TY - JOUR A1 - Bedbur, Stefan A1 - Kamps, Udo A1 - Schmiedt, Anja Bettina T1 - A flexible model of ordered random variables for non-metallic inclusions in steels and related statistical inference JF - Applied Mathematical Modelling N2 - In a data set of non-metallic inclusion sizes in samples from engineering steel, common order statistics fail to serve as a suitable model for ascendingly ordered measurements within single samples. Therefore, a flexible model of ordered random variables is proposed, which allows for changes of distributions described by model parameters. Joint maximum likelihood estimation of these parameters and the shape parameter of an underlying left-truncated Weibull distribution is considered, and a model test is developed for the null-hypothesis of common order statistics being an adequate model. To overcome small data situations, a link-function approach is examined in order to reduce the number of involved model parameters as well as to propose to use a link-function parameter as a material indicator. An asymptotic test is provided to check for the presence of a linear link function, and tests for hypotheses about two link-function parameters are studied. Moreover, the construction of simultaneous confidence regions for the link-function parameters as well as of confidence bands for the entire graph of the link function are presented. Throughout, the findings are applied to the real metallurgical data set. Similar problems and data structures arise in other fields of material science and applications such as geology. Y1 - 2026 U6 - https://doi.org/10.1016/j.apm.2025.116284 SN - 0307-904X N1 - Corresponding author der OTH Regensburg: Anja Bettina Schmiedt VL - 149 PB - Elsevier ER - TY - JOUR A1 - Winter, Lina A1 - Artinger, Annalena A1 - Böck, Hendrik A1 - Ramakrishnan, Vignesh A1 - Reible, Bruno A1 - Albin, Jan A1 - Schüffler, Peter J. A1 - Raptis, Georgios A1 - Brochhausen, Christoph T1 - Developing a smart and scalable tool for histopathological education—PATe 2.0 JF - Journal of Pathology Informatics N2 - Digital microscopy plays a crucial role in pathology education, providing scalable and standardized access to learning resources. In response, we present PATe 2.0, a scalable redeveloped web-application of the former PATe system from 2015. PATe 2.0 was developed using an agile, iterative process and built on a microservices architecture to ensure modularity, scalability, and reliability. It integrates a modern web-based user interface optimized for desktop and tablet use and automates key workflows such as whole-slide image uploads and processing. Performance tests demonstrated that PATe 2.0 significantly reduces tile request times compared to PATe, despite handling larger tiles. The platform supports open formats like DICOM and OpenSlide, enhancing its interoperability and adaptability across institutions. PATe 2.0 represents a robust digital microscopy solution in pathology education enhancing usability, performance, and flexibility. Its design enables future integration of research algorithms and highlights it as a pivotal tool for advancing pathology education and research. Y1 - 2026 U6 - https://doi.org/10.1016/j.jpi.2025.100535 SN - 2153-3539 VL - 20 PB - Elsevier ER - TY - JOUR A1 - Rueckert, Tobias A1 - Rauber, David A1 - Maerkl, Raphaela A1 - Klausmann, Leonard A1 - Yildiran, Suemeyye R. A1 - Gutbrod, Max A1 - Nunes, Danilo Weber A1 - Moreno, Alvaro Fernandez A1 - Luengo, Imanol A1 - Stoyanov, Danail A1 - Toussaint, Nicolas A1 - Cho, Enki A1 - Kim, Hyeon Bae A1 - Choo, Oh Sung A1 - Kim, Ka Young A1 - Kim, Seong Tae A1 - Arantes, Gonçalo A1 - Song, Kehan A1 - Zhu, Jianjun A1 - Xiong, Junchen A1 - Lin, Tingyi A1 - Kikuchi, Shunsuke A1 - Matsuzaki, Hiroki A1 - Kouno, Atsushi A1 - Manesco, João Renato Ribeiro A1 - Papa, João Paulo A1 - Choi, Tae-Min A1 - Jeong, Tae Kyeong A1 - Park, Juyoun A1 - Alabi, Oluwatosin A1 - Wei, Meng A1 - Vercauteren, Tom A1 - Wu, Runzhi A1 - Xu, Mengya A1 - Wang, An A1 - Bai, Long A1 - Ren, Hongliang A1 - Yamlahi, Amine A1 - Hennighausen, Jakob A1 - Maier-Hein, Lena A1 - Kondo, Satoshi A1 - Kasai, Satoshi A1 - Hirasawa, Kousuke A1 - Yang, Shu A1 - Wang, Yihui A1 - Chen, Hao A1 - Rodríguez, Santiago A1 - Aparicio, Nicolás A1 - Manrique, Leonardo A1 - Palm, Christoph A1 - Wilhelm, Dirk A1 - Feussner, Hubertus A1 - Rueckert, Daniel A1 - Speidel, Stefanie A1 - Nasirihaghighi, Sahar A1 - Al Khalil, Yasmina A1 - Li, Yiping A1 - Arbeláez, Pablo A1 - Ayobi, Nicolás A1 - Hosie, Olivia A1 - Lyons, Juan Camilo T1 - Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge JF - Medical Image Analysis N2 - Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context – such as the current procedural phase – has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. KW - Surgical phase recognition KW - Instrument keypoint estimation KW - Instrument instance segmentation KW - Robot-assisted surgery Y1 - 2026 U6 - https://doi.org/10.1016/j.media.2026.103945 SN - 1361-8415 N1 - Corresponding author der OTH Regensburg: Tobias Rueckert Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/solrsearch/index/search/start/0/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/2507.16559 VL - 109 PB - Elsevier ER - TY - CHAP A1 - Deutel, Mark