Search Results (486)

Tourism accessibility is an important indicator for assessing the coordinated development of transport and tourism. Using counties and districts in Gansu Province as the units of analysis, this study developed a three-dimensional evaluation framework comprising temporal accessibility, economic accessibility, and balanced accessibility, based on real-world travel data between county and district centres and China’s A-rated tourist attractions obtained from the Amap API. Spatial autocorrelation analysis, the Geographical Detector, the Spatial Durbin Model (SDM), and Multiscale Geographically Weighted Regression (MGWR) were employed to systematically investigate the spatial patterns and driving mechanisms of tourism accessibility in Gansu Province. The results indicate that: (1) tourism accessibility exhibits significant spatial clustering, with high-value areas primarily concentrated in the Hexi Corridor and low-value areas mainly distributed in the mountainous regions of central and southern Gansu; (2) distance to the provincial capital, elevation, and the number of adjacent counties constitute the core determinants of tourism accessibility, while interactions among factors generally exhibit bi-factor enhancement or nonlinear enhancement effects; and (3) tourism accessibility exhibits significant spatial spillover effects and spatial heterogeneity, with the effects of different driving factors varying considerably across space. The findings provide a theoretical basis for optimising tourism transport and promoting balanced regional tourism development in Gansu Province. Full article

Understanding the spatiotemporal dynamics of dominant desert-steppe species under climate change is critical for sustainable management of arid ecosystems. Sophora alopecuroides L., a perennial drought-tolerant leguminous herb with substantial medicinal and forage value, is an important component of these ecosystems. Based on 137 occurrence records and 10 environmental variables, we applied the MaxEnt model and ArcGIS to simulate suitable habitats and range shifts of S. alopecuroides across multiple periods, including the Last Interglacial (LIG), Last Glacial Maximum (LGM), Mid-Holocene (MH), present, and the 2050s and 2070s under four representative concentration pathways (RCP 2.6, 4.5, 6.0, and 8.5). The model showed high predictive performance, with an area under the receiver operating characteristic curve (AUC) greater than 0.9. Temperature annual range (Bio7), elevation, precipitation of the warmest quarter (Bio18), mean temperature of the driest quarter (Bio9), and annual mean temperature (Bio1) were identified as key environmental drivers, indicating that the distribution of this species is mainly shaped by temperature regimes, seasonal water availability, and topography. Under current conditions, the total suitable habitat accounts for 28.9% of China’s territory, closely matching the known distribution. Since the LIG, the total suitable area has fluctuated slightly, being larger during the LIG and LGM, smaller during the MH, and close to the present level thereafter. The distribution centroid remained within the Jinta Basin of the Hexi Corridor, Gansu Province, with only limited displacement from the LIG to the present, suggesting that this area, together with the Tianshan–Junggar Basin margins and the Alxa Plateau–Helan Mountain region, constitute key topographically and climatically inferred potential refugial areas for S. alopecuroides. Future projections indicate that suitable habitat may expand slightly under low-emission scenarios but contract under medium- to high-emission scenarios, with centroid shifts varying among pathways. These findings highlight the potential importance of such bioclimatically inferred putative refugia in maintaining the distribution of dominant dryland herbs and provide a spatially explicit basis for monitoring, germplasm conservation, and adaptive management of S. alopecuroides under ongoing climate change. Full article

Long-term agricultural irrigation in the loess platform region of Northwest China has raised the groundwater level and triggered numerous irrigation-induced loess landslides. The widely developed fissures in loess provide preferential pathways for irrigation water infiltration and serve as key factors that control the hydrological evolution and stability of landslides. The Jiaojiayatou landslide in the Heifangtai platform, Gansu Province, was selected as the study case. A coupled saturated-unsaturated seepage–stress numerical model incorporating fissure structures was established to systematically investigate the effects of fissure depth, location, and number on the seepage field evolution, stability, and deformation characteristics of loess landslides under long-term irrigation. The results show that fissures significantly accelerate the advance of the wetting front, enlarge the high-water-content zone, increase pore water pressure, and reduce the factor of safety. Among these parameters, the effect of fissure depth is the most significant: for fissure depths of 5 m and 10 m, the simulated average annual rise in groundwater level is 0.63 m/a and 1.21 m/a, respectively. When the fissure depth increases to 15 m, irrigation water directly recharges the groundwater, leading to landslide instability (factor of safety drops to 0.97). The displacement at the slope shoulder increases by 54% compared with that in the no-fissure case, and the displacement pattern shifts from predominantly horizontal sliding to vertical settlement. Furthermore, the closer the fissure is to the platform edge and the greater the number of fissures, the lower the stability becomes and the larger the soil displacement at the slope shoulder. Full article

