Objectives. To synthesize contemporary scientific approaches to interpreting physical education of schoolchildren within the logic of a managed learning process and to clarify the role of pedagogical control, modelling, and age-related developmental regularities in shaping learning outcomes.
Materials and Methods. The study was conducted as a narrative review of publications addressing physical education theory, pedagogical control, modelling of the learning process, age-related developmental regularities, and the teaching of physical exercises in general secondary education. The analysis was carried out from systems-based and learning-oriented perspectives on the organisation of physical education.
Results. The review supports interpreting physical education of schoolchildren as a managed learning process in which learning outcomes emerge through the interaction of pedagogical control, modelling, and learners’ age-related developmental characteristics. Age-related regularities are best treated as parameters of learning models that define the boundaries for valid interpretation of pedagogical-control results. Pedagogical control acquires a regulatory function only when embedded within a model of the learning process. The synthesis also allows the learning of physical exercises to be interpreted as the formation and dynamics of learning states that can serve as objects of pedagogical control and regulation.
Conclusions. The proposed synthesis enables interpreting outcomes of physical education as consequences of the organisation of the learning process rather than as autonomous normative indicators. This narrative review delineates theoretical and methodological frames for further research aimed at empirically testing models of managed physical education and refining tools of pedagogical control in general secondary education practice.
Georeferenced Access Points as a Strategic Node in the Evolution of Territorial Information Systems - This paper explores the strategic role of georeferenced access points and civic numbering as foundational components of advanced Territorial Information Systems (SIT) within Italian public administrations. The quality and consistency of georeferenced street and building numbers represent a fundamental component of territorial data infrastructures, enabling reliable integration between cadastral datasets, administrative services and emergency response systems.
Drawing from the experience of LdP Progetti GIS — involving more than 130 municipalities across five regions — the article demonstrates how the integration of Accesses, Buildings and Street Toponyms enables an interoperable Web-GIS ecosystem supporting digital services, data governance and operational decision-making. Real case studies from the municipalities of Siena, Arezzo, Empoli and Pistoia illustrate concrete applications such as emergency management, fiscal intelligence (TARI compliance), housing planning and economic activity monitoring. The results highlight significant improvements in administrative efficiency, transparency and open-data availability, positioning geospatial infrastructures as a key enabler of digital transformation in the Public Sector.
Study region: The Three Gorges Reservoir Area (TGRA) Study focus: TGRA faces increasing vulnerability to extreme precipitation events driven by complex convective weather systems. Radar echo extrapolation—predicting future precipitation patterns from current radar data—is essential for early warning systems but faces significant challenges in this topographically complex region. While data-driven approaches have advanced the field, current convolutional neural network-based diffusion models struggle with the TGRA's dynamic meteorological conditions due to their reliance on translational invariance, which often fails to capture rapid weather transitions in complex terrain. New hydrogeological insights from the region: To address these limitations, we introduce RadarDiT, a Vision Transformer-based diffusion model specifically engineered for radar extrapolation in the TGRA. First, we develop a five-year radar dataset capturing diverse convective weather phenomena unique to this region. Then, leveraging this dataset, RadarDiT employs multi-layer Vision Transformers that effectively model global dependencies and complex spatial relationships, enabling accurate prediction of convective cell evolution. Our model demonstrates superior performance in maintaining strong echo and spatial coherence over longer forecast horizons. Quantitative evaluations across multiple metrics and thresholds confirm RadarDiT's enhanced skill in forecasting heavy precipitation events, with particular improvements in Critical Success Index at higher radar echo values. This work establishes a foundation for more reliable nowcasting systems in regions with complex terrain and dynamic weather patterns, directly supporting enhanced disaster preparedness and response strategies.
This paper addresses the two-dimensional bin packing problem with guillotine constraints. The problem requires a set of rectangular items to be cut from larger rectangles, known as bins, while only making use of edge-to-edge (guillotine) cuts. The goal is to minimize the total bin area needed to cut all required items. This paper also addresses variants of the problem which permit 90° rotation of items and/or a heterogeneous set of bins. A novel heuristic is introduced which is based on the ruin and recreate paradigm combined with a goal-driven approach. When applying the proposed heuristic to benchmark instances from the literature, it outperforms the current state-of-the-art algorithms in terms of solution quality for all variants of the problem considered.
Lucía Prieto-Santamaría, Alba Cortés Iglesias, Claudio Vidal Giné
et al.
