Miracle-dz/UniFAR-FTUs huggingface dataset:
Prior Entrepreneurial Exposure and Action of Women Entrepreneurs: Exploring the Moderation Effects of Entrepreneurial Competencies in a Developing Country Context

While the differences between men and women with regard to entrepreneurial activity is well-acknowledged, few scholars have explored models explaining the differences through an objectivist lens. This research addresses this gap by investigating the relationship between prior entrepreneurial exposure and entrepreneurial action, moderated by entrepreneurial competencies (ECs). This paper draws from two psychology theories to develop and test a three-factor model of entrepreneurial action. The structuration theory formulates a theoretical model that explains how entrepreneurs’ interaction with their environment, and their concomitantly learned behavioral scripts (i.e., entrepreneurial competencies), impacts a newly formulated typology of entrepreneurial gestation activities based on the mindset theory of action phases. Furthermore, the ECs in this paper are drawn from a systematic framework of entrepreneurship competency development, which categorizes ECs into (1) entrepreneurial attitudes and personal characteristics and (2) entrepreneurial motives. By dividing entrepreneurial action into a predecisional, preactional, and actional phase, a novel approach is used in taking the context of the entrepreneurial process into account. It is proposed that prior entrepreneurial exposure is a significant and positive predictor of future entrepreneurial action in the predecisional and preactional phases. However, once entering the actional phase, this factor is no longer important, as women entrepreneurs have crossed the entrepreneurial Rubicon. The sample consists of South African entrepreneurs of which 346 women entrepreneurs and a sample of 804 male entrepreneurs are used to compare the results of the first hypothesis. Structural equation modeling (SEM) is used to model the relationship between prior entrepreneurial exposure and entrepreneurial action. Results confirm that prior entrepreneurial exposure in the form of role models, entrepreneurial parents, or any other form of exposure to entrepreneurship before starting a business is particularly important to encourage women to pursue business start-up (action). Furthermore, the development of certain ECs is crucial for improving the strength of the relationship between prior entrepreneurial exposure and entrepreneurial action for women entrepreneurs. These results have important implications for women entrepreneurs, educators, as well as entrepreneurship models, which have been traditionally male dominated.
Broad Learning System for Predicting Student Dropout in Massive Open Online Courses

At a time when the number of Massive Open Online Courses (MOOCs) users, courses and participating universities is increasing rapidly, a short-time training and reliable prediction model for MOOC extremely high dropout rates is needed. This paper proposes a MOOC dropout prediction model which is based on Broad Learning System (BLS) for MOOC dropout prediction. The model first maps the input into the feature node layer, then generates the enhanced node layer according to the feature node layer through activation, and finally performs linear transformation by combining the feature layer and the enhancement layer. The output layer is used for dropout prediction. Experiments are carried out on the dataset provided by KDD CUP 2015, and the experimental results show that the BLS significantly reduces training time and has a better prediction of dropouts than other mainstream research methods.
A Novel Method for Extending V2V System

Autonomous vehicles requires sufficient perception of the surrounding environment to make proper driving behavior. Vehicle-to-vehicle (V2V) is a technology that allow vehicles exchange location information (i.e. velocity, position) which can improve the perception capabilities of traditional on-board sensors. However, there are still obstacles preventing the roll-out of the V2X technology, mainly the fact that, unless almost the totality of the existing vehicles adopt it, its effectiveness is rather limited. We can't guarantee that all vehicles are V2V vehicles in real environment due to many reasons. In the traditional V2V system, only V2V vehicle have the ability to broadcast their own location information, but non-V2V vehicle can't. But, the situation is somewhat different in our V2V system. Although, non-V2V vehicles don't have the ability to broadcast their own location information, we can let V2V vehicle detect the location information of non-V2V vehicle and broadcast them out. Therefore, we can think that the non-V2V vehicle can also have the ability to broadcast its own location information in our V2V system. In this way, we extend the ability of traditional V2V system to a certain extent. The proposed method is validated under real-world conditions in urban area.
The Moderating Role of Regulatory Institutional Environment in the Relationship Between Emotional Job Demands and Employee Absenteeism Likelihood of Healthcare Workers. Evidence From the Low-Income Country Context

