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In recent years, the research pertaining to speech recognition technology in the Kazakh language has gained significant importance. This is due to the increasing demand for natural language processing applications in the region where Kazakh is predominantly spoken. Thus, there exists an urgent requirement for precise and dependable speech recognition systems. The research study examines the application of sophisticated deep learning methodologies, such as Natural Language Processing (NLP) and Hidden Markov Model (HMM), in facilitating speech recognition for the Kazakh language. Additionally, the investigation delves into how various techniques, including data preprocessing, acoustic modeling, and language modeling, can aid in devising effective speech recognition systems. The article deliberates on the feasible uses of speech recognition technology in the geographic area where Kazakh language is spoken and outlines its future research prospects. The investigation underscores the significance of persistent inquiry in this realm to confront distinctive obstacles encountered in creating speech recognition systems for languages with restricted resources. [ABSTRACT FROM AUTHOR]

To generate a research overview of digital banking, we did a bibliometric analysis. In our bibliometric analysis, we identified 2475 papers about digital banking that were published in the previous 10 years (2013–2023) from 1492 different sources under the direction of 6777 academics. The bibliographic information was gathered using the Scopus database. The dataset was examined using the bibliometric R program Biblioshiny. The number of digital banking research publications published each year, the most relevant author, the most cited articles, thematic maps, and trending topics are all listed. A keyword analysis showed that "blockchain", "security", "digital banking", and "digital storage" appeared most often. The research results will be significant to scholars, researchers, and policymakers in banking, as the study showed the roadmap and pathways to scientifically understanding current and future research trends. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

This conceptual paper aims to investigate the interrelationship between intellectual capital (IC) and frugal innovation (FI) in higher education institutions (HEI), considering the mediating role of information technology (IT) capabilities. With many private HEI facing financial challenges, it is crucial to explore how IC can be effectively managed to achieve frugal innovation. By drawing on the knowledge-based view (KBV), this study examines key factors that influence intellectual capital and its impact on frugal innovation in HEI. The findings emphasize the importance of leveraging intangible assets in HEI to enhance their capabilities and address the dynamic business environment. Specifically, the study highlights the need to identify the extent to which HEI can exploit IT capabilities to develop intellectual capital aligned with the criteria of frugal innovation for value creation. This research contributes to the development of evolving approaches in IC knowledge for effective IC management and highlights the benefits for HEI, including a focus on core functions, stronger integration with industry and local/international communities, and enhanced operational efficiency. By shedding light on the formalization, capture, and leveraging of intangible assets, rather than solely focusing on tangible assets, HEI can achieve frugal innovation. Overall, this paper provides a novel perspective by bridging the gap between frugal innovation and IC research in the context of HEI. It highlights the mediating role of IT capabilities in the relationship between IC and frugal innovation, adding valuable insights to the existing literature and contributing to the ongoing scholarly discourse in this field. [ABSTRACT FROM AUTHOR]

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey. [ABSTRACT FROM AUTHOR]

The traditional pen and paper-based administrative processes in Pondok Pesantren have been time-consuming and error-prone. To address these challenges, the E-Santren app system was developed to modernize and streamline administrative tasks. This research paper examines the acceptance and impact of the E-Santren app system as an alternative to the traditional approach. The study aimed to assess educators' perceptions and experiences regarding the implementation of the app system. Gorunded in the Technology Acceptance Model (TAM), a mixed-methods approach was employed, including a questionnaire survey and a focused group discussion. The results showed a positive acceptance of the E-Santren app system, with participants recognizing its benefits in terms of efficiency, accuracy, monitoring capabilities, and student compliance. This highlights a willingness among educators to adopt the new method, shifting away from traditional practices. The implications of these findings emphasize the potential for improved administrative processes in Pondok Pesantren through the adoption of the E-Santren app system. [ABSTRACT FROM AUTHOR]

In a construction environment, project scheduling is an essential tool to measure the success of all projects. This research paper will use project crashing of Activity to decrease the project completion schedule through the CPM method of time-cost trade-off and to minimize project cost. It includes linear programming with the aid of Microsoft Excel Solver to determine the result. And at the end of the research paper, it will show the impact of crashing the activities for the HVAC mechanical installation in a project for both normal and crash time, which displays that the selected Activity that had been crashed generated an increase of 9.8 % total cost from the total project cost. [ABSTRACT FROM AUTHOR]

Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The paper discusses the possibility of increasing the effectiveness of strategic management of research and development activities in higher education institutions. To achieve this goal, the authors proposed an algorithm for selecting competencies, which allows for the distribution of university employees and students among research and development teams in accordance with their individual competencies for carrying out research and development work. The paper covers the problem statement of competence selection, an overview of natural language processing methods for algorithm development, problematic areas of the algorithm, and prospects for its implementation in university management. [ABSTRACT FROM AUTHOR]

