Search Results (843)

Beautiful City construction is a core initiative to realize the Beautiful China vision and advance China’s progress toward the Sustainable Development Goals (SDGs). This study develops a unified evaluation framework aligned with official Chinese assessment guidelines and global sustainability objectives. Based on a 2015–2022 dataset of 126 prefecture-level cities in the Yangtze River Economic Belt (YREB), we adopt Dagum’s Gini coefficient decomposition, kernel density estimation (KDE), and Moran’s I to investigate the spatiotemporal characteristics of beautiful city construction. The results indicate that the regional Beautiful City Construction Index (BI) increased steadily at both the regional and subregional levels, with all sample cities achieving continuous improvement. A consistent development gradient of lower reach > middle reach > upper reach was identified, with gradually narrowing inter-regional gaps. Importantly, the primary source of regional disparity shifted from inter-subregional differences to intra-subregional differences around 2021. Statistically significant positive spatial clustering was observed, with high-value clusters concentrated in downstream areas and low-value clusters in upstream areas, while the overall clustering effect gradually weakened. This study provides empirical evidence for targeted urban governance and regional coordinated development, offering a typical Chinese practice for global SDG research. Full article

Grounded in a human–land coupling framework, this study takes Purple Mountain, a representative composite urban mountain heritage site, as the research object. It integrates historical archives, field survey data, and multi-source geospatial data, and adopts a set of GIS-based spatial statistical methods, including the nearest neighbour index, kernel density estimation, standard deviational ellipse, coupling coordination degree model, and Geodetector. This paper systematically explores the spatial differentiation, spatiotemporal evolution, human–land coupling patterns, and multidimensional driving mechanisms of four heritage types: geomorphological relics, ritual architecture, modern commemorative heritage, and eco-scenic heritage. The results show that: (1) Heritage resources across Purple Mountain display statistically significant clustering, with ritual architectural heritage exhibiting the highest agglomeration degree; heritage sites form an east–west high-density corridor along the southern foothills, presenting a consistent northeast–southwest spatial orientation. (2) Purple Mountain heritage has undergone multi-stage diachronic evolution. Jointly driven by topographic constraints and socio-cultural forces, its heritage quantity, spatial coverage, and functions fluctuated across dynasties, with an overall expanding trend. (3) A total of 63.07% of the study area’s grid units are in a near-dissonant human–land coupling state, while highly coordinated units are concentrated in the southern core corridor of the Ming Xiaoling Mausoleum, Sun Yat-sen Mausoleum, and Linggu Temple, with remarkable disparities among heritage types in coupling patterns among the four heritage categories. (4) Historical and cultural aggregation density dominates heritage spatial differentiation, while topographic factors show weak explanatory power, and all influencing factor interactions present prominent non-linear enhancement effects. This study establishes a three-level quantitative framework of “spatial pattern—coupling coordination—driving mechanism”, enriching the theoretical framework of human–land coupling for urban mountain heritage, and provides scientific support for refined coordinated governance of mountain heritage embedded in high-density urban environments. Full article

Reliable attribution of artificial intelligence (AI)-generated text to a specific large language model (LLM) matters increasingly as LLMs proliferate, yet where quantum machine learning actually stands on this task has, to our knowledge, never been measured systematically. We benchmark the quantum support vector machine (QSVM) for binary attribution between Gemma 3 and Qwen 2.5 on a 5800-sample corpus from paired prompts: 83 configurations sweeping qubit count, regularization, training-set size, feature-map family, and circuit depth under exact, noiseless classical statevector simulation. QSVM validation accuracy plateaus at approximately 88%, whereas a classical support vector machine with a radial basis function kernel reaches approximately 97.8% on the identical fourteen-dimensional inputs: the ceiling belongs to the quantum (fidelity) kernel, not to the input representation. We measure the mechanism: off-diagonal quantum kernel values shrink exponentially with qubit count, the signature of exponential kernel concentration. The same classical model recovers the stylometric attribution fingerprint, showing it belongs to the shared feature pipeline rather than to the quantum kernel. All large-scale headline results generalize to an independent 1000-text test set produced after every design decision was frozen. The study provides a cautionary, reproducible benchmark for quantum kernel natural language processing and outlines an open-set extension as future work. Full article

