bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wearable Sensor-Based Human Activity Recognition for Worker Safety in Manufacturing Line
Authors: Suh Sungho , Rey Vitor Fortes , Lukowicz Paul 
Published: February 2024

AbstractImproving worker safety and productivity is of paramount importance in the manufacturing industry, driving the adoption of advanced sensing and control systems. This concern is particularly relevant within the framework of Industry 5.0. In this context, wearable sensors offer a promising solution by enabling continuous and unobtrusive monitoring of workers’ activities in the manufacturing line. This book chapter focuses on wearable sensor-based human activity recognition and its role in promoting worker safety in manufacturing environments. Specifically, we present a case study on wearable sensor-based worker activity recognition in a manufacturing line with a mobile robot. As wearable sensors comprise various sensor types, we investigate and compare sensor data fusion approaches using neural network models to effectively handle the multimodal sensor data. In addition, we introduce several deep learning-based techniques to improve the performance of human activity recognition. By harnessing wearable sensors for human activity recognition, this book chapter provides valuable insights into improving worker safety on the manufacturing line, aligning with the principles of the Industry 5.0 paradigm. The chapter sheds light on the potential of wearable sensor technologies and offers avenues for future research in this field.
Boosting AutoML and XAI in Manufacturing: AI Model Generation Framework
Authors: Barroso Marta , Hinjos Daniel , Martin Pablo A. , Gonzalez-Mallo Marta , Gimenez-Abalos Victor , Alvarez-Napagao Sergio 
Published: February 2024

AbstractThe adoption of AI in manufacturing enables numerous benefits that can significantly impact productivity, efficiency, and decision-making processes. AI algorithms can optimize production schedules, inventory management, and supply chain operations by analyzing historical data and producing demand forecasts. In spite of these benefits, some challenges such as integration, lack of data infrastructure and expertise, and resistance to change need to be addressed for the industry to successfully adopt AI. To overcome these issues, we introduce the AI Model Generation framework (AMG), able to automatically generate AI models that adjust to the user’s needs. More precisely, the model development process involves the execution of a whole chain of sub-processes, including data loading, automated data pre-processing, cost computation, automatic model hyperparameter tuning, training, inference, explainability generation, standardization, and containerization. We expect our approach to aid non-expert users into more effectively producing machine and deep learning algorithms and hyperparameter settings that are appropriate to solve their problems without sacrificing privacy and relying on third-party services and infrastructure as few as possible.
Anomaly Detection in Manufacturing
Authors: Scholz Jona , Holtkemper Maike , Graß Alexander , Beecks Christian 
Published: February 2024

AbstractThis chapter provides an introduction to common methods of anomaly detection, which is an important aspect of quality control in manufacturing. We give an overview of widely used statistical methods for detecting anomalies based on k-means, decision trees, and Support Vector Machines. In addition, we examine the more recent deep learning technique of autoencoders. We conclude our chapter with a case study from the EU project knowlEdge, where an autoencoder was utilized in order to detect anomalies in a manufacturing process of fuel tanks. Throughout the chapter, we emphasize the importance of humans-in-the-loop and provide an example of how AI can be used to improve manufacturing processes.
Process and Product Quality Optimization with Explainable Artificial Intelligence
Authors: Sesana Michele , Cavallaro Sara , Calabresi Mattia , Capaccioli Andrea , Napoletano Linda , Antonello Veronica , Grandi Fabio 
Published: February 2024

AbstractIn today’s rapidly evolving technological landscape, businesses across various industries face a critical challenge: maintaining and enhancing the quality of both their processes and the products they deliver. Traditionally, this task has been tackled through manual analysis, statistical methods, and domain expertise. However, with the advent of artificial intelligence (AI) and machine learning, new opportunities have emerged to revolutionize quality optimization. This chapter explores the process and product quality optimization in a real industrial use case with the help of explainable artificial intelligence (XAI) techniques. While AI algorithms have proven their effectiveness in improving quality, one of the longstanding barriers to their widespread adoption has been the lack of interpretability and transparency in their decision-making processes. XAI addresses this concern by enabling human stakeholders to understand and trust the outcomes of AI models, thereby empowering them to make informed decisions and take effective actions.
Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive Review
Authors: Martinez-Gil Jorge , Hoch Thomas , Pichler Mario , Heinzl Bernhard , Moser Bernhard , Kurniawan Kabul , Kiesling Elmar , Krause Franz 
Published: February 2024

