Miracle-dz/UniFAR-FTUs huggingface dataset:
Loop parallelization in the cloud using OpenMP and MapReduce

A busca por paralelismo sempre foi um importante objetivo no projeto de sistemas computacionais, conduzida principalmente pelo constante interesse na reducao de tempos de execucao de aplicacoes. Programacao paralela e uma area de pesquisa ativa, na qual o interesse tem crescido devido a emergencia de arquiteturas multicore. Por outro lado, aproveitar as grandes capacidades de computacao e armazenamento da nuvem e suas caracteristicas desejaveis de flexibilidade e escalabilidade oferece varias oportunidades interessantes para abordar problemas de pesquisa relevantes em computacao cientifica. Infelizmente, em muitos casos a implementacao de aplicacoes na nuvem demanda conhecimento especifico de interfaces de programacao paralela e APIs, o que pode se tornar um fardo na programacao de aplicacoes complexas. Para superar tais limitacoes, neste trabalho propomos OpenMR, um modelo de execucao baseado na sintaxe e nos principios da API OpenMP que facilita a tarefa de programar sistemas distribuidos (isto e, clusters locais ou a nuvem remota). Especificamente, este trabalho aborda o problema de executar a paralelizacao de lacos, usando OpenMR, em um ambiente distribuido, atraves do mapeamento de iteracoes do laco para nos MapReduce. Assim, a interface de programacao para a nuvem se torna a propria linguagem, livrando o desenvolvedor da tarefa de se preocupar com detalhes da distribuicao de cargas de trabalho e dados. Para avaliar a validade da proposta, modificamos benchmarks da suite SPEC OMP2012 para se encaixarem no modelo proposto, desenvolvemos outros toy benchmarks que sao I/O-bound e executamo-os em duas configuracoes: (a) um cluster de computadores disponivel localmente atraves de uma LAN padrao; e (b) clusters disponiveis remotamente atraves dos servicos Amazon AWS. Comparamos os resultados com a execucao utilizando OpenMP em uma arquitetura SMP e mostramos que a tecnica de paralelizacao proposta e factivel e demonstra boa escalabilidade. Abstract
Electrocardiographic abnormalities in Cameroonian university athletes: a cross-sectional study

Background: Pre-participation screening is not commonly done in Cameroonian amateur athletes. There are therefore very few studies describing the athlete’s heart among Cameroonians. This study aims to describe electrocardiogram (ECG) abnormalities in a group of Cameroonian athletes participating in university games. Methods: This was a cross-sectional study conducted at the Yaounde University Teaching Hospital. All participants underwent a 12-lead ECG. The ECG analysis was done using the Seattle criteria. Results: We recruited 145 athletes, of whom 37.2% were female. The average age is 23±3 years. The pathological abnormalities including left atrial hypertrophy, T wave inversion, left axial deviation and right ventricular hypertrophy. These were found in 5.6% of athletes, mostly in the males. Conclusions: The abnormalities observed on the ECG in the University of Yaounde 1 athletes are similar to those described in the literature.
Globally optimal vector quantizer design using stochastically competitive learning algorithm

This paper presents a learning scheme called stochastically competitive learning algorithm (SCLA) for globally optimal vector quantizer design. The SCLA incorporates the idea of stochastic relaxation into the on-line learning scheme of the Kohonen Learning Algorithm (KLA). The key of the SCLA is to replace the Euclidean winner rule with the stochastic competition such that at a given instant any codevector may be updated according to a probability related with its distance to the input. With computer simulations, the effectiveness of the SCLA has been demonstrated by comparing its performance with that of the GLA. <<ETX>>
A Serological Investigation of Avian Reovirus Infections in Poultry Enterprises in Balikesir and Bolu Provinces in Turkey

