Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Assistant for the Assessment University Competencies in B-Learning Environments

Competency assessment is a complex problem that has been approached from multiple perspectives but remains a work in progress. This paper presents a new competency assessment model accompanied by a computational assistant that guides its implementation. The model covers seven phases of an evaluation process, beginning with the definition of the competency and ending with the feedback of evidence provided from the teacher to the students. In each phase, the assistant provides suggestions to the teacher, which are obtained through text analysis algorithms and improve as information is added. The model was validated through pre and post-test study requiring the participation of a group of university teachers. The results indicate that with the use of the model, the time invested by the teacher in evaluation tasks is reduced, the clarity and completeness of the process improves, and an integrated evaluation of generic and specific competences is achieved.
OprF and OprL Conjugate as Vaccine Candidates against Pseudomonas aeruginosa; an in Silico Study

Results: The structures of OprF and OprL were successfully modeled and assessed using 4RLC-A and 4G4V-A as template structures. The regions of the proteins with a high B cell epitope density were identified as candidates for vaccine design. These regions contain functional and exposed amino acids. In these regions, the majority of amino acids were hydrophilic, flexible and accessible. Conclusion: It should be noted that in silico approaches are appealing alternatives for empirical methods. These approaches could pave the way for precise vaccine design efforts with lower cost and time. J Med Microbiol Infec Dis, 2018, 6 (1): 1-7.
A Human Embryonic Stem Cell Model of Aβ-Dependent Chronic Progressive Neurodegeneration

We describe construction and phenotypic analysis of a human embryonic stem cell model of progressive Aβ-dependent neurodegeneration (ND) with potential relevance to Alzheimer’s disease (AD). We modified one allele of the normal APP locus to directly express a secretory form of Aβ40 or Aβ42, eliminating the need for amyloidogenic APP proteolysis. Following neuronal differentiation edited cell lines specifically accumulate aggregated/oligomeric Aβ, exhibit a synaptic deficit and have an abnormal accumulation of endolysosomal vesicles. Edited cultures progress to a stage of overt ND. All phenotypes appear at earlier culture times for Aβ42 relative to Aβ40. Whole transcriptome RNA-Seq analysis identified 23 up and 70 down regulated genes (DEGs) with similar directional fold change but larger absolute values in the Aβ42 samples suggesting common underlying pathogenic mechanisms. Pathway/annotation analysis suggested that down regulation of extracellular matrix and cilia functions are significantly overrepresented. This cellular model could be useful for uncovering mechanisms directly linking Aβ to neuronal death and as a tool to screen for new therapeutic agents that slow or prevent human ND.
A Study of the Secondary Electrons

Slow secondary electrons (SEs) (E<50 eV) are those emitted from a sample as the result of bombardment by energetic particles. They are the most important signal source for SEM and for other advanced microanalysis techniques. SE yield δ and spectrum N(E) are the two important parameters evaluating the capability of a sample on producing SEs and the energy distribution of SEs generated from the sample respectively. Measuring δ and N(E) is not easy since SEs are easily affected by sample surface condition and by experimental configuration. Though SE has been studied since its first find in 1902, experimental date of it are inconsistent and unsystematic. Theoretical models on the SE production are not well established. To better understand the secondary electrons, an optimization of a scattered experimental SE yield database was carried out by fitting the data to a semi-empirical universal curve and by a Monte Carlo simulation. The profiles of SE yield versus beam energy and the values of SE excitation energyε and mean SE escape depthλ were generated for 44 elements. An atomic shell filling effect was found on the maximum SE yields and the corresponding beam energies. To obtain more accurate and systematic SE yield data, a novel experimental method by collecting electron spectra on an AES instrument (PHI 680 SAN) equipped with a cylindrical mirror analyzer (CMA) was developed. The principles of the CMA and the micro channel plate (MCP) in analyzing and multiplying electron signal were studied. The efficiency of the PHI 680 SAN in response to the electron energy in the range of 0~3200 eV was deduced to calibrate intensity of any as received spectrum. Measurement on elements and the Cr-W and Cu-Au binary alloys were conducted. The measured SE yields were 50% lower than the optimized values but showed the atomic shell filling effect as expected. Both the SE and BSE yields of the binary alloys showed linear relationship with atomic percent of specimen constituents. Linear synthesis of the elemental SE spectra of the Cr-W alloys agreed well with the measured.
Constant-Voltage and Constant-Current Output Using P-CLCL Compensation Circuit for Single-Switch Inductive Power Transfer

