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 article explores the current challenges and strategies related to EU immigration policy, incorporating various statistical and econometric methods. It begins with a review of the development of EU immigration policies, highlighting how globalization and the EU's economic stature have attracted significant immigration, leading to challenges in socio-economic, cultural diversity, and social stability areas. The article then analyzes the current state of immigration issues within the EU, particularly focusing on social integration and cultural conflict. Through case studies of Germany, Italy, and Sweden, the effectiveness of different immigration policies is examined. The research utilizes descriptive statistics, ANOVA (Analysis of Variance), and crosstab analyses to provide a comprehensive understanding of public opinion on immigration issues. The findings reveal that despite various strategies in border control, diplomatic cooperation, and social integration, the EU still faces significant challenges, with public satisfaction with current policies being relatively low. The article suggests that future immigration policies should place greater emphasis on social integration and sustainable development, promoting cross-cultural education, diversity management, and cultural exchange to address the complex issues brought by immigration. This analysis offers valuable insights for policymakers to develop immigration policies that better align with public expectations.

Existing deep learning-based image fire detection algorithms require training the model with a large number of diverse image datasets and accurate annotations to achieve high accuracy and strong anti-interference capabilities. However, in the field of fire detection, there is a lack of sufficiently rich datasets of real fire scenes to train the detection models, leading to unreliable detection results. In this paper, we propose a new flame image generation method that aims to enhance the efficiency and adaptability of fire detection systems, particularly when the number of samples is unbalanced. By constructing extensive datasets containing different environments (e.g., factories, warehouses, and forests), we address the practical challenges of safety control and fire initiation.

Our approach is based on two main networks: the flame generation network and the hybrid network. The flame generation network utilizes the SCGAN technique to generate diverse flame images by controlling the shape of  flames based on the input reference information. The hybrid network synthesizes fire images from different scenes into an improved DDPM to create realistic images by fine-tuning textures and styles. Our approach has three main advantages: the ability to control the generated flame images, the preservation of high-quality background details, and training on real datasets, making the generated images suitable for engineering application scenarios.

Allport proposed the Intergroup Contact Hypothesis, which posits that intergroup contact facilitates greater understanding of outgroups and, on this basis, reduces ethnic prejudices, playing a positive role in mitigating interethnic conflicts and tensions. Based on extensive empirical research, Allport outlined four optimal conditions for intergroup contact that promote positive outcomes among ethnic groups. For China, a multi-ethnic developing country, communication, exchange and integration among ethnic groups represent crucial avenues for enhancing mutual understanding and fostering interethnic friendships. Active interethnic contact and exchange strengthen ethnic unity and reinforce the centripetal force of the Chinese nation. This paper analyzes the Communication, Exchange and Integration among ethnic groups at the Naadam Fair in Inner Mongolia through the lens of the four optimal conditions for intergroup contact proposed by the theory of interethnic contact: equal status, shared goals, intergroup cooperation, and institutional support. The aim is to provide theoretical insights for the interethnic development of China's diverse ethnic groups and the construction of harmonious ethnic relations

As marine resources become increasingly scarce, marine aquaculture, an integral part of the marine economy, necessitates scientific management and rational utilization. Image segmentation technology for marine aquaculture, a critical tool for monitoring and managing aquaculture areas, has garnered widespread attention in research and application. This paper reviews the research progress in marine aquaculture image segmentation techniques, analyzes the advantages and limitations of various methods, and explores future development directions.

Knee osteoarthritis (KOA) is a chronic degenerative disease commonly found in middle-aged and elderly people, characterized mainly by degenerative changes in knee joint cartilage and secondary bone hyperplasia. The exact mechanism of knee osteoarthritis is not yet clear, and Western medicine primarily focuses on symptomatic treatment, which cannot effectively curb the progression of the disease. With the continuous research and innovation in traditional Chinese medicine (TCM), TCM has shown advantages such as minimal side effects, significant and stable therapeutic effects, and relatively low costs. Thus, TCM methods for treating knee osteoarthritis have been widely applied in clinical practice. This article reviews the relevant mechanisms and pathways of knee osteoarthritis by searching literature from databases such as China National Knowledge Infrastructure (CNKI), PubMed, and Wanfang from 2003 to 2024, and provides an overview of the treatment of knee osteoarthritis from the perspectives of TCM monotherapy and compound TCM formulations.

Classical Ballet and Modern Ballet are two significant periods in the history of ballet, each having a profound impact on the development of the art form. Both periods have produced numerous classic works and cultivated a wealth of outstanding ballet choreographers and performers. Classical ballet is characterized by its strict conventions in creation, performance, and training methods. In contrast, modern ballet is more diverse in its dance vocabulary, creative approaches, and stage design. "Swan Lake," a hallmark of ballet art, has enjoyed worldwide acclaim and continues to be performed regularly. A hundred years after its debut, a modern version of "Swan Lake" was created in England, featuring powerful male swans that captivated audiences. By examining these two epoch-making classics, we can discern the distinct charms of classical and modern ballet and the innovative aspects of modern ballet. This also highlights that contemporary ballet is gaining and will continue to gain more attention in the present and future.

