Hasil untuk "Recreation. Leisure"

Leonardo Varasano

Beginning in the 1980s, the mediatization of soccer also manifested itself through the phenomenon of fanzines, instruments of information, but even more so of counter-information, dissonant and free voices. In this panorama, an original case study is that of the fanzine “Brigata Ultrà,” a folio of the eponymous group of supporters from Perugia’s north curve between 1994 and 2008. The “Brigata” has a strong territorial identity tied to a specific area of the city and with a political orientation dissonant with that of the Perugia curve: it is a “black” (i. e. right wing) group in a “red” curve (i. e. left wing). This composite identity takes on visible forms and manners that attract national attention. The group travels for years on away trips with a black bus recognizable by an eagle, a tricolor and a Grifo (the symbol of the city). Added to this is the frequent practice of physically following the Italian national team’s matches as well. The fanzine becomes the “voice” of the “Brigata” by proposing and nurturing, around soccer, multiple identities: that of the home neighborhoods, the one of the city, the one of the national, and the one of the politically right-wing oriented.

Kyriakos Stylianopoulos, Panagiotis Gavriilidis, Gabriele Gradoni et al.

Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces of scene objects based on the scattered field at distributed receivers. To solve this difficult inverse scattering problems, data-driven methods are often employed that extract patterns from similar training examples, while offering minimal latency. In this paper, we first provide an approximate yet fast electromagnetic model, which is based on the electric field integral equations, for data generation, and subsequently propose a Deep Neural Network (DNN) architecture to learn the corresponding inverse model. A graph-attention backbone allows for the system geometry to be passed to the DNN, where residual convolutional layers extract features about the objects, while a UNet head performs the final image reconstruction. Our quantitative and qualitative evaluations on two synthetic data sets of different characteristics showcase the performance gains of thee proposed advanced architecture and its relative resilience to signal noise levels and various reception configurations.

Peter Raidl, Barbara Wessner, Robert Csapo

Introduction The response to stress is driven by two interdependent systems: the hypothalamic-pituitary-adrenal (HPA) axis and the autonomic nervous system (ANS), which regulate cardiac control, endocrine levels, and immune function (Tsigos et al., 2000). Previous research suggests that regular exercisers show reduced responses to acute psychosocial stressors (Mücke et al., 2018). Nevertheless, it is currently unknown if the stress response in exercisers depends on the type of stressor and physiological marker of interest. Also, little research has directly compared male and female participants. Understanding the specificity of the response is crucial for designing future research on exercise and stress. This pilot study should clarify the feasibility of the presented design and methods to address this research gap. Methods We adopted a crossover design, exposing subjects to two laboratory-based stress tests in random, counterbalanced order. The Trier Social Stress Test (TSST; Kirschbaum et al., 1993) induces psychosocial stress, while the Maastricht Acute Stress Test (MAST; Smeets et al., 2012) incorporates additional physiological components by immersing one hand in ice water. Young, healthy, physically active subjects (n = 12; 6 females, 6 males; age = 20-3 yrs) were invited to the laboratory twice, one month apart. Females were eumenorrheic and invited within the self-reported mid-luteal phase. Participants were asked to arrive well-rested and under standardized dietary conditions. After a 15-minute resting period during which baseline measures were taken, participants underwent the stress test. Subsequently, participants sat in a quiet room for follow-up sampling of heart rate (HR), serum blood (+0 min, +5 min, +25 min), saliva, and subjective stress via a 100 mm visual analog scale (VAS100; +0 min, +5 min, +10 min, +15 min, +25 min). Free cortisol and HR were defined as primary markers for HPA and ANS activity, respectively, and analyzed using a [timepoint x test x sex] ANOVA followed by the Games-Howell. VAS100 was analyzed using continuous ordinal regression. Results HR was higher during the TSST than the MAST (∆ HR = 21.1 bpm, 95% CI [8.8, 33.4]). No sex differences for HR were found. Sex differences indicate lower cortisol in females (g = 0.87, 95% CI [0.49, 1.26]), but no time- or interaction effects were found (p > 0.05). VAS100 significantly increased following the stress tests. The MAST evoked higher VAS100 than the TSST (g = 0.46, 95% CI [0.13, 0.79]), and women reported higher levels of subjective stress than men (g = 0.62, 95% CI [0.29, 0.95]). Discussion While HR is a marker of ANS activity, the amount of movement during the interview phase might increase HR during the TSST. On the other hand, the VAS100 might reflect the physical pain experienced by the ice water and less so the psychosocial component. Despite increases in subjective stress, cortisol levels exhibited no change. This difference is in line with previous work hypothesizing even a protective effect of cortisol on subjective stress (Het et al., 2012). We conclude that the design is promising for testing hypotheses concerning the physiological and subjective stress response during acute laboratory stress tests in an exercising population. As this is a pilot study, inferential statistics should be read cautiously. This study was designed to facilitate a larger-scale project with sufficient power. References Het, S., Schoofs, D., Rohleder, N., & Wolf, O. T. (2012). Stress-induced cortisol level elevations are associated with reduced negative affect after stress: Indications for a mood-buffering cortisol effect. Psychosomatic Medicine, 74(1), 23–32. https://doi.org/10.1097/PSY.0b013e31823a4a25 Kirschbaum, C., Pirke, K. M., & Hellhammer, D. H. (1993). The ’Trier Social Stress Test’—A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology, 28(1–2), 76–81. https://doi.org/10.1159/000119004 Mücke, M., Ludyga, S., Colledge, F., & Gerber, M. (2018). Influence of regular physical activity and fitness on stress reactivity as measured with the Trier Social Stress Test Protocol: A systematic review. Sports Medicine, 48(11), 2607–2622. https://doi.org/10.1007/s40279-018-0979-0 Smeets, T., Cornelisse, S., Quaedflieg, C. W. E. M., Meyer, T., Jelicic, M., & Merckelbach, H. (2012). Introducing the Maastricht Acute Stress Test (MAST): A quick and non-invasive approach to elicit robust autonomic and glucocorticoid stress responses. Psychoneuroendocrinology, 37(12), 1998–2008. https://doi.org/10.1016/j.psyneuen.2012.04.012 Tsigos, C., Kyrou, I., Kassi, E., & Chrousos, G. P. (2000). Stress: Endocrine physiology and pathophysiology. In K. R. Feingold, B. Anawalt, M. R. Blackman, A. Boyce, G. Chrousos, E. Corpas, W. W. de Herder, K. Dhatariya, K. Dungan, J. Hofland, S. Kalra, G. Kaltsas, N. Kapoor, C. Koch, P. Kopp, M. Korbonits, C. S. Kovacs, W. Kuohung, B. Laferrère, … D. P. Wilson (Eds.), Endotext. http://www.ncbi.nlm.nih.gov/books/NBK278995/

