Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Assisted Objective Structured Clinical Examination: a useful tool for dermatology undergraduate assessment.

BACKGROUND Dermatology is a minor subject in undergraduate medical curriculum in India. The dermatology clinical postings are generally poorly attended, and the clinical acumen of an average medical graduate in this specialty is quite low. AIMS To develop and implement Computer Assisted Objective Structured Clinical Examination (CA-OSCE) as a means of end of dermatology posting evaluation. Also, to assess its effectiveness in improving the motivation, attendance and learning of undergraduate students with respect to their visual recognition skills and problem solving ability. METHODS We designed and introduced CA-OSCE as a means of end of posting assessment. The average attendance and assessment scores of students undergoing CA-OSCE were compiled and compared using 'independent t test' with the scores of previous year's students who had undergone assessment with essay type questions. RESULTS The average attendance and average assessment scores for the candidates undergoing CA-OSCE were found to 83.36% and 77.47%, respectively as compared to 64.09% and 52.07%, respectively for previous years' students. The difference between the two groups was found to be statistically significant. Student acceptability of the technique was also high, and their subjective feedback was encouraging. CONCLUSION CA-OSCE is a useful tool for assessment of dermatology undergraduates. It has the potential to drive them to attend regularly as well as to test their higher cognitive skills of analysis and problem solving.
Urban fine-scale forecasting reveals weather conditions with unprecedented detail

CapsuleFeasibility of Numerical Weather Prediction at urban neighborhood and street scales demonstrated for summer conditions in the Amsterdam metropolitan region (Netherlands). As the number of urban dwellers increases from an estimated 4 billion in 2014 to an expected 6.5 billion by 2050 (UN 2014), urbanization is putting an increasing strain on human comfort, productivity and health in cities worldwide.
Statistics of magnetic field measurements in OBA stars and the evolution of their magnetic fields

We review the measurements of magnetic fields of OBA stars. Based on these data we confirm that magnetic fields are distributed according to a lognormal law with a mean log(B)=-0.5 (B in kG) with a standard deviation sigma=0.5. The shape of the magnetic field distribution is similar to that for neutron stars. This finding is in favor of the hypothesis that the magnetic field of a neutron star is determined mainly by the magnetic field of its predecessor, the massive OB star. Further, we model the evolution of an ensemble of magnetic massive stars in the Galaxy. We use our own population synthesis code to obtain the distribution of stellar radii, ages, masses, temperatures, effective magnetic fields and magnetic fluxes from the pre-main sequence (PMS) via zero age main sequence (ZAMS) up to the terminal age main sequence (TAMS) stages. A comparison of the obtained in our model magnetic field distribution (MFD) with that obtained from the recent measurements of the stellar magnetic field allows us to conclude that the evolution of magnetic fields of massive stars is slow if not absent. The shape of the real MFD shows no indications of the magnetic desert proposed previously. Based on this finding we argue that the observed fraction of magnetic stars is determined by physical conditions at the PMS stage of stellar evolution.
Random homogenization of Pseudo-elliptic equations in the presence of long-range media

This paper deals with the homogenization theory for one-dimensional pseudo-elliptic equations with highly oscillatory random coefficients displaying long-range correlation. We prove that the corrector to homogenization, i.e. the difference between the random solution and the homogenized solution, convergence to stochastic integrals with respect to Hermite process.
Introduction: Big data and partial differential equations†

Partial differential equations (PDEs) are expressions involving an unknown function in many independent variables and their partial derivatives up to a certain order. Since PDEs express continuous change, they have long been used to formulate a myriad of dynamical physical and biological phenomena: heat flow, optics, electrostatics and -dynamics, elasticity, fluid flow and many more. Many of these PDEs can be derived in a variational way, i.e. via minimization of an ‘energy’ functional. In this globalised and technologically advanced age, PDEs are also extensively used for modelling social situations (e.g. models for opinion formation, mathematical finance, crowd motion) and tasks in engineering (such as models for semiconductors, networks, and signal and image processing tasks). In particular, in recent years, there has been increasing interest from applied analysts in applying the models and techniques from variational methods and PDEs to tackle problems in data science. This issue of the European Journal of Applied Mathematics highlights some recent developments in this young and growing area. It gives a taste of endeavours in this realm in two exemplary contributions on PDEs on graphs [1, 2] and one on probabilistic domain decomposition for numerically solving large-scale PDEs [3].
Advection on Cut-Cell Grids for an Idealized Mountain of Constant Slope