Hate Speech already been phenomenal expansion over the past decade. The paper proposed a new model that combines advanced CNN and Bidirectional Encoder Representations from Transformers (BERT) context embedding to predict hate speech in social media. This research trained contextual embedding on the datasets and used the learned information to identify objectionable language and hate speech in text. The paper evaluated supervised machine learning classifiers for bigoted and offensive content on Twitter using two datasets and found that advanced CNN context embeddings produced superior results. This research generated optimistic outcomes, which achieves 73% F1-score for Davidson dataset and 56% F1-score for TRAC-1 dataset [ABSTRACT FROM AUTHOR]

This paper explores the characteristics of a two-step procedure (dimension reduction and function approximation) in discrete choice modeling with high-dimension data. This study proposes the SS-GAM procedure which is an extension of the Super Sparse Principal Component Analysis (SSPCA) where the results are further processed with the Generalized Additive Model (GAM) in a classification problem. Moreover, the Orthogonal Sparse Principal Component Analysis with GAM (OS-GAM) is also proposed. For baseline comparison, the General Adaptive Sparse-PCA with GAM (GAS-GAM) is considered in this paper. The performance of these three sparse PCA methods are investigated with varied underlying correlation structure. In the simulation study, it is demonstrated that with varied degree of dimensionality, and levels of correlation structure, SS-GAM performed better compared to OS-GAM and GAS-GAM in terms of its predictive rate, on the average. It was observed that the OS-GAM performed best when data exhibits low correlation structure. However, with high correlation structure, OS-GAM and GAS-GAM obtained comparable result. Moreover, in terms of computational time, OS-GAM seems to be not affected by the increase of feature dimension. [ABSTRACT FROM AUTHOR]

Phishing emails, which masquerade as legitimate correspondence, are a significant modern hazard that cybercriminals use to deceive people into disclosing personal information. This paper presents the complex tactics used by cyber adversaries in the always changing field of phishing detection by using a real-time dataset and sophisticated classification tools to thoroughly examine email properties. In addition to detection, this paper highlights the need for constant watchfulness, delves into the psychological and social engineering elements included in phishing emails, and promotes continuous user awareness. Furthermore, the research broadens its scope to encompass the wider implications for enterprises, clarifying possible financial and reputational fallout. It offers helpful advice on strengthening defences and acts as a crucial manual for protecting private and corporate data against the ever-changing and persistent danger landscape that phishing attempts provide. This research gives an exploration of new insights by cybercriminals used for insertion of malicious software using emails. By using attributes like email text, Email type, the research identifies the signs of fraud emails. [ABSTRACT FROM AUTHOR]

In this research paper, a detailed investigation presents the utilization of the BiT5 Bidirectional NLP model for detecting vulnerabilities within codebases. The study addresses the pressing need for techniques enhancing software security by effectively identifying vulnerabilities. Methodologically, the paper introduces BiT5, specifically designed for code analysis and vulnerability detection, encompassing dataset collection, preprocessing steps, and model fine-tuning. The key findings underscore BiT5's efficacy in pinpointing vulnerabilities within code snippets, notably reducing both false positives and false negatives. This research contributes by offering a methodology for leveraging BiT5 in vulnerability detection, thus significantly bolstering software security and mitigating risks associated with code vulnerabilities. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of neural style transfer to video sequences, leveraging pre-trained Convolutional Neural Network (CNN) models. By combining content and style losses in an optimization process, the approach transforms video frames into visually captivating compositions, opening new avenues for artistic expression in visual storytelling and filmmaking. The procedure begins with meticulous preprocessing of video frames to align them with the specifications of the powerful VGG16 model. A content model is then constructed to extract activations from the 14th layer of the VGG network, forming the groundwork for nuanced content depiction. Style information is computed by aggregating data from a combination of convolutional layers, resulting in a multifaceted, artistically complex tapestry. The paper introduces a well-balanced loss function that combines style and content losses, serving as the foundation for the ensuing optimization procedure. This careful balancing between style adherence and content authenticity yields visually stunning synthesis, enhancing video sequence quality and artistic resonance. The paper also delves into a detailed analysis of hyper parameters, particularly style weights, and introduces a novel pixel value scaling method for enhanced visual coherence. Experimental findings demonstrate the efficiency and adaptability of the proposed approach, showcasing the seamless integration of content and style for an immersive visual experience. This accomplishment marks a significant stride toward unlocking the creative potential of video content, ushering in a period of enhanced visual storytelling. [ABSTRACT FROM AUTHOR]

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. [ABSTRACT FROM AUTHOR]