A1 - Mallah, Maen A1 - Wissing, Julio A1 - Scheele, Stephan ED - Vermesan, Ovidiu ED - Pagani, Alain ED - Meloni, Paolo T1 - Recent Trends in Edge AI: Efficient Design, Training and Deployment of Machine Learning Models T2 - Charting the Intelligence Frontiers – Edge AI Systems Nexus N2 - With a rising demand for ubiquitous smart systems, processing and interpreting large quantities of data generated on the edge at a high velocity is becoming an increasingly important challenge. Machine learning (ML) models such as Deep Neural Networks (DNNs) are an essential tool of today’s artificial intelligence due to their ability to make accurate predictions given complex tasks and environments. However, Deep Learning is computationally complex and energy intensive. This seems to contradict the characteristics of many edge devices, which have only limited memory, computational resources, and energy budget available. To overcome this challenge, an efficient ML model design is crucial that incorporates available optimization techniques from hardware, software, and methodological perspective to enable energy-efficient deployment and operation on the edge. This work comprehensively summarizes recent techniques for training, optimizing, and deploying ML models targeting edge devices. We discuss different strategies for finding deployable ML models, scalable DNN architectures, neural architecture search, and multi-objective optimization approaches, to enable feasible trade-offs considering available resources and latency. Furthermore, we give insight into DNN compression methods such 182as quantization and pruning. We conclude by investigating different forms of cascaded processing, from simple multi-level approaches to highly branched compute graphs and early-exit DNNs. Y1 - 2026 SN - 9788743808862 U6 - https://doi.org/10.1201/9788743808862-9 SP - 181 EP - 220 PB - River Publishers CY - New York ER - TY - INPR A1 - Feeß, Alexander A1 - Weiß, Martin T1 - Uniqueness Domains of Offset Wrist Robots via a 4D Workspace Representation N2 - We present a new method for studying the workspace of 6R robots using a four-dimensional representation. This is applied to numerically determine connected workspace components with a constant number of solutions of the inverse kinematics, and to describe critical values that form the boundaries these regions. To define and maximize uniqueness domains, nonsingular transition of these boundaries are investigated. This provides a method to determine workspace regions in which the robot can move avoiding configuration changes and singularities. KW - kinematic workspace analysis KW - kinematic singularities KW - cuspidal robot Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-88635 CY - Regensburg ER - TY - INPR A1 - Feeß, Alexander A1 - Weiß, Martin T1 - Analysis of a Cuspidal 6R Robot N2 - We present a theoretical and numerical analysis of the kinematics for the Transpressor, a cuspidal 6R robot. It admits up to 16 inverse kinematics solutions which are described geometrically. For special target poses, we provide the solutions analytically and present a simple numerical solver for the general case. Moreover, an analytical estimate of the Jacobian determinant on a path between two solutions proves cuspidality for a class of robots similar to the transpressor. KW - Kinematik KW - Industrierobotik KW - inverse kinematics KW - kinematic singularities Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-88626 CY - Regensburg ER - TY - JOUR A1 - Westner, Markus A1 - Reicheneder, Matthias A1 - Matschi, Markus T1 - From Captive Centers to Global Integration BT - Organizational Models and Good Practices in Information Technology Nearshoring and Offshoring JF - Information Resources Management Journal N2 - Contemporary developments in virtual work and global talent management are transforming information technology (IT) nearshoring and offshoring practices, yet research on organizational responses remains limited. Through expert interviews with 33 IT executives (76% chief information officers) from Germany's top 500 companies, this article examines organizational models, strategies, and good practices in IT nearshoring and offshoring. It reveals a shift from traditional captive centers toward fully integrated global teams, enabling high-value task delivery and reflecting increased workforce maturity. Companies are expanding their operations driven by talent needs rather than cost savings, with success factors including employee integration and robust on-site management. This study contributes to theory and practice by providing a framework for understanding organizational models in global IT delivery and offering actionable insights for executives, emphasizing workforce integration over geographical separation. KW - Offshoring KW - Informationstechnik KW - Internationale Arbeitsteilung KW - Organisationsstruktur KW - IT-Nearshoring KW - IT-Offshoring KW - Global IT-Teams KW - Organizational Models KW - Distributed Work Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-80406 SN - 1533-7979 N1 - Corresponding author der OTH Regensburg: Markus Westner VL - 38 IS - 1 PB - IGI Global ER - TY - CHAP A1 - Rush, Logan A1 - Schmid, Marina A1 - Raptis, Georgios T1 - Privacy