A new dataset for spatial, temporal, and tactical analysis of female handball player movements.

This paper presents a comprehensive dataset detailing the precise indoor positioning of players from a female amateur handball team across 10 real matches. Utilizing Ultra-Wideband (UWB) technology, the dataset captures each player's x and y coordinates every second throughout the games. Additionally, a preliminary game analysis is included, specifying the initiation and termination times of each team's possession.

Which videos are better for the students? Analyzing the student behavior and video metadata.

Due to its inherent flexibility and adaptability, e-learning has become a versatile and effective educational option, with video-based learning (VBL) enabling asynchronous engagement. Despite the wealth of video resources, evaluating their effectiveness remains challenging. This study employs a comprehensive approach, analyzing student behavior and video metadata to assess educational videos' efficacy.

Smartphone-based serious games for mental health: a scoping review.

Unlabelled: The use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g.

Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app.

Objective: While exposure therapy (ET) has the potential to help people tolerate intense situation-specific emotions and change avoidance behaviours, no smartphone solution exists to guide the process of in-vivo ET. A geolocation-based smartphone software component was designed and developed to instrumentalize patient guidance in in-vivo ET and its psychological validity was assessed by a group of independent psychology experts.

Methods: A team of computer scientists and psychologists developed the ET Component for in-vivo ET using geolocation-based technology, following the process-centred design methodology.

A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.

Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the diagnosis, prognosis and prediction of the response to therapy has aroused the interest of researchers in their use together with machine learning and deep learning models. Most of the research in this field has been model-centric, meaning it has been based on finding better performing algorithms.

Reminiscence interventions for loneliness reduction in older adults: a systematic review.

Objectives: This study aims to review the use of reminiscence interventions in the reduction of loneliness in older adults by categorising the interventions by type, and comparing their effectiveness and methodological quality.

Method: This is a systematic review following PRISMA guidelines. The search was restricted to studies published in the Cochrane, CINAHL, Web of Science, Psycarticles, Psycinfo, and PubMed databases until December 2022.

TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density.

Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density remains an unanswered issue. To address this gap, this work introduces TUJI1, an anonymized IEEE 802.

Unveiling Fundamentals of Multi-Beam Pulsed Laser Ablation in Liquids toward Scaling up Nanoparticle Production.

Pulsed laser ablation in liquids (PLAL) is a versatile technique to produce high-purity colloidal nanoparticles. Despite considerable recent progress in increasing the productivity of the technique, there is still significant demand for a practical, cost-effective method for upscaling PLAL synthesis. Here we employ and unveil the fundamentals of multi-beam (MB) PLAL.

Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing.

Brain-Computer Interfacing (BCI) has shown promise in Machine Learning (ML) for emotion recognition. Unfortunately, how data are partitioned in training/test splits is often overlooked, which makes it difficult to attribute research findings to actual modeling improvements or to partitioning issues. We introduce the "data transfer rate" construct (i.

Improving the Antibacterial Properties of Dental Bonding Materials Loaded with Silver Compounds.

Biofilm accumulation, the appearance of white spot lesions and the development of secondary caries are the main complications in orthodontic patients. A promising approach to fight this situation is the development of adhesive cements with improved antibacterial properties. The aim of the present study was to evaluate the possibility of improving the antibacterial properties of glass ionomer cements by incorporating different types of antimicrobial compounds without altering their physical and mechanical properties.

Dataset of inertial measurements of smartphones and smartwatches for human activity recognition.

This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively.

A new database of the analysis of the physiological needs in amateur female basketball during official matches.

The PHYAFB database is a valuable resource for studying the physiological demands of female amateur basketball players during high-stress official games. It contains heart rate data from ten players aged 18 to 26, collected during ten crucial relegation phase matches, with 348,232 HR samples in CSV and Excel formats for easy access and analysis. The database includes Python source code for initial examination.

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health.

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications.

Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal components.

This paper introduces the reconstructed dataset along with procedures to implement air quality prediction, which consists of air quality, meteorological and traffic data over time, and their monitoring stations and measurement points. Given the fact that those monitoring stations and measurement points are located in different places, it is important to incorporate their time series data into a spatiotemporal dimension. The output can be used as input for various predictive analyses, in particular, we used the reconstructed dataset as input for grid-based (Convolutional Long Short-Term Memory and Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms.

Long-Term Analyses of the Rate of Perceived Exertion as an Indicator of Intensity in Women's Basketball during a Relegation Play-off.

Knowledge of internal load is essential to understand the effect of training and competition on athletes. The aim of this study was to analyse the validity of the rate of perceived exertion (RPE) scale as an indicator of intensity in amateur female basketball players during a relegation play-off. The heart rate and RPE of 10 players (age: 21.

Advances in Indoor Positioning and Indoor Navigation.

Locating devices in indoor environments has become a key issue for many emerging location-based applications and intelligent spaces in different fields [...

Developing a smartphone App based on the Unified Protocol for the transdiagnostic treatment of emotional disorders: A qualitative analysis of users and professionals' perspectives.

Emotional Disorders have become the most prevalent mental disorders in the world. In relation to their high prevalence, mental health care from public health services faces major challenges. Consequently, finding solutions to deliver cost-effective evidence-based treatments has become a main goal of today's clinical psychology.

Exploring the effects of social capital on the compulsive use of online social networks in civil unrest contexts.

The use of online social networking sites has become part of everyday life for more than three billion people worldwide. However, its use may go beyond being a habit, leading to compulsive use behaviours that jeopardize the well-being of an individual and the whole society. This study proposes and evaluates a theoretical model that examines the four dimensions of social capital, mediated by bonding and bridging social capital, as drivers of compulsive use of online social networks in the context of civil unrest.

Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19.

The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks.

Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments.

The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers.

Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid.

Nitrogen dioxide is one of the pollutants with the most significant health effects. Advanced information on its concentration in the air can help to monitor and control further consequences more effectively, while also making it easier to apply preventive and mitigating measures. Machine learning technologies with available methods and capabilities, combined with the geospatial dimension, can perform predictive analyses with higher accuracy and, as a result, can serve as a supportive tool for productive management.

Inactivation of SARS-CoV-2 by a chitosan/α-AgWO composite generated by femtosecond laser irradiation.

In the current COVID-19 pandemic, the next generation of innovative materials with enhanced anti-SARS-CoV-2 activity is urgently needed to prevent the spread of this virus within the community. Herein, we report the synthesis of chitosan/α-AgWO composites synthetized by femtosecond laser irradiation. The antimicrobial activity against Escherichia coli, Methicilin-susceptible Staphylococcus aureus (MSSA), and Candida albicans was determined by estimating the minimum inhibitory concentration (MIC) and minimal bactericidal/fungicidal concentration (MBC/MFC).

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios-A Systematic Review.

Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems.

Data on CO, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic.

In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020-2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors.

Glimpse: A Gaze-Based Measure of Temporal Salience.

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data.