Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship between Support Behavior of Physical Education Teachers and Amotivation of Students in Physical Education Activities at Schools

The aim of this study is to investigate the relationship between the social support behavior of physical education teachers and amotivation (absence of motivation) of students in P.E activities at schools. This is a descriptive and correlation study which is done as a field study. In doing so, we chose 381 high school students of Boukan city (201 of boys and 180 of girls), to fill out, 1) the questionnaire of amotivation inventory – physical education (AI PE), and 2) interpersonal behavior scale (IBS). For data analyzing we used the Spearman test of correlation. The results showed that there is a significant but negative relationship between the social support behavior of P.E teachers and amotivation of students in doing P.E activities of schools (r = -0.252, p≤0.01). There are respectively significant but negative relationships between factors of social support behavior (i.e. autonomy support, competence support, and relatedness support) and amotivation (r = -0.207, r = -0.255, r = -0.216, p≤0.01). According to the fact that, reduction of P.E teachers support behavior may result in amotivation of students, we recommend that teachers should identify the multi-lateral nature of amotivation, and then increase the amount of students social support, to finally improve their participation at school P.E activities.
Outbreak of Legionnaires' disease in Glasgow Royal Infirmary: microbiological aspects

SUMMARY The bacteriological investigation of an outbreak of Legionnaires' disease in Glasgow Royal Infirmary affecting 16 patients is described. Most of the patients had been treated in high-dependency areas on two floors of the hospital supplied by the same two air-conditioned ventilation systems. The source of infection was traced to contamination of a cooling tower from which a plume of spray discharged into the intake vents of the two ventilation systems. Rubber grommets within the cooling tower probably provided a nidus of infection there. The control and management of the outbreak are discussed: a policy of frankness about the course and progress of the investigations was adopted and helped to allay anxiety on the part of both staff and media.
A thin near-field RFID antenna for file managing system

In this paper, a thin near-field RFID antenna has been proposed which is designed for a file managing system and used in closed metal filing cabinet. The antenna is circular polarization, while its working band is from 910MHz to 930MHz.The VSWR of the antenna at 925MHz is less than 1.2, and the maximum gain is 5.2dB. Indicated through the system conjunction, the antenna can meet the system to satisfy the requirements of reading 30 tags at the same time in the range of 0.1~1 meter.
Search-based semi-supervised clustering algorithms for change detection in remotely sensed images

In real life change detection for remotely sensed images suffers due to the problem of inadequate labeled patterns. When a few labeled patterns can be collected by experts, semi-supervised (learning) clustering can be opted for change detection instead of the unsupervised approach to make full utilization of both labeled and unlabeled patterns. In the present work, a study has been carried out by applying some of the semi-supervised clustering techniques for changed detection. A comparative analysis between K-Means, COP-KMeans, Seeded-KMeans and Constrained-KMeans algorithms is being performed based on the results obtained using two multi-temporal remotely sensed images. It can be concluded from the experiments that the Constrained-KMeans is well suited for changed detection of remotely sensed images under semi-supervised framework.
What Is a Good Day for Outdoor Photometric Stereo?

Photometric Stereo has been explored extensively in laboratory conditions since its inception. Recently, attempts have been made at applying this technique under natural outdoor lighting. Outdoor photometric stereo presents additional challenges as one does not have control over illumination anymore. In this paper, we explore the stability of surface normals reconstructed outdoors. We present a data-driven analysis based on a large database of outdoor HDR environment maps. Given a sequence of object images and corresponding sky maps captured in a single day, we investigate natural factors that impact the uncertainty in the estimated surface normals. Quantitative evidence reveals strong dependencies between expected accuracy and the normal orientation, cloud coverage, and sun elevation. In particular, we show that partially cloudy days yield greater accuracy than sunny days with clear skies; furthermore, high sun elevation-recommended in previous work-is in fact not necessarily optimal when taking more elaborate illumination models into account.
Antioxidant Activity of Natural Products Isolated from Red Seaweeds