Hongliang Zhong, Can Wang, Jingbo Zhang et al.

Generating and inserting new objects into 3D content is a compelling approach for achieving versatile scene recreation. Existing methods, which rely on SDS optimization or single-view inpainting, often struggle to produce high-quality results. To address this, we propose a novel method for object insertion in 3D content represented by Gaussian Splatting. Our approach introduces a multi-view diffusion model, dubbed MVInpainter, which is built upon a pre-trained stable video diffusion model to facilitate view-consistent object inpainting. Within MVInpainter, we incorporate a ControlNet-based conditional injection module to enable controlled and more predictable multi-view generation. After generating the multi-view inpainted results, we further propose a mask-aware 3D reconstruction technique to refine Gaussian Splatting reconstruction from these sparse inpainted views. By leveraging these fabricate techniques, our approach yields diverse results, ensures view-consistent and harmonious insertions, and produces better object quality. Extensive experiments demonstrate that our approach outperforms existing methods.

Jose Lukose, Abayomi Agbeyangi

The leisure and hospitality industry is a significant driver of the global economy, with the adoption of new technologies transforming service delivery and customer experience. Despite the transformative potential and benefits associated with adopting technology, there remains a low level of adoption in rural areas, particularly among small-scale players. This study explores the role of ICT literacy and sustainable practices in influencing ICT adoption among small-scale players in the hospitality industry in rural Eastern Cape Province, South Africa, specifically focusing on Mnquma Municipality. The study employs a non-probability sampling and purposive technique, utilising a case study research design within a positivist paradigm. A random sample of 21 small-scale players (BnBs, guest houses, and non-serviced accommodations) was selected, and data were collected through a face-to-face interview and questionnaire featuring closed-ended questions. The data were analysed using descriptive statistics and the Kruskal-Wallis H Test to examine differences in ICT usage levels. The test yielded a Kruskal-Wallis H of 2.57 with a p-value of 0.277. The findings reveal that businesses with more educated workforces demonstrate higher ICT adoption levels. Moreover, key factors such as ICT literacy, awareness of sustainable practices, access to ICT resources, and contextual challenges significantly impact ICT adoption. Recommendations include integrating ICT literacy and sustainability education into training programs and developing targeted policies and support mechanisms to enhance ICT integration.

