bluuebunny/crossref_metadata_2025_split huggingface dataset:
Information as Key for Strategic Stakeholder Opportunities Within the Value Chain of an Advanced Circular Economy
Authors: Wallat Phillip , Lawrenz Sebastian , Nippraschk Mathias 
Published: December 2024

AbstractDesigning resource-efficient products, aided by digitalization, offers additional potential to consider improved circularity of the product for the whole value chain. Distribution of data is crucial to provide key information for relevant stakeholders, to guide and support their decisions regarding the product of interest. Stakeholders have different interests in certain sets of data. Streamlining the data exchange will lead to improved efficiency for all involved parties. Therefore, the lifecycle of products has to be examined; the most relevant stakeholders, with their ability to extract and distribute data, are identified; and who has an interest in combined datasets from all stakeholders at what point in time were investigated. Hence, combining datasets has to take into account that time is a factor. Loss of information over time, as well as the necessary data-velocity, are important to provide valid datasets.The result is a schematic overview of the data and information exchange between stakeholders over time. Additional stakeholder data sets are defined and who is involved in providing data to X. Tailored information will be generated out of those data sets, depending on individual interests. The Stakeholder-Datasets are structured in static and dynamic information, potential loss of information, and information velocity between stakeholders. The mobility value chain is used to schematically show which stakeholders need which data set as information, in order to utilize potential that has not yet been tapped. To integrate these results along recent research, the product passport model is applied. The developed schematics are still on a meta-level and shall provide a procedure for further research based on case studies to enhance the advanced circular economy (Definition: Advanced Circular Economy: A circular economy supported with digitized information systems to aid optimization of the whole value chain in the economic system).
A Markovian Approach to Automated Detection of Product Traceability Anomalies and Gaps in Complex Discrete Manufacturing
Authors: Bakir Roudi , Blume Christine 
Published: December 2024

AbstractProduct traceability is essential in modern manufacturing to ensure quality standards and regulatory compliance. It also provides transparency that resonates with customers’ growing emphasis on sustainability. Traditional methods of traceability verification often involve manual inspections, which are time-consuming and prone to human error. This paper proposes an automated approach leveraging machine learning to detect anomalies and gaps in product traceability within discrete production lines. The production line is modeled as discrete sequences, with process parameter names for each station hashed and represented as states in Markov Chains. Through continuous analysis, the system updates the Markov Chain model, adapting to changing production scenarios without manual intervention. Anomalies are detected based on deviations in transition probabilities, allowing for the automatic identification of traceability anomalies and gaps. This not only ensures the integrity of the production process but also optimizes operational efficiency, significantly reducing the need for manual oversight. The proof-of-concept is demonstrated in a case study at the automotive electronic manufacturing plant of Robert Bosch Elektronik GmbH, showcasing the effectiveness and scalability of the automated approach in real-world manufacturing environments.
Multi-Sensor Data Fusion for Application of Machine Learning in Milling Processes on Press Tool Surfaces
Authors: Paturri Skender , Wendland Lasse , Gravert Marvin , Duka Enea , Mackiewicz Maik 
Published: December 2024

AbstractIn today’s global markets, companies must ensure product quality, transparency, and responsiveness to remain competitive. Efficient and precise milling processes are now more crucial than ever due to the utilization of advanced manufacturing technologies. Deviations of nominal geometry occurring from the milling process can result in time consuming and costly reworking or scrapping. Predicting these deviations before machining has a significant value for optimizing the workflow. Data-based methods have demonstrated their ability to surpass the limitations of classical analysis methods in optimizing machining processes in production. However, their performance depends on an extensive and diverse structured data set. In this paper, we propose a method for fusing all the necessary machine signals collected during the milling process into a large-scale data set suitable for machine learning in milling. This is achieved by recognizing patterns and reference points in various types of sensor systems to transform the signals into a unified data model. Specifically, our data set includes the milling program, internal data of the tool machine, data collected by external accelerometers and spindle sensors, as well as CAD data and 3D scans of the finalized part. Together, this serves as a digital end-to-end representation of the milling process, providing a foundation for machine learning based methods. We apply and evaluate our approach on a test geometry.
Systematic Planning and Early-Stage Development of Industrial AI Systems for Plant Optimization
Authors: Voigt Jorrit , Gillmann Marlene Judith , Dröder Klaus 
Published: December 2024

