Understanding and Exploring the Concept of Fear, in the Work Context and Its Role in Improving Safety Performance and Reducing Well-Being in a Steady Job Insecurity Period
All articles published by are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by , including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https:///openaccess.
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
City Of Evanston Proposed Budget By City Of Evanston
Editor’s Choice articles are based on recommendations by the scientific editors of journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
With the continuous breakthrough and innovation of artificial intelligence technology, the demand for diversified and multi-level compound intelligent manufacturing talents keeps growing. However, the current pace of intelligent manufacturing talent education in colleges and universities is still difficult to keep up with the advances in science and technology in the context of the new generation of artificial intelligence. This work conducted visual research of the literature on artificial intelligence in the field of manufacturing. All the literature was retrieved from the Web of Science Core Collection and divided into three periods (1979–1994, 1995–2007 and 2008–2021) according to the fluctuation of literature volume. Bibliometric and content analysis of the related literature during these periods were conducted to track the hotspots and trend of artificial intelligence in the field of manufacturing. The results showed that the internet of things, deep learning, cyber physical systems and smart manufacturing have been the new research hotspots. Finally, a series of suggestions were given for the sustainable education of intelligent manufacturing talents in the context of the new generation of artificial intelligence. This work may provide references for the construction of sustainable education systems for intelligent manufacturing talents in the context of the new generation of artificial intelligence.
Intelligent manufacturing (IM) is a human-machine integrated intelligent system composed of intelligent machines and expert intelligence which can carry out intelligent activities in the manufacturing process, such as analysis, reasoning, judgment, conception and decision-making [1, 2, 3]. New technologies such as cloud computing, big data analytics, virtual/augmented reality, Internet of Things (IoT), mobile devices and robots continue to emerge in the context of the new generation of artificial intelligence (AI 2.0) [4, 5, 6, 7, 8]. These technologies greatly promote further changes of IM technology and make modern industrial manufacturing automation flexible, intelligent and highly integrated. Therefore, IM technology has attracted worldwide attention from academia and industry. Governments around the world have issued a series of policies on the development of intelligent manufacturing technology. In 2011, the Advanced Manufacturing Partnership (AMP) was proposed by the United States aiming to connect industry, universities and research institutes with the government to jointly invest in advanced technologies and create high-quality products [9, 10]. Germany launched the “Industry 4.0” plan in 2013, which sought a highly flexible production mode [11]. In 2015, The Chinese government implemented the “Made in China 2025” plan, which emphasized that the deep integration of information technology and manufacturing technology is the commanding point of the new round of industrial competition [12, 13]. In the same year, the 17 objectives were adopted at the United Nations Sustainable Development Summit. The Sustainable Development Goals (SDGs) aim to address the social, economic and environmental dimensions of development. The ninth goal is industrial innovation, which aims to promote sustainable industries and drive innovation [14, 15, 16]. All the policies mention the importance of IM talent education, and colleges and universities are the main sources of talent to transfer to enterprises. However, the talent architecture and attribute requirements of IM systems in colleges and universities have not been clearly revealed due to the continuous change of AI 2.0. Obviously, this will lead to the talent education of IM in colleges and universities being divorced from the actual needs of social enterprises, which is detrimental to the sustainable education of IM talents.
Pdf) The Other Side Of The Brain: The Politics Of Split Brain Research In The 1970s 1980s
Education for sustainable development (ESD) refers to an education that emphasizes the pursuit of harmonious balance among society, economy and environment [17, 18, 19]. This concept was proposed by the United Nations Educational, Scientific, and Cultural Organization (UNESCO). One of the main ideas is to make education always maintain the vigor and vitality of sustainable development and cultivate talents with sustainable development abilities [20, 21]. Many efforts have been made in education sustainability [22, 23]. A. Bieler et al. presented a content analysis for the strategic plans of Canadian higher education institutions (HEIs) and found three characteristic types of response for education sustainability [24]. K. Mintz et al. used a mixed-methods design method to integrate sustainability into the curriculum and improve students’ knowledge, skills and motivation [25]. J. J. Salovaara et al. explored educational programs and the representation of theory-based key competencies for sustainability through a qualitative content study of master’s programs [26]. Obviously, the core competencies of sustainable education are different for different fields and different levels of education. As new and innovative technologies continue to emerge, the development of artificial intelligence has entered a new stage, referred to as AI 2.0 [27, 28, 29, 30]. Nowadays, the main difficulty of sustainable education for IM talents in the context of AI 2.0 is to keep pace with the times of the education mechanism and cultivate talents’ abilities of continuous innovation.