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions requiring speed and accuracy. Machine learning and artificial intelligence have become critical in supporting extensive data collection and real-time processing to assist decision-makers in configuring scarce resources. In this paper, we present four different applications that investigate issues related to the broader maritime supply chain security domain affecting the planning, execution, and performance of complex systems while exploring novel frontiers in cyber research and education. This paper will focus on Machine Learning and AI applications on Unmanned Aerial Systems and Cryptography related to Cybersecurity in Maritimes and Shipbuilding Spheres. [ABSTRACT FROM AUTHOR]

The rapid advancement of robotics and artificial intelligence paves the way for collaborative robotics to revolutionise industrial operations. Collaborative robotics can bring significant improvements in productivity, efficiency, and safety. Recently, the concept of Symbiotic Human-Robot Collaboration (SHRC) has emerged within the field of collaborative robotics, emphasising the seamless and adaptive integration of human and robotic capabilities within a shared workspace to optimise overall production performance. However, existing collaborative systems frequently face difficulties in establishing dynamic relationships with humans and adapting to evolving conditions, ultimately restricting their overall potential. This paper investigates the essential features required for collaborative workstations to enable SHRC. Moreover, with the aim of fully harnessing the power of collaborative robotics, the paper introduces a design methodology that supports the development of symbiotic workstations for specific assembly operations. A real-world case study is provided, demonstrating the practical implementation and benefits of the proposed design approach. [ABSTRACT FROM AUTHOR]

The need of undertaking effective actions against the climate change is pushing industrial companies to find new business models and technological solutions to reduce the resource utilization and the waste production. In this context, several companies are moving towards servitization, that has been proved as a promising strategy to enable sustainable business models in a wide spectrum, namely encompassing environmental, economic and social aspects. Nevertheless, companies lacks of tools for evaluating different servitization models with a clear view of economic and environmental benefits. This paper presents a model to estimate the profitability of servitization opportunities by combining economic Net Present Value index and environmental impacts estimation. The model is targeted on the steel sector, for which a preliminary application has been developed and is discussed in the paper. The results show that adopting different servitization business models, several benefits could be exploited both from providers and customers. [ABSTRACT FROM AUTHOR]

Autonomous Mobile Robots, as the advanced version of Automated Guided Vehicles have received a lot of interest and recognition in recent years. Simultaneous Localization and Mapping (SLAM) techniques enable the vehicles to independently navigate and map their surroundings so that they can drive autonomously in changing and uncharted areas. Due to the increasing importance and contributive development of SLAMs for automated guided vehicles and autonomous mobile robots, this study seeks to provide an in-depth analysis of well-known SLAM techniques developed and applied during the previous ten years. Well-known SLAM algorithms considered in this paper include GMapping, Cartographer, LIO-SAM, and so on. They are mainly examined and compared from the viewpoints of basic principles, sensor requirements, computing complexity, and performance. The aim of this paper is to offer insights into various SLAM approaches to researchers, practitioners, and developers in the field of automated guided vehicles and autonomous mobile robots, facilitating the selection of suitable SLAM methods for specific applications and fostering innovation in autonomous navigation and mapping. [ABSTRACT FROM AUTHOR]

Blockchain technology has become a reality and its experimentation promises a future of innovative and beneficial technology. In almost all fields, this technology has seduced the actors because of its potentialities, namely traceability, transparency, security, immutability, etc. However, setting up such a technology adapted to one's needs is far from being a classic project. Indeed, a technical and organizational decision support is very important during a blockchain project to measure its actions and anticipate the risks. In this paper, we will focus on the choice of the public or private blockchain according to technical, functional criteria, and a decision aid module based on the Fuzzy method is proposed to do so. The results obtained prove the effectiveness of our approach used in this paper. [ABSTRACT FROM AUTHOR]

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the Russian-Ukrainian war revealed vulnerabilities in supply chains, emphasizing the need for resilience in multiple industries. While Industry 4.0 is valuable, it cannot fully address complex supply chain challenges. Therefore, exploring additional drivers like Operations Management has become imperative. More clearly, the adoption of Operations Management tools and practices helps implement and use enabling technologies more effectively. In this context, the aim of the paper is to conduct a literature review to study supply chain resilience considering these two concepts. In other words, the objective of the work consists in examining the effectiveness of the mentioned combination. The paper presents a conceptual framework for enhancing supply chain resilience by exploring the potential of Industry 4.0 and Operations Management. The paper also underlines the positive impact issued from hybridizing both solutions to achieve resilience. As it discusses enhancing features of each concept aside the other. The paper provides a basic model for researchers to further detailed studies, and for managers to bringing the proposed framework from theory to practice. [ABSTRACT FROM AUTHOR]