While the relationship between body mass index (BMI) and physical fitness in youth often follows an inverted U-shape, populations-based data from sub-plateau settings remain scarce. This study characterized the cross-sectional BMI–fitness association among 14,744 students (9931 females) at a single university in Gansu Province, China (altitude ~1483 m). Fitness was assessed across seven components (vital capacity, speed, power, flexibility, endurance, and muscular strength/endurance) and summarized as a standardized Physical Fitness Index (PFI). Overweight/obesity prevalence was 11.2% (males 18.3%, females 7.7%)—lower than the 14.0% national average for same-aged university students. BMI displayed a monotonic inverse association with PFI; underweight and normal-weight groups performed comparably, while overweight and obese groups showed progressively lower PFI, with a stronger association in males (β = −0.46) than females (β = −0.36). The weight-normalized vital capacity index (VCWI)—a derived indicator of pulmonary function—showed the strongest inverse correlation (males r = −0.47; females r = −0.40). Associations were most pronounced for VCWI and endurance, whereas flexibility exhibited a negligible BMI-related gradient. These findings describe a cohort-specific pattern, emphasizing the need for broader investigation of regional variations, and should not be interpreted as evidence of an altitude-mediated effect. Full article

Resource-based cities in Northwest China face increasing ecological, geotechnical, and socio-economic challenges caused by long-term mining, including subsidence, slope instability, waste rock accumulation, soil erosion, industrial decline, and settlement deterioration. Post-mining land reuse is constrained by geological safety, foundation stability, slope safety, drainage capacity, erosion risk, and waste rock dump stability, yet existing restoration studies often separate engineering remediation from landscape reuse, industrial pathway selection, and long-term governance. Taking a mining area in City A, Gansu Province, as a case study, this paper develops a geotechnical constraint-based ecology–landscape–economy framework for post-mining land reuse and sustainable human settlement improvement. Unlike conventional reclamation approaches that mainly emphasize engineering remediation, vegetation recovery, or single-function land reuse, this study integrates geotechnical constraints, land-unit classification, pathway-specific compatibility assessment, and capital–space coupling into a planning-scale decision-support framework. Post-mining land was classified into five units, and their compatibility with three restoration plus industrial pathways was assessed using five indicators: geological safety, ecological sensitivity, land-use availability, landscape and cultural value, and industrial operation potential. The results indicate that backfilled mining voids and reclaimed platforms are most suitable for modern agriculture, tailings ponds and subsidence waterbodies for cultural tourism and wellness, and waste rock dump platforms and other stable, low-sensitivity open land for new energy development. A capital–space coupling mechanism is further proposed to link restoration, support, and development zones with government funds, corporate capital, social capital, green finance, and industrial income. This framework provides a planning-scale engineering-suitability screening tool for sustainable post-mining land transformation. Full article

Hexi cattle are a local cattle population endemic to the Hexi Corridor in Gansu Province, China, and exhibit strong adaptability to the region’s arid continental environment. However, comprehensive genomic investigations of this population are still lacking. In the present study, we integrated population genomic analyses with a genome-wide association study (GWAS) to dissect the genetic architecture of Hexi cattle. Whole-genome resequencing data were generated for 264 Hexi cattle, and public genomic datasets from six representative cattle breeds were obtained from the NCBI database for comparative analysis. Multiple analytical approaches—including principal component analysis (PCA), linkage disequilibrium (LD) decay analysis, neighbor-joining (NJ) phylogenetic tree construction, and ADMIXTURE analysis—were adopted to evaluate population structure and evolutionary relationships. A mixed linear model was then used to identify significant SNPs associated with five major body conformation traits in six-month-old cattle: body weight (BW), withers height (WH), hip height (HH), heart girth (HG), and abdominal girth (AG). Our results confirm the admixed nature of Hexi cattle, whose genome is derived primarily from Simmental cattle and secondarily from Mongolian cattle. A total of 69 trait-associated significant SNPs were identified and functionally annotated. Specifically, TBC1D31, DERL1 and MCPH1 were linked to BW; FSCN3 and PLBD1 to WH; MCPH1 to HH; NPAS3, TBC1D31, DERL1 and MCPH1 to HG; and SOX5, NTAQ1, FAM83A, TBC1D31, DERL1, CDH11, CPLX4 and ADAM18 to AG. This study deepens our understanding of the genetic basis of growth traits in Hexi cattle and offers valuable molecular resources for future selective breeding, genetic improvement, and long-term conservation of this indigenous cattle population. Full article

