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Predicting exercise behaviors and intentions of Taiwanese urban high school students using the theory of planned behavior
No full text[[abstract]]Purpose: This study applied the Theory of Planned Behavior to predict exercise behaviors and intentions of teenagers and analyzed sex differences. Design and methods: A prospective study design was employed to survey tenth-grade students in Taipei, Taiwan. The 951 participants reported their exercise attitudes, subjective norms, perceived behavioral control (PBC), and intentions, and their exercise behaviors were tracked 6 months later. Results: Results revealed that 22.1% of all students and more male students than female students exercised for ?30 min/day on 5 or more days/week. Hierarchical multiple regression analyses demonstrated that intentions, PBC, attitudes, and subjective norms explained 32.5% of the variation in exercise behavior (p < .001). Intentions, attitudes, and PBC were related to exercise behavior regardless of sex. Attitudes, subjective norms, and PBC explained 67.0% of the variation in intentions (p < .001). Attitudes and PBC were related to intentions regardless of sex. Conclusions: The findings support that the main constructs of the Theory of Planned Behavior can effectively predict regular exercise intentions and behaviors among adolescents. Practice implications: The results can serve as a reference for nurses and other healthcare professionals when formulating effective strategies to encourage adolescents to engage in exercise practices
De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update
No full text[[abstract]]Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeutics have been suggested to cause fewer adverse effects than the traditional small-molecule drugs. In light of current advancements in deep learning techniques, generative adversarial network (GAN) algorithms are being leveraged to a wide variety of applications in the process of generative chemistry and computer-aided drug design and discovery. In this review, we focus on the up-to-date developments for de novo peptide and protein design research using GAN algorithms in the interdisciplinary fields of generative chemistry, machine learning, deep learning, and computer-aided drug design and discovery. First, we present various studies that investigate GAN algorithms to fulfill the task of de novo peptide and protein design in the drug development pipeline. In addition, we summarize the drawbacks with respect to the previous studies in de novo peptide and protein design using GAN algorithms. Finally, we depict a discussion of open challenges and emerging problems for future research
Neural basis of operant behaviors maintained on the differential-reinforcement-of-low-rate (DRL) schedule in rodents.
No full text[[abstract]]Various schedules of reinforcement have long been used in experimental psychology to establish and maintain operant behaviors. These reinforcement contingencies have also been widely applied in preclinical psycho- and neurobiology research. However, the differential reinforcement of low-rate response (DRL) schedule has received less attention than other schedules based on response ratios or different types of intervals. Hence, little is known about the neural basis of DRL schedule-controlled behavior. Herein, we review early and recent reports of rodent experiments utilizing brain lesions and intracranial drug infusions to respectively elucidate the neural substrates and neuropharmacological basis of DRL behavior. Overall, the available evidence implies that 1) certain cortical and subcortical areas are differentially involved in the DRL behavior and 2) disruption of dopamine or serotonin neurotransmission alters DRL behavior. We further identify remaining challenges in the field and suggest future work that will be helpful for understanding the neurobehavioral mechanisms of the DRL schedule of reinforcement
3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction
No full text[[abstract]]Motivation
Drug–drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificial intelligence methods predict and mine potential DDI, they ignore the 3D structure information of drug molecules and do not fully consider the contribution of molecular substructure in DDI.