AbstractThe integration of Knowledge Graphs (KGs) in the manufacturing industry can significantly enhance the efficiency and flexibility of production lines and improve product quality. By integrating and contextualizing information about devices, equipment, production resources, location, usage, and related data, KGs can be a powerful operational tool. Moreover, KGs can contribute to the intelligence of manufacturing processes by providing insights into the complex and competitive manufacturing landscape. This research work presents a comprehensive analysis of the current trends utilizing KG in the manufacturing sector. We provide an overview of the state of the art in KG applications in manufacturing and highlight the critical issues that need to be addressed to enable a successful implementation. Our research aims to contribute to advancing KG technology in manufacturing and realizing its full potential to enhance manufacturing operations and competitiveness.
Architecture of a Software Platform for Affordable Artificial Intelligence in Manufacturing
Authors: Cutrona Vincenzo , Landolfi Giuseppe , Alonso Rubén , Montini Elias , Falconi Andrea , Bettoni Andrea 
Published: February 2024

AbstractThe fourth industrial revolution has driven companies of all sizes to embrace digitalization, recognizing the potential of AI technologies for data analysis and real-time decision-making. However, the adoption of AI by manufacturing SMEs faces challenges related to cost, accessibility, and the need for expertise. To address these challenges, this chapter introduces a groundbreaking platform developed as part of the EU H2020 KITT4SME project. The platform aims to democratize the adoption of AI tools by leveraging the “as-a-service” model, making them affordable and readily available for SMEs. It follows a five-step workflow (diagnose–compose–sense–intervene–evolve) to provide tailor-made AI solutions to SMEs. The distinctive functionality of the platform allows for the composition of AI components from a marketplace into a customized service offering for companies, filling a gap in existing AI platforms. The KITT4SME platform has been successfully applied in four use cases within the project and to 18 external demonstrators via Open Calls. This chapter presents one of the internal use cases to showcase the capabilities and benefits of the KITT4SME platform.
Conclusion: In Need of a Curricular Revolution
Authors: Gellman Mneesha 
Published: February 2024

AbstractIn the previous chapters, the textbook analysis showed what little progress US history textbooks have made in the last seventy years. White stories of victory and domination continue to disproportionally fill textbooks. While representation of non-White perspectives and more accurate information about BIPOC groups does increase in more recent textbooks, the same limited and narrow-minded tropes, events, and passivity still persist. So, what can be done? In the conclusion, I reiterate the link between culturally relevant curricula and youth wellbeing. Curricular reform is a beginning step that educators, policy-makers, and school districts could make to ensure their BIPOC students feel seen, heard, and valued as students and as people. Such a proposition is deeply political, but is necessary to correct past wrongs when it comes to misrepresentation in school curricula. Such reforms will move us toward providing a more inclusive environment for all students.
Artificial Intelligence for Digital Twin
Authors: Zhang Yan 
Published: February 2024

AbstractArtificial intelligence (AI) is a promising technology that enables machines to learn from experience, adjust to environments, and perform humanlike tasks. Incorporating AI with digital twin (DT) makes DT modelling flexible and accurate, while improving the learning efficiency of AI agents. In this chapter, we present the framework of AI-empowered DT and discuss some key issues in the joint application of these two technologies. Then, we introduce the incorporation paradigms of three AI learning approaches with DT networks.
Blockchain for Digital Twin
Authors: Zhang Yan 
Published: February 2024

