3,831 research outputs found

    Characterizing Sleep Patterns in Youth with CP and its Impact on Mood

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    Background. Cerebral palsy (CP) is a lifelong neurodevelopmental condition characterized by limitations in movement and posture (Oskoui et al., 2013; Rosenbaum et al., 2007). There is a growing consensus that sleep difficulties are common and life-long in individuals with CP (Lélis et al., 2016; Newman et al., 2006; Simard-Tremblay et al., 2011). These difficulties encompass various aspects such as sleep duration, sleep quality, staying asleep, and experiencing more difficulty getting up in the morning (Lélis et al., 2016; Newman et al., 2006); however, much remains unknown about the specific sleep patterns in CP and whether they are distinct from those observed in other conditions such as autism or fetal alcohol spectrum disorder (FASD). Additionally, the link between sleep and mood in CP is not well understood (Gadie et al., 2017). While in neurotypical youth, better sleep has been linked to improvements in social, emotional, and psychological well-being (e.g., mood), the extent to which sleep may impact mood within the context of CP remains uncertain (Hamilton et al., 2007). This manuscript-based thesis aims to address these significant gaps in knowledge by examining the sleep patterns in youth with CP and investigate the subsequent temporal association between sleep and mood. Methods. For this exploratory manuscript-based thesis, we analyzed secondary data from baseline questionnaires and weekly data (accelerometers and daily sleep diaries) collected from a larger study that examined the associations between physiological factors and mental health in youth with CP. In the first study, we investigated the sleep patterns of 45 youth with CP using caregiver and youth reports, the Child/Adolescent Sleep-Wake Scale (CSWS/ASWS), Insomnia Severity Index (ISI), and measurements from actigraphs that youth wore for one week. First, the sleep characteristics were described in relation to available demographic variables (e.g., sex, age, Gross Motor Functioning Classification System level [GMFCS]), using descriptive statistics. Second, to determine the impact of the presence of a mental health diagnosis on sleep patterns and problems, a hierarchical regression analyses was conducted. In the second study, we focused on a subsample of youth (n = 32) who had sufficient daily diaries of sleep and mood. In paper 2, the impact of intraindividual variability (IIV) in sleep patterns on mood (i.e., positive and negative affect) was examined using a series of fixed-effects multi-level modelling. Analyses included age, sex, and GMFCS as covariates as these factors contribute to sleep and mood. Results. In the first study of 45 youth, the average sleep duration was 10 hours per night (SD = 0:59), ranging from 7.5 to 12.85 hours. Youth experienced an average of 14 awakenings (>5 min) per night (SD = 5.3), which is substantially higher than previous literature in youth without CP. Most youth reported poor sleep quality based on sleep quality scores from the combined CSWS and ASWS (M = 3.67, SD = 1.24). Hierarchical linear regression analysis revealed a significant positive association between mental health diagnosis and insomnia severity, even after controlling for participant demographics (age, sex, GMFCS) (p = .010). For the second study, fixed-effect models were used to examine the association between IIV sleep duration and quality and next-day negative and positive affect over a 7-day period. While controlling for covariates, higher within-subjects variability of sleep quality was related to lower next-day negative mood (b = -.03, p < .001) and increased next-day positive mood (b = .05, p = .018). To determine the directionality of this association, mood variability predicting next day sleep was examined; however, only higher within-subject variability of negative mood was related to next-day sleep quality (b = -1.12, p = .011). Conclusions. This thesis is the first of its kind to examine the group and individual characteristics of sleep patterns among youth with CP (Study 1) and the temporal impact of IIV sleep on daily positive and negative affect (Study 2). Sleep is a complex phenomenon, and further investigation is necessary to understand the influence of various other factors, which were not available for this thesis. Nevertheless, sleep timing and sleep consistency may be important characteristics of sleep health. Overall, more research is needed to help inform prevention of mental health issues in this already vulnerable population and to help inform the development of supports for sleep

    Fake News: Finding Truth in Strategic Communication

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    Fake news is an old phenomenon that has become a new obsession and a menace to society due to technological advancement and the proliferation of social media, which has changed traditional journalism norms. As the spread of false information has increased these past few years, it has become increasingly difficult for information consumers to distinguish between facts and fakes. A comprehensive systematic literature review to extract themes revealed the major factors responsible for spreading fake news. This qualitative interpretative meta-synthesis (QIMS) aims to better understand and offer solutions to combat fake news. This Ph.D. dissertation will serve as a guide for ethical communication practice and a reference for future research studies

    A survey on vulnerability of federated learning: A learning algorithm perspective

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    Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

    A survey on vulnerability of federated learning: A learning algorithm perspective

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    Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centralizing data, FL holds promise for scenarios where data integrity, privacy and security and are critical. However, this decentralized training process also opens up new avenues for opponents to launch unique attacks, where it has been becoming an urgent need to understand the vulnerabilities and corresponding defense mechanisms from a learning algorithm perspective. This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into their methodology and impact. In this survey, we focus on threat models targeting the learning process of FL systems. Based on the source and target of the attack, we categorize existing threat models into four types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and composite attacks. For each attack type, we discuss the defense strategies proposed, highlighting their effectiveness, assumptions and potential areas for improvement. Defense strategies have evolved from using a singular metric to excluding malicious clients, to employing a multifaceted approach examining client models at various phases. In this survey paper, our research indicates that the to-learn data, the learning gradients, and the learned model at different stages all can be manipulated to initiate malicious attacks that range from undermining model performance, reconstructing private local data, and to inserting backdoors. We have also seen these threat are becoming more insidious. While earlier studies typically amplified malicious gradients, recent endeavors subtly alter the least significant weights in local models to bypass defense measures. This literature review provides a holistic understanding of the current FL threat landscape and highlights the importance of developing robust, efficient, and privacy-preserving defenses to ensure the safe and trusted adoption of FL in real-world applications. The categorized bibliography can be found at: https://github.com/Rand2AI/Awesome-Vulnerability-of-Federated-Learning

