Miracle-dz/UniFAR-FTUs huggingface dataset:
School Demands and Coping Resources−Associations with Multiple Measures of Stress in Mid-Adolescent Girls and Boys

Stress, and stress-related health complaints, are common among young people, especially girls. Since studies have shown that school demands are an important driver of stress in adolescents, identifying if school-based resources can protect against stress is highly relevant. The aim of this study was to analyse task-related demands and task-related coping resources as aspects of the school work environment of potential relevance for stress in mid-adolescent girls and boys. The data came from “The School Stress and Support study” (TriSSS) conducted among students in grades 8 and 9 (aged 14–16 years). Self-reports of demands, coping resources, stress, as well as recurrent pain, were collected through questionnaires (n = 411). A subsample of students (n = 191–198) also provided salivary samples, which were analysed for the stress marker cortisol. Linear (OLS) and binary logistic regression analyses showed that higher demands were associated with more perceived stress, a higher likelihood of recurrent pain, and a lower cortisol awakening response. Greater coping resources were associated with less perceived stress and a lower likelihood of recurrent pain, but there was no association with cortisol. The strength of the associations differed by gender. The findings suggest that schools can promote student wellbeing by providing clear and timely information and teacher support to the students, especially for boys. Identifying specific features of the schoolwork that give rise to stress and to modify these accordingly is also of importance, especially for girls.
Adaptive Polling in Hierarchical Social Networks Using Blackwell Dominance

This paper presents adaptive polling algorithms and their analysis for social networks having a hierarchical influence structure. The adaptive polling problem on the social network is formulated as a partially observed Markov decision process (POMDP). Our main results exploit the structure of the polling problem to determine novel conditions for Blackwell dominance that arise in hierarchical social influence networks. The Blackwell dominance conditions enable the construction of myopic policies that provably upper bound the optimal policy of the POMDP for adaptive polling. Adaptive versions of intent polling and expectation polling are developed using Blackwell dominance, and they are inexpensive to implement. For polling problems not having a Blackwell dominance structure, the Le Cam deficiency is used to determine approximate Blackwell dominance; this is used to develop an adaptive version of the recently proposed Neighbourhood Expectation Polling algorithm. The proposed Blackwell dominance conditions also facilitate the comparison of Rényi divergence and Shannon capacity of more general channel structures that arise in polling hierarchical social influence networks. Numerical examples are provided to illustrate the adaptive polling policies with parameters estimated from YouTube data.
On the application of quantization for mobile optimized convolutional neural networks as a predictor of realtime ageing biomarkers

In this paper we propose a mobile optimized deep learning network based on the VGG16 architecture. Compared to the classical approach, after training has been performed the model is converted to a quantized equivalent where 32 bit floating point operations are exchanged for 8 bit ones. This reduces the strain on mobile memory and local caches while simultaneously reducing the computational complexity and energy requirement of the entire deep learning model. Aggregated testing has been performed to validate the complexity hypothesis and the quantized model has been compared to the original model in terms of accuracy. The results show that for a modest decrease in accuracy, the quantized model takes up 75% less disk space and through the 8 bit operations the computational complexity is reduced, showing a load and inference speed up of 3 – 4 times faster than the original model.
Current Concepts in the Operative Management of Acromioclavicular Dislocations: A Systematic Review and Meta-analysis of Operative Techniques

Background: Acromioclavicular (AC) instability is a frequent injury affecting young and athletic populations. Symptomatic, high-grade dislocations may be managed by a myriad of operative techniques that utilize different grafts to achieve reduction. Comparative data are lacking on the ability of these techniques to achieve excellent patient outcomes and stable AC reduction and to minimize complications. Purpose: To systematically review the outcomes and complications of different techniques of AC joint reconstruction. Study Design: Systematic review and meta-analysis. Methods: The MEDLINE, Scopus, Embase, and Cochrane Library databases were accessed to perform a systematic review of the scientific literature from 2000 to 2018 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria with the following keywords: “acromioclavicular” and “reconstruction. ” Included articles were evaluated for loss of reduction, complication rate, revision rate, and change in coracoclavicular distance. Articles were stratified by graft and surgical material used: suture only, Endobutton with suture, TightRope, GraftRope, synthetic artificial ligament, tendon graft, and Weaver-Dunn coracoacromial ligament transfer. These outcomes were pooled using a random-effects model and stratified by surgical technique and arthroscopic versus open reconstruction. Results: Fifty-eight articles were included in the analysis, with 63 homogeneous populations composed of 1704 patients. The mean age was 37.1 years (range, 15-80 years) with a mean follow-up of 34.3 months (range, 1.5-186 months). The overall failure rate was 20.8% (95% CI, 16.9%-25.2%). The overall pooled complication rate was 14.2% (95% CI, 10.5%-18.8%). The most common complications were infection (6.3% [95% CI, 4.7%-8.2%]), fracture to the coracoid or distal clavicle (5.7% [95% CI, 4.3%-7.6%]), and hardware/button failure (4.2% [95% CI, 3.1%-5.8%]). There were no differences between arthroscopic and open techniques in regard to loss of reduction (P = .858), overall complication rate (P = .774), and revision rate (P = .390). Open surgery had a greater rate of clavicular/coracoid fractures than arthroscopic surgery (P = .048). Heterogeneity, best assessed from the pooled loss of reduction, was measured as I2 = 64.0%. Conclusion: Open and arthroscopic AC joint reconstruction techniques have no differences in loss of reduction, the complication rate, and the revision rate based on the available literature. Complications are significant, and profiles vary between surgical techniques, which should be evaluated in the decision making of selecting the technique.
Kinetic Analysis of Isometric Back Squats and Isometric Belt Squats

