Gender-Specific Determinants of eHealth Literacy: Results from an Adolescent Internet Behavior Survey in Taiwan

Adolescents’ Internet health information usage has rarely been investigated. Adolescents seek all kinds of information from the Internet, including health information, which affects their Health Literacy that eHealth Literacy (eHL). This study is a retrospective observational study, we have total of 500 questionnaires were distributed, 87% of which were recovered, and we explored the channels that adolescents use to search for health information, their ability to identify false information, and factors affecting the type and content of health information queried. Adolescents believe that the Internet is a good means to seek health information because of its instant accessibility, frequent updating, convenience, and lack of time limits. More boys use the Internet to seek health information than girls in junior high schools (p = 0.009). The Internet is an important source of health information for adolescents but contains extensive misinformation that adolescents cannot identify. Additionally, adolescent boys and girls are interested in different health issues. Therefore, the government should implement measures to minimize misinformation on the Internet and create a healthy, educational online environment to promote Adolescents’ eHealth Literacy (eHL).

Intelligent Tourism Personalized Recommendation Based on Multi-Fusion of Clustering Algorithms

Actual tourism mining models are often used to discover potential information in documents, but tourism models without human knowledge often produce unexplainable topics. This paper combines big data technology to build a personalized recommendation system for smart tourism, model the contextual information usage ontology under the tourism information system, and give the association between various ontologies. Then, this paper uses a matrix to describe each discrete attribute and interval attribute and uses a vector to model the user’s preferences. In addition, this paper constructs an intelligent recommendation system based on the actual needs of travel recommendation and verifies the system in combination with experimental research. Through experimental analysis, it can be known that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect.

The Impact of Privacy Regulations on DB Systems

Personal data usage and collection are activities that used to grow unrestricted. However, several laws in the physical world ensure rights to people regarding their privacy and information usage. In the last years, legislators passed many laws, regulations, and acts to replicate these rights to the digital world. By doing so, new constraints, rights, and duties appear on every component of the data usage and collection workflow. In this paper, we discuss legislations’ implications, identifying impacts that these regulations introduce to current DBMS, and survey recent works that aim to solve the problems raised by these impacts, highlighting research opportunities and identifying how solutions can be achieved for the problems.

Personal Information Privacy: Some Findings on Gender Difference

The implementation of effective cybersecurity by organizations is a prerequisite to privacy protection for personal information collected, used, stored, and shared online. The trend for the potential of online privacy breaches has been moving upward with our daily reliance on the Internet and cloud computing. While online, individuals may choose to use a credit card to complete a transaction, access email, access social media sites, and store pictures through a cloud storage. In some cases, law enforcement agencies may access and use personal information stored online. Do individuals approve of the usage of their personal information by these agencies to solve crimes? Do demographic characteristics such a gender, education, and age provide a reliable set of predictors for the probability of approval? Do females and males differ with respect to the decision to approve information usage to solve crimes? This study reports on the analysis of data from a 2019 Pew Research Center survey of 1,365 individuals in the USA. Most respondents (63%) approve of personal information usage by law enforcement agencies to solve crimes. The purpose of this study is to determine the trend in the citizens’ approval for personal information usage by law enforcement agencies, especially distinguishing the genders. The results of a regression analysis showed that the demographic variables -gender, education, and age- provide a statistically significant power to predict the probability for information usage approval. A t-Test revealed that there is a statistically significant difference between genders. Females are more likely to offer the approval.

Key factors affecting the technical efficiency of bee farms in the province of La Pampa (Argentina): A two-stage DEA approach

This paper analyzes how decision-making, management capacity and technology adoption by beekeepers, affect the technical efficiency (TE) of Argentinean beekeeping through the case study of the province of La Pampa (Argentina). The assessment of TE is currently receiving ever-growing attention as an indicator of sustainability and usage of sufficient natural resources in beekeeping activities. This study aimed to identify the key factors affecting the technical efficiency of bee farms in the province of La Pampa. The study included a sample of 40 bee farms and estimated their TE score through an input-oriented Data Envelopment Analysis (DEA) model. In a second stage, Tobit regression was determined to evaluate the technical inefficiency determinants. This paper found that most beekeeping production units have low TE levels. Only 25 % of bee farms produce either at or close to the frontier. The Tobit model revealed that variables such as marital status, educational level, primary family income, source information usage, planning and health area, affect positively on pure technical efficiency. These results are considered to be of great interest for structured beekeeping systems on small-scale and family farms, as well as for political decision-makers, regarding a public program in apiculture. Highlights: Argentina is the leading country in America, exporting honey worldwide. DEA approach and Tobit model based on a two-stage analysis is a useful tool when evaluating livestock production systems. Bee farmers in La Pampa (Argentina) are operating below the production frontier, which indicates there is still scope for improvement. Variables such as marital status, educational level, primary family income, source information usage, planning and health area, affect positively on technical efficiency. Beekeepers should be encouraged by the government to improve their efficiency through training programs to ease decision-making and management, therefore enhancing productivity.

Customer Accounting Information and Omani Service Companies’ Performance

The main objective of this paper is to investigate the link between customer accounting information and companies' performance in Sultanate of Oman. This is a cross-sectional study with quantitative method where the quantitative data was accumulated through questionnaire. The sample size of 160 usable questionnaires were received from employees work in the banks that are considered as a sample of the current study. The findings show that there is a positive link between the customers' accounting information and the companies’ performance. The findings pointed out that companies' extent of customer accounting information usage have significantly impact the institutional performance. This research is a new in its type to be applied in Sultanate of Oman context via testing the relation between its predictors of customer accounting information features towards their impact on company performance.

A review on prediction of diabetes type 2 by machine learning techniques

Machine learning is considered to be one of the most promising tools when it comes to working with heterogeneous data. It provides a new dimension which enables one to extract relevant data and take decision for the effective functioning of the network, making use of network generated data. Every sphere of our life is now dependent on machine learning. It has flourished in every dimension. Making it versatile and ever demanding. Department of healthcare contains very abundant and sensitive information which is needed to be carefully handled. Diabetes mellitus is increasing exponentially and is spreading like anything in the world. A reliable prediction system should be present for diagnosing diabetes. Variety of machine learning techniques find their use in the examination of data from variant perspectives and summarizing it into effective information. Usage of new patterns is done to elucidate these patterns in order to deliver relevant information for their users. By making use of techniques such as SVM, random forest, logistic regression, naïve bayes etc the prediction of diabetes can be done easily and accurately. In this study we will make use of different machine learning techniques and try to find accurate prediction regarding the same.

A General Framework Information Loss of Utility-Based Anonymization in Data Publishing

To build anonymization, the data anonymizer must determine the following three issues: Firstly, which data to be preserved? Secondly, which adversary background knowledge used to disclosure the anonymized data? Thirdly, The usage of the anonymized data? We have different anonymization techniques from the previous three-question according to different adversary background knowledge and information usage (information utility). In other words, different anonymization techniques lead to different information loss. In this paper, we propose a general framework for the utility-based anonymization to minimize the information loss in data published with a trade-off grantee of achieving the required privacy level.