Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Distribution of the Sum of Nakagami-$ m$ Random Variables

In this paper, we investigate the classical problem of finding the probability density function (pdf) of the sum of Nakagami-m random variables. Exact infinite series representations are derived for the sum of three and four identically and independently distributed (i.i.d.) Nakagami-m random variables, and subsequently, it is extended to the sum of general number of random variables. A useful pattern emerged as a result of these extensions, and a new Fourier transform pair that involves parabolic cylinder function and Gauss hypergeometric function is obtained. Bounds on the error resulting from truncation of the infinite series of pdfs are also presented. These pdfs are used to analyze the performance of dual, triple, and quadruple branch predetection equal gain combining (EGC) receivers over Nakagami-m fading environment. Furthermore, a hypergeometric relation is derived as a result of those derivations. Subsequently, the analysis is extended to the case of L branch EGC receiver performance as well. Selected simulation plots are provided to compare the results with the approximate results available in the literature and to illustrate the validity of the formulation.
Characterization of the Tritium-Labeled Analog of L-threo-β-Benzyloxyaspartate Binding to Glutamate Transporters

l-Glutamate is the major excitatory neurotransmitter in the mammalian central nervous system. Termination of glutamate receptor activation and maintenance of low extracellular glutamate concentrations are primarily achieved by glutamate transporters (excitatory amino acid transporters 1-5, EAATs1-5) located on both the nerve endings and the surrounding glial cells. To identify the physiological roles of each subtype, subtype-selective EAAT ligands are required. In this study, we developed a binding assay system to characterize EAAT ligands for all EAAT subtypes. We recently synthesized novel analogs of threo-β-benzyloxyaspartate (TBOA) and reported that they blocked glutamate uptake by EAATs 1-5 much more potently than TBOA. The strong inhibitory activity of the TBOA analogs suggested that they would be suitable to use as radioisotope-labeled ligands, and we therefore synthesized a tritiated derivative of (2S,3S)-3-{3-[4-ethylbenzoylamino]benzyloxy}aspartate ([3H]ETB-TBOA). [3H]ETB-TBOA showed significant high-affinity specific binding to EAAT-transfected COS-1 cell membranes with each EAAT subtype. The Hill coefficient for the Na+-dependence of [3H]ETB-TBOA binding revealed a single class of noncooperative binding sites for Na+, suggesting that Na+ binding in the ligand binding step is different from Na+ binding in the substrate uptake process. The binding was displaced by known substrates and blockers. The rank order of inhibition by these compounds was consistent with glutamate uptake assay results reported previously. Thus, the [3H]ETB-TBOA binding assay will be useful to screen novel EAAT ligands for all EAAT subtypes.
A Data Mining Based Pervasive User Requests Prediction Method in e-Learning Systems

A user request prediction method based on data mining approaches has been proposed in this paper. By analyzing a large amount of learning history data accumulated over a long period of time, the presented solution enables the coming request to be predicted in advance, and preprocessed beforehand. It takes advantage of the integration of data mining technology and parallel computing, which maximizes the server's ability of parallelization from the knowledge acquired from the experiential data. The proposed protocol has been applied to a practical e-learning system, and the experiment result shows that the average response time with the proposed scheme is much shorter than the one without request prediction
Intraosseous injection as an adjunct to conventional local anesthetic techniques: A clinical study

Background: The achievement of successful local anesthesia is a continual challenge in dentistry. Adjunctive local anesthetic techniques and their armamentaria, such as intraosseous injection (the Stabident system and the X-tip system) have been proposed to be advantageous in cases where the conventional local anesthetic techniques have failed. Aim: A clinical study was undertaken using intraosseous injection system by name X-tip to evaluate its effectiveness in cases where inferior alveolar nerve block has failed to provide pulpal anesthesia. Materials and Methods: Sixty adult patients selected were to undergo endodontic treatment for a mandibular molar tooth. Inferior alveolar nerve block was given using 4% articaine with 1:100,000 epinephrine. Twenty-four patients (40%) had pain even after administration of IAN block; intraosseous injection was administered using 4% articaine containing 1:100,000 epinephrine, using the X-tip system. The success of X-tip intraosseous injection was defined as none or mild pain (Heft-Parker visual analog scale ratings ≤ 54 mm) on endodontic access or initial instrumentation. Results: Intraosseous injection technique was successful in 21 out of 24 patients (87.5%), except three patients who had pain even after supplemental X-tip injection. Conclusion: Within the limits of this study, we can conclude that supplemental intraosseous injection using 4% articaine with 1:100,000 epinephrine has a statistically significant influence in achieving pulpal anesthesia in patients with irreversible pulpitis.
Development and in-house validation of an LC-MS/MS method for the quantification of the mycotoxins deoxynivalenol, zearalenone, T-2 and HT-2 toxin, ochratoxin A and fumonisin B1 and B2 in vegetable animal feed