To satisfy the rising demand for higher product quality and giga-casting requirements, the casting process is undergoing significant changes. However, current control methods rely significantly on human expertise and experience, making process availability and stability difficult to ensure. The semisolid casting process is more complicated than conventional liquid casting due to the additional casting parameters incorporated during the slurry preparation, which can have an effect on the quality of the final product. Therefore, an efficient tool is required to simplify the complete process of semisolid casting. The introduction of an AI system to aid in the supervision of the casting manufacturing procedure is one potential solution. This paper introduces a new casting system named "Smart-Cast" developed for this specific purpose. The paper describes the functions of the system and its current development process. Using an AI system as an assistant can help to achieve the goal of enhancing the efficacy of casting process control, and it can also help foundries step into the Industry 4.0 era. [ABSTRACT FROM AUTHOR]

Software-Defined Radios are radio communications devices that have been growing and developing on a larger scale in recent years. Communications are intrinsically embedded in our day by day, thus presenting a higher motivation to use software-defined radios due to its attractive cost. However they present technical limitations. This paper addresses this problem, which is the non-linearity behaviour of gain and frequency in the LimeSDR-USB. That is, this equipment is used to produce a FM signal with an associated frequency and gain before being parameterised according to the internal parameters of each software-defined radio. Each software-defined radio presents a value of frequency and gain of its own, which correlates to the generated signals at the output level. To avoid this, machine learning networks were used, in which networks were trained to adapt to the non-linearity of these devices and correct it without the user noticing. This way, the user sets a desired frequency and gain in a signal, at the output of the software-defined radios, and a neural network calculates which values the software-defined radios should be parameterised, thus mitigating the non-linearity behaviour. This paper presents the evaluation of a laboratory prototype based on low-cost commercial software-defined radios equipment, to replace an expensive metrologically calibrated equipment used for radio frequency tests on a new concept of industrial test station, with description of the integration of Digital Twins, with their physical and virtual parts. [ABSTRACT FROM AUTHOR]

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research. [ABSTRACT FROM AUTHOR]

In the era of twin transition companies face complex challenges. Economical resource usage and efficiency are particularly important in the manufacturing sector. Product-service systems are seen a promising solution that can meet the expectations regarding efficiency in a sustainable way. However, there is a huge potential regarding the evaluation of sustainable value creation. The paper focuses on the value creation process in product-service systems including offers with remanufactured products. Against this background, the goal of this paper is to describe how data driven industrial services can create sustainable value in the meaning of the triple bottom line. Based on previous work, a quantitative model for the assessment and optimization of this value creation is extended to include additional remanufacturing strategies. The value optimization model integrates the different perspectives of provider, custumer and society. The numerical evaluation of this model for a specific application case shows that economic and ecological value creation can be jointly achieved and optimized, and which service arrangements lead to this optimization. [ABSTRACT FROM AUTHOR]

The paper presents the stages of designing prototype production processes based on two methods: design based on the action-challenge-tactics model and value stream mapping (VSM). There was highlighted the mutual importance of the interaction of activities, challenges and tactics in the preliminary stage of production process designing. The importance of the collected information in relation to the creation of a map of the designed process was also emphasized. The paper presents the first results of designing the prototype production process of magnetic materials used in the medical industry (materials with increased quality requirements). Noteworthy is the use of two design methods: systemic (actions-challenges-tactics) and visual (method of mapping value stream flows). [ABSTRACT FROM AUTHOR]

Industrial maintenance plays a crucial role in manufacturing by significantly reducing machine failure time and minimizing costs, especially in the revolution of Industry 4.0. Consequently, researchers and industrial engineers have continuously focused on this area. Manufacturing companies possess extensive maintenance reports or logs containing valuable textual information, which offers a new avenue for exploring effective industrial maintenance methods. Natural Language Processing (NLP), a subfield of Artificial Intelligence, has demonstrated remarkable potential in analyzing maintenance reports and achieving promising results in various tasks. This paper presents a comprehensive systematic literature review that specifically concentrates on the applications of NLP approaches employed in the field of industrial maintenance. Additionally, this review analyzed the datasets utilized in previous studies and the evaluation measures adopted, which can serve as a valuable resource for other researchers seeking potential solutions in maintenance. Furthermore, the paper discusses the challenges encountered in applying NLP to industrial maintenance and outlines future research directions in this domain. By conducting this systematic literature review, we provide a comprehensive understanding of the current state of NLP applications in industrial maintenance, identify gaps in the existing literature, and guide future research efforts in leveraging NLP techniques for enhanced maintenance practices. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel "AI triangle" with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications. [ABSTRACT FROM AUTHOR]

This paper discusses the digital transformation journey of a small and medium enterprise (SME) based in the UK. With the proposed digital transformation archetype, the paper highlights the improvement in various key performance indices (KPIs) for the case SME. The core KPIs and operational KPIs show improvement through the technology adoption as part of the digital manufacturing initiative. While embracing technology, such as Industry 4.0, it is important to highlight the importance of the change and other benefits of technological changes. The paper uses socio-technological system principles to achieve a successful transition. An action research approach and a specific soft system thinking methodology known as Process-Oriented Holonic (PrOH) modeling were used in this paper. A digital twin architecture is presented in the paper that showcases the use of integrated technologies for a digital manufacturing roadmap. [ABSTRACT FROM AUTHOR]