We propose a resolution-adaptive Bayesian wavelet-denoising method for noisy one-dimensional signals. The main contribution is a spike-and-slab prior whose continuous slab is a mixture of a compactly supported Wendland-type polynomial kernel and the semicircle density, with data-adaptive, resolution-specific mixture weights, produced by a low-dimensional empirical-Bayes trend. The Wendland component concentrates mass near zero and vanishes smoothly at the support boundary, whereas the semicircle component is more dispersed. This construction combines explicit sparsity and support control with an interpretable mechanism for adapting the shrinkage shape across resolutions. Under squared-error loss, we derive the posterior-mean estimator; establish key symmetry, boundedness, continuity, and limiting properties; define pointwise fixed-hyperparameter bias, variance, and risk; and develop an empirical-Bayes estimation procedure. The Wendland contribution has finite-sum expressions under a Laplace working likelihood, while the semicircle contribution is evaluated by stable one-dimensional integration. Simulations using the Bumps, Blocks, Doppler, and HeaviSine signals compare the proposed Gaussian- and Laplace-likelihood versions with universal thresholding, false-discovery-rate (FDR) thresholding, cross-validation (CV), Stein’s unbiased risk estimate (SURE), the Bayesian adaptive multiresolution shrinker (BAMS), and a nonlocal-prior (NLP)-based method. In the primary Gaussian-error simulation study, the Gaussian-likelihood version was the strongest non-NLP method in 24 of the 36 design cells, including 11 of the 12 low signal-to-noise ratio (SNR) cells, and had a substantially more favorable computational profile than the Laplace-likelihood version. Analysis of a seismic acceleration trace from the 2008 Chino Hills earthquake illustrates attenuation of rapid fluctuations and preservation of the dominant acceleration event under the chosen diagnostics. Using the processed channel-1 trace as surrogate truth, the corresponding semi-synthetic validation showed that WS–Gaussian improved on the noisy observation at lower and moderate SNRs but not at the highest SNR. Full article

The city center of Kahramanmaraş suffered considerable damage as a result of the Mw 7.7 Pazarcık and Mw 7.6 Elbistan earthquakes, which took place on 6 February 2023. The present study examines the impact of urban development, groundwater conditions, and paleo-drainage systems on the pattern of earthquake damage in the Dulkadiroğlu and Onikişubat districts, which make up the urban center of Kahramanmaraş. Geological, hydrogeological, geomorphological, urban development, and building damage data were combined and analyzed using a GIS environment. Kernel Density Estimation (KDE) and spatial overlay analyses were carried out in order to show the spatial concentration of the damage. The results show that severely damaged or collapsed buildings were not distributed at random; rather, they were found in regions featuring largely young geological units, shallow groundwater levels, and former stream channels. In several areas where rapid urban expansion has occurred over the past thirty years, there is a clear spatial relationship between high damage densities and paleo-drainage systems. These findings reveal a spatial relationship between severe building damage and areas characterized by alluvial deposits, reconstructed paleo-drainage corridors, and potentially shallow groundwater conditions. Although the available data do not allow the damage to be directly attributed to site amplification or other geotechnical mechanisms, the observed spatial patterns identify areas where local ground conditions warrant further geotechnical and seismological investigation. Full article

Co-endemic zoonoses may occur in the same broad geographic settings but not necessarily the same communities or time periods. This study investigated whether Crimean–Congo hemorrhagic fever (CCHF) and brucellosis exhibit convergent or divergent spatiotemporal patterns across geographic scales in a livestock-dependent, CCHF-endemic province of Türkiye. This retrospective study analyzed confirmed human CCHF and brucellosis cases reported to the national infectious disease surveillance system between 2011 and 2024. Temporal analyses, kernel density estimation, settlement-level random labeling, and within- and cross-disease Knox tests were performed. Among the 611 eligible cases, CCHF (n = 442; 72.3%) and brucellosis (n = 116; 19.0%) accounted for 91.3%. CCHF was strongly seasonal, with 99.3% of cases occurring between April and September, whereas brucellosis occurred throughout the year. Although broad density surfaces partially overlapped, same-settlement co-occurrence was substantially lower than expected (O/E = 0.37; p < 0.001) and remained so in the sensitivity analysis (O/E = 0.74; p = 0.001). Cross-disease analysis showed no evidence that CCHF and brucellosis occurred closer together in both space and time than expected. Annualized CCHF concentration peaked in 2020–2021, whereas brucellosis peaked in 2022–2024. Integrated surveillance may use shared regional infrastructure, but interventions should be tailored to disease-specific settlements, transmission pathways, and periods of risk. Full article