The present work describes more than 60 natural products from marine red seaweeds (Rhodophyta) and their antioxidant activities. The results indicate that algae belonging to the order Ceramiales, family Rhodomelaceae are the most promising as potential producers of antioxidants. This activity seems to be related to the ability to synthesize polyphenols and their derivatives, as bromophenols.
Study on a new nonuniform frequency density multicarrier radar signal and its performances

In target searching stage of radar seeker in modern encounter, the transmitted signal is required to have characteristics of high resolution, low probability of intercept and high Doppler tolerance. In order to overcome the disadvantage of OFDM signal's low Doppler tolerance, a new type of multicarrier signal with nonuniform frequency density is proposed, whose subcarriers and amplitude weighting are derived from nonlinear frequency modulation signals. By theoretical analysis and numerical simulation methods, characteristics of spectrum and ambiguity-function of the signal are derived and computed, and be compared with those of typical OFDM signal. The results show the signal has advantages of higher Doppler tolerance, range-Doppler decoupling, faster out-of-band attenuation and lower sidelobe of autocorrelation function, as is suitable to search and detect high speed target.
Formulation of buffer energy and experimental results of energy factor of DC–DC converters

Two concepts of buffer energy and energy factor for switched‐mode power converters are proposed and examined. They can describe the nonactive power as well as predict and evaluate the performance of DC–DC converters. The concepts can also find usage in both DC and AC systems. The energy factors of six basic topologies of DC–DC converter have been studied, and they can be used to compare the performance of different topologies. It can be shown that both buffer energy and energy factor provide an alternative understanding of the energy processes in DC–DC converters. Experimental results demonstrate that energy factors and buffer energy can be measured. The measured results for some basic converters are consistent with the theoretical values. Buffer energy, energy factor, and their related concepts proposed in this article have promising application in the design of DC–DC converters. Copyright © 2011 John Wiley & Sons, Ltd.
Personal exposure to fine particulate matter concentrations in central business district of a tropical coastal city

ABSTRACT In the present study, personal exposure to fine particulate matter (particulate matter with an aerodynamic diameter <2.5 μm [PM2.5]) concentrations in an urban hotspot (central business district [CBD]) was investigated. The PM monitoring campaigns were carried out at an urban hotspot from June to October 2015. The personal exposure monitoring was performed during three different time periods, i.e., morning (8 a.m.−9 a.m.), afternoon (12.30 p.m.–1.30 p.m.), and evening (4 p.m.–5 p.m.), to cover both the peak and lean hour activities of the CBD. The median PM2.5 concentrations were 38.1, 34.9, and 40.4 µg/m3 during the morning, afternoon, and evening hours on the weekends. During weekdays, the median PM2.5 concentrations were 59.5, 29.6, and 36.6 µg/m3 in the morning, afternoon, and evening hours, respectively. It was observed that the combined effect of traffic emissions, complex land use, and micrometeorological conditions created localized air pollution hotspots. Furthermore, the total PM2.5 lung dose levels for an exposure duration of 1 hr were 8.7 ± 5.7 and 12.3 ± 5.2 µg at CBD during weekends and weekdays, respectively, as compared with 2.5 ± 0.8 µg at the urban background (UB). This study emphasizes the need for mobile measurement for short-term personal exposure assessment complementing the fixed air quality monitoring. Implications: Personal exposure monitoring at an urban hotspot indicated space and time variation in PM concentrations that is not captured by the fixed air quality monitoring networks. The short-term exposure to higher concentrations can have a significant impact on health that need to be considered for the health risk–based air quality management. The study emphasizes the need of hotspot-based monitoring complementing the already existing fixed air quality monitoring in urban areas. The personal exposure patterns at hotspots can provide additional insight into sustainable urban planning.
Learning mixtures of arbitrary distributions over large discrete domains