Continuing technical vocational education and training (TVET) and professional development of the entire automotive sector towards implementing Industry 4.0 and 5.0 tools, methods and technologies has become an essential component of today's knowledge society. The principle of lifelong learning permeates all areas of professional and private life and has manifested itself as an essential component of today's knowledge society. Especially the automotive sector is challenged to successfully master the green and digital transformation. This needs engineers with expertise to actively shape the dual transformation. In this context, continuing education and training on a postgraduate level is indispensable. This paper illustrates continuing technical vocational education and training at Graz University of Technology, managed by the Life Long Learning organizational unit. As an example, a course concept is described in more detail on the basis of the university course Automotive Mechatronics. The aim of this paper is to present a successful TVET program and to show how postgraduate education over the lifespan can succeed sustainably for engineering professionals in the automotive sector. [ABSTRACT FROM AUTHOR]

The human-centered view emphasizes various aspects including usability, acceptance, and understandability, as they emerge in the interaction between humans and intelligent systems. For intelligent assistance systems to provide long-term added value in working environments, it is of major importance to develop a positive user experience. However, a positive user experience does not appear by itself but must be systematically investigated and designed. In this paper, we focus on the role of operator 5.0 in manufacturing and provide an overview of the main challenges and potentials related to working with intelligent systems. Drawing upon state-of-the-art literature and findings from industry (context and expert interviews and user experience workshops), this paper investigates the main discontinuous production processes (i.e., process monitoring, process optimization, process maintenance) that will undergo a series of changes due to integration of cutting-edge technologies (such as Digital Twins, Extended Reality, and Artificial Intelligence). It then identifies requirements and discusses key challenges for the realization of human-centered assistance systems. By highlighting underexplored research questions and potential improvements in industrial settings, this paper provides researchers and practitioners to establish a holistic understanding of the user experience along the industry 5.0 journey [ABSTRACT FROM AUTHOR]

Numerous research has for long studied the challenges and opportunities within digitalization of Small and Medium-sized Enterprises (SMEs). While SMEs are financially limited compared to larger organization, previous research has succeeded in identifying potential focus areas that allow SMEs to participate in the industrial progression. Among these, pragmatic research on exploitation of existing resources have been highlighted. Therefore, this paper intends to report the findings of a single case study in a Danish SME, where the objective is to understand how technological sub-factors influence the prototyping of digital initiatives for advancing maintenance activities. The study begins with mapping the maintenance activities, along with the IT infrastructure. From here, relevant data is analyzed to understand their shortcomings, where the most dominant relates to how technicians report service orders. The paper concludes by identifying two sets of three technological sub-factors that strongly influence the digitalization of the case company's maintenance activities. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing recognition of the importance of balance and coordination in maintaining overall health, particularly among the elderly population. As societies around the world experience demographic shifts with an increasing number of elderly individuals, age-related declines in balance and coordination can lead to an elevated risk of falls and fall-related injuries, making it a significant public health concern. The need for effective balance rehabilitation and fall prevention strategies has become paramount. This paper presents a motion platform designed for balance rehabilitation exercises using exergames. The system consists of a 3 degrees of freedom (3DOF) platform actuated by four servomotors controlled by Omron-DeltaTau real-time motion controller. As a novelty with respect to other State of Art (SoA) actuated platforms, in the present work the movements of the user's body, which determine the platform motion, are tracked by exploiting a markerless motion capture system with an RGB camera and pose-estimation algorithm MediaPipe. The human displacement is used to generate the input for the roll rotation of the platform according to a one d.o.f. transfer function, whose parameters can be set to get different levels of difficulties in the exercise. The device offers therefore the flexibility to set different parameters to modify the level of the users' training. The proposed device has the potential for a larger variety of rehabilitation activities and exergames. This paper describes the elements adopted for the implementation of this human-in-the-loop system, presents some preliminary tests to explore the influence of transfer function parameters on human and platform movements, and concludes with future development possibilities. [ABSTRACT FROM AUTHOR]

In the sphere of Engineer-to-Order (ETO) manufacturing, the demand for highly customized and rapidly adaptable solutions is escalating, particularly in the context of Industry 4.0 and the burgeoning focus on hyper-customization in Industry 5.0. Traditional, rigid simulation structures often fall short in adapting to these dynamic requirements, creating a need for more flexible simulation frameworks. This paper introduces a novel auto-generative Discrete Event Simulation (DES) system specifically designed to address the unique challenges of ETO manufacturing. Unlike traditional rigid simulation architectures, the proposed system offers substantial flexibility and modularity, significantly easing the development effort. Developed in MATLAB and Simulink, this paper not only presents the foundational logic and architecture of the system but also provides a practical implementation guide. By aligning with the imperatives of Industry 4.0 and 5.0, this research fills a significant gap in the literature and offers a scalable, adaptable simulation-based Decision Support System (DSS) for ETO manufacturing. [ABSTRACT FROM AUTHOR]

