bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reinforcement Learning-Based Approaches in Manufacturing Environments
Authors: Fernández Martínez Andrea , González-Val Carlos , Martín Daniel Gordo , López Alberto Botana , Muros Jose Angel Segura , Llopis Afra Maria Petrusa , Masood Jawad , Muiños-Landin Santiago 
Published: February 2024

AbstractThe application of reinforcement learning often faces limitations due to the exploration phase, which can be costly and risky in various contexts. This is particularly evident in manufacturing industries, where the training phase of a reinforcement learning agent is constrained, resulting in suboptimal performance of developed strategies. To address this challenge, digital environments are typically created, allowing agents to freely explore the consequences of their actions in a controlled setting. Strategies developed in these digital environments can then be tested in real scenarios, and secondary training can be conducted using hybrid data that combines digital and real-world experiences.In this chapter, we provide an introduction to reinforcement learning and showcase its application in two different manufacturing scenarios. Specifically, we focus on the woodworking and textile sectors, which are part of ongoing research activities within two distinct European Research Projects. We demonstrate how reinforcement learning is implemented in a digital context, with the ultimate goal of deploying these strategies in real systems.
Towards Industry 5.0 by Incorporation of Trustworthy and Human-Centric Approaches
Authors: Vyhmeister Eduardo , Castane Gabriel Gonzalez 
Published: February 2024

AbstractThe industrial sector has been a major adopter of new technologies for decades, driving economic and societal progress. The path by which industry embraces new techniques has a significant impact on the environment and society and thus must be guided by principles of sustainability and trustworthiness. In this chapter, we explore the current paradigm in which Industry 4.0 is evolving towards Industry 5.0, where artificial intelligence (AI) and other advance technologies are being used to build services from a sustainable, human-centric, and resilient perspective. We examine how AI can be applied in industry while respecting trustworthy principles and collect information to define how well these principles are adopted. Furthermore, it is presented a perspective on the industry’s approach towards adopting trustworthy AI (TAI), and we propose steps to foster its adoption in an appropriate manner. We also examine the challenges and risks associated with the adoption of AI in industry and propose strategies to mitigate them. This chapter intends to serve researchers, practitioners, and policymakers interested in the intersection of AI, industry, and sustainability. It provides an overview of the latest developments in this field and offers practical guidance for those seeking to promote the adoption of TAI.
Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0
Authors: Hoch Thomas , Martinez-Gil Jorge , Pichler Mario , Silvina Agastya , Heinzl Bernhard , Moser Bernhard , Eleftheriou Dimitris , Estrada-Lugo Hector Diego , Leva Maria Chiara 
Published: February 2024

AbstractAI has gained significant traction in manufacturing, offering tremendous potential for enhancing production efficiency, cost reduction, and safety improvements. Consequently, developing AI-based software platforms that facilitate collaboration between human operators and AI services is crucial. However, integrating the different stakeholder perspectives into a common framework is a complex process that requires careful consideration. Our research has focused on identifying the individual relevance of varying quality characteristics per stakeholder toward such a software platform. Therefore, this work proposes an overview on the vital success factors related to human-AI teaming that can be used to measure fulfillment.
Holistic Production Overview: Using XAI for Production Optimization
Authors: Perez-Castanos Sergi , Prieto-Roig Ausias , Monzo David , Colomer-Barbera Javier 
Published: February 2024