IgA nephropathy (IgAN), also known as Berger’s disease, is a primary glomerulonephritis characterized by the deposition of galactose-deficient IgA1 (Gd-IgA1) in the glomerular mesangium, leading to progressive renal damage. This condition is prevalent in young adults and frequently follows respiratory or gastrointestinal infections. Approximately 20-40% of patients with IgAN progress to end-stage renal disease (ESRD) within 20 years of diagnosis. The pathogenesis of IgAN involves genetic, environmental, and immunological factors. Central to the disease is the production of Gd-IgA1, which forms immune complexes with IgG and IgA antibodies. These complexes deposit in the glomeruli, triggering mesangial cell proliferation and inflammation. Environmental triggers, particularly infections, are thought to exacerbate this immune response. Recent research has focused on understanding the molecular mechanisms underlying aberrant glycosylation of IgA1 and the role of immune complexes in disease progression. Targeting these pathways offers promising therapeutic strategies, such as modulating glycosylation enzymes and developing monoclonal antibodies to reduce pathogenic immune complex formation. Advances in diagnostic tools, such as identifying specific glycosylation patterns, are also aiding in early detection and treatment. Despite these advancements, challenges remain in personalizing treatment and addressing the disease’s heterogeneous clinical presentation.

The market for the transfer of land contract management rights has played an important role in the liberation and development of rural productive forces.The scale of rural land circulation has been expanding,and the circulation methods have been gradually diversified,mainly including subcontracting,leasing,exchange,shareholding and transfer.However,with the diversification of circulation methods,trust problems arising between various subjects of interest in the land process occur from time to time.Rural land circulation integrity problems affect the use of rural land resources and the process of circulation,so it is necessary to pay great attention to the integrity problems between the various subjects of interest brought about by rural land circulation.Therefore,the existing integrity problems of rural land transfer will be analysed,and the legal system optimization path of rural land transfer integrity problems will be proposed from three aspects.

This study examines cultural participation patterns across different regions and demographic groups in Estonia, utilizing data from the Estonian Statistics Bureau. The analysis reveals significant disparities in cultural engagement, with Harju County, including Tallinn, exhibiting the highest participation rates in theatre, cinema, and museum visits. In contrast, rural areas such as Hiiu and Jõgeva counties show markedly lower engagement, highlighting a pronounced urban-rural divide. Demographically, middle-aged adults (25-44 years) and those with tertiary education are the most culturally active, while women participate more in cultural activities compared to men. Cinema visits emerged as the most popular cultural activity, appealing broadly across all groups, whereas theatre and museum visits were more associated with higher educational attainment and urban residency. Based on these findings, the study recommends targeted cultural policies, including increased investment in rural cultural infrastructure, the promotion of cultural education, and the enhancement of digital access to cultural content. Additionally, tailored cultural programs to address demographic disparities are suggested. The study also identifies areas for future research, including longitudinal studies on cultural participation trends, the impact of digital platforms, and the economic implications of cultural engagement. These insights aim to inform more equitable and effective cultural policy-making in Estonia.

With the rapid advancement of information technology, smart healthcare has become a crucial tool for improving the quality and efficiency of medical services. This study analyzes and predicts outpatient revenue based on time series forecasting models using medical data from a hospital's outpatient department. The objective is to optimize resource allocation and enhance hospital management efficiency. For the first and second rehabilitation wards, Long Short-Term Memory (LSTM) and BERT models were constructed to predict outpatient revenue. The experimental results indicate that the BERT model outperforms in capturing global features of the time series. Subsequently, a stacked ensemble method was employed, combining the predictions from the LSTM and BERT models with XGBoost for final prediction, successfully forecasting the outpatient revenue for the third rehabilitation ward. Validation results show that the stacked ensemble model achieved the best performance in outpatient revenue prediction, providing effective support for revenue management in smart healthcare. This paper demonstrates the potential of new productivity in the healthcare field and offers valuable insights for the further development of smart healthcare.

The purpose of this study is to use the gradient boosting decision tree ( GBDT ) model to predict the debris flow disaster in the Palongzangbu Basin, and to combine ArcGIS for spatial visualization. We selected key features such as terrain slope, accumulation fan area, longitudinal slope, source area, precipitation, and distance to the fault to construct the GBDT model to quantify the impact of each feature on the occurrence of debris flow. The results show that slope ( 21.30 % ) and accumulation fan area ( 20.10 % ) are the main influencing factors of debris flow. Combined with ArcGIS mapping technology, we generated a debris flow risk distribution map, clearly identified high-risk areas in the basin, and provided a scientific basis for disaster prevention and mitigation. This study verifies the effectiveness of the GBDT model in debris flow disaster prediction, and shows the key role of geographic information system in disaster risk management, which provides an important reference for future disaster warning and prevention.

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.