We give an algorithm for learning a mixture of unstructured distributions. This problem arises in various unsupervised learning scenarios, for example in learning topic models from a corpus of documents spanning several topics. We show how to learn the constituents of a mixture of k arbitrary distributions over a large discrete domain [n]={1, 2, . ,n} and the mixture weights, using O(n polylog n) samples. (In the topic-model learning setting, the mixture constituents correspond to the topic distributions.) This task is information-theoretically impossible for k > 1 under the usual sampling process from a mixture distribution. However, there are situations (such as the above-mentioned topic model case) in which each sample point consists of several observations from the same mixture constituent. This number of observations, which we call the "sampling aperture", is a crucial parameter of the problem. We obtain the first bounds for this mixture-learning problem without imposing any assumptions on the mixture constituents. We show that efficient learning is possible exactly at the information-theoretically least-possible aperture of 2k-1. Thus, we achieve near-optimal dependence on n and optimal aperture. While the sample-size required by our algorithm depends exponentially on k, we prove that such a dependence is unavoidable when one considers general mixtures. A sequence of tools contribute to the algorithm, such as concentration results for random matrices, dimension reduction, moment estimations, and sensitivity analysis.
Prevalence of celiac disease in at-risk and not-at-risk groups in the United States: a large multicenter study.

BACKGROUND Celiac disease (CD) is an immune-mediated enteropathic condition triggered in genetically susceptible individuals by the ingestion of gluten. Although common in Europe, CD is thought to be rare in the United States, where there are no large epidemiologic studies of its prevalence. The aim of this study was to determine the prevalence of CD in at-risk and not-at-risk groups in the United States. METHODS Serum antigliadin antibodies and anti-endomysial antibodies (EMA) were measured. In EMA-positive subjects, human tissue transglutaminase IgA antibodies and CD-associated human leukocyte antigen DQ2/DQ8 haplotypes were determined. Intestinal biopsy was recommended and performed whenever possible for all EMA-positive subjects. A total of 13 145 subjects were screened: 4508 first-degree and 1275 second-degree relatives of patients with biopsy-proven CD, 3236 symptomatic patients (with either gastrointestinal symptoms or a disorder associated with CD), and 4126 not-at-risk individuals. RESULTS In at-risk groups, the prevalence of CD was 1:22 in first-degree relatives, 1:39 in second-degree relatives, and 1:56 in symptomatic patients. The overall prevalence of CD in not-at-risk groups was 1:133. All the EMA-positive subjects who underwent intestinal biopsy had lesions consistent with CD. CONCLUSIONS Our results suggest that CD occurs frequently not only in patients with gastrointestinal symptoms, but also in first- and second-degree relatives and patients with numerous common disorders even in the absence of gastrointestinal symptoms. The prevalence of CD in symptomatic patients and not-at-risk subjects was similar to that reported in Europe. Celiac disease appears to be a more common but neglected disorder than has generally been recognized in the United States.
Speech recognition using deep neural networks trained with non-uniform frame-level cost functions

The aim of this paper is to present two new variations of the frame-level cost function for training a deep neural network in order to achieve better word error rates in speech recognition. Minimization functions of a neural network are salient aspects to deal with when researchers are working on machine learning, and hence their improvement is a process of constant evolution. In the first proposed method, the conventional cross-entropy function can be mapped to a nonuniform loss function based on its corresponding extropy (a complementary dual function), enhancing the frames that have ambiguity in their belonging to specific senones (tied-triphone states in a hidden Markov model). The second proposition is a fusion of the proposed mapped cross-entropy and the boosted cross-entropy function, which emphasizes those frames with low target posterior probability. The developed approaches have been performed by using a personalized mid-vocabulary speaker-independent voice corpus. This dataset is employed for recognition of digit strings and personal name lists in Spanish from the northern central part of Mexico on a connected-words phone dialing task. A relative word error rate improvement of 12.3% and 10.7% is obtained with the two proposed approaches, respectively, regarding the conventional well-established crossentropy objective function.
Factors Predicting Physical Activity Among Children With Special Needs

