Miracle-dz/UniFAR-FTUs huggingface dataset:
Jumper's knee: An epidemiological study of volleyball players

In brief: We studied 407 volleyball players to investigate the factors that cause patellar or quadriceps tendinitis (jumper's knee). Athletes from 30 teams in various divisions of the Italian Volleyball Federation championship were interviewed personally or by questionnaire. Factors investigated included age and sex, years of play, frequency of training sessions and games, type of playing surface, and type of training. The results showed that symptoms occurred most often among athletes playing five times a week or more and among those playing on hard surfaces, such as cement. Age, sex, years of play, and type of training were less significant factors.
Deep Next-Generation Sequencing Enables Mutational Profiling of Classical Hodgkin Lymphoma

Effective treatments are available for classical Hodgkin Lymphoma (cHL); however, for many patients the success of initial treatment is often followed by a high rate of treatment-related conditions including infertility and secondary malignancies. Due to the rarity of the malignant Hodgkin and Reed/Sternberg (HRS) cells in cHL, molecular genotyping has not been successfully implemented. The inability to obtain the mutational profile currently precludes genotyping of cHL which is informative for clinical management in numerous other cancers. The genetic pathways implicated in the pathogenesis and oncogenic activity of cHL have been described and leveraged for the selection of first-line therapeutics. However, a comprehensive evaluation of relevant mutations in the SOCS1 gene remain elusive despite its role in cHL being verified in several studies. To assess the feasibility of next-generation sequencing (NGS) for genotyping in cHL, an NGS panel employing Anchored Multiplex PCR (AMP) was designed to target and sequence three relevant genes: B2M, JAK2, and SOCS1. The primary endpoint was suitable detection of low-lying mutations in the rare HRS cells which could be adequately detected by a clinical-grade laboratory assay. Secondary endpoints were the prevalence of mutations and establishing the limit of detection (LOD). Sufficient average sequencing depth for SOCS1 was achieved in normal feasibility samples at 236 total reads. In cHL cases sequencing coverage over genes B2M, JAK2 and SOCS1 was achieved at an average of 503 reads, 462 reads, and 191 reads respectively, which demonstrated the technical performance of the method. Diagnostic performance was satisfactory with mutations successfully detected in each of the three targeted genes for all sequenced cHL cases, with an established LOD of 5% allelic fraction establishing the analytical performance. Despite the technical barriers imposed by the rarity of tumor cells and the necessity to test DNA extracted from tissue biopsies with variable quality, the application of this method can reliably detect somatic mutations with a sufficient level of high quality sequencing coverage across the coding regions of B2M, JAK2, and SOCS1. The demonstrated levels of technical, diagnostic, and analytical performance are adequate to move this approach forward towards implementation of a clinical-grade laboratory assay.
Two Perspectives on Graph-based Traffic Flow Management

New en-route Traffic Flow Management (TFM) tools are needed to handle the predicted air traffic growth by mitigating congestion, reducing delays and maintaining high safety levels. A large-scale graph abstraction of traffic through an airspace center is extracted from historical navigation data. The network graph model represents aggregated air traffic flows through the airspace. Although exemplified for a typical center, the methodology is generalizable to any airspace. The network is used to support two perspectives on TFM: a centralized approach based on network flow optimization with workload-derived sector constraints, and a decentralized approach based on mean field games.
Cost-effectiveness Analysis of Policosanol and Rosuvastatin in Treatment of Senior Patients with Hyperlipidemia

Objective To evaluate the cost-effectiveness analysis(CEA),efficacy and safety of Policosano and Rosuvastatin in the treatment of senior patients with hyperlipoidemia.Methods 60 senior patients with hyperlipidemia admitted into our hospital during 2011 and 2010 were randomly divided into Policosano group(n=30,10 mg per day)and Rosuvastatin group(n=30,5 mg per day). 6 weeks after treatment,the efficacy was observed,and CEA was performed.Results The Policosanol was similar to Rosuvastatin in decreasing lipidemia.The effective rates in Policosano group and Rosuvastatin group were 90% and 96.67% respectively,and the differences were significant(P=0.69). There were no adverse reactions in the two groups.CEA showed that C/E of Policosano was 1240,and 193 of Rosuvastatin.Conclusion Policosano and Rosuvastatin are effective and safe in lowering lipidemia,but Rosuvastatin has a higher economic value.
THE EFFECTIVENESS OF CALIBRATED VS. DEFAULT DISTANCE DECAY FUNCTIONS FOR GEOGRAPHIC PROFILING

