The purpose of this study is to examine the impact of tourist spending and the growth of Oman’s tourism industry on the country’s GDP from 1996 to 2018. The study uses the error correction model and other tests for assessing the link among variables, such as the cointegration test and the Granger causality test, to accomplish its aims. Findings from the error correlation model and cointegration test show that there is a link between the variables in Oman over the long and short term. There is a positive and statistically significant relationship between tourist expenditures and economic growth, as well as a negative and statistically significant relationship between tourism expansion and economic growth. We now use ARDL regression estimators to assess the robustness of the empirical results. There is no evidence of a direct relationship between increased tourism and GDP growth, according to the study’s results. According to the research, sustainable tourism development is an achievable economic growth driver, and Oman should prioritize economic policies that support this trend.

This study explores the role of arts management in regional economic development within major Chinese cities, including Beijing, Shanghai, and Shenzhen. Cultural organizations—such as museums, theaters, and galleries—contribute significantly to local economies through tourism, job creation, and the enhancement of cultural branding. Using a qualitative approach, 18 semi-structured interviews with arts managers and policymakers selected based on their influential roles in cultural organizations across these cities. The interviews were analyzed using thematic analysis, which identified key themes including the economic impact of cultural organizations, the influence of government policies, challenges in arts management, and the role of cultural tourism in fostering regional growth. The findings reveal that while government policies play a pivotal role in supporting cultural organizations, providing crucial funding, tax incentives, and infrastructure development, concerns remain about the long-term sustainability of funding due to shifting political and economic priorities. Additionally, arts managers face challenges related to balancing artistic goals with financial viability, particularly as the sector becomes increasingly competitive and technology-dependent. Key challenges identified include securing stable funding sources, adapting to digital technologies, talent retention, and maintaining artistic integrity amid commercial pressures. The study highlights the need for diversified funding models such as public-private partnerships and alternative revenue streams and suggests further exploration into the role of smaller cultural organizations in rural regions to promote inclusive regional development. Practical recommendations include developing strategies to enhance financial sustainability, investing in digital capabilities, and formulating policies that provide long-term support for the cultural sector. Overall, the research contributes to a better understanding of how effective arts management can drive regional economic development and offers practical recommendations for strengthening the sustainability of China’s cultural sector.

This study explores the role of intercultural communicative competence (ICC) and STEM education in building the soft infrastructure necessary for economic development within Kazakhstan’s transforming education system. The authors conducted an interdisciplinary analysis, emphasizing the cognitive and communicative aspects of foreign language education in secondary schools, proposing a model for integrating ICC through the use of information and analytical technologies. The research focuses on personalized education, teacher competencies, and student engagement, with experimental methods applied in a Karaganda-based school. The study aims to identify mechanisms and principles that enhance ICC development, contributing to Kazakhstan’s modernization efforts in fostering globally competitive graduates prepared for the demands of the international arena. This research lays the foundation for further practical experimentation in profiled schools, aligning education with national development goals.

Working Capital Management (hereafter WCM) is the strategic tool that helps a company navigate through challenging economic growth, and influence its competitive performance. Thus, this study examines the impact of WCM on the competitiveness of firms operating in the non-financial sectors in Pakistan. We use the Generalized Method of Moments (GMM) technique to ensure the robustness of our results. The study findings reveal that both a large net trade cycle and surplus working capital have a substantial negative impact on firms’ competitiveness within their respective industries. These results suggest that companies should streamline their investments in working capital accounts and concentrate more resources on long-term projects that maximize value to improve their competitiveness compared to other companies. Therefore, firms that are effectively managing their short-term financial affairs are experiencing much better performance in all aspects of firm performance. The research findings highlight the urgent need for governmental initiatives designed to improve WCM practices in these industries. It is imperative for the management of  companies with excess net working capital to maximize their working capital efficiency, aligning it with industry standards to enhance competitiveness. Moreover, policymakers should prioritize easing access to financial alternatives that allow enterprises to maintain an efficient working capital structure without relying on excessive measures. Furthermore, policymakers should be cautious when determining minimum cash balance requirements in a cash-strapped economy where external financing is relatively more expensive than in other regional economies.

Onion (Allium cepa L.) is one of the important vegetables in Egypt. The study was conducted in the vegetable field to study the effect of different rates of phosphorus fertilizers and foliar application of Nano-Boron, Chitosan, and Naphthalene Acidic Acid (NAA) on growth and seed productivity of Onion plant (Allium cepa L., cv. Giza 6 Mohassan). The experiments were carried out in a split-plot design with three replicates. The main plot contains 3 rates of phosphorus treatments (30, 45 and 60 kg P₂O₅/feddan), Subplot includes foliar application of Nano-Boron, Nano-Chitosan and Naphthalene Acidic Acid (NAA) at a concentration of 50 ppm for each and sprayed at three times (50, 65 and 80 days after transplanting). Increasing the phosphorus fertilizers rate to 60 kg P₂O₅/fed significantly affects the growth and seed production of the Onion plant. Foliar application of nano-boron at 50 ppm concentration gave maximum values of onion seed yield in both seasons. Results stated that the correlation between yield and yield contributing characters over two years was highly significant. It could be recommended that P application at a rate of 60 kg P₂O₅ and sprayed onion plants at 50 ppm nano-boron three times (at 50, 65, and 80 days from transplanting) gave the highest seed yield of onion plants. Moreover, the maximum increments of inflorescence diameter (94.4%) were recorded to nano-boron foliar spray (60 p × nB) compared to the other treatments in both seasons.

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.