AbstractThis chapter introduces the work performed in XMANAI to address the need of explainability in manufacturing AI systems applied to optimize production lines. The XMANAI platform is designed to meet the needs of manufacturing factories, offering them a unified framework to leverage their data and extract valuable insights. Within the project, the Ford use case is focused on forecasting production in a dynamically changing manufacturing line, serving as a practical illustration of the platform capabilities. This chapter focuses on the application of explainability using Hybrid Models and Heterogeneous Graph Machine Learning (ML) techniques. Hybrid Models combine traditional AI models with eXplainable AI (XAI) tools and Heterogeneous Graph ML techniques using Graph Attention (GAT) layers to extract explainability in complex manufacturing scenarios where data that can be represented as a graph. To understand explainability applied to the Ford use case, this chapter describes the initial needs of the scenario, the infrastructure behind the use case and the results obtained, showcasing the effectiveness of this approach, where models are trained in the XMANAI platform. Specifically, a description is given on the results of production forecasting in an engine assembly plant while providing interpretable explanations when deviations from expected are predicted.
Toward Explainable Metrology 4.0: Utilizing Explainable AI to Predict the Pointwise Accuracy of Laser Scanning Devices in Industrial Manufacturing
Authors: Lavasa Eleni , Chadoulos Christos , Siouras Athanasios , Etxabarri Llana Ainhoa , Rodríguez Del Rey Silvia , Dalamagas Theodore , Moustakidis Serafeim 
Published: February 2024

AbstractThe field of metrology, which focuses on the scientific study of measurement, is grappling with a significant challenge: predicting the measurement accuracy of sophisticated 3D scanning devices. These devices, though transformative for industries like manufacturing, construction, and archeology, often generate complex point cloud data that traditional machine learning models struggle to manage effectively. To address this problem, we proposed a PointNet-based model, designed inherently to navigate point cloud data complexities, thereby improving the accuracy of prediction for scanning devices’ measurement accuracy. Our model not only achieved superior performance in terms of mean absolute error (MAE) across all three axes (X, Y, Z) but also provided a visually intuitive means to understand errors through 3D deviation maps. These maps quantify and visualize the predicted and actual deviations, which enhance the model’s explainability as well. This level of explainability offers a transparent tool to stakeholders, assisting them in understanding the model’s decision-making process and ensuring its trustworthy deployment. Therefore, our proposed model offers significant value by elevating the level of precision, reliability, and explainability in any field that utilizes 3D scanning technology. It promises to mitigate costly measurement errors, enhance manufacturing precision, improve architectural designs, and preserve archeological artifacts with greater accuracy.
Human-AI Interaction for Semantic Knowledge Enrichment of AI Model Output
Authors: Chala Sisay Adugna , Graß Alexander 
Published: February 2024

AbstractModern manufacturing requires developing a framework of AI solutions that capture and process data from various sources including from human-AI collaboration. This chapter tries to describe the concept of domain knowledge fusion in human-AI collaboration for manufacturing. Human interaction with AI is enabled in such a way that the domain expert not only inspects the output of the AI model but also injects engineered knowledge in order to retrain AI models for iterative improvement. Domain knowledge fusion is a technique that involves combining knowledge from multiple domains or sources to produce a more complete solution by augmenting learned knowledge, i.e., the knowledge generated by the AI model with engineered knowledge, i.e., the knowledge provided by the domain expert. The concept developed in this chapter demonstrates how the domain expert interacts with AI systems to observe and decide the veracity of the learned knowledge with respect to the given context. It enables humans to collaborate with AI systems through intuitive interfaces that help domain experts in interpreting insights, validating the findings, and applying domain knowledge to gain a deeper understanding of the data.
Leveraging Semantic Representations via Knowledge Graph Embeddings
Authors: Krause Franz , Kurniawan Kabul , Kiesling Elmar , Martinez-Gil Jorge , Hoch Thomas , Pichler Mario , Heinzl Bernhard , Moser Bernhard 
Published: February 2024