AbstractSecurity and privacy are critical issues in digital twin edge networks. Blockchain, as a tamper-proof distributed database, is a promising solution for protecting data and edge resource sharing in digital twin edge networks. In this chapter, we first introduce the architecture of the blockchain-empowered digital twin edge network, to show the integration angles of the blockchain and digital twin techniques. Then, we show the block generation and consensus process in the developed blockchain-empowered digital twin edge network architecture.
Prompt Patterns for Agile Software Project Managers: First Results
Authors: Sainio Kari , Abrahamsson Pekka , Ahtee Tero 
Published: February 2024

AbstractIn the evolving field of Agile Project Management (APM), the role of the project manager is in transition. This paper identifies common ‘pain points’ in APM through a literature review and constructs a theoretical model to address them. The study introduces ‘Prompt Engineering’ as a novel approach to leverage artificial intelligence (AI), specifically ChatGPT, for mitigating these challenges. Empirical research evaluates ChatGPT's capabilities and reliability in managing various project tasks using engineered prompts. The findings suggest that while ChatGPT cannot fully replace human project managers, it excels in assisting, guiding, and automating specific tasks when guided by well-crafted prompts. As an outcome, prompt engineering patterns for project managers is proposed to facilitate the application of AI in agile settings. In this paper, we introduce patterns for requirements management, stakeholder and management teams and role clarification. The paper concludes that ChatGPT's knowledge is generally reliable but emphasizes the need for expert evaluation in critical areas.
Starting Collaborations Between SMEs and Researchers in Software Engineering
Authors: Rico Sergio , Dobslaw Felix , Öberg Lena-Maria 
Published: February 2024

AbstractIn software engineering research, academia-industry collaboration is predominantly understood as partnerships between academic institutions and large companies. Small and Medium-sized Enterprises (SMEs) are vital contributors to the industry, and they are numerous. Their unique preconditions and challenges differentiate their collaboration dynamics from larger corporations. We seek to identify guiding principles and practices for initiating collaborations between researchers and SMEs. Through a meta-synthesis approach drawn from two systematic literature reviews, we introduce a collaborative model canvas. This emphasizes the importance of SMEs’ business contexts and the relationships between researchers and SMEs. Our research offers insights for those looking to collaborate with SMEs, considering potential challenges and limitations.
Positive Customer Experience is Enhanced by Effective Agile Practices
Authors: Piiroinen Riina , Jormanainen Ilkka , Tukiainen Markku 
Published: February 2024

AbstractThis paper explores the connection between agile methods and digital customer experience, aiming to identify what are the hallmarks of a good agile way of working. The research is an exploratory case study consisting of interviews and analysis. In summary, the research suggests that the hallmarks of a good agile way of working are 1) breaking down tasks into sufficiently small pieces, 2) defining tasks precisely and releasing them to production evenly, 3) continuous improvement, and 4) good planning of sprints. These good agile operating methods can be seen in the development measures as a short lead time, a short time to export to production, low errors, and a high deployment frequency. According to the findings, these metrics are linked to the Net Promoter Score (NPS), a measure of customer experience. A team with sufficient technical capabilities team that utilizes agile operating methods is able to produce the desired things for customers at exactly the right time while constantly improving, so that the NPS is positive, and its direction is improving. On the other hand, the team’s bad operating methods are also visible in the NPS meter – in this case, the NPS fluctuates strongly. Teams can obtain insightful supplementary data about their own practices by keeping track of development measures.
Investigating Open Innovation Practices to Support Requirements Management in Software Ecosystems
Authors: Malcher Paulo , Viana Davi , Antonino Pablo Oliveira , dos Santos Rodrigo Pereira 
Published: February 2024

