During crisis events, the government implements many policies to control the development of the crisis and stimulate the economy damaged by the crisis. The government plays a very important role during the crisis. The stock market is a reflection of a country’s economic situation. This article takes the Chinese government policies during the COVID-19 crisis as the research object and analyzes the impact of government policies on the CSI300 index. The following conclusion is drawn: not all government restrictions will cause a decline in stock market prices, among which the Wuhan lockdown policy has promoted the rise of the CSI300 index. The two stimulus policies implemented by the Chinese government are both conducive to the rise of CSI300 index. During the COVID-19 crisis, investors holding high assets, high leverage, and low profitability companies will be significantly negatively affected after the government implements restrictive policies. After the government implements stimulus policies, investors holding high asset and high leverage companies will suffer losses. Investors who hold low asset, low leverage, and high profitability companies will have profits. And this article also finds that the size of company assets is an important driving factor for abnormal returns.

The extent to which businesses incorporate Naga worship into their strategies and operations and its effect on their success remains ignored. This study employed a multidisciplinary approach to examine the diverse practices of Naga worship in business contexts across different regions. This study utilized a mixed-methods research design to provide insights into the strategic integration of Naga worship into business practices and its impact on business performance. It employed a questionnaire to gather insights from respondents about their demographic data, awareness of Naga worship, its integration into business practices, consumer perceptions and behaviors, and overall business performance. Follow-up, in-depth interviews were developed to probe deeper into respondents’ experiences, motivations, and perceptions regarding the integration of Naga worship into their business practices. Most respondents agreed to integrate Naga worship into their company practices or marketing plans by using Naga symbols in branding, doing rituals for success, providing Naga-themed products and services, and scheduling activities on auspicious Naga-related dates. Respondents perceived companies that venerate Naga as culturally genuine and focused on the community. Worshipping the Naga deity improved the brand’s and corporation’s image and reputation. People patronized these enterprises by buying products and services associated with Naga culture. A substantial portion of respondents believe that worshiping Naga enhances commercial prosperity. Yet, a few participants from different regions mentioned difficulties regarding the integration of Naga religious customs.

Increasing number of smart cities, the rise of technology and urban population engagement in urban management, and the scarcity of open data for evaluating sustainable urban development determines the necessity of developing new sustainability assessment approaches. This study uses passive crowdsourcing together with the adapted SULPiTER (Sustainable Urban Logistics Planning to Enhance Regional freight transport) methodology to assess the sustainable development of smart cities. The proposed methodology considers economic, environmental, social, transport, communication factors and residents’ satisfaction with the urban environment. The SULPiTER relies on experts in selection of relevant factors and determining their contribution to the value of a sustainability indicator. We propose an alternative approach based on automated data gathering and processing. To implement it, we build an information service around a formal knowledge base that accumulates alternative workflows for estimation of indicators and allows for automatic comparison of alternatives and aggregation of their results. A system architecture was proposed and implemented with the Astana Opinion Mining service as its part that can be adjusted to collect opinions in various impact areas. The findings hold value for early identification of problems, and increasing planning and policies efficiency in sustainable urban development.

Consumers, particularly women, pursue beauty and health in order to uphold their image within society, which has contributed to consistent demand for cosmetics. The cosmetics market, driven by globalization and cultural exchange, sees Thai cosmetics gaining popularity among Chinese women. There has been a significant rise in the popularity of Thai cosmetics, known for their natural ingredients and innovative formulations. With a growing interest in cross-cultural consumer behaviour, particularly in the context of skincare and make-up products, understanding how different age groups perceive and choose Thai cosmetics is crucial for effective marketing strategies. The main issue is the development of consumer preferences over time among Chinese women who have only recently been given the opportunity to choose among many brands. This qualitative study explores the intergenerational differences in Chinese female consumers’ preferences for Thai cosmetics, aiming to uncover rich insights into their perceptions, attitudes, and behaviours. The target population is female Chinese who have visited Thailand and purchased or used Thai-branded cosmetics. Key themes emerge regarding the perception of product efficacy, the cultural authenticity and the role of digital media and trends in influencing product choices. Findings highlight nuanced generational preferences, with older cohorts emphasizing trust and familiarity with established brands, while younger cohorts prioritize innovation, sustainability, and personalized beauty experiences. These insights provide valuable implications for marketers seeking to tailor strategies and product offerings to engage effectively diverse generational segments within the competitive cosmetics market.

Regardless of the importance of accreditation and the role faculty play in a such process, not much attention was given to those in dental colleges This study aimed to explore faculty perceptions of accreditation in the College of Dental Medicine and its impact, the challenges that hinder their involvement in accreditation, and countermeasures to mitigate these barriers using a convergent mixed methods approach. The interviewees were faculty who hold administrative positions (purposeful sample). The remaining faculty were invited for the survey using convenience sampling. Quantitative data were analyzed by Mann-Whitney and Kruskal-Wallis tests at 0.05 significance. A consensus was achieved on the positive impact of accreditation with an emphasis on the collective responsibility of faculty for the entire process. Yet their involvement was not duly recognized in teaching load, promotion, and incentives. Quality Improvement and Sustainability Tools and Benchmarking were identified as common themes for the value of accreditation to institutions and faculty. Global ranking and credibility as well as seamless service were key themes for institutional accreditation, while education tools and guidance or unifying tools were central themes for faculty. Regarding the challenges, five themes were recognized: Lack of Resources, Rigorous Process, Communication Lapse, Overwhelming Workload, and Leadership Style and Working Environment. To mitigate these challenges, Providing Enough Resources and Leadership Style and Working Environment were the identified themes. This research endeavors to achieve a better understanding of faculty perceptions to ease a process that requires commitment, resources, and readiness to change.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.