AbstractArtificial Intelligence (AI) offers promising capabilities for many industrial applications and the use of AI has gained traction within manufacturing processes. However, working procedures lack criteria to decide about the benefit of AI systems for particular use cases and do not describe any uncertainty analysis in the development stage, since they assume readily available significant data. The objective of the present study is to introduce a process model, which includes decision criteria to determine the necessity of AI and evaluate the uncertainty of the system, consisting of suitable sensors and algorithms, in early development phases. The procedure is verified by analyzing a battery production line and identifying the prediction of success for MIG-welding as a suitable point of improvement by an AI system. A qualitative extraction of domain knowledge and data source selection leads to a fuzzy model and a clear distinction between the epistemic and aleatoric uncertainty of the system to estimate the prediction capability. The presented method allows the distinction between such uncertainties in a systematic development process and thus allows for a targeted optimization of AI systems. Extending the introduced systematic to other industrial fields can help to increase the implementation rates of AI in manufacturing.
Cross-Process X-ray Inspection Strategy in Battery Cell Assembly
Authors: Masuch Steffen , Gräfnitz Sophie , Dröder Klaus 
Published: December 2024

AbstractThe production of lithium-ion battery cells (LIBs) for electric vehicles requires a considerable amount of energy and raw materials. Due to the increasing added value along the production chain, it is essential to recognise deviations in the intermediate products as early as possible using 100% inline measurement processes. For this reason, LIBs are inspected during cell assembly using plain radiography to qualitatively check their internal geometry. For safety-critical features, such as the anode-cathode-overhang (AC-overhang) in the composite, measuring methods are required that are not fulfilled by plain radiography. The use of computer tomography (CT) offers a solution for this demand. However, to realise a 100% CT inspection, the scan time of conventional systems and the number of applications along the cell assembly must be reduced.This research presents an approach to overcome this limitation in the form of a cross-process X-ray inspection strategy based on a technical and economic analysis of the cell assembly. To reduce scan time and the number of X-ray applications, multiple internal features are measured in a single CT scan prior to electrolyte filling. An initial feasibility assessment of this inspection position is being investigated using a state-of-the-art metal-jet tube and photon counting detector. This provides an inspection time of 1 s, enabling detailed inspection to be combined in a time-efficient process. However, moving the CT inspection downstream would contradict the key objective of early defect detection. To overcome these challenges, an upstream vision system was developed. This vision system detects outliers immediately after the stacking process by detecting the position of the external feature separator in the electrode separator composite (ESC) without X-rays and at a much lower cost than CT. This approach aligns with the primary objective of early defect detection in the production chain through the cross-process inspection strategy.
Enhancing Automotive Production Through Integrated Data Analysis and Simulation
Authors: Bublitz Enno , Warsönke Kai 
Published: December 2024

AbstractIn the context of automotive production, the variability in component tolerances poses a significant challenge, often leading to production downtimes. Deviations in dimensional accuracy and geometry can disrupt automated assembly processes, emphasizing the critical link between input quality and assembly outcomes. As automation gains prominence, robust process monitoring becomes imperative for quality assurance and maintenance cycle optimization. However, a prevalent issue persists data fragmentation and underutilization in production. Our approach is centred on leveraging existing production data to optimize automotive production and streamline final assembly processes. We investigate the interplay of upstream process influences on downstream assembly processes within large-scale production environments. Real-world manufacturing data is analysed and validated through finite element analysis (FEA) simulations coupled with Monte Carlo simulations. Focusing on one specific automotive assembly process—the automated assembly of cockpits—we examine the influence of various body concepts on assembly automation compatibility. Data analysis from two assembly lines identifies body concepts conducive to efficient assembly, subsequently validated using our innovative coupled simulation method. This holistic approach identifies production-suitable product concepts and integrates our novel simulation method into the design of future products, bridging the gap between production data fragmentation and optimized assembly in the automotive industry. By connecting real manufacturing processes with product concept suitability, we enhance production efficiency, advance data mining and simulation methods, and improve overall production quality.
Industrial workspace detection of a robotic arm using combined 2D and 3D vision processing
Authors: Schorr Logan , Cobilean Victor , Mavikumbure Harindra S. , Manic Milos , Hadimani Ravi L. 
Published: December 2024

AbstractAutomation via robotic systems is becoming widely adopted across many industries, but intelligent autonomy in dynamic environments is challenging to implement due to the difficulty of 3D vision. This paper proposes a novel method that utilizes in-situ 2D image processing to simplify 3D segmentation for robotic workspace detection in industrial applications. Using a TOF sensor mounted on a robotic arm, depth images of the workspace are collected. The algorithm identifies the contour of a table, filters extraneous data points, and converts only relevant data to a 3D pointcloud. This pointcloud is processed to identify the precise location of the workspace with regard to the robot. This method has been shown to be 10% more accurate and over 10,000% faster than a human analyzing the data in a GUI-based software using an octree region-based segmentation algorithm and provides consistent results, only limited by the resolution of the camera itself.
In-vitro activity of the novel β-lactam/β-lactamase inhibitor combinations and cefiderocol against carbapenem-resistant Pseudomonas spp. clinical isolates collected in Switzerland in 2022
Authors: Le Terrier Christophe , Bouvier Maxime , Kerbol Auriane , Dell’Acqua Chloé ,   Nara Network members, Nordmann Patrice , Poirel Laurent 
Published: December 2024