Results
We proposed a new deep learning architecture, 3DGT-DDI, a model composed of a 3D graph neural network and pre-trained text attention mechanism. We used 3D molecular graph structure and position information to enhance the prediction ability of the model for DDI, which enabled us to deeply explore the effect of drug substructure on DDI relationship. The results showed that 3DGT-DDI outperforms other state-of-the-art baselines. It achieved an 84.48% macro F1 score in the DDIExtraction 2013 shared task dataset. Also, our 3D graph model proves its performance and explainability through weight visualization on the DrugBank dataset. 3DGT-DDI can help us better understand and identify potential DDI, thereby helping to avoid the side effects of drug mixing
Are Healthcare Expenditures Related to Economic Growth in China? Bootstrap ARDL Approach
No full text[[abstract]]This study attempts to investigate whether healthcare expenditures (HCE) are related to economic growth in China using a newly developed Bootstrap autoregressive distributed lag (ARDL) test for China over the period of 1990–2019. To avoid omitted variable bias, we use the ratio of the population of 65 years old over the total population (aging ratio) as a control variable. Empirical result indicates that no cointegration among these three variables. Granger causality test based on Bootstrap ARDL model demonstrates that one-way Granger causality running from HCE to aging ratio and from economic growth to both HCE and aging ratio. Empirical results have important policy implications for China understud
Exploring the Impacts of Students in Hospitality Programs of Vocational High Schools Watching YouTubers’ Travel Programs on Travel Intention from the Perspective of Digital Business Opportunities in the Postpandemic Era
No full text[[abstract]]The purpose of this study is to explore the impacts of students in the hospitality programs of vocational high schools watching YouTubers’ travel programs on their travel intentions, as viewed from the perspective of digital business opportunities in the postpandemic era. Students in the hospitality programs of vocational high schools were taken as the research subjects, and a questionnaire survey was conducted with convenience sampling. A total of 350 questionnaires were distributed, and 320 questionnaires were recovered for a recovery rate of 91%. After 35 invalid questionnaires were excluded, there were 285 valid questionnaires for an effective recovery rate of 89%. The software, Statistical Package for the Social Sciences and Analysis of Moment Structures, was used for statistical analysis. The research results show that (1) there was no significant difference between different background variables in travel intention; (2) the degree of involvement had a significant impact on travel intention; (3) trust had a significant impact on travel intention; (4) perceived value had a significant impact on travel intention; and (5) destination image had a significant impact on travel intention. Finally, based on the results of this study, relevant suggestions were provided for practical application
A Study on Carbon Fiber Composites with Low-Melting-Point Polyester Nonwoven Fabric Reinforcement: A Highly Effective Electromagnetic Wave Shield Textile Material
No full text[[abstract]]In this study, a low-melting-point polyester nonwoven fabric (L), a nylon spacer fabric (N), and a carbon fiber woven fabric (C) are laminated in different orders and then needle-bonded at a depth of 15.0 cm to form NLC, NLN, CLC, and CLN composites with a sandwich construction. Regardless of the lamination order, four composite types exhibit high tensile strengths and tearing strengths. Based on the ASTM D4935-18 test standard, the electromagnetic wave shielding measurement is conducted in a frequency range of 1~3 GHz. The two groups—NLC and CLN—demonstrate different electromagnetic wave shields, which are ?45~?65 dB for the former, and ?60 dB for the latter. According to FTTS-FA-003, in the specified requirements of the test method for electromagnetic shielding textiles, the proposed composites achieve level III, which is the highest standard, and are thus qualified for use in the aviation, construction, and commerce fields
Visual responses of patients with generalized anxiety disorder who cycling in the virtual sportscapes with different tree cover densities