Avian reoviruslari (ARV) viral artrit sendromunun nedenidir. Ayrica, ARV tek basina ya da diger ajanlar ile birlikte, enterik solunum yolu hastaliklari, miyokardit, hepatit ve tavuklarda buyume geriligi/malabsorpsiyon sendromu gibi klinik tablolara da neden olabilir. Bu calismada, Turkiye’nin onemli kanatli yetistiriciligi merkezlerinden olan Balikesir ve Bolu illerindeki broiler ve broiler damizlik yetistiren tavukculuk isletmelerinde ARV infeksiyonunun seroepizootiyolojik durumu arastirildi. Ticari Enzyme Linked Immunosorbent Assay (ELISA) (Avian Reovirus Antibody test Kit ® , BioChek, Gouda, Hollanda) kullanilarak 11 broiler ve 6 broiler damizlik yetistiriciligi isletmesindeki ARV’una karsi asili ve asisiz tavuklardan alinan toplam 920 serum ornegi ARV antikorlari yonunden test edildi. Test sonucunda butun kumeslerde ARV antikorlari bulundu. Etci tavuklar gozonune alindiginda, asilanmis tavuklarin %78’i (156/200) ve asilanmamis tavuklarin %84.18 (266/316)’inde antikor varligi saptandi. Bu oranlar, asilanmis ve asilanmamis damizlik tavuklarda sirasiyla %86.5 (173/200) ve %89.71 (183/204) idi. Balikesirdeki etci tavuklar gozonune alindiginda, seropositivite asilanmamis hayvanlarda asilanmislara oranla daha yuksekti (%92.03 ve %78, P<0.001). Ancak Bolu’daki damizlik tavuklarda asilanmis ve asilanmamis hayvanlar arasi seropositivite oranlari benzerdi (asilanmis hayvanlarda 93% ve asilanmamislarda 89.71%, P=0.35). Ayni ilden toplanan ornekler karsilastirildiginda, Balikesir’den toplanan etci ve damizlik tavuk orneklerindeki seropositivite orani benzerdi (78% ve 80%, P=0.69). Ancak Bolu’daki asilanmamis hayvanlar gozonune alindiginda, seropositivite damizlik tavuklarda etci tavuklara oranla daha yuksekti (78.09 ve 89.71, P<0.01). Bu calismada elde edilen sonuclar kanatli endustrisinde onemli ekonomik ve verim kaybina yol acan ARV enfeksiyonlarinin Bolu ve Balikesir illerinde cok yaygin oldugunu gostermektedir. Asilanmamis hayvanlardaki yuksek antikor titresi bu illerde asilama programlarinin duzenli olarak uygulanmadigini dusundurmektedir. Bu yuzden ARV enfeksiyonlarini onlemek icin virolojik ve epidemiyolojik calismalarin baslatilmasi, mucadele programlarinin gelistirilmesi ve hayvanlarin duzenli olarak asilanmalari gerekmektedir.
Predicting prognosis of endometrioid endometrial adenocarcinoma on the basis of gene expression and clinical features using Random Forest

Traditional clinical features are not sufficient to accurately judge the prognosis of endometrioid endometrial adenocarcinoma (EEA). Molecular biological characteristics and traditional clinical features are particularly important in the prognosis of EEA. The aim of the present study was to establish a predictive model that considers genes and clinical features for the prognosis of EEA. The clinical and RNA sequencing expression data of EEA were derived from samples from The Cancer Genome Atlas (TCGA) and Peking University People's Hospital (PKUPH; Beijing, China). Samples from TCGA were used as the training set, and samples from the PKUPH were used as the testing set. Variable selection using Random Forests (VSURF) was used to select the genes and clinical features on the basis of TCGA samples. The RF classification method was used to establish the prediction model. Kaplan-Meier curves were tested with the log-rank test. The results from this study demonstrated that on the basis of TCGA samples, 11 genes and the grade were selected as the input features. In the training set, the out-of-bag (OOB) error of RF model-1, which was established using the ‘11 genes’, was 0.15; the OOB error of RF model-2, which was established using the ‘grade’, was 0.39; and the OOB error of RF model-3, established using the ‘11 genes and grade’, was 0.15. In the testing set, the classification accuracy of RF model-1, model-2 and model-3 was 71.43, 66.67 and 80.95%, respectively. In conclusion, to the best of our knowledge, the VSURF was used to select features relevant to EEA prognosis, and an EEA predictive model combining genes and traditional features was established for the first time in the present study. The prediction accuracy of the RF model on the basis of the 11 genes and grade was markedly higher than that of the RF models established by either the 11 genes or grade alone.
Predictors of problematic substance use 18 years after treatment: a longitudinal cohort study of persons with substance use disorders

Abstract Objective To examine the extent of substance use and explore which baseline factors predicted current problematic substance use 18 years after treatment in surviving patients. Methods: This longitudinal cohort study used a mailed self-report questionnaire on a group of patients with long-term problematic substance use, and high psychiatric comorbidity, 18 years after they received treatment for substance use disorders. A consecutive sample of patients with substance use disorders (n = 287, mean age 38.6 years, 72% male; 45% alcohol use disorder only and 55% other substance use disorders) was recruited in 1997 and 1998. Baseline measurements included the Composite International Diagnostic Interview, the Millon Clinical Multiaxial Inventory II, the Hopkins Symptom Checklist 25 and demographic data. In the 18-year follow-up study (n = 91), the Alcohol Use Disorders Identification Test and the Drug Use Disorders Identification Test were used. A multivariate logistic regression analysis was used to identify predictors of having current problematic substance use 18 years after treatment for participants with full data collection at baseline (n = 75). Results: Of the 91 participants who responded at the 18-year follow-up study, 47 (52%) had no current problematic substance use and 44 (48%) had current problematic substance use during the past 12 months. Of participants with no current problematic substance use, 17 (36%) reported total abstinence from alcohol, illicit substances and misuse of pharmaceuticals during the past 5 years. Mental distress measured with the Hopkins Symptom Checklist 25 at baseline predicted having current problematic substance use 18 years after treatment (n = 75). Conclusions: In a group of patients with long lasting substance use disorders and high psychiatric comorbidity that entered treatment, close to half of those participating at a follow-up study had current problematic substance use during the past 12 months 18 years later. Mental distress at baseline predicted having current problematic substance use 18 years after treatment, adjusted for other factors. Self-reported general mental health symptoms can be a relevant predictor of the long-term course of substance use disorders.
Accident Detection Using Android Smart Phone