AbstractCut cells use regular or nearly regular polygonal cells to describe fields. For a given orography, some cells may be completely under the mountain, some completely above the mountain, and some are partially filled with air. While there are reports indicating considerably improved simulations with cut cells, inaccuracies may arise with some approximations, producing noise in fields near the surface. This behavior may depend strongly on the approximations made for the advection terms near the surface. This paper investigates the accuracy of advection for numerical schemes for a nondivergent flow near a mountain surface. The schemes use C-grid staggering with densities located at cell centers or on the corners of cells. Also, a nonconserving scheme is considered, which was used in the past with real-data cut-cell simulations. Since the cut cells near the surface create an irregular resolution, the accuracy and order of some approximations may break down near the surface. The objective of this paper i.
Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations

In this paper we describe an improved neural network method to predict T‐cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence‐encoding schemes has a performance superior to neural networks derived using a single sequence‐encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence‐encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix‐driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high‐binding peptides. Finally, we use the method to predict T‐cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
Identification of related proteins with weak sequence identity using secondary structure information

Molecular modeling of proteins is confronted with the problem of finding homologous proteins, especially when few identities remain after the process of molecular evolution. Using even the most recent methods based on sequence identity detection, structural relationships are still difficult to establish with high reliability. As protein structures are more conserved than sequences, we investigated the possibility of using protein secondary structure comparison (observed or predicted structures) to discriminate between related and unrelated proteins sequences in the range of 10%–30% sequence identity. Pairwise comparison of secondary structures have been measured using the structural overlap (Sov) parameter. In this article, we show that if the secondary structures likeness is >50%, most of the pairs are structurally related. Taking into account the secondary structures of proteins that have been detected by BLAST, FASTA, or SSEARCH in the noisy region (with high E value), we show that distantly related protein sequences (even with <20% identity) can be still identified. This strategy can be used to identify three‐dimensional templates in homology modeling by finding unexpected related proteins and to select proteins for experimental investigation in a structural genomic approach, as well as for genome annotation.
Identification and Phenotype of MAIT Cells in Cattle and Their Response to Bacterial Infections

Mucosal-associated invariant T (MAIT) cells are a population of innate-like T cells that utilise a semi-invariant T cell receptor (TCR) α chain and are restricted by the highly conserved antigen presenting molecule MR1. MR1 presents microbial riboflavin biosynthesis derived metabolites produced by bacteria and fungi. Consistent with their ability to sense ligands derived from bacterial sources, MAIT cells have been associated with the immune response to a variety of bacterial infections, such as Mycobacterium spp. , Salmonella spp. and Escherichia coli. To date, MAIT cells have been studied in humans, non-human primates and mice. However, they have only been putatively identified in cattle by PCR based methods; no phenotypic or functional analyses have been performed. Here, we identified a MAIT cell population in cattle utilising MR1 tetramers and high-throughput TCR sequencing. Phenotypic analysis of cattle MAIT cells revealed features highly analogous to those of MAIT cells in humans and mice, including expression of an orthologous TRAV1-TRAJ33 TCR α chain, an effector memory phenotype irrespective of tissue localisation, and expression of the transcription factors PLZF and EOMES. We determined the frequency of MAIT cells in peripheral blood and multiple tissues, finding that cattle MAIT cells are enriched in mucosal tissues as well as in the mesenteric lymph node. Cattle MAIT cells were responsive to stimulation by 5-OP-RU and riboflavin biosynthesis competent bacteria in vitro. Furthermore, MAIT cells in milk increased in frequency in cows with mastitis. Following challenge with virulent Mycobacterium bovis, a causative agent of bovine tuberculosis and a zoonosis, peripheral blood MAIT cells expressed higher levels of perforin. Thus MAIT cells are implicated in the immune response to two major bacterial infections in cattle. These data suggest that MAIT cells are functionally highly conserved and that cattle are an excellent large animal model to study the role of MAIT cells in important zoonotic infections.
Observer-based robust H∞ control for uncertain Markovian jump systems via fuzzy Lyapunov function

This paper investigates the problem of observer-based robust H ∞ control for a class of continuous-time nonlinear Markovian jump systems (MJSs) with uncertainties, external disturbance and unavailable states that can be represented by Takagi-Sugeno (T-S) fuzzy models. Based on a mode-dependent fuzzy Lyapunov function and by introducing slack matrix variables, a sufficient condition for the existence of the state observer and observer-based robust H ∞ controller for such MJSs are derived by constructing an augmented fuzzy system. Further, by means of congruent transformation in matrix and linear matrix inequality (LMI) method, the results are given in the form of LMIs that can be easily solved by using the convex optimization techniques. Moreover, we also give the result obtained via common stochastic Lyapunov function to compare with the proposed approach. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.
Twisted second moments and explicit formulae of the Riemann zeta-function

