scholarly journals An Experimental Study of Influential Elements on Cyberloafing from General Deterrence Theory Perspective Case Study: Tehran Subway Organization

2015 ◽  
Vol 8 (3) ◽  
Author(s):  
Hosseini Mirza Hassan ◽  
Daraei Mohammad Reza ◽  
Mostafa Abdol-Alivandi Farkhad
Keyword(s):  
Experimental Study ◽  
Deterrence Theory ◽  
General Deterrence ◽  
General Deterrence Theory

scholarly journals Internal and external drivers for compliance with the COVID-19 preventive measures in Slovenia: The view from general deterrence and protection motivation

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259675
Author(s):  
Anže Mihelič ◽  
Luka Jelovčan ◽  
Kaja Prislan
Keyword(s):  
Preventive Measures ◽  
External Factors ◽  
Protective Behavior ◽  
Deterrence Theory ◽  
Cross Sectional ◽  
Internal Factors ◽  
General Deterrence ◽  
General Deterrence Theory ◽  
Cross Sectional Design

The emergence of a pandemic is usually accompanied by different measures–economic, social, preventive, and (self)protective. In the case of the COVID-19, several preventive measures were formally enforced by state authorities in the majority of countries worldwide. Thus, during the COVID-19 pandemic, the intertwining of formal and informal social control could be observed. Hence, in this study a cross-sectional design was chosen to explore the issue in Slovenia. To the best of our knowledge, this research is the first in the current literature to empirically test the general deterrence theory in pandemic circumstances (as external factors predicting individuals’ compliance with the COVID-19 preventive measures). The results suggest an important role of informal punishment, with perceived informal severity being the only statistically significant factor from the general deterrence theory. In contrast to external factors, internal factors play a significantly greater role in promoting people’s self-protective behavior in pandemic circumstances. During the unknown, the uncertain and delicate situations with which people have no previous experience, both personal beliefs about the effectiveness of measures and perceived self-efficacy are more important than fear of formal sanctions.

scholarly journals An Experimental Study on the Sources of Strontium in Mineral Water and General Rules of Its Dissolution—A Case Study of Chengde, Hebei

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 699
Author(s):  
Ruifeng Wang ◽  
Xiong Wu ◽  
Yanliang Zhai ◽  
Yuxuan Su ◽  
Chenhui Liu
Keyword(s):  
Experimental Study ◽  
Mineral Water ◽  
Surrounding Rock ◽  
Mechanism Of Formation ◽  
High Quality ◽  
General Rules ◽  
Leaching Experiment ◽  
High Level ◽  
Impact Experiments

Chengde City boasts a wealth of high-quality mineral water resources characterized by a high level of strontium (Sr), a low level of sodium, and low alkalinity. In order to study the mechanism of formation of Sr-bearing mineral water in Chengde and to scientifically guide future mineral water exploration, taking three typical mineral water exploration areas in Chengde as examples, this paper studies the sources of Sr in mineral water and the general rules of its dissolution via a laboratory static leaching experiment and impact experiments, and it provides an analysis of the characteristics of typical rock samples. The research results indicate that the content of Sr in surrounding rock and the characteristics of minerals existing in surrounding rock jointly control the dissolution of Sr in water; that CO2 can promote the formation of mineral water containing Sr; and that temperature increases may boost the dissolution of Sr from carbonate minerals but also inhibit the dissolution of Sr from silicate minerals.

Twitter Users' Classification Based on Interest

2020 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Noura A. AlSomaikhi ◽  
Zakarya A. Alzamil
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Knowledge Sharing ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Software System ◽  
Web Based ◽  
Twitter Users

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.

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