No full text[[abstract]]The tree density of virtual sportscape is the main factor that determines the benefits that generalized anxiety disorder (GAD) patients can obtain when they exercise with virtual environment. By using pupil size, fixation count and time as metrics, this research aimed to clarify the relationship between tree cover density and stress in the virtual environment. Ninety GAD patients were randomly grouped into the 36–60% tree density (high tree density, HTDS), 20–35% tree density (medium tree density, MTDS), or control groups (n = 30). Researchers used eye-tracking technology to analyze fixation time, fixation count and changes in pupil size to evaluate the stress changes of participants after 20 min of aerobic exercise in a virtual environment. The results showed that pupil size expanded in GAD patients after exercising in the virtual environment. Furthermore, GAD patient cycling in the MTDS group can show smaller pupil size than those in HTDS. Those results suggest that GAD patient cycling 20 min in the MTDS group can perceived lower stress. The results of eye tracking analysis showed that GAD patients spend more time and counts observing tree elements in HTDS and MTDS sportscapes. Specifically, they spent more 48% and 27% time on tree and green plants in the HTDS condition and MTDS condition, respectively, than in non-natural sportsscapes. Although 36–60% tree density of virtual natural sportscape can get more visual attention from GAD patients, 20–35% tree density of virtual natural sportscape is more capable of reducing their stress
以計畫行為理論擴展模式探討大學生運動行為意圖之影響因素─加入手機成癮因子
No full text[[abstract]]本研究目的在瞭解科技大學學生規律運動現況，並且進一步利用國外健身領域應用最廣泛的計畫行為理論來探討科技大學學生從事規律運動行為的影響因素。本研究採便利取樣，以臺灣科技大學與東南科技大學學生為研究對象，總計發出500份量表，經回收整理後有效量表為442份，有效回收率達88.4%。研究工具為研究者以Ajzen（2007）所推薦的問卷編製方法，依開放式引導問卷填答的結果，自編而成的結構式問卷。研究結果主要發現為：一、研究對象在未來三個月從事規律運動行為的意圖以正向者居多，但目前有規律運動行為者，僅占總樣本數的22%；二、態度、主觀規範與知覺行為控制對規律運動行為意圖具有42%的解釋量；態度與主觀規範最能有效解釋從事規律運動的意圖；三、計畫行為理論應用在規律運動行為意圖之研究上，比理性行為理論更為合適；四、父母親有運動習慣與平日有收看運動性電視節目者，可在三個理論變項外，增加對意圖的解釋變異量。本研究結論：根據本研究發現，實務者可以擬定有效策略，強化受試者之態度、主觀規範與知覺行為控制，並營造家庭親子運動的情境、發揮運動性電視節目的影響力，以提高其從事規律運動的意圖。[[abstract]]The main objective of this study was to understand the current status of regular exercise behavior among university students. Furthermore, the determinants of students' intention to participate in regular exercise were examined by employing the theory of planned behavior (TPB). This study adopted the convenience sampling procedure, and data were collected from 442 students of National Taiwan University of Science and Technology and Tungnan University, respectively. According to the Ajzen's (2007) TPB questionnaire construction procedures, we developed this research instrument based on the outcomes of the proposed open-ended questions. The major findings were as follows: (a) respondents had positive tendencies toward taking part in regular exercise, while about 22% of them had cultivated a habit of regular exercise; (b) the attitude, subjective norm and perceived behavioral control explained 42% unique variance in intentions of involving in regular exercise; (c) the TPB was found to be better than TRA (theory of reasoned action) in predicting the behavioral intention of students to participate in regular exercise; and (d) along with three TPB predictors, two other variables, including parent's habit of regular exercise and their habit of watching sports programs on TV, would provide additional variance that may explain regular exercise behavioral intention. These findings conclude that practitioners can formulate effective strategies to strengthen the subject's attitude, subjective norm and perceived behavioral control. Furthermore, combined participation of parents and children in exercise, and watching sports programs on TV may enhance their intention of participating in regular exercise
Automated detection of ADHD: Current trends and future perspective
No full text[[abstract]]Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit combinations of inattention, impulsiveness, and hyperactivity. With early treatment and diagnosis, there is potential to modify neuronal connections and improve symptoms. However, the heterogeneous nature of ADHD, combined with its comorbidities and a global shortage of diagnostic clinicians, means diagnosis of ADHD is often delayed. Hence, it is important to consider other pathways to improve the efficiency of early diagnosis, including the role of artificial intelligence. In this study, we reviewed the current literature on machine learning and deep learning studies on ADHD diagnosis and identified the various diagnostic tools used. Subsequently, we categorized these studies according to their diagnostic tool as brain magnetic resonance imaging (MRI), physiological signals, questionnaires, game simulator and performance test, and motion data. We identified research gaps include the paucity of publicly available database for all modalities in ADHD assessment other than MRI, as well as a lack of focus on using data from wearable devices for ADHD diagnosis, such as ECG, PPG, and motion data. We hope that this review will inspire future work to create more publicly available datasets and conduct research for other modes of ADHD diagnosis and monitoring. Ultimately, we hope that artificial intelligence can be extended to multiple ADHD diagnostic tools, allowing for the development of a powerful clinical decision support pathway that can be used both in and out of the hospital