Ashutosh Singh, Khushdeep Singh, Amit Kumar et al.

In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and non-stress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector Machines have a maximum accuracy for Stress, reaching 95\%. The study contributes to a deeper understanding of stress determinants. It aims to improve college student's overall quality of life and academic success, addressing the multifaceted nature of stress.

Zhengyang Liang, Hao He, Ceyuan Yang et al.

Diffusion transformers (DiT) have already achieved appealing synthesis and scaling properties in content recreation, e.g., image and video generation. However, scaling laws of DiT are less explored, which usually offer precise predictions regarding optimal model size and data requirements given a specific compute budget. Therefore, experiments across a broad range of compute budgets, from 1e17 to 6e18 FLOPs are conducted to confirm the existence of scaling laws in DiT for the first time. Concretely, the loss of pretraining DiT also follows a power-law relationship with the involved compute. Based on the scaling law, we can not only determine the optimal model size and required data but also accurately predict the text-to-image generation loss given a model with 1B parameters and a compute budget of 1e21 FLOPs. Additionally, we also demonstrate that the trend of pre-training loss matches the generation performances (e.g., FID), even across various datasets, which complements the mapping from compute to synthesis quality and thus provides a predictable benchmark that assesses model performance and data quality at a reduced cost.

Audrey Blizard, Stephanie Stockar

Strict user demands and large variability in external disturbances, along with limited richness in the data collected on the daily operating conditions of district heating networks makes the design and testing of novel energy-reducing control algorithms for district heating networks challenging. This paper presents the development of a dynamically similar lab-scale district heating network that can be used as a test bench for such control algorithms. This test bench is developed using the Buckingham pi theorem to the match the lab-scale components to the full-scale. By retaining the relative thermodynamics and fluid dynamics of a full-scale network in the lab-scale system, the experimental setup allows for repeatability of the experiments being performed and flexibility in the testing conditions. Moreover, the down-scaling of the experiment is leveraged to accelerate testing, allowing for the recreation of operating periods of weeks and months in hours and days. A PID controller is implemented on the lab-scale test bench to validate its response against literature data. Results show 63% efficiency during heating operations compared to 70% efficiency for a similar full-scale system, with comparable pressure losses across the system.

Slava Rychkov

Numerical evidence suggests that the Random Field Ising Model loses Parisi-Sourlas SUSY and the dimensional reduction property somewhere between 4 and 5 dimensions, while a related model of branched polymers retains these features in any $d$. These notes give a leisurely introduction to a recent theory, developed jointly with A. Kaviraj and E. Trevisani, which aims to explain these facts. Based on the lectures given in Cortona and at the IHES in 2022.

Abdul Sittar, Dunja Mladenic, Marko Grobelnik

News headlines can be a good data source for detecting the news spreading barriers in news media, which may be useful in many real-world applications. In this paper, we utilize semantic knowledge through the inference-based model COMET and sentiments of news headlines for barrier classification. We consider five barriers including cultural, economic, political, linguistic, and geographical, and different types of news headlines including health, sports, science, recreation, games, homes, society, shopping, computers, and business. To that end, we collect and label the news headlines automatically for the barriers using the metadata of news publishers. Then, we utilize the extracted commonsense inferences and sentiments as features to detect the news spreading barriers. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods. The results show that the proposed approach using inferences-based semantic knowledge and sentiment offers better performance than the usual (the average F1-score of the ten categories improves from 0.41, 0.39, 0.59, and 0.59 to 0.47, 0.55, 0.70, and 0.76 for the cultural, economic, political, and geographical respectively) for classifying the news-spreading barriers.

Tariku Adane Gelaw, Misgina Tsighe Hagos

Poor sitting habits have been identified as a risk factor to musculoskeletal disorders and lower back pain especially on the elderly, disabled people, and office workers. In the current computerized world, even while involved in leisure or work activity, people tend to spend most of their days sitting at computer desks. This can result in spinal pain and related problems. Therefore, a means to remind people about their sitting habits and provide recommendations to counterbalance, such as physical exercise, is important. Posture recognition for seated postures have not received enough attention as most works focus on standing postures. Wearable sensors, pressure or force sensors, videos and images were used for posture recognition in the literature. The aim of this study is to build Machine Learning models for classifying sitting posture of a person by analyzing data collected from a chair platted with two 32 by 32 pressure sensors at its seat and backrest. Models were built using five algorithms: Random Forest (RF), Gaussian Naïve Bayes, Logistic Regression, Support Vector Machine and Deep Neural Network (DNN). All the models are evaluated using KFold cross-validation technique. This paper presents experiments conducted using the two separate datasets, controlled and realistic, and discusses results achieved at classifying six sitting postures. Average classification accuracies of 98% and 97% were achieved on the controlled and realistic datasets, respectively.