The prefectures and cities along the Yunnan–Vietnam border constitute a key cultural corridor connecting China and ASEAN. Under the dual pressures of globalization and modernization, the regional cultural landscape is undergoing profound structural transformation. This study takes 194 traditional villages and 942 municipal-level and above intangible cultural heritage (ICH) items in Honghe, Wenshan, and Pu’er as the research objects. Kernel density estimation, the standard deviation ellipse, and the gravity center model were employed to identify their spatial distribution patterns, while the spatial mismatch index and GeoDetector were used to examine their spatial coupling relationship and influencing mechanisms. The results indicate the following: (1) traditional villages exhibit a single-core clustered distribution concentrated in the Ailao Mountains-Honghe River Basin of Honghe Prefecture, whereas ICH displays a one-core-two-cluster pattern with relatively weak agglomeration, and the gravity centers of the two heritage systems are separated by 61.21 km; (2) a significant systematic spatial mismatch exists between traditional villages and ICH, with Honghe characterized as a high negative mismatch region, while Pu’er and Wenshan represent positive mismatch regions; (3) their spatial relationship is jointly shaped by nonlinear interactions among natural geographical, socioeconomic, and historical-cultural factors, forming an interaction mechanism dominated by the ecological constraints of hydrothermal conditions and topography together with the spatial organizational effects of border ports and transportation networks. This study reveals the spatial reorganization pattern of cultural heritage elements in the three prefectures and cities along the Yunnan–Vietnam border and provides a scientific basis for the coordinated conservation and spatial optimization of traditional villages and intangible cultural heritage. Full article

Landslide hazard assessment provides an important basis for regional geological disaster prevention and mitigation. Landslide hazard is commonly characterized in terms of three fundamental dimensions: spatial occurrence, temporal occurrence, and potential magnitude. Based on this conceptual framework, Guiyang, a typical mountainous city in western China, was selected as the study area, and slope units were adopted as the basic assessment units. First, eight topographic, geological, and hydrological conditioning factors were incorporated into an information-value model to evaluate the landslide susceptibility of each slope unit. The resulting min–max-normalized susceptibility index was used as the spatial susceptibility component. Second, the historical landslide inventory was combined with kernel density estimation, and a Poisson-based exceedance probability model was used to estimate the model-based exceedance probability of one or more landslides occurring in each slope unit under a 10-year scenario time window. This probability was used as the temporal component. Third, the potential landslide volume of each slope unit was predicted using machine-learning algorithms. The predicted small-, medium-, and large-volume classes were assigned ordinal magnitude weights of 1, 2, and 3, respectively, and were used as the magnitude component. Finally, the three components were integrated using a multiplicative composite index to obtain the relative landslide hazard of each slope unit. The results were as follows. The overall landslide hazard in Guiyang was dominated by medium and low hazard levels. Furthermore, 422 high-hazard slope units were identified, accounting for 15.58% of all slope units. These units were mainly concentrated in the central and northern regions. In terms of administrative divisions, Xifeng County, Kaiyang County, Xiuwen County, Qingzhen city, and Huaxi district ranked among the top five administrative regions with the most high-hazard slope units. Therefore, these regions should be considered key areas for disaster prevention and mitigation. Based on the hazard zonation, 21 selected high-hazard slopes were examined using UAV imagery and on-site inspection to document their representative geomorphological and deformation characteristics. The observations provided qualitative field support for the geological plausibility of selected high-hazard predictions. The findings of this study provide an important decision-making basis for landslide mitigation efforts by the Guiyang Municipal Government. Full article