Animal feed can be contaminated with various mycotoxins. To ensure animal health and safe food and feed production, the European Commission has recommended increased monitoring of the co-occurrence of deoxynivalenol, zearalenone, ochratoxin A, fumonisin B1 and B2, T-2 and HT-2 toxin in feed. Thus, there is a need for an analytical method that enables their simultaneous detection and quantification. This paper describes the development and in-house validation of such a method, in which the mycotoxins were extracted from spiked and naturally contaminated cereal-based compound feed, corn and wheat. The extracts were divided into two aliquots where one was diluted and then analysed directly and the other was cleaned by using MultiSep®226 and then diluted and analysed. Separation and detection was achieved with LC-ESI-MS/MS by using a triple quadrupole instrument in the SRM mode. The precision (in terms of intra-day repeatability and inter-day reproducibility), accuracy, linearity, apparent recovery and expanded measurement uncertainty in feed, corn and wheat were evaluated. The LODs ranged from 1.0 to 72 μg/kg, and the LOQs ranged from 2.5 to 115 μg/kg. The apparent recovery was higher than 86% for all the mycotoxins, and the precision was better than that defined by the Horwitz equation for all concentrations. Proficiency test materials were analysed to assess the accuracy of the method, and the results were satisfactory for all seven mycotoxins. The method will be used to monitor the occurrence of these mycotoxins in products intended for animal feeding in Sweden.
Signal extraction method in electromagnetic coupling system based on LM-ABC algorithm

In the electromagnetic coupling system, the sinusoidal coupling signal extraction is crucial. It will be difficult to extract the signal parameters because of the possibility of signal frequency offset and saturation. Hence, this paper proposes an algorithm combined Artificial Bee Colony algorithm (ABC) and the Levenberg-Marquardt(LM) algorithm to extract sinusoidal parameters from saturation sinusoidal signal with frequency deviation. The LM-ABC algorithm take full advantage of the global search ability of ABC Algorithm and the high-precision local optimization capability of LM Algorithm to recover the signal under certain noise conditions and achieve high signal accuracy. Firstly, the unsaturated sinusoidal signal sample is extracted from the sample of the saturated sinusoidal signal. Then, we use the ABC algorithm to calculate an initial value. LM iteration based on the initial value optimizes the signal parameters. The simulation results show that if there are enough sampling points, the method can obtain he accurate parameters at a certain noise level from saturated sinusoidal signal under frequency offset.
Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks

Consider a random preferential attachment model G(p) for network evolution that allows both node and edge arrivals. Starting with an arbitrary nonempty graph G0, at each time step, there are two possible events: with probability p > 0 a new node arrives and a new edge is added between the new node and an existing node, and with probability 1 - p a new edge is added between two existing nodes. In both cases, the involved existing nodes are chosen at random according to preferential attachment, i.e., with probability proportional to their degree. G(p) is known to generate power law networks, i.e., the fraction of nodes with degree k is proportional to k-β. Here β=(4-p)/(2-p) is in the range (2,3]. Denoting the number of nodes of degree k at time t by mk,t, we significantly improve some long-standing results. In particular, we show that mk,t is concentrated around its mean with a deviation of O(√t), which is independent of k. We also tightly bound the expectation Emk,t with an additive error of O(1/k), which is independent of t. These new bounds allow us to tightly estimate mk,t for a considerably larger k values than before. This, in turn, enables us to estimate other important quantities, e.g., the size of the k-rich club, namely, the set of all nodes with a degree at least k. Finally, we introduce a new generalized model, G(pt, rt, qt), which extends G(p) by allowing also time-varying probabilities for node and edge arrivals, as well as the formation of new components. We show that the extended model can produce power law networks with any exponent β in the range (1,∞). Furthermore, the concentration bounds established for mk,t in G(p) also apply in G(pt, rt, qt).
Optical vortex metrology for non-destructive testing

