Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between power strength and anaerobic power index as a clear picture of the effect of strength training among young soccer elite players

Purpose: muscle contraction has both mechanical to produce movement and metabolic properties to produce energy. However, manipulation of strength training planes (horizontal or vertical) subject to be an important training strategy to develop soccer-specific power-based actions. Depending on mode of exercise and more important the energy demands. Material & Methods: this cross-sectional study was conducted to inspect the relation between power strength and anaerobic power index, proper to mode of exercise (vertical vs horizontal) among young soccer elite players. To achieve this goal, we based on anaerobic capacity repeat sprint test power index for103 male players with mean age (18.55±0.48) years, involved in the Oran league competition division one. In addition to their strength in Vertical Jump (VJ) and Standing Long Jump Test (SLJ), conducted in the Omni sports complex of the institute physical education and sport Chlef. Results: significant inverse correlations record between power strength and anaerobic power index in all comparisons practised. Support by the index of Max power anaerobic and fatigue as super predictors of players performance in horizontal plane. The opposite of vertical plane where the index of fatigue and Min power anaerobic are the super predictors. Conclusions: our results confirmed that to have a clear picture of the effect of strength training on physical performance. Trainers must take in their consideration, the mode of exercise and their energy index power demands affirmed in present via the development of max power anaerobic to enhance not only minimal but also the index of fatigue requiring the good developments of neuromuscular function, in which max power anaerobic levels play a big part in maximizing scores not only in vertical plane but also in horizontal jump.
Serum cystatin C is a useful biomarker but not superior to serum creatinine assessment for the diagnosis of acute kidney injury in septic children: a prospective cohort study

Sepsis is a multisystem disease and is associated with significant morbidity and mortality. Acute kidney injury (AKI) is a relatively common complication in septic children and is a major cause of death in these patients. While serum creatinine is the gold standard for the diagnosis of acute kidney injury, it has many drawbacks. Cystatin C has emerged as a biomarker for AKI. We aim to assess if serum cystatin C can predict early AKI in septic children. This is a cross sectional study conducted in King Abdulaziz University Hospital. Patients who were suspected of being septic were eligible for inclusion in the study. Both serum creatinine and serum cystatin C levels were measured in patients on admission (0 h) and 12 h after admission. AKI was diagnosed according to the modified pRIFLE criteria. Receiver operating characteristic (ROC) curve analysis was used to assess whether cystatin C is a useful biomarker for diagnosing early AKI in children with sepsis. A total of 40 patients with sepsis were included. Twenty-eight patients were diagnosed with AKI (70%). Serum cystatin C was significantly higher in patients with AKI (1595.79 ± 303.830) compared with patients without AKI (1344.67 ± 378.23) with p=0.033, and the mean difference was 250.119 [95% CI, 478.60 to 21.637]. The area under the curve (AUC) for serum cystatin C suggested it was a good marker for the diagnosis of AKI with a sensitivity of 92% and a specificity of 66.7% at 0 hours and a sensitivity of 90% and specificity of 75% at 12 hours. The best cut-off value was 0.721 mg/l. There was no significant difference in the AUC curves for serum creatinine and serum cystatin C at 0h and 12 h (p-value of 0.2 and 0.3, respectively). While serum cystatin is a sensitive and specific marker for the early diagnosis of sepsis-associated AKI, it is not superior to serum creatinine.
Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution

We explore the use of Convolutional Neural Networks (CNNs) for multi-label Authorship Attribution (AA) problems and propose a CNN speciﬁcally designed for such tasks. By averaging the author probability distributions at sentence level for the longer documents and treating smaller documents as sentences, our multi-label design adapts to single-label datasets and various document sizes, retaining the capabilities of a traditional CNN. As a part of this work, we also create and make available to the public a multi-label Authorship Attribution dataset (MLPA-400), consisting of 400 scientiﬁc publications by 20 authors from the ﬁeld of Machine Learning. Proposed Multi-label CNN is evaluated against a large number of algorithms on MLPA-400 and PAN-2012, a traditional single-label AA benchmark dataset. Experimental results demonstrate that our method outperforms several state-of-the-art models on the proposed task.
Weak Form of Market Efficiency: Evidences from Selected NSE Indices

Stock market efficiency is and important parameter to gauge the efficiency of a financial system. It assumes extreme importance especially in developing countries like India. The efficiency of Indian stock markets, especially the leading stock exchange of India - the NSE, attracts the attention of researchers and analysts in view of recent fluctuations in investments levels and the global financial turmoil. The efficiency tests conducted by the researchers so far have produced contradictory results and it is precisely difficult to comment on Indian stock market efficiency with definitiveness. We found it interesting to examine the impact of various macro economic factors both on Indian and global front on Indian stock markets in relation to the rate and manner of incorporation of information, which is the concern of every economic participant in recent times. This paper therefore, attempts to seek evidence for the weak form efficient market hypothesis using the daily data for stock indices of the National Stock Exchange for the period of 1 January 2000 to 31 Oct 2008. We use Kolmogrov -Smirnov, Unit Root and run test to test weak-form efficiency.
Binding to type I collagen is essential for the infectivity of Vibrio parahaemolyticus to host cells