AbstractThe representation and exploitation of semantics has been gaining popularity in recent research, as exemplified by the uptake of large language models in the field of Natural Language Processing (NLP) and knowledge graphs (KGs) in the Semantic Web. Although KGs are already employed in manufacturing to integrate and standardize domain knowledge, the generation and application of corresponding KG embeddings as lean feature representations of graph elements have yet to be extensively explored in this domain. Existing KGs in manufacturing often focus on top-level domain knowledge and thus ignore domain dynamics, or they lack interconnectedness, i.e., nodes primarily represent non-contextual data values with single adjacent edges, such as sensor measurements. Consequently, context-dependent KG embedding algorithms are either restricted to non-dynamic use cases or cannot be applied at all due to the given KG characteristics. Therefore, this work provides an overview of state-of-the-art KG embedding methods and their functionalities, identifying the lack of dynamic embedding formalisms and application scenarios as the key obstacles that hinder their implementation in manufacturing. Accordingly, we introduce an approach for dynamizing existing KG embeddings based on local embedding reconstructions. Furthermore, we address the utilization of KG embeddings in the Horizon2020 project Teaming.AI (www.teamingai-project.eu.) focusing on their respective benefits.
Self-Reconfiguration for Smart Manufacturing Based on Artificial Intelligence: A Review and Case Study
Authors: Cruz Yarens J. , Castaño Fernando , Haber Rodolfo E. , Villalonga Alberto , Ejsmont Krzysztof , Gladysz Bartlomiej , Flores Álvaro , Alemany Patricio 
Published: February 2024

AbstractSelf-reconfiguration in manufacturing systems refers to the ability to autonomously execute changes in the production process to deal with variations in demand and production requirements while ensuring a high responsiveness level. Some advantages of these systems are their improved efficiency, flexibility, adaptability, and cost-effectiveness. Different approaches can be used for designing self-reconfigurable manufacturing systems, including computer simulation, data-driven methods, and artificial intelligence-based methods. To assess an artificial intelligence-based solution focused on self-reconfiguration of manufacturing enterprises, a pilot line was selected for implementing an automated machine learning method for finding and setting optimal parametrizations and a fuzzy system-inspired reconfigurator for improving the performance of the pilot line. Additionally, a deep learning segmentation model was integrated into the pilot line as part of a visual inspection module, enabling a more efficient management of the production line workflow. The results obtained demonstrate the potential of self-reconfigurable manufacturing systems to improve the efficiency and effectiveness of production processes.
Teaching to the Test: Undermining Academic Rebellion (2020s)
Authors: Gellman Mneesha 
Published: February 2024

AbstractAdvanced Placement US History (APUSH) courses offered in high schools, and the materials learned from them, have important implications for students. Since APUSH courses can be used as college credit, some young people might not even take a subsequent history course when in college—making the curricula in these classes all the more important. This chapter looks specifically at a 2020 textbook titled Fabric of a Nation used for APUSH courses. Out of all the textbooks looked at in this analysis, Fabric of a Nation is undoubtedly the most inclusive and most up to date. It prompts critical thinking and includes more accurate historical context. However, the book is not perfect. It continues to reinforce Native American passivity and doesn’t expand enough on issues of immigration when it comes to Mexican and im/migrant representations.
Digital Twin Models and Networks
Authors: Zhang Yan 
Published: February 2024

AbstractA digital twin (DT) model reflects one or a group of physical objects that exist in a complex real system to a virtual space. By interconnecting and coordinating multiple independent models, a DT network (DTN) can be built to map the associations and interactions between physical objects. In this chapter, we present DT models in terms of modelling frameworks, modelling methods, and modelling challenges. Then we elaborate the concept of DTNs and compare it with the concept of DT. The communication mechanisms, application scenarios, and open research issues of DTNs are then discussed.
Edge Computing for Digital Twin
Authors: Zhang Yan 
Published: February 2024

AbstractMobile edge computing is a promising solution for analysing and processing a portion of data using the computing, storage, and network resources distributed on the paths between data sources and a cloud computing centre. Mobile edge computing thus provides high efficiency, low latency, and privacy protection to sustain digital twin. In this chapter, we first introduce a hierarchical architecture of digital twin edge networks that consists of a virtual plane and a user/physical plane. We then introduce the key communication and computation technologies in the digital twin edge networks and present two typical cooperative computation modes. Moreover, we present the role of artificial intelligence (AI) for digital twin edge networks, and discuss the unique edge association problem.
Digital Twin for the Internet of Vehicles
Authors: Zhang Yan 
Published: February 2024

