Related Reasons Research Articles

Prevalence of supplement usage and related attitudes and reasons among fitness athletes in the gyms of Kashan and its relationship with feeding behavior: a cross-sectional study

BackgroundThe overuse of supplements among athletes is a widespread issue affecting the health of both male and female athletes. However, research on supplements usage among female fitness athletes is limited, and there is little information on the feeding behavior of fitness athletes who use supplements. This study aimed to fill the gap in knowledge by examining the prevalence of supplement usage and its related attitudes and reasons among fitness athletes in the gyms of Kashan. It further aimed to investigate the correlation between supplements usage and the feeding behavior of fitness athletes.MethodsFor these purposes, in this cross-sectional study, 433 fitness athletes (15‒46 years old) in 28 gyms in the city of Kashan were surveyed using a researcher-made questionnaire in 2023. Five experts confirmed the validity of the questionnaire. The present study considered the supplements based on the Australian Institute of Sport position statement. A Chi-square analysis was conducted to examine the relationship between the study variables and supplement usage.ResultsOverall, 272 male and 161 female fitness athletes participated in this study. The results revealed that 57.9% of participants used supplements, most commonly vitamin C, vitamin D, omega-3 fatty acids, and whey protein. The main reason for using supplements was to speed up body repair after exercise (69.5%). Additionally, 41.8% of these athletes believed that using supplements improves their overall performance, and 21.9% thought that supplements do not harm the body. Moreover, a correlation was observed between feeding behavior and the consumption of supplements. It was found that athletes who use supplements tend to eat more white meat, seeds, and nuts and fewer high-fat dairy products than those who do not consume them.ConclusionUsing supplements among fitness athletes in the gyms of Kashan is common. The main reason for using these substances was to speed up body repair after exercise, and nearly half of the athletes believed that supplements improved their performance. In addition, it was revealed that athletes who take supplements have healthier feeding behaviors than those who do not. Thus, these findings confirm the necessity of informing fitness athletes about using supplements.

Impact of Covid-19 Pandemic on the Access to Congenital Heart Disease Health Services A Single Center Study

Abstract Background The SARS-COV2 virus has been extensively researched on a microbiological basis with overview on its various symptoms, complications, modes of transmission and how to possibly treat or even prevent it. But in our research, we are pre-determined to look on how much damage the pandemic has cost our health care system indirectly causes many people to suffer and have their services delayed or postponed. Aim and objectives To quantify the impact that COVID-19 had on access to congenital heart disease heath care, comparing data at different time intervals, during March 2020, September-October 2020 and November 2021. In the congenital and structural heart disease unit in Ain Shams university hospitals. Patients and Methods This is a Cross-sectional observational study of a 260-patient presented to Ain Shams University Hospitals Structural heart disease in the congenital heart disease cath lab. Patients or their guardians were asked via survey over the Telephone to assess their demographic data, their medical condition, delay in health care services and the respondent own perception to the delay in effect. The answers were compared according to the different time frames and their relation to COVID-19 pandemic. Results We have revealed a difference in number of cases between March 2020 (peak pandemic), September-October 2020 and 1 year after in November 2021; number of respondents 31,80 and 149 respectively. There was a statistically significant relation between the delay time (time interval from decision taken to actual intervention done) and (date of the actual procedure); patients who underwent their procedure in the group of March 2020; patients experienced less delay, with 42% of the patients had their procedure done in less than one month. While the group Sep-Oct 2020; Patients experienced the most delay with 50% of the patients had their procedure done in more than 6 months. In group of Nov 2021; 43% of the population had less than one month delay. Over the course of the population studied, almost 21% of respondents believed that there had been a delay in their case due to non -COVID-19 related reasons (hard paperwork and/or long waiting lines). In the group who had their procedure done in March 2020, 60% of respondents believed that their cases were delayed because of non-Covid-19 related reasons (hard paperwork and/or long waiting lines). In the group who had their procedure done in Sep-Oct 2020; 58.9% of respondents thought that their cases were delayed mainly because of COVID-19 related reasons. In the group who had their procedure done in Nov 2021; 39% of respondents believed that their delay was because of COVID-19 related reasons (fear of infection and/or Turing of hospitals into isolation centers/cancelling elective procedures). Conclusion We concluded that COVID-19 pandemic has affected the provided the health care services provided. Most of the delayed population, had their procedures delayed because of COVID-19 related reasons. Risk stratification of ACHD according to complexity of illness or severity of symptoms is a must to allow intervention and follow up during drastic times according to a pre-set list of patients’ priorities.