Introduction Obesity is especially prevalent among children with special needs. Both lack of physical activity and unhealthful eating are major contributing factors. The objective of our study was to investigate barriers to physical activity among these children. Methods We surveyed parents of the 171 children attending Vista Del Mar School in Los Angeles, a nonprofit school serving a socioeconomically diverse group of children with special needs from kindergarten through 12th grade. Parents were asked about their child’s and their own physical activity habits, barriers to their child’s exercise, and demographics. The response rate was 67%. Multivariate logistic regression was used to examine predictors of children being physically active at least 3 hours per week. Results Parents reported that 45% of the children were diagnosed with attention deficit hyperactivity disorder, 38% with autism, and 34% with learning disabilities; 47% of children and 56% of parents were physically active less than 3 hours per week. The top barriers to physical activity were reported as child’s lack of interest (43%), lack of developmentally appropriate programs (33%), too many behavioral problems (32%), and parents’ lack of time (29%). However, child’s lack of interest was the only parent-reported barrier independently associated with children’s physical activity. Meanwhile, children whose parents were physically active at least 3 hours per week were 4.2 times as likely to be physically active as children whose parents were less physically active (P = .01). Conclusion In this group of students with special needs, children’s physical activity was strongly associated with parental physical activity; parent-reported barriers may have had less direct effect. Further studies should examine the importance of parental physical activity among children with special needs.
RFID Enabled Visualisation of Product Flows: A Data Analytics Approach

Radio frequency identification (RFID) is an information facilitator that can directly improve decision‐ making; thus many retailers and suppliers have adopted it. A vast amount of RFID data streams has been gathered, however, it remains unutilized, or it is been exploited solely for inventory count purposes. This research proposes a way to analyze the immense volume of RFID data reflecting the behavior of products in retail stores, in order to produce information for inventory availability and inventory flows at different stages of the supply chain. We propose an RFID data analytics artifact that transforms RFID data captured in retail stores to the flows of the inventory/ products between locations in the stores. By mining the RFID data streams, we reveal the flow patterns of the products; these patterns correspond to the frequent product paths in the stores, and we provide them to the retailers in a visual manner. This unprecedented knowledge is valuable, because it can enable decisions ranging from shelves space allocation, dynamic pricing programs for slow-moving fresh products to product assortment. Furthermore, to testify artifacts’ correctness and usefulness, we have put it in practice, using real data provided by an Italian fashion retailer, in order to show how it can really support such decisions.
Dinamika model vaksinasi virus influenza dengan perubahan laju pemberian vaksinasi

Dalam tulisan ini, disajikan dinamika populasi model virus influenza dari Alexander dengan pengubahan laju pemberian vaksinasi. Model memiliki dua titik tetap yaitu titik tetap tanpa penyakit dan titik tetap endemik. Banyaknya titik tetap endemik dipengaruhi oleh suatu persamaan kuadrat sehingga keberadaannya bergantung pemilihan nilai parameter. Pada saat titik tetap ini ada dua, kestabilannya titik tetap ini akan dipengaruhi oleh suatu parameter yang merupakan tingkat vaksinasi. Jika nilai ini diperbesar, maka populasi yang rentan akan semakin membesar sedangkan populasi terinveksi akan semakin kecil. Penggambaran dinamika populasi model akan dilakukan dengan bantuan software Maple.
Identifying People based on their Motion Signature

Introduction In the past, using motion for recognition has been demonstrated mainly for classifying certain actions or gait styles ([1, 2, 3, 4, 5]). Using the more ambiguous motion of a person that appears during talking and performing “body language” is harder to quantify. This is similar to what the acoustic speech community calls “speaker recognition”. It is not important what the person says, but how it is said. We developed a new video-based feature extraction technique and methods to train statistical models that classify body motion signatures. The recognition architecture is inspired by recent progress in speaker recognition research. This abstract has 3 main contributions: 1) a visual feature estimation technique, based on sparse flow computations and motion angle histograms that we call “Motion Orientation Signatures” (MOS). 2) Integration of this feature into a 3-stage recognition system, 3) an integration method with a state-of-the-art face recognition architecture [6]. We demonstrate how this new technique can be used to identify people just based on their motion, or it can be used to significantly improve “hard-biometrics” techniques. For example, face verification achieves on this domain 6.45% Equal Error Rate (EER), and the combined verification performance of motion features and face reduces the error to 4.96% using an adaptive score-level integration method. The more ambiguous motion-only performance is 17.1% EER. This is to the best of our knowledge the first time a system has been demonstrated that can significantly improve a state-of-the-art face recognition system with such complex and ambiguous signals like a person’s body-language.
Exercise Effect in Children with Attention-Deficit Hyperactivity Disorder : Meta-Analysis of Domestic Study