Menno Yap, Oded Cats

Urban metro and tram networks are regularly subject to planned disruptions, including closures, resulting from the need to maintain and renew infrastructure. In this study, we first empirically analyse the passenger demand response to planned public transport disruptions based on individual passenger travel behaviour, based on which we infer generalised journey time and cost elasticities for different passenger groups and time periods of the day. Second, we develop a model which enables predicting public transport demand for individual origin-destination pairs affected by a closure. The model is trained based on the empirically observed travel behaviour. The proposed method is applied to a case study closure in Amsterdam, the Netherlands, based on which we empirically derive generalised journey time and generalised journey cost elasticities. Our results suggest that passengers demand response is lower for frequent users of the public transport network, as well as during weekdays, especially during the peak periods. Arguably, this stems from a higher share of captive passengers with a mandatory journey purpose in these segments, who will continue making their journey nevertheless. During weekends, with typically higher shares of leisure related journeys, a much more pronounced demand response is found. The estimated neural network regression model is able to predict passenger demand during public transport closures with a high level of accuracy. This provides public transport agencies more precise insights into the impact of closures on their revenue losses and on the potential need for resources reallocation.

Bekir Mendeş, Mehmet Cevher İşeri

Bu çalışmada, 8 haftalık mini-trambolin egzersizinin 11-12 yaş grubundaki erkek çocukların fiziksel uygunluk bileşenlerine etkisinin incelenmesi amaçlandı. Çalışmaya 11-12 yaşlarında 45 erkek öğrenci gönüllü olarak katıldı. Bunlar Deney Grubu (DG, N=23) ve Kontrol Grubu (KG, N=22) olmak üzere iki gruba ayrıldı. DG’na 8 hafta boyunca haftada 2 gün 30 dakika mini-trambolin egzersizi uygulanırken, KG’na herhangi bir egzersiz uygulanmadı. Gruplara çalışma öncesi ve sonrası; denge, bacak kuvveti ve vücut yağ yüzdesi testleri uygulandı. Tespit edilen özellikler bakımından grupların ön ve son test ölçümlerinin karşılaştırılmasında Tekrarlanan Ölçümlü Varyans Analizi tekniği kullanıldı. Analiz sonucunda vücut yağ oranı (P=0,045) ve bacak kuvveti (P=0,000) Grup x Ön test-son test interaksiyon etkisi önemli bulundu. Dolayısıyla grupların bu özelliklere etkisi ön test ve son teste göre anlamlı farklılıklar gösterdi. Anterior/Posterior bakımından sadece grup ortalamaları arasındaki fark DG lehine anlamlı bulunmuşken (P=0,000), Medial Lateral İndeks bakımından sadece ön test-son test arasındaki fark anlamlı bulundu (P=0,004). Overall Stabilite İndeks bakımından ise ne interaksiyon etkisi (P=0,154), ne grup etkisi (P=0,078) ne de ön test-son test etkisi (P=0,234) anlamlı bulundu. Konuyla ilgili olarak farklı yaş ve denek gruplarında yapılacak daha geniş tabanlı çalışmaların yapılmasının uygun olacağı düşünülmektedir.

Gael Chareyron, Saskia Cousin, Sébastien Jacquot

According to the World Tourism Organization (UNWTO), the pandemic has reduced international tourism by 70% by 2020. In the media, the cessation of international mobility is translated into a discourse on “the end of tourism” or “the death of travel”. However, in the summer of 2020, most French territories will experience a summer season that is as busy or even busier than in previous years. How can this discrepancy be explained? Based on a critical analysis of statistical tourism indicators, field observations and the study of comments posted on tourism booking platforms, this article proposes the following hypothesis: the pandemic reveals the fragility of the international tourism industry, and its hold on the norms, values and imaginaries of the elsewhere and of “other” time. Escaping the tourism industry and its indicators, popular vacation practices have resisted rather than been radically transformed. Above all, they have become visible and valued. In this context, the notion of “differential valence” allows us to understand the current inversion of values respectively associated with international tourism and popular vacations.

David J. Aldous, Madelyn Cruz

Card shuffling models have provided simple motivating examples for the mathematical theory of mixing times for Markov chains. As a complement, we introduce a more intricate realistic model of a certain observable real-world scheme for mixing human players onto teams. We quantify numerically the effectiveness of this mixing scheme over the 7 or 8 steps performed in practice. We give a combinatorial proof of the non-trivial fact that the chain is indeed irreducible.