Rural cultural tourism has become an important pathway for rural revitalization in underdeveloped regions, yet its spatial organization and driving mechanisms remain insufficiently understood. This study investigates the spatial patterns, functional typology, and driving mechanisms—understood as statistically significant spatial associations—of 300 policy-designated rural cultural tourism villages in Gansu Province, China, using an integrated geospatial analytical framework incorporating the Average Nearest Neighbor Index, Kernel Density Estimation (KDE), the Lorenz Curve, Global Moran’s I, and the Geo-detector model. The results reveal a significant clustered distribution characterized by a pronounced “high-density southeast–low-density northwest” pattern. Five functional village types exhibit distinct spatial concentration characteristics associated with differences in resource endowments and development orientations. Geo-detector analysis identifies transportation accessibility, economic development, and topographic conditions as the factors most strongly associated with spatial heterogeneity. Interaction effects among natural, socioeconomic, and policy variables are also detectable. These findings advance the understanding of the spatial organization of rural cultural tourism and demonstrate the value of GIS-based spatial analysis for supporting evidence-based rural revitalization planning and sustainable territorial development in ecologically fragile regions. Full article

Walnut kernels are highly susceptible to lipid oxidation because of their high content of unsaturated fatty acids. This study screened five lipid-soluble antioxidants—tert-butylhydroquinone (TBHQ), butylated hydroxytoluene (BHT), propyl gallate (PG), dilauryl thiodipropionate (DLTP), and ascorbyl palmitate (AP), and further evaluated binary and ternary formulations based on BHT. Treated walnut kernels were subjected to accelerated oxidation, and the oxidative stability was assessed using peroxide value, thiobarbituric acid-reactive substances (TBARS), DPPH radical scavenging activity, electron spin resonance, fatty acid composition, thermogravimetric analysis, and sensory evaluation. Among the individual antioxidants evaluated at their respective maximum permitted concentrations, BHT showed the most consistent protective effect, with the lowest oxidation indices, the strongest DPPH radical scavenging activity, and the weakest thermally induced radical signal. The BHT + TBHQ formulation performed significantly better than BHT alone in several oxidation indices (p < 0.05). The ternary BHT + TBHQ + DLTP formulation showed the best overall performance, with antioxidant activity comparable to that of the commercial antioxidant blend and significantly lower TBARS values than the BHT + TBHQ group (p < 0.05). Linoleic, oleic, and α-linolenic acids remained at 58.78–62.54%, 17.62–21.33%, and 10.64–11.31%, respectively. No significant differences in aroma or flavor were observed among treatments (p > 0.05). These findings indicate that BHT + TBHQ + DLTP effectively improves the oxidative stability of walnut kernels during accelerated storage. Full article

