Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Birth Order and Academic Proficiency Influence Perfectionistic Self-presentation Among Undergraduate Engineering Students? A Descriptive Analysis

Background: Perfectionism is a multifaceted concept. It had both advantages and disadvantages. Perfectionistic traits have been associated with leadership and very intellectual people. The present study is an attempt to understand if engineering students possess perfectionistic orientation and whether it influences self-efficacy, social connectedness, and achievement motivation. Materials and Methods: The present study adopts a random sampling design to evaluate the presence of perfectionism as a personality trait among undergraduate engineering students (N = 320). Standardized inventories such as Almost Perfect Scale-Revised were administered first to identify perfectionists and second to differentiate the adaptive from the maladaptive perfectionists. Scheduled interviews were conducted with students to obtain information regarding birth order and family functioning. Results: Findings from the study reveal that there were a significant number of maladaptive perfectionists and that they experienced higher levels of personal and societal demands leading to a negative emotional well-being in comparison to the adaptive perfectionists. We also observed that first-born children were more likely to display a perfectionistic self-presentation and from scheduled interviews, we understood that paternal influences were stronger when it came to decision-making and display of conscientiousness. Conclusion: The study draws on important implications for helping students to understand perfectionism and to respond to demands of the family and societal subsystems in a positive and an adaptive manner.
Convergence of a Semi-Lagrangian Scheme for the BGK Model of the Boltzmann Equation

Recently, a new class of semi-Lagrangian methods for the BGK model of the Boltzmann equation has been introduced [F. Filbet and G. Russo, Kinet. Relat. Models, 2 (2009), pp. 231–250; G. Russo and P. Santagati, A new class of large time step methods for the BGK models of the Boltzmann equation, arXiv:1103.5247; P. Santagati, High Order Semi-Lagrangian Methods for the BGK Model of the Boltzmann Equation, Ph.D. thesis, University of Catania, Italy, 2007]. These methods work in a satisfactory way either in a rarefied or a fluid regime. Moreover, because of the semi-Lagrangian feature, the stability property is not restricted by the CFL condition. These aspects make them very attractive for practical applications. In this paper, we prove that the discrete solution of the scheme converges in a weighted $L^1$ norm to the unique smooth solution by deriving an explicit error estimate.
A Residual Error Control Scheme in Single-Hop Wireless Sensor Networks

In this paper a new energy-efficient error control mechanism based on Redundant Residue Number System (RRNS) is proposed for Wireless Sensor Networks (WSNs). In general, WSNs suffer from burst errors due to their inherent nature. An error detection and correction approach has been developed to handle burst errors more efficiently. Residue encoders transform the sensed data to small-sized and independent code-words with redundant information. When an error is detected at the base station, a new module of remainder code-words will be requested from the encoder nodes. This approach creates spatial diversity against fading in a wireless channel. Overall, the proposed scheme results in saving transmission energy, adding negligible encoder energy consumption.
Women conductors : a qualitative study of gender, family, 'the body' and discrimination

The purpose of this study is to develop a broader appreciation of women’s experiences of conducting, focusing on issues concerning embodiment, body language, clothing and so forth in order to place emphasis on their life experiences. The research is based upon thematic analysis (TA) of qualitative interviews conducted with women conductors living in the UK (N=8) alongside detailed Media Content Analysis (MCA) of data derived from online magazines, online newspapers, online blogs and Internet articles. The results reveal prevalent gender discrimination, bias, sexism and misogyny against women in the conducting profession. Specifically, six broad themes emerged through the data analysis. These are: 1) gender discrimination (TA and MCA suggest that gender bias, discrimination, sexist and misogynist comments and attitudes still occur within the conducting profession); 2) factors that influence women conductors’ career development (TA suggests that families as well as a musical background of women conductors have motivated them to pursue and develop a musical career); 3) achieving balance (TA and MCA support that a balance between professional and personal life can be difficult to achieve); 4) clothing (TA and MCA suggest that clothing choices are very important for women conductors, affecting them personally in terms of how they are perceived by other musicians and/or audiences); 5) the conducting body (TA and MCA conclude that gender is not a factor affecting women’s bodily communication; however, their gestures may be perceived differently by people); and 6) conducting and leadership (TA and MCA suggest that the concept of the male ‘tradition’ is the main factor that has prevented women conductors from being seen as great leaders, therefore, women lack the experience and practice to develop their leadership abilities). Taken together, these factors highlight the continuing struggle that women experience in conducting today and provide an insight of how they cope with their profession.
BBA: A Binary Bat Algorithm for Feature Selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques.
Dimensionality Reduction Using Automatic Supervision for Vision-Based Terrain Learning

