Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
19,552
result(s) for
"lin, Min"
Sort by:
The prosody of formulaic sequences : corpus and discourse
\"To apply the same approaches to analysing spoken and written formulaic language is problematic; to do so masks the fact that the contextual meaning of spoken formulaic language is encoded, to a large extent, in its prosody. In The Prosody of Formulaic Sequences, Phoebe Lin offers a new perspective on formulaic language, arguing that while past research often treats formulaic language as a lexical phenomenon, the phonological aspect of it is a more fundamental facet. This book draws its conclusions from three original, empirical studies of spoken formulaic language, assessing intonation unit boundaries as well as features such as tempo and stress placement. Across all studies, Lin considers questions of methodology and conceptual framework. The corpus-based descriptions of prosody outlined in this book not only deepen our understanding of the nature of formulaic language but have important implications for English Language Teaching and automatic speech synthesis\"-- Provided by publisher.
Book
A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss
Background
This systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were effective in changing physical activity, healthy eating, weight management behaviors, and other related health outcomes.
Methods
In collaboration with a medical librarian, six electronic bibliographic databases (PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, and IEEE) were searched to identify relevant studies. Only randomized controlled trials or quasi-experimental studies were included. Studies were screened by two independent reviewers, and any discrepancy was resolved by a third reviewer. The National Institutes of Health quality assessment tools were used to assess risk of bias in individual studies. We applied the AI Chatbot Behavior Change Model to characterize components of chatbot interventions, including chatbot characteristics, persuasive and relational capacity, and evaluation of outcomes.
Results
The database search retrieved 1692 citations, and 9 studies met the inclusion criteria. Of the 9 studies, 4 were randomized controlled trials and 5 were quasi-experimental studies. Five out of the seven studies suggest chatbot interventions are promising strategies in increasing physical activity. In contrast, the number of studies focusing on changing diet and weight status was limited. Outcome assessments, however, were reported inconsistently across the studies. Eighty-nine and thirty-three percent of the studies specified a name and gender (i.e., woman) of the chatbot, respectively. Over half (56%) of the studies used a constrained chatbot (i.e., rule-based), while the remaining studies used unconstrained chatbots that resemble human-to-human communication.
Conclusion
Chatbots may improve physical activity, but we were not able to make definitive conclusions regarding the efficacy of chatbot interventions on physical activity, diet, and weight management/loss. Application of AI chatbots is an emerging field of research in lifestyle modification programs and is expected to grow exponentially. Thus, standardization of designing and reporting chatbot interventions is warranted in the near future.
Systematic review registration
International Prospective Register of Systematic Reviews (PROSPERO):
CRD42020216761
.
Journal Article
Prevalence of Internet Addiction during the COVID-19 Outbreak and Its Risk Factors among Junior High School Students in Taiwan
The coronavirus disease 2019 (COVID-19) outbreak has significantly disrupted normal activities globally. During this epidemic, people around the world were expected to encounter several mental health challenges. In particular, Internet addiction may become a serious issue among teens. Consequently, this study aimed to examine the prevalence of Internet addiction and identify the psychosocial risk factors during the COVID-19 outbreak. This study was constructed using a cross-sectional design with 1060 participants recruited from among junior high school students around Taiwan using stratified and cluster sampling methods. Taiwan’s first COVID-19 case was diagnosed on 28 January 2020. New cases exploded rapidly in February, and as a result, participants were surveyed during March 2 through 27 March 2020. The prevalence of Internet addiction was found to be 24.4% during this period. High impulsivity, high virtual social support, older in age, low subjective well-being, low family function, and high alexithymia was all independently predictive in the forward logistic regression analyses. The prevalence rate of Internet addiction was high among junior high school students during the COVID-19 outbreak. Results from this study can be used to help mental health organizations and educational agencies design programs that will help prevent Internet addiction in adolescents during the COVID-19 pandemic.
Journal Article
Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy
Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.
Journal Article
Accelerated weight gain, prematurity, and the risk of childhood obesity: A meta-analysis and systematic review
The purpose of this systematic review and meta-analysis of the literature was to analyze and evaluate the impact of prematurity and accelerated weight gain on the risk of childhood and adolescent obesity. CINAHL, Embase, PubMed, and Web of Science databases were searched until December 2019 which yielded 19 studies with a total of 169,439 children enrolled were systematically reviewed. The results revealed that preterm infants had a greater likelihood of childhood obesity (defined as BMI ≥95th percentile for age-sex), than term infants (OR = 1.19, 95% CI [1.13, 1.26]). However, no difference of childhood obesity was found between \"small for gestational age\"(SGA) and \"appropriate for gestational age\"(AGA) among preterms. Accelerated weight gain (defined as weight gain velocity during first two years after birth) significantly increased the likelihood of subsequent childhood obesity among preterms (aOR = 1.87, 95% CI [1.57, 2.231]). In conclusion, accelerated weight gain at infancy among preterm children may be a critical contributor to obesity in later life. Establishing optimal growth trajectories and timely referral to health care providers may be of clinical importance.
