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This book sheds light on the overall description and explanation of the current socio-political, economic and cultural environment concerning the development of China's audio streaming programs industry. It interprets the emergence of the “ear economy” through the subjects of media ecology, media psychology, communication studies and cultural criticism, media industrial studies, sociology and anthropology. The book skillfully weaves together historical, cultural, and industry studies, along with textual and critical discourse analysis. This interdisciplinary work contributes to multiple academic fields including literary and cultural studies, media and communication studies, China/Asia studies, and political theory. Ying Huang is an Assistant Professor, Academy of Film, Macau University of Science and Technology, a post-doctoral fellow in School of Journalism and Communication, Tsinghua University.

Digital media. --- Mass media and culture. --- Communication and traffic. --- Communication. --- Information theory. --- Mass media. --- Digital and New Media. --- Media Culture. --- Media Industries. --- Media and Communication Theory. --- Media Sociology.

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This book systematically presents 40 pests, 7 natural pest enemies, and 20 diseases and weeds commonly encountered in sugarcane production, combining clear colour photos with detailed scientific descriptions. It covers a range of related topics, including morphological identification, habits and frequency of occurrence, prevention and control measures, symptom identification, characteristics of infections and epidemics, parasitic (predator) characteristics, ways of utilising natural pest enemies, main species and distribution, fluctuation in the field, and chemical control of weeds. With novel content presented in simple, straightforward language, the book provides a valuable reference guide for scientific researchers, educators and industrial practitioners, as well as students and advisers at agricultural universities.

Sugarcane --- Diseases and pests. --- Agriculture. --- Plant diseases. --- Plant breeding. --- Plant Pathology. --- Plant Breeding/Biotechnology. --- Crops --- Agriculture --- Breeding --- Botany --- Communicable diseases in plants --- Crop diseases --- Diseases of plants --- Microbial diseases in plants --- Pathological botany --- Pathology, Vegetable --- Phytopathology --- Plant pathology --- Plants --- Vegetable pathology --- Agricultural pests --- Crop losses --- Diseased plants --- Phytopathogenic microorganisms --- Plant pathologists --- Plant quarantine --- Farming --- Husbandry --- Industrial arts --- Life sciences --- Food supply --- Land use, Rural --- Pathology --- Diseases and pests --- Diseases --- Wounds and injuries --- Plant pathology.

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This work aims to provide an overview of the history and the current academic landscape of the field of ‘social entrepreneurship, considered a sub-field of ‘entrepreneurship. The field of Entrepreneurship as a research field is fairly new and the term ‘entrepreneurship’ is broadly defined amongst its many uses. The definition of social entrepreneurship that will be used as the guiding baseline of this work is the definition proposed by Wee-Liang Tan, John Williams & Teck-Meng Tan(2005)(1) . The main body of this research will consist of a scientometric review, including a co-author analysis, co-word analysis, co-citation analysis. The base for this will be a sample of papers obtained from the Web of Science, of which the parameters will be clarified further in this research. The results will be mapped and visualized through node networks, offering a timeline perspective of the prominent authors, institutions, countries, journals and documents in the field. In addition, it will investigate co-occurrences of citations of authors, documents and journals across the sample. The visualized information will then be analyzed to identify potential drivers, leading actors or leading institutions or research topics of the field. The conclusions of these analyses will be discussed against other reviews, critiques and supporting publications on the field of social entrepreneurship.

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In this thesis, we disclose the evolution and business applications of the emerging field of machine learning by means of a scientometric analysis. This scientometric analysis will examine the scientific research of machine learning of the period 2013-2017. Machine learning will be introduced in the theoretical background to provide a clear perspective to the reader on what ML is and what its building blocks are. First it will focus on how, and in what way machine learning research has evolved by means of analyzing the publication activity of ML research. The publication activity will be analyzed in terms of publication output, document types, research categories, the countries active in the production of the research and its related institutions. Then we will focus on why this period was subjected to an explosive growth of the research by means of a structural analysis. In this structural analysis two scientometric methods will be applied. First, we will examine the dataset by co-occurrence of Author Keywords. After this, we will proceed with a bibliographic-coupling analysis. The last part consists of discussing the results and analyzing how the identified trends in ML research relate to the entrepreneurial sphere.

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Innovation and knowledge play a crucial role in the competitiveness of companies and the process of economic development of regions and countries (Furková, 2019). The distributions of innovation resources in different areas are uneven, but the demands of regional development for scientific and technological resources are increasing. Each region has specific assets, unique capabilities and industrial policies that make it different from other regions (Sleuwaegen & Boiardi, 2014). Regional integration and cooperation of different levels have become the important strategy of dealing with the new round of technology revolution, seizing the commanding heights of science and technology development, and improving the regional innovation competitiveness. In recent years, European Union (EU) regions have proposed several policies to promote regional science, technology and innovation through the involvement of regional stakeholders (ZabalaIturriagagoitia, Jiménez-Sáez, & Castro-Martínez, 2008). Based on the above background, this paper integrates social network analysis, patentmetric analysis and multiple regression analysis to answer the following questions: What is the development trend of EU’s inter-regional technological cooperation? What are the cooperation patterns in different timeframes? Which variables have a significant influence on technological cooperation? These answers can be used for strategies and policies of EU science & technology cooperation in the future.