The characteristic of constant-current output (CCO) and constant-voltage output (CVO) on full-bridge topology can be realized by using a traditional double-sided <italic>LCC</italic> compensation network. The switching from CCO mode to CVO mode can be realized by switching the operating frequency in CCO mode (<italic>f</italic><sub>CC</sub>) to the operating frequency in CVO mode (<italic>f</italic><sub>CV</sub>). However, the operating frequency is generally high, the value of <italic>L</italic><sub>P</sub>, <italic>L</italic><sub>S,</sub> and <italic>M</italic> in the loosely coupled transformer will have a significant influence on the operating frequency of the circuit, which makes it difficult to carry out a long-distance transmission at a lower operating frequency, otherwise, the self-inductance of the coils will be high, even though it is used to transfer low-power. Therefore, in this article, a novel P-<italic>CLCL</italic> compensation topology based on a single-switching circuit (SSC) is proposed to address these problems. The switching from CCO to CVO can also be realized by changing the operating frequency from <italic>f</italic><sub>CC</sub> to <italic>f</italic><sub>CV</sub>, and a relatively lower operating frequency and a relatively farther transmission distance than full-bridge topology are realized. The calculation method of circuit parameters, implementation of zero-voltage-switching, sensitivity analysis, and load-independent output are discussed. Finally, the simulation and experiment are carried out to verify the correctness of the analysis.
Demographics Structure effect on Stock Prices: An Evidence from (China, India, Pakistan)

This study investigates the impact of investment on the demographic structure of population i.e.; peak earning age and retirement age and its impact on the stock price of China, India and Pakistan. The current study employs data of these countries for the period of 1991to 2017 by using Autoregressive Regressive Distributed Lag (ARDL) approach. This study indicates that investment made by peak earning age investors has a positive and significant impact on stock price. Whereas, it is observed the retirement age has a significant and negative impact on stock price. The results of the study also suggested that in the peak earning age people invest in the stock market and at retirement age, they withdraw their investment from stock markets.
People Counting Using IR-UWB Radar Sensor in a Wide Area

Conventional radar-based people counting systems are designed mainly for dense spatial distributions in a small region of interest (ROI). Therefore, a system with only conventional energy-based features, which are effective for a small ROI with a limited spatial distribution of individuals, generally fails to cope with the diverse and complex spatial distributions that arise from the freer movements of individuals as the ROI widens. To address this problem, a novel approach that achieves robust people counting in both wide and small ROIs is presented in this study. The proposed technique incorporates modified CLEAN-based features in the range domain and energy-based features in the frequency domain to efficiently address both dense and dispersed distributions of individuals. Subsequently, principal component analysis and an appropriate normalization of the proposed features are performed for improving the people counting system further. Based on several experiments in practical environments with wide ROIs and severe multipath effects, we observed that the proposed approach yields significantly improved performance compared with traditional people counting systems.
Adaptive Tunable Q Wavelet Transform-Based Emotion Identification

Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition from electroencephalogram (EEG) signals plays a vital role in the development of a brain–computer interface (BCI). Extracting the important information from raw EEG signals is difficult due to its nonstationary nature. Fixing a factual predefined basis function for efficient decomposition using a tunable $Q$ wavelet transform is an arduous task. In this article, an adaptive tunable $Q$ wavelet transform is proposed for the automatic selection of tuning parameters. Optimum tuning parameters are obtained using gray wolf optimization (GWO). Tuning parameters obtained by GWO are used to decompose the EEG signals into subbands (SBs). The set of time-domain features elicited from the SBs are used as an input to multiclass least-squares support vector machine. Classification accuracy of four basic emotions, namely, happy, fear, sad, and relax, is tested and compared with existing methods. An accuracy of 95.70% is achieved with a radial basis function kernel that is about 5% more than the existing methods using the same data set. This article proposes the development of a nonparameterized decomposition method for efficient decomposition of EEG signals. This method can be used with machine learning algorithms to take a step forward in the development of BCI systems.
Flocking behaviour in the twilight ascents of Common Swifts Apus apus

Among the many unique flight behaviours of Common Swifts Apus apus, the most puzzling may be their ascents to high altitudes during both dusk and dawn. Twilight ascents have been hypothesized to be functionally related to information acquisition, including integration of celestial orientation cues, high-altitude visual landmarks and sampling of weather conditions. However, their exact purpose remains unknown. We tracked Common Swifts with tracking radar at their breeding grounds in southern Sweden, and present evidence that during the dusk ascent and dawn descent they often occur in flocks, whereas during the dusk descent and dawn ascent phase they do not. This flocking behaviour suggests that swifts may benefit from conspecific interactions during twilight ascents and descents, possibly through more robust cue acquisition and information exchange in groups, or extending social behaviour also seen in screaming parties before dusk. (Less)
INTRASPECIFIC SYSTEMATICS OF GENUS NEZARA (HEMIPTERA: PENTATOMIDAE) INFERRED FROM ANALYSIS OF THE MITOCHONDRIAL CYT B GENE SEQUENCES

In the present study, the genetic divergence among genus Nezara of family Pentatomidae collected from different localities of Northern India was estimated using partial Cyt b gene sequences. Intraspecific divergence ranged from 0.2% to 7.2% with an average of 4.16±3.2% while the interspecific divergence between N. viridula and N. antennata was ranged from 6.1% to 9.8% with an average of 7.54 ± 1.2%. Mitochondrial Cyt b gene sequences clearly showed the overlapping of intra and interspecific divergence in the genus Nezara as the intraspecific divergence in N. viridula of the present study was quite high.
The Influence of Impulsivity and Values on Impulsive Buying

This study examined the influence of impulsivity and values orientations on impulsive buying tendencies on convenience sample from the City of Zagreb (N = 220, 56% women) using the Baratt impulsivity scale, Buying Impulsivity Scale, and Value Orientation Scale. Participants were between the ages 20 years and 55 years, and all were employed. As predicted, bivariate correlations showed that impulsive buying tendencies were positively related to attention, motor and non-planning impulsivity. In addition, the results revealed positive association between impulsive buying tendencies and hedonistic value orientation. Results of hierarchical regressions analyses showed that motor and non-planning impulsivity, but not attention impulsivity, positively predicted impulsive buying tendencies. Moreover, the multiple regression analysis has shown that impulsivity and values explained 35% of the variance in impulsive buying tendencies. The results are in line with the theoretical assumptions proposed that some aspects of impulsive behaviour are associated with utilitarian-hedonistic value orientation.
Digital Predictive Current-Mode Control of Three-Level Flying Capacitor Buck Converters

This article investigates the use of digital predictive current-mode control (DPCMC) in dc–dc three-level flying capacitor (3LFC) buck converters. In particular, stability and flying capacitor (FC) balancing properties of predictive peak, average, and valley current-mode controllers are studied when operated in single- and multisampled modes. A variant of the multisampled DPCMC obtained through a fast-update of the duty cycle command is also disclosed and analyzed. Results indicate that single-sampled DPCMC is always stable, and that fast-update approaches can strongly improve the converter dynamic response. Multisampled controllers are also shown to be inherently more robust than single-sampled ones against timing mismatches in the control signals, resulting in a smaller FC voltage imbalance. The analysis is validated in simulation and experimentally on a 500 kHz, 12–1.5 V, 500 mA 3LFC buck case study.
Characterization of Evolutionarily Conserved Trypanosoma cruzi NatC and NatA-N-Terminal Acetyltransferase Complexes