AbstractAs a working combination of smart vehicles, advanced communication infrastructures, and intelligent transportation units, the Internet of Vehicles (IoV) has emerged as a new paradigm for safe and efficient urban life in the future. However, various types of smart vehicles with distinct capacities, diverse IoV applications with different resource demands, and unpredictive vehicular topology pose significant challenges to fully realize IoV systems. To cope with these challenges, we leverage digital twin (DT) technology to model complex physical IoV systems in virtual space, to identify the relation between application characteristics and IoV services, which facilitates effective service scheduling and resource management. In this chapter, we discuss the motivation, benefits, and key issues of applying DT in IoV systems. Then, we use vehicular edge computing and caching as two typical IoV application scenarios to present DT-empowered task offloading and content caching scheduling schemes and their performance.
An Evaluation of the Product Security Maturity Model Through Case Studies at 15 Software Producing Organizations
Authors: Baninemeh Elena , Toomey Harold , Labunets Katsiaryna , Wagenaar Gerard , Jansen Slinger 
Published: February 2024

AbstractCybersecurity is becoming increasingly important from a software business perspective. The software that is produced and sold generally becomes part of a complex landscape of customer applications and enlarges the risk that customer organizations take. Increasingly, software producing organizations are realizing that they are on the front lines of the cybersecurity battles. Maintaining security in a software product and software production process directly influences the livelihood of a software business. There are many models for evaluating security of software products. The product security maturity model is commonly used in the industry but has not received academic recognition. In this paper we report on the evaluation of the product security maturity model on usefulness, applicability, and effectiveness. The evaluation has been performed through 15 case studies. We find that the model, though rudimentary, serves medium to large organizations well and that the model is not so applicable within smaller organizations.
Requirements Tool Practices that Drive Business Agility
Authors: Birk Andreas 
Published: February 2024

AbstractCONTEXT: Successful agile teams advance their work practices continuously. The continuous improvement of effective tool-based requirements practices is an important foundation of business agility. However, requirements tool practices are still widely rooted in plan-based approaches. They are not yet suited well for agile teams or agile businesses. OBJECTIVE: Report and make available an approach for continuous improvement of requirements practices so that tool-based requirements management can drive business agility. METHOD: Industry experience report based on a series of cases from different sources, including ones with involvement of the author. RESULTS: Processes and work practices for evolutionarily introducing and adapting requirements tools and tool-based requirements practices, in a way that supports business agility. CONCLUSION: The presented practices can guide organizations towards establishing effective, tool-based requirements practices that support business agility. A foundation is laid for further systematic investigation and development of the approach.
The type 1 submovement conundrum: an investigation into the function of velocity zero-crossings within two-component aiming movements
Authors: Roberts James W. , Burkitt James J. , Elliott Digby 
Published: February 2024

AbstractIn rapid manual aiming, traditional wisdom would have it that two components manifest from feedback-based processes, where error accumulated within the primary submovement can be corrected within the secondary submovement courtesy of online sensory feedback. In some aiming contexts, there are more type 1 submovements (overshooting) compared to types 2 and 3 submovements (undershooting), particularly for more rapid movements. These particular submovements have also been attributed to a mechanical artefact involving movement termination and stabilisation. Hence, the goal of our study was to more closely examine the function of type 1 submovements by revisiting some of our previous datasets. We categorised these submovements according to whether the secondary submovement moved the limb closer (functional), or not (non-functional), to the target. Overall, there were both functional and non-functional submovements with a significantly higher proportion for the former. The displacement at the primary and secondary submovements, and negative velocity peak were significantly greater in the functional compared to non-functional. The influence of submovement type on other movement characteristics, including movement time, was somewhat less clear. These findings indicate that the majority of type 1 submovements are related to intended feedforward- and/or feedback-based processes, although there are a portion that can be attributed an indirect manifestation of a mechanical artefact. As a result, we suggest that submovements should be further categorised by their error-reducing function.
Salacia spp.: recent insights on biotechnological interventions and future perspectives
Authors: Chavan Jaykumar , Patil Priyanka , Patil Avdhoot , Deshmukh Akshay , Panari Pallavi , Mohite Ashwini , Lawand Pramod , Yadav Pradnya , Bodhe Minal , Kadam Abhijit , Namdas Dada , Pawar Bandu , Jadhav Amol , Shekhawat Mahipal , Santa-Catarina Claudette 
Published: February 2024

