Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations between college/university campus characteristics and student body mass index

Background Campus environments are associated with undergraduate weight. However, few studies have examined campus type and geographic location in relation to student weight. This article aimed to identify college/university students with elevated BMIs by campus type and region. Methods Linear mixed effects regression models were fit to data from the American College Health Association-National College Health Assessment II. Analyses tested associations between campus type/region and student self-reported BMI. Results The sample included 404,987 students from 445 schools with mean BMI 24.9 ± 5.8. Across all school types/regions, BMI confidence intervals included overweight values. Two-year and public school students had higher BMIs compared to four-year and private school students, respectively. Students in the Midwest had higher BMIs compared to students in the Northeast. In the South only, Minority Serving Institution (MSI) students had higher BMIs compared to non-MSI students. Conclusion Healthy weight maintenance programs should be made available to undergraduate students.
Factors influencing crowdsourcing riders’ satisfaction based on online comments on real-time logistics platform

ABSTRACT Real-time logistics (RTL), which is mainly organized by crowdsourcing, has grown rapidly in recent years. Crowdsourcing riders are the main undertakers of RTL. This paper uses crowdsourcing riders’ online comments as data sources, and uses text mining techniques such as sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling to analyze the factors that bring satisfaction and dissatisfaction to riders. The research results show that in addition to basic income, riders expect the platform to provide them with better services, skills training and safety insurance before work can bring satisfaction to riders. The lack of timely information feedback on the current platform and inaccurate order matching are the reasons for the dissatisfaction of riders. Research also shows that riders can easily gain a sense of accomplishment to help others in the process of completing RTL distribution. Interactions with merchants and customers will also affect riders’ satisfaction.
How Do Distributed and Transformational Leadership Teams Improve Working Conditions and Student Learning in Underperforming High-Needs Schools?

Theories of distributed leadership suggest that organizational learning and change results not from the efforts of a single individual, but rather from a network of people working within their broader systems. Team empowering leadership enhances human resources development of the organization to promote the sharing of knowledge that is necessary for change. In this study, we study transformational and distributed leadership team that have been linked to improving working conditions and students’ learning in high-needs schools. Specifically, we highlight a team-based intervention where positive organizational improvements were made to academically struggling schools, and then qualitatively examined the associated processes to understand what enabled the occurrence of those positive changes. We find that the team structure allowed for the clarification of expectations, enhancement of communication, and improvement of educator working conditions through professional development support and distribution of leadership responsibility, which ultimately resulted in improvement in school culture and performance.
Development of Magnetic Saturation Controllable Soft Starter for Motors

A new scheme of magnetic control for the soft starting of motors is introduced in this paper. Magnetic saturation controllable reactor is connected in series with stator of the motor to realize soft start. It can make the motor start smoothly according to the starting characteristic required by the load, and reduce the strike to the power system. Emulated results by MATLAB are compared with the real waves.
Structural, optical, and magnetic properties of pristine and Cr doped WO3 nanoparticles

Abstract WO3 nanoparticles doped with chromium were prepared via acid-assisted precipitation method. These nanoparticles were characterized by XRD, FTIR, Raman, UV-DRS, SEM-EDS and photoluminescence (PL) spectral techniques. The XRD peaks confirmed the monoclinic arrangement with decrease in crystallite size on increasing the dopant concentration. The surface characterizations exposed the doping of chromium ions within the host lattices of WO3. From the SEM images, it can be noticed that doping changes the morphology of WO3 nanoparticles. The structure and crystal quality were studied by FTIR and Raman spectra. The absorption spectra reveals that the optical band gap energy is reduced after doping and there is rise in Urbach energies on increasing the Cr concentration. The PL spectra give an insight of increasing numbers of oxygen vacancies in WO3 after doping. The M–H hysteresis loops reveal that the ferromagnetic character is enhanced on low dopant concentration while at higher dopant concentrations, antiferromagnetism (FM) dominates.
Stick-Slip Interactions of the Soft-Solid Contact: An Integrated LuGre/Beam Network Model Approach

Contact model of the soft-solid interactions plays an important role for robotic and mechatronic systems design. We present a new model of the soft-solid contact that integrates the LuGre dynamic friction model with the beam network model. The LuGre dynamic friction model uses the bristle deformation to capture the friction characteristics and dynamics while the beam network model represents the elastic contact interactions. The new model is applied to a fingertip-like stick-slip interaction application. We present the model prediction and validation results with the experiments. The comparison results demonstrate the effectiveness of the modeling development.Copyright © 2015 by ASME
Similarity Model Test of Coal Mining under Ordovician Limestone Nappe Aquifer

