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. © 2025 Elsevier Ltd

Abstract

Abstract
Thanks to the vast amount of available resources and unique propagation properties, terahertz (THz) frequency bands are viewed as a key enabler for achieving ultrahigh communication performance and precise sensing capabilities in future wireless systems. Recently, the European Telecommunications Standards Institute (ETSI) initiated an Industry Specification Group (ISG) on THz which aims at establishing the technical foundation for subsequent standardization of this technology, which is pivotal for its successful integration into future networks. Starting from the work recently finalized within this group, this article provides an industrial perspective on potential use cases and frequency bands of interest for THz communication systems. We first identify promising frequency bands in the 100 GHz-1 THz range, offering over 500 GHz of available spectrum that can be exploited to unlock the full potential of THz communications. Then, we present key use cases and application areas for THz communications, emphasizing the role of this technology and its advantages over other frequency bands. We discuss their target requirements and show that some applications demand multi-Tb/s data rates, latency below 0.5 ms, and sensing accuracy down to 0.5 cm. Additionally, we identify the main deployment scenarios and outline other enabling technologies crucial for overcoming the challenges faced by THz systems. Finally, we summarize past and ongoing standardization efforts focusing on THz communications, while also providing an outlook toward the inclusion of this technology as an integral part of the future sixth generation (6G) and beyond communication networks.

Abstract
This paper studies the process of cutting steel bars in a truck suspension factory with the objective of reducing its inventory costs and material losses. A mathematical model is presented that focuses on decisions for a medium-term horizon (4 periods of 2 months). This approach addresses the one-dimensional 3-level integrated lot sizing and cutting stock problem, considering demand, inventory costs and stock level limits for bars (objects-level 1), springs (items-level 2) and spring bundles (final products-level 3), as well as the acquisition of bars as a decision variable. The solution to the proposed mathematical model is reached through an optimization package, using column generation along with a method for achieving integer solutions. The results obtained with real data demonstrate that the method provides significantly better solutions than those carried out at the company, whilst using reduced computational time. Additionally, the application of tests with random data enabled the analysis of both the effect of varying parameters in the solution, which provides managerial insights, and the overall performance of the method.

Abstract
Objective.The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction. Approach. A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets. Main results. The heart rate (HR) of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error of 1.31 bpm comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 bpm. Significance. The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of HR (70-115 bpm), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

Abstract
The rapid growth of data traffic and evolving service demands are driving a shift from traditional network architectures to advanced solutions. While 5G networks provide reduced latency and higher availability, they still face limitations due to reliance on integrated hardware, leading to configuration and interoperability challenges. The emerging Open Radio Access Network (O-RAN) paradigm addresses these issues by enabling remote configuration and management of virtualized components through open interfaces, promoting cost-effective, multi-vendor interoperability. Network slicing, a key 5G enabler, allows for tailored network configurations to meet heterogeneous performance requirements. The main contribution of this paper is a private Standalone 5G network based on O-RAN, featuring a dynamic Data Radio Bearer Management xApp (xDRBM) for real-time metric collection and traffic prioritization. xDRBM optimizes resource usage and ensures performance guarantees for specific applications. Validation was conducted in an emulated environment representative of real-world scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.