Abstract
The plants of the genus Salacia L. are the storehouse of several bioactive compounds, and are involved in treating human diseases and disorders. Hitherto, a number of reports have been published on in vitro biotechnology as well as microbial involvement in the improvement of Salacia spp. The present review provides comprehensive insights into biotechnological interventions such as tissue culture for plant propagation, in vitro cultures, and endophytic microbes for up-scaling the secondary metabolites and biological potential of Salacia spp. Other biotechnological interventions such as molecular markers and bio-nanomaterials for up-grading the prospective of Salacia spp. are also considered. The in vitro biotechnology of Salacia spp. is largely focused on plant regeneration, callus culture, cell suspension culture, somatic embryogenesis, and subsequent ex vitro establishment of the in vitro–raised plantlets. The compiled information on tissue cultural strategies, involvement of endophytes, molecular markers, and nanomaterials will assist the advanced research related to in vitro manipulation, domestication, and commercial cultivation of elite clones of Salacia spp. Moreover, the genetic diversity and other molecular-marker based assessments will aid in designing conservation policies as well as support upgrading and breeding initiatives for Salacia spp.

Key points:
• Salacia spp. plays a multifaceted role in human health and disease management.
• Critical and updated assessment of tissue culture, endophytic microbes, metabolites, molecular markers, and bio-nanomaterials of Salacia spp.
• Key shortcomings and future research directions for Salacia biotechnology.
Towards adaptive drag reduction of a flat-back 3D bluff body in variable pitch and crosswind by flow orientation at the base separation
Authors: Fan Y. , Parezanović V. , Fichera S. , Cadot O. 
Published: February 2024

AbstractThe sensitivity of the drag to the rear design of a flat-back body is investigated under different body attitudes defined by the pitch ($$-1.5^\circ , 0^\circ , +1.5^\circ$$

-
1
.

5
∘

,

0
∘

,
+
1
.

5
∘

) and yaw (up to $$15^\circ$$

15
∘

). The rear design consists of taper angles at the top and bottom trailing edge varying from $$0^\circ$$

0
∘

(no taper) to $$12.5^\circ$$

12
.

5
∘

. Compared to the fixed optimal rear design that minimizes drag at the wind-aligned body attitude, the rear design adaptation to the change of attitude produces a noticeable drag reduction up to 5% depending on the pitch angle within a yaw range smaller than $$2^\circ$$

2
∘

. It is shown that this drag reduction is related to the vertical wake steady instability interfering with the rear design. For yaw larger than $$2^\circ$$

2
∘

and up to $$12^\circ$$

12
∘

, an almost constant drag reduction of 2% is found and shown to be a compromise between a beneficial pressure recovery on the flat base and a detrimental pressure drag on the tapers. At larger yaw angles and whatever the pitch angle is, there is no compromise anymore such that any taper angle different from $$0^\circ$$

0
∘

produces a drag increase leading eventually to the squareback rear design as the optimal design. Overall, the study emphasizes the potential of adaptive control of the top and bottom trailing edge tapers to arbitrary body attitude even at small yaw angles when the pitch is varied.