The construction of researcher profiles is crucial for modern research management and talent assessment. Given the decentralized nature of researcher information and evaluation challenges, we propose a profile system for Chinese researchers based on unsupervised machine learning and algorithms. This system builds comprehensive profiles based on researchers’ basic and behavior information dimensions. It employs Selenium and Web Crawler for real-time data retrieval from academic platforms, utilizes TF-IDF and BERT for expertise recognition, DTM for academic dynamics, and K-means clustering for profiling. The experimental results demonstrate that these methods are capable of more accurately mining the academic expertise of researchers and performing domain clustering scoring, thereby providing a scientific basis for the selection and academic evaluation of research talents. This interactive analysis system aims to provide an intuitive platform for profile construction and analysis.

Financial literacy is an essential life skill today and plays a crucial role in business success. This study examined the relationship between college students’ financial literacy, financial management behavior, and entrepreneurial opportunity recognition. A survey was conducted among college students in the Busan and Gyeongnam regions, and a total of 272 responses were analyzed using SPSS 28.0. The results showed that financial literacy partially positively affects financial management behavior. Furthermore, financial management behavior positively influences entrepreneurial opportunity recognition. Financial management behavior partially mediates the relationship between financial literacy and entrepreneurial opportunity recognition. Improving the financial literacy of college students during adolescence serves as a motivation for entrepreneurship and significantly impacts their exploration and practice of various income activities to achieve their expected future living standards. The study’s findings indicate that for potential entrepreneurs, recognizing and promoting entrepreneurship as a source of innovation and growth requires incorporating financial literacy and desirable financial management behavior education into university curricula.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.

On 17 February 2008, Kosovo declared its independence from Serbia, receiving recognition from over half of the UN member states, the majority of the European Union, Council of Europe and NATO member states, as well as the most industrialized states in the global economic forum. However, Kosovo did not receive recognition from Serbia, China, Russia, India, certain states with diplomatic grievances with the USA, communist dictatorial states like North Korea, and five EU member states, including Romania, Greece, Cyprus, Slovakia, and Spain. This article focuses on Spain’s possibilities and reasons for recognizing Kosovo or not. Using qualitative methodology, five university professors—two from Madrid, one from Barcelona, and two Kosovar professors, one from the University of Pristina and the other from the University of Winchester, England—were interviewed with open-ended questions in November-December 2023. The research identified opportunities and reasons for Spain’s hesitation in recognizing Kosovo, including Spain’s domestic context, historical relations with the Western Balkans and the newly formed countries after the dissolution of Yugoslavia in the early 1990s, as well as the European and international political context. The research results show that Spain has been hesitant to recognize new states quickly, not only in the case of Kosovo, due to the context of autonomist aspirations within Spain and reluctance to draw parallels between Kosovo and Spain’s autonomous regions.

Organizations are gradually focusing on creating a healthy workplace for their employees and becoming more people-centric. This occurs because a healthy workforce increases the work performance of the organisation and the personal development of its employees. This study aims to investigate the HR functions that impact employee motivation in the Malaysian banking sector. The three HR functions that were selected were training and development, rewards and recognition, and career management. The study utilised a cross-sectional design, and the research instruments were adapted from a number of past studies. A total of 350 respondents from the Malaysian banking industry were recruited. Using SPSS Version 26.0, the research hypotheses were examined. The results show that rewards and recognition are not significant predictors of employee motivation in the Malaysian banking industry; however, training and development and career management are significant predictors of employee motivation. These results will help the human resources department develop and improve its HR operations.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.