This paper considers the problem of learning to recognize different terrains from color imagery in a fully automatic fashion, using the robot’s mechanical sensors as supervision. We present a probabilistic framework in which the visual information and the mechanical supervision interact to learn the available terrain types. Within this framework, a novel supervised dimensionality reduction method is proposed, in which the automatic supervision provided by the robot helps select better lower dimensional representations, more suitable for the discrimination task at hand. Incorporating supervision into the dimensionality reduction process is important, as some terrains might be visually similar but induce very different robot mobility. Therefore, choosing a lower dimensional visual representation adequately is expected to improve the vision-based terrain learning and the final classification performance. This is the first work that proposes automatically supervised dimensionality reduction in a probabilistic framework using the supervision coming from the robot’s sensors. The proposed method stands in between methods for reasoning under uncertainty using probabilistic models and methods for learning the underlying structure of the data. The proposed approach has been tested on field test data collected by an autonomous robot while driving on soil, gravel and asphalt. Although the supervision might be ambiguous or noisy, our experiments show that it helps build a more appropriate lower dimensional visual representation and achieves improved terrain recognition performance compared to unsupervised learning methods.
An approach to joint analysis of longitudinal measurements and competing risks failure time data

Joint analysis of longitudinal measurements and survival data has received much attention in recent years. However, previous work has primarily focused on a single failure type for the event time. In this paper we consider joint modelling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint which occurs frequently in clinical trials. Our model uses latent random variables and common covariates to link together the sub‐models for the longitudinal measurements and competing risks failure time data, respectively. An EM‐based algorithm is derived to obtain the parameter estimates, and a profile likelihood method is proposed to estimate their standard errors. Our method enables one to make joint inference on multiple outcomes which is often necessary in analyses of clinical trials. Furthermore, joint analysis has several advantages compared with separate analysis of either the longitudinal data or competing risks survival data. By modelling the event time, the analysis of longitudinal measurements is adjusted to allow for non‐ignorable missing data due to informative dropout, which cannot be appropriately handled by the standard linear mixed effects models alone. In addition, the joint model utilizes information from both outcomes, and could be substantially more efficient than the separate analysis of the competing risk survival data as shown in our simulation study. The performance of our method is evaluated and compared with separate analyses using both simulated data and a clinical trial for the scleroderma lung disease. Copyright © 2006 John Wiley & Sons, Ltd.
Improving QPF by blending techniques at the Meteorological Service of Catalonia

Abstract. The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.
Pruning population size in XCS for complex problems

In this paper, we show how to prune the population size of the Learning Classifier System XCS for complex problems. We say a problem is complex, when the number of specified bits of the optimal start classifiers (the problem dimension) is not constant. First, we derive how to estimate an equivalent problem dimension for complex problems based on the optimal start classifiers. With the equivalent problem dimension, we calculate the optimal maximum population size just like for regular problems, which has already been done. We empirically validate our results. Furthermore, we introduce a subsumption method to reduce the number of classifiers. In contrast to existing methods, we subsume the classifiers after the learning process, so subsuming does not hinder the evolution of optimal classifiers, which has been reported previously. After subsumption, the number of classifiers drops to about the order of magnitude of the optimal classifiers while the correctness rate nearly stays constant.
Links to Occupational Plans and Work Values