Entomopathogenic nematodes (EPNs) are among the most promising biocontrol agents; however, their short shelf life constrains commercial application. This study evaluated the effects of storage temperature (6 °C and 25 °C) and sponge substrate moisture content (42%, 48%, and 55%) on the 18-week survival and post-storage virulence of two indigenous EPN species from Gansu Province, China: Heterorhabditis megidis 0627M and Steinernema feltiae 0619HT. A Generalized Linear Mixed Model (GLMM) revealed that storage duration, temperature, moisture content, and species all significantly affected survival of H. megidis 0627M and S. feltiae 0619HT (all p < 0.001). Low-temperature storage (6 °C) reduced mortality odds by 82.4% compared with room temperature (OR = 0.176, 95% CI: 0.117–0.263, p < 0.001). High moisture content (55%) increased mortality odds by 10.1-fold relative to moderate moisture (48%; OR = 10.114, 95% CI: 6.155–16.618, p < 0.001), whereas low moisture (42%) showed no significant difference from 48% (OR = 0.942, p = 0.810). The two species exhibited distinct adaptation strategies: S. feltiae 0619HT achieved the highest survival under low-temperature storage at 48% moisture content (56.48% at week 18), whereas H. megidis 0627M demonstrated a delayed competitive advantage under room-temperature, low-moisture conditions. Virulence assays revealed that low-temperature storage better preserved infectivity under most conditions. Notably, survival and virulence were not always concordant, necessitating their evaluation as complementary metrics. Species-specific storage protocols are proposed, providing a scientific basis for the future development of regionally targeted and cost-effective native EPN formulations, as well as for regionally targeted biocontrol applications. Full article

Biomass allocation patterns affect plant functions across all levels, ranging from plant growth and reproduction to the quality and energy flow of entire communities. Revealing the biomass allocation and allometric growth relationships among the dominant plant formations in alpine peat swamp wetlands not only can help elucidate the life history strategies of swamp plants, but also plays a crucial role in understanding the uncertainty of plant carbon sinks in peat swamp wetlands. Based on community surveys, this study employed analysis of variance (ANOVA) and standardized major axis estimation (SMA) to analyze the species composition, biomass allocation of different organs, and allometric growth relationships of the dominant plant formation in the alpine peat swamp wetlands of the Yellow River on the Gannon Plateau, Gansu Province, China. The results showed the following: (1) Peat swamp plants can be classified into six formations dominated by Carex muliensis, Blysmus sinocompressus, Carex atrofusca, Kobresia tibetica, Kobresia kansuensis, and Carex kansuensis. Environmental filtering was identified as the primary factor influencing the distribution of formations in this region. (2) The biomass allocation ratios of the dominant plant formations were ordered as follows: root mass ratio > leaf mass ratio > stem mass ratio. There were also significant differences in the biomass allocation of roots, stems, and leaves among different plant formations. (3) Isometric growth was observed between the leaf and stem biomass of the dominant plant formations (p > 0.05), while allometric growth relationships existed between root/leaf biomass and root/stem biomass (p < 0.05), with the growth rate of root biomass (RB) being higher than that of leaf biomass (LB) and stem biomass (SB). The biomass allocation patterns and allometric growth relationships among the roots, stems, and leaves of the dominant plant formations in peat swamp wetlands reflect the environmental plasticity mechanism of functional plant traits in heterogeneous habitats. Moreover, combining optimal allocation theory and allometric growth theory can better explain the biomass variation and adaptation mechanisms of dominant plant formations in peat swamp wetlands, providing a theoretical basis for understanding the habitat adaptation patterns of plants in alpine peat swamp wetlands. Full article