Vibrio parahaemolyticus is a globally present marine bacterium that often leads to acute gastroenteritis. Two type III secretion systems (T3SSs), T3SS1 and T3SS2, are important for host infection. Type I collagen is a component of the extracellular matrix and is abundant in the small intestine. However, whether type I collagen serves as the cellular receptor for V. parahaemolyticus infection of host cells remains enigmatic. In this study, we discovered that type I collagen is not only important for the attachment of V. parahaemolyticus to host cells but is also involved in T3SS1‐dependent cytotoxicity. In addition, 2 virulence factors, MAM7 and VpadF enable V. parahaemolyticus to interact with type I collagen and mediate T3SS2‐dependent host cell invasion. Type I collagen, the collagen receptor α1 integrin, and its downstream factor phosphatidylinositol 3‐kinase (PI3K) are responsible for V. parahaemolyticus invasion of host cells. Further biochemical studies revealed that VpadF mainly relies on the C‐terminal region for type I collagen binding and MAM7 relies on mce domains to bind to type I collagen. As MAM7 and/or VpadF homologues are widely distributed in the genus Vibrio, we propose that Vibrios have evolved a unique strategy to infect host cells by binding to type I collagen.
Labour supply responses to an increase in minimum wages: an overview of the literature

The paper provides an overview of economic theory on labour supply and discusses factors that can affect the labour supply decision including financial and non-financial benefits or disincentives, individual or household characteristics and labour demand. This review found relatively few research papers on the labour supply responses to increases in minimum and otherwise low wages, both in Australia and internationally. Findings from empirical studies reviewed were ambiguous and varied in their methodology. Some population subgroups have been found to be more responsive to wage changes, such as females (particularly partnered females), and these subgroups are also more likely to be award-reliant and therefore more likely to respond to minimum wage changes. More research is needed to better understand the supply side effects of an increase in minimum wages. Authors: David Rozenbes, Rebecca Kenny, Lucy Nelms and Samantha Farmakis-Gamboni
Worst-case Analysis of the Time-To-React Using Reachable Sets

Collision mitigation and collision avoidance systems in intelligent vehicles reduce the severity and number of accidents. To determine the optimal point in time at which such systems should intervene, time-based criticality metrics such as the Time-To-React (TTR) are commonly used. The TTR describes the last point in time along the current trajectory at which an evasive trajectory exists. In this paper, we present a novel approach to determine the point in time after which it is guaranteed that no evasive maneuver exists, i.e., by using reachable sets, we over-approximate the TTR. Our deterministic upper bound of the TTR can be used to trigger a collision mitigation system or to find a feasible emergency maneuver which avoids the collision. We demonstrate the efficient computation of the tight over-approximated TTR in different urban and rural traffic scenarios, and compare our results to an estimated TTR using an optimization-based trajectory planner.
The Nature, History and Significance of the Concept of Positional Goods

Reflecting its title, this article is divided into three sections. The first section outlines the contrasting ways in which the term ‘positional goods’ has been defined since it was first coined by Fred Hirsch in 1976. It is argued in this section that whereas Hirsch was thinking of goods of which it is true that for some of the members of a society part or all of the satisfaction derived from possessing them is the enhancement of social status due to the fact that such satisfaction is possible only for a minority, R. C. O. Matthews, Robert Frank and Ugo Pagano each defined the term in a substantially different way, so as to support his particular line of argument. The second section assesses the extent to which Hirsch’s concept was anticipated by earlier writers, including Adam Smith, John Rae, Nassau Senior, Augustin Cournot, Karl Marx, John Stuart Mill, Thorstein Veblen, Philip Wicksteed, A. C. Pigou, James Meade, James Duesenberry, Harvey Leibenstein, Roy Harrod, W. G. Runciman and Staffan Linder. The third section discusses the significance of recognition of the existence of positional goods for the predictive and policy analysis of markets, inequality, and economic growth.
An examination of antecedents of conversion rates of e-commerce retailers

