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Heat staking is a joining process in which thermoplastic pins are formed by heat and pressure in a form-fitting and insoluble way. This study evaluates the mechanical performance and microstructure of selective laser sintered (SLS) polyamide 12 (PA 12) components before and after heat staking, compared with conventionally turned reference specimens. The components were characterized using tensile tests, micrographs, microscopy, and micro-CT measurements. For the tests, the forces and temperatures during heat staking were varied to determine the best process parameters. Tensile tests revealed that SLS joints achieved strengths of up to 33.6 MPa, approaching the 39.9 MPa of the turned references. Microstructural analysis showed a marked reduction in porosity due to heat staking. Porosity decreased from 3.9% to 1.56% at a staking force of 300 N and from 4.29% to 0.81% at 1000 N, highlighting the beneficial effect of increased force. These results demonstrate that heat staking parameters significantly influence local densification and mechanical performance, and that, under suitable conditions, SLS components can achieve joint strengths comparable to conventionally manufactured parts. The study shows that the heat staking process parameters have a significant influence on the local microstructure and thus on the mechanical performance and provides a basis for optimizing SLS components for new heat staking applications.
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.
Isogeometric analysis (IGA) combined with explicit dynamics is increasingly used in academia and has already been successfully applied in industrial simulations, including crash and sheet-metal forming. Since explicit schemes are only conditionally stable, accurate estimation of the critical time step is essential for both stability and efficiency. Adaptive mesh refinement is widely used to balance accuracy and computational cost. In IGA, THB- and LR-splines break the tensor-product structure of standard B-splines and enable local refinement, but their effect on the critical time step under trimming has not been systematically studied - a key requirement for reliable time step estimation.
We investigate the critical time step in explicit dynamic simulations using trimmed B-splines, LR-splines, and THB-splines, based on a lumped mass matrix obtained by simple row summation. One-dimensional bar, two-dimensional membrane, and trimmed shell models are analyzed to determine how trimming and local refinement influence element and system eigenfrequencies, which directly control the stable time step. Refined boundary elements in open knot vectors are identified as the main bottleneck. Trimming these elements can increase the stable time step, though certain trimming configurations introduce new restrictions.
Results show that LR- and THB-splines impose time step constraints similar to B-splines, making them equally suitable for explicit simulations. We also present a general method for computing element-wise Bézier extraction operators for LR- and THB-splines, enabling straightforward integration into standard finite element solvers. The findings are validated through nonlinear sheet-metal forming simulations in LS-DYNA using shells discretized with trimmed B-, LR-, and THB-splines. This represents the first such application and demonstrates their practical feasibility for industrial use.
The growing demand for green hydrogen necessitates a rapid scale-up of production and exports to meet decarbonization targets globally. However, current ramp-up efforts remain insufficient, calling for policies that unlock the potential of hydrogen as a low-carbon energy carrier. A key lever is the offtaker regulation, which impacts the pace and sustainability of export projects.
This study investigates minimum renewable share requirements for hydrogen exports in countries with high renewable electricity shares. Using New Zealand as a case study, we develop a fully sector-coupled capacity expansion and dispatch model, integrating hydrogen and electricity network planning based on PyPSA-Earth. The model optimizes New Zealand’s energy system under varying export scenarios, renewable electricity shares, and resulting system impacts.
We find that domestic electricity demand and renewable expansion rates dominate long-term outcomes, while progressive regulation enables short-term hydrogen and Power-to-X exports. Relaxing the renewable threshold from 80% to 60% triples export volumes from 2.5 TWh to 8.2 TWh by 2030. We propose a two-stage requirement: an initially progressive threshold to attract investment with low consequential emissions, followed by stricter regulation to prevent high emissions, rising domestic electricity prices, and declining hydrogen competitiveness. This framework, demonstrated for New Zealand, can guide hydrogen-exporting countries worldwide.
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.
Person-specific evaluation method for occupational exoskeletons - Biomechanical body heat map
(2025)
Human-centred and ergonomic work design is one of the most important drivers for increasing the competitiveness of the European Union. As a flexible, person-specific occupational measure, exoskeletons promise great potential for effectively reducing individual ergonomic stress. Digital human models can provide important insights and offer great potential for systematising the effect and targeted use of exoskeletons, supporting their effective implementation in practice. In this article, digital human models are applied on two levels. Firstly, a realistic industrial logistics scenario in which boxes had to be relocated is designed with the help of a digital human model for workplace and process planning and secondly, a new biomechanical evaluation methodology to analyse intended and unintended effects on internal stress on the human body is demonstrated by applying musculoskeletal exoskeleton human models of four test subjects. Finally, the modelled biomechanical support tendencies of one exoskeleton are preliminary validated using EMG measurement data of the back muscles collected from the four male workers. The preliminary analysis of two back-support exoskeletons to demonstrate the new methodological approach confirms the expected, intended effects in the lower back and reveals unintended effects, such as e.g. changes in knee kinetics when applying a soft or hard-frame exoskeleton. Furthermore, the exemplary results to demonstrate the methodological approach expose notable differences between the test subjects, which underlines the relevance of person-specific evaluation and consideration of exoskeleton support. The preliminary validation shows a correlation between the modelled and the EMG-measured biomechanical exoskeleton support of the considered back muscles.
