Abstract
Background & Aim: This scoping review aims to identify and summarize how technology can help prevent work-related musculoskeletal injuries in healthcare professionals. Methods & Materials: We conducted a scoping review following the steps provided by the Joanna Briggs Institute. The PRISMA (R) - Preferred Reporting Items for Systematic Reviews and Meta-Analyses model was used to organize the information, following the recommendations described in PRISMA-ScR (PRISMA Extension for Scoping Reviews) for the article presentation. A search of PubMed, Scopus, and CINAHL databases was conducted for all articles in December 2023. Results: Of the 964 initial articles identified, 7 met the inclusion criteria. The reviewed studies highlight the effectiveness of various technological interventions in reducing musculoskeletal injuries among healthcare professionals. Wearable technologies, such as inertial measurement units, have been effective in promoting correct posture and reducing the risk of musculoskeletal disorders. However, the studies also identified significant challenges, including the generalizability of findings, the need for more robust empirical evidence, and issues related to the long-term sustainability and cost-effectiveness of these technologies. Conclusion: The conclusion of this analysis highlights the need for scalable, effective, and customized therapies and calls for more study and development in gamification, wearable technologies, and tailored mobile applications.

Abstract
Reliability analysis of large power networks requires accurate aggregate models of low voltage (LV) networks to allow for reasonable calculation complexity and to prevent long computational times. However, commonly used lumped load models neglect the differences in spatial distribution of demand, type of phase-connection of served customers and implemented protection system components (e.g., single-pole vs three-pole). This paper proposes a novel use of state enumeration (SE) and Monte Carlo simulation (MCS) techniques to formulate more accurate LV network reliability equivalents. The combined SE and MCS method is illustrated using a generic suburban LV test network, which is realistically represented by a reduced number of system states. This approach allows for a much faster and more accurate reliability assessments, where further reduction of system states results in a single-component equivalent reliability model with the same unavailability as the original LV network. Both mean values and probability distributions of standard reliability indices are calculated, where errors associated with the use of single-line models, as opposed to more detailed three-phase models, are quantified.

Abstract
Many clinical applications involve in-bed patient activity monitoring, from intensive care and neuro-critical infirmary, to semiology-based epileptic seizure diagnosis support or sleep monitoring at home, which require accurate recognition of in-bed movement actions from video streams. The major challenges of clinical application arise from the domain gap between common in-the-lab and clinical scenery (e.g. viewpoint, occlusions, out-of-domain actions), the requirement of minimally intrusive monitoring to already existing clinical practices (e.g. non-contact monitoring), and the significantly limited amount of labeled clinical action data available. Focusing on one of the most demanding in-bed clinical scenarios - semiology-based epileptic seizure classification - this review explores the challenges of video-based clinical in-bed monitoring, reviews video-based action recognition trends, monocular 3D MoCap, and semiology-based automated seizure classification approaches. Moreover, provides a guideline to take full advantage of transfer learning for in-bed action recognition for quantified, evidence-based clinical diagnosis support. The review suggests that an approach based on 3D MoCap and skeleton-based action recognition, strongly relying on transfer learning, could be advantageous for these clinical in-bed action recognition problems. However, these still face several challenges, such as spatio-temporal stability, occlusion handling, and robustness before realizing the full potential of this technology for routine clinical usage.

Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph, will operate an internal Single Conjugate Adaptive Optics (SCAO) system, which will mainly serve the science cases targeting exoplanets and disks around bright stars. The Extremely Large Telescope (ELT) is expected to have its first light in 2028, and the entire instrument recently passed its final design phase. The adaptive optics (AO) of METIS SCAO is designed to correct for atmospheric distortions, and is essential for diffraction-limited observations with METIS. The computational and data transfer requirements for these next generation ELT AO Real-Time Computers (RTCs) are enormous, and require advanced data processing and pipelining techniques. METIS SCAO will use a pyramid wavefront sensor (WFS), which captures incoming wavefronts at 1 kHz with a raw throughput of 148 MB/s. The RTC will ingest these WFS images on a frame-by-frame basis, compute the corrections and send them to the deformable mirror M4 and the tip/tilt mirror M5. The RTC is split up into two distinct systems: the Hard Real-Time Computer (HRTC) and the Soft Real-Time Computer (SRTC). The HRTC is responsible for computing the time sensitive wavefront control loop, while the SRTC is responsible for supervising and optimising the HRTC. A working prototype for the HRTC has been completed and operates with an RTC computation time of roughly 372 mu s. This computation is memory limited and runs on two NVIDIA A100 GPUs. This paper shows a breakdown of the HRTC on a CUDA kernel level, focusing on the tasks that run on the GPUs. We also present the performance of the HRTC and possible improvements for it.

Abstract
Pupil size can be used as an important biomarker for occupational risks. In recent years, there has been an increase in the development of open-source tools dedicated to obtaining and measuring pupil diameter. However, it remains undetermined determined whether these tools are suitable for use in occupational settings. This study explores the significance of pupil size variation as a biomarker for occupational risks and evaluates existing opensource methods for potential use in both research and occupational settings, with the goal of to prevent occupational accidents and improve the health and performance of workers. To this end, a two-phase systematic literature review was conducted in the Web of Science TM, ScienceDirect (R), and Scopus (R) databases. For the relevance of monitoring pupil size variation in occupational settings, 15 articles were included. The articles were divided into three groups: mental workload, occupational stress, and mental fatigue. In most cases, pupil dilation increased with workload enhancement and with higher levels of stress. Regarding fatigue, it was noted that an increase in this condition corresponded with a decrease in pupil size. With respect to the open-source methodologies, 16 articles were identified, which were categorized into two groups: algorithms and software. Convolutional neural networks (CNN) 1 have exhibited superior performance among the various algorithmic approaches studied. Building on this insight, and considering the evaluations of software options, MEYE emerges as the premier open-source system for deployment in occupational settings due to its compatibility with a standard computer webcam. This feature positions MEYE as a particularly practical tool for workers in stable environments, like those of developers and administrators.

Abstract