In the global manufacturing sector, the ever-increasing digital transformation of Supply Chains (SCs) has become imperative, leading to building new digital and analytical capabilities to achieve SC competitiveness. However, building these new capabilities to integrate end-to-end Digital Supply Chains (DSCs) is a major challenge. There is a need to develop a SCM holistic view that facilitates the understanding of the fundamentals behind successful strategic alignment, information sharing, and collaborative decision-making across supply chains and networks. This paper examines key enabling digital and smart technologies transforming supply chains and networks through an explorative review of the scientific literature, identifying emerging DSC capabilities and technology adoption challenges that supply chains and networks face to achieve their digital transformation. Furthermore, this paper concludes that digital strategic alignment must be practised among DSC partners. The findings contribute to understanding how digital and analytical capabilities are built for DSC competitiveness – with implications for SCM theory and practice. [ABSTRACT FROM AUTHOR]

The paper focuses on social sustainability within manufacturing industries by measuring workplace well-being. Firstly, the study focused on what the scientific literature says about the main variables and aspects that impact on employee well-being and deepen the productivity and efficiency of industries. To better understand the problems and address the best solution to improve the company environment, the paper analyses more than 20 innovative startups operating in the field of well-being and Human Resource Management. After having defined the impactful and meaningful variables, it was structured a particular case study on a manufacturing field, regarding a Calabrian SME. Using Business Intelligence tools and applying machine learning techniques, comes to light an overall result of company well-being and the value of the main areas. In addition, different company cluster analyses are conducted. Moreover, it shows a correlation network between variables highlighting the most affecting ones on well-being. Thanks to the information obtained, a strategic investment can be made to enhance people's well-being and to make companies more productive. [ABSTRACT FROM AUTHOR]

Supply chain (SC) conditions are becoming more challenging, and its systems are gaining more complexities over time. However, managing a complex SC depends on multiple variables related to the SC and its environment. In addition, digital technologies have shown potential to reshape the present understanding of SC management. In this regard, blockchain technology (BT) can help business models by enhancing transparency, improving traceability, reducing costs, minimizing risks, and bringing resiliency to the SC processes. This paper follows the Dynamic Capabilities View (DCV) theory as a theoretical lens to investigate the capabilities of the BT and its impact on supply chain resilience (SCR). The paper uses a PLS-SEM method for analyzing the collected data and drawing evidence from the manufacturing and assembling industries to demonstrate the technological benefits and potential of BT, as well as its capacity to drive SCR and enhance efficiency in business models. The findings of the paper show that the sensing, seizing, and transforming capabilities of the BT have a substantial effect on SCR (R2 adjusted= 0.701). The obtained results from this study highlight the potential of adopting BT to help policymakers in their query to build SCR. [ABSTRACT FROM AUTHOR]

This paper delves into the transformative realm of Industry 4.0, emphasizing the pivotal role of Human-Robot Collaboration (HRC) in redefining manufacturing landscapes. Central to this revolution is the integration of cyber-physical systems, automation, and the Internet of Things (IoT), culminating in the advent of smart manufacturing. We explore how HRC fosters a synergistic work environment, blurring the traditional boundaries between human workers and robotic counterparts. This research emphasizes the importance of intelligent task allocation in optimizing operational efficiency while safeguarding the ergonomic well-being of human operators. A simulation-based approach is presented, focusing on task allocation within HRC contexts to enhance both human operators' productivity and fatigue levels. The study provides an analysis of fatigue dynamics and its repercussions on industrial settings. The paper outlines various collaborative scenarios, including sole human operation, robot support, a Flexible Workstation (F-WS) support, and combined robot-FWS support, evaluated through a case study of an assembly station. The results underscore the tangible benefits of integrating robotic and F-WS support, demonstrating a balanced improvement in processing speed, operator efficiency, and a significant reduction in fatigue accumulation. In conclusion, this paper advocates for a balanced, human-centric approach to Industry 4.0, where the integration of HRC and ergonomic considerations pave the way for a sustainable, efficient, and health-preserving industrial future. [ABSTRACT FROM AUTHOR]

