Abstract

Depressive symptoms are common among older adults and can significantly impact their quality of life. Yet, many older adults face barriers to accessing psychological treatment. Internet-based cognitive behavioural therapy (iCBT) is a promising alternative to face-to-face treatments, but its feasibility among older adults is less researched.

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Volatility in energy prices, alongside the European Commission's decarbonization strategy, has led to reforming the European electricity market and the creation of a hydrogen strategy. Hydrogen and electricity have a symbiotic relationship: hydrogen production through electrolysis relies on electricity, while its production provides flexibility to the power system utilizing renewable energy surpluses. This research provides a joint electricity and hydrogen market model based on Cournot equilibrium, solved with an equivalent optimization problem, incorporating contracts for both goods. Results for the MIBEL show that contracts increase market competition, reduce prices, and enhance renewable energy utilization. Wholesale electricity and hydrogen prices decrease by 10 % and 8 %, respectively, while electrolytic hydrogen production rises by 10 %. Profits increase by over 20 %, with the hydrogen sector doubling its gains. The model also identifies contract prices that ensure profitability and emissions reduction. These findings highlight the potential of PPAs and HPAs to support energy transition goals.

Abstract
Stroke, a vascular disorder affecting the nervous system, is the third-leading cause of death and disability combined worldwide. One in every four people aged 25 and older will face the consequences of this condition, which typically causes loss of limb function, among other disabilities. The proposed review analyzes the mechanisms of stroke and their influence on the disease outcome, highlighting the critical role of rehabilitation in promoting recovery of the upper limb (UL) and enhancing the quality of life of stroke survivors. Common outcome measures and the specific targeted UL features are described, along with emerging supplementary therapies found in the literature. Stroke survivors often develop compensatory strategies to cope with limitations in UL function, which must be detected and corrected during rehabilitation to facilitate long-term recovery. Recent research on the automated detection of compensatory movements has explored pressure, wearable, marker-based motion capture systems, and vision sensors. Although current approaches have certain limitations, they establish a strong foundation for future innovations in post-stroke UL rehabilitation, promoting a more effective recovery.

Abstract
Effective project management is crucial to the success of any industry, particularly in metalworking, where deadlines, resources, and costs play critical roles. However, accurately predicting project execution times remains a significant challenge, directly impacting companies' competitiveness and profitability. In this context, the integration of Artificial Intelligence (AI) tools emerges as a promising solution to improve the accuracy of time predictions and optimise project management in the metal-working industry.AI, particularly through techniques such as Machine Learning (ML), has demonstrated significant potential in predicting timeframes for engineering projects. Predictive activity-based models can be trained with historical data to identify patterns and forecast future durations with high accuracy. In the metalworking sector, where projects are often complex and subject to variability, AI can provide notable advantages in terms of precision and efficiency.This study aims to formulate an activity-based model, represented in IDEF0 (part of the Integration Definition for Function Modelling), for predicting activity durations using AI to support project management in the metalworking industry. By applying the principles of the IDEF0 tool, the objective is to develop a robust and adaptable system capable of analysing historical data, environmental factors, project characteristics, and other relevant inputs to produce more accurate time forecasts.With this work, we aim to contribute to the advancement of Project Management (PM) in the metal-working industry, particularly by providing an activity-based model to support the creation of an innovative AI tool for predicting execution times with greater accuracy.

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