Ordovician limestone in North China coalfield has the characteristics of karst fissure development and strong water yield. Coal mines in North China have been threatened by Ordovician limestone water for a long time. Taking the mining of 32 coal seam in the first eastern mining area of Qianyingzi coal mine in Anhui Province in China as the engineering geological prototype, this paper uses the self-developed fluid solid coupling similarity model test device to carry out the similarity model test of overburden stress and pore water pressure response of coal mining under Ordovician limestone nappe aquifer. Based on the variation law of pore water pressure and overburden stress in a stope, it can be seen that the variation law of pore water pressure in confined aquifer is jointly affected by two factors: the vertical distance between the monitoring point and coal seam and the horizontal distance between the monitoring point and DF200 fault. According to the analysis of experimental results, the maximum height of a water flowing fracture zone in the stope is fifteen times the mining thickness. Compared with the theoretical calculation results of empirical formula, the coupling effect of seepage field and stress field and the geological structure characteristics of the stope promote the development of a water flowing fracture zone. The research results of this paper have important theoretical and practical significance not only for the safe mining of Qianyingzi coal mine but also for the safe mining of the coal mine facing the double threat of roof Ordovician limestone confined aquifer and reverse fault.
A framework for context-aware driver status assessment systems

The automotive industry is actively supporting research and innovation to meet manufacturers' requirements related to safety issues, performance and environment. The Green ITS project is among the efforts in that regard. Safety is a major customer and manufacturer concern. Therefore, much effort have been directed to developing cutting-edge technologies able to assess driver status in term of alertness and suitability. In that regard, we aim to create with this thesis a framework for a context-aware driver status assessment system. Context-aware means that the machine uses background information about the driver and environmental conditions to better ascertain and understand driver status. The system also relies on multiple sensors, mainly video and audio. Using context and multi-sensor data, we need to perform multi-modal analysis and data fusion in order to infer as much knowledge as possible about the driver. Last, the project is to be continued by other students, so the system should be modular and well-documented. With this in mind, a driving simulator integrating multiple sensors was built. This simulator is a starting point for experimentation related to driver status assessment, and a prototype of software for real-time driver status assessment is integrated to the platform. To make the system context-aware, we designed a driver identification module based on audio-visual data fusion. Thus, at the beginning of driving sessions, the users are identified and background knowledge about them is loaded to better understand and analyze their behavior. A driver status assessment system was then constructed based on two different modules. The first one is for driver fatigue detection, based on an infrared camera. Fatigue is inferred via percentage of eye closure, which is the best indicator of fatigue for vision systems. The second one is a driver distraction recognition system, based on a Kinect sensor. Using body, head, and facial expressions, a fusion strategy is employed to deduce the type of distraction a driver is subject to. Of course, fatigue and distraction are only a fraction of all possible drivers' states, but these two aspects have been studied here primarily because of their dramatic impact on traffic safety. Through experimental results, we show that our system is efficient for driver identification and driver inattention detection tasks. Nevertheless, it is also very modular and could be further complemented by driver status analysis, context or additional sensor acquisition.
Asystole in a COVID-19 patient without systemic illness: a case report

ABSTRACT There is growing evidence that patients with severe systemic illness from coronavirus disease 2019 (COVID-19) are at risk for developing a variety of cardiac arrhythmias. Less is known about patients with milder symptoms. Here, we report on the case of a 62-year-old male, admitted to the hospital following an episode of syncope, who experienced multiple episodes of cardiac arrest due to asystole lasting up to 30 seconds. History revealed a recent asymptomatic COVID-19 infection, and recurrent episodes of prolonged asystole necessitated permanent pacemaker placement. To our knowledge, this is the first report of an asymptomatic COVID-19 patient experiencing prolonged asystole. Cardiac arrhythmias in asymptomatic or oligosymptomatic COVID-19 patients may be underestimated.
Hierarchical ZnO nanostructure flowers loaded with AgI nanoparticles for photodegradation of methylene blue under UV visible radiation

Abstract Hierarchical ZnO spherical flowers loaded with different weight contents of AgI nanoparticles were synthesized by deposition-precipitation method at room temperature and tested for photodegradation of methylene blue (MB) under UV visible light irradiation. The as-prepared photocatalysts were characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM) connected with energy dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS). XRD patterns and SEM images of ZnO and AgI/ZnO hierarchical spherical flowers present pure phase of hexagonal wurtzite ZnO structure flowers and hexagonal wurtzite ZnO flowers supported with cubic AgI nanoparticles with size range of 80–90 nm on top. The photodegradation of MB was increased with increasing in the weight content of the loaded AgI nanoparticles on the surface of ZnO flowers. The 10% AgI/ZnO nanocomposites have the highest photocatalytic efficiency in degrading MB by h+ and ●OH as the main active species under visible light irradiation within 60 min.
A Novel Glucose-Sensitive Scaffold Accelerates Osteogenesis in Diabetic Conditions