AbstractSoftware ecosystems (SECO) affect requirements management when considering multiple actors (i.e., keystone, third-party developer, users) from different organizations using several communication channels such as issue trackers and forums. To deal with this scenario, professionals involved in requirements management in SECO have resorted to several open innovation (OI) practices. Our study aims to investigate OI practices applied to support requirements management in SECO. We conducted a field study based on interviews with 21 professionals involved in requirements management activities in SECO. We identified 10 OI practices to support requirements management in SECO and 14 communication channels to receive/provide requirements from/to external actors. OI practices identified in this study can help practitioners manage requirements in the SECO context in which they are engaged, making this process more informal, open, and collaborative.
Dynamic Capabilities for Sustainable Digital Transformation Amid Crisis: Insights from Law Firms in Emerging Economy
Authors: Adisa Mikhail O. , Ojikutu Gbadebo A. , Abdullai Larry , Oyedeji Shola , Porras Jari 
Published: February 2024

AbstractAmidst the evolving crises and disruptions threatening firms’ competitiveness, businesses are faced with increased dynamism necessitated by technological development, digitalization, and sustainability requirements for survival and growth. This study delves into the intersection of dynamic capabilities (DC), digital transformation (DT), and sustainable resilience among law firms in developing countries. With Nigerian law firms as our case study, this research investigates the strategic integration of dynamic capabilities and digital transformation to foster long-term sustainability of law firms’ resilience during a crisis. Through empirical analysis and qualitative exploration, the study unveils obstacles ranging from digital resistance to technical constraints yet uncovers valuable insights from adopting innovative digital strategies that enhance operational resilience and contribute to driving positive economic, environmental, and social impact while ensuring long-term sustainability objectives. The study reaffirms the significance of dynamic capabilities for digital transformation and contributes to the broader discourse on how digital technology enables firms in emerging economies to maneuver disruptions during crises.
On Public Procurement of ICT Systems: Stakeholder Views and Emerging Tensions
Authors: Ghezzi Reetta , Mikkonen Tommi 
Published: February 2024

AbstractThe public sector is a significant consumer of ICT systems. In countries like Finland, where openness, objectivity, and fairness in public acquisitions are deemed essential, public ICT procurement is based on tenders initiated by public sector organizations. The tendering process is regulated by laws that aim to eliminate unfair advantages and provide all potential stakeholders with similar opportunities to participate. However, depending on the stakeholders’ perspectives, they may interpret the tendering process differently, leading to tensions among them. In this paper, we examine Finland’s public procurement of ICT systems using semi-structured interviews as our data collection method and analyze the results thematically. The interviewees include individuals familiar with tendering and acquisition processes in public organizations and those involved in delivering systems as vendors, representing two different perspectives on the tendering process. The results indicate that although there are significant differences in maturity among public sector organizations participating in procurement, several common themes emerged from nearly all the interviews. Furthermore, in light of contrasting views between public organizations and vendors, recurring tensions arise due to different interpretations of acquisition laws.
Who Does What? Evolving Division of Responsibilities in a B2B Platform
Authors: Vuolasto Jaakko 
Published: February 2024

AbstractTo remain vital, a digital platform ecosystem requires governance. In the extant literature a platform ecosystem typically has a single focal actor who is responsible for the governance. We conducted a case study in heavy industry to understand how the responsibilities of a focal actor in governing a business-to-business platform ecosystem are shared and how they change. We observe the division of responsibilities and their changes as configurations. We conclude that the focal actor’s responsibilities in a platform ecosystem are more multifaceted than the established view where a single actor has a stable set of responsibilities. The division of responsibilities in an ecosystem is subject to actor strategies and their positions in the supply chain. Thus, the strategic moves in an ecosystem are not made by a single actor but by multiple focal actors with multiple strategies.
Limit Theorems for the Cubic Mean-Field Ising Model
Authors: Contucci Pierluigi , Mingione Emanuele , Osabutey Godwin 
Published: February 2024

AbstractWe study a mean-field spin model with three- and two-body interactions. The equilibrium measure for large volumes is shown to have three pure states, the phases of the model. They include the two with opposite magnetization and an unpolarized one with zero magnetization, merging at the critical point. We prove that the central limit theorem holds for a suitably rescaled magnetization, while its violation with the typical quartic behavior appears at the critical point.
Falsch- und Desinformation in sozialen Medien: Ansätze zur Minimierung von Risiken in digitaler Kommunikation über Gesundheit
Authors: Schüz Benjamin , Jones Christopher 
Published: February 2024