Optical vortices or phase singularities in optical fields have been known for a long time, and their basic properties have been studied extensively since the seminal work by Nye and Berry in the early 1970s. Recently, phase singularities have come to attract new attentions for different reasons in the fields of applications such as optical metrology and photon manipulation. Because of their orbital photon angular momentum, optical vortices have proved themselves a useful means for optical trapping and manipulation of atoms and micro particles. On the contrary, phase singularities have been regarded as a nuisance by researchers in optical interferometry and profilometry, because the phase singularities hinder unique phase unwrapping of the measured phase values, which are usually wrapped into the range [-π, π). Therefore, main efforts in optical metrology have been concentrated on how to get rid of the effect of phase singularities. A typical example is the phase unwrapping algorithm that directly kills out all phase singularities by superposing vortex fields with the phase singularities of opposite sign [1]. A question then naturally arises “Can phase singularities also prove themselves to be useful in optical metrology? ”
Comparison of Ensemble Techniques for Incremental Learning of New Concept Classes under Hostile Non-stationary Environments

We have recently introduced Learn++, an incremental learning algorithm, inspired by the multiple classifiers structure of AdaBoost. Both algorithms generate an ensemble of classifiers trained on bootstrapped replicates of the training data, and the classifiers are then combined through a voting process. Learn++, however, generates additional ensembles as new data become available, and uses a different distribution update rule to resample the data. While AdaBoost was originally designed to improve the performance of a weak classifier, whether it can still achieve incremental learning through its ensemble structure is still an open question. In this paper, we compare the incremental learning ability of AdaBoost.M1 and Learn++ under very hostile nonstationary learning environments, which may introduce new concept classes. We also compare the algorithms under several combination rules to determine which of the three key components -ensemble structure, resampling procedure, or the combination rule -has the primary impact on incremental learning in nonstationary environments.
Enhanced image quality evaluation based on SIFT feature

In the study of Retinex image enhancement methods, there is a need to determine the quality of the image enhancement. But the images obtained through different Retinex image enhancement methods were quite similar. Thus, subjective evaluation criteria are not reliable. In this paper, the SIFT feature points are presented as an objective evaluation of images. Image quality evaluation method based on SIFT features were turned out to be feasible. Experimental results are consistent with the conclusions drawn by synthesizing information entropy, standard deviation, and the mean squared error.
Dlx5/6‐enhancer directed expression of Cre recombinase in the pharyngeal arches and brain

Dlx5 and Dlx6, two members of the Distalless gene family, are required for development of numerous tissues during embryogenesis, including facial and limb development. This gene pair is expressed in tandem, transcribed toward each other and separated by a short intergenic region containing multiple putative enhancers. Targeted inactivation of Dlx5 and Dlx6 in mice results in multiple developmental defects in craniofacial and limb structures, suggesting that these genes are crucial for aspects of both neural crest and nonneural crest development. To further investigate potential developmental roles of Dlx5 and Dlx6, we used one of the Dlx5/6 intergenic enhancers to drive Cre recombinase expression in transgenic mice. Crossing Dlx5/6‐Cre transgenic mice with mice from the R26R strain results in β‐galactosidase staining in the apical ectodermal ridge, brain, and neural crest‐derived mesenchyme of the pharyngeal arches, with staining in term embryos observed in the facial skeleton and specific brain structures. However, in contrast to endogenous expression patterns of Dlx5 and Dlx6, Cre expression within the pharyngeal arches occurs during a very narrow window in early development. Our studies suggest that Dlx5/6‐Cre mice may prove useful both in further understanding the function and regulation of Distalless genes during development and in studies of gene function in conditional knockout mice. genesis 37:188–194, 2003. © 2003 Wiley‐Liss, Inc.
A predictive optimal torque control of synchronous reluctance motor drive

In this paper, a predictive digital control of the synchronous reluctance motor drive is presented. The proposed control, implemented in rotor coordinates, is based on a recursive algorithm in which the reference values of d and q current components are imposed. The digital control system directly synthesizes the output vector which allows the timing control of the inverter by means of on/off state of the switches. This results in avoiding the implementation of the traditional PI controllers and PWM technique. Under transient operation, both the reference d-q current components are set by imposing the maximum allowable torque variation, during the actual sampling interval, taking into account the inverter voltage saturation. In this way, the optimal (fastest) torque trajectory can be followed. The proposed algorithm guarantees optimal torque step response. The d-q current values, corresponding to steady state torque, do not guarantee any other additional condition. Hence, after reference torque is reached, the drive can be controlled by means of an additional predictive algorithm which assures minimum Joule losses. A computer simulation study, using Matlab Simulink tool, is conducted on the drive with the aim of highlighting the good performance of the proposed algorithms compared to traditional and to predictive (not optimal) ones.
Nodal points and complexity of Notch-Ras signal integration