Hintergrund und Zielsetzung
Zunehmend werden digitale Anamneseverfahren, wie z. B. Chatbots, entwickelt und evaluiert, sind jedoch in Arztpraxen in Deutschland noch nicht flächendeckend implementiert. Ziel dieser Arbeit ist die Erhebung der Akzeptanz der Bevölkerung in Deutschland gegenüber KI-gestützten Verfahren im Anamneseprozess, durch die es möglich wird, Rückschlüsse auf die Nutzung der Verfahren und ihre Eignung als Bestandteil von Digitalisierungsstrategien der Praxen zu ziehen.
Methode
Die Studie basiert auf einer Onlinebefragung der Bevölkerung in Deutschland (Feldphase 18.-24.11.2024, n = 1.000). Zurückgehend auf die „Unified Theory of Acceptance and Use of Technology“ (UTAUT) wurden drei zentrale Hypothesen zur Nutzung von Künstlicher Intelligenz (KI) im Anamneseprozess aufgestellt und mit linearen Regressionsmodellen getestet.
Ergebnisse
Die Akzeptanz für die Nutzung von KI-gestützten Systemen fällt in beiden untersuchten Szenarien, der Nutzung eines Chatbots zu Hause und der Nutzung eines Chatbots in einer Arztpraxis, hoch aus. Dabei bestätigen sich die erwarteten Zusammenhänge von Leistungserwartung, Aufwandserwartung und erwartetem Sozialem Einfluss mit der Verhaltensabsicht, einen Chatbot in der Arztpraxis oder vor dem Besuch einer Arztpraxis zu nutzen. Die Einflüsse von Alter, Geschlecht oder Erfahrung mit der Technologie zeigen jedoch nicht die vermuteten Zusammenhänge.
Diskussion und Schlussfolgerung
Die Praktikabilität ist von entscheidender Bedeutung für eine positive Einstellung gegenüber der Nutzung. Dabei zeigt sich kein Unterschied zwischen den Geschlechtern. Bei der Implementierung von Chatbots im Anamneseprozess sollten die Aspekte Praktikabilität, Datenschutz und Datensicherheit eine wesentliche Rolle spielen. Angesichts der Tatsache, dass ein großer Teil der älteren Bevölkerung in Deutschland nach wie vor über begrenzte Erfahrung mit digitalen Technologien verfügt, ist Gebrauchstauglichkeit für diese Bevölkerungsgruppe sowie für Menschen mit Beeinträchtigungen von hoher Relevanz.
Generalized Ng–Kundu–Chan model of adaptive progressive Type‐II censoring and related inference
(2023)
The model of adaptive progressive Type‐II censoring introduced by Ng et al. (2009) (referred to as Ng–Kundu–Chan model) is extended to allow switching from a given initial censoring plan to any arbitrary given plan of the same length. In this generalized model, the joint distribution of the failure times and the corresponding likelihood function is derived. It is illustrated that the computation of maximum likelihood and Bayesian estimates are along the same lines as for standard progressive Type‐II censoring. However, the distributional properties of the estimators will usually be different since the censoring plan actually applied in the (generalized) Ng–Kundu–Chan model is random. As already mentioned in Cramer and Iliopoulos (2010), we directly show that the normalized spacings are independent and identically exponentially distributed. However, it turns out that the spacings themselves are generally dependent with mixtures of exponential distributions as marginals. These results are used to study linear estimators. Finally, we propose an algorithm for generating random numbers in the generalized Ng–Kundu–Chan model and present some simulation results. The results obtained also provide new findings in the original Ng–Kundu–Chan model; the corresponding implications are highlighted.
Intra-abdominal pressure is a significant physiological parameter influencing spinal stability and pelvic floor health. This study investigates the potential of a transformer encoder model to predict IAP using motion capture data and musculoskeletal modeling. Data from 211 subjects performing walking, fast walking, and running were used to train a transformer encoder. The model showed promising results with an overall Mean Absolute Percentage Error of 13.5% and a Pearson correlation coefficient of 0.85. Predictions for fast walking and running proved to be more challenging compared to walking, which was attributed to the greater variability and complexity of faster movements.
Background: Group treatments and interdisciplinary collaboration are recommended in evidence-based guidelines for neurorehabilitation, including physical and speech-language therapy. Evidence suggests that activating overlapping neural networks for upper extremity motor control and speechlanguage processing produces synergistic effects during therapy. This systematic
review aims to overview and appraise the efficacy of group treatments in traditional rehabilitation and telerehabilitation. In addition to summarizing evidence on monodisciplinary approaches in physical and speech-language therapy, it seeks data on integrative approaches involving one or both disciplines to inform further interdisciplinary collaboration.
Methods: The review was registered with PROSPERO (CRD42021288012) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Systematic searches were conducted in PubMed, CINAHL, and the Cochrane Library. Two reviewers independently screened studies, extracted data, and assessed quality using AMSTAR 2, the Physiotherapy Evidence Database (PEDro) scale, or the Joanna Briggs Institute (JBI) Checklist, as appropriate. The evidence was summarized in a systematic narrative synthesis and its certainty rated based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.
Results: A total of 29 studies were included: 16 on speech-language therapy (861 participants) and 13 on physical therapy (1757 participants). No studies addressed interdisciplinary group interventions, and only two evaluated group telerehabilitation. Outcome domains and measures varied across studies and the certainty of evidence was predominantly low. However, moderate-certainty evidence supports that group speech-language therapy improves quality of life,
communication, and language in stroke survivors, especially when interventions emphasize verbal production in communicative settings with multimodal materials and cueing. In physical therapy, circuit class training may be more effective than other group approaches for enhancing quality of life and mobility.
Conclusion: Group treatments in neurorehabilitation show some benefits, but further research is needed – especially regarding interdisciplinary approaches and telerehabilitation.