Abstract
Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%–40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation. This study uses learning models based on sequential data from Electronic Health Records (EHR). Decompensation prediction was performed based on 24-h in-mortality prediction at each hour of the patient's stay in the Intensive Care Unit (ICU). A cohort of 2261 TBI patients was selected from the MIMIC-III dataset based on age and ICD-9 disease codes. Logistic Regressor (LR), Long-short term memory (LSTM), and Transformers architectures were used. Two sets of features were also explored combined with missing data strategies by imputing the normal value, data imbalance techniques with class weights, and oversampling. The best performance results were obtained using LSTMs with the original features with no unbalancing techniques and with the added features and class weight technique, with AUROC scores of 0.918 and 0.929, respectively. For this study, using EHR time series data with LSTM proved viable in predicting patient decompensation, providing a helpful indicator of the need for clinical interventions.

Abstract
Two essential tasks in production planning and control are the generation and the release of orders to the shop floor. In this study order, generation is based on the Demand Driven Materials Requirement Planning system, while order release is based on the CONstant Work-in-Process system. Although the two systems alone have been extensively studied, their combination has received much less attention. In this paper, we address the problem of sequencing replenishment orders generated by the Demand Driven Materials Requirement Planning system to be released by the CONstant Work-in-Process system. Four pool-sequencing rules have been considered. Two of these are used by Demand Driven Materials Requirement Planning for establishing priorities for order planning and order execution. The other two are the First-Come-First-Served rule and a virtual due date rule. Results of a simulation study show that the rules proposed in the Demand Driven Materials Requirement Planning literature for planning and for execution are not the best options for pool-sequencing, particularly for restricted levels of workload allowed on the shop floor. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
In Virtual Reality, feedback is usually provided through the handheld controllers. However, it is often limited to vibrotactile feedback. Some research has been done on property changing haptics, which can adapt to some characteristics of the virtual object being interacted with, thus being able to represent distinct objects. However, these are mostly focused on shape or texture and are mostly grounded devices. In this work, we propose a device that can change the material exposed to the user and is simultaneously untethered and ungrounded. Results from a user evaluation with 21 participants showed that our device can successfully convey different types of materials and that the same physical material can be used to make the user perceive different virtual materials.

Abstract
Recent work by Oberti et al, (Astron. Astrophys., 667, 48, 2022) argued and made a compelling case that classical astronomical adaptive optics (AO) tomography performance can be further enhanced by carefully designing and optically configuring the system to leverage inherent super-resolution (SR) capabilities. Our goal here is to further materialise the concept by providing the means to compute SR-enabling tomographic reconstructors for AO and showcase its broad uptake on soon every 10 m-class VIS/NIR telescopes and Giant Segmented Mirror Telescopes of up to 40 m in diameter. To that end we indicate the necessary tomography generalisations where we: (i) clarify how model-and-deploy is a generic methodological umbrella for linear minimum-mean-squared-error (LMMSE) tomographic reconstructors arising naturally from the solution of the tomographic inverse problem, thus unifying various solutions presented as distinct in the literature within a single framework, (ii) recall how such solutions are found as limiting cases of a model-based optimal control problem, thus elucidating how pseudo-open-loop control is a feature of the latter that allows LMMSE reconstructors to be adapted to closed-loop systems, (iii) review the two forms of the LMMSE tomographic reconstructors, highlighting the necessary adaptations to accommodate super-resolution, (iv) review the implementation in either dense-format vector-matrix-multiplication or sparse iterative forms and (v) discuss the implications for runtime and off-line real-time implementations, anticipating widespread adoption. We illustrate our examples with physical-optics numerical simulations for 10 m and 40 m-scale systems showing the performance benefits of super-resolution in the order of several tens of nm rms and the computational burden associated.

Abstract
This work investigates how inverter control modelling influences protection in distribution networks with Inverter-Based Distributed Generation (IIDG). This paper analyse two representative fault-response schemes - (i) conventional, positive-sequence only, and (ii) advanced, with coordinated positive- and negative-sequence injection - both configured to satisfy Fault Ride-Through (FRT) requirements and to provide dynamic voltage support during disturbances. As international standards (e.g., IEEE 1547-2018; IEEE 2800-2021) continue to evolve, incorporating such models into protection assessments is increasingly essential. Our objective is to validate MATLAB/Simulink fault-response models and benchmark them against implementations available in OMICRON RelaySimTest software, quantifying their impact on relay-measured currents in medium-voltage feeders. The results validate the model across multiple fault types and elucidate the direct implications of inverter control choices on the behaviour of protection devices in distribution networks.

Abstract
Bees play a critical role as pollinators in ecosystem services, contributing significantly to the sexual reproduction and diversity of plants. The Caatinga biome in Brazil, home to around 200 bee species, provides an ideal habitat for these species due to its unique climate conditions. However, this biome faces threats from anthropogenic processes, making it urgent to characterise the local bee populations efficiently. Traditional taxonomic surveys for bee identification are complex due to the lack of suitable keys and expertise required. As a result, molecular barcoding has emerged as a valuable tool, using genome regions to compare and identify bee species. However, little is known about Caatinga bees to develop these molecular tools further. This study addresses this gap, providing an updated list of 262 Caatinga bee species across 86 genera and identifying similar to 40 primer sets to aid in barcoding these species. The findings highlight the ongoing work needed to fully characterise the Caatinga biome's bee distribution and species or subspecies to support more effective monitoring and conservation efforts.