Effective communication in food supply chains is increasingly important, especially in supply chains that strive to provide improved levels of information from producer to consumer. Such supply chains can be found in alternative food networks. Alternative food networks often adopt local and short supply chain strategies to provide embedded information between producers and consumers. Information in the supply chain of alternative food networks is communicated through face-to-face, proximate, or extended supply chain structures. The tools used to communicate information vary amongst the supply chains in alternative food networks; for example, face-to-face may adopt word-of-mouth, while proximate and extended supply chain structures may use labels, digital platforms, and websites. This paper uses Principal Component Analysis to provide an understanding of the categories and underlying dimensions of information communication tools in the supply chains of alternative food networks. A living lab approach and survey method were used for data collection. Using Principal Component Analysis, the data revealed two principal components of information communication tools, highlighting the use of on-packaging information communication tools such as packaging and labelling tools as a principal component. Another principal component was identified, backing a need for off-packaging information communication tools such as digital technologies, certificates, social media, and packaging technologies, enabling stakeholders who desire a further understanding of information regarding the processes and products. The paper concludes with the implications, limitations, and areas of future work. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) has emerged as a highly innovative response to market needs, particularly in logistics. This paper presents a structured case study on the implementation of I4.0 technologies for material flow and handling management. The study explores the contextual framework, historical development, and key characteristics of I4.0, focuses on the necessary characteristics for I4.0 certification and outlines the specific features exhibited by the company under study. Objectives include enhanced traceability of metallic materials and optimization of crane management. The paper outlines the project phases, highlighting the positive impact on lead time reduction and providing insights into operational procedures. This research contributes to understanding I4.0 practices in logistics and offers valuable insights for future implementations, driving enhanced efficiency and productivity. [ABSTRACT FROM AUTHOR]

The urgent need to act against climate change is emphasized in many initiatives, such as the Sustainable Development Goals, the European Green Deal, and the Circular Economy Action Plan. The role of the industrial sector is crucial. Companies, however, have to operate in a very complex environment driven by digitization and are supposed to be more sustainable. This paper explores how companies can implement the ecological transformation, with a focus on the role of digital technologies and data in enabling the twin transition. This paper proposes an approach to support SMEs in the twin transition through a combination of digital and sustainable business model development. The concept includes an interdisciplinary methodology that combines qualitative and quantitative approaches, focusing on the latter. The suggested method collection includes adaptations of the business model canvas, value proposition analysis, data-driven decision-making, and the integration of environmental and economic considerations using the system of environmental-economic accounting. In particular, approaches to define the quantification of economic as well as ecological value will be key levers for sustainable implementation. By leveraging these approaches, SMEs can navigate the challenges of digital transformation and sustainability and contribute to a more sustainable future. [ABSTRACT FROM AUTHOR]

This paper focuses on the implementation of predictive maintenance processes using both physics-based and data-driven methods, specifically applied to a Salvagnini panel bender. The primary objective is to enhance production data analysis through digital twin predictions, enabling the calculation of process parameters that cannot be directly measured. Two key aspects are addressed in this study: Firstly, two global measures of the effective cumulative load on a machine during its lifetime are formulated. These measures can be used as criteria for estimating the health condition of a machine and for comparing different machines. To achieve this, dimensionless parameters are derived from actual production data. Secondly, a remaining useful life (RUL) model is developed for two machine components relying on production data analysis and digital twin predictions. The first component under consideration is a hybrid hydraulic actuator. Internal leakage prediction for this component is performed based on its data history, resulting in a corresponding trend curve. Additionally, wear of critical bearings is calculated using an analytical model that depends on the production history log. Through the proposed hybrid approach, this paper aims to enhance the predictive maintenance process by leveraging both physics-based and data-driven methodologies. The findings from this study can offer valuable insights for optimizing maintenance strategies and improving the overall efficiency of similar manufacturing systems. [ABSTRACT FROM AUTHOR]

In recent years, multiple crises have shown the fragility of global supply chains. In particular, climate change sheds light on the never-ending waste of resources caused by the linear economic model. In response, circular economy emerges as a promising alternative. Companies must rethink and innovate their business models, product life cycles, and underlying mindsets for a systemic transition. Cross-sectoral collaboration in networks is necessary to redesign, reuse, recycle, and remanufacture products and close the loop of material flows. First, projects and research on the concepts of open source hardware, open innovation, and open design show that openness can be a game-changer for the circular economy and circular business models. Although there has been growing interest, there is still a lack of comprehensive research examining possible synergies of openness and circularity, as well as identifying patterns for integrating these concepts into business models. This paper explores possible ways, prerequisites, and obstacles of open source concepts enabling circular value creation in industry. We combine explorative data from a literature review and more than 10 interviews with open source and circular economy experts. Furthermore, a total of 38 case studies of circular or open or open & circular business models were identified and selected for analysis. The empirical results were triangulated with existing business model patterns from the literature. The study presents a framework to describe open and circular business models and four prototypical patterns. In an exploratory approach, we identify necessary lines of future research in order to facilitate open source movements toward a circular economy. The paper provides insights for academics and practitioners from production and supply chain management. It combines interdisciplinary perspectives to unlock the potential of open source concepts and collaboration for enabling a circular economy. [ABSTRACT FROM AUTHOR]