Several aspects connecting analytic number theory and the Riemann zeta-function are studied and expanded. These include: 1. explicit formulae relating the Mobius function to the non-trivial zeros of the zeta function; 2. generalized results on sums of Ramanujan sums; 3. new results on the combinations of Riemann $\Xi$-functions on bounded vertical shifts and their zeros on the critical line; 4. a generalization of moment integrals involving the Riemann $\Xi$-function; 5. asymptotics for the mean square of the product of the Riemann $\xi$-function and new Dirichlet polynomials; 6. zeta regularization on tori and a new proof of the Chowla-Selberg formula.
INFERENCE IN INSTRUMENTAL VARIABLE MODELS WITH HETEROSKEDASTICITY AND MANY INSTRUMENTS

This paper proposes novel inference procedures for instrumental variable models in the presence of many, potentially weak instruments that are robust to the presence of heteroskedasticity. First, we provide an Anderson–Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson–Rubin-type tests. Second, we consider the case of testing a subset of parameters under the assumption that a consistent estimator for the parameters not under test exists. We show that under the null, the proposed statistics have Gaussian limiting distributions and derive alternative chi-square approximations. An extensive simulation study shows the competitive finite sample properties in terms of size and power of our procedures. Finally, we provide an empirical application using college proximity instruments to estimate the returns to education.
Effects of hyperscaling violation and dynamical exponents on drag force

: In this paper, we calculate drag force of a charged particle in systems with a hyperscaling violation, by using the AdS/CFT correspondence. We obtain energy loss, friction coef-ﬁcient, diﬀusion and quasi normal modes of a heavy point particle. Here, we consider two cases. The ﬁrst, we assume the heavy point particle moves with constant velocity v . In the second case, we assume the heavy point particle rotates along a circle of radius L with angular velocity ω . Also, we add some electromagnetic ﬁeld and obtain the energy loss. Finally, we draw some ﬁgures for the drag force with respect to the velocity and the temperature. As well, we show how the drag force and energy loss change with the hyperscaling violation parameter θ , dynamical parameter z , mass m and charge Q .
Purely Sequential Fixed Accuracy Confidence Intervals for P(X < Y) under Bivariate Exponential Models

SYNOPTIC ABSTRACT This article deals with the estimation of the reliability or the availability parameter θ = P(X < Y) in a stress-strength model. We consider separate cases where (X, Y) has a bivariate exponential distribution due to Marshall and Olkin (1976) and Freund (1961). We construct a purely sequential estimation methodology to find a fixed accuracy confidence interval for the unknown θ in each case. First-order asymptotic efficiency and consistency properties are also established of our proposed procedures. We then supplement extensive sets of simulation studies and real data analysis to show encouraging performances of our proposed stopping strategies.
Investigating the effects of personality traits on cyberbullying

Cyberbullying is a complicated and multifaceted phenomenon especially among youngsters, thus understanding the determinants of cyberbullying is prominent to explain what causes youngsters experience cyberbullying. Since little is known about the link between personality traits and cyberbullying, the current study aimed to explore the effects of vocational high school students’ personality traits on their cyberbullying behaviors. In this regard, “Cyber victim/bully Scale”, and “Adjective Based Personality Scale” were administered to 246 participant students. A structural regression model was developed with predictor variables and tested to see the effects of personality traits on cyberbullying. The goodness of fit indices indicated acceptable fitness ( χ 2 /df :2.63, RMSEA: .08, GFI: .55, AGFI: .53, CFI: .61, IFI: .61). The results illustrated that openness is the strongest significant predictor of both cyberbullying ( R 2 =-.83, p<.05) and being bullied ( R 2 =-.77, p <.05). Yet another significant but positive predictor of cyberbully is extroversion ( R 2 =.54, p <.05). However, the weakest positive significant predictor of cyberbully is agreeableness ( R 2 =.14, p <.05). In addition to openness, conscientiousness was also found to be another significant predictor of being bullied ( R 2 =.64, p <.05). Surprisingly, neuroticism has no significant effect on cyberbullying and being bullied.
Vortex decay in quasi-2D MHD ducts: Application to Kármán vortex streets behind turbulence promoters

A vortex decay model for predicting temporal evolution of peak vorticity in a wake behind a cylinder is presented. Where the wake vortices are in stable region, results have shown that the correlation has a good capability of predicting temporal evolution of peak vorticity within an advecting vortex across a wide range of flow parameters. The correlation is also generalized to predict the dacay behaviour of wake vortices in a class of magnetohydrodynamic duct flows. Comparison with published data demonstrates the capability of this model in predicting vortex strength with a relative standard error of less than 2%.
An Alternative Specification for Technical Efficiency Effects in a Stochastic Frontier Production Function

