Abstract
In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California speci fi cally experienced heavy droughts and heat -wave conditions, causing the operations of hydropower stations to halt; fl oods caused substantial damage to energy infrastructure in central Europe, Australia, and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of U.S. dollars. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission, and consumption, and summarize estimations of economic losses.

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Abstract
The Electroencephalogram (EEG) is a medical tool that captures, in a non-invasive way, electrical signals from the brain activities performed by neurons. EEG signals have been the target of study as a biomarker of Parkinsons disease (PD), where several methods of analysis are applied. The present work aims to evaluate features extracted from EEG signals, through methodologies such as HOS, Haralick descriptors, and Fractal Features, as new biomarkers for PD identification. Data from 50 individuals, available at the Open Neuro repository, who underwent an attentional cognitive task were analyzed. RF and SVM algorithms were employed for the classification of the extracted features. The best accuracy achieved was 79.49% in differentiating between Parkinsons subjects and control subjects using Haralick descriptors and RF classifier, suggesting that these features can identify activations in brain areas caused by dopaminergic medication.

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This paper introduces the Large Events Model (LEM) for soccer, a novel deep learning framework for generating and analyzing soccer matches. The framework can simulate games from a given game state, with its primary output being the ensuing probabilities and events from multiple simulations. These can provide insights into match dynamics and underlying mechanisms. We discuss the framework's design, features, and methodologies, including model optimization, data processing, and evaluation techniques. The models within this framework are developed to predict specific aspects of soccer events, such as event type, success likelihood, and further details. In an applied context, we showcase the estimation of xP+, a metric estimating a player's contribution to the team's points earned. This work ultimately enhances the field of sports event prediction and practical applications and emphasizes the potential for this kind of method.

Abstract
Direct blood pressure (BP) measurements require cuff compression, which not only is time-consuming but also inconvenient for frequent monitoring. This study addresses the challenge of continuous BP estimation (both Systolic (SBP) and Diastolic (DBP)) during exercise in a cuff-less manner, utilizing photoplethysmography (PPG) signals acquired by low-cost wearables. Leveraging Reflective Pulse-wave Transit Time (R-PTT), state-of-the-art algorithms were put to the test in two datasets (total subjects = 18). DATASET1 contains PPG signal and BP measurements of subjects in resting state, while DATASET2 comprises data of subjects in resting state and during exercise. The results reveal competitive performance, with Mean Absolute Error (MAE) of the estimation algorithm for DATASET1 being SBP=7.9 mmHg and DBP=5.2 mmHg and SBP=14.4 mmHg and DBP=7.7 mmHg for DATASET2. DATASET1 consistently outperforms DATASET2, affirming the algorithm's efficacy during resting states and that estimation during physical activity introduces challenges, requiring further refinement and research for real-world applications. In conclusion, this research unveils a viable solution for continuous cuff-less BP monitoring, while emphasizing the need for refinement and validation to enhance its clinical applicability and accessibility.

Abstract
Lack of timeliness and capacity are seen as fundamental problems that jeopardise healthcare delivery systems everywhere. Many believe the shortage of medical providers is causing this timeliness problem. This action research presents how one doctor implemented the theory of constraints (TOC) to improve the throughput (quantity of patients treated) of his ophthalmology imaging practice by 64% in a few weeks with little to no expense. The five focusing steps (5FS) guided the TOC implementation - which included the drum-buffer-rope scheduling and buffer management - and occurred in a matter of days. The implementation provided significant bottom-line results almost immediately. This article explains each step of the 5FS in general terms followed by specific applications to healthcare services, as well as the detailed use in this action research. Although TOC successfully addressed the practice problems, this implementation was not sustained after the TOC champion left the organisation. However, this drawback provided valuable knowledge. The article provides insightful knowledge to help readers implement TOC in their environments to provide immediate and significant results at little to no expense.