Lithological mapping with multispectral remote sensing remains challenging when diagnostic spectral information is limited and reliable labeled samples are scarce. This problem is particularly relevant when convolutional neural networks (CNNs) are applied to lithological classification, because limited spectral dimensionality and scarce training samples may hinder the learning of discriminative spatial–spectral features. In this study, we developed a limited-sample lithological mapping framework for the Shibaocheng area of Subei County, Gansu Province, China, using band-integrated ASTER and Sentinel-2A multispectral imagery. ASTER shortwave infrared (SWIR) bands were co-registered and resampled to Sentinel-2A imagery, and then integrated with Sentinel-2A visible and near-infrared (VNIR) and red-edge bands to construct a complementary multispectral dataset. A compact spectrally enhanced multi-scale CNN was designed, incorporating a residual spectral feature enhancement module for inter-band representation learning and a parallel multi-scale hybrid convolution module for capturing spatial–spectral features. Eight lithological units were classified under limited-label conditions using 8158 training samples and 3497 spatially independent validation samples. Experimental results show that the band-integrated ASTER–Sentinel-2A dataset improved classification performance compared with single-sensor inputs. Using the proposed model, the band-integrated dataset achieved an overall accuracy (OA) of 94.12%, average accuracy (AA) of 94.04%, and Kappa coefficient of 0.932, compared with OA values of 93.14% and 92.40% obtained using ASTER and Sentinel-2A alone, respectively. The positive effect of band-level integration was also observed for spectral angle mapper (SAM), support vector machine (SVM), and 3D-CNN, whose OA values increased to 54.33%, 86.12%, and 92.29%, respectively. The proposed CNN achieved the highest OA among the evaluated methods, outperforming SAM, SVM, and the conventional 3D-CNN. In addition, t-SNE visualization indicated that incorporating spatial texture features produced more compact and better-separated lithological clusters than using spectral features alone. Ablation experiments further demonstrated that the proposed spectral feature enhancement and multi-scale hybrid convolution modules each contributed to improving lithological classification performance. These results demonstrate that integrating freely available multispectral data with a lightweight spectral–spatial CNN provides a practical and cost-effective solution for lithological mapping in bedrock-exposed arid to semi-arid regions, especially where hyperspectral imagery and dense field samples are unavailable. Full article

Driven by the demand for high-quality housing, fourth-generation residential buildings—known internationally as “Vertical Forest” and in China as “Urban Forest Garden”—have developed rapidly. Initially built in mild southern regions, they have recently expanded to colder northern areas, with over 50 projects underway in provinces such as Shanxi, Hebei, Shaanxi, and Gansu. Several cities have introduced design standards and incentives, and the China Association for Standardization of Engineering Construction has issued the “Design Standards for Urban Forest Garden Housing.” However, in cold regions, where winters are long and cold and summers are short and hot, there is a lack of systematic quantitative research on how balcony design affects building carbon reduction, energy efficiency, and indoor thermal comfort. To address this research gap, this paper poses the following research questions: (1) In fourth-generation residential buildings in cold regions, how do different combinations of balcony orientations affect annual energy consumption and indoor thermal comfort? (2) Which balcony configurations offer the best balance between carbon reduction, energy efficiency, and thermal comfort? Based on statistical analysis of terrace configurations from more than 40 projects, 12 typical configuration models were identified. Using Ladybug and Honeybee tools on the Grasshopper platform, building energy consumption and indoor thermal comfort were simulated. Multi-objective trade-off analysis was performed using the Pareto front method. In this study, indoor thermal comfort was evaluated using the PMV (Predicted Mean Vote) index. PMV is an index proposed by Professor Fanger that comprehensively reflects human thermal sensation, taking into account air temperature, humidity, wind speed, mean radiant temperature, human metabolic rate, and clothing thermal resistance. Its typical range is −3 (cold) to +3 (hot); in this study, the comfort zone was defined as −1 ≤ PMV ≤ 1. Key findings: (1) The southwest + south terrace configuration shows the highest annual energy consumption, exceeding the lowest (northwest + west) by 2.7%, indicating that south-facing terraces are less favorable for carbon reduction. (2) The best thermal comfort is achieved with east, west, and south orientations. Compared to the least comfortable combination (southwest + northwest), the difference in PMV comfort percentage reaches 2.4%. (3) The Pareto front reveals that beyond a certain comfort level, energy consumption increases sharply. The west + south and east + south combinations yield the highest thermal comfort (49.4%) while maintaining relatively low energy consumption (17.98 kWh/m²). Therefore, in cold regions, fourth-generation residential designs should prioritize terrace combinations integrating south-facing and side-facing orientations and avoid pure corner configurations to balance winter solar gain and summer shading. Full article