Mandibular bone regeneration is still a big challenge in those diabetic patients with poorly controlled blood glucose. In this study, we prepared a novel glucose-sensitive controlled-release fiber scaffold (PVA-HTCC/PEO-rhBMP2-glucose oxidase (PHPB-G)), which contained the recombinant human bone morphogenetic protein 2 (rhBMP2) by coaxial cospinning and grafted with glucose oxidase (GOD). We presented evidence that PHPB-G could undergo a series of structural changes with the blood glucose and promoted bone regeneration in diabetic rat. PHPB-G expanded the voids in nanofibers when blood glucose levels elevated. More importantly, its slow-release rhBMP2 effectively promoted the healing of bone defects. These data suggested that the PHPB-G delivery system may provide a potential treatment strategy for patients with severe diabetic alveolar bone defects.
Segmentation of kidney stones in endoscopic video feeds

Image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process as possible. For the segmentation task, we adapted and analyzed three baseline deep learning models – U-Net, U-Net++, and DenseNet – to predict annotations on the frames of the endoscopic videos with the highest accuracy above 90%. To show clinical potential for real-time use, we also confirmed that our best trained model can accurately annotate new videos at 30 frames per second. Our results demonstrate that the proposed method justifies continued development and study of image segmentation to annotate ureteroscopic video feeds.
Hybrid data mining based fault diagnosis for distribution systems

For improving the deficiencies of single data mining method for distribution systems fault diagnosis,a new fault diagnosis technique with hybrid data mining methods is proposed in this paper.Firstly,the original fault data is reduced by using the rough set theory,and a streamlined rules set is formed.Then,a neural network is trained by calling the simple rules set,the training time is short and the diagnosis accuracy is improved.Finally,an example illustrates the effectiveness of the proposed method.
Synthesis of Fe3O4 Nanocatalyst Capped Citric Acid (Fe3O4-CA) from Sargassum filipendula

The nanocatalyst Fe3O4 capped citric acid (Fe3O4-CA) was successfully synthesized using brown seaweed Sargassum filipendula. Sulfated polysaccharides contained in Sargassum filipendula extract contain sulfate, hydroxy, and aldehyde groups which cause Fe3+ reduction and nanoparticle stabilization. The FT-IR results of Sargassum filipendula extract showed the presence of CO-SO3 stretching vibrations at 1040 cm-1, sulfate groups at 1241 cm-1, aromatic CC at 1413 cm-1, carbonyl at 1604 cm-1, CH stretching vibrations at 2932 cm-1. 1, and the hydroxy group at 3316 cm-1\. Meanwhile, citric acid was used as capping to prevent agglomeration of the synthesized nanocatalyst. Fe3O4-CA nanocatalysts were characterized using XRD, PSA, and SEM-EDX. The XRD results were processed using the Debye-Scherrer equation and the crystal size of Fe3O4-CA was 8.5 nm. PSA results show that Fe3O4-CA particles have a radius of 45.09 nm or diameter of 90.18 nm. This nanocatalyst was also tested for the synthesis of pyrimidine-derived compounds at optimum conditions using 7.5% mol of catalyst, 50 °C for 6 hours, in order to obtain a yield of 83.2%.
Visual perceptual salience and novel referent selection in children with and without autism spectrum disorder

Background & Aims Many young children with autism spectrum disorder (ASD) demonstrate striking delays in early vocabulary development. Experimental studies that teach the meanings of novel nonwords can determine the effects of linguistic and attentional factors. One factor that may affect novel referent selection in children with ASD is visual perceptual salience—how interesting (i.e., striking) stimuli are on the basis of their visual properties. The goal of the current study was to determine how the perceptual salience of objects affected novel referent selection in children with ASD and children who are typically developing (TD) of similar ages (mean age 3–4 years). Methods Using a screen-based experimental paradigm, children were taught the names of four unfamiliar objects: two high-salience objects and two low-salience objects. Their comprehension of the novel words was assessed in low-difficulty and high-difficulty trials. Gaze location was determined from video by trained research assistants. Results Contrary to initial predictions, findings indicated that high perceptual salience disrupted novel referent selection in the children with ASD but facilitated attention to the target object in age-matched TD peers. The children with ASD showed no significant evidence of successful novel referent selection in the high-difficulty trials. Exploratory reaction time analyses suggested that the children with autism showed “stickier” attention—had more difficulty disengaging (i.e., looking away)—from high-salience distracter images than low-salience distracter images, even though the two images were balanced in salience for any given test trial. Conclusions and Clinical Implications These findings add to growing evidence that high perceptual salience has the potential to disrupt novel referent selection in children with ASD. These results underscore the complexity of novel referent selection and highlight the importance of taking the immediate testing context into account. In particular, it is important to acknowledge that screen-based assessments and screen-based learning activities used with children with ASD are not immune to the effects of lower level visual features, such as perceptual salience.
The Effect of Galvanic Vestibular Stimulation on Visuospatial Cognition in an Incomplete Bilateral Vestibular Deafferentation Mouse Model