Previous research has not clearly studied how the effects of emotional job demands on absenteeism likelihood are moderated by the contingent absenteeism-related regulatory institutional environments of low-income countries. In this regard, we surveyed 487 healthcare workers in a low-income country in order to test for the effect of emotional job demands on healthcare workers’ absenteeism likelihood. We also explored the mediating role of work engagement and the contingent role of context-specific regulatory institutional environments on the link between emotional job demands and absenteeism likelihood. The main findings of this study are as follows: (1) emotional job demands have a direct positive effect on healthcare workers’ absenteeism likelihood, (2) work engagement plays a mediating role on the link between emotional job demands and healthcare workers’ absenteeism likelihood, and (3) the regulatory institutional environment related to absenteeism moderates the negative link between work engagement and absenteeism likelihood. Results in this study demonstrate the crucial role that the context-specific regulatory institutional environment related to absenteeism plays in suppressing the effect of emotional job demands on absenteeism likelihood when considered through the work-engagement pathway. The study’s findings clarify the mechanism through which emotional job demands affect absenteeism likelihood in a low-income country context. The study thus offers a new refined theoretical perspective on how emotional job demands, work engagement, and context-specific regulatory institutional environments interact in ways that predict absenteeism likelihood.
The Diversity of the CRISPR-Cas System and Prophages Present in the Genome Reveals the Co-evolution of Bifidobacterium pseudocatenulatum and Phages

Diverse CRISPR-Cas systems constitute an indispensable part of the bacterial adaptive immune system against viral infections. However, to escape from this immune system, bacteriophages have also evolved corresponding anti-defense measures. We investigated the diversity of CRISPR-Cas systems and the presence of prophages in the genomes of 66 Bifidobacterium pseudocatenulatum strains. Our findings revealed a high occurrence of complete CRISPR-Cas systems (62%, 41/66) in the B. pseudocatenulatum genomes. Subtypes I-C, I-U and II-A, were found to be widespread in this species. No significant association was found between the number of bacterial CRISPR spacers and its host’s age. This study on prophages within B. pseudocatenulatum genomes revealed that prophage genes related to distinct functional modules became degraded at different levels, indicating that these prophages were not likely to enter lytic cycle spontaneously. Further, the evolutionary analysis of prophages in this study revealed that they might be derived from different phage ancestors. Notably, self-targeting phenomenon within B. pseudocatenulatum and Anti-CRISPR (Acr) coding genes in prophages was observed. Overall, our results indicate that the competition between B. pseudocatenulatum and phages is a major driving factor for the genomic diversity of both partners.
Contention-aware application performance prediction for disaggregated memory systems

Disaggregated memory has recently been proposed as a way to allow flexible and fine-grained allocation of memory capacity to compute jobs. This paper makes an important step towards effective resource allocation on disaggregated memory systems. Specifically, we propose a generic approach to predict the performance degradation due to sharing of disaggregated memory. In contrast to prior work, cache capacity is not shared among multiple applications, which removes a major contributor to application performance. For this reason, our analysis is driven by the demand for memory bandwidth, which has been shown to have an important effect on application performance. We show that profiling the application slowdown often involves significant experimental error and noise, and to this end, we improve the accuracy by linear smoothing of the sensitivity curves. We also show that contention is sensitive to the ratio between read and write memory accesses, and we address this sensitivity by building a family of sensitivity curves according to the read/write ratios. Our results show that the methodology predicts the slowdown in application performance subject to memory contention with an average error of 1.19% and max error of 14.6%. Compared with state-of-the-art, the relative improvements are almost 24% on average and 33% for the worst case.
Gait Energy Image Based on Static Region Alignment for Pedestrian Gait Recognition

The Gait Energy Image (GEI) spatially aligns, accumulates, and averages all the frames of a gait cycle, so there is a very high requirement for the registration of moving targets. Accurate registration of moving targets is important for the synthesis of Gait Energy Image (GEI). In this paper, we propose a new Gait Energy Image to improve the registration effect: Gait Energy Image based on static region alignment (SRA-GEI). Firstly, we select the minimum circumscribed rectangle containing the moving human body from the gait sequence. Secondly, we scale the minimum circumscribed rectangle to the specified height and calculate the gait cycle by analyzing the distance between the two feet. Finally, we propose a new registration method to generate Gait Energy Image by calculating and aligning the centroid of the static region of the gait image. This paper explores the performance of SRA-GEI with KNN based on the CASIA Dataset B. The experimental results have shown that the proposed method achieves better recognition rate compared with GEI which aligned by overall centroid.
Ankle gait-pattern generators

Different lower limbs robotic rehabilitation devices are under study to help patients to recover gestures altered, affected or lost after traffic accident injury, illness or disability. Especially ankle because is a complex part of the human skeleton. In this paper we present our contribution to the ankle foot robotic device. We will present our strategy to generate the setpoint trajectory for ankle devices that is based on neural networks. The latter will generate the trajectory of the ankle from the angular position sensors of the hip joint and the knee joint. This gave good results, but we noticed during the tests that the ankle trajectory contained sporadic peaks. This was the case of the right and left ankle. An optimization algorithm is developed to eliminate this phenomenon; it will be presented soon.
Customer Churn Prediction In Telecommunication Industry Using Machine Learning Classifiers

