance product characteristics (expensive and fragile components, rare materials), the stack assembly process (precise alignment of cells and pressure), the supply chain (immature, limited supplier base, quality issues, delivery constraints), and the production equipment (immature). Designing a high efficiency electrolyser assembly line is thus a challenging task. This paper presents results of a study with the aim to assess design alternatives for electrolyser stack assembly with focus on logistics and flow efficiency. Design elements such as the number and locations of buffers and quality inspection are considered. A discrete-event simulation model is developed and tested based on scenarios reflecting situations related to for instance poor quality yield of incoming components and equipment downtime. Data from an empirical case involving the design of a new assembly line, are used to set up the model. Based on the findings from the simulations, recommendations for the design of industrialized electrolyser assembly lines are formulated. This study contributes with insight to critical aspects and guidance to design decisions for efficient electrolyser assembly lines. Moreover, it shows how simulation can support assessment of different design alternatives in the development of efficient stack assembly lines

Facial Deblurring and Recognition Using Image Processing and Machine Learning Techniques

Part 4: SDG 11 Sustainable Cities and CommunitiesInternational audienceThe project addresses the challenge of accurately identifying blurred faces in computer vision and facial recognition. It introduces a novel framework that integrates deblurring techniques, utilizing point spread function deconvolution to enhance facial image quality. Principal Component Analysis (PCA) is employed for feature extraction, and a K-Nearest Neighbors (KNN) classifier is applied for face identification. The combined deblurring and PCA-transformed features improve matching and identification accuracy, particularly in scenarios with initially blurred images. Experimental validation on a real-world dataset demonstrates the efficacy of the proposed methodology. This approach not only enhances facial recognition accuracy but also lays the groundwork for future research in challenging practical applications, such as security and law enforcement

A Holistic Approach to Developing Intervention Strategies Against Digital Piracy

Part 2: PrivacyInternational audienceAddressing the global challenge of digital piracy, this study concludes a research series that explores the multifaceted drivers behind copyright infringement activities. Integrating findings from a PRISMA-guided systematic literature review and the application of behavioural psychology through the Theoretical Domains Framework (TDF), this work identifies key factors of digital piracy, including accessibility, awareness, education and social and cultural influences, alongside a consideration of previous behaviour. Crucially, the research leverages expert reviews analysed through ATLAS.ti, enhancing the development of the Digital Piracy Conceptual Framework (DPCF). The study’s findings, derived from interviews with one (1) participant from each of the six (6) sectors, provide sector-specific insights that, while informative, should not be interpreted as broadly generalisable across industries. This detailed approach has refined the DPCF, offering a comprehensive blueprint for devising effective digital piracy intervention strategies, marking a significant step towards mitigating this pervasive issue and protecting intellectual property rights globally

Research Agenda for Speaker Authentication

Part 3: Technical Attacks and DefensesInternational audienceIn this study, we thoroughly examined every component of speaker authentication, analyzing the input, process, and output phases to identify flaws and new threats. Our investigation is organized around specific research topics that aim to effectively address and minimize the identified dangers. By methodically exploring each component of the speaker authentication process, we not only identify possible issues but also recommend proactive methods to protect these systems from unauthorized access. Our research questions act as significant probes, allowing for a deeper knowledge of the underlying difficulties and leading to the creation of tailored authentication solutions. This study goes beyond theoretical analysis and provides practical insights and strategic recommendations for improving the security and reliability of speaker authentication systems in a variety of sectors, including cybersecurity and forensic analysis. We highlight the interrelated nature of the input, process, and output stages, emphasizing the importance of remaining vigilant in the face of emerging security risks. Our goal is to provide the necessary knowledge and tools to effectively handle the complexities of speaker authentication in the changing digital world. This work establishes a solid foundation for the development of safe and durable speaker authentication methods

A Deep Neural Network-Based Segmentation Method for Multimodal Brain Tumor Images

Part 2: Image UnderstandingInternational audienceMedical image segmentation plays an important role in medical diagnosis. Accurate segmentations of brain tumor images require well-designed segmentation models and sufficient high-quality well-labeled training samples, but it is difficult for existing segmentation methods to meet these requirements. In this paper, we propose a segmentation method, which involves a GAN-nested model and an improved UNet. The GAN-nested model is used to automatically generate sufficient well-labeled brain tumor images, which are used as training samples; the improved UNet is good at extracting the detailed features of brain tumor images, and therefore it can conduct the accurate segmentation for brain tumor images using high-quality training samples generated by the GAN-nested model. Extensive experimental results prove that the proposed method is effective and obtains state-of-the-art performance on the given datasets