Abstract Layer, JS, Grenz, C, Hinshaw, TJ, Smith, DT, Barrett, SF, and Dai, B. Kinetic analysis of isometric back squats and isometric belt squats. J Strength Cond Res 32(12): 3301–3309, 2018—Belt squats seem to provide an alternative to back squats. However, it is not clear how musculoskeletal loading differs between the two. This study compared lower extremity and low-back kinetics during isometric back squats and isometric belt squats. Sixteen men (age: 22.6 ± 3.4 years; height: 1.74 ± 0.11 m; mass: 82.0 ± 5.6 kg) and 10 women (age: 21.5 ± 2.5 years; height: 1.64 ± 0.10 m; mass: 68.9 ± 7.1 kg) performed isometric back squats and belt squats at 4 squat depths. Joint resultant moments were calculated from kinematic and ground reaction force data. Linear interpolation was used to estimate peak vertical forces and joint moments at a 45° thigh segment angle. Subjects increased peak forces, ankle moments, and knee moments but decreased low-back moments from back to belt squats (p ⩽ 0.023). Hip moments did not significantly change between 2 squats. Subjects demonstrating smaller ankle and knee moments during back squats showed greater increases in these moments from back to belt squats (p ⩽ 0.012, R2 ⩽ 0.24). Subjects whose back squats were characterized by greater low-back moments displayed greater decreases in low-back moments from back to belt squats (p < 0.001, R2 = 0.98). Compared with isometric back squats, isometric belt squats may provide a similar or greater external loading for the musculoskeletal system of the lower extremities while reducing external spinal loading. Belt squats may be considered by individuals with upper-body or spinal injuries and those displaying excessive external back moments.
GraPU: Accelerate Streaming Graph Analysis through Preprocessing Buffered Updates

Streaming graph analysis extracts timely insights from evolving graphs, and has gained increasing popularity. For current streaming graph analytics systems, incoming updates are simply cached in a buffer, until being applied onto existing graph structure to construct a new snapshot. Iterative graph algorithms then work on the new snapshot to produce up-to-date analysis result. Nevertheless, we find that for widely used monotonic graph algorithms, the buffered updates can be effectively preprocessed to achieve fast and accurate analysis on new snapshots. To this end, we propose GraPU, a streaming graph analytics system for monotonic graph algorithms. Before applying updates, GraPU preprocesses buffered updates in two sequential phases: 1) Components-based Classification first identifies the effective graph data that are actually affected by current updates, by classifying the vertices involved in buffered updates according to the predetermined connected components in underlying graph; 2) In-buffer Precomputation then generates safe and profitable intermediate values that can be later merged onto underlying graph to facilitate the convergence on new snapshots, by precomputing the values of vertices involved in buffered updates. After all updates are applied, GraPU calculates new vertex values in subgraph-centric manner. GraPU further presents Load-factors Guided Balancing to achieve subgraph-level load balance, by efficiently reassigning some vertices and edges among subgraphs beforehand. Evaluation shows that GraPU outperforms state-of-the-art KineoGraph by up to 19.67x, when running four monotonic graph algorithms on real-word graphs.
Predicting American Board of Emergency Medicine Qualifying Examination Passage Using United States Medical Licensing Examination Step Scores