The aim of this meta-analysis study was to examine the effectiveness of exercise interventions on attention-deficit hyperactivity disorder (ADHD) pertaining to cognitive function such as attention and impulsivity in children.ZA comprehensive literature search was conducted using databases including Korean Information Service System, DBpia, Research Information Sharing Service, and National Assembly Library from 1995 to 2014. Search terms included ‘ADHD’, ‘children’, ‘exercise’, ‘physical activity’, and ‘sports’. Standardized mean difference and 95% confidence intervals were calculated, and the heterogeneity of the studies was estimated using Q statistic. That is, these results revealed that effectiveness of exercise groups was higher by approximately 32% compared with control groups.ZEight studies (n=150) satisfied the inclusion criteria. The meta-analysis suggested that exercise has a moderate to large effect on core symptoms involved in attention [effect size (ES)=0.93] and impulsivity (ES=0.92) in children with ADHD. The main findings indicate that exercise, based on several types of exercise intervention, appears to be effective for mitigating symptoms such as attention, impulsivity in children with ADHD. From this perspective, exercise might be an effective adjunctive therapy for enhancing the effects of medication in children with ADHD. Future study related to exercise effect on ADHD symptoms is required in order to understand the specific evidence of relationship between exercise and core symptoms. J Korean Neuropsychiatr Assoc 2015;54(4):399-405
Decreased regional left ventricular myocardial strain in type 1 diabetic children: a first sign of diabetic cardiomyopathy?

Abstract Background and Objectives Type 1 diabetes is a major cardiovascular risk factor associated with an excess of mortality in young adults due to premature cardiovascular events, which includes heart failure. The relation between type 1 diabetes and cardiac structure and function in children was poorly documented. Our study investigates (1) whether type 1 diabetic children have echocardiographic signs of subclinical cardiac dysfunction assessed by tissue Doppler strain and (2) whether state of metabolic control and diabetes duration have any influence on the cardiac event. Methods Standard echocardiography and tissue Doppler imaging were prospectively performed in type 1 diabetic children. Left ventricular dimensions, standard indices of systolic and diastolic function, and septal longitudinal strain were investigated. Results Thirty consecutive asymptomatic diabetic children (age: 12.4 [5–17] years; males: 53%) were compared to 30 age and sex-matched healthy control subjects. Left ventricular mass index and diastolic septal thickness were significantly increased in diabetic children. There was no difference between two groups as regards the left ventricular ejection fraction and conventional mitral Doppler parameters (E, A, Ea). The global longitudinal systolic strain and strain rate were found to be decreased in children with diabetes. The global longitudinal early diastolic strain rate (Esr) was negatively correlated with metabolic control. Longitudinal strain was not correlated with diabetes duration. Conclusion Children with Type 1 diabetes had subclinical alterations in left ventricular size and longitudinal myocardial deformation.
IT@Home: unraveling complexities of networked devices in the home

The home is becoming a complex and hard to manage collection of computers and digital lifestyle devices. The work to setup and maintain a network of digital living devices in the home is similar to the work of IT professionals. Indeed the growing complexity of interconnected digital devices results in more and more time spent solving problems with those devices and their configurations, an important part of computer use that we call "IT@Home". This workshop explores the complexity of framing the right problems, methods for studying those problems, and designing to support users' IT@Home.