Protein N-terminal acetylation is a co- and posttranslational modification, conserved among eukaryotes. It determines the functional fate of many proteins including their stability, complex formation, and subcellular localization. N-terminal acetyltransferases (NATs) transfer an acetyl group to the N-termini of proteins, and the major NATs in yeast and humans are NatA, NatB, and NatC. In this study, we characterized the Trypanosoma cruzi (T. cruzi) NatC and NatA protein complexes, each consisting of one catalytic subunit and predicted auxiliary subunits. The proteins were found to be expressed in the three main life cycle stages of the parasite, formed stable complexes in vivo, and partially cosedimented with the ribosome in agreement with a cotranslational function. An in vitro acetylation assay clearly demonstrated that the acetylated substrates of the NatC catalytic subunit from T. cruzi were similar to those of yeast and human NatC, suggesting evolutionary conservation of function. An RNAi knockdown of the Trypanosoma brucei (T. brucei) NatC catalytic subunit indicated that reduced NatC-mediated N-terminal acetylation of target proteins reduces parasite growth.
Acquisition of visual priors and induced hallucinations in chronic schizophrenia

Prominent theories suggest that symptoms of schizophrenia stem from learning deficiencies resulting in distorted internal models of the world. To further test these theories, we here use a visual statistical learning task known to induce rapid implicit learning of the stimulus statistics (Chalk et al., 2010). In this task, participants are presented with a field of coherently moving dots and need to report the presented direction of the dots (estimation task) and whether they saw any dots or not (detection task). Two of the directions were more frequently presented than the others. In controls, the implicit acquisition of the stimuli statistics influences their perception in two ways: 1-motion directions are perceived as being more similar to the most frequently presented directions than they really are (estimation biases); 2-in the absence of stimuli, participants sometimes report perceiving the most frequently presented directions (a form of hallucinations). Such behaviour is consistent with probabilistic inference, i.e. combining learnt perceptual priors with sensory evidence. We investigated whether patients with chronic, stable, treated schizophrenia (n=20) differ from controls (n=23) in the acquisition of the perceptual priors and/or their influence on perception. We found that, although patients were slower than controls, they showed comparable acquisition of perceptual priors, correctly approximating the stimulus statistics. This suggests that patients have no statistical learning deficits in our task. This may reflect our patients relative wellbeing on antipsychotic medication. Intriguingly, however, patients made significantly fewer hallucinations of the most frequently presented directions than controls and fewer prior-based lapse estimations. This suggests that prior expectations had less influence on patients’ perception than on controls when stimuli were absent or below perceptual threshold.
Data-driven development of personality predictive lexica from social media

Automatic personality prediction is getting more popular because it is convenient and reliable. Lexicon-based analysis has been successful in the ﬁelds of sentiment analysis and emotion. Many studies have used linear models for personality prediction, which suggests that we can also use lexical-based analysis for personality prediction. In the current study, we developed weighted word lexicons (words and scores) on each dimension of MBTI personality. The lexicons are built based on eight MBTI datasets, different features (unigram, 1-2 grams, 1-2-3 grams) and weightings (TF, TF-IDF, TF-logIDF), and different supervised learning models. Then we ran correlation analysis between our MBTI lexicons and other existing lexicons, such as Big-5, emotion, sentiment, age, gender. The correlation analysis shows interesting and reasonable correlation between different personality dimensions and other psychological traits, and it also provides evidence for the robustness of our lexicons.
The Effect of Power Distance in School on Instructor-Student Interaction, Help Seeking, Critical Thinking, and Convergence Thinking