Background: The objective of the current study was to determine whether emergency medicine residents' United States Medical Licensing Examination (USMLE) scores are significantly associated with first-attempt passage of the American Board of Emergency Medicine (ABEM) qualifying (written) examination. We hypothesized that USMLE Step 2 Clinical Knowledge (CK) scores would be useful in predicting students who passed the ABEM qualifying examination on their first attempt. Methods: For this retrospective cohort study, we examined the data of residents who successfully completed training at two emergency medicine residency programs between the years 2002-2013. Because scores on the USMLE Step examinations varied greatly across years, we obtained means and standard deviations from the National Board of Medical Examiners. We subtracted the mean score for the year each resident took the examination from the resident's examination score, creating centered Step 1 and centered Step 2 CK scores. Results: A multivariate logistic regression analysis indicated that centered Step 2 CK scores could be used to predict the odds of passing the ABEM qualifying examination (odds ratio = 1.05 [95% confidence interval 1.02 to 1.08, P < 0.001]). Using a Step 2 CK score cutoff of 7 points lower than the mean yielded 64% sensitivity and 81% specificity for predicting passage of the ABEM written examination on the first attempt. Conclusion: Program directors and selection committees may wish to consider whether applicants' Step 2 CK scores are near the national average when making ranking decisions, as this variable is highly predictive of passing the ABEM qualifying examination on the initial attempt.
Robust Optimization of Renewable Energy Based Distribution Networks Considering Electrical Energy Storage and Fuel Cell

The uncertainty of power distribution networks parameters such as power market price and power generation output of renewable energy sources defines the requirement for a strong method to handle the uncertain parameters. This paper proposes a rzobust optimization (RO) model for dealing with uncertainty of power market price in day-ahead optimal scheduling of distribution networks. The effect of fuel cell (FC) and electrical energy storage systems in handling peak demand management are considered in optimal scheduling of network containing wind turbine and photovoltaic cell. The proposed model is applied on IEEE 33-bus system in order to obtain the minimum operation cost of the system with optimal generation scheduling of the production units and optimal charge-discharge scheduling of the energy storage units and fuel cell.
Collaborative filtering with temporal dynamics

Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics is essential for designing recommender systems or general customer preference models. However, this raises unique challenges. Within the ecosystem intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance decay approaches cannot work, as they lose too many signals when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long-term patterns. We show how to model the time changing behavior throughout the life span of the data. Such a model allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie-rating dataset underlying the Netflix Prize contest. Results are encouraging and better than those previously reported on this dataset. In particular, methods described in this paper play a significant role in the solution that won the Netflix contest.
A Qualitative Study of the Real-world Experiences of Infectious Diseases Fellows Regarding Antibiotic Stewardship

Abstract Background Antibiotic-resistant bacterial infections are a major threat to public health, yet improper use of antibiotics remains high. Infectious Diseases (ID) fellows play a major role in antibiotic stewardship efforts, but there is little research on how they view stewardship activities. We performed a qualitative study to explore ID fellows’ experiences and perspectives regarding their antibiotic stewardship training and their role as future antibiotic stewards. Methods We conducted 17 in-depth interviews with ID fellows across the country. The interviews were transcribed verbatim by the study team, and we used grounded theory to generate themes from these interviews Results Fellows focused on concrete tasks of stewardship such as performing antibiotic approvals, didactic and case-based education, and interactions with other physicians and pharmacists. There was little focus on the broader public health relevance of antibiotic stewardship. Pharmacists, not ID physician leaders, were identified as fellows’ primary resource for antibiotic teaching. Several fellows suggested that stewardship programs should be led by pharmacists. Conclusions ID fellowship training is not successfully conveying the public health importance of antibiotic stewardship or the role of ID physicians as leaders of antibiotic stewardship programs. Fellows are more focused on concrete tasks related to stewardship. ID training programs and societies should consider developing robust curricula involving fellows in the operation of the stewardship program itself, not solely in antibiotic approvals, emphasizing aspects of the program such as complex problem solving that fellows find most compelling, and emphasizing the important role these programs serve in improving public health.
Deciphering the Genetic Architecture of Cooked Rice Texture

The textural attributes of cooked rice determine palatability and consumer acceptance. Henceforth, understanding the underlying genetic basis is pivotal for the genetic improvement of preferred textural attributes in breeding programs. We characterized diverse set of 236 Indica accessions from 37 countries for textural attributes, which includes adhesiveness (ADH), hardness (HRD), springiness (SPR), and cohesiveness (COH) as well as amylose content (AC). A set of 147,692 high quality SNPs resulting from genotyping data of 700K high Density Rice Array (HDRA) derived from the Indica diversity panels of 218 lines were retained for marker-trait associations of textural attributes using single-locus (SL) genome wide association studies (GWAS) which resulted in identifying hotspot on chromosome 6 for AC and ADH attributes. Four independent multi-locus approaches (ML-GWAS) including FASTmrEMMA, pLARmEB, mrMLM, and ISIS_EM-BLASSO were implemented to dissect additional loci of major/minor effects influencing the rice texture and to overcome limitations of SL-based GWAS approach. In total 224 significant quantitative trait nucleotide (QTNs) were identified using ML-GWAS, of which 97 were validated with at least two out of the four multi-locus methods. The GWAS results were in accordance with the very significant negative correlation (r = −0.83) observed between AC and ADH, and the significant correlation exhibited by AC (r < 0.4) with HRD, SPR, and COH. The novel haplotypes and putative candidate genes influencing textural properties beyond AC will be a useful resource for deployment into the marker assisted program to capture consumer preferences influencing rice texture and palatability.
Physical Conditions for Full Control of Transmission Through Non-Reflecting Metasurfaces