This paper proposes an alternative specification for technical efficiency effects in a stochastic production frontier model. The proposed specification is distribution free and thus eschews one-sided error term present in almost all the existing inefficiency effects models. The efficiency effects are represented by the standard normal cumulative distribution function of exogenous variables which ensures the efficiency scores to lie in a unit interval. An empirical exercise based on widely used Philippines rice farming data set illustrates the simplicity and usefulness of the proposed model.
Blossom: an Anytime Algorithm for Computing Optimal Decision Trees

We propose a simple algorithm to learn optimal decision trees of bounded depth. This algorithm is essentially an anytime version of the state-of-the-art dynamic programming approach. It has virtually no overhead compared to heuristic meth-ods and is comparable to the best exact methods to prove optimality on most data sets. Experiments show that whereas existing exact methods hardly scale to deep trees, this algorithm learns trees comparable to standard heuristics without computational overhead, and can significantly improve their accuracy when given more computation time, even for deep trees.
An extension of Birnbaum-Saunders distributions based on scale mixtures of skew-normal distributions with applications to regression models

SOBRENOME, A. B. C. An extension of Birnbaum-Saunders distributions based on scale mixtures of skew-normal distributions with applications to regression models. 2010. 109 f. Tese (Doutorado) Instituto de Matemática e Estat́ıstica, Universidade de São Paulo, São Paulo, 2010. The aim of this work is to present an inference and diagnostic study of an extension of the lifetime distribution family proposed by Birnbaum and Saunders (1969a,b). This extension is obtained by considering a skew-elliptical distribution instead of the normal distribution. Specifically, in this work we develop a Birnbaum-Saunders (BS) distribution type based on scale mixtures of skew-normal distributions (SMSN). The resulting family of lifetime distributions represents a robust extension of the usual BS distribution. Based on this family, we reproduce the usual properties of the BS distribution, and present an estimation method based on the EM algorithm. In addition, we present regression models associated with the BS distributions (based on scale mixtures of skew-normal), which are developed as an extension of the sinh-normal distribution (Rieck and Nedelman, 1991)). For this model we consider an estimation and diagnostic study for uncensored data.
Accident Analysis and Prevention

As use of handheld multimedia devices has exploded globally, safety experts have begun to consider the impact of distraction while talking, text-messaging, or listening to music on traffic safety. This study was designed to test how talking on the phone, texting, and listening to music may influence pedestrian safety. 138 college students crossed an interactive, semi-immersive virtual pedestrian street. They were randomly assigned to one of four groups: crossing while talking on the phone, crossing while texting, crossing while listening to a personal music device, or crossing while undistracted. Participants distracted by music or texting were more likely to be hit by a vehicle in the virtual pedestrian environment than were undistracted participants. Participants in all three distracted groups were more likely to look away from the street environment (and look toward other places, such as their telephone or music device) than were undistracted participants. Findings were maintained after controlling for demographics, walking frequency, and media use frequency. Distraction from multimedia devices has a small but meaningful impact on college students’ pedestrian safety. Future research should consider the cognitive demands of pedestrian safety, and how those processes may be impacted by distraction. Policymakers might consider ways to protect distracted pedestrians from harm and to reduce the number of individuals crossing streets
CFD analysis of human powered submarine to minimize drag

This paper deals with finding the optimum fineness ratio, i.e. ratio of length to maximum diameter, of human-powered submarine of different shapes to reduce the drag force on the body using Computational Fluid Dynamics (CFD) analysis. These types of submarines are used in events like ISR and eISR. This paper focuses on finding the total drag force on submarine models with a constrained diameter and different fineness ratios. The analysis is done by using ANSYS Fluent. In this paper, only the fully submerged flow is considered on a hull without any appendages.The total drag on a body is caused in three different parts that are wave drag, skin friction drags and base drag.The analysis is done different shapes of submarines like Conic shape hull, Elliptical shape hull, Ogive shape hull and Parallel mid-body hull by flowing water at velocities of 3 m/s, 4m/s and 5 m/s. The fineness ratios at which the drag is minimum are found in all submarine shapes. The optimum value of fineness ratio, which gives minimum drag is obtained by the analysis is 6 for Conical shape hull, Elliptical shape hull and Ogive shape hull whereas for the submarine with Parallel mid-body hull shape the optimum fineness ratio is 7