Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection, which requires many workers without a steady and good detection effect. In order to solve the problem, a visual defect detection method based on a new deep convolutional neural network (CNN) is designed in this paper. First, we develop a CNN model by adjusting the depth and width of the model. Then, the optimal CNN model structure is developed by comparing the performance of different depth and width combinations. This research focuses on finding a way to distinguish defects in solar cells from the background texture of busbars and fingers. The characteristics of solar cell color images are analyzed. We find that defects exhibited different distinguishable characteristics in various structures. The deep CNN model is constructed to enhance the discrimination capacity of the model to distinguish between complicated texture background features and defect features. Finally, some experimental results and K-fold cross-validation show that the new deep CNN model can detect solar cell surface defects more effectively than other models. The accuracy of defect recognitionreaches 85.80%. In solar cell manufacturing, such an algorithm can increase the productivity of solar cell manufacturing and make the manufacturing process smarter.

The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions of themes related to Nytimes.com pandemic discourse, revealed according to their conceptual definitions. Additionally, we compared the performance of six machine learning algorithms on the task of text classification. Considering the findings, it is possible to conclude that in Nytimes.com (2019-nCOV) discourse, some concepts have changed on a regular basis while others have remained constant. The pandemic discourse focused on specific concepts that were seen to guide human behavior and presented content that may cause anxiety to readers of Nytimes.com. The results of the sentiment analysis supported these findings. Another result was that the findings showed us that the contents of the coronavirus (2019-nCOV) articles supported official policies. It can be concluded that regarding the coronavirus (2019-nCOV), which has caused profound societal changes and has results such as death, restrictions, and mask use, the discourse did not go beyond a total of 15 main themes and about 100 concepts. The content analysis of Nytimes.com reveals that it has behavioral effects, such as causing fear and anxiety in people. Considering the media dependency of society, this result is important. It can be said that the agenda-setting of society does not go beyond the traditional discourse due to the tendency of individuals to use newspapers and news websites to obtain information.

Error detection has a vital function in the production stages. Computer-aided error detection applications bring significant technological innovation to the production process to control the quality of products. As a result, the control of product quality has reached an essential point because of computer-aided image processing technologies. Artificial intelligence methods, such as Convolutional Neural Network (CNN), can detect and classify product errors. However, detecting acceptable and small defects on base parts cannot be done with a high rate of accuracy. At this point, it is possible to detect such minor errors with the help of the graph convolutional network, which has emerged as a new method. In this study, the defect elements on the surfaces of metal nut parts are determined through the graph convolutional network, and quality control is ensured. First, the surface images of the metal nut parts are captured. For this, a python-based Raspberry pi card and a modified camera system were installed. Adapters with three different zoom options are used on the camera system, depending on the part to be captured. The images obtained in the second step are sent to the other computer, which is used for image processing via the local server. In the third stage, image transformations are obtained by graphically separating the obtained images in white and black color tones on the second computer, and histogram maps of these images are drawn. Value ranges of these maps are determined and classified according to the value ranges obtained from the images of the defective parts. As a result, nine different models were analyzed. According to the analysis results, the graph convolutional neural network method gives 2.9554% better results than conventional methods.

This research aims to assess the readiness of professionals working in offices to accept robots as co-workers, and to provide insight for robot developers and organizations in promoting robot acceptance. This study investigates the acceptance of robots in office environments using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, extended with a specific focus on perceived sociability. A two-country comparative approach was employed. The research involved participants from the United Kingdom and Turkey to explore differences on robot acceptance. Data were collected via a structured questionnaire with demographics, robot usage or intention to use, and robot appearance preferences, targeting working professionals in office environments. The findings highlight key factors influencing behavioral intentions to use robots, including performance expectancy, effort expectancy, social influence, and perceived sociability. Our research results indicate that robots will likely to be accepted in our future office work environments. The results provide actionable insights for designing socially interactive robots and utilizing them in diverse workplace environments. Future research directions include expanding the cultural scope and utilizing qualitative methods for the additional investigation of factors that may enhance our understanding of robot acceptance.

The aim of this conceptual study is to analyze the effects of state-of-the-art research streams on supply chain risk management (SCRM) based on organizational theoretical background and direct future research toward the use of other related organizational theories. This paper seeks to provide a framework for understanding various organizational theories that can impact the understanding of SCRM. A systematic literature review on articles published from 1998 to 2020 was conducted manually in the following databases: Emerald, Science Direct, Taylor & Francis Online, and Wiley online library. Among these articles, the paper by Smeltzer and Siferd (1998) is the first article published on the topic. Therefore, that serves as a starting point for the papers' analysis. A total of 109 articles have been selected and reviewed in detail. The results of the study indicate that the articles which utilize theories in SCRM research have been mostly published in the last three years. The quantitative and case studies have been prevalently applied methods in the articles. In total, 34 theories are listed from the investigated articles. The four commonly studied theories among these are the information processing theory, transaction cost theory, contingency theory, and resource-based view. This paper is the pioneer in the sense that the paper specifically and directly reviews the SCRM literature in terms of organizational theory usage. For future research, this study offers a diffusion of innovation (DOI) theory that explains the factors which can affect the adoption or diffusion of SCRM practices.

Closed-loop supply chain management has begun to gain more importance in recent years due to increased environmental concerns, reduced natural resources, and legal regulations. In addition, with the shortening of product life cycles, the rate of returning products is increasing day by day. Therefore, businesses are trying to find ways to get more value from these returning products. Here, the closed-loop supply chain (CLSC), which comes into effect at this point, refers to the design, operation, and control of the system to ensure maximum value from returning products of different breeds and quantities. Recycling these returned products by different methods will provide significant savings in terms of both the production costs by providing return of the economically valuable materials to the economy as an input and the waste disposal costs by reducing the amount of waste. This chapter investigates the concept of closed-loop supply chain and its benefits to the businesses.