Individualism-collectivism (IC) constitutesa cultural variable thought toinfluence a widevariety of variables including career planning and decision making. To examine this possibility, college students (216 women, 106 men, 64% racial-ethnic minorities) responded to measures of IC, occupational plans, and work values. Multivariate analysis of variance (MANOVA) results indicated significant main effects for gender and race. Men, more so than women, endorsed vertical individualism (VI)—a cultural pattern characterized by independence and dominance—and intrinsic work values. African Americans expressed more fit of their occupational plans with personal goals and endorsed extrinsic work values more than did European Americans. A hypothesized inverse relationship between collectivism and consistency of occupational plans with personal goals was not supported. Hypothesized positive relationships between collectivism and work values stressing relationship and interdependence were supported, whereas expected inverse relationships between collectivism and work values emphasizing independence and personal gain were not supported. Findings are discussed in terms of acculturation issues, career assessment implications, and future research directions.
Publishing Reference Geodata on the Web: Opportunities and Challenges for IGN France

The French national mapping agency (IGN) produces several different but complementary geographic vector reference databases delivered in traditional GIS formats. However, linked data users have different expectations and habits, such as the need to browse an entire data catalogue in RDF using the "follow-your-nose" navigation capacity from one graph to another. Besides, traditional GIS data formats are not interoperable with RDF. Yet, all these geographic datasets could be used with benefits on the Web of data, either with direct georeferencing through geographic primitives, or indirect one through postal addresses. In this paper, we aim to contribute to the georeferencing of datasets published on the Web of data by providing such resources for French context. Firstly, we propose two vocabularies designed for representing structured geometries defined with coordinates expressed in any Coordinates Reference System (CRS). Secondly, we reuse these vocabularies and the CRSs' dataset to publish a reference dataset on administrative units that can also be reused for indirect georeferencing purposes. Finally , we also propose two vocabularies for describing geographic feature types. In addition to these resources, we also present a comprehensive workflow for easily publishing geographic data on the Web of data.
Production, characterization and determination of the real catalytic properties of the putative ‘succinate dehydrogenase’ from Wolinella succinogenes

Both the genomes of the epsilonproteobacteria Wolinella succinogenes and Campylobacter jejuni contain operons (sdhABE) that encode for so far uncharacterized enzyme complexes annotated as ‘non‐classical’ succinate:quinone reductases (SQRs). However, the role of such an enzyme ostensibly involved in aerobic respiration in an anaerobic organism such as W. succinogenes has hitherto been unknown. We have established the first genetic system for the manipulation and production of a member of the non‐classical succinate:quinone oxidoreductase family. Biochemical characterization of the W. succinogenes enzyme reveals that the putative SQR is in fact a novel methylmenaquinol:fumarate reductase (MFR) with no detectable succinate oxidation activity, clearly indicative of its involvement in anaerobic metabolism. We demonstrate that the hydrophilic subunits of the MFR complex are, in contrast to all other previously characterized members of the superfamily, exported into the periplasm via the twin‐arginine translocation (tat)‐pathway. Furthermore we show that a single amino acid exchange (Ala86→His) in the flavoprotein of that enzyme complex is the only additional requirement for the covalent binding of the otherwise non‐covalently bound FAD. Our results provide an explanation for the previously published puzzling observation that the C. jejuni sdhABE operon is upregulated in an oxygen‐limited environment as compared with microaerophilic laboratory conditions.
The complexity of satisfiability problems

The problem of deciding whether a given propositional formula in conjunctive normal form is satisfiable has been widely studied. I t is known that, when restricted to formulas having only two literals per clause, this problem has an efficient (polynomial-time) solution. But the same problem on formulas having three literals per clause is NP-complete, and hence probably does not have any efficient solution. In this paper, we consider an infinite class of satisfiability problems which contains these two particular problems as special cases, and show that every member of this class is either polynomial-time decidable or NP-complete. The infinite collection of new NP-complete problems so obtained may prove very useful in finding other new NP-complete problems. The classification of the polynomial-time decidable cases yields new problems that are complete in polynomial time and in nondeterministic log space. We also consider an analogous class of problems, involving quantified formulas, which has the property that every member is either polynomial time decidable or complete in polynomial space.
Biological activity and composition of teas and tinctures prepared from Rosa rugosa Thunb.