Objectives To evaluate the efficacy of galvanic vestibular stimulation (GVS) for recovering from the locomotor and spatial memory deficits of a murine bilateral vestibular deafferentation (BVD) model. Methods Male C57BL/6 mice (n = 36) were assigned to three groups: bilateral labyrinthectomy with (BVD_GVS group) and without (BVD_non-GVS group) the GVS intervention, and a control group with the sham operation. We used the open field and Y maze, and Morris water maze (MWM) tests to assess locomotor and visuospatial cognitive performance before (baseline) and 3, 7, and 14 days after surgical bilateral labyrinthectomy. For the GVS group, a sinusoidal current at the frequency at 1 Hz and amplitude 0.1 mA was delivered for 30 min daily from the postoperative day (POD) 0 to 4 via electrodes inserted subcutaneously close to both the bony labyrinths. Results Short-term spatial memory was significantly impaired in bilaterally labyrinthectomized mice (BVD_non-GVS group), as reflected by decreased spontaneous alternation performance in the place recognition test and time spent in the novel arm and increased same arm return in the Y-maze test, compared with the control. Long-term spatial memory was also impaired, as indicated by a longer escape latency in the hidden platform trial and a lower percentage of time spent in the target quadrant in the probe trial of the MWM. GVS application significantly accelerated the recovery of locomotion and short-term and long-term spatial memory deficits in the BVD mice. Conclusions Our data demonstrate that locomotion, short-term, and long-term (at least 2 weeks) spatial memory were impaired in BVD mice. The early administration of sinusoidal GVS accelerated the recovery of those locomotion and spatial memory deficiencies. GVS could be applied to patients with BVD to improve their locomotion and vestibular cognitive functioning.
Endoplasmic Reticulum Stress-Related Four-Biomarker Risk Classifier for Survival Evaluation in Esophageal Cancer

Purpose Esophageal cancer (EC) is a lethal digestive tumor worldwide with a dismal clinical outcome. Endoplasmic reticulum (ER) stress poses essential implications for a variety of tumor malignant behaviors. Here, we set up an ER stress-based risk classifier for assessing patient outcome and exploiting robust targets for medical decision-making of EC cases. Methods 340 EC cases with transcriptome and survival data from two independent public datasets (TCGA and GEO) were recruited for this project. Cox regression analyses were employed to create a risk classifier based on ER stress-related genes (ERGs) which were strongly linked to EC cases' outcomes. Then, we detected and confirmed the predictive ability of our proposed classifier via a host of statistical methods, including survival analysis and ROC method. In addition, immune-associated algorithm was implemented to analyze the immune activity of EC samples. Results Four EGRs (BCAP31, HSPD1, PDHA1, and UBE2D1) were selected to build an EGR-related classifier (ERC). This classifier could distinguish the patients into different risky subgroups. The remarkable differences in patient outcome between the two groups were observed, and similar results were also confirmed in GEO cohort. In terms of the immune analysis, the ERC could forecast the infiltration level of immunocytes, such as Tregs and NK cells. Conclusion We created a four-ERG risk classifier which displays the powerful capability of survival evaluation for EC cases.
A survey on big data systems

With the development of the latest technologies, a large amount of data is generated from various domains (such as optical observation and control, healthcare, sensors, user-generated data, Internet and nancial companies, supply chain systems, etc.) during the last two decades. (A more appropriate description could be "in nite" data, e.g., in the application of optical observation and control, data are continuously generated, cre-ating a data disaster.) The term of big data is coined to capture the profound meaning of this emerging trend.Compared with traditional data, big data exhibits some unique characteristics besides the sheer volume, such as commonly un-structured data and more real-time analysis requirements. The development of big data calls for new system architectures for data storage and large-scale data processing mechanisms. In this paper, we present a literature survey of big data analytics. Firstly, the de nition of big data and big data challenges are presented. Secondly, a systematic framework to decompose big data system into four sequential modules, namely data generation, data acquisition, data storage, and data analytics, which form the value chain for big data, is proposed. A detailed survey of numerous approaches and mechanisms related to each module, from research and industry communities is discussed. Finally, some evaluation benchmarks and potential scienti c problems in big data systems are outlined.