To evaluate the efficiency of decision-making units, researchers continually develop models simulating the production process of organizations. This study formulates a network model integrating undesirable outputs to measure the efficiency of Vietnam’s banking industry. Employing methodologies from the data envelopment analysis (DEA) approach, the efficiency scores for these banks are subsequently computed and comparatively analyzed. The empirical results indicate that the incorporation of undesirable output variables in the efficiency evaluation model leads to significantly lower efficiency scores compared to the conventional DEA model. In practical terms, the study unveils a deterioration in the efficiency of banking operations in Vietnam during the post-Covid era, primarily attributed to deficiencies in credit risk management. These findings contribute to heightening awareness among bank managers regarding the pivotal importance of credit management activities.

In modern society, English, as an important language, is an indispensable tool for people to communicate and exchange. However, learning English is not limited to knowledge points, grammar and other aspects. With the development and progress of the times and the rapid improvement of the level of science and technology, the problem of how to cultivate students' interests has become increasingly prominent. Interest is the best teacher to learn, and it is also the most effective, direct and lasting way for students to learn English well and improve their level and ability. Cultivating good teaching habits can help us master knowledge and skills better. Starting from the importance of students' interest in learning, this paper discusses how to stimulate students' interest in learning and find the correct teaching methods in order to help students have a strong interest in English learning in class and help them acquire knowledge actively.

In response to the increasing global emphasis on sustainability and the specific challenges faced by small and medium-sized enterprises (SMEs) in China, this study explores the integration of green reverse logistics within these enterprises to enhance their sustainability and competitiveness. The aim of this study is to understand the relationship between reverse logistics, green logistics, and sustainable development. Data analysis was conducted utilizing a combination of descriptive statistics and correlation analysis. A survey of 311 participants examined SMEs’ performance in reverse logistics practices and their initiatives in green logistics and sustainable development. The empirical findings reveal significant progress in reverse logistics practices among SMEs, reducing environmental impact and improving resource efficiency. Moreover, a notable positive correlation was identified between reverse logistics promotion and advancements in green logistics and sustainable development. SMEs’ investment in reverse logistics is closely linked to their efforts in environmentally conscious and sustainable supply chain management. These insights benefit SMEs and supply chain practitioners and offer a valuable reference for future research and practical applications in this field.

The education of Chinese language and culture in the teaching of college Chinese can achieve the goal of cultural education and allow students to improve their thinking in the context of cultural learning. Chinese language and culture education in colleges and universities provides new requirements and new goals for college personnel training. In the whole teaching process, it is necessary to pay attention to the cultivation of students’ values and help students establish a correct humanistic spirit. This paper analyzes the value of Chinese language and culture education in colleges and universities, and discusses the teaching reform of “college Chinese” course “based on Chinese language and culture”.

The world has changed to a massive degree in the past thousands of years. Most of the time, the amount of carbon dioxide in the atmosphere remains constant. In the late 18th century, according to the sources of CDIAC and NOOA, the level of carbon dioxide began to rise, and then in the 20th century, it went through the roof, reaching levels that had not been seen in nature for millions of years. The increase in carbon in the atmosphere is the major contributing factor to climate change. The key to reversing the damage is restoring the earth’s delicate, balanced carbon cycle. As carbon cycle depicts the way carbon moves around the earth. It consists of sources that emit the carbon component into the atmosphere. The biological side of the carbon cycle is well balanced due to respiration, where carbon dioxide is released into the atmosphere, then plants, bacteria, and algae take carbon dioxide out of the atmosphere during photosynthesis and the process they use to generate chemical energy. On the other hand, oceans are the best sources and sinks; carbon dioxide is endlessly being absorbed into the ocean and released from the oceans almost exactly at the same rate, which is rapidly influencing the carbon cycle. Similarity is a methodology that has many applications in the real world. The current research article is destined to study how statistics of carbon emission metrics are alike and belong to one cluster. In the current study, the research is destined to derive a similarity analysis of several countries’ carbon emission metrics that are alike and often fall in the range of [0, 1]. And deriving the proximity of the carbon emission metrics leading to similarity or dissimilarity. In the current context of data matrices of numerical data, an Euclidian measure of distance between two data elements will yield a degree of similarity. The current research article is destined to study the similarity analysis of carbon emission metrics through fuzzy entropy clustering.