Abstract
Advancements in large language model (LLM)-based conversational assistants have transformed search experiences into more natural and context-aware dialogues that resemble human conversation. However, limited access to interaction log data hinders a deeper understanding of their real-world usage. To address this gap, we analyzed 16,952 prompt logs from 904 unique users of Labadain Chat, an LLM-based conversational assistant designed for Tetun speakers, to uncover patterns in user search behavior, engagement, and intent. Our findings show that most users (29.87%) spent between one and five minutes per session, with an average of 43 unique daily users. The majority (93.97%) submitted multiple prompts per session, with an average session duration of 16.9 minutes. Most users (95.22%) were based in Timor-Leste, with education and science (28.75%) and health (28.00%) being the most searched topics. We compared our findings with a study on Google Bard logs in English, revealing similar search characteristics - including engagement duration, command-based instructions, and requests for specific assistance. Furthermore, a comparison with two conventional search engines suggests that LLM-based conversational systems have influenced user search behavior on traditional platforms, reflecting a broader trend toward command-driven queries. These insights contribute to a deeper understanding of how user search behavior evolves, particularly within low-resource language communities. To support future research, we publicly release LabadainLog-17k+, a dataset of over 17,000 real-world user search logs in Tetun, offering a unique resource for investigating conversational search in this language. © 2025 Elsevier B.V., All rights reserved.

Abstract
Underwater exploration relies heavily on autonomous underwater vehicles and sensor platforms for sustained monitoring of marine environments, yet their operational duration is limited by energy constraints. To enhance energy efficiency, various control strategies have been proposed, including robust, optimal, and disturbance-aware approaches. Recent work introduced a variable structure controller (VSC) with a constant-amplitude control action for depth control of a platform equipped with a variable buoyancy module, achieving an average 22% reduction in energy use in comparison with conventional PID-based controllers. In a separate paper, the conditions for its closed-loop stability were proven. This study extends these works by proposing a controller with a variable-amplitude control action designed to minimize energy consumption. A formal proof of stability is provided to guarantee safe operation even under conservative assumptions. The controller is applied to a previously developed depth-regulated sensor platform using a validated physical model. Additionally, this study analyzes how the controller parameters and mission requirements affect stability regions, offering practical guidelines for parameter tuning. A method to estimate oscillation amplitude during hovering tasks is also introduced. Simulation trials validate the proposed approach, showing energy savings of up to 16% when compared to the controller using a constant-amplitude control action.

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
Local flexibility markets are a promising solution to aid system operators in managing the network as it faces the growth of distributed resources and the resulting impacts on voltage control, among other factors. This paper presents and simulates a proposal for an intra-day local flexibility market based on grid segmentation. The design provides a market-based solution for distribution system operators (DSOs) to address near-real-time grid issues. The grid segmentation computes the virtual buses that represent each zone and the sensitivity indices that approximate the impact of activating active power flexibility in the buses within the zone. This approach allows DSOs to manage and publish their flexibility needs per zone and enables aggregators to offer flexibility by optimizing their resource portfolios per zone. The simulation outcomes allow for the assessment of market performance according to the number of zones computed and show that addressing overloading and voltage control through zonal approaches can be cost-effective and counterbalance minor errors compared to node-based approaches.