Impacts of Major Changes in China's COVID-19 Prevention and Control Policies on Emergency Department Visits: A Quasi-Experiment.

This study aims to evaluate the impact of COVID-19 prevention and control policies on the frequency of emergency department (ED) visits in a large tertiary hospital in central China, from January 2018 to September 2023. We conducted a multi-stage interrupted time series analysis to investigate the impact of various epidemic control policies on weekly ED visits at a tertiary hospital in Hunan Province, China. The study period ranged from January 1, 2018, to September 30, 2023, and was divided into four distinct periods: pre-epidemic, pandemic, normalized control, and end of control. Using a quasi-Poisson regression model, we examined the specific effects of these policies on emergency visits, with a particular focus on stratifying patients based on respiratory versus non-respiratory diseases. Compared to the pre-pandemic period, the number of ED visits in a tertiary hospital decreased by 38.5% (95% CI: 25.1% to 49.8%) during the COVID-19 pandemic, of which the number of ED visits for respiratory diseases increased by 79.4% (95% CI: 13.2% to 177.2%) and the number of ED visits for non-respiratory diseases decreased by 45.9% (95% CI: -55.7% to -34.2%). After the end of the epidemic control, the total number of ED visits increased by 31.5% (95% CI: 19.1% to 45.0%), with the number of ED visits for respiratory diseases rising by 379.2% (95% CI: 275.9% to 511.8%), but with no significant change in the number of ED visits for non-respiratory emergencies. Control policies were associated with people avoiding emergency care for non-respiratory related reasons during the pandemic, while the end of control policies was associated with a sharp rise in emergency care for respiratory diseases. This study provides a scientific basis for the different changes in ED visits under the implementation of varying epidemic prevention and control policies.

Geometric-Contextual Mutual Infomax Path Aggregation for Relation Reasoning on Knowledge Graph

Geometric-Contextual Mutual Infomax Path Aggregation for Relation Reasoning on Knowledge Graph

Effectiveness assessment of recent large vision-language models

The advent of large vision-language models (LVLMs) represents a remarkable advance in the quest for artificial general intelligence. However, the models’ effectiveness in both specialized and general tasks warrants further investigation. This paper endeavors to evaluate the competency of popular LVLMs in specialized and general tasks, respectively, aiming to offer a comprehensive understanding of these novel models. To gauge their effectiveness in specialized tasks, we employ six challenging tasks in three different application scenarios: natural, healthcare, and industrial. These six tasks include salient/camouflaged/transparent object detection, as well as polyp detection, skin lesion detection, and industrial anomaly detection. We examine the performance of three recent open-source LVLMs, including MiniGPT-v2, LLaVA-1.5, and Shikra, on both visual recognition and localization in these tasks. Moreover, we conduct empirical investigations utilizing the aforementioned LVLMs together with GPT-4V, assessing their multi-modal understanding capabilities in general tasks including object counting, absurd question answering, affordance reasoning, attribute recognition, and spatial relation reasoning. Our investigations reveal that these LVLMs demonstrate limited proficiency not only in specialized tasks but also in general tasks. We delve deep into this inadequacy and uncover several potential factors, including limited cognition in specialized tasks, object hallucination, text-to-image interference, and decreased robustness in complex problems. We hope that this study can provide useful insights for the future development of LVLMs, helping researchers improve LVLMs for both general and specialized applications.

