Abstract
The Real Time Controllers (RTCs) for the W. M. Keck Observatory Adaptive Optics (AO) systems have been upgraded from a Field Programmable Gate Array (FPGA) to a Graphics Processing Unit (GPU) based solution. The previous RTCs, operating since 2007, had reached their limitations after upgrades to support new hardware including an Infra-Red (IR) Tip/Tilt (TT) Wave Front Sensor (WFS) on Keck I and a Pyramid WFS on Keck II. The new RTC, fabricated by a Microgate-led consortium with SUT leading the computation engine development, provides a flexible platform that improves processing bandwidth and allows for easier integration with new hardware and control algorithms. Along with the new GPU-based RTC, the upgrade includes a new hardware Interface Module (IM), new OCAM2K EMCCD cameras, and a new Telemetry Recording Server (TRS). The first system upgrade to take advantage of the new RTC is the Keck I All-sky Precision Adaptive Optics (KAPA) Laser Tomography AO (LTAO) system, which uses the larger and more sensitive OCAM2K EMCCD camera, tomographic reconstruction from four Laser Guide Stars (LGS), and improvements to the IR TT WFS. On Keck II the new RTC will enable a new higher-order Deformable Mirror (DM) as part of the HAKA (High order Advanced Keck Adaptive optics) project, which will also use an EMCCD camera. In the future, the new RTC will allow the possibility for new developments such as the proposed 'IWA (Infrared Wavefront sensor Adaptive optics) system. The new RTC saw first light in 2021. The Keck I system was released for science observations in late 2023, with the Keck II system released for science in early 2024.

Abstract
This article summarises the research studies published in the special issue on Project Management and Scheduling devoted to the 18th International Conference on Project Management and Scheduling (PMS). The special issue contains state-of-the art research in the field of (non-)robust project and machine scheduling and the contribution of each individual study to the academic literature are discussed. We notice that there is a growing interest in the research community to investigate robust scheduling approaches and optimisation problems observed in real-life business settings. This allows us to derive some interesting future research directions for the project and machine scheduling community.

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
A basic combinatorial optimization problem, the Set Covering Problem (SCP) finds extensive use in computer science, operations research, and logistics, among other domains. The SCP’s goal is to determine the smallest number of subsets, or sets, needed to cover every element precisely once given a finite set of items and a collection of subsets of these elements. Numerous practical uses for the SCP exist, such as crew scheduling, truck routing, and facility locating. This paper focuses on obtaining feasible solutions, applying to the obtained solutions constructive heuristics (CH), followed by a redundancy elimination procedure to remove unnecessary sets. To further optimize the quality of the solution, a local search method is also implemented based on the First and Best improvement algorithms. Additionally, the Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Search (VNS) metaheuristics are designed and implemented. Each implemented heuristic underwent testing across 42 instances, with the average deviation from the optimal solution calculated for each instance. The GRASP heuristic demonstrated the most favorable performance, achieving a maximum deviation of 2.26% from the optimal solution, while the VNS approach yielded a maximum deviation of 11.46% from the optimal solution at its best.