We develop a design-conditional limit theory for kernel estimators of conditional U-functionals based on locally stationary functional random fields observed at irregular random locations and under incomplete response observation. The covariates take values in a separable Hilbert space, the responses are allowed to take values in a general Polish space, and the target is indexed by a class of symmetric kernels of a fixed order. Functional localization is induced by single-index semi-metrics, while spatial localization is performed on the rescaled observation domain. Missing responses are incorporated through a complete-case construction under a Missing At Random condition and a uniform-positivity assumption. The resulting estimator is a ratio of spatially weighted U-statistics with random tuplewise observation indicators. The asymptotic analysis must account simultaneously for four sources of complexity: dependence within the spatial field, nonstationarity across an expanding domain, concentration in an infinite-dimensional covariate space, and the random thinning generated by missing responses. Conditioning on the sampling locations removes the randomness of the spatial design weights but does not eliminate dependence among the observations. We therefore derive a design-conditional projection decomposition adapted to the triangular-array structure of the model. The leading component is represented by a spatially dependent complete-case empirical process, whereas the higher-order canonical terms are controlled uniformly over the response kernels, functional-target points, single-index directions, and rescaled spatial locations. The proofs combine stationary tangent-field approximations for locally stationary random fields, large-block–small-block decompositions, coupling arguments under spatial absolute regularity, small-ball probability estimates, and entropy bounds for the joint indexing class. These arguments yield a uniform stochastic expansion in which the empirical fluctuation, the spatial–functional smoothing bias, and the local-stationarity approximation error appear as distinct contributions. In particular, the local-stationarity remainder has no counterpart in the strictly stationary theory and quantifies the cost of replacing the observed nonstationary field with its stationary tangent approximation. Under the MAR and positivity conditions, complete-case sampling reduces the effective local information and modifies the covariance structure, but it does not change the formal order of the uniform-convergence rate. Under strengthened moment, mixing, entropy, and negligibility conditions, we establish weak convergence of the normalized conditional U-process in the corresponding supremum-norm function space to a tight centered Gaussian process. The limiting covariance is determined by the complete-case first-order projection and consequently retains the effect of the observation propensity and the spatial dependence structure. We also introduce a complete-case leave-tuple-out spatial prediction criterion for bandwidth selection and prove oracle optimality over admissible bandwidth families. The general theory applies to conditional rank association, discrimination probabilities, set-indexed conditional distribution functionals, and related pairwise statistical-learning criteria. Simulation experiments and applications to spatial environmental and epidemiological data illustrate the finite-sample implications of the theory and the stabilizing role of single-index localization. Viewed through the lens of data-driven science, the framework addresses a fundamental asymmetry between the information carried by irregular, locally heterogeneous functional covariates and the selectively observed response tuples. By combining design conditioning, complete-case normalization, tangent-field localization, and single-index dimension reduction, the proposed approach resolves this inferential asymmetry at the level of the model by matching estimation and uncertainty quantification to the information actually available locally, without imposing artificial stationarity or complete-data symmetry. Full article

Accommodation establishments constitute a core component of tourism infrastructure, and their location choices directly affect water resource utilization, land pressure, and the spatial equilibrium of tourism development—issues that are particularly acute in vast arid regions. Yet the spatial organization of accommodation supply across extensive drylands characterized by fragmented oasis distribution, and the reasons why standard and non-standard accommodation follow divergent location logics, remain poorly understood. This study addresses three questions: (1) How are nine accommodation categories, differentiated by type and quality, distributed across Xinjiang? (2) Do directional spatial associations exist among categories that are consistent with hierarchical, path-dependent development? (3) Which factors drive these patterns, and do their effects exhibit the nonlinearity and threshold behavior predicted by location theory? Drawing on 12,073 accommodation establishments from the Ctrip platform, we construct a staged analytical framework in which each technique answers a specific question: the nearest-neighbor index and standard deviational ellipse characterize global patterns; kernel density estimation and OPTICS clustering identify local agglomerations; directional local co-location quotients measure asymmetric spatial associations; and XGBoost–SHAP isolates nonlinear drivers and threshold effects. Results reveal a highly concentrated “single-core, multi-center” structure anchored by Urumqi, Yining, and Kashgar, with rapid expansion toward the Ili Valley, Kashgar, and Altay since 2019. Standard accommodation tracks urban centrality and transport nodes, while non-standard accommodation tracks tourism resource endowments, consistent with location-theoretic expectations. Directional co-location analysis reveals hierarchical spatial associations among categories, and driving factors exhibit pronounced nonlinear threshold effects. From a sustainability perspective, the identified thresholds—elevation (1360 m), water-body proximity, and distance to rural tourism demonstration sites (3 km)—constitute quantifiable, spatially explicit sustainability indicators that can be incorporated into planning tools to monitor and steer accommodation development away from ecologically sensitive zones. Global Moran’s I diagnostics of model residuals (reduction of 83–99.7%) suggest that these findings are unlikely to be artifacts of spatial autocorrelation; this diagnostic, however, complements rather than replaces spatially blocked validation. The study contributes category-differentiated, spatially directed evidence for policies balancing tourism expansion against water security and ecosystem integrity, serving sustainable tourism development in arid-region destinations. Full article