AbstractTo evaluate the in-vitro activity of the novel commercially-available drugs, including meropenem-vaborbactam (MEV), ceftazidime-avibactam (CZA), ceftolozane-tazobactam (C/T), imipenem-relebactam (IPR) as well as cefiderocol (FDC), against carbapenem-resistant Pseudomonas spp. (CRP) isolates. All CRP isolates collected at the Swiss National Reference Laboratory (NARA) over the year 2022 (n = 170) have been included. Most of these isolates (n = 121) were non-carbapenemase producers. Among the 49 carbapenemase producers, 47 isolates produced metallo-β-lactamases (MBL) including NDM-1 (n = 11), VIM-like (n = 28), IMP-like (n = 7), and both NDM-1 and VIM-2 (n = 1) and two isolates produced the class A carbapenemase GES-5. Susceptibility testing was determined by broth microdilution method (BMD), or disk diffusion test, and results interpreted following EUCAST guidelines. The susceptibility rates for MEV, CZA, C/T and IPR were found to be 41%, 45%, 59% and 58%, respectively, for the whole set of isolates tested. Among non-carbapenemase producers, susceptibility rates for these β-lactam/β-lactamase inhibitors (BL/BLI) combinations were higher, determined at 55%, 61%, 83%, and 82%, respectively. The overall susceptibility of carbapenemase-producing Pseudomonas spp. to novel BL/BLI was relatively low, while 80% of these isolates demonstrated susceptibility to FDC, with a similar proportion (79%) observed among MBL producers. A total of 10 MBL-producing isolates (6%), mainly NDM-1, were found to exhibit resistance to all drugs tested, with the exception of colistin. FDC exhibited an excellent in-vitro activity against this collection of CRP recovered from Switzerland in 2022, including MBL producers. The new BL/BLI combinations displayed significant activity against non-carbapenemase CRP, with IPR and C/T showing the highest susceptibility rates.
How are informal diagrams used in software engineering? An exploratory study of open-source and industrial practices
Authors: Jongeling Robbert , Cicchetti Antonio , Ciccozzi Federico 
Published: December 2024

AbstractIn software engineering practice, models created for communication and documentation are often informal. This limits the applicability of powerful model-driven engineering mechanisms. Understanding the motivations and use of informal diagrams can improve modelling techniques and tools, by bringing together the benefits of both informal diagramming and modelling using modelling languages and modelling tools. In this paper, we report on an initial exploration effort to investigate the use of informal diagramming in both open-source software repositories and industrial software engineering practices. We carried out a repository mining study on open-source software repositories seeking informal diagrams and classified them according to what they represent and how they are used. Additionally, we describe industrial practices that rely to some extent on informal diagramming, as gathered through unstructured interviews with practitioners. We compare the findings from these data sources and discuss how informal diagrams are used in practice.
Genetic biomarkers and machine learning techniques for predicting diabetes: systematic review
Authors: Khan Sulaiman , Mohsen Farida , Shah Zubair 
Published: December 2024

Abstract
Diabetes mellitus is a long-term metabolic condition marked by high blood sugar levels due to issues with insulin production, insulin effectiveness, or a combination of both. It stands as one of the fastest-growing diseases worldwide, projected to afflict 693 million adults by 2045. The escalating prevalence of diabetes and associated health complications (kidney disease, retinopathy, and neuropathy) underscore the imperative to devise predictive models for early diagnosis and intervention. These complications contribute to increased mortality rates, blindness, kidney failure, and an overall diminished quality of life in individuals living with diabetes. While clinical risk factors and glycemic control provide valuable insights, they alone cannot reliably predict the onset of vascular complications. Genetic biomarkers and machine learning techniques have emerged as promising tools for predicting diabetes development risk and associated complications. Despite the emergence of numerous smart AI models for diabetes prediction, there is still a need for a thorough review outlining their progress and challenges. To address this gap, this paper offers a systematic review of the literature on AI-based models for diabetes identification, following the PRISMA extension for scoping reviews guidelines. Our review revealed that multimodal diabetes prediction models outperformed unimodal models. Most studies focused on classical machine learning models, with SNPs being the most used data type, followed by gene expression profiles, while lipidomic and metabolomic data were the least utilized. Moreover, some studies focused on identifying genetic determinants of diabetes complications relied on familial linkage analysis, tailored for robust effect loci. However, these approaches had limitations, including susceptibility to false positives in candidate gene studies and underpowered AI models capabilities due to sample size constraints. The landscape shifted dramatically with the proliferation of genomic datasets, fueled by the emergence of biobanks and the amalgamation of global cohorts. This surge has led to a more than twofold increase in genetic discoveries related to both diabetes and its complications using AI. Our focus here is on these genetic breakthroughs, particularly those empowered by AI models. However, we also highlight the existing gaps in research and underscore the need for further advancements to propel genomic discovery to the next level.
Specification overfitting in artificial intelligence
Authors: Roth Benjamin , Luz de Araujo Pedro Henrique , Xia Yuxi , Kaltenbrunner Saskia , Korab Christoph 
Published: December 2024