The life and music of the Oroqen people are not only treasures of their ethnic culture but also an important component of the diverse and unified cultural pattern of the Chinese nation. They carry the historical memory, emotional expression, and cultural identity of the Oroqen people, bearing profound cultural significance. In modern society, the lifestyle and musical culture of the Oroqen have received more attention and protection, serving as a bridge between the past and the future, and between tradition and modernity.

In today's globalized world, cultural exchange and dissemination have become vital links connecting people from different countries and regions. The dragon and lion dance culture, a gem of traditional Chinese culture, has showcased its powerful vitality on the international stage with its unique artistic charm and profound cultural heritage. However, how to achieve its localization in foreign countries is a subject that requires in-depth research and discussion. The Confucius Institute at Veliko Tarnovo University in Bulgaria, as a crucial platform for cultural exchange between China and Bulgaria, is committed to promoting Chinese language teaching and disseminating Chinese culture. It has successfully introduced dragon and lion dance activities to Bulgaria and facilitated their dissemination and popularization through various cultural events, providing new insights into international cultural dissemination. This study uses this Confucius Institute as a case to explore the issues of international dissemination and localization of dragon and lion dance culture, revealing the underlying cultural logic and dissemination mechanisms.

In recent years, alarming temperature reports have underscored the reality of global warming. This research utilizes ARIMA and LSTM models to analyze historical global temperatures, predict future trends, and identify key influencing factors. Data collected up to 2021, supplemented by interpolation, facilitated the analysis of the temperature anomaly in March 2022. Predictions using ARIMA and LSTM models suggest that the global average temperature will reach 20 degrees Celsius by 2046 and 2065, respectively, with LSTM demonstrating higher accuracy based on RMSE analysis. The study also investigates the impacts of volcanic eruptions, forest fires, and COVID-19 on temperature. Finally, we compiled the research findings and recommendations into relevant suggestions, offering some strategies to address global warming based on the comprehensive analysis.

Telecom fraud Financial fraud is committed by telephone, Internet and SMS, and it is important to identify and prevent these activities. In this paper, a telecom fraud prediction model is established based on the collected telecom fraud data set. Firstly, the environment in which fraud occurs is analyzed through data visualization technology, and then the influence of bank card usage on fraud probability is discussed by using logistic regression model, and it is found that there is a significant correlation between improper use of bank card and fraud risk. Then, the relationship between transaction pattern and fraud is revealed through the correlation analysis of variables, and the complex relationship between different transaction types and fraud probability is deeply discussed. Finally, convolutional neural network and short-duration memory network model combined with Bahdanau attention mechanism were used to improve the prediction accuracy, and the accuracy of the model was up to 99%. This study not only improves the prediction ability of the model, but also shows the extensive application potential of the model, which provides important technical support and theoretical basis for the identification and prevention of telecom fraud.

In the era of big data, the scale and complexity of spatial data are continuously increasing, making the effective partitioning and classification of spatial vector point sets a critical and challenging problem. This study proposes a spatial vector point set partitioning model based on deep learning, which leverages techniques such as Convolutional Neural Networks (CNN) and PointNet. Through an end-to-end learning process, the model automatically extracts the intrinsic structures and patterns of the data. The method employs 3D-CNN and PointNet models to process point cloud data, achieving efficient and accurate partitioning results. The findings indicate that the model demonstrates stronger robustness and higher accuracy when handling large-scale, high-dimensional data, with a classification accuracy reaching 100%. In conclusion, the spatial vector point set partitioning model based on deep learning holds significant theoretical and practical value, offering more precise and reliable technical support for related fields.

The establishment of the right of residence in the Civil Code aims to meet the living and residential needs of the right holders, ensuring housing security for specific vulnerable groups, including the elderly, and maintaining social stability. However, in judicial practice, there are still some challenges concerning the establishment methods, rights and obligations, grounds for termination, and applicable conditions of the right of residence. These issues prevent the full protection of elderly individuals' residential rights and the realization of the legislative intent. To address these problems, it is necessary to further clarify the conditions for establishing the right of residence, balance legal provisions with public order and morality, uphold a unified standard in judicial adjudication, and clearly define the boundaries of the rights and obligations of the right holders to provide robust housing security for the elderly.

This article comprehensively analyzes and summarizes the research status of alkaline electrolysis hydrogen production technology.Discuss technical principles, development history, key issues, and solutions.Through literature review and case studies, the important role and development potential of technology in the field of energy conversion have been revealed.At the same time, combining experimental data and theoretical models, the advantages and disadvantages of alkaline electrolysis hydrogen production technology were deeply explored, and future research directions and suggestions were proposed.This technology has a development t rend and application prospects in the field of industrial technology, with advantages such as high efficiency, low cost, and environmental protection, gradually becoming one of the hotspots in the research of clean energy.It has broad application prospects in new energy vehicles, energy storage, and other fields.However, there are still some technical challenges that need to be addressed, such as electrode materials, electrolytes, and electrolytic cell design.In order to promote the further development and application of this technology, this article proposes some future research directions and suggestions, which are of great significance for further exploration in this field.