Purpose - – This study aims to theorize and empirically examine the relationship between “purchase intention and conversion rate”, “website satisfaction and conversion rate” and “purchase intention and conversion rate”. E-Commerce conversion rate represents the percentage of visits to an e-tailer’s website that includes a purchase transaction. Despite the importance of conversion rates for e-tailers, prior research predominantly used purchase intention and website satisfaction as main dependent variables and implicitly assumed that these variables will influence the actual purchase. Design/methodology/approach - – Data on 85 US retail websites were used to test the hypotheses. The unit of the analysis is the online retail website. Regression analysis was used to perform the data analysis. Findings - – The results indicate that both purchase intention and website satisfaction positively influence conversion rates. It was also found that website satisfaction positively influences purchase intention. Research limitations/implications - – Only data from 85 US e-tailers from the top-100 US online retailers are used to test the hypotheses. Also, conversion rate is only one of the several important success metrics used by e-tailers. Originality/value - – This study not only examines antecedents of e-commerce conversion rates, but also theorizes and tests if there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”. This is because, although previous studies used purchase intention and website satisfaction as main dependent variables and proxies for actual purchase behavior, they did not validate this relationship. This study shows that: there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”, there is also a statistically significant relationship between “website satisfaction and purchase intention” and this study used firm-level data to theorize, measure and analyze the data, whereas prior literature used only individual-level data.
THE ANTI-AGING PROTEIN KLOTHO ENHANCES OLIGODENDROCYTE MATURATION AND MYELINATION OF THE CENTRAL NERVOUS SYSTEM

We have previously shown that myelin abnormalities and loss characterize the normal aging process of the brain and that an age-associated reduction in Klotho is conserved across species. Predominantly generated in brain and kidney, Klotho overexpression extends life span, whereas loss of Klotho accelerates the development of aging-like phenotypes. While the function of Klotho in brain is unknown, loss of Klotho expression leads to cognitive deficits. In the present study, we found significant effects of Klotho on oligodendrocyte functions including induced maturation of rat primary oligodendrocytic progenitor cells (OPCs) in vitro and myelination. Phosphoprotein Western analysis indicated Klotho's downstream effects involve Akt and ERK signal pathways. Klotho increased OPCs maturation, and inhibition of Akt or ERK function blocked this effect on OPCs. In vivo studies of Klotho knockout mice and their control littermates revealed that knockout mice have a significant reduction in major myelin protein and gene expression. By immunohistochemistry, the number of total and mature oligodendrocytes was significantly lower in Klotho knockout mice. Strikingly, at the ultrastructural level, Klotho knockout mice exhibited significantly impaired myelination of the optic nerve and corpus callosum. These mice also displayed severe abnormalities at the nodes of Ranvier. In order to decipher the mechanisms by which Klotho affects oligodendrocytes, we used luciferase pathway reporters to identify the transcription factors involved. Taken together, these studies provide novel evidence for Klotho as a key player in myelin biology, which may thus be a useful therapeutic target in efforts to protect brain myelin against age-dependent changes. Address correspondence to: Carmela R. Abraham, Department of Biochemistry, Boston University School of Medicine, 72 E. Concord Street, K304, Boston, MA 02118, Tel. 617 638-4308; Fax. 617 638-5339; cabraham@bu.edu. *These authors contributed equally NIH Public Access Author Manuscript J Neurosci. Author manuscript; available in PMC 2013 July 30. Published in final edited form as: J Neurosci. 2013 January 30; 33(5): 1927–1939. doi:10.1523/JNEUROSCI.2080-12.2013. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript
Printed Organic Thin Film Transistors, Photodiodes, and Phototransistors for Sensing and Imaging

Author(s): Pierre, Adrien | Advisor(s): Arias, Ana Claudia | Abstract: Light and image sensors are ubiquitous and used in a wide variety of applications ranging from consumer products to healthcare and industrial applications. The signal-to-noise ratio (SNR) from a photodetector element increases with larger photoactive area, which is costly to scale up using silicon wafers and wafer-based microfabrication. On the other hand, the performance of solution-processed photodetectors and transistors is advancing considerably. It is proposed that the printability of these devices on plastic substrates can enable low-cost areal scaling for high SNR light and image sensors.This thesis advances the performance of printed organic thin film transistor (OTFT), pho- todiode (OPD), and phototransistor (OPT) devices optimized for light and image sensing applications by developing novel printing techniques and creating new device architectures. An overview is first given on the essential figures of merit for each of these devices and the state of the art in solution-processed image sensors. A novel surface energy-patterned doc- tor blade coating technique is presented to fabricate OTFTs on flexible substrates over large areas. Using this technique, OTFTs with average mobility and on-off ratios of 0.6 cm^(2)/Vs and 10^(5) are achieved, which is competitive with amorphous silicon TFTs. High performance OPDs are also fabricated using doctor blade coating and screen printing. These printing processes give high device yield and good controllability of photodetector performance, enabling an average specific detectivity of 3.45×10^(13) cm·Hz^(0.5)·W^(-1) that is higher than silicon photo- diodes (10^(12-13)). Finally, organic charge-coupled devices (OCCDs) and a novel OPT device architecture based on an organic heterojunction between a donor-acceptor bulk heterojunction blend and a high mobility semiconductor that allows for a wide absorption spectrum and fast charge transport are discussed. The OPT devices not only exhibit high transistor and photodetector performance, but are also able to integrate photogenerated charge at video frame rates up to 100 frames per second with external quantum efficiencies above 100%. Applications of these devices include screen printed OTFT backplanes, large-area OPDs for pulse oximeter applications, and OPT-based image sensors.
Nesting Biology of Sympatric Species of Megachilidae Bees in a Conservation Area in Brazilian Atlantic Forest