Document-level biomedical relation extraction via hierarchical tree graph and relation segmentation module.

Biomedical relation extraction at the document level (Bio-DocRE) involves extracting relation instances from biomedical texts that span multiple sentences, often containing various entity concepts such as genes, diseases, chemicals, variants, etc. Currently, this task is usually implemented based on graphs or transformers. However, most work directly models entity features to relation prediction, ignoring the effectiveness of entity pair information as an intermediate state for relation prediction. In this article, we decouple this task into a three-stage process to capture sufficient information for improving relation prediction. We propose an innovative framework HTGRS for Bio-DocRE, which constructs a hierarchical tree graph (HTG) to integrate key information sources in the document, achieving relation reasoning based on entity. In addition, inspired by the idea of semantic segmentation, we conceptualize the task as a table-filling problem and develop a relation segmentation (RS) module to enhance relation reasoning based on the entity pair. Extensive experiments on three datasets show that the proposed framework outperforms the state-of-the-art methods and achieves superior performance. Our source code is available at https://github.com/passengeryjy/HTGRS.

HierarT: Multi-hop temporal knowledge graph forecasting with hierarchical reinforcement learning

Temporal knowledge graph (TKG) multi-hop reasoning is a dominant approach that infers the target entity by walking along the path connecting entities and relations. However, in most TKGs, there are multiple relations related to an identical entity and multiple tail entities for an identical pair of head entity and relation. This characteristic leads to a significant amount of redundant data in the binary action space which is a collection of binary tuples composed of relations and entities, thereby impeding model inference, i.e., action space explosion. We propose a method to address this issue that dismantles the reasoning process into a relation level for relation reasoning and an entity level for entity reasoning. In addition, hybrid time encoding is proposed to enhance the utilization of the timestamp information in the reasoning process. Moreover, K-means-based reward shaping is proposed to alleviate the issue of sparse reward matrices by comparing the pre-clustered labels of both the predicted entity and the target entity. A text transformer module is used to deal with the limitation of a single information modality by integrating the text information of TKG into model reasoning. Our method is evaluated using four benchmark datasets, and the results verify its superiority over state-of-the-art baselines.

BCRA: bidirectional cross-modal implicit relation reasoning and aligning for text-to-image person retrieval

BCRA: bidirectional cross-modal implicit relation reasoning and aligning for text-to-image person retrieval

The impact of cardiorespiratory fitness on COVID-19 related outcomes: the ETHOS Study

Abstract Background Comorbidities such as cardiovascular disease, diabetes, obesity and hypertension are associated with more severe COVID-19 outcomes, but the relative impact of cardiorespiratory fitness (CRF) on outcomes in COVID-positive patients is largely unknown. Objective To assess the association between objectively measured CRF and COVID-19-related health outcomes including mortality, hospitalization, and mechanical ventilation in non-COVID vaccinated patients. Design, Setting and Participants. Retrospective analysis of 750,302 subjects (23,140 [mean age 65.8±12.0 years] with a positive COVID-19 test between March 2020 and September 2021) who underwent a maximal exercise test between 1999 and 2020. Main Outcomes and Measures. Association between all-cause mortality, hospitalization due to COVID-19, and need for intubation with risk for severe COVID-19 outcomes after adjustment for 15 covariates. Subjects were stratified into five age-specific CRF categories (Least-fit, Low-fit, Moderate-fit, Fit and High Fit), based on peak metabolic equivalents achieved. Results During a median of follow-up of 100 days, 1,643 (7.1%) patients died, 4,995 (21.6%) were hospitalized, and 927 (4.0%) required intubation for COVID-19 related reasons. When compared to the Least-Fit subjects (referent), the Low-Fit, Moderately-Fit, Fit, and High-Fit subjects had hazard ratios for mortality of 0.82 (95% CI; 0.72-0.93), 0.73 (95% CI; 0.63-0.86), 0.61 (95% CI; 0.53-0.72), and 0.54 (95% CI; 0.45-0.65), respectively (Figure). Subjects who were more fit also had significantly lower need for hospital admissions and intubation. Similar patterns were observed for elderly subjects and subgroups with comorbidities including hypertension, diabetes, cardiovascular disease, and chronic kidney disease; for each of these conditions, those in the High-Fit category had mortality rates that were roughly half those in the Low-Fit category. Conclusion Among subjects positive for COVID-19, higher CRF had a favorable impact on survival, need for hospitalization, and need for intubation regardless of age, BMI, or the presence of comorbidities.

Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning

Simulating high-resolution detector responses is a computationally intensive process that has long been challenging in Particle Physics. Despite the ability of generative models to streamline it, full ultra-high-granularity detector simulation still proves to be difficult as it contains correlated and fine-grained information. To overcome these limitations, we propose Intra-Event Aware Generative Adversarial Network (IEA-GAN). IEA-GAN presents a Transformer-based Relational Reasoning Module that approximates an event in detector simulation, generating contextualized high-resolution full detector responses with a proper relational inductive bias. IEA-GAN also introduces a Self-Supervised intra-event aware loss and Uniformity loss, significantly enhancing sample fidelity and diversity. We demonstrate IEA-GAN’s application in generating sensor-dependent images for the ultra-high-granularity Pixel Vertex Detector (PXD), with more than 7.5 M information channels at the Belle II Experiment. Applications of this work span from Foundation Models for high-granularity detector simulation, such as at the HL-LHC (High Luminosity LHC), to simulation-based inference and fine-grained density estimation.

Person re-identification by utilizing hierarchical spatial relation reasoning

Deep learning-based methods have become increasingly popular in person re-identification (re-id) as they enhance the discriminative features while suppressing extraneous or irrelevant details. However, these methods face a potential limitation in overlooking the relationships between semantic regions focusing solely on salient features or pixel-level matching information. As a result, the model performance becomes susceptible to adverse factors such as pose variations, misalignment, and background clutter. To overcome this issue, we propose a novel deep relational learning network (DRLNet) that learns relationship knowledge between semantic regions and “local–global” information to improve discriminative feature learning effectively. We manually establish the relation between local regions using a simple partition strategy that captures global structural information and improves relation-aware attention learning. Dividing images into uniform partitions allows each part-level feature to represent itself and the entire image area, facilitating the learning of more refined and discerning relationships. Our approach's efficacy is evaluated on three datasets, where experimental results exhibit its superiority. Significantly, our approach achieves a new state-of-the-art Rank-1 accuracy of 84.7%, 80.9%, and 83.5% on CUHK03 (Labeled), CUHK03 (Detected), and MSMT17 datasets, respectively, outperforming current state-of-the-art methods. In conclusion, our proposed DRLNet demonstrates its potential as a highly effective solution to overcoming the limitations of deep learning-based methods by providing superior means of learning discriminative features while incorporating relationships between semantic regions.

A Private Function: Independent Providers of Vocational Education and Training in Post-War England

ABSTRACT This paper focuses on independent training providers (ITPs) – in other words, private companies – as suppliers of vocational education and training in post-war England. Whilst acknowledging the central role of further education (FE) colleges in delivering vocational learning, it draws attention to a large, diverse sector of ITPs operating alongside FE colleges, particularly during the 1960s and 1970s. Data suggest that around 15–20% of vocational learners were enrolled as fee-paying customers with private providers at that time – a figure broadly similar to today. There are, it is argued, three related reasons for this. First, the post-war policy environment, and the varied and uneven nature of colleges at that time, allowed significant room for ITPs to operate as alternative providers of vocational education. Second, the far-reaching ‘efficiency’ gains required since FE colleges left local authority control have largely attenuated the space in which ITPs previously operated. Third, neoliberal assumptions about the superiority of private enterprise mean that ITPs now receive significant funding from the state, largely to deliver Apprenticeships and other programmes of work-related learning – which has, somewhat perversely, reduced the incentive for them to act as bone fide commercial providers of a broader range of vocational learning.