Customer churn is one of the main problems in telecommunication industry. This study aims to identify the factors that influence customer churn and develop an effective churn prediction model as well as provide best analysis of data visualization results. The dataset has been collected from Kaggle open data website. The proposed methodology for analysis of churn prediction covers several phases: data pre-processing, analysis, implementing machine learning algorithms, evaluation of the classifiers and choose the best one for prediction. Data preprocessing process involved three major action, which are data cleaning, data transformation and feature selection. Machine learning classifiers was chosen are Logistic Regression, Artificial Neural Network and Random Forest. Then, classifiers were evaluated by using performance measurement which are accuracy, precision, recall and error rate in order to find the best classifier. Based on this study, the output shows that logistic regression outperform compared to artificial neural network and random forest.
Confidence Intervals for Coefficient of Variation of Inverse Gaussian Distribution

The coefficient of variation is an useful indicator to measure and compare separated data in different units. This paper proposes the new confidence interval for the single coefficient of variation and the difference between coefficients of variation of Inverse Gaussian distribution using the generalized confidence interval (GCI) and the bootstrap percentile confidence interval. A Monte Carlo simulation is used to construct and compare the performance of these confidence intervals based on the coverage probability and average length. The results of the simulation study showed that the GCI is an appropriate method to construct the confidence interval for the single coefficient of variation and the difference between coefficients of variation. The proposed approaches are illustrated based on the real data.
A Dropout Prediction Framework Combined with Ensemble Feature Selection

In recent years, with the rapid development of large-scale open online courses, low completion rate and high dropout rate have been important challenges for open online courses. Therefore, it is necessary to make effective prediction and timely intervention to ensure the completion of the course. Some of the traditional prediction models only use the features extracted manually from students' clickstream data, which is too subjective to guarantee the quality of features and affect the prediction accuracy. Others generate features automatically with finer granularity, but the problem of feature redundancy appears. In order to solve this problem, this paper proposes a comprehensive dropout prediction framework of MOOCs students. The framework can automatically extract features from clickstream data, and filter features with an integrated feature selection strategy based on clustering and weighted MaxDiff, and finally predict. Experiments show that the model can effectively improve the accuracy of prediction of dropout.
Diabetic Retinopathy Diagnosis with Ensemble Deep-Learning

Diabetic retinopathy is an eye disease, is a condition in which retina is damaged due to diabetes mellitus. It is a major cause of blindness. Although many artificial intelligence methods has been applied to diabetic retinopathy diagnosis. This method is a new approach in this problem domain. It's early detection can help in tackling the future damages due to the disease. Here, model is ensemble of pre-existing GoogleNet, AlexNet and ResNet50 architectures. The best machine learning models that were used was GoogLeNet achieving highest accuracy for this job. Here, The results are standing out with the GoogLeNet's accuracy.
Introduction

Intercultural discourse has been a field of study of increasing interest for the last five years. What makes it so attractive? How does this collection of papers relate to other recent publications on related topics? This introduction answers these questions from the perspective of the struggle between linguistic theory and the international practice of business and technology. What is a sound description of intercultural discourse and when is it appropriate to apply the results of research and theory? After a brief account of the appropriate content and methodology of such discourse analyses, we present our criteria for the selection of the papers in this collection, explain their order of presentation, and briefly summarize their focus and scope. Finally we outline the future of such analyses from the perspective of communication research. The title of this issue is Intercultural discourse in business and technology. By intercultural we mean between cultures, reserving the terms crosscultural for a comparison of two or more cultures, and monocultural for interaction within one culture. Culture is defined operationally äs the specific psychosocial orientation (Hofstede, 1991) of a group of people, be it a business, a nation or other grouping (for further clarification see the paper by Ulijn and Li in this volume). Cultures included here ränge from those of groups within one nation (e.g., Japanese, Chinese and Spanish cultures within the US) to those of national groups (e.g., Dutch, French, English, German, Italian, Finnish and Chinese). Discourse äs defined here can be either written or oral. The first two papers (Touchstone et al. and Connor et al.) focus on the written text, with some attention paid to the reader of the text. The remaining five papers deal with oral interactions such äs requests (van der Wijst), conflict mediating conversations (Maleville), meetings (Bargiela and Harris) and negotiations (Fant and Grindsted and Ulijn and Li). All papers involve business contexts (banks, a university, and insurance and technologybased companies); one involves technology and business: Ulijn and Li's
Stoichiometry for activation of neuronal α7 nicotinic receptors