Mohamed R. Ibrahim, James Haworth, Nicola Christie et al.

Cycling is a promising sustainable mode for commuting and leisure in cities, however, the fear of getting hit or fall reduces its wide expansion as a commuting mode. In this paper, we introduce a novel method called CyclingNet for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built, the visual conditions and without any restrictions on the riding behaviour. CyclingNet is a deep computer vision model based on convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks that aim to understand near misses from both sequential images of scenes and their optical flows. The model is trained on scenes of both safe rides and near misses. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities and elsewhere, which could help planners and policy-makers to better understand the requirement of safety measures when designing infrastructure or drawing policies. As for future work, the model can be pipelined with other state-of-the-art classifiers and object detectors simultaneously to understand the causality of near misses based on factors related to interactions of road-users, the built and the natural environments.

Peng Xu, Xiatian Zhu

Existing person re-identification (re-id) works mostly consider short-term application scenarios without clothes change. In real-world, however, we often dress differently across space and time. To solve this contrast, a few recent attempts have been made on long-term re-id with clothes change. Currently, one of the most significant limitations in this field is the lack of a large realistic benchmark. In this work, we contribute a large, realistic long-term person re-identification benchmark, named as DeepChange. It has several unique characteristics: (1) Realistic and rich personal appearance (e.g., clothes and hair style) and variations: Highly diverse clothes change and styles, with varying reappearing gaps in time from minutes to seasons, different weather conditions (e.g., sunny, cloudy, windy, rainy, snowy, extremely cold) and events (e.g., working, leisure, daily activities). (2) Rich camera setups: Raw videos were recorded by 17 outdoor varying resolution cameras operating in a real-world surveillance system. (3) The currently largest number of (17) cameras, (1, 121) identities, and (178, 407) bounding boxes, over the longest time span (12 months). Further, we investigate multimodal fusion strategies for tackling the clothes change challenge. Extensive experiments show that our fusion models outperform a wide variety of state-of-the-art models on DeepChange. Our dataset and documents are available at https://github.com/PengBoXiangShang/deepchange.

Janae Davis

The Wilderness Act of 1964 defines wilderness as “an area where the earth and its community of life are untrammeled by man, where man himself is a visitor who does not remain”. It goes on to limit acceptable activities in designated wilderness areas to those associated with leisure, scenic viewing, education, and scientific inquiry. These precepts are the basis for federal wilderness management in national parks, national forests, national wildlife refuges, and lands administered by the Bureau of Land Management. They are derived from the interests and values held by the early environmental movement's predominantly white middle and upper class patrons, and imposed on diverse groups who may not hold the same views. This study examined how the imposition of wilderness management at Congaree National Park greatly restricted local African Americans' traditional fishing practices and how fishers made meaning of their displacement. Participants' experience of alienation is a result of their perceptions of racial discrimination in the park's preferential treatment of white visitors. This study argues that African American presence in the Great Outdoors is erased both materially and symbolically by racial bias in the Wilderness Act, a general lack of attention to black outdoor spaces, and the use of white outdoor values and pursuits as the criterion for which to assess African American outdoor ethos.

أحمد فتحي, عبد الرحمن رجب

C. S. Abhaydev, J. Bhukar, R. K. Thapa

The purpose of this study was to find the effects of a 12 weeks IAAF Kid’s Athletics programme on the psychological and motor fitness abilities of sedentary school-going children. Materials and methods. The study involved 40 students (age 10 to 14 years) with no previous history of systematic training. The subjects were further sub-divided based on their age, i.e. low age (10 to 11 years) and high age (13 to 14 years), and then randomly assigned to either an experimental group (Kid’s Athletics) or a control group. The psychological variables selected were stress tolerance reactive, simple motor speed, simple reaction speed, visual perception, and focused attention, whereas motor variables selected were sit and reach test, standing broad jump, 50m sprint, T-test, and 150m sprint. Tests were conducted pre-training, mid-training, and post-training for motor variables while only pre-training and post-training tests were conducted for psychological variables. Results. The two-way mixed ANOVA revealed a significant difference in all the selected variables (motor and psychological variables) in group × time interaction (p = 0.001 to <0.001) with large effect sizes. Lager effect sizes in motor fitness variables were observed after 12 weeks (ES = 2.09 to 5.72) than 6 weeks (ES = 1.92 to 3.47) when compared to baseline in the experimental group. Conclusion. The study shows that Kid’s Athletics recommended by IAAF may be considered as an effective programme to improve psychological as well as motor abilities in sedentary school-going children.