As with many countries worldwide, Central Asian countries, including Kazakhstan, are under water crisis. With the depletion of the surface rivers and lakes' water resources, including Aral, Caspian, and Balkhash Lakes' intensive shrinking, Kazakhstan is increasing groundwater use. The permanent river basin's surface groundwater resources' quantity and quality investigation monitoring is required for proper resilient future strategies development. The current project provided the Kazakhstan-Kyrgyzstan Shu-Talas's river basins groundwater project, Assessment to support water use management activities. Field groundwater surveys were conducted at 22 groundwater intakes in the research area, located in the Zhambyl region, Kazakhstan, where groundwater samples were analyzed for major ions, physicochemical parameters, and trace elements, followed by geospatial mapping. The integrated hydrogeological maps, monitoring records, and field observations were developed. The developed groundwater contour maps were used to identify recharge and discharge areas and groundwater flow directions. Groundwater was predominantly fresh, with mineralization ranging from 152 to 897 mg/L and pH values of 6.45-8.03, generally meeting drinking-water standards. Most samples belonged to the HCO3-Ca-Mg and mixed hydrochemical facies, while elevated lithium concentrations were identified at Kainar (0.043 mg/L), Sarybulak (0.031 mg/L), Kyzylkainar (0.032 mg/L), and Zhasorken (0.033 mg/L). Hydrochemical investigations identified localized elevated lithium concentrations, with a maximum value of 0.043 mg/L, exceeding the applicable drinking-water standard of 0.03 mg/L by approximately 43%. This project investigation indicates that groundwater recharge occurs mainly in Kyrgyzstan, whereas groundwater flow is directed toward Kazakhstan, confirming the transboundary hydraulic connection of the groundwater system. Kazakhstan will need to improve additionally in the surface water diplomacy and the groundwater cooperation activities with Kyrgyzstan, which has been missing till now. This project investigation should provide support in such cases of surface-groundwater transboundary issues to improve water sustainability programs.
Religious buildings are important spatial nodes for ritual practice, collective memory, and everyday public life in multiethnic traditional villages. However, quantitative evidence on how their intra-village spatial configurations vary across ethnic inter-embedding contexts-and how such variation may inform sustainable spatial planning and cultural landscape conservation-remains limited. This study examines 73 settlement spatial units derived from 72 national-level traditional villages in the Hehuang Region, China. Building on a typology of ethnic inter-embedding comprising Clustered-Isolation, Boundary-Permeation, and Cellular-Symbiosis contexts, we compiled 82 spatial records of major religious buildings. Standardized distances were calculated from each religious building to the mean center of the built-up area, the built-up-area boundary, and the main street. Relative inter-building distances were also examined for different religious buildings in Cellular-Symbiosis villages. Differences among contexts were assessed using the Kruskal-Wallis H test and Dunn-Bonferroni post hoc comparisons. The results show significant differences in distances to the settlement center and main street across the three contexts, whereas distances to the built-up-area boundary do not differ significantly. Religious buildings in Clustered-Isolation villages are generally closer to both the built-up-area center and the main street, while those in Boundary-Permeation villages are relatively displaced from internal core spaces. The strongest between-group difference is observed for main-street distance (H = 17.08, p < 0.001, epsilon(2) = 0.215). A subsample analysis of Tibetan Buddhist-related buildings yields a similar significant difference in main-street distance (H = 10.51, p = 0.005, epsilon(2) = 0.161), suggesting that the overall pattern is not fully attributable to variation in religious-building composition. In Cellular-Symbiosis villages, nine valid building pairs show substantial heterogeneity in standardized inter-building distance, ranging from 0.1557 to 1.5611, and include both closely juxtaposed and spatially separated multi-nodal configurations. These findings indicate that religious spatial configurations should not be interpreted solely through a center-periphery framework. Sustainable planning should consider the relationships of religious buildings with street networks, residential clusters, topographic interfaces, public facilities, and other religious nodes. The study provides a transparent and reproducible multiscalar framework to support context-sensitive spatial planning and cultural landscape conservation in multiethnic traditional villages.
Invasive albizia (Falcataria falcata (L.) Greuter & R. Rankin) trees pose an increasing threat to infrastructure, transportation networks, and public safety in Hawai'i because their rapid growth and shallow root systems make them susceptible to windthrow during severe weather events. However, comprehensive spatial information on albizia distribution remains limited, constraining invasive-species management, hazard mitigation, and sustainable infrastructure planning. This study evaluates the applicability of a GeoAI-based framework for identifying invasive-tree-related infrastructure exposure at a regional planning scale. This study develops a GeoAI framework using a U-Net convolutional neural network with a ResNet-34 backbone to detect and map albizia canopy from 0.6 m National Agriculture Imagery Program (NAIP) aerial imagery. Training data from two geographically distinct areas, M & amacr;noa and Kahalu'u, were used for model development and iterative refinement. The final model achieved a precision of 0.76, compared with 0.64 in the initial iteration, and was applied to estimate potential tree-fall exposure through spatial proximity analysis of roads and buildings. Following manual quality control, approximately 2 km2 of albizia canopy were identified within the study area. More than 53 km of roads and 2300 buildings were located within the potential tree-fall exposure zone, including portions of major transportation corridors such as Pali Highway, Likelike Highway, and Interstate H-3. By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance. The approach contributes to sustainability by supporting more targeted use of management resources, reducing potential disruption to critical infrastructure and essential access, and strengthening long-term community and infrastructure resilience.
Artificial intelligence (AI) is a general-purpose technology driving technological and industrial transformation. In this study, we construct an AI adoption indicator based on textual analyses of firms' annual reports and measure innovation quality using the cumulative three-year forward citation counts-excluding self-citations-of invention patents at the firm-year level. Based on a sample of Chinese A-share manufacturing firms spanning 2011-2021, we found that AI adoption is positively associated with innovation quality. This conclusion remains robust across a range of endogeneity-mitigating specifications and robustness checks. Exploratory mechanism analyses suggest that this positive association is partly accounted for by three channels-R&D investment, human capital, and R&D efficiency-although these results should be interpreted as indicating an association rather than causal mechanisms. Heterogeneity analysis further showed that such association is concentrated in larger enterprises, and it is stronger for enterprises with lower financialization and those in asset-intensive industries. However, no significant difference was observed across firms with different ownership or regional locations. This study expands the literature on the innovation effects of AI and offers new insights into the factors influencing innovation quality in the AI era. It also has implications for business management and public policy with regard to leveraging AI to foster high-quality innovation capabilities, thereby providing a knowledge foundation for industrial upgrading and sustainable development.
Transformative sustainability education often uses disruption to unsettle learners' assumptions, but the claim that disruption fails when "demands exceed resources" gives teachers little guidance. This conceptual paper introduces re-anchoring: the support a teacher plans to follow a disruption. Such support varies in amount (resource sufficiency), in whether its kind matches the problem created (functional congruence), and in timing and order (temporal sequencing). The propositions are theoretical and untested. The framework is useful only if it predicts what existing accounts of support amount, scaffolding, and fit do not: that order matters when content and amount are equal; that early closure may bring relief now but weaker reflection later; and that different mismatches produce different outcome patterns. Four anchors-meaning and values, responsibility and complexity, relational and future orientation, and feasible action-are linked to four types of post-disruption demand in a table of expected effects. Moral licensing, need for cognitive closure, and task completion are treated as competing explanations of "false resolution." A tourism extension proposes consumer-professional identity overlap (CPIO) as an unvalidated moderator. The paper ends with testable propositions, failure conditions, and a longitudinal testing strategy.
