Issue 4 (September)

At present, some urban water plants in China have started using chloramine disinfection. So how to determine whether the disinfected water is drinkable? This article collected a water quality prediction data, including indicators such as chloramine and trihalomethanes. Firstly, descriptive statistics and Pearson correlation analysis were conducted between the data of chloramine and trihalomethanes and the target variable (whether it is drinkable). It is known that water quality cannot be judged solely based on these two indicators, so more indicators such as pH value will be used. In order to establish a more accurate prediction model, the dataset is first preprocessed, including statistical analysis of missing values, determination of box plot outliers, and filling with KNN algorithm. Then, feature engineering is performed, including Yeo Johnson transformation, correlation analysis, and calculation of Shap values. Subsequently, the processed data was input into the established Stacking, Voting, and attention based CNN-LSTM classification prediction models. Random search and cross validation were used to train each model, resulting in the optimal hyperparameters for each model. The relevant evaluation indicators for each model were calculated to measure its accuracy.

Lithium cobalt oxide (LiCoO2 ) cathode material, with its high energy density and operating voltage, is currently a mainstream material for lithium-ion battery cathodes.   However,  the  crystal  structure  collapse  and  lattice  oxygen  evolution under high-voltage conditions lead to rapid capacity decay, severely limiting its practical applications.   This paper  analyzes the main factors affecting the cycle performance and rate performance of lithium cobalt oxide, considering the physic- ochemical properties of the particles, including elemental content and particle size, and provides a mathematical model linking these properties to electrochemical per- formance.  The study offers insights for the practical production of high-voltage lithium cobalt oxide materials.

At the beginning, we embarked on investigating the correlation between the physicochemical attributes (encompassing elemental composition and particle size) of lithium cobalt oxide and its cycle performance. An Ordinary Least Squares (OLS) linear regression model was formulated, yielding a robust fit with an R-Squared value of 0.94. This model was subsequently optimized through the application of an XGBoost algorithm, achieving an R-Squared value nearing unity, signifying a remarkable enhancement in model accuracy. Visual analysis of the results pinpointed the primary determinants of cycle performance, arranged in descending order of significance: 'Cycle Index,' Mg content, particle size distribution (D10), Zn, and Al.

Then, our attention shifted to examining the link between the aforementioned physicochemical characteristics and the rate performance of lithium cobalt oxide. The Jarque-Bera and Shapiro-Wilk tests confirmed the normality of the data, fulfilling the prerequisites for hypothesis testing. An OLS linear regression model was developed, demonstrating a strong goodness-of-fit with an R-Squared value exceeding 0.8. This model was further honed with an XGBoost model, which achieved an R-Squared score approaching 1, indicating a substantial refinement in model precision. The visualization of model outcomes illuminated the key factors influencing rate performance, ranked in descending order of importance: particle size distribution (D50), Al, Mn, Zn, Mg, Ni, and Fe.

Lastly, we devised an optimal strategy that encompasses the incorporation of strategic doping elements, the utilization of high-temperature sol-gel methods to bolster cycle performance, and coating modifications aimed at enhancing rate performance. This holistic approach fosters the structural stability of lithium cobalt oxide crystals, fortifying their high-potential cycle capabilities, and concurrently elevating their rate performance.

This study explores the current status and influencing factors of the integration between classical photography and contemporary art, aiming to analyze key variables that affect audience perception of art integration. A total of 100 valid survey responses were collected, and Ordinary Least Squares (OLS) regression analysis was employed to examine the impact of independent variables such as art exhibition participation frequency, technical familiarity, social media usage frequency, and cultural openness on the perception of art integration. The results indicate that all hypotheses were validated: art exhibition participation frequency, technical familiarity, social media usage frequency, and cultural openness significantly influence respondents' perception of the integration between classical photography and contemporary art. Among these factors, cultural openness had the most significant impact, suggesting that exposure to diverse art forms helps enhance the understanding of art integration. The significance of technical familiarity and social media usage further demonstrates the importance of technology and social media in art dissemination. Based on these findings, the study provides recommendations for artists, curators, and educators, including promoting interdisciplinary art creation, organizing interactive exhibitions, and strengthening multicultural education. Future research could further explore these influencing factors across different cultural contexts.

This paper analyzes the connotations and functions of dormitory culture in universities under the background of the new era, recognizing the importance and urgency of innovating dormitory culture construction in higher education. Based on this understanding, the paper explores innovative approaches to constructing dormitory culture in the new era from four aspects: improving dormitory management service systems, setting examples through student leaders and Party members, establishing dormitory culture exchange platforms, and creating outstanding dormitory culture projects. These approaches are discussed in light of the basic requirements for strengthening cultural soft power, promoting information technology development, and emphasizing the educational value of culture.

This study explores the potential of AI-enabled project-based learning (PBL) in enhancing bilingual international communication in Sichuan. By utilizing AI tools, the proposed educational model provides students with real-time, personalized feedback, helping them improve their language fluency, pronunciation, and cultural adaptability in both Chinese and English. The study presents a PBL framework where students are tasked with creating a bilingual virtual tour for an international audience, focusing on the development of 21st-century skills such as collaboration, problem-solving, and critical thinking. The research highlights the scalability of AI-enabled PBL across different linguistic and cultural contexts, making it applicable not only in educational settings but also in professional fields such as diplomacy and international business. The findings emphasize the transformative potential of AI in language education, fostering both language proficiency and global communication skills, and call for future studies to implement and evaluate this model in real-world settings.

The purpose of this article is to study the level of synergistic development of air cargo in the Yangtze River Delta airport cluster and to analyze the factors affecting its development. The article first reviews the rapid growth of the air cargo industry and its role in driving global trade, and then analyzes the current situation of the Yangtze River Delta airport cluster in terms of air cargo, including its uneven development and homogeneous competition. By constructing an indicator system that includes the scale of airport cargo operations, infrastructure development, cargo connectivity, and the level of urban economic development, and applying the entropy weighting method to determine the weights of the indicators, the article evaluates the level of synergistic development of the Yangtze River Delta airport cluster. The results of the study show that although the synergy of the Yangtze River Delta airport cluster has increased in 2023, its overall synergy is still in the low synergy stage. The article further explores the impact of factors such as market concentration, airport strategic positioning and route network overlap on the level of synergy development. Finally, the article puts forward suggestions to improve the level of air cargo synergy development in the Yangtze River Delta airport cluster

Demand for air transportation determines the connectivity of routes between airports and influences the development of airline route networks. The demand for air transportation is affected by many factors, and socio-economic factors are one of the most important ones. In this paper, we use input-output analysis to explore the correlation between the air transportation industry and the three industries at the national level and Xinjiang level, and find that the tertiary industry has the highest direct demand for air transportation. Thus, we construct an indicator system for considering the demand for air transportation, and through the grey correlation analysis method, we find that the integration of Xinjiang's air transportation industry with the tertiary industry is in the friction stage, and at the same time, we select the contribution rate of the tertiary industry, the number of tourists, the total retail sales of consumer goods, and the passenger traffic volume of roads and railroads as the important drivers of the connectivity of Xinjiang's regional airports to the aviation network, and then we use these four drivers as the drivers for priority connectivity of the air routes of Xinjiang regional airports. The simulation network is constructed by prioritizing the connectivity of the network, and compared with the real network, it is found that the number of tourists, the passenger traffic volume of road and railroad, and the total retail sales of social consumer goods are more important drivers for the connectivity of the aviation network.