Roof-contact backfilling is a critical determinant of stope stability in cut-and-fill mining, and the rheological properties of backfill slurry decisively influence the quality of roof contact. To investigate the flow characteristics of unclassified tailings backfill slurry and their effect on rheological parameters, this study uses the Daye Iron Mine as its engineering case. It adopts a combined laboratory and numerical simulation approach. The physicochemical characteristics of the unclassified tailings and the rheological behavior of the slurry were systematically characterized using particle-size analysis, density measurements, spreadability tests, and rheometer measurements. Subsequently, a numerical model of the L-type flow tester was developed in COMSOL Multiphysics (6.4) to simulate the flow process at varying concentrations. Based on the simulation results, a Gaussian process regression (GPR)-based inversion model for rheological parameters was proposed, and the predictive performance of different kernel functions was compared and evaluated. Finally, the existing backfilling scheme at the Daye Iron Mine was optimized based on the obtained rheological characteristics to improve the roof-contact rate. The results indicate that the unclassified tailings from the Daye Iron Mine have a median particle size of 12.1 μm and a density of 2855 kg·m⁻³, with CaO, Al₂O₃, and MgO as the primary active components. Under the same cement-to-tailings ratio, slurry flowability decreases markedly with increasing concentration. The rheological curves exhibit three stages, with the third conforming to the Bingham model; both yield stress and viscosity increase exponentially with concentration. Evaluation of the inversion results demonstrates that the GPR model with the Rational Quadratic (RQ) kernel achieves optimal performance. The recommended slurry concentration for the Daye Iron Mine is determined to be in the range of 69–71%, and the recommended spacing between filling pipelines is 13.34–18 m. This study reveals the flow evolution patterns of unclassified tailings backfill slurry, demonstrates the potential of the GPR-based inversion approach, and optimizes the roof-contact backfilling scheme, offering a scientific reference for flow characterization and backfill optimization in analogous mining operations. Full article

Maize (Zea mays L.) landraces are rich sources of genetic and nutritional diversity, yet their compositional traits remain undercharacterized compared to modern hybrids. This study compiled and analyzed data from 1554 maize samples—995 landraces and 559 hybrids—evaluated at the CIMMYT Maize Quality Laboratory over more than a decade. Traits assessed included kernel structure, macronutrients, iron, zinc, carotenoids, and anthocyanins. Landraces showed greater variability and higher average concentrations of protein (10.88 vs. 9.92%), iron (17.63 vs. 13.91 mg kg⁻¹), zinc (22.70 vs. 18.98 mg kg⁻¹), and anthocyanins, whereas hybrids, particularly those developed through provitamin A biofortification programs, exhibited higher average provitamin A (3.33 vs. 1.84 µg g⁻¹) and total carotenoid concentrations. Correlations between kernel structural components and nutrient composition highlighted the structural basis of grain quality variation. These findings demonstrate that landraces and hybrids provide complementary nutritional profiles that can jointly support breeding, food innovation, and sustainable food systems. Full article

Beech (Fagus sylvatica L.) kernels are a sustainable plant material suitable for human consumption. This study analyzes the impact of moistening and roasting (thermal treatment) beech kernels on the development of novel edible oils. Crude beech kernels (CBKs) and thermal-treated beech kernels (TTBKs) were analyzed for their chemical and bioactive compound compositions to assess the influence of thermal treatment prior to different extraction methods (cold pressing, cold solvent extraction—Folch, and hot extraction—Soxhlet). The predominant phenolic compounds found in beech kernels belong to the flavanol group, with the most abundant being kaempferol-rutinoside (773.34 µg/g in CBK and 714.77 µg/g in TTBK). Lutein was the most significant carotenoid present in the oils (384.80 µg/g in crude oil and 456.21 µg/g in thermal-treated oil), and its concentration increased due to kernel conditioning. Thermal treatment had a minimal impact on the overall fatty acid composition, while the majority of these acids are predominantly mono- and polyunsaturated. Cold-pressed oils have higher viscosity than oils extracted using Folch’s technique. Overall, beech kernels represent a sustainable plant material, while seed conditioning through moistening and roasting enhanced bioactive compound transfer to extracted oils. Full article