The present work aims to identify the hotspots and research trends in AI. The purpose of the paper is to provide an informative overview of the past and present of research on AI and prospective future research directions. The main research questions to be addressed are:
In this work, the AI literature retrieved from the Web of Science Core Collection (WoSCC) was used to study the progress of AI in the field of manufacturing. The literature from 1979 to 2021 was divided into three periods according to the corresponding the periods of rising AI literature volume. The work used the bibliometric method to analyze the annual distribution of publications, countries, institutes, authors, journals and categories of this literature. The publication trend from 1979 to 2021 was tracked to indicate the heat volatility of AI. Key topics and highly cited papers were analyzed to show the main concern of researchers and influences of specific topics. Furthermore, high frequency terms and clusters were found and cluster analysis was conducted to demonstrate the theme turnover in the three different periods. Finally, suggestions for the cultivation of AI were given according to the analysis results.
Recruitment Marketing Resume Samples
The AI literature retrieved from the WoSCC were used to conduct the research on the progress of AI in the field of manufacturing. The data for the study were retrieved in November 2021 from the WoSCC. In order to find literature suitable for the topic, the data retrieval strategies were set as:
A total of 10, 154 results were retrieved from the database. The rise of published literature year by year means the rise of research heat. When the volume of literature reaches a peak, it means that the current technology has reached a bottleneck and it also means that a stage has ended. The retrieved results were classified as three periods according to the annual peak volume of literature, which are 1979–1994 (n = 424), 1995–2007 (n = 2102) and 2008–2021 (n = 7628). Bibliometric methods were used to analyze the geographic distribution, topic terms, highly cited articles and sources and categories of AI research. Topic terms were utilized to conduct keyword cluster analysis, which can indicate the hotspots of different periods. The highly cited articles were also classified as hot topics. By this means, the hotspots of three periods were revealed and the changes of different hotspots in these periods also give a reference for the final decision. VOSviewer was utilized as a tool for bibliometric analysis and visualization of the results. The advantage of VOSviewer is the ability to display graphics for analyzed literature. In this way, we can obtain the research topics and hotspots in a certain field. The overall methodology is shown in Figure 1.
The publications trend of annual papers in AI research from 1979 to 2021 (the time of data retrieval) is shown in Figure 2. We chose the WoSCC as the source of data for the research. We used the topic search terms “artificial intelligence” and “manufacturing” to search the literature, and the time span of the literature was selected from 1979 to 2021. A line chart of ten countries with
Noble Gases And Nitrogen In Samples Of Asteroid Ryugu Record Its Volatile Sources And Recent Surface Evolution
The AI literature retrieved from the WoSCC were used to conduct the research on the progress of AI in the field of manufacturing. The data for the study were retrieved in November 2021 from the WoSCC. In order to find literature suitable for the topic, the data retrieval strategies were set as:
A total of 10, 154 results were retrieved from the database. The rise of published literature year by year means the rise of research heat. When the volume of literature reaches a peak, it means that the current technology has reached a bottleneck and it also means that a stage has ended. The retrieved results were classified as three periods according to the annual peak volume of literature, which are 1979–1994 (n = 424), 1995–2007 (n = 2102) and 2008–2021 (n = 7628). Bibliometric methods were used to analyze the geographic distribution, topic terms, highly cited articles and sources and categories of AI research. Topic terms were utilized to conduct keyword cluster analysis, which can indicate the hotspots of different periods. The highly cited articles were also classified as hot topics. By this means, the hotspots of three periods were revealed and the changes of different hotspots in these periods also give a reference for the final decision. VOSviewer was utilized as a tool for bibliometric analysis and visualization of the results. The advantage of VOSviewer is the ability to display graphics for analyzed literature. In this way, we can obtain the research topics and hotspots in a certain field. The overall methodology is shown in Figure 1.
The publications trend of annual papers in AI research from 1979 to 2021 (the time of data retrieval) is shown in Figure 2. We chose the WoSCC as the source of data for the research. We used the topic search terms “artificial intelligence” and “manufacturing” to search the literature, and the time span of the literature was selected from 1979 to 2021. A line chart of ten countries with
0 komentar
Posting Komentar