An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China

Air pollution is a serious problem in China, and the government has taken a series of steps to solve it. However, it is still u2nclear how the situation has improved after years of atmospheric pollution control. Shandong Province, which has the second largest population and the highest coal consumption in China, was chosen to analyze the spatiotemporal air quality trends. We obtained daily air quality index (AQI) values from 91 stations in the province from 1 January 2014, to 31 December 2019, based on an adaptive data analysis method, empirical mode decomposition (EMD). The distribution of AQI in Shandong Province was heterogeneous at both spatial and temporal scales. All the stations could be divided into four clusters whose AQI trends decreased from 75 to 53, 95 to 68, 128 to 82, and 148 to 82, respectively. The overall trend of pollution became more serious from east to west in the province. The AQI is the largest in winter, followed by spring and autumn, and the smallest index occurs in summer. There are four types of annual trends of the AQI of each city. The overall downward trend indicates that the air quality of each city was improving annually.

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three years.

Computer-supported collaborative learning (CSCL) system research - Edutab box

Collaborative/active learning has proven to be an effective educational method. It involves groups of two or more learners working together to solve a problem or complete a task and the benefits are numerous. For example, it enhances problem-solving skills, inspires critical thinking, improves social interactions and helps in the development of self-management skills and oral communication skills, to name but a few. There is potential to further enhance these benefits using information and communications technology (ICT). Professor Hirotaka Takahashi has spent his career looking at a wide range of topics, including adaptive data analysis methods, information theory, gravitational wave physics and astronomy. His current work centres around computer-supported collaborative/active learning (CSCL) systems, which he is seeking to further develop by collaborating with experts in data science, ICT system development and education. Specifically, Takahashi is working with experts to develop a CSCL system/software called the 'Edutab Box'.

Computer-supported collaborative learning (CSCL) system research - Edutab box

Collaborative/active learning has proven to be an effective educational method. It involves groups of two or more learners working together to solve a problem or complete a task and the benefits are numerous. For example, it enhances problem-solving skills, inspires critical thinking, improves social interactions and helps in the development of self-management skills and oral communication skills, to name but a few. There is potential to further enhance these benefits using information and communications technology (ICT). Professor Hirotaka Takahashi has spent his career looking at a wide range of topics, including adaptive data analysis methods, information theory, gravitational wave physics and astronomy. His current work centres around computer-supported collaborative/active learning (CSCL) systems, which he is seeking to further develop by collaborating with experts in data science, ICT system development and education. Specifically, Takahashi is working with experts to develop a CSCL system/software called the 'Edutab Box'.

Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management

The accidents caused by hazardous material during road transportation may result in catastrophic losses of lives and economics, as well as damages to the environment. Regarding the deficiencies in the information systems of hazmat transportation accidents, this study conducts a survey of 371 accidents with consequence Levels II to V involving road transportation in China from 2004–2018. The study proposes a comprehensive analysis framework for understanding the overall status associated with key factors of hazmat transportation in terms of characteristics, cause, and severity. By incorporating the adaptive data analysis techniques and tackling uncertainty, the preventative measures can be carried out for supporting safety management in hazmat transportation. Thus, this study firstly analyzed spatial–temporal trends to understand the major characteristics of hazmat transportation accidents. Secondly, it presented a quantitative description of the relation among the hazmat properties, accident characteristics, and the consequences of the accidents using the decision tree approach. Thirdly, an enhanced F-N curve-based analysis method that can describe the relationship between cumulative probability F and number of deaths N, was proposed under the power-law distribution and applied to several practical data sets for severity analysis. It can evaluate accident severity of hazmat material by road transportation while taking into account uncertainty in terms of data sources. Through the introduction of the as low as reasonably practicable (ALARP) principle for determining acceptable and tolerable levels, it is indicated that the F-N curves are above the tolerable line for most hazmat accident scenarios. The findings can provide an empirically supported theoretical basis for the decision-makers to take action to reduce accident frequencies and risks for effective hazmat transportation management.