Electrically thin composite layers (metasurfaces) can be used to shape transmitted fields. It is usually desirable that reflections from the metasurface are absent, while the phase of the transmission coefficient can be fully controlled by tuning the metasurface structure. More generally, it is desirable to shape both the phase and amplitude of transmitted waves, still allowing no reflections. In this presentation we discuss necessary physical conditions for realizing these properties, both for normal and oblique illuminations, and present examples of new topologies of metasurfaces for control of transmitted waves.
On RF Telemetry for Implantable Medical Devices: A Communication Theory Perspective

Bio-engineering is one of the emerging and fast-growing fields today where medical and engineering disciplines converge. Several technologies appeared to provide an efficient treatment of diseases or human deficiencies. One of these technologies is the implantable medical devices (IMDs). These devices are supported with computing, decision-making and communication capabilities. In this paper, a survey on the RF telemetry, including the parameters that have effects on the overall system performance, such as power consumption, RF bands and security, is presented. Furthermore, this paper provides an overview of different channel models of wireless body area networks (WBANs). Then, the effects of channel behavior and challenges in channel modeling are considered. Additionally, many communications protocols which can be used to increase the data rates, including ultra-wide band techniques, infra-red transmission technique, and also body conduction techniques, are discussed.
Sequence-aware recommendation

In recent years, more and more recommendation algorithms have been proposed that are based on time-ordered user interaction logs. Algorithms for session-based recommendation tasks are among the most prominent examples of such approaches. Differently from the more traditional matrix completion algorithms, where for each user-item pair only one interaction (e.g., a rating) is considered, sequence-aware algorithms are typically designed to learn sequential patterns from user behavior data. These patterns can then be used to predict the user's next action within an ongoing session or to detect short-term trends in the community. In this tutorial, we first outline the application areas of sequence-aware recommendation. We then focus on sequential and session-based recommendation techniques and discuss algorithmic proposals as well as evaluation challenges. Finally, the tutorial will be concluded by an hands-on session.
Power Management and Control of HEV using Model Predictive Control

In this date to improve the fuel economy and get less emission, Hybrid Electric Vehicle (HEV) is developed. Energy management system has been developed where we can exploit the operating points of both the electrical system and the internal combustion engine. Typical power-split HEV model is built in this paper. To improve the tracking of the desired speed set points, Model Predictive control is used when subjected to the input and output constraints. The combination of both the series and parallel architecture is used in power split HEV to get different modes of operation. The combination and split of mechanical power generation is done with the use of planetary gear set. So near optimal fuel and energy management is done for the different modes of operation of the HEV with the implementation of Model Predictive Control.
Prototype-based Classifier for Automatic Diagnosis of Depressive Mood

Psychosocial disorders are a major public health issue, possibly leading to severe short and/or long-term consequences on both personal and professional levels. These troubles have to be diagnosed by a specialist doctor. However, Affective Computing (AC) can provide him with an assistance, as a both fast and inexpensive monitoring to the patient. Therefore, we propose a real-time automated tool for evaluating depressive moods, by way of a simple webcam observing the face. We have trained a classifier on AVEC2014 challenge database to extract prototypes of depressive faces, with respect to the Beck Depression Inventory II score (BDI-II). The system achieves with succes rates above 90%, an RMSE estimated at 4.30 and a realtime diagnosis abilitv,
The role of 5G wireless networks in the internet-of- things (IoT)

The number of mobile hosts that will be used in 2020 will be exceeding 50 billion hosts [5]. This increase in the amount of mobile devices is intersected with the evolution of the Internet of things (IoT) technology. The demand to create intelligent environments such as smart cities and smart homes is increased. The evolution in this area results on the concept of IoT where devices of everyday are connected [5]. These interactions between a large numbers of heterogeneous devices increase a substantial demand on providing high connectivity, extremely high data rates, low latency, designing special applications to serve the IoT and many other communication requirements [8]. Thus, the promising 5G cellular networks can be considered as the key enabler for IoT technology. Moreover, the 5G wireless networks can be used to achieve the high communication requirements of the IoT. This survey is conducted to investigate the role of 5G cellular networks in the evolution of IoT. Also, this survey aims to compare existing wireless networks to emphasize the role of 5G as enabler driver for IoT. This survey highlights both requirements and enabling technologies of the IoT. Furthermore, it discusses some of the challenges that face the evolution of IoT.