Geographic profiling systems use distance decay functions to predict where serial offenders live. These functions assume that the likelihood of an offender residing at a particular location decreases with increasing distance from the offender's crime sites. Currently, each system relies on a default function, which was validated on data that are unrelated to the crimes being submitted to these systems during investigations. This occurs despite the possibility that a single decay function cannot be used with equal effectiveness across data collected under varying conditions. This study determined whether a decay function calibrated for a particular crime type or geographic region resulted in greater predictive power than an uncalibrated, default function. Decay functions were calibrated for three different types of serial crime (residential burglary, theft, and auto theft) collected from the same geographic region (Glendale, AZ) and for serial burglary collected from three different geographic regions (Glendale, AZ, Baltimore, MD, and Dorset, UK). The two default functions (truncated negative exponential and negative exponential) used for comparison purposes came from CrimeStat (v 3.1), a computerized geographic profiling system. The hypothesis that calibrated functions would possess more predictive power than the two default functions, as measured by error distance and hit percentage, was not supported to the extent that was expected, with the majority of analyses finding non-significant differences across the various functions within each data file. Potential explanations for these findings are provided, implications are discussed, and directions for future research are presented.
Children’s Story Classification in Indian Languages Using Linguistic and Keyword-based Features

The primary objective of this work is to classify Hindi and Telugu stories into three genres: fable, folk-tale, and legend. In this work, we are proposing a framework for story classification (SC) using keyword and part-of-speech (POS) features. For improving the performance of SC system, feature reduction techniques and combinations of various POS tags are explored. Further, we investigated the performance of SC by dividing the story into parts depending on its semantic structure. In this work, stories are (i) manually divided into parts based on their semantics as introduction, main, and climax; and (ii) automatically divided into equal parts based on number of sentences in a story as initial, middle, and end. We have also examined sentence increment model, which aims at determining an optimum number of sentences required to identify story genre by incremental selection of sentences in a story. Experiments are conducted on Hindi and Telugu story corpora consisting of 300 and 150 short stories, respectively. The performance of SC system is evaluated using different combinations of keyword and POS-based features, with three well-established machine learning classifiers: (i) Naive Bayes (NB), (ii) k-Nearest Neighbour (KNN), and (iii) Support Vector Machine (SVM). Performance of the classifier is evaluated using 10-fold cross-validation and effectiveness of classifier is measured using precision, recall, and F-measure. From the classification results, it is observed that adding linguistic information boosts the performance of story classification. In view of the structure of the story, main, and initial parts of the story have shown comparatively better performance. The results from the sentence incremental model have indicated that the first nine and seven sentences in Hindi and Telugu stories, respectively, are sufficient for better classification of stories. In most of the studies, SVM models outperformed the other models in classification accuracy.
Genetic diversity of Picea orientalis (L.) Link populations in Turkey

Knowledge of genetic variation is needed to understanding the genetic structure in forest tree populations. In addition, the determination of the genetic structure in the natural distribution areas of forest trees has become easier depending on the development of the isoenzyme technique. Determining the genetic structure and variations of Picea orientalis (L.) Link, which is limited local natural distribution areas on the world, transfer of this genetic to the future generations with sustainable forestry is important to ensure the continuity of the species. In this study, genetic differences and similarities were determined for P. orientalis populations in selected regions (Artvin, Torul-Örümcek, Tirebolu-Akılbaba, Ordu-Çambaşı, Artvin-Şavşat, Ardanuç-Ovacık, Şavşat-Sahara, Artvin-Saçinka, Ardahan-Posof and Maçka-Hamsiköy) in Turkey. In the study using 10 gene loci in different enzyme systems to determine the genetic variation, the values of heterozygosity (Ho), number of alleles per locus (AL), genetic diversity (v), intrapopulational differentiation (dT), multi locus diversity (Vgam) and differentiation among populations (Dj) were determined in these populations. The grand means were obtained as 0.154, 1.74 and 1.719 for the observed heterozygosity, alleles per locus and genetic diversity, respectively. Moreover, when the genetic diversity was considered, three different groups arose in terms of selected populations. Since Torul-Örümcek population had relatively higher results in contrast to other populations, this population has high importance for sustainability of gene resource of oriental spruce.
One Intelligent Facial Blackheads Identification Method Based on Computer Vision Technology

Blackheads are a subtype of acne. As a cosmetic problem, it seriously affects patient's facial appearance and psychological condition. So it has attracted more and more public attention in recent years. However due to the evaluation standards are not uniformed, the grade methods of existing acne still are lack of objective quantitative standard. Even for professionals with long training, there remains great variability among the evaluators. The experimental shows that the new intelligent method is similar to the results of professional dermatologists in terms of blackheads counting and has high efficiency advantages. What's more, it achieves the leap from qualitative to quantitative analysis in blackheads identification field.
Stepping Stone Graph