Understanding the real-world effects of recreational drug use remains a critical challenge in public health and biomedical research, especially as traditional surveillance systems often underrepresent user experiences. In this study, we leverage social media (specifically Twitter) as a rich and unfiltered source of user-reported effects associated with three emerging psychoactive substances: ecstasy, GHB, and 2C-B. By combining a curated list of slang terms with biomedical concept extraction via MetaMap, we identified and weakly annotated over 92,000 tweets mentioning these substances. Each tweet was labeled with a polarity reflecting whether it reported a positive or negative effect, following an expert-guided heuristic process. We then performed descriptive and comparative analyses of the reported phenotypic outcomes across substances and trained multiple machine learning classifiers to predict polarity from tweet content, accounting for strong class imbalance using techniques such as cost-sensitive learning and synthetic oversampling. The top performance on the test set was obtained from eXtreme Gradient Boosting with cost-sensitive learning (F1 = 0.885, AUPRC = 0.934). Our findings reveal that Twitter enables the detection of substance-specific phenotypic effects, and that polarity classification models can support real-time pharmacovigilance and drug effect characterization with high accuracy.
Elena-Andreea Trandafirescu, Vladimir Potop, Ilie Mihai
et al.
Background and Study Aim. Fear of failure, choking under pressure, financial disparities in income, and concerns related to body image and social pressure may intensify on-field challenges. These factors make the competitive environment particularly demanding for female soccer players. The aim of the current review is to map the available evidence on anxiety in women's football over the past decade and to identify interventions designed to manage anxiety among female soccer players.
Material and Methods. A scoping review was conducted following the PRISMA-ScR guidelines. An extensive search was carried out across four major databases for publications dated between 2014 and 2024. Two independent reviewers screened titles and abstracts, followed by full-text screening and data extraction. Any disagreements were resolved by a third researcher. The findings were tabulated and synthesized in a narrative format. The study was pre-registered on the Open Science Framework.
Results. The search yielded 2885 unique records. A total of 21 texts were reviewed in full, and the final sample included 14 studies. Some of the records included mentioned modifications to psychosocial interventions, such as Virtual Reality (VR) relaxation sessions, pre-exercise sporting massage, and psychological training programs (including psycho-neuromuscular theory and visualization techniques). These interventions led to a reduction in anxiety, though their effectiveness varied significantly across different approaches. Our results suggest that research on women's football may disproportionately focus on psychological interventions for managing anxiety. This highlights the need for broader investigations into other factors that influence athletes' performance and well-being.
Conclusions. This scoping review helps clarify the current landscape of anxiety research in women's soccer. It highlights both promising interventions and gaps in the research over the past decade. Although the body of evidence on anxiety interventions is small, it suggests that psychological interventions may be effective in reducing anxiety among female soccer players.
Background: Studies about the combined effects of gaseous air pollutants and particulate matters are still rare. Objectives: This study was performed based on baseline survey of the Diverse Life-Course Cohort in the Beijing-Tianjin-Hebei (BTH) Region of North China to evaluate the association of long-term air pollutants with blood pressure and the combined effect of the air pollutants mixture among 32821 natural han population aged 20 years or above. Methods: Three-year average exposure to air pollutants (PM10, PM2.5, PM1, O3, SO2, NO2, and CO) and PM2.5 components [black carbon (BC), ammonium (NH4+), nitrate (NO3−), sulfate (SO42−), and organic matter (OM)] of residential areas were calculated based on well-validated models. Generalized linear mixed models (GLMMs) were used to estimate the associations of air pollutants exposure with the systolic blood pressure (SBP), diastolic blood pressure (DBP), Mean arterial pressure (MAP), pulse pressure (PP) and prevalent hypertension. Quantile g-Computation and Bayesian Kernel Machine Regression (BKMR) were employed to assess the combined effect of the air pollutant mixture. Results: We found that long-term exposures of O3, PM2.5, and PM2.5 components were stably and strongly associated with elevated SBP, DBP, and MAP and prevalent hypertension. O3 increased SBP, DBP, and MAP at a similar extent, but with greater effects; while, PM2.5 and PM2.5 components had a greater impact on SBP than DBP, which increased PP simultaneously. In multi-pollutant models, the combined effects of the air pollutant mixture on blood pressure and prevalent hypertension was predominantly influenced by O3, PM2.5, and O3, OM in different models, respectively. For example, O3, PM2.5 contributed 57.25 %, 39.22 % of the positive combined effect of the air pollutant mixture on SBP; and O3, OM positively contributed 70.00 %, 30.00 % on prevalent hypertension, respectively. There were interactions between O3, CO, SO2 and PM2.5 components on hbp, SBP and PP. Conclusions: The results showed positive associations of air pollutant mixtures with blood pressure, where O3 and PM2.5 (especially OM) might be primary contributors. There were interactions between gaseous air pollutants and PM2.5 components on blood pressure and prevalent hypertension.
Tehreem Arshad, Muhammad Shahid Rafique, Shazia Bashir
et al.
