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Discusses how data is replacing money as the driver of market behavior and shares predictions for how data will revolutionize the market economy and make cash, banks, and big companies obsolete. --Publisher.

Understanding the influence of COVID-19 on China's agricultural economy and the Chinese government's emergency measures to ease the economic impacts of viral spread can offer urgently-needed lessons while this virus continues to spread across the globe. Thus, this study collected over 750,000 words upon the topic of COVID-19 and agriculture from the largest two media channels in China: WeChat and Sina Weibo, and employed web crawler technology and text mining method to explore the influence of COVID-19 on agricultural economy and mitigation measures in China. The results show that: (1) the impact of COVID-19 on China's agricultural economy at the very first phase is mainly reflected in eight aspects as crop production, agricultural products supply, livestock production, farmers' income and employment, economic crop development, agricultural products sales model, leisure agriculture development, and agricultural products trade. (2) The government's immediate countermeasures include resuming agricultural production and farmers' work, providing financial support, stabilizing agricultural production and products supply, promoting agricultural products sale, providing subsidies, providing agricultural technology guidance and field management, and providing assistance to poor farmers to reduce poverty. (3) The order of government's immediate countermeasures is not all in line with the order of impact aspects, which indicates that more-tailored policies should be implemented to mitigate the strikes of COVID-19 on China's agricultural economy in the future.

This book shows how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Topics include: exploratory data analysis; visualization; decision trees; rule induction; k-nearest neighbors; naive Bayesian; artificial neural networks; support vector machines; ensemble models; bagging; boosting; random forests; linear regression; logistic regression; association analysis using Apriori and FP growth; k-means clustering; density based clustering; self organizing maps; text mining; time series forecasting; anomaly detection and feature selection. --

As measures of concentration, especially for market (industry) concentration based on market shares, a variety of different measures or indices have been proposed. However, the various indices, including the two most widely used ones, the concentration ratio and the Herfindahl-Hirschman index (HHI), lack an important property: the value-validity property. An alternative index with this and other desirable properties is introduced. The new index makes it permissible to properly assess the extent of the concentration and make order and difference comparisons between index values as being true representations of the real concentration characteristic (attribute). Computer simulation data and real market-share data are used in the analysis. It is shown that the new index has a close functional relationship with the HHI index and has a firm theoretical relationship with market power as measured by the price-cost margin. Corresponding modifications to existing merger guidelines are presented.

Railway accidents, particularly suicides and suicide attempts, significantly disrupt operations, cause delays in passenger and freight services, and result in varying degrees of infrastructure damage. This study focuses on identifying the relationship between suicide-related railway incidents, as the most frequent type of railway accidents, and socio-economic factors, utilising data from 2015 to 2022 provided by the Railways of the Slovak Republic. Using a data mining approach, a logistic regression model was developed to predict the accident rate based on key socio-economic factors. This model demonstrates high prediction performance, with significant predictors including interest, marriage, and fertility rates. The data mining approach allows for the efficient extraction of relevant patterns and relationships and ensures that the model can be easily adjusted in response to significant changes in input factors or conditions. The findings contribute to understanding railway safety, offering practical insights for improving safety measures and aiding suicide prevention efforts. The high explanatory power of the predictive model underscores the critical role of societal influences in the dynamics of railway-related suicides and suicide attempts, providing valuable guidance for enhancing safety protocols and planning.

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including \"updates about confirmed cases,\" \"COVID-19 related death,\" \"cases outside China (worldwide),\" \"COVID-19 outbreak in South Korea,\" \"early signs of the outbreak in New York,\" \"Diamond Princess cruise,\" \"economic impact,\" \"Preventive measures,\" \"authorities,\" and \"supply chain.\" Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.

PurposeThrough a comprehensive review of the literature on open innovation (OI), this study aimed to achieve two objectives: (1) to identify the main thematic areas discussed in the past and track their evolution over time; and (2) to provide recommendations for future research avenues.Design/methodology/approachTo achieve the first objective, a method based on text mining was implemented, with the analysis focusing on 1,772 journal articles published between 2003 and 2018. For the second objective, a review based on recent and relevant papers was conducted for each thematic area.FindingsThe paper identified nine thematic areas explored in existing research: (1) context-dependency of OI, (2) collaborative frameworks, (3) organizational dimensions of OI, (4) performance and OI, (5) external search for OI, (6) OI in small and medium-sized enterprises, (7) OI in the pharmaceutical industry, (8) OI and intellectual property rights, and (9) technology. The analysis of the most recent papers belonging to the more investigated areas offers suitable suggestions for future research avenues.Originality/valueTo the best of the authors’ knowledge, no review has yet been undertaken to reorganize the OI literature.

Mineral wealth serves as a crucial driver of economic growth and infrastructure development in many countries, including Uganda. Artisanal and Small-Scale Gold Mining (ASGM) plays a significant role in enhancing incomes and livelihoods. However, ASGM remains largely informal in Uganda, not reflected in the country's Gross Domestic Product (GDP). Many methods of gold processing are available around the globe. The borax method has become one of the favorable methods for extracting gold due to its lower operating cost, higher recovery, human and environmental consideration. This study aimed to assessing the impact of borax utilization in gold processing on the income and livelihoods of miners and stakeholders in Buhweju, Busia and Kassanda districts in Uganda where the Free Your Mine project is being implemented. A cross-sectional study using mixed methods was employed between October 2022 and January 2023 with data collected from 161 miners through semi-structured questionnaires, Key Informant Interviews (KIIs), Focus Group Discussions (FGDs), and observations. Analysis was conducted using Atlas ti version 7.0 and Stata Version 15.0. Findings indicate low adoption of borax and limited training coverage on the use of borax. While 80.1% of respondents saved money attained from gold mining activities, only 21.5% had investments. FGDs revealed that miners often resort to mercury use for quick income as using mercury takes little time compared to mercury-free methods, while KIIs emphasized the need for government intervention and need for policies to promote safer gold processing methods for sustainable livelihoods. Study underscores need for awareness, policy to improve safety in Uganda's ASGM sector. In addition, there is need for funding to support scale up the Free Your Mine Project to other gold mining districts in Uganda.

The different facets of the sharing economy offer numerous opportunities for businesses? particularly those that can be distinguished by their creative ideas and their ability to easily connect buyers and senders of goods and services via digital platforms. At the beginning of the growth of this economy, the advanced digital technologies generated billions of bytes of data that constitute what we call Big Data. This book underlines the facilitating role of Big Data analytics, explaining why and how data analysis algorithms can be integrated operationally, in order to extract value and to improve the practices of the sharing economy. It examines the reasons why these new techniques are necessary for businesses of this economy and proposes a series of useful applications that illustrate the use of data in the sharing ecosystem.