In this study, daily averages of air quality parameters were measured in two stations (S1 and S2) of the organized industrial district in Samsun. The meteorological variables were measured at only one station (S1), such as temperature, relative humidity, wind speed, solar radiation, and ambient pressure in 2007, and the daily promised limit for nitrogen dioxide has been especially exceeded at 206 times for 1^st station. However, exceeds of the limit value in 2006 for 1^st station was reduced by approximately 3.5 times. The daily nitrogen dioxide concentration did not exceed the daily limit of WHO^[1] as for 2^nd station. The results obtained showed that under the influence of dominant wind direction, the second station measurement results are higher than that of the first station. To determine all of the possible environmental effects, the measurements should be analyzed from a multi-point perspective.

This study analyzes the influence of five primary factors—inflation, capital ratio, deposits, non-performing loans, and bank size—on the performance of banks in Vietnam. Our sample encompasses 26 commercial banks from 2014 to 2023. The analysis incorporates data sourced from commercial banks’ financial statements and annual reports. Our findings indicate that banks with higher capital ratios and sizes generally exhibit superior performance. Moreover, inflation positively influences the performance of Vietnamese commercial banks throughout the selected timeframe. In contrast, non-performing loans and deposits are inverse to bank performance. Our findings offer novel insights into the factors influencing bank performance in a growing economy like Vietnam, along with recommendations for Vietnamese commercial banks and the State Bank of Vietnam to implement effective methods to improve bank performance.

In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.