The study aims to enhance the understanding of Korean learners concerning globalization and localization of educational contents. The study explores power distance in school, and examines its effects on instructor-student interaction, help-seeking, critical thinking, and convergence thinking. Four hundred sixteen students participated. Power distance in school is analyzed as aspects of course driver, instructor's role, and instructor-student equality. It is found that students have tendencies for large power distance and it has significant effects on instructor-student interaction, help-seeking, critical thinking, and convergence thinking. Considerations to localize international educational contents for Korean learners are discussed. study limitations and future research direction are addressed.
Role of Panax Ginseng as an Antioxidant and Hepatoprotective after Liver Toxicity caused by Flutamide in Adult Male Rats

Aim: This study was conducted to determine antioxidant, hepatoprotective and anti  inflammatory effects of panax ginseng extract against hepatotoxic effect of flutamide, a drug widely used in the treatment of metastatic prostate adenocarcinoma. Materials and Methods: Thirty rats (weight 200-300 g) were used and had access to water and food in the animal house for two week. The rats were divided randomly into six equal groups: group (A): received normal saline as control, group(B): received flutamide (25 mg/kg b.wt) orally for 7 days, group (C): injected p. ginseng extract (200 mg / kg b.wt) intraperitonialy daily for 30 days, group (D):injected p. ginseng extract (400mg/kg b.wt) intrapretonialy daily for 30 days, group (E):received flutamide (25mg/kg b.wt) orally for 7 days with p. ginseng extract (200 mg / kg b.wt) intrapretonialy daily for 30 days, group (F): received flutamide (25mg/kg b.wt) orally for 7days with p. ginseng extract (400 mg/kg b.wt) intrapretonialy daily for 30 days. In this study, we assessed the hepatoprotective effect of red ginseng in rats treated with flutamide. Serum concentrations of the hepatic marker enzymes alanine amino transferase (ALT), aspartate amino transferase (AST) and histopathological analysis have been performed for this purpose. The hepatic antioxidant status was assessed using measurement of GSH levels. Results: Administration of flutamide to adult male rats causes severe hepatic injury. Hepatosomatic index, ALT and AST were significantly increased in comparison with control and ginseng treated groups. While a significant decrease in the contents of reduced hepatic glutathione (GSH) was observed Histological examination of liver tissues showed that flutamide caused significant increase in the diameter of central vein, bloody congestion in the central vein with infiltration of Inflammatory cells, swelling of hepatocytes, narrowing of blood sinusoids and vacoules appear inside the hepatocytes. Conclusion: The results of this current study indicate that the administration of ginseng (200,400 mg / kg b.w) to flutamide treated animals resulted in an improvement in the histological picture of the liver as well as biochemical parameters, mainly through down regulation of oxidative stress and inflammatory response.
Spectral–Spatial Joint Sparse NMF for Hyperspectral Unmixing

The nonnegative matrix factorization (NMF) combining with spatial–spectral contextual information is an important technique for extracting endmembers and abundances of hyperspectral image (HSI). Most methods constrain unmixing by the local spatial position relationship of pixels or search spectral correlation globally by treating pixels as an independent point in HSI. Unfortunately, they ignore the complex distribution of substance and rich contextual information, which makes them effective in limited cases. In this article, we propose a novel unmixing method via two types of self-similarity to constrain sparse NMF. First, we explore the spatial similarity patch structure of data on the whole image to construct the spatial global self-similarity group between pixels. And according to the regional continuity of the feature distribution, the spectral local self-similarity group of pixels is created inside the superpixel. Then based on the sparse expression of the pixel in the subspace, we sparsely encode the pixels in the same spatial group and spectral group respectively. Finally, the abundance of pixels within each group is forced to be similar to constrain the NMF unmixing framework. Experiments on synthetic and real data fully demonstrate the superiority of our method over other existing methods.