Mediating effect of personal and situational characteristics of Arab tourists in Malaysia and their influence on information sources and information channels of tourist information choice strategies

Statistics show that the influx of tourists in Malaysia has continued to increase by 14.3%; information sources and information channels have played crucial roles in this regard. However, existing studies are yet to distinguish how information sources and information channels influence information choice strategies of Arab tourists. Therefore, this study examines the mediating effects of personal and situational characteristics on information sources and information channels to provide clearer understanding on how information sources and information channels influence the information choice strategies of Arab tourists. Data was collected from 358 Arab tourists in Malaysia through the self-administered questionnaire procedure, and the data was analysed using the multiple regression analysis. Overall, the study found that information sources and information channels had significant influence on the information choice strategies of the Arab tourists. As for the mediating effects, the study also found that personal characteristics significantly mediate the influence of information sources on the information choice strategies of the Arab tourists. The result showed that situational characteristics significantly mediate the influence of information sources on the information choice strategies of the Arab tourists. Therefore, the study concluded that information sources and information channels influence the information choice strategies of the Arab tourists, while personal and situational characteristics significantly mediate influence of the information sources and information channels on the information choice strategies. The study contributes to the body of literature in this area and also provides several insights that may contribute to the development of knowledge that would effectively enhance the tourism industry in Malaysia and also attract more Arab tourists to Malaysia. The study recommends that both the Arab tourists and the Malaysian tourist agencies should always consider personal and situational characteristics in planning for tourism adventure policies on tourism. Finally, the study highlights the limitation of the study and the suggestions for future study

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students' skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development

The Impact of Software Team Project Measurements on Students' Performance in Software Engineering Education

It is essential to the software engineering instructors to monitor the students' performance in their course projects. Detecting key measures of software engineering project helps to get a better assessment for students' performance, resolve difficulties of low expectation-team's, and consequently improves the overall learning outcomes. Several studies attempted to present the important measures of software project but they only captured the early phases of the whole project time period. This paper introduces a hybrid approach of classification and feature selection techniques, which aims to comprehensively cover all phases of software development through investigating all product and process measures of software project. Experiments were conducted using five classifiers and two feature selection techniques. The results show the significant process and product measures for the software engineering team projects, which primarily improves the students' performance assessment. The performance prediction of our proposed assessment model outperforms prediction of the previous models. Keywords: Assessment, Classification, Feature selection, Software engineering education, Software team DOI: 10.7176/JEP/11-31-02 Publication date: November 30th 2020

The impact of incentives on the performance of employees in public sector: Case study in Ministry of labor

The ultimate purpose of this search is to evaluate of incentives on the performance of employees in Jordan Case study :( Ministry of Labor). Results show that Incentives affect the performance of staff in the public sector in Jordan, do not affect the practical experience on the performance of staff in the public sector and the qualification does not affect the performance of  staff in the government sector. And recommendations are made in In order to focus on providing fair and adequate compensation when retired employees and  salaries compatable with the level of their performance at work and the interest in providing moral support and praise for the staff to raise the level of their performance. Keywords: incentives, practical experience, performance of employees

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accurac

Time Series Forecasting of New Cases for COVID-19 Pandemic in Jordan Using Enhanced Hybrid EMD-ARIMA

In this study, the enhanced hybrid empirical mode decomposition with autoregressive integrated moving average (EMD- ARIMA) method is proposed and applied to forecast daily new COVID-19 reported cases in Jordan. The EMD method is applied to decompose the COVID-19 data into a number of IMFs components as a simple time series. Then, the appropriate ARIMA(p,d,q) model is applied to evaluate the forecasting value for the low-frequency components. Then, the forecasting results are collected together. Data for this study are collected from the Jordanian Ministry of Health. Seven forecasting accuracy measures are employed to compare the forecasting results of the proposed technique with the results of seven forecasting methods. The comparison of forecasting results shows that the enhanced EMD-ARIMA method is better than selecting forecasting methodologies in COVID-19 data

