An analysis of the attitudes of internet users towards the islamic financial system

In this article has been explored the attitude of Internet users toward the Islamic financial system using data from Google Trends and WorldData.AI. An attitudes towards Islamic finance, Islamic economics and Islamic banking have been compared using from Google Trends survey statistics. Also, in the WorldData. AI. source has been analyzed that the news texts on Islamic finance for the last 12 months and found that the public attitude towards Islamic finance was positive.

HOW MACHINE LEARNING METHOD PERFORMANCE FOR IMBALANCED DATA

This study will examine the application of several classification methods to machine learning models by taking into account the case of imbalanced data. The research was conducted on a case study of classification modeling for working status in Banten Province in 2020. The data used comes from the National Labor Force Survey, Statistics Indonesia. The machine learning methods used are Classification and Regression Tree (CART), Naïve Bayes, Random Forest, Rotation Forest, Support Vector Machine (SVM), Neural Network Analysis, One Rule (OneR), and Boosting. Classification modeling using resample techniques in cases of imbalanced data and large data sets is proven to improve classification accuracy, especially for minority classes, which can be seen from the sensitivity and specificity values that are more balanced than the original data (without treatment). Furthermore, the eight classification models tested shows that the Boost model provides the best performance based on the highest sensitivity, specificity, G-mean, and kappa coefficient values. The most important/most influential variables in the classification of working status are marital status, education, and age.

iMakerSpace Best Practices for Shaping the 21st Century Workforce

Innovations in engineering education are undergoing a noticeable transformation. Higher education institutions are practicing distance education, remote laboratories, studio pedagogies and several other approaches in order to increase their students’ retention, success, and preparedness for the job market. In engineering education, maker spaces have become popular in the last ten years in universities as well as community colleges, high schools and community innovation hubs. A large number of engineering colleges have allocated significant spaces, and at some universities entire buildings, as maker spaces to be used for curricular and extracurricular activities. Success stories of these types of spaces are well documented. This paper describes the core activities and programs held at Tennessee Tech University’s maker space called ‘iMakerSpace.’ Accomplishments include several innovative workforce development activities. The impact and effectiveness of the iMakerSpace, presented through workforce innovations and survey statistics, is a unique contribution to this paper. In addition, the modification of operations under COVID-19 pandemic restrictions has resulted in changes that may be carried out post-pandemic.

Dithakga Tša Gobala: A Collaborative Book Creation Project

A 2018 survey (Statistics South Africa, n.d.) reported that almost half of South African children aged four years had never read a book with their parents. In light of the current literacy crisis that South Africa is experiencing, providing appropriate materials to encourage and aid reading and storytelling activities is increasingly important but remains a challenge. Wordless picturebooks are an under researched literary genre in the South African context but, we argue, they can be used to spark a culture and love of reading because they can be enjoyed by readers of various backgrounds, language preferences, and literacy levels. Using participative research, the Dithakga Tsa Gobala project investigated whether and how the development of wordless picturebooks in local communities could help parents engage in cognitively stimulating activities such as shared reading and storytelling. Stories sourced from two communities were used to create a series of wordless picturebooks that were then circulated in the communities. Initial results indicated that not only did the project have a positive impact on the participants' self-concept and their relationship with reading, but that the books were also positively received by the wider community. Results indicated that the books were easy to use, created positive parent-child experiences, encouraged imagination, and that the content of the books was relatable. Challenges that arose included issues of authorship and misunderstandings between authors and illustrators. Nevertheless, the findings suggest that wordless picturebooks are a valuable genre in the South African reading landscape and that a participatory model for creating relevant, local content for reading is not only viable but also beneficial for communities and other stakeholders.

LATs in the United States

This chapter presents the findings of my 2016 surveys of LATs in New York State and nationally and my qualitative interviews with LATs in the United States and England, which support many of the findings of the literature from other countries and diverge from them in certain respects. The survey statistics show that living apart is as common here as in Europe, with couples displaying similar arrangements and interactions. Data about economic interrelationship of the partners and mutual caregiving differ somewhat from those reported in previous studies.

Outliers in official statistics

The purpose of this manuscript is to provide a survey on the important methods addressing outliers while producing official statistics. Outliers are often unavoidable in survey statistics. They may reduce the information of survey datasets and distort estimation on each step of the survey statistics production process. This paper defines outliers to be focused on each production step and introduces practical methods to cope with them. The statistical production process is roughly divided into the following three steps. The first step is data cleaning, and outliers to be focused are that may contain mistakes to be corrected. Robust estimators of a mean vector and covariance matrix are introduced for the purpose. The next step is imputation. Among a variety of imputation methods, regression and ratio imputation are the subjects in this paper. Outliers to be focused on in this step are not erroneous but have extreme values that may distort parameter estimation. Robust estimators that are not affected by remaining outliers are introduced. The final step is estimation and formatting. We have to be careful about outliers that have extreme values with large design weights since they have a considerable influence on the final statistics products. Weight calibration methods controlling the influence are discussed based on the robust weights obtained in the previous imputation step. A few examples of practical application are also provided briefly, although multivariate outlier detection methods introduced in this paper are mostly in the research stage in the field of official statistics.

The COVID-19 Pandemic and the Antecedants for the Impulse Buying Behavior of US Citizens

In an analysis based on the theory of Fear, this study examines impulse purchase patterns during the COVID-19 Pandemic across major US urban centers. Data from 889 US consumers were collected from leading US cities to evaluate impulse buying behavior fluctuations using SEM-based multivariate approaches to examine the survey statistics. We used COVID-19 as a moderating variable of this impulse purchase behavior. The results confirmed that Fear of a complete lockdown, peers buying, scarcity of essential products on shelves, US stimulus checks, the limited supply of essential goods, and panic buying have had a compelling and affirmative influence on the sharp swings of impulse buying patterns. The findings further confirm that Fear Appeal and social media fake news have had a strong positive impact on impulse buying as mediating factors. Finally, it was concluded that COVID-19 is a significant moderating factor influencing the impulse buying behavior of US citizens. The practical implications suggest that marketers and brand managers should devise novel strategies to enhance their brand’s market share to attain a competitive advantage in COVID-19 or similar panic situations in the future. These research findings are essential to comprehend the sharp fluctuations of impulse buying patterns in the current cutthroat competition environment across the US and other parts of the world.

Rescue Work and Progress of Analog Seismograms in China

Rescue work involving scanning and digitization research on historical analog seismograms nationwide in China was launched in 2017. A series of results have been accomplished. There are more than 13 million analog seismic records existing in China, and about 3 million of them were scanned by December 2019. The Second Monitoring and Application Center of China Earthquake Administration completed the phased construction of the “China Analog Seismic Record Rescue Project” in 2019, which has received a great deal of funding support. Most of the analog seismograms and geophysical records in China will be scanned in the following decade, the corresponding stations and instrument parameters will be also collected during this project to provide retrieval and download service. This article first introduces the survey statistics about scanning and digitalization progress of analog seismic records in China. Second, a management and service system for the storage and query of large scale of seismograms has been established based on distributed database and search engine. Finally, future prospects of the rescue work of analog seismograms are mentioned.