ZusammenfassungInsbesondere im Kontext der COVID-19-Pandemie sind Falsch- und Desinformationen in sozialen Medien zu einer Herausforderung für effektive Public-health-Maßnahmen geworden. Hier betrachten wir Einflussfaktoren auf das Glauben und Teilen von Falschinformationen auf individueller, sozialer und situativ-kontextueller Ebene und diskutieren Interventionsmöglichkeiten.Auf individueller Ebene spielen Wissensdefizite, mangelnde Kompetenzen und emotionale Motivation eine Rolle. Menschen mit geringer Gesundheitskompetenz und bestimmten Überzeugungen sind anfälliger für Falschinformationen. Auf sozialer Ebene beeinflussen die Glaubwürdigkeit von Informationsquellen und soziale Normen das Teilen von Falschinformationen. Das Bedürfnis nach Status und Gruppenzugehörigkeit spielt ebenfalls eine Rolle. Auf kontextueller Ebene wirken Emotionen und die Wiederholung von Nachrichten auf das Glauben und Teilen von Falschinformationen.Interventionen auf individueller Ebene beinhalten Maßnahmen zur Verbesserung von Wissen und Kompetenzen. Auf sozialer Ebene können soziale Prozesse und soziale Normen angesprochen werden, um das Teilen von Falschinformationen zu reduzieren. Auf kontextueller Ebene wird die Regulierung von sozialen Netzwerken als wichtiger Ansatzpunkt betrachtet.Es wird darauf hingewiesen, dass soziale Ungleichheiten in der Exposition gegenüber Falschinformationen und im Umgang damit eine wichtige Rolle spielen. Es bleibt unklar, ob die Anfälligkeit für Falschinformationen eine generelle Tendenz oder kontextabhängig ist. Die Entwicklung von Interventionen muss daher vielfältige Einflussfaktoren berücksichtigen.
Doxorubicin concentrations in bone tumour-relevant tissues after bolus and continuous infusion: a randomized porcine microdialysis study
Authors: Jørgensen Andrea René , Bue Mats , Hanberg Pelle , Petersen Elisabeth Krogsgaard , Harlev Christina , Hansen Jakob , Baad-Hansen Thomas , Safwat Akmal , Stilling Maiken 
Published: February 2024

Abstract
Purpose
Doxorubicin is a widely used chemotherapeutic drug that can be administered intravenously as both a bolus infusion and a continuous infusion. The latter is believed to lower the risk of cardiotoxicity, which is a critical long-term complication of doxorubicin treatment. The local tissue concentrations of doxorubicin will be reflected in both treatment efficacy and toxicity, but very limited information is available. The aim of this study was to measure the concentration of doxorubicin after continuous and bolus infusion in tissue compartments around a typical location of a bone tumour.

Methods
Sixteen pigs (female, Danish Landrace, mean weight 77 kg) were randomized into two groups of eight. Both groups received an intravenous infusion of 150 mg doxorubicin; Group 1 received a bolus infusion (10–15 min) and Group 2 received a continuous infusion (6 h). Before infusion, microdialysis catheters were placed intravenously and in four bone tumour-relevant tissue compartments (cancellous bone, subcutaneous tissue, synovial fluid of the knee joint and muscle tissue). Sampling was done (n = 15) over 24 h, and venous blood samples were collected as a reference.

Results
Area under the concentration–time curve (AUC0–24 h) for plasma (total concentration) was significantly different between the two groups, while peak drug concentration (Cmax) was significantly higher in two compartments (plasma and synovial fluid of the knee joint) in Group 1 compared to Group 2. Overall, the unbound tissue concentrations were extremely low with values below 0.20 µg/mL.

Conclusion
The pharmacokinetic profile for doxorubicin in the investigated tissues is very similar when comparing bolus and 6 h continuous infusion.