Journal Article
An overview of GabRat edge disruption and its new extensions for unbiased quantification of disruptive camouflaging patterns using randomization technique
Disruptive colorations are camouflaging patterns that use contrasting colorations to interrupt the continuity of object’s edge and disturb the observer’s visual recognition. The GabRat method has been introduced and widely used to quantify the strength of edge disruption. The original GabRat method requires a composite image where a target object is placed on a particular background. It computes the intensities of ‘frequency components’ parallel and perpendicular to the edge direction at each edge point using Gabor filters, and summarizes the ratios of these two intensities around the perimeter of the shape. However, we found that the original GabRat method has an issue that produces false signals and biases to overestimating the GabRat value depending on the edge angle. Here, we introduce GabRat-R, which can diminish that angle dependency using Gabor filters with randomized base angles. Additionally, we developed GabRat-RR, which iteratively places a target object on a background with random positions and rotation angles to average the effects of the heterogeneity and anisotropy of background. Compared with the original GabRat, our GabRat-R and GabRat-RR programs run more efficiently using multithreading techniques. Those programs are provided as built-in features of the Natsumushi 2.0 software and available from the GitHub repository, https://github.com/mtlucanid/GabRat-R .
Journal Article
Metal to non-metal sites of metallic sulfides switching products from CO to CH4 for photocatalytic CO2 reduction
The active center for the adsorption and activation of carbon dioxide plays a vital role in the conversion and product selectivity of photocatalytic CO
2
reduction. Here, we find multiple metal sulfides CuInSnS
4
octahedral nanocrystal with exposed (1 1 1) plane for the selectively photocatalytic CO
2
reduction to methane. Still, the product is switched to carbon monoxide on the corresponding individual metal sulfides In
2
S
3
, SnS
2
, and Cu
2
S. Unlike the common metal or defects as active sites, the non-metal sulfur atom in CuInSnS
4
is revealed to be the adsorption center for responding to the selectivity of CH
4
products. The carbon atom of CO
2
adsorbed on the electron-poor sulfur atom of CuInSnS
4
is favorable for stabilizing the intermediates and thus promotes the conversion of CO
2
to CH
4
. Both the activity and selectivity of CH
4
products over the pristine CuInSnS
4
nanocrystal can be further improved by the modification of with various co-catalysts to enhance the separation of the photogenerated charge carrier. This work provides a non-metal active site to determine the conversion and selectivity of photocatalytic CO
2
reduction.
The product selectivity of photocatalytic carbon dioxide reduction from carbon monoxide to methane is determined by the active center from metal to sulfur site in metal sulfides. Non-metal sulfur in CuInSnS4 octahedral nanocrystal acts as carbon dioxide activation center for switching selectivity to methane.
Journal Article
Cr dopant mediates hydroxyl spillover on RuO2 for high-efficiency proton exchange membrane electrolysis
Simultaneously improving the activity and stability of catalysts for anodic oxygen evolution reaction (OER) in proton exchange membrane water electrolysis (PEMWE) remains a notable challenge. Here, we report a chromium-doped ruthenium dioxide with oxygen vacancies, termed Cr
0.2
Ru
0.8
O
2-x
, that drives OER with an overpotential of 170 mV at 10 mA cm
−2
and operates stably over 2000 h in acidic media. Experimental and theoretical studies show that the synergy of Cr dopant and oxygen vacancy induces an unconventional dopant-mediated hydroxyl spillover mechanism. Such dynamic hydroxyl spillover from Cr dopant to Ru active site changes the rate-determining step from OOH* formation to O
2
formation and thus greatly improves the OER performance. Moreover, the Cr dopant and oxygen vacancy also play a crucial role in stabilizing surface Ru and lattice oxygen in the Ru-O-Cr structural motif. When assembled into the anode of a practical PEMWE device, Cr
0.2
Ru
0.8
O
2-x
enables long-term durability of over 200 h at an ampere-level current density and 60 degrees centigrade.
Developing highly active and stable anode catalysts for green hydrogen production is crucial but challenging. Here, the authors report a Cr0.2Ru0.8O2-x catalyst with an unconventional dopant-mediated hydroxyl spillover mechanism for high-efficiency proton exchange membrane water electrolysis.
Journal Article
Association between mild anemia and physical fitness in a military male cohort: The CHIEF study
Anemia defined as reduced hemoglobin levels of red blood cells may carry less oxygen to skeletal muscle and impair physical performance. Previous studies have shown that exercise intolerance was related to moderate or severe anemia, however, the relationship to mild anemia was unknown. We investigated the cross-sectional association of mild anemia defined as a hemoglobin level of 10.0–13.9 g/dL with physical fitness in 3,666 military young males in Taiwan in 2014. Aerobic fitness was evaluated by 3000-meter run test, and anaerobic fitness was evaluated by 2-minute sit-ups and 2-minute push-ups, respectively. Multiple logistic regressions for the best 10% and the worst 10% performers were used to determine the relationship. There were 343 mild anemic males in whom 47.8% were microcytic anemia and 3,323 non-anemic males for the analysis. The multiple logistic regression shows that as compared with non-anemic males, mild anemic males were more likely to be the worst 10% performers in the 3000-meter run test (odds ratios (OR) and 95% confidence intervals: 1.47, 1.01–2.14) after adjusting for age, service specialty, body mass index, waist size, mean blood pressure, unhealthy behaviors, lipid profiles, and exercise frequency. On the contrary, mild anemic males had higher possibility to be the best 10% performers in the 2-minute push-ups test (OR: 1.48, 1.08–2.04). However, there was no association between mild anemia and 2-minute sit-ups. Our findings suggest that unspecified mild anemia might be associated with lower cardiorespiratory fitness but not with anaerobic fitness in physically active military males.
Journal Article