There are many real world applications that require identifying public movements such as identifying movement corridors in cities and most popular paths. If one is not given user trajectories but rather sporadic location data, such as location-based social network data, finding movement related information becomes difficult. Rather than processing all points in a dataset given a query, a clever approach is to construct a graph, based on user locations, and query this graph to answer questions such as shortest paths, most popular paths, and movement corridors. Shortest path graph is one of the popular graphs. However, the shortest path graph can be inefficient and ineffective for analysing movement data, as it calculates the graph edges considering the shortest paths over all the points in a dataset. Therefore, edge sets resulting from shortest path graphs are usually very restrictive and not suitable for movement analysis because of its global view of the dataset. We propose the stepping stone graph, which calculates the graph considering point pairs rather than all points; the stepping stone graph focuses on possible local movements, making it both efficient and effective for location-based social network related data. We demonstrate its capabilities by applying it in the Location-Based Social Network domain and comparing with the shortest path graph. We also compare its properties to a range of other graphs and demonstrate how stepping stone graph relates to Gabriel graph, relative neighbourhood graph, and Delaunay triangulation.
A Metric-Guided Method for Discovering Impactful Features and Architectural Insights for Skylake-Based Processors

The slowdown in technology scaling puts architectural features at the forefront of the innovation in modern processors. This article presents a Metric-Guided Method (MGM) that extends Top-Down analysis with carefully selected, dynamically adapted metrics in a structured approach. Using MGM, we conduct two evaluations at the microarchitecture and the Instruction Set Architecture (ISA) levels. Our results show that simple optimizations, such as improved representation of CISC instructions, broadly improve performance, while changes in the Floating-Point execution units had mixed impact. Overall, we report 10 architectural insights—at the microarchitecture, ISA, and compiler fronts—while quantifying their impact on the SPEC CPU benchmarks.
Transliteration of Arabizi into Arabic Script for Tunisian Dialect

The evolution of information and communication technology has markedly influenced communication between correspondents. This evolution has facilitated the transmission of information and has engendered new forms of written communication (email, chat, SMS, comments, etc.). Most of these messages and comments are written in Latin script, also called Arabizi. Moreover, the language used in social media and SMS messaging is characterized by the use of informal and non-standard vocabulary, such as repeated letters for emphasis, typos, non-standard abbreviations, and nonlinguistic content like emoticons. Since the Tunisian dialect suffers from the unavailability of basic tools and linguistic resources compared to Modern Standard Arabic, we resort to the use of these written sources as a starting point to build large corpora automatically. In the context of natural language processing and to benefit from these networks’ data, transliterating from Arabizi to Arabic script is a necessary step because most recently available tools for processing the Tunisian dialect expect Arabic script input. Indeed, the transliteration task can help construct and enrich parallel corpora and dictionaries for the Tunisian dialect and can be useful for developing various natural language processing applications such as sentiment analysis, opinion mining, topic detection, and machine translation. In this article, we focus on converting the Tunisian dialect text that is written in Latin script to Arabic script following the Conventional Orthography for Dialectal Arabic. Then, we propose two models to transliterate Arabizi into Arabic script for the Tunisian dialect, namely a rule-based model and a discriminative model as a sequence classification task based on conditional random fields). In the first model, we use a set of transliteration rules to convert the Tunisian dialect Arabizi texts to Arabic script. In the second model, transliteration is performed both at word and character levels. In the end, our models got a character error rate of 10.47%.
Towards Elderly Assistance Identification for Service Robot Using Combination of Mappings

Identifying request or task for assistance in an elderly or retirement home is a challenge for service robot. It is an essential task for service robot to give their services along with human. Identifying human position in an environment, such as elderly home, is the most challenging task, as it involves static and dynamic object. During its operation, a service robot must avoid collision to objects, and also prepares path for its journey. This work makes use of RGB-D camera, such as Kinect, to identify the static and dynamic object via 3D maps. To manage its path finding, the prediction of people location and position is enabled via Long Term and Short Term Memory been implemented in Real-Time Appearance Based Mapping. The loop closure detection with real time constraints wrapping the collected information that uses RGB-D SLAM method. To identify the human position and flow, a technique of pedestrian detection is added to find group of people, which will gain more understanding on how human will interact to robot. We have demonstrated the work of this initial work by generating 3D point cloud for specific environment through an intensive mapping experiments.
A Hierarchical CNN-RNN Approach for Visual Emotion Classification