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments. Full article

Accurate wind-power forecasting is essential for grid scheduling when renewable generation becomes highly variable. This study developed WindPower-SAFusion, an Informer-inspired forecasting model designed for long wind-power sequences. The framework is built around three complementary designs. First, ProbSparse self-attention is used to lower the attention cost from $O\left(L^2\right)$ to $O\left(L\log L\right)$ while retaining informative temporal dependencies. Second, convolutional distillation is embedded in the encoder to summarize local fluctuations and form multi-scale representations. Third, historical theoretical power and wind speed are fused in a recursive forecasting scheme for multi-step prediction. The model is evaluated using measured data from the Daliang Wind Farm in Guazhou, Gansu Province, China. Experiments conducted using 1-day, 3-day, and 7-day horizons show that WindPower-SAFusion obtained lower errors and higher explanatory ability than the selected statistical and deep learning baselines. The ablation results further confirm the contributions of sparse attention, convolutional feed-forward extraction, and sequence distillation. These findings indicate that the proposed framework can provide an effective data-driven tool for wind-farm dispatching and power-management applications. Full article

Traditional meteorological data spatial interpolation methods often rely on linear or static assumptions, which are inadequate for complex terrain and fail to exploit continuous spatiotemporal variation information. This paper proposes a Spatiotemporal Graph Network with Adaptive Temporal Decay (DG) that integrates a learnable graph convolution module and a temporal attenuation mechanism, enabling accurate precipitation estimation for target stations or regions at consecutive time steps. The method is evaluated using daily precipitation data from nine stations in Longnan City, Gansu Province, China, along with ERA5 (0.25°) and GPCP (0.5°) gridded reanalysis products. In the station-to-station interpolation scenario, DG significantly outperforms ordinary Kriging (OK), reducing the average RMSE from 1.4 mm/day to 1.2 mm/day, with a 28.6% improvement at mountainous stations. The DG model also exhibits superior performance in grid-to-station interpolation, achieving an average RMSE of 1.9 mm/day (OK: 2.5 mm/day). On heavy precipitation days (≥20 mm/day), DG reduces the RMSE nearly by half (11.7 mm/day) compared to OK (23.2 mm/day). A temporal-only LSTM baseline and three ablation variants (spatial-only OSI, temporal-only OTI and dgcn-only OD) are also compared, and DG consistently outperforms them, confirming the essential role of spatiotemporal integration. Additional baselines including IDW and Co-Kriging further validate the superiority of DG. The proposed method offers a promising new approach for high-precision spatiotemporal interpolation of meteorological elements in complex terrain. Full article

Longnan, located in Gansu Province, China, at the junction of Shaanxi, Gansu, and Sichuan provinces, represents one of the key corridors of the Northern Tea-Horse Ancient Road. This region preserves abundant traditional timber covered bridges with distinct local characteristics. This study employs ArcGIS spatial analysis and documentary research methods to explore the spatial distribution, spatiotemporal evolution, and influencing factors of these bridges. Spatial analyses (nearest neighbor index, kernel density, and standard deviational ellipse) are based on 71 bridges with traceable coordinates, while the temporal evolution analysis incorporates 80 bridges (64 with definite construction periods and 16 with unknown dates; the latter are handled through a sensitivity analysis as described later in this paper The results indicate that the timber covered bridges in Longnan exhibit a significantly clustered distribution, presenting a pattern of “dense in the southwest and sparse in the northeast”, with Wen County and Kang County as the core clustering areas. Temporally, they follow a unimodal evolution pattern: initiation in the Ming Dynasty, peak in the Qing Dynasty, decline in the Republic of China period, and near stagnation in modern times. The location and distribution of the covered bridges show a strong statistical association with natural conditions (e.g., topography, hydrology) and exhibit spatial coincidence with modern vegetation coverage—the latter treated solely as a contemporary context variable rather than a historical driver. Spatial coincidence with the ancient road is quantified (60.56% within a 2000 m buffer), while settlement proximity is only qualitatively noted as background. Socio-economic factors (e.g., population, transportation, and settlements) are examined qualitatively and display spatial coincidence rather than quantitatively measured influence; these factors cannot be directly compared with natural factors. Full article