Characterization of Raman gain for different gain medium

By using different fiber types as gain medium such as 500 m HNLF, 215 cm Bi-EDF, 7.2 km DCF, 11 km DCF, 18.2 km DCF, as well as the composition of Bi-EDF with 11 km DCF and finally the composition of Bi-EDF with the 18.2 km DCF, Raman gain was characterized and measured by the same set up as proposed in our study. The results revealed that the composition of Bi-EDF with the 18.2 km DCF provided the highest effective Raman gain among the other fibers investigated

INFORMATION SOURCE, INFORMATION CHANNELS AND INFORMATION CHOICE: THE MEDIATING EFFECT OF PERSONAL CHARACTERISTICS

ABSTRACT. Although a strong relationship has been established between information source, information channels and information choice strategies, however, a mediator is required to further explain why or how the independent variables (information source and information channels) predict the dependents variable (information choice strategy). As a result, the collected data from 358 respondents of Arab tourists in Malaysia was analyzed through the regression analysis. Overall, the findings show significant relationships between information source, information channels and information choice. The result further shows that personal characteristic significantly mediate the relationship between information source, information channels and information choice. Hence, the study concludes that personal characteristic significantly explains the relationship between information source, information channels and information choice. Based on the findings obtained in this study, implications for the study and future research including the limitations are discussed

A Wideband Bear-Shaped Compact Size Implantable Antenna for In-Body Communications

Biomedical implantable antennas play a vital role in medical telemetry applications. These types of biomedical implantable devices are very helpful in improving and monitoring patients' living situations on a daily basis. In the present paper, a miniaturized footprint, thin-profile bear-shaped in-body antenna operational at 915 MHz in the industrial, scientific, and medical (ISM) band is proposed. The design is a straightforward bear-shaped truncated patch excited by a 50-W coaxial probe. The radiator is made up of two circular slots and one rectangular slot at the feet of the patch, and the ground plane is sotted to achieve a broadsided directional radiation pattern, imprinted on a Duroid RT5880 roger substrate with a typical 0.254-mm thickness ( er = 2.2, tan d = 0.0009). The stated antenna has a complete size of 7 mm x 7 mm x 0.254 mm and, in terms of guided wavelength, of 0.027lg x 0.027lg x 0.0011lg. When operating inside skin tissues, the antenna covers a measured bandwidth from 0.86 GHz to 1.08 GHz (220 MHz). The simulations and experimental outcomes of the stated design are in proper contract. The obtained results show that the calculated specific absorption rate (SAR) values inside skin of over 1 g of mass tissue is 8.22 W/kg. The stated SAR values are lower than the limitations of the federal communications commission (FCC). Thus, the proposed miniaturized antenna is an ultimate applicant for in-body communications.This project received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation program, under the Marie Sklodowska-Curie Grant 801538. It also received partial funding from the Researchers Supporting Project number (RSP- 2021/399), King Saud University, Riyadh, Saudi Arabia

Double spacing multi-wavelength L-band Brillouin erbium fiber laser with Raman pump

A new multi-wavelength Brillouin erbium fiber laser (BEFL), which operates in the L-band region with double frequency Brillouin spacing, is demonstrated. This design uses a Raman pump (RP) and a piece of 2 km highly nonlinear fiber as a gain medium. The double frequency spacing is achieved by employing a dual ring configuration, which is formed by utilizing a four-port circulator that removes the odd-order Stoke signals. Twenty Stokes and seventeen anti-Stokes lines, which have optical signal to noise ratio (OSNR) greater than 15 dB, are generated simultaneously with a spacing of 0.16 nm when Brillouin pump and RP powers were fixed at the optimum values of 8 dBm and 40 mW, respectively. The BEFL can be tuned in the range between 1591 nm to 1618 nm. The proposed configuration increases the number of lines generated and the OSNR, and thus allows a compact multi-wavelength laser source to be realized