Nowadays, nobody in this world is ready to lookwhat’s happening around them. Even though, if any accident occurs no one cares about it. This is an intention to implement an innovative solution for this problem by developing an Accident detection System using android smart phone from the accident zone.This system has been developed and implemented using the heart beat sensor based mobile technology integrated with the evolving android smart phone. The application for accident detection which primarily measures the heart beat rate using heart beat sensor. After getting the signal from sensor this system filter out the background noise and detect only the sound of the pulse. The microphone would need to be held near to the heart or somewhere the movement of blood made enough noise to be detect using some sort of audio signal processing. Then count the time between each pulse to get the pulse rate. The normal human heart beat range is 60 to 100 Beats Per Minute (BPM). If there is any variation from the normal heart beat range, then the system detects that may be an accident or not. Then the system will immediately transmit the location of the accident to the pre-configured contacts through Short Message Service (SMS). In case of an accident is occurred then the driver is prompted to respond by touch or voice in order to eliminate any false detection So the proposed system ensures that to reduce the human death ratio by accidents.
Plan Projection, Execution, and Learning for Mobile Robot Control

Most state-of-the-art hybrid control systems for mobile robots are decomposed into different layers. While the deliberation layer reasons about the actions required for the robot in order to achieve a given goal, the behavioral layer is designed to enable the robot to quickly react to unforeseen events. This decomposition guarantees a safe operation even in the presence of unforeseen and dynamic obstacles and enables the robot to cope with situations it was not explicitly programmed for. The layered design, however, also leaves us with the problem of plan execution. The problem of plan execution is the problem of arbitrating between the deliberationand the behavioral layer. Abstract symbolic actions have to be translated into streams of local control commands. Simultaneously, execution failures have to be handled on an appropriate level of abstraction. It is now widely accepted that plan execution should form a third layer of a hybrid robot control system. The resulting layered architectures are called three-tiered architectures, or 3T architectures for short. Although many high level programming frameworks have been proposed to support the implementation of the intermediate layer, there is no generally accepted algorithmic basis for plan execution in three-tiered architectures. In this thesis, we propose to base plan execution on plan projection and learning and present a general framework for the self-supervised improvement of plan execution. This framework has been implemented in Appeal, an Architecture for Plan Projection, Execution And Learning, which extends the well known Rhino control system by introducing an execution layer. This thesis contributes to the field of plan-based mobile robot control which investigates the interrelation between planning, reasoning, and learning techniques based on an explicit representation of the robot’s intended course of action, a plan. In McDermott’s terminology, a plan is that part of a robot control program, which the robot cannot only execute, but also reason about and manipulate. According to that broad view, a plan may serve many purposes in a robot control system like reasoning about future behavior, the revision of intended activities, or learning. In this thesis, plan-based control is applied to the self-supervised improvement of mobile robot plan execution.
EFFECTS OF ARTERIAL PLATOON PROGRESSION ON CAPACITY

Platoon progression aims at reducing stops and delays in undersaturated conditions. Numerous factors militate against its effectiveness. Unfavorable intersection spacing, turning vehicles and platoon dispersion - the latter abetted by midblock friction, lane changes, unbalanced lane use and differences in vehicle and driver characteristics - all restrict the successful operation of progression to comparatively moderate traffic volumes, and they all tend to lower system capacity. Transferring green signal time to the arterial in order to maintain uninterrupted progression reduces cross flow capacity, while a split adjustment to increase cross flow capacity interferes with satisfactory progression on the arterial. Designed to minimize stops and delays, progression does not maximize system capacity as well; the two goals of minimum delay and maximum capacity are in conflict. The problems of traffic in free-flowing and in congested conditions, it has been said, are so different that different methods have to be employed in their solution: more efforts should be directed at capacity improvements and towards staving off the onset of congestion. Geometric improvements rather than signal timing strategies are the proper answer to capacity limitations.
Nonstochastic adaptive decision fusion in distributed-detection systems

