• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.507-517
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• 2020

The present research intends to examine the relationship between the Big Five personality traits and leadership initiations among the marketing executives in Delhi NCR (INDIA), and seeks to uncover the predictors of leadership initiations within personality traits. The data are collected through online survey method using different social media platforms. A sample of 233 (male =136 and female =97) marketing executive's responses were included. The data collected with the help of self-reported Big Five model inventory and leadership initiation test. The collected data were analyzed statistically by using descriptive statistics, correlation. and stepwise multiple regression analysis. The results revealed that the age of respondents inversely correlated with leadership initiation. Neuroticism revealed significant inverse correlation with leadership initiation, whereas significant positive correlations were found between extraversion, conscientiousness, agreeableness, and leadership initiations, while openness to experience revealed insignificant positive correlation with leadership initiation. Extraversion and conscientiousness appeared as the most dominant personality traits among marketing executives, irrespective of gender, that positively influenced leadership initiation and appeared as the predictor of leadership initiation. In male executives extraversion and age emerged as the predictors of leadership behavior, while in female executives extraversion and openness to experience personality traits appeared as the predictors of leadership initiation.

• Steel and Composite Structures
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• 제43권3호
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• pp.403-417
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• 2022

Recently, there has been an increasing demand for buildings that allow rapid assembly of construction elements, have ample open space areas and are flexible in their final intended use. Accordingly, researchers have developed new competitive structures in terms of cost and efficiency, such as cold-formed steel and timber composite floors, to satisfy these requirements. Cold-formed steel and timber composite floors are light floors with relatively high stiffness, which allow for longer spans. As a result, they inherently have lower fundamental natural frequency and lower damping. Therefore, they are likely to undergo unwanted vibrations under the action of human activities such as walking. It is also quite expensive and complex to implement vibration control measures on problematic floors. In this study, a finite element model of a composite floor reported in the literature was developed and validated against four-point bending test results. The validated FE model was then utilised to examine the vibration behaviour of the investigated composite floor. Predictions obtained from the numerical model were compared against predictions from analytical formulas reported in the literature. Finally, the influence of various parameters on the vibration behaviour of the composite floor was studied and discussed.

• 한국통신학회논문지
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• 제26권11B호
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• pp.1501-1509
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• 2001

최근 들어 무선 통신 시스템의 급격한 증가로 인해, 한정된 주파수 자원의 효과적인 배분이 큰 문제로 대두되고 있으며, 이의 근본적인 해결 방안으로서, 초광대역 시간도약 임펄스 라디오 시스템이 많은 관심을 모으고 있다. 임펄스 라디오 시스템은 PPM 변조된 데이터를 1 nsec 이하의 매우 짧은 펄스폭을 갖는 가우시안 모노사이클 펄스를 사용하여 전송한다. 따라서 전송 신호는 매우 낮은 전력의 수 GHz에 이르는 초광대역 스펙트럼을 갖게 되며, 기존의 통신 시스템에 거의 간섭을 미치지 않는 것으로 알려져 있다. 본 논문의 목적은 임펄스 라디오 신호의 다중 경로 전파 특성을 파악하고 다중 경로 환경에서 상관기를 사용하는 수신기의 성능을 평가하는데 있다. 이러한 목적을 위하여 본 논문에서는 결정론적인 2-path 모델과 Saleh와 Valenzuela의 통계적 실내 다중 경로 모델을 고려하였다. 2-path 모델에 대하여 이상적인 기준 파형을 사용하는 상관기의 출력이 간접 경로의 지연 시간과 PPM 이진 데이터의 시간 지연의 상대적 차와 간접 경로의 경로 이득에 의존함을 보이고 모의 실험을 통하여 두 모델에 대한 임펄스 라디오의 비트 오율 특성을 관측하였다. 모의 실험 결과에 의하면 임펄스 라디오의 성능은 간접 경로의 지연 시간과 PPM 이진 데이터의 시간 지연의 상대적 차에 따라서 변하는 것이 관측되었다. 또한 기존의 AWGN (additive white Gaussian noise) 채널 환경에서 사용되는 기준 파형을 다중 경로 채널에서 적용할 수 없음을 관측하였다.