The study was designed to determine the total phenolic, flavonoid, o-dihydroxyphenol, tannin, and carotenoid content as well as the antiradical, antitumor and antimicrobial properties of two types of galenic preparations from Rosa rugosa Thunb. Such extracts obtained from various plant parts have not been studied to date. Our findings have revealed high antiradical activity of the examined galenic preparations, with root, leaf and flower extracts (IC50 ranging from 0.27 to 0.19 mg of dry extract per mg DPPH·) showing the greatest potential. MIC and MBC values against 8 reference bacterial strains (i.e. Staphylococcus epidermidis, Staphylococcus aureus, Bacillussubtilis, Micrococcus luteus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Proteus mirabilis) were determined. Generally, tinctures were found to be more active than teas with MIC ranging from 0.08 to 2.5 mg mL−1 and 0.31 to 1.25 mg mL−1, respectively. Anticancer activities against ovarian (TOV-112D), cervical (HeLa), breast (T47D) and lung cancer (A549) cell lines were evaluated using the BrdU test. The data obtained demonstrate considerable impact of polyphenols on the anticancer activity of extracts (ethanolic, in particular).
Neural Correlates of Motion-induced Blindness in the Human Brain

Motion-induced blindness (MIB) is a visual phenomenon in which highly salient visual targets spontaneously disappear from visual awareness (and subsequently reappear) when superimposed on a moving background of distracters. Such fluctuations in awareness of the targets, although they remain physically present, provide an ideal paradigm to study the neural correlates of visual awareness. Existing behavioral data on MIB are consistent both with a role for structures early in visual processing and with involvement of high-level visual processes. To further investigate this issue, we used high field functional MRI to investigate signals in human low-level visual cortex and motion-sensitive area V5/MT while participants reported disappearance and reappearance of an MIB target. Surprisingly, perceptual invisibility of the target was coupled to an increase in activity in low-level visual cortex plus area V5/MT compared with when the target was visible. This increase was largest in retinotopic regions representing the target location. One possibility is that our findings result from an active process of completion of the field of distracters that acts locally in the visual cortex, coupled to a more global process that facilitates invisibility in general visual cortex. Our findings show that the earliest anatomical stages of human visual cortical processing are implicated in MIB, as with other forms of bistable perception.
Dexamethasone treatment causes resistance to insulin-stimulated cellular potassium uptake in the rat.

Patients treated with glucocorticoids have elevated skeletal muscle ouabain binding sites. The major Na(+)-K(+)-ATPase (NKA) isoform proteins found in muscle, alpha2 and beta1, are increased by 50% in rats treated for 14 days with the synthetic glucocorticoid dexamethasone (DEX). This study addressed whether the DEX-induced increase in the muscle NKA pool leads to increased insulin-stimulated cellular K+ uptake that could precipitate hypokalemia. Rats were treated with DEX or vehicle via osmotic minipumps at one of two doses: 0.02 mg.kg(-1). day(-1) for 14 days (low DEX; n = 5 pairs) or 0.1 mg.kg(-1). day(-1) for 7 days (high DEX; n = 6 pairs). Insulin was infused at a rate of 5 mU.kg(-1). min(-1) over 2.5 h in conscious rats. Insulin-stimulated cellular K+ and glucose uptake rates were assessed in vivo by measuring the exogenous K+ infusion (K+(inf)) and glucose infusion (Ginf) rates needed to maintain constant plasma K+ and glucose concentrations during insulin infusion. DEX at both doses decreased insulin-stimulated glucose uptake as previously reported. Ginf (in mmol.kg(-1). h(-1)) was 10.2 +/- 0.6 in vehicle-treated rats, 5.8 +/- 0.8 in low-DEX-treated rats, and 5.2 +/- 0.6 in high-DEX-treated rats. High DEX treatment also reduced insulin-stimulated K+) uptake. K+(inf) (in mmol.kg(-1). h(-1)) was 0.53 +/- 0.08 in vehicle-treated rats, 0.49 +/- 0.14 in low-DEX-treated rats, and 0.27 +/- 0.08 in high-DEX-treated rats. DEX treatment did not alter urinary K+ excretion. NKA alpha2-isoform levels in the low-DEX-treated group, measured by immunoblotting, were unchanged, but they increased by 38 +/- 15% (soleus) and by 67 +/- 3% (gastrocnemius) in the high-DEX treatment group. The NKA alpha1-isoform level was unchanged. These results provide novel evidence for the insulin resistance of K+ clearance during chronic DEX treatment. Insulin-stimulated cellular K+ uptake was significantly depressed despite increased muscle sodium pump pool size.