Metazoans use a handful of highly conserved signaling pathways to create a signaling backbone that governs development. How these few signals have such a versatile action likely depends upon the larger-scale network they form through integration, as exemplified by cross-talk between the Notch and receptor tyrosine kinase (RTK) pathways. We examined the transcriptional output of Notch–RTK cross-talk during Drosophila development and present in vivo data supporting a role for selected mutually regulated genes in signal integration. Interestingly, Notch–RTK integration did not lead to general antagonism of either pathway, as is commonly believed. Instead, integration had a combinatorial effect on specific cross-regulated targets, which unexpectedly included numerous core components of the RTK and other major signaling pathways (TGF-β, Hh, Jak/Stat, nuclear receptor and Wnt). We find the majority of Ras-responsive genes are also Notch-responsive, suggesting Notch may function to specify the response to Ras activation.
Measuring Currency Risk of Foreign Exchange Reserve in China with Extreme Theory and Multivariate Time-varying Copula Model

This paper first uses extreme value theory and the ARMA-AGACH-t model to derive the residual series of daily returns of the five exchange rates of RMB against the U.S.dollar,euro,yen,Hang Kong dollar,and pound.And then it uses multivariate static and time-varying copula-t model respectively to esti-mate its VaR and CVAR value.Finally it calculates the optimal structure of for-eign exchange reserves under the target of different return ratios.The result shows that generalized Pareto distribution can well fit the tail of exchange rate re-turns series.Compared with multivariate static copula-t model,time-varying copu-la-t model can better measure the currency risk of foreign reserves.Given the tar-get of return ratio,the optimal holding ratio of dollar declines when the target of return ratio increases while that of yen increases in the same direction.
Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives

Background The Food and Drug Administration’s (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. Objective The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. Methods We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. Results The annotated corpus had an agreement of over . 9 Cohen’s kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. Conclusions In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance.
Wind speed and direction forecasting for wind power generation using ARIMA model

Wind Power plays a major role in both large utility grids and small microgrids due to a wide range of socio-economic benefits. Due to this reason, current research has an emerging trend to enhance its reliability and usability. Highly random nature of the wind speed and direction leads to having a poor accuracy of wind power forecasting and thereby poor reliability, increased cost and reduced efficiency of electrical systems. Most updated studies are focused mainly on wind speed, and their prediction errors are above the industry expectations. In this paper, both the wind speed and wind direction are analyzed to develop a statistical model based forecasting technique. This paper uses an Autoregressive Integrated Moving Average method to build the estimating model for wind measured in Western Australia to yield the forecasted values. The resultant model can be used to improve the system reliability, quality of the wind power generation system.
The Impact of the Wellness Guide/Guía on Hispanic Women's Well-Being-Related Knowledge, Efficacy Beliefs, and Behaviors: The Mediating Role of Acculturation

This study examined how acculturation mediated the impact of the California Wellness Guide/La Guía del Bienestar on Hispanic women's knowledge, efficacy beliefs, and behaviors. Knowledge gaps were found between acculturated and less acculturated women. Acculturated mothers had more confidence in their abilities to acquire wellness-related information both before and after Guide/Guía distribution and also had more assistance-seeking efficacy. Guide/Guía recipients were more knowledgeable, had greater confidence in their knowledge and their abilities to acquire information, and possessed better information acquisition strategies than nonrecipients. The publication also enhanced low-acculturated recipients' assistance-seeking self-efficacy. High-acculturated and low-acculturated recipients of the Guide/Guía were equally likely to have retained and used their copies, to anticipate using the publication in the future, and to have made a change in behavior as a result of the guide. Finally, the Guide/Guía did not increase the knowledge gap between high- and low-acculturated guide recipients.
Performance Evaluation in the UEFA Champions League

This article aims to evaluate the sports performance of teams that have participated in the Union of European Football Associations (UEFA) Champions League (UCL) during the last 10 seasons (2004-2005 to 2013-2014). Technical efficiency is estimated using well-known data envelopment analysis (DEA) approaches and a bootstrapped DEA model. To solve the problem of measuring sporting results as output in knockout competitions, we propose the use of the coefficients applied by the UEFA from UCL revenue distribution. The results obtained show first that there is a high level of inefficiency in UCL over the period studied: Only 10% of the teams seem to be efficient. Also, the teams have many problems in maintaining their efficiency during the seasons. Second, the champion is always efficient. Third, we identify two sources of inefficiency: waste of sports resources and the selection of sporting tactics. Finally, from a methodological perspective, the output measure proposed seems to be suitable to represent reliably the sports results achieved by clubs in this qualifying competition type. Furthermore, our results are robust when applying alternative estimation methods. Regarding the results, some management implications are discussed and suggestions are made to boost the efficiency in inefficient clubs.