Abstract
Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Consequently, regulatory bodies struggle with containing this technology’s potential negative side effects. High-level requirements such as fairness and robustness need to be formalized into concrete specification metrics, imperfect proxies that capture isolated aspects of the underlying requirements. Given possible trade-offs between different metrics and their vulnerability to over-optimization, integrating specification metrics in system development processes is not trivial. This paper defines specification overfitting, a scenario where systems focus excessively on specified metrics to the detriment of high-level requirements and task performance. We present an extensive literature survey to categorize how researchers propose, measure, and optimize specification metrics in several AI fields (e.g., natural language processing, computer vision, reinforcement learning). Using a keyword-based search on papers from major AI conferences and journals between 2018 and mid-2023, we identify and analyze 74 papers that propose or optimize specification metrics. We find that although most papers implicitly address specification overfitting (e.g., by reporting more than one specification metric), they rarely discuss which role specification metrics should play in system development or explicitly define the scope and assumptions behind metric formulations.
An end-to-end occluded person re-identification network with smoothing corrupted feature prediction
Authors: Zhao Caijie , Qin Ying , Zhang Bob , Zhao Yajie , Wu Baoyun 
Published: December 2024

Abstract
Occluded person re-identification (ReID) is a challenging task as the images suffer from various obstacles and less discriminative information caused by incomplete body parts. Most current works rely on auxiliary models to infer the visible body parts and partial-level features matching to overcome the contaminated body information, which consumes extra inference time and fails when facing complex occlusions. More recently, some methods utilized masks provided from image occlusion augmentation (OA) for the supervision of mask learning. These works estimated occlusion scores for each part of the image by roughly dividing it in the horizontal direction, but cannot accurately predict the occlusion, as well as failing in vertical occlusions. To address this issue, we proposed a Smoothing Corrupted Feature Prediction (SCFP) network in an end-to-end manner for occluded person ReID. Specifically, aided by OA that simulates occlusions appearing in pedestrians and providing occlusion masks, the proposed Occlusion Decoder and Estimator (ODE) estimates and eliminates corrupted features, which is supervised by mask labels generated via restricting all occlusions into a group of patterns. We also designed an Occlusion Pattern Smoothing (OPS) to improve the performance of ODE when predicting irregular obstacles. Subsequently, a Local-to-Body (L2B) representation is constructed to mitigate the limitation of the partial body information for final matching. To investigate the performance of SCFP, we compared our model to the existing state-of-the-art methods in occluded and holistic person ReID benchmarks and proved that our method achieves superior results over the state-of-the-art methods. We also achieved the highest Rank-1 accuracies of 70.9%, 87.0%, and 93.2% in Occluded-Duke, Occluded-ReID, and P-DukeMTMC, respectively. Furthermore, the proposed SCFP generalizes well in holistic datasets, yielding accuracies of 95.8% in Market-1510 and 90.7% in DukeMTMC-reID.
Iterative local search for preserving data privacy
Authors: Arbelaez Alejandro , Climent Laura 
Published: December 2024

Abstract
k-Anonymization is a popular approach for sharing datasets while preserving the privacy of personal and sensitive information. It ensures that each individual is indistinguishable from at least k-1 others in the anonymized dataset through data suppression or generalization, which inevitably leads to some information loss. The goal is to achieve k-anonymization with minimal information loss. This paper presents an efficient local search framework designed to address this challenge using arbitrary information loss metrics. The framework leverages anytime capabilities, allowing it to balance computation time and solution quality, thereby progressively improving the quality of the anonymized data. Our empirical evaluation shows that the proposed local search framework significantly reduces information loss compared to current state-of-the-art solutions, providing performance improvements of up to 54% and 43% w.r.t. the k-members and l-greedy heuristic solutions, the leading algorithms for large datasets. Additionally, our solution approach outperforms the Hun-garian-based solution, the best solution approach for small-size instances, by up to 4.7% on these instances.