• Steel and Composite Structures
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• 제37권1호
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• pp.37-49
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• 2020

In this paper, free vibration finite element analysis of axially moving laminated composite beams subjected to axial tension is studied. It is assumed that the beam has a constant axial velocity and is subject to uniform axial tension. The analysis is based on higher-order theories that have been presented by Carrera Unified Formulation (CUF). In the CUF technique, the three dimensional (3D) displacement fields are expressed as the approximation of the arbitrary order of the displacement unknowns over the cross-section. This higher-order expansion is considered in equivalent single layer (ESL) model. The governing equations of motion are obtained via Hamilton's principle. Finally, several numerical examples are presented and the effect of the ply-angle, travelling speed and axial tension on the natural frequencies and beam stability are demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3533-3549
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• 2019

A big database utilizes the establishing network technology, and it became an emerging trend in the computing field. Therefore, there is a necessity for an optimal and effective data distribution approach to deal with this trend. This research presents the practical perspective of designing and implementing distributed database features. The proposed system has been establishing the satisfying, reliable, scalable, and standardized use of information. Furthermore, the proposed scheme reduces the vast and recurring efforts for designing an individual system for each university, as well as it is effectively participating in solving the course equivalence problem. The empirical finding in this study shows the superiority of the distributed system performance based on the average response time and the average waiting time than the centralized system. The system throughput also overcomes the centralized system because of data distribution and replication. Therefore, the analyzed data shows that the centralized system thrashes when the workload exceeds 60%, while the distributed system becomes thrashes after 81% workload.

• Computers and Concrete
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• 제10권2호
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• pp.197-217
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• 2012

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

• Journal of electromagnetic engineering and science
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• 제9권2호
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• pp.71-77
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• 2009

OFDM(Orthogonal Frequency Division Multiplexing) signals sutler serious nonlinear distortion in the nonlinear HPA(High Power Amplifier) because of high PAPR(Peak Average Power Ratio). Nonlinear distortion can be improved by a pre-distorter, but this pre-distorter is insufficient when the PAPR is very high in an OPFDM system. In this paper, a DFT(Discrete Fourier Transform) transform technique is introduced for PAPR reduction. It is especially important to consider the memory effect of HPA for more precise predistortion. Therefore, in this paper, we consider two models, the TWTA(Traveling-Wave Tube Amplifier) model of Saleh without a memory effect and the HPA memory polynomial model that has a memory effect. We design a pre-distorter and an adaptive pre-distorter that uses the NLMS(Normalized Least Mean Square) algorithm for the compensation of this nonlinear distortion. Without the consideration of a memory effect, the system performance would be degraded, even if the pre-distorter is used for the compensation of the nonlinear distortion. From the simulation results, we can confirm that the proposed system shows an improvement in performance.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.833-843
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• 2021

This research examines the impact of audit quality on the extent to which firm-specific information is integrated with a firm's share price - which is determined inversely using stock price synchronicity. The study sample consists of non-financial companies listed on the Amman Stock Exchange i.e., the Jordanian Stock Market, from 2014-2018. After examining 810 firm-year observations from Jordanian industrial companies listed on the ASE, during the study period, we find that the companies using one of the BIG4 audit firms for auditing have less synchronous and more informative stock prices, suggesting high-quality audit improved governance and reduce information asymmetry between firms' insiders and investors which enhances the capitalization of firm's specific information into the stock price, thus less synchronous and more informative stock return. The findings remain consistent over 2 separate measurements of stock price synchronicity (Market and Industry model and Market Model) and show robustness for fixed effect tests. Our multivariate regression results are also robust after controlling for a number of features at the firm level with potential associations with stock price synchronicity. These include the firm size, leverage, return on assets (ROA), and market to book value (MBV).

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.145-156
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• 2022

Children are the foundation and future of this society and understanding their impressions and behaviors is very important and the child's behavioral problems are a burden on the family and society as well as have a bad impact on the development of the child, and the early diagnosis of these problems helps to solve or mitigate them, and in this research project we aim to understand and know the behaviors of children, through artificial intelligence algorithms that helped solve many complex problems in an automated system, By using this technique to read and analyze the behaviors and feelings of the child by reading the features of the child's face, the movement of the child's body, the method of the child's session and nervous emotions, and by analyzing these factors we can predict the feelings and behaviors of children from grief, tension, happiness and anger as well as determine whether this child has the autism spectrum or not. The scarcity of studies and the privacy of data and its scarcity on these behaviors and feelings limited researchers in the process of analysis and training to the model presented in a set of images, videos and audio recordings that can be connected, this model results in understanding the feelings of children and their behaviors and helps doctors and specialists to understand and know these behaviors and feelings.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.