Lahore (Pakistan), being an industrial city, has high emission of aerosols that affects and contaminates the air quality. Therefore, the abatement/inactivation of aerosols is necessary to restrict their infectious activities. In this project, ionic wind isolated from dielectric barrier discharge plasma (DBD plasma) has been utilized to abate the aerosols trapped in the Surgical Mask and KN95 Respirator. To infer the chemical and elemental detection of ambient aerosols, FTIR and LIBS have been employed. “From the results, it is noteworthy that abatement/removal of aerosols has been successfully carried out by the ionic wind irradiation and highlights the potential of DBD plasma technology in removing the aerosols pollution.”
Environmental sciences, Public aspects of medicine
Julia S. Schmid, Sean Simmons, Mark S. Poesch
et al.
Recreational fishing is an important economic driver and provides multiple social benefits. To predict fishing activity, identifying variables related to variation, such as gender or Covid-19, is helpful. We conducted a Canada-wide email survey of users of an online fishing platform and analyzed responses focusing on gender, the impact of Covid-19, and variables directly related to fishing effort. Genders (90% men and 10% women) significantly differed in demographics, socioeconomic status, and fishing skills but showed similar fishing preferences, fishing effort in terms of trip frequency, and travel distance. Covid-19 altered trip frequency for almost half of fishers, with changes varying by gender and activity level. A Bayesian network revealed travel distance as the main determinant of trip frequency, negatively impacting fishing activity for 61% of fishers, with fishing expertise also playing a role. The results suggest that among active fishers, socio-economic differences between genders do not drive fishing effort, but responses to Covid-19 were gender-specific. Recognizing these patterns is critical for equitable policy-making and accurate socio-ecological models, thereby improving resource management and sustainability.
Edward Gunning, Steven Golovkine, Andrew J. Simpkin
et al.
We present a multivariate functional mixed effects model for kinematic data from a large number of recreational runners. The runners' sagittal plane hip and knee angles are modelled jointly as a bivariate function with random effects functions used to account for the dependence among measurements from either side of the body. The model is fitted by first applying multivariate functional principal component analysis (mv-FPCA) and then modelling the mv-FPCA scores using scalar linear mixed effects models. Simulation and bootstrap approaches are introduced to construct simultaneous confidence bands for the fixed effects functions, and covariance functions are reconstructed to summarise the variability structure in the data and thoroughly investigate the suitability of the proposed model. In our scientific application, we observe a statistically significant effect of running speed on both the hip and knee angles. We also observe strong within-subject correlations, reflecting the highly idiosyncratic nature of running technique. Our approach is more generally applicable to modelling multiple streams of smooth kinematic or kinetic data measured repeatedly for multiple subjects in complex experimental designs.
This dissertation is based on a project co-founded by the Health Market Quality Program (now Rozetta Institute) and the Australian Institute of Health and Welfare. The overall objective of this work is to provide a framework and a tool for classification and clustering of homogeneous geographic areas based on aggregated population data. Thus, to enable the presentation and reporting of comparable information of individual units with peers, I develop the Homogeneity and Location indices to measure respectively the dispersion and central tendency of a categorical ordinal distribution. The advantages of such indices include statistical efficiency and a simple presentation of results. Our approach is founded on the general theory of probability distributions, and our aim is to provide a natural benchmark for a homogeneity measure in terms of what is a "high" and "low" concentration of a probability distribution. Currently, there is no accepted benchmark that could be used to assess the homogeneity of a categorical ordinal variable. In this work, the proposed statistical indices are used to assess the socioeconomic homogeneity of the commonly used SA3 Australia census geography and analyse the variation of GP attenders in the metropolitan area of Sydney. The approach can be used to classify any geographic area and explore variation across any specified geographical boundaries. The SA3 dataset and scripts (R/Python) to develop these indices have been made available on my GitHub account: https://github.com/lpinzari/homogeneity-location-index
Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario of ethnic minority education in academic practices. Ethnic minority group has grown in their own culture and would prefer to be taught in their native way. We have formulated such linguistic anthropology(how people learn)based engagement as semi-supervised problem. Then, we have developed an conditional deep generative adversarial network algorithm namely LA-GAN to classify linguistic ethnographic features in student engagement. Theoretical justification proves the objective, regularization and loss function of our semi-supervised adversarial model. Survey questions are prepared to reach some form of assumptions about z-generation and ethnic minority group, whose learning style, learning approach and preference are our main area of interest.
Laura Gustafson, Megan Richards, Melissa Hall
et al.
Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of inequity. Addressing such performance gaps remains a challenge, as little is understood about why performance degrades across incomes or geographies. We take a step in this direction by annotating images from Dollar Street, a popular benchmark of geographically and economically diverse images, labeling each image with factors such as color, shape, and background. These annotations unlock a new granular view into how objects differ across incomes and regions. We then use these object differences to pinpoint model vulnerabilities across incomes and regions. We study a range of modern vision models, finding that performance disparities are most associated with differences in texture, occlusion, and images with darker lighting. We illustrate how insights from our factor labels can surface mitigations to improve models' performance disparities. As an example, we show that mitigating a model's vulnerability to texture can improve performance on the lower income level. We release all the factor annotations along with an interactive dashboard to facilitate research into more equitable vision systems.
These are notes on the impact of Lagrange's memoir on the construction of geographical maps. We mention the relations of some ideas and questions introduced in this memoir with other notions that appeared later in the works of several mathematicians, including in particular Chebyshev (19th c.) and Darboux (19th-20th c.), two mathematicians who were particularly interested in geography.
Bruno Gonçalves Galdino da Costa, Jean-Philippe Chaput, Marcus Vinicius Veber Lopes
et al.
Background: Physical activity, sleep, and sedentary behaviors compose 24-h movement behaviors and have been independently associated with depressive symptoms. However, it is not clear whether it is the movement behavior itself or other contextual factors that are related to depressive symptoms. The objective of the present study was to examine the associations between self-reported and accelerometer-measured movement behaviors and depressive symptoms in adolescents. Methods: Cross-sectional data from 610 adolescents (14–18 years old) were used. Adolescents answered questions from the Center for Epidemiological Studies Depression scale and reported time spent watching videos, playing videogames, using social media, time spent in various physical activities, and daytime sleepiness. Wrist-worn accelerometers were used to measure sleep duration, sleep efficiency, sedentary time, and physical activity. Mixed-effects logistic regressions were used. Results: Almost half of the adolescents (48%) were classified as being at high risk for depression (score ≥20). No significant associations were found between depressive symptoms and accelerometer-measured movement behaviors, self-reported non-sport physical activity, watching videos, and playing videogames. However, higher levels of self-reported total physical activity (odd ratio (OR) = 0.92, 95% confidence interval (95%CI): 0.86–0.98) and volume of sports (OR = 0.88, 95%CI: 0.79–0.97), in minutes, were associated with a lower risk of depression, while using social media for either 2.0–3.9 h/day (OR = 1.77, 95%CI: 1.58–2.70) or >3.9 h/day (OR = 1.67, 95%CI: 1.10–2.54), as well as higher levels of daytime sleepiness (OR = 1.17, 95%CI: 1.12–1.22), were associated with a higher risk of depression. Conclusion: What adolescents do when they are active or sedentary may be more important than the time spent in the movement behaviors because it relates to depressive symptoms. Targeting daytime sleepiness, promoting sports, and limiting social media use may benefit adolescents.
Introduction: the aim of the study is to analyze the recreational needs of the population of the South of Russia, to study the geography of tourist flows in the macroregion, the ranking of preferences for tourist destinations, and motives for tourist trips. The importance of studying the recreational needs of the population as the main factor generating the demand for tourism services is substantiated. Materials and methods of the research: the study is based on a generalization of the results of a sociological survey of the population of seven subjects of Southern Russia. The survey was implemented using the Google Forms online service. The data obtained in the course of a sociological survey supplement statistical information and allow comprehensive study of various aspects of tourism in the macro-region. The results of the study and their discussion: as a result, the main directions of tourist lows in the South of Russia were identified, the most popular tourist centers were revealed, the most preferred types of transport and types of accommodation establishments were determined. The analysis of the main factors restraining the development of tourism in the South of Russia is presented. Conclusions: an analysis of the data of a sociological survey allows to identify: the neighboring position and transport accessibility as a key factor in choosing a destination for recreation; traditional high demand for resorts on the Black Sea coast of the Krasnodar Territory and the emerging demand for mountain climatic resorts, especially in the summer; insignificant interest of potential tourists in the excursion centers of the plain part of the South of Russia.
Physical activity is known to help improve and maintain one's health. In particular, recreational running has become increasingly popular in recent years. Yet, lack of motivation often interferes with people's routines and thus may prohibit regular uptake. This is where running tracking applications are frequently used to overcome one's weaker self and offer support. While technology artifacts, such as sport watches or running applications, usually count as extrinsic drivers, they can also impact one's intrinsic motivation levels. The aim of this study was thus to investigate upon the motivational impact of distinct features found within applications specifically used for running. Focusing on the 22 most famous running applications, a semi-structured, problem-centered interview study with $n=15$ recreational runners showed that intrinsic motivation is stimulated from diverting runners, aiding them in their goal setting, decreasing their efforts, improving and sharing their run performance, allowing them to receive acknowledgements, as well as providing them with guidance, information, and an overall variety in their training routines.