Challenges in Genomic Data: A Scoping Review of Risks, Mitigation Strategies, and Research Gaps. T2 - Information and Communication Technology: 13th International Symposium, SOICT 2024, Danang, Vietnam, December 13–15, 2024, Proceedings, Part II N2 - Advances in genomic research have created new privacy challenges. This scoping review analyzes the risks associated with the processing, storage, and sharing of genomic data including epigenetics, and examines current privacy protection strategies. It also attempts to identify research gaps in this area. Using the PRISMA methodology, 37 relevant studies were identified and analyzed. The results of the risk assessment can be grouped into four main themes: Risks posed by processing of functional genomic data, sharing of genomic data, patient (re-)identification, and dividuality, i.e. the extending of privacy risks to blood relatives. The identified risk mitigation strategies were systematically categorized into five classes: pre-release measures, governance, secure data processing and exchange, access restriction and transparency, anonymization and masking. However, there are some important research gaps that still need to be addressed. The current literature neglects to assess the likelihood of potential breaches and tends to focus only on assessing possible scenarios of privacy risks. It also mainly fails to assess the role of contextualized data and the effectiveness of policies and governance systems with respect to privacy risks. Y1 - 2025 U6 - https://doi.org/10.1007/978-981-96-4285-4_34 SP - 416 EP - 430 PB - Springer CY - Singapore ER - TY - JOUR A1 - Holler, Nathalie A1 - Westner, Markus T1 - Factors Influencing Cloud Computing Adoption in Small and Medium-Sized Enterprises: A Systematic Review JF - Informatica N2 - This paper investigates the factors influencing cloud computing adoption in small and medium-sized enterprises (SMEs) through a systematic literature review. The analysis identified twelve key factors influencing the adoption of cloud computing in SMEs. Based on the Technology-Organisation-Environment (TOE) model and the Technology Acceptance Model (TAM), a conceptual framework was developed for future research. The most important factors are cost, organisational readiness, compatibility, relative advantage and top management support. Other influential factors include security, perceived usefulness, firm size, government support, perceived ease of use, vendor support and competitive pressure. The majority of studies were conducted in Asian countries, including developing countries, limiting the generalisability of the findings to SMEs in more developed economies. This research highlights the need for cloud computing solutions that not only reduce costs and ensure high levels of security and privacy, but are also easy to use and integrate. Further research is recommended to explore these factors within SMEs in more developed economies. KW - cloud computing adoption KW - small and medium-sized enterprises (SMEs) KW - technology-organization-environment (TOE) framework KW - systematic literature review KW - digital transformation Y1 - 2025 U6 - https://doi.org/10.31449/inf.v49i1.6971 SN - 1854-3871 VL - 49 IS - 1 PB - Slovenian Association Informatika ER - TY - JOUR A1 - Beck, Nico A1 - Dovern, Jonas A1 - Vogl, Stefanie T1 - Mind the naive forecast! a rigorous evaluation of forecasting models for time series with low predictability JF - Applied Intelligence N2 - In the field of time series forecasting, numerous machine learning studies have assessed the performance of new methods on highly volatile data from macroeconomics and finance. Unlike in other domains, where models are also compared to simpler statistical or naive baselines, they mostly compare the performance solely relative to other complex models. This approach may lead to limited conclusions and reduce the practical significance of the results, as it overlooks the unpredictability of some highly volatile time series in the datasets used. We apply state-of-the-art methods from time-series econometrics and machine learning, including autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), Bayesian vector autoregressive model (BVAR), long-short term memory neural networks (LSTM), historical consistent neural networks (HCNN), deep vector autoregressive neural networks (DeepVAR), temporal fusion transformers (TFT), and extreme gradient boosting (XGBoost). Our results demonstrate that no method consistently outperforms the naive (no-change) forecast for highly volatile time series from two popular datasets containing exchange rates and stock prices, rendering comparative analysis between complex models less meaningful. In contrast, when applied to more predictable macroeconomic price indices, many of the methods significantly outperform naive forecasts. We find that the performance of machine learning models deteriorates more than that of statistical models for high-volatility time series. This study highlights the critical importance of using appropriate benchmark models, including cost-effective, simple approaches, on datasets that permit meaningful conclusions. Y1 - 2025 U6 - https://doi.org/10.1007/s10489-025-06268-w VL - 55 IS - 6 PB - Springer ER -