Implementing the optimal Neyman-Pearson (NP) fusion rule in distributed detection systems requires the sensor error probabilities to be a priori known and constant during the system operation. Such a requirement is practically impossible to fulfil for every resolution cell in a multiflying target multisensor environment. The true performance of the fusion center is often worse than expected due to fluctuations of the observed environment and instabilities of sensor thresholds. This work considers a nonparametric data fusion situation in which the fusion center knows only the number of the sensors, but ignores their error probabilities and cannot control their thresholds. A data adaptive approach to the problem is adopted, and combining P reports from Q independent distributed sensors through a least squares (LS) formulation to make a global decision is investigated. Such a fusion scheme does not entail strict stationarity of the noise environment nor strict invariance of the sensor error probabilities as is required in the NP formulation. The LS fusion scheme is analyzed in detail to simplify its form and determine its asymptotic behavior. Conditions of performance improvement as P increases and of quickness of such improvement are found. These conditions are usually valid in netted radar surveillance systems.
Appraisal of state-of-the art Systems-ADME/Tox: Resources and network approaches

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.
Data predistortion with adaptive fuzzy systems

Presents two data predistortion methods for communication transmitters that employ adaptive fuzzy systems. The first is based on indirect learning and does not require a model of the high power amplifier. The second requires an estimated model of the pulse shaping filter and the high power amplifier before training. Simulations with 16 QAM signals show that the performance of fuzzy predistorters are roughly the same as the performance of a third order polynomial predistorter with odd-ordered terms only. Fuzzy and polynomial predistorters in the second method both contain nonlinear parameters, which are sensitive to initial conditions. The fuzzy predistorter has the unique advantage in that the initial values of all adjustable parameters can be set easily based on their linguistic meanings.
Augmented Mean Arterial Blood Pressure And Vasopressor Selection In Patients With Spinal Cord Injury

Blood pressure management in the acute period following spinal cord injury is a critical concern; one that anesthesia and critical care providers are often able to directly measure and regulate. It has been hypothesized that supraphysiologic blood pressure maintenance during this acute phase may improve recovery, however there is limited high-quality evidence to reinforce and guide management. Based on included prospective and retrospective studies, which provide the highest level of evidence available, The American Association of Neurologic Surgeons does provide the following level III recommendation: MAP goals of 85–90 mm Hg for 5–7 days postinjury should be considered. With regard to the optimal vasopressor, dopamine should be avoided. Norepinephrine should be considered as a first-line agent for cervical and upper thoracic spinal cord injuries, given evidence that it has a lower risk profile than dopamine. For injuries in the mid to lower thoracic spine, norepinephrine or phenylephrine should be considered as first-line agents. In summary, blood pressure management should not be overlooked and proper utilization could result in significant improvements in quality of life and hope for recovery.
Two dimensional nesting problem: artificial neural network and optimization approach

An approach that integrates artificial neural networks and optimization methods for automating regularly-shaped pattern generation processes is proposed. In the proposed approach the artificial neural network model is used for generating rectangular pattern configurations with an acceptable scrap. Rectangular patterns of different sizes are used as the input of the neural network to generate location and rotation of each pattern when they are combined. The pattern configurations generated through the neural network are represented as decision variables of a mathematical programming model for determining an efficient nesting of different sizes of rectangular patterns for meeting the demand of a given planning horizon. The initial results obtained based on rectangular patterns are reported. <<ETX>>
Predictive Values of Serum Uric Acid, Mean Platelet Volume and Plateletcrit on Benign Paroxysmal Positional Vertigo

Benign paroxysmal positional vertigo (BPPV) is a common healthcare problem in the elderly. BPPV is characterized by short vertigo episodes with movement of the head (1). BPPV has a high incidence rate, approximately 17-20% of peripheral vertigo (2). Most BPPV cases are idiopathic. However, some risk factors such as ear surgery, labyrinthitis, vestibular neuritis, and head trauma are common causes for BPPV. In addition, the symptoms and prevalence increase with age (3). The common diagnostic procedure includes visualization of nystagmus and medical history of the patient (4). The prevalence of BPPV is reported as 25% in older people over 70 years with complaints of more than one year (5). More than 90% of BPPV patients can be treated with maneuvers. Most patients with vertigo state that closing the eyes reduces the symptoms (6). Current treatments include manual repositioning procedures such as Gufoni, Semont, Epley or Barbecue procedures (2).
Development of correction chain for multispectral airborne video data

A correction chain for the spectral and spatial distortions of multispectral airborne video data is developed in order to enable the use of the data in quantitative studies of forest and land use. The correction chain includes corrections for sensor and system induced effects, for bidirectional effects and for slope and aspect effect. The usefulness of the corrections was tested by a land use classification in the German Alps using uncorrected and corrected data. The classification accuracy was increased from 64.3% to 85.8% by the correction chain.