Megachilidae bees are important pollinators in the Neotropical region, however information on the ecology and behavior of these species is still scarce. The objective of this study was to analyze the nesting biology of sympatric species in the União Biological Reserve, a remnant of Atlantic Forest in the southeastern Brazil. Our results indicated the occurrence of 17 species, representing significant richness compared to other areas in the Atlantic Forest. Five sympatric species built ¾ of all nests found and the architecture of their nests was studied including, for the first time, nests of a species of Megachile (Ptilosarus). The use of petals or leaf fragments in the construction of the nests was observed for species of Megachile (Chrysosarus), confirming previous data. The nesting activity period in the trap-nests occurred mainly in the rainy season, with different peaks among the species. The results indicate that the distinctive characteristics of the species, such as the type of material used in the nests, the dimensions of cavities, and the asynchronous nesting period, could be important for the niche differentiation of these sympatric species, allowing the maintenance and the survival of the most abundant Megachilidae populations in the area.
Long-term wind speed and power forecasting using local recurrent neural network models

This paper deals with the problem of long-term wind speed and power forecasting based on meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park on the Greek island of Crete. As inputs our models use the numerical forecasts of wind speed and direction provided by atmospheric modeling system SKIRON for four nearby positions up to 30 km away from the wind turbine cluster. Three types of local recurrent neural networks are employed as forecasting models, namely, the infinite impulse response multilayer perceptron (IIR-MLP), the local activation feedback multilayer network (LAF-MLN), and the diagonal recurrent neural network (RNN). These networks contain internal feedback paths, with the neuron connections implemented by means of IIR synaptic filters. Two novel and optimal on-line learning schemes are suggested for the update of the recurrent network's weights based on the recursive prediction error algorithm. The methods assure continuous stability of the network during the learning phase and exhibit improved performance compared to the conventional dynamic back propagation. Extensive experimentation is carried out where the three recurrent networks are additionally compared to two static models, a finite-impulse response NN (FIR-NN) and a conventional static-MLP network. Simulation results demonstrate that the recurrent models, trained by the suggested methods, outperform the static ones while they exhibit significant improvement over the persistent method.
Robust 6D Object Pose Estimation with Stochastic Congruent Sets

Object pose estimation is frequently achieved by first segmenting an RGB image and then, given depth data, registering the corresponding point cloud segment against the object's 3D model. Despite the progress due to CNNs, semantic segmentation output can be noisy, especially when the CNN is only trained on synthetic data. This causes registration methods to fail in estimating a good object pose. This work proposes a novel stochastic optimization process that treats the segmentation output of CNNs as a confidence probability. The algorithm, called Stochastic Congruent Sets (StoCS), samples pointsets on the point cloud according to the soft segmentation distribution and so as to agree with the object's known geometry. The pointsets are then matched to congruent sets on the 3D object model to generate pose estimates. StoCS is shown to be robust on an APC dataset, despite the fact the CNN is trained only on synthetic data. In the YCB dataset, StoCS outperforms a recent network for 6D pose estimation and alternative pointset matching techniques.
Are There Personality Traits That Predispose Applicants to Fake Noncognitive Measures in Personnel Selection

Previous research has shown that applicants’ faking on personality questionnaires could lower their predictive validity and reduce the quality of selection decisions. Hence, several models that specify key determinants of applicants’ faking responses on noncognitive measures have emerged. One of the common elements of these models is the important role of applicants’ personality in predicting their motivation to fake and consequently faking behavior. Nevertheless, this assumption lacks systematical empirical validation. The goal of the present study was to investigate the role of applicants’ personality facets in predicting the amount of faking on a noncognitive questionnaire. The total of 202 participants responded to personality inventory twice – first under instructions to respond honestly and afterwards under instructions to respond as an applicant in a simulated selection program. The difference between personality scores in two conditions represented the individual amount of faking, which was then regressed on the facet scores. As expected, the results confirmed significant contribution of Conscientiousness and Neuroticism facets in explaining the variance of faking. However, the most efficient predictors seem to be the facets of Openness. The obtained results provide support for personality traits having the important role in predicting applicants’ faking behavior on noncognitive measures in personnel selection.