In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

Taking the geographic information industry as the research object, using the authorized invention patent data, this paper puts forward the research method of industrial innovation chain structure based on the geographic information industry chain. Then, from the perspective of overall structure and specific regional structure, the development status of the innovation chain is quantitatively evaluated, which is helpful to all countries in the world. The structural integrity and leading links of the innovation chain especially in China, the United States and Japan are compared and analyzed. The results show that: (1) from the perspective of the overall structure, the global innovation chain presents an “inverted triangle” structure due to the weak innovation ability of downstream links. From the perspective of specific regional structure, the innovation chain of geographic information industry in most countries and regions is incomplete, and there are broken links or isolated links. The global innovation chain except China has cracks between the upstream and downstream due to the relative weakness of the midstream links, showing “hourglass-shaped” structure with a wide upper part, narrow lower part and narrow middle part. (2) Relatively speaking, China’s industrial innovation chain is relatively complete, and the midstream link has significant comparative advantages in the global market. However, the industry university research cooperation in the innovation chain is weak, the degree of marketization is low, and the technological competitiveness lags behind that of the United States.

Human capital, which is a key resource of every organization, is characterized by high sensitivity to social, cultural and other factors that are not necessarily economic in nature. In the process of managing this capital, employee satisfaction becomes key, resulting from various reasons. In this study, we attempted to examine the level of satisfaction of university employees. The aim of this study was to gather information on the level of employee satisfaction with their job positions and to examine the relationships between selected, identified factors influencing their job satisfaction. The paper used multivariate statistical analysis, mainly Wilcoxon tests and Spearman rank correlation. Analysis of the survey results confirmed significant relationships between factors such as work atmosphere, appreciation of work effects, proper division of responsibilities and possible help in the team.

Training is an important content of enterprise human resource management, which is a purposeful, planned and step-by-step learning, its goal is to enable managers to master specific professional knowledge, working methods, skills, and improve their working attitudes and values, so that the potential of managers can be fully exerted, so that the performance of individuals and enterprises can be improved, and promote the development of individuals and organizations. For grassroots managers, as the front-line managers of the enterprise, about 60% of their employees are directly managed by them. One of the important bases for leaders to support training is the training program, how to write an attractive training program is a mandatory topic for every human resource management, this article will be from the training theme extraction, program logic concept, training safeguard measures several aspects to discuss, in order to enlighten the reader.

This article measures the performance of listed commercial banks in Vietnam and identifies factors influencing their efficiency. The study follows a two-stage approach: (i) In the first stage, scale efficiency scores from 2016 to 2022 are assessed using the Data Envelopment Analysis (DEA) method; (ii) In the second stage, Tobit regression analyzes internal factors, macroeconomic conditions, and the impact of Covid-19. Key findings show that internal factors such as return on assets positively affect efficiency, while the ratio of equity to total capital has a negative and statistically significant impact. Bank size positively influences efficiency scores. Macroeconomic factors, including economic growth and inflation, were statistically insignificant. However, the Covid-19 pandemic had a significant negative effect on bank efficiency.