This study aimed to investigate the entrepreneurial attitudes among youth in Palestine by testing the effect of some psychological factors and entrepreneurial skills on young people attitudes toward entrepreneurship in Palestine. In order to achieve the purpose of the study, three hypotheses were developed and tested using field data collected by questionnaires, which were administered to a convenience a sample of (270) young people in the governorates of the northern West Bank. The study found that risk taking, locus of control, and entrepreneurial skills are positively influencing youth's entrepreneurial attitudes. The study provided a set of recommendations to foster youth's attitudes toward entrepreneurship and turn it into a tangible reality.

The study aimed to identify the person-environment fit and its dimensions (Person–Job Fit, Person–Organisation Fit, Person–Group Fit, Person–Supervisor Fit), and organisational commitment; and to measure the impact of the person-environment fit and its dimensions on organisational commitment at Taif university. To meet the study’s aims, a descriptive and analytical approach was used, and a questionnaire was developed, which consisted of 42 items reflecting the person-environment fit and its dimensions, and 12 items measuring organisational commitment. The target population comprised all administrators at Taif University, and 291 employees were randomly selected. The appropriate statistical tests were employed. The study showed that there were strong positive significant relationships between person-environment fit and its dimensions, on the one hand, and organisational commitment, on the other. Moreover, among the dimensions of person-environment fit the one most strongly correlated with organisational commitment was person-organisation fit, and the one with the weakest correlation was person-group fit. Also, the results found a significant impact of person-job fit and person-organisation fit on organisational commitment, but it did not find a significant impact of person-group fit and person-supervisor fit on organisational commitment. Finally, the study concluded with a number of theoretical implications and practical recommendations.

In this article, a highly flexible and fast framework for estimating and exploring the dynamic latent dependencies among main cryptocurrency log‐returns is introduced. Our approach is inspired by latent factor analysis models capturing simultaneously the persistence of volatility clustering and the presence of leverage effect in the crypto market. A recursive quasi-maximum likelihood strategy is proposed to infer the latent cross-sectional correlation structure of the data and then to estimate the parameters of the model. Our algorithm consists of recursively alternating between Kalman filtering and smoothing recursions and the expectation maximization (EM) algorithm. This produces economically interpretable estimates for the common latent factors, which can be used to predict the mean return vector and the return covariance matrix, particularly useful for stock selection and portfolio allocation problems. To illustrate the performance of our sequential strategy, the model is applied to daily log-returns of the last 5 years of the Bitcoin, Ethereum, Litecoin, Monero, Binance Coin and Waves currencies. The out-of-sample forecast encompassing tests show that the factorial approach yields better forecasts than those given by typical benchmarks. In terms of cumulative financial returns, our framework seems to provide the best performing mean-variance portfolio.

Nowadays, video-on-demand (VoD) applications are becoming one of the tendencies driving vehicular network users. In this paper, considering the of ve-hicular network opens up to different types of communications in order to meet the needs of the wide variety of new applications envisaged within the framework of the Intelligent Transport System (ITS). In this work, we seek to establish a list of possibilistic concepts in order to efficiently identify the strict parameters of ur-ban VANET networks. To this end, we use linear optimization under constraints. We apply in parallel to this first proposition a minimization of a validated quadrat-ic criterion with the appearance of fuzzy least squares. To arrive at a quadratic resolution under constraints, different distances were managed and various con-straints were introduced in the optimization problem. We have shown that the da-ta independent criterion in urban VANETs can overcome the failure problem in terms of robustness. To assess the comparative effectiveness of our solutions, many experiments are carried out. The obtained results showed that the proposed identification scheme will allow an increase in the performance of Urban VANET networks with different load conditions.

With the rise of the internet, startups number has substantially increased in the last two decades. Although many of them have succeeded to revolutionize their sectors, many of them have shut down a few months or even a few years after the foundation. In this paper, we propose a machine learning approach for predicting startups success based on historical data. Almost 840 startup data have been finely preprocessed to extract 35 features that characterize each a particular aspect of the studied startups. Afterward, computational models based on machine learning techniques were developed and tested using a cross-validation approach. The main objective is to predict the success of the startup, especially in terms of mergers and acquisitions. In particular, several models have been applied namely Artificial Neural Networks (a.k.a. ANNs), Support Vector Machines (a.k.a. SVMs), Random Forests, Bagging, Stacking, and Gradient Boosting. Overall results were very promising since the best model succeeded in the prediction with an accuracy rate of 85%. Furthermore, a feature importance study was also conducted to analyze the best predictors of a startup acquisition.