Using the Intercultural Competence and Inclusion in Education Scale (ICIES), this study examines variations in intercultural competence and inclusion between mainstream and multiethnic high schools. The sample consisted of 384 high school students, aged 17 to 18, from both rural and urban areas in Western Romania, enrolled in grades 11 and 12. The ICIES demonstrated strong reliability, with a Cronbach’s alpha of 0.721. Exploratory factor analysis revealed three distinct dimensions: Intercultural opportunities and activities, Comfort in diverse settings, and Cultural reflection and values. Independent samples t-tests identified significant differences between mainstream and multiethnic schools across several items, with students in multiethnic schools reporting higher levels of intercultural competence and inclusion. These findings highlight the critical role of multicultural educational settings in fostering students' cultural awareness and inclusive attitudes. This study provides actionable insights for enhancing multicultural education practices and policies, including teacher training programs, inclusive curricula, and extracurricular initiatives that promote intercultural engagement and reduce intergroup biases.

As the complexity and scale of software applications increase, the challenges associated with testing these systems grow correspondingly, necessitating innovative and sustainable testing strategies. This paper explores a multifaceted approach aimed at addressing the intricate challenges inherent in testing large-scale software applications. Through a comprehensive examination of current industry practices and emerging trends, this study introduces a novel framework that integrates advanced testing techniques with state-of-the-art tools. This framework not only mitigates the challenges posed by the complexity and size of modern applications but also enhances the efficiency and effectiveness of the testing process. Key aspects of this research include a detailed exploration of test methodologies suited for large-scale applications, an evaluation of advanced tools designed for complex test scenarios, and an analysis of the impact of the test environment on sustainability. The findings offer valuable insights and actionable strategies for software development and testing professionals aiming to optimize testing processes and improve the quality and sustainability of their software in a rapidly evolving technological landscape.

Based on the research on 31 provincial-level administrative regions at the end of 2022, we used the geographic concentration index, geographic imbalance index, SPSS and ARCGIS spatial analysis techniques to study the spatial distribution, distribution factor correlation, and accessibility of national 5A-level scenic spots. The research results show that the overall distribution of my country's 5A-level scenic spots is unbalanced, with a low degree of concentration, showing a pattern of denseness in the east and sparseness in the west, with large inter-provincial differences. The density of traffic highways is positively correlated with the distribution density of 5A-level scenic spots. The traffic lines in the central and eastern regions are dense, and there are a large number of 5A-level scenic spots, especially the Beijing-Tianjin-Hebei region, the Yangtze River Delta region, and the middle and lower reaches of the Yangtze River and Yellow River. Therefore, the spatial distribution of China's 5A-level tourist attractions is mainly affected by the interaction of economic, transportation and social factors, among which GDP, transportation network and attraction of scenic spots are the most critical factors. These research results can provide a reference for optimizing the spatial layout of China's scenic resources and promoting regional socio-economic development.

In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.

In this study, nano-scale microstructural evolution in 6061-T6 alloy after laser shock processing (LSP) was studied. 6061-T6 alloy plate was subjected to multiple LSP. The LSP treated area was characterized by X-ray diffraction and the microstructure of the samples was analyzed by transmission electron microscopy. Focused Ion Beam (FIB) tools were used to prepare TEM samples in precise areas. It was found that even though aluminum had high stacking fault energy, LSP yielded to formation of ultrafine grains and deformation faults such as dislocation cells, stacking faults. The stacking fault probability (PSF) was obtained in LSP-treated alloy using X-Ray diffraction. Deformation induced stacking faults lead to the peak position shifts, broadening and asymmetry of diffraction. XRD analysis and TEM observations revealed significant densities of stacking faults in LSP-treated 6061-T6 alloy. And mechanical properties of LSP-treated alloy were also determined to understand the hardening behavior with high concentration of structural defects.

Low levels of financial literacy cause people to have lower savings rates, higher transaction costs, larger debts and the loans acquisition with higher interest rates, therefore it becomes relevant to analyze the determinants of financial literacy. The aim of this research is to identify whether there is an association between the financial literacy level and sociodemographic characteristics. The Mexican Petroleum Company (Pemex) employees is the population analyzed. Pemex is the state-owned oil and natural gas producer, transporter, refiner and marketer in Mexico. A non-probabilistic convenience sampling was performed and 404 responses were obtained. The analysis of data was carried out with the Bayesian method. The results show that there is an association between Pemex employees’ level of financial literacy and their level of education, income, age and type of retirement saving. No association was found between their level of financial literacy and gender, marital status and whether or not they have children.