Significance Nicotinic α7 receptors are neurotransmitter-gated ion channels that participate in cognitive, cytoprotective, and anti-inflammatory processes and have emerged as targets for neurological disorders and therapeutic drugs. They are associated with extrasynaptic volume transmission where they are exposed to low ACh concentrations and thus function with partial occupancy of their five identical binding sites. By generating α7 receptors with different numbers of functional ACh binding sites and measuring open-channel lifetimes of individual receptors, we find that occupancy of one site produces a maximal response and that the additional sites enhance agonist sensitivity. Thus, ACh occupancy of only one of five sites in α7 produces a maximal response, adapting it to extrasynaptic volume transmission in neuronal and nonneuronal cells. Neuronal α7 nicotinic receptors elicit rapid cation influx in response to acetylcholine (ACh) or its hydrolysis product choline. They contribute to cognition, synaptic plasticity, and neuroprotection and have been implicated in neurodegenerative and neuropsychiatric disorders. α7, however, often localizes distal to sites of nerve-released ACh and binds ACh with low affinity, and thus elicits its biological response with low agonist occupancy. To assess the function of α7 when ACh occupies fewer than five of its identical binding sites, we measured the open-channel lifetime of individual receptors in which four of the five ACh binding sites were disabled. To improve the time resolution of the inherently brief α7 channel openings, background mutations or a potentiator was used to increase open duration. We find that, in receptors with only one intact binding site, the open-channel lifetime is indistinguishable from receptors with five intact binding sites, counter to expectations from prototypical neurotransmitter-gated ion channels where the open-channel lifetime increases with the number of binding sites occupied by agonist. Replacing the membrane-embedded domain of α7 by that of the related 5-HT3A receptor increases the number of sites that need to be occupied to achieve the maximal open-channel lifetime, thus revealing a unique interdependence between the detector and actuator domains of these receptors. The distinctive ability of a single occupancy to elicit a full biological response adapts α7 to volume transmission, a prevalent mechanism of ACh-mediated signaling in the nervous system and nonneuronal cells.
The African elephant, Loxodonta africana : a field method for the estimation of age

A new field method, now termed the “FM technique”, for age estimation in wild African elephants was outlined in a previous paper (Sikes, 1966a). In this technique, the stage reached in the molar progression which occurs throughout the life of any elephant, is related to a fixed point, namely the foramen mentale, in the lower jaw. The stage reached in any individual elephant of either sex may thus be described as its “molar age” by the “FM formula” (a descriptive, non-mathematical formula). Up to the age of 30 years, molar age may be converted with reasonable accuracy to year age. Above this point, however, any such conversion must be regarded as arbitrary and of doubtful value until such time as adequate additional data from older African elephants of known age become available. The molars are briefly described, and the molar progression in the species outlined. A hypothesis is offered as a possible explanation of the mechanism of the progression. The field procedure for using the FM technique is explained, and its advantage over previous methods discussed. A comprehensive Age Reference Chart for field use is given, covering the whole potential life span of the African elephant.
Interpopulation variation in egg size in the winter cherry bug relative to oviposition site selection

To investigate whether or not oviposition on substrates other than host plants (e.g., non‐host plants, abiotic materials) would affect the evolution of egg size in phytophagous insects, we studied the winter cherry bug, Acanthocoris sordidus (Thunberg) (Heteroptera: Coreidae), as a model organism for its interpopulation variation in oviposition preference. The rate of oviposition off host plants is much higher in the Amami Island population than in either the Kyoto or Kochi populations. We compared egg size and number among the three local populations from Kyoto, Kochi, and Amami Island. In addition, to evaluate the adaptive significance of larger eggs for offspring in terms of searching for host plants, we examined the relationship between egg size and first‐instar body size. We also searched for a relationship between egg size and starvation tolerance in the second instars because first instars can develop to second instars without food intake, and thus the substantial host‐searching stage is the second instar, when females lay their eggs off host plants. Females from the Amami population produced fewer larger eggs than females from either the Kyoto or Kochi population. Regardless of the local population, the body size of first instars that emerged from larger eggs was larger, and the second instars originating from larger eggs had a higher starvation tolerance. The larger body size and higher starvation tolerance should enable nymphs to disperse further, which may enhance the probability of successfully reaching host plants. These results suggest that egg size in A. sordidus may be determined in relation to its oviposition habits to maximize reproductive success, resulting in interpopulation variation in egg size.