This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

Space is a product of society. Driven by industrialization, urbanization, informatization and government policies, China’s rural space is undergoing drastic reconstruction. As one of the core contents of international rural geography research, rural space research are multi-disciplinary, multi perspective, multi-dimensional and multi-method, forming a rich research field. In order to comprehensively grasp the progress of rural space research abroad, this study reviewed international rural space research literature in recent 40 years. The study found that foreign scholars described the connotation of rural space from the aspects of material, imagination and practice, emphasize the importance of daily life practice. It introduced living space to construct a more systematic research framework of rural space by establishing a “three-fold model of rural space”. With regard to the theoretical perspective, international research on rural space has experienced three stages: functionalism, political economics and social constructivism. In the evolution of time, it has realized the transformation from productivism to post-productivism; in the spatial dimension, it realizes the multiple superposition of settlement space, economic space, social space and cultural space. As a whole, international research on rural space has realized the transformation from material level to social representation, from objective space to subjective space, and from static one-dimensional space to dynamic multi-dimensional space, which enlightens us on the importance of interdisciplinary research and “social cultural” research on rural space. The construction of rural space in China needs to pay attention to the subject status of farmers and multifunction of rural space, respect the role of locality and difference of various places, and recover the function of production of meaning of rural space.

Soil salinization is a difficult challenge for agricultural productivity and environmental sustainability, particularly in arid and semi-arid coastal regions. This study investigates the spatial variability of soil electrical conductivity (EC) and its relationship with key cations and anions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, CO₃²⁻, HCO₃⁻, SO₄²⁻) along the southeastern coast of the Caspian Sea in Iran. Using a combination of field-based soil sampling, laboratory analyses, and Landsat 8 spectral data, linear Multiple Linear Regression and Partial Least Squares Regression (MLR, PLSR) and nonlinear Artifician Neural Network and Support Vector Machine (ANN, SVM) modeling approaches were employed to estimate and map soil EC. Results identified Na⁺ and Cl⁻ as the primary contributors to salinity (r = 0.78 and r = 0.88, respectively), with NaCl salts dominating the region’s soil salinity dynamics. Secondary contributions from Potassium Chloride KCl and Magnesium Chloride MgCl₂ were also observed. Coastal landforms such as lagoon relicts and coastal plains exhibited the highest salinity levels, attributed to geomorphic processes and anthropogenic activities. Among the predictive models, the SVM algorithm outperformed others, achieving higher R² values and lower RMSE (RMSE_Test = 27.35 and RMSE_Train = 24.62, respectively), underscoring its effectiveness in capturing complex soil-environment interactions. This study highlights the utility of digital soil mapping (DSM) for assessing soil salinity and provides actionable insights for sustainable land management, particularly in mitigating salinity and enhancing agricultural practices in vulnerable coastal systems.

Purpose: The aim of the study is to apply policy analysis matrix (PAM) to identify international competitiveness of marketing channels and policy impacts of government on each marketing channels. Methodology: Policy analysis matrix is employed to evaluate influences of macroeconomic policy on the Tuong-mango value chain. The study investigated 213 sampling observation of eight main actors in chain. Findings: The findings indicate that although domestic channel 4 exhibits competitiveness (Private cost ratio (PRC) < 1), channels 1, 2, and 3 possess both comparative and competitive advantages (PRC < 1, Domestic Resource Cost (DRC) < 1, and social benefit-cost (SBC) > 1). The government’s strategy on production protection, referred to as Nominal protection coefficient on tradable output (NPCO) 0.16, together with the plan for enhancing added value, denoted as Effective protection coefficient (EPC) 0.14 and Subsidy ratio to producers (SRP) −0.18, place a significant emphasis on the first export channel. The government’s subsidy plan grants preferential treatment to Channel 4 in terms of the pricing of commercially available products, with a Nominal protection coefficient on tradable input (NPCI) value of 0.75. A value-added strategy is implemented for export channels 2 and 3, which have EPCs of 0.76 and 0.85, respectively. Policy implications: If the tradable cost is modified by 20%, there will be a change in the ratio of DRC, SBC, EPC, and SRP. While the EPC does not see a 20% reduction in domestic prices, the DRC and SBC do benefit from this cost reduction. A reduction of 20% in the local cost, coupled with a corresponding rise of 20% in the Free on Board (FOB) price, would result in a significant elevation of the SRP for export channels 1, 2, and 3. Conclusion: This is as evidence for the combination of quantitative is a dynamic tool in the policymaking process to ensure targets, constrictions, and consistent policies for agricultural fields. This permits policies to be changed in steps with an alteration in the economy and priorities set up for the tropical fruits and vegetables field.