Visual emotion classification is predicting emotional reactions of people for the given visual content. Psychological studies show that human emotions are affected by various visual stimuli from low level to high level, including contrast, color, texture, scene, object, and association, among others. Traditional approaches regarded different levels of stimuli as independent components and ignored to effectively fuse different stimuli. This article proposes a hierarchical convolutional neural network (CNN)-recurrent neural network (RNN) approach to predict the emotion based on the fused stimuli by exploiting the dependency among different-level features. First, we introduce a dual CNN to extract different levels of visual stimulus, where two related loss functions are designed to learn the stimuli representation under a multi-task learning structure. Further, to model the dependency between the low- and high-level stimulus, a stacked bi-directional RNN is proposed to fuse the preceding learned features from the dual CNN. Comparison experiments on one large-scale and three small scale datasets show that the proposed approach brings significant improvement. Ablation experiments demonstrate the effectiveness of different modules from our model.
The Effect of Co-infection of HIV and Hepatotropic Viruses on Selected Biochemical and Haematological Markers of Patients in Northeastern Nigeria

Background: As deaths caused by HIV declines with the use of HAART, liver disease associated with co-infection of HIV with hepatotropic viruses has become an increasing cause of morbidity and mortality. Aim: To assess the effect of HIV-mono and co-infections with hepatotropic viruses on haematological and biochemical markers of the patients. Methodology: 109 HIV patients from tertiary health facilities in northeastern Nigeria were initially screened with Immuno chromatographic kit for HIV antibody and confirmed by western blot prospectively and consecutively. However, Hepatitis B virus surface antigen (HBsAg) and Hepatitis C virus (HCV) antibody were detected on the HIV positive patients Original Research Article International Journal of TROPICAL DISEASE & Health, 4(5): 568-581, 2014 569 by ELISA. Blood donors served as control. The study was conducted between January and October 2012. Results: Of the HIV patients 12.8% and 4.6% had HBsAg and HCV antibody respectively. The prevalence rate of Hepatitis B virus (HBV) infection among males was 12.8% while females had 12.9% but lower rates of HCV were obtained in both males (5.1%) and females (3.3%). However, HIV mono-infections had higher mean baseline values for haemogblobin (Hb), CD4 and platelet counts, protein C (PC) and protein S (PS) in comparison with HIV/ HBV/HCV co-infections (P 0.05) in both groups. Conclusion: Co-infections of HIV and hepatotropic viruses do occur. Haematological and biochemical parameters serve as pointers for early detection of liver disease in HIV patients. The development of novel therapeutic approaches to impede co-infection of HIV and hepatotropic viruses is encouraged.
Combined Stochastic Process and Value at Risk: A Real-World Information System Decision Case

In this study, we used a combined stochastic process and value-at-risk (VaR) method to examine an electronic commerce expansion decision. By modeling uncertain benefits as a stochastic process, maximum losses of alternative decisions were quantified and compared to help managers to make information system/information technology (IS/IT) project decisions. Our results, based on the maximum loss perspective, demonstrated that uncertainty plays a critical role in evaluating IS/IT projects. More importantly, the results illustrate that VaR serves as a useful tool in decision-making for managers to quantify the value of maximum possible loss and to help them reach decisions.
Tachykinins and Kisspeptins in the Regulation of Human Male Fertility

Infertility is a global disease affecting one out of six couples of reproductive age in the world, with a male factor involved in half the cases. There is still much to know about the regulation of human male fertility and thus we decided to focus on two peptide families that seem to play a key role in this function: tachykinins and kisspeptins. With this aim, we conducted an exhaustive review in order to describe the role of tachykinins and kisspeptins in human fertility and their possible implications in infertility etiopathogenesis. Many advances have been made to elucidate the roles of these two families in infertility, and multiple animal species have been studied, including humans. All of this knowledge could lead to new advances in male infertility diagnosis and treatment, but further research is needed to clarify all the implications of tachykinins and kisspeptins in fertility.
Learning to build SPARQL queries from natural language questions

The amount of information available in the web of data using the semantic web standards increased tremendously in the last decade. The Linked Open Data Cloud, for example, has over 1,200 datasets and 16,000 links. Despite that, the access to this data is still difficult because users must have specialised knowledge to query knowledge graphs. Question answering systems have been proposed as an alternative to address this problem in order to provide the benefits of interconnected data further accessible to people. In this approach, systems translate natural language (NL) questions into structured queries. In this work, we study, construct and evaluate a query builder component called MSQG to learn how to build SPARQL queries from NL questions. Our solution explores the combination of distinct sentence encoder to provide better latent sentence representations in the query construction. We evaluate the solution based on Lc-Quad dataset. Obtained results indicate the benefits of our approach in handling several types of questions as input.
OP-ROFF170022 2073..2108

We exploit variation in consumer price inflation across seventy-one Russian regions to examine the relationship between the perceived stability of the domestic currency and financial dollarization. Our results show that regions with higher inflation experience an increase in the dollarization of household deposits and a decrease in the dollarization of loans. The impact of inflation on credit dollarization is weaker in regions with less integrated banking markets. This suggests that the currencyportfolio choices of households and firms are constrained by the asset-liability management of banks. JEL classification: E31, E44, F36, G21, P22, P24