We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns.

Wind erosion poses a major threat to ecosystem stability and land productivity in arid and semi-arid regions. Accurate identification of its spatiotemporal dynamics and underlying driving mechanisms is a critical prerequisite for effective risk forecasting and targeted erosion control. This study applied the Revised Wind Erosion Equation (RWEQ) model to assess the spatial distribution, interannual variation, and seasonal dynamics of the Soil Wind Erosion Modulus (SWEM) across Inner Mongolia from 1990 to 2022. The GeoDetector model was further employed to quantify dominant drivers, key interactions, and high-risk zones via factor, interaction, and risk detection. The results showed that the average SWEM across the study period was 35.65 t·ha⁻¹·yr⁻¹ and showed a decreasing trend over time. However, localised increases were observed in the Horqin and Hulun Buir sandy lands and central grasslands. Wind erosion was most intense in spring (17.64 t·ha⁻¹·yr⁻¹) and weakest in summer (5.57 t·ha⁻¹·yr⁻¹). Gale days, NDVI, precipitation, and wind speed were identified as dominant drivers. Interaction detection revealed non-linear synergies between gale days and temperature (q = 0.40) and wind speed and temperature (q = 0.36), alongside a two-factor interaction between NDVI and precipitation (q = 0.19). Risk detection indicated that areas with gale days > 58, wind speed > 3.01 m/s, NDVI < 0.2, precipitation of 30.17–135.59 mm, and temperatures of 3.01–4.23 °C are highly erosion-prone. Management should prioritise these sensitive and intensifying areas by implementing site-specific strategies to enhance ecosystem resilience.

To address the challenges of water scarcity and the limited accuracy of terrestrial water storage (TWS) estimation in Inner Mongolia, this study integrates GRACE satellite observations, the GLDAS-Noah hydrological model, and ground-based precipitation records, in combination with Theil–Sen median trend analysis and the Mann–Kendall test, to systematically evaluate the spatiotemporal evolution of TWS from 2003 to 2016. The results demonstrate that: (1) GRACE data reliably capture regional water storage dynamics. Over the study period, TWS exhibited a significant overall decline, with an average rate of −5.2 × 10⁻⁴ cm/year, and seasonal variations were strongly coupled with precipitation patterns. (2) Spatially, TWS anomalies (TWSa) decreased from northeast to southwest, with values ranging from approximately +1.22 cm to −2.94 cm. The most pronounced decline was detected in the southern Ordos region. (3) Soil water changes were more substantial than those in canopy or snow water, with sharp reductions occurring during 2004–2007 and 2013–2015. Soil water exhibited clear stratification across different depths, and variations in deep soil water and groundwater were primarily influenced by non-precipitation factors. These findings provide a scientific basis for the sustainable utilization of water resources in Inner Mongolia and yield important insights for regional water management and policy formulation.

The research on the daily living space of residents in rural tourist destinations focuses on the spatial change and functional transformation at the regional level; however, it dedicates insufficient attention to the daily behavior and emotional changes of “individuals” in the human-land relationship. This study proposes the analysis framework of residents' daily living space constructed by a material-behavioral-emotional ternary space, and expands the application of the cognitive map from the large scale urban-structure spatial image to the small-scale daily living space of rural residents; thus, it aims to enhance the visualization and effectiveness of the research. The study observed the following: (1) In the cognitive map, for the residents' of Naobao Village, the cognition pertaining to the material space changes of their daily life has shifted from road-oriented to area-oriented; In the behavioral space, the residential space has shifted from scattered to focused, the employment space has shifted from the “circular focus + extended sector” structure to the “circular focus” structure, the consumption space has shifted from explicit space to implicit space, and the leisure space has shifted from the “point set” mode to “circle layer” mode. In the emotional space, the pattern of “one ring, one network and four centers” has shifted to the “single ring type” pattern. (2) Based on the findings of the interviews, natural conditions, policies, and institutions, self-organizations and other organizations jointly form the “driving system-pattern evolution-spatial representation” driving mechanism, which promotes the evolution of the daily living space of Naobao Village. This research provides relevant reference for the construction and development of residents' living space in the development of rural tourism; meanwhile, it also attempts to analyze the change pertaining to residents' living space form and spatial cognition using a new method.

Trophic state index (TSI) is a critical ecological and environmental issue in water resource management that has garnered significant attention. Given the complexity of optical characteristics in aquatic environments, this study employs fuzzy classification methods (FCM) and composite nutrient status indices to meticulously classify in-situ remote sensing reflectance data, aiming to develop evaluation models for different nutrient status categories to facilitate the assessment of the Daihai River in Inner Mongolia, China. Subsequently, we applied this model to MSI data to analyze the nutrient status of Daihai Lake from 2016 to 2021. Furthermore, a structural equation model (SEM) was utilized to explore the primary driving factors influencing nutrient status. The results indicated that the water bodies in Daihai Lake can be broadly classified into three categories, with the nutrient status models demonstrating robust performance for each category (R² = 0.80, R² = 0.83, and R² = 0.74). Comparisons were made between nutrient status accuracies obtained through the NCM and FCM based on measured data, yielding R² values of 0.74 and 0.85, respectively. Furthermore, the TSI results derived from MSI inversion were validated, with NCM achieving an R² of 0.49, RMSE of 6.88, and MAPE of 10.36%, while FCM exhibited an R² of 0.55, RMSE of 8.89, and MAPE of 13.18%. An SEM–based analysis revealed that over the long term, human activities exerted a more substantial impact on eutrophication in Daihai Lake, while climatic factors played an accelerating and reinforcing role. These results are consistent with prior research in the Daihai area, indicating a state of mild eutrophication and the potential of the fuzzy classification method and comprehensive trophic status index method in eutrophication assessment.

Identifying and ranking the key drivers of grassland conversion at the county level is crucial for developing targeted policies and improving protection efficiency. However, this process faces methodological challenges because of spatial and temporal variability. Partial order theory offers a robust framework for addressing these complexities. This study applies partial order theory (POT) combined with the Hasse diagram technique (HDT) to analyze grassland conversion in the Hohhot–Baotou–Ordos region during two time periods (2000–2010 and 2010–2020). First, patterns of grassland transformation are quantified, and the dominant driving factors of grassland conversion out (GCO) are identified and ranked, highlighting regional differences and temporal shifts. By integrating POT and HDT, this study offers a novel approach to handling complex, nonlinear, and hierarchical relationships among multiple drivers. The results provide scientific insight and policy recommendations for region-specific grassland management and sustainable land-use planning. The results show that (1) transitions between grasslands and other land-use types became more frequent across the two periods. Specifically, the rates of grassland conversion out and conversion increased from 2.1% and 3.5% during the period 2000–2010 to 4.7% and 4.8% during the period 2010–2020, respectively. (2) Urbanization was the primary driver of grassland conversion in 11 and 10 of the 18 counties during the first and second periods, respectively, followed by factors related to weather variables. (3) In the future, the eastern region of the study area needs to prioritize mitigating the impacts of urban development, while the western region should focus on enhancing ecological construction projects. This study recommends adopting region-specific ecological protection and economic strategies for balanced outcomes in conservation and development.