Missing Data is of concern in smart manufacturing. There are various reasons why data may be missing. Buffering issues, sensor failure, or protocol issues can cause missing entries or skipped captures. This missing data leads to distorted datasets, failure to train models, and either to disregard, deletion of records or typically to vast manual re-work efforts. This paper shows types of missing data and various approaches to detect them. Replacement or fill-in approaches are presented and their limitations are highlighted. A more precise imputation method beyond the state of the art is explained in detail. The implementation of this physics-informed imputation method was performed and experiments were carried out. The results of the experiments are presented in this paper and the results discussed. [ABSTRACT FROM AUTHOR]

The proposal of Industry 5.0 has made sustainability, human-centric and resilience the core of digital manufacturing, which also puts forward new requirements for the human-machine interaction (HMI) paradigm in human-centric smart manufacturing (HCSM). In the manufacturing scenario, the process of HMI can be divided into four parts: 1) Sensors and hardware, where the environment information and input signals are collected, 2) Data processing, where the signals are converted into data, 3) Transmission mechanism, where the data is transmitted to the processing centre, and 4) Interaction and collaboration. Among them, sensors and data are expected to become breakthrough points in optimising HMI. This is not only due to the emergence of new research, innovation and technologies but also because they are closely influenced by the new design concepts brought about by Industry 5.0. This paper analyses the latest studies and technologies in the sensor field and their possible applications in HCSM scenarios. Then, opportunities and challenges of data analysis in the HMI in Industry 5.0 are discussed. Finally, based on the design concepts and requirements of Industry 5.0, this paper demonstrates how they will become the key points for future HMI development. [ABSTRACT FROM AUTHOR]

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysis-capable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication. [ABSTRACT FROM AUTHOR]

he literature on Lean Manufacturing has predominantly emphasized mass production and batch systems, leaving a considerable knowledge gap regarding the challenges and considerations specific to implementing Lean principles in Engineer-to-Order (ETO) manufacturing contexts. This paper addresses this gap by exploring the interaction between Project Cost Deployment (PCD), a tailored method for ETO environments, and the digital technologies of Industry 4.0. The research aims to identify the synergies between Lean Manufacturing and Industry 4.0, investigating how their integration can enhance productivity, quality, and competitiveness in the manufacturing industry. While Lean Manufacturing is adept at addressing loss reduction and elimination across various industrial contexts, Industry 4.0 leverages digital technologies to revolutionize manufacturing operations and enable more efficient and intelligent production systems. The paper demonstrates the effectiveness of the PCD method through an industrial application involving an international company specializing in customized paper converting machinery and lines. By utilizing PCD in conjunction with Industry 4.0 technologies, analysts were able to uncover hidden losses and quantify them from an economic perspective, while also evaluating the potential impacts of improvement activities. The results highlight the significance of synergizing Lean Manufacturing and Industry 4.0 for ETO environments, offering valuable insights into how digital technologies can be effectively integrated into Lean principles. [ABSTRACT FROM AUTHOR]

This paper presents an academic exploration of the shifting landscape of business models toward greater sustainability, considering the environmental, social, and economic dimensions. The United Nations has emphasized the need for companies to prioritize sustainability, leading to scholarly discussions on the topic. While economic sustainability remains important, decision-makers must now navigate the transformation of their business models to incorporate environmental and social sustainability, driven by emerging regulations such as the European Union's Green Deal. This transition may initially appear as an additional task accompanied by potential investment costs and reduced profits. However, it also presents opportunities for economic growth through the creation of new value propositions, entry into untapped markets, access to novel customer segments, the provision of innovative services, and the facilitation of data-driven supply chains. This paper provides a comprehensive literature review on sustainability as a pivotal future value proposition for companies and identifies potential pathways that can leverage its transformative potential. [ABSTRACT FROM AUTHOR]

The integration of Adaptive Business Intelligence systems in healthcare has garnered significant attention due to their potential to manage the ever-growing volume of healthcare data and enhance the quality of care provided to society. ABI systems also play a crucial role in supporting hospital administrators in making strategic decisions. To facilitate the transparency and interoperability of these solutions, the scientific community has embarked on various studies to develop technologic architectures capable of meeting the complex requirements of healthcare settings. One of the key challenges in adopting this technology is the creation and integration of prediction and optimization models in an automated and semi-autonomous manner. This article presents a novel and robust microservices architecture designed to streamline the deployment of intelligent models and seamlessly integrate them within the ABI system. This paper begins by introducing the problem of deploying and integrating intelligent models into ABI systems, providing essential context on ABI systems within the healthcare domain. Subsequently, it details the proposed architecture, outlining its technical approaches and highlighting the advantages it brings to the healthcare ecosystem. Finally, the paper concludes by summarizing the contributions and future directions for research in this critical area, emphasizing the potential impact of this architecture on improving healthcare intelligence systems. [ABSTRACT FROM AUTHOR]