Rhetorical Risk Zones in Mediatexts of Turbulent Times

The concept of rhetorical risk is introduced and actualized within the framework of the rhetorical decoding of the text concept aimed at studying the mechanisms of effective text formation. The phenomenon of textual effectiveness gains particular significance in turbulent periods of human existence. The research material consists of a corpus of mediatexts created during the COVID-19 pandemic and in April 2024 during the devastating flooding in the Southern Urals and Western Siberia. The aim of the article is to identify the zones of rhetorical risks in the text and to identify their indicators. In accordance with the methodological principles of linguorhetorical reconstruction, the zones of rhetorical risks in the text were identified as inventive, dispositival, and elocutive. Indicators of the inventive zone include the implicitness of the speaker’s practical goal-setting and the orthodoxy of the thesis put forward. Indicators of dispositival risk zones include one-dimensionality of argumentation, excessive use of statistical data as arguments, and frequency of related reasoning. The elocutive zone of rhetorical risks arises due to the speakers’ neglect of such a significant communicative criterion for turbulent times as dialogism. The research results can have broad professional applications — from educational activities to mass media and linguo-expert practices.

Understanding doctoral students’ needs for thesis discussion writing and supervisory curriculum development: a sociocultural theory perspective

ABSTRACT The global blossoming of doctoral education calls for a better understanding of doctoral candidates’ needs concerning thesis writing. Although scholars proclaim the particular difficulty of writing a thesis discussion, empirical studies on doctoral students’ needs for the discussion chapter are insufficient. To fill the gap, using individual interviews, we investigated seven candidates’ views on challenges and helpful resources, noting L1/L2 students’ differences regarding writing the discussion. We found that students perceived challenges differently: challenges (or absence of challenge) are connected with individual situations. We also found that students relied mainly on supervisors to help them with thesis discussion writing and yet supervisors sometimes were not able to provide sufficient guidance. Lastly, we found L1 candidates had ideologically related reasons for writing choices whereas L2 candidates’ reasons were to fit in. Another L1/L2 difference is that L2 students reported using more resources for language difficulty than L1 students did. We draw on sociocultural theory to make sense of these findings to offer insights into pedagogy. We conclude the paper with a discussion on the implications for supervisors, writing instructors and students.

In-hospital and readmission outcomes of patients with cancer admitted for pulmonary embolism treated with or without catheter-based therapy

BackgroundCancer patients are at risk of pulmonary embolism (PE). Catheter-based therapies (CBT) are novel reperfusion options for PE though data in patients with cancer is lacking. Study design and methodsPatients with intermediate- or high-risk PE were identified using the National Readmission Database (NRD) from 2017 to 2020. Primary outcome were in-hospital death and 90-day readmission. Secondary outcomes were in-hospital bleeding, 90-day readmission for venous thromboembolism (VTE)-related or right heart failure-related reasons and bleeding. Propensity scores were estimated using logistic regression and inverse-probability treatment weighting (IPTW) was utilized to compare outcomes between CBT and no CBT as well as CBT versus systemic thrombolysis. ResultsA total of 7785 patients were included (2511 with high-risk PE) of whom 1045 (13.4%) were managed with CBT. After IPTW, CBT was associated with lower rates of index hospitalization death (OR 0.89, 95% CI 0.83–0.96) and 90-day readmission (HR 0.75, 95% CI 0.69–0.81) but higher rates of in-hospital bleeding (OR 1.11, 95% CI 1.03–1.20) which was predominantly post-procedural bleeding. CBT was associated with lower risk of major bleeding (20.8% vs 24.8%; OR 0.80, 95% CI 0.68–0.94) compared with systemic thrombolysis. InterpretationAmong patients with cancer with intermediate or high-risk PE, CBT was associated with lower in-hospital death and 90-day readmission. CBT was also associated with decreased risk of index hospitalization major bleeding compared with systemic thrombolysis. Prospective, randomized trials with inclusion of patients with cancer are needed to confirm these findings.

Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguisticmeaning.

Determination of cyberloafing level of speech and Language Therapy Department students

Cyberloafing is the use of (e.g. smartphones, tablets, laptops, and the Internet) for purposes other than work related reasons during work hours. Although cyberloafing in the workplace has been widely investigated, there is relatively a small number of studies on cyberloafing behaviors in educational settings, which refer to students' tendencies to use technology for non-class-related purposes during lectures. The goal of the current study is to determine how frequently and for what purposes speech and language therapy (SLT) students at Biruni University engage in cyberloafing during lectures. In this quantitative study, The Cyberloafing Scale was administered to 264 undergraduate students (235 female; 27 male; 2 preferred not to disclose). The results revealed that SLT students’ cyberloafing behavior was very high. However, there is no statistically significant difference between the gender variable and the overall mean score for cyberloafing. There is a statistically significant difference between genders in gaming/gambling subscale favoring males and in shopping subscale favoring females. Further studies should be conducted to analyze cyberloafing behavior in health education.

A psychological study of Cantonese tone confusion based on computer experimental data

The paper is based on computer experimental data and aims at investigating the pronunciation of Cantonese tones by 40 participants in Guancheng. Accordingly, three methods, namely, the preparatory pronunciation, pronunciation experiment and perception experiment were applied to examine the tones. The methods correspond to three levels of psychological identification. As the result, confusions of Cantonese tones were summarized and the related reasons were analyzed. The psychological identification of different levels was found to show different trends in the confusion of the tone. Moreover, according to the preparatory experiment, as shallow level psychological identity, the similarity of the adjustment and modulation can be argued to be the internal reason of the confusion. As the medium level psychological identity, the pronunciation experiment suggested that the weakening of socialized pronunciation can lead to tone confusion. Finally, as the deep psychological identity, the experimental study of perception demonstrated the perceptual similarity as the direct psychological motivation of the confusion..

The effectiveness of a fall detection device in older nursing home residents: a pilot study.

Real-world research to evaluate the effect of device technology in preventing fall-related morbidity is limited. This pilot study aims to investigate the effectiveness of a non-wearable fall detection device in older nursing home residents. The study was conducted in a nursing home with single-resident rooms. Fall detection devices were randomly set up in half of the rooms. Demographic data, comorbidities, lists of medications, and functional, nutritional, and frailty status were recorded. The residents were followed up for 3 months. The primary outcome was falls and the secondary outcome was all-cause mortality. A total of 26 participants were enrolled in the study. The study group consisted of 13 residents who had a fall detection device in their rooms. The remaining 13 residents on the same floor formed the control group. Participants had a mean age of 82 ± 10 years and 89% of the residents were female. The most prevalent comorbidity was dementia. Two residents from the control group and one resident from the study group experienced a fall event during follow-up. The fall events in the control group were identified retrospectively by the nursing home staff, whereas the fall in the study group received a prompt response from the staff who were notified by the alarm. One resident was transferred to the hospital and died due to a non-fall related reason. Device technology may provide an opportunity for timely intervention to prevent fall-related morbidity in institutionalized older adults.

Knowledge Graph Multi-Hop Question Answering Based on Dependent Syntactic Semantic Augmented Graph Networks

In the rapidly evolving domain of question answering systems, the ability to integrate machine comprehension with relational reasoning stands paramount. This paper introduces a novel architecture, the Dependent Syntactic Semantic Augmented Graph Network (DSSAGN), designed to address the intricate challenges of multi-hop question answering. By ingeniously leveraging the synergy between syntactic structures and semantic relationships within knowledge graphs, DSSAGN offers a breakthrough in interpretability, scalability, and accuracy. Unlike previous models that either fall short in handling complex relational paths or lack transparency in reasoning, our framework excels by embedding a sophisticated mechanism that meticulously models multi-hop relations and dynamically prioritizes the syntactic–semantic context.