    A synthesis of evidence for policy from behavioural science during COVID-19

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    DATA AVAILABILITY : All data and study material are provided either in the Supplementary information or through the two online repositories (OSF and Tableau Public, both accessible via https://psyarxiv.com/58udn). No code was used for analyses in this work.Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.The National Science Foundation; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education); the Ministry of Science, Technology and Innovation | Conselho Nacional de Desenvolvimento Científico e Tecnológico (National Council for Scientific and Technological Development); National Science Foundation grants; the European Research Council; the Canadian Institutes of Health Research.http://www.nature.com/naturehj2024Gordon Institute of Business Science (GIBS)Non

    Non-Market Food Practices Do Things Markets Cannot: Why Vermonters Produce and Distribute Food That\u27s Not For Sale

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    Researchers tend to portray food self-provisioning in high-income societies as a coping mechanism for the poor or a hobby for the well-off. They describe food charity as a regrettable band-aid. Vegetable gardens and neighborly sharing are considered remnants of precapitalist tradition. These are non-market food practices: producing food that is not for sale and distributing food in ways other than selling it. Recent scholarship challenges those standard understandings by showing (i) that non-market food practices remain prevalent in high-income countries, (ii) that people in diverse social groups engage in these practices, and (iii) that they articulate diverse reasons for doing so. In this dissertation, I investigate the persistent pervasiveness of non-market food practices in Vermont. To go beyond explanations that rely on individual motivation, I examine the roles these practices play in society. First, I investigate the prevalence of non-market food practices. Several surveys with large, representative samples reveal that more than half of Vermont households grow, hunt, fish, or gather some of their own food. Respondents estimate that they acquire 14% of the food they consume through non-market means, on average. For reference, commercial local food makes up about the same portion of total consumption. Then, drawing on the words of 94 non-market food practitioners I interviewed, I demonstrate that these practices serve functions that markets cannot. Interviewees attested that non-market distribution is special because it feeds the hungry, strengthens relationships, builds resilience, puts edible-but-unsellable food to use, and aligns with a desired future in which food is not for sale. Hunters, fishers, foragers, scavengers, and homesteaders said that these activities contribute to their long-run food security as a skills-based safety net. Self-provisioning allows them to eat from the landscape despite disruptions to their ability to access market food such as job loss, supply chain problems, or a global pandemic. Additional evidence from vegetable growers suggests that non-market settings liberate production from financial discipline, making space for work that is meaningful, playful, educational, and therapeutic. Non-market food practices mend holes in the social fabric torn by the commodification of everyday life. Finally, I synthesize scholarly critiques of markets as institutions for organizing the production and distribution of food. Markets send food toward money rather than hunger. Producing for market compels farmers to prioritize financial viability over other values such as stewardship. Historically, people rarely if ever sell each other food until external authorities coerce them to do so through taxation, indebtedness, cutting off access to the means of subsistence, or extinguishing non-market institutions. Today, more humans than ever suffer from chronic undernourishment even as the scale of commercial agriculture pushes environmental pressures past critical thresholds of planetary sustainability. This research substantiates that alternatives to markets exist and have the potential to address their shortcomings

    The Epidemiology and Management of Kawasaki Disease in Australia

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    Kawasaki disease (KD) is a syndrome of systemic inflammation with the potential to cause life-threatening aneurysms of the coronary arteries. I sought to contribute to our understanding of this important condition, particularly with regard to Australian children. By determining the hospitalisation rate and IVIG-treatment rate I estimated the incidence of KD to be about 14 per 100,000 children under the age of 5 between 2007 and 2015. I also showed that the hospitalisation rate nationally had increased on average 3.5% annually between 1993 and 2018, with significant changes in the age distribution over that period. In collaboration with the Paediatric Active Enhanced Disease Surveillance (PAEDS) network, I undertook a large multicentre prospective surveillance study of KD in Australia. My analysis of that cohort confirmed several of the findings from the survey, such as the preference of Australian clinicians for low-dose aspirin from the time of diagnosis, and the considerable variability around how IVIG resistance is diagnosed and managed. Importantly, I observed that a significant subset of children diagnosed with, and treated for, KD do not meet the diagnostic criteria outlined in the 2017 statement by the American Heart Association. This work has contributed significantly to the understanding of KD’s epidemiology, management, and outcomes in Australia. I have shown that the incidence of the condition is increasing, and the clinical picture is changing. I identified important areas of practice variation and highlighted the need for international collaboration around agreed definitions (such as for IVIG resistance). Finally, I have played a central role in establishing an important resource for future resource: prospective surveillance of KD in Australia continues, with well over 700 cases recruited so far. It is hoped that this work will be of benefit to the researchers, clinicians, patients, and families affected by KD now, and into the future

    Combined Nutrition and Exercise Interventions in Community Groups

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    Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
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