Search Results (945)

Achieving robust environmental responsiveness in early stage architectural spatial layout design remains a critical challenge under the imperatives of global carbon neutrality and climate-adaptive building practice. Conventional parametric design and multi-objective optimization approaches suffer from computational inefficiency, inadequate constraint satisfaction, and opaque generative logic when operating in high-dimensional design spaces. This paper presents a mathematically rigorous, climate-responsive spatial layout generation framework that unifies category theory with conditional diffusion modeling. The proposed method formalizes site-specific environmental parameter systems and architectural spatial topologies as two small categories, and establishes structure-preserving environment-to-space mappings via covariant functors; natural transformations are further introduced to characterize morphological transitions across distinct design strategies. A conditional diffusion model (CDM) serves as the generative engine, producing candidate spatial topological configurations subject to environmental parameter conditioning. A three-stage categorical constraint screening mechanism—constructed from groupoid structures and pullback limits—enforces simultaneous compliance with functional adjacency requirements, topological coherence, and multi-criteria environmental performance targets. Extensive experiments across three climatically contrasting sites (Hangzhou, Qingdao, and Lijiang) demonstrate that the framework substantially enhances environmental response performance while preserving spatial topological rationality, achieving competitive generation efficiency and constraint satisfaction relative to conventional parametric optimization baselines. These findings establish that categorical structures can serve as interpretable, mathematically consistent constraint engines within AI-driven generative design pipelines, offering a principled computational paradigm for climate-responsive architectural layout synthesis. Full article

We propose a new class of spatio-temporal GARCH models designed to capture volatility dynamics that propagate jointly across time and space. Existing spatio-temporal GARCH formulations typically account for either lagged spatial spillovers or contemporaneous interactions separately, and therefore fail to capture the combined effect of instantaneous spatial volatility feedback and its propagation over time. To address this gap, we introduce a unified framework that incorporates both contemporaneous and lagged spatial volatility interactions within a single coherent model. At each time point, conditional variances evolve according to a temporal GARCH recursion combined with both contemporaneous and lagged spatial volatility interactions defined on a lattice. This structure allows volatility shocks to diffuse instantaneously across neighboring locations and persist over time through spatially structured feedback mechanisms, extending existing spatial and spatio-temporal GARCH formulations. We establish sufficient conditions for the existence of a unique strictly stationary and ergodic solution based on contraction properties of a combined spatial–temporal operator. Statistical inference is conducted via Gaussian quasi-maximum likelihood estimation (QMLE). We derive consistency and asymptotic normality of the QMLE under two asymptotic regimes: (i) increasing temporal domain with fixed spatial size, and (ii) joint asymptotics where both the number of time periods and spatial locations diverge. In both cases, the asymptotic covariance matrix admits a standard sandwich form and can be consistently estimated. An extensive Monte Carlo study confirms the theoretical results. The simulations show that the QMLE performs well even under strong spatial and temporal persistence and remains robust to heavy-tailed innovations. In particular, increasing the spatial domain substantially improves estimation accuracy, highlighting the efficiency gains induced by spatial information. The proposed model provides a flexible and tractable framework for analyzing volatility processes evolving jointly in time and space. Full article

Dry soil spectral reflectance provides a stable baseline for characterizing soil optical properties and supporting the retrieval of soil attributes from remote sensing. However, despite the large number of studies on soil spectral reflectance, most existing research primarily focuses on empirical relationships between spectra and soil properties. The representation and prediction of dry soil reflectance as a baseline condition, particularly under the influence of environmental factors, remain insufficiently explored, and the generalizability of existing models still needs improvement. Therefore, this study collects 700 dry soil samples with laboratory-measured spectral reflectance from Northeast China and quantitatively analyzes the contribution of environmental covariates (soil properties, parent material, and geographical location) using the SHAP method. Then, an environmental and edaphic-factor-driven smooth dry soil reflectance model (EEDSR) model covering 400–2500 nm is developed based on gradient boosting regression (GBR), and its accuracy is evaluated using global ISRIC soil datasets. Our results indicate the following: (1) the reflectance of dry soil is closely related to the soil properties in the VIS to SWIR range. The reflectance of dry soil of 400–2500 nm is positively correlated with clay percentage, longitude, and parent material but negatively correlated with latitude, sand percentage and silt percentage. And its correlation with other variables (such as soil organic matter, pH, and EC) varies with wavelength. (2) The EEDSR model exhibited high predictive accuracy across the 400–2500 nm spectral range (R² = 0.93, RMSE = 0.018). Additionally, incorporating parent material (PM) and geographical factors into the predictor set enhanced the accuracy of dry soil reflectance prediction by 13.4%. (3) The spatial consistency between the predicted soil reflectance in Northeast China and the satellite observations indicates that the EEDSR model has good performance in predicting soil reflectance, as the bias of reflectance gradually increasing from west to east is consistent with the precipitation distribution in Northeast China. (4) The generalization ability of the EEDSR model was confirmed by global ISRIC datasets (R = 0.94), outperforming the deep learning-based Soil Optical Generative Model (SOGM) (R = 0.27). Overall, this study presents an efficient and interpretable framework for modeling dry soil spectral reflectance, providing a robust reference for soil reflectance prediction and remote sensing-based soil property retrieval. Full article

Gait impairment is a common motor manifestation of Parkinson’s disease (PD), which is also frequently accompanied by other motor abnormalities such as bradykinesia, rigidity, postural instability, and movement asymmetry. These motor impairments are closely associated with reduced mobility and increased fall risk. Although wearable plantar insole sensing provides a promising basis for objective gait assessment, existing studies have mainly focused on conventional time- or frequency-domain descriptors, whereas the nonlinear complexity of gait, laterality-related imbalance, and deviation from normal gait patterns remain insufficiently characterized in an integrated manner. To address this gap, this paper proposes FID-Gait, which is a three-domain fusion framework for PD identification using instrumented insole data. The framework combines automated gait-cycle segmentation with multidomain feature modeling, including a fractal domain for nonlinear gait complexity, a plantar-loading–phase imbalance (PLPI) domain for loading asymmetry and temporal disturbance, and a covariance-adjusted deviation (CAD) domain for measuring deviation from normal gait patterns. Experiments on the PhysioNet Gait in Parkinson’s Disease dataset showed that FID-Gait achieved strong discriminative performance under multiple evaluation protocols. At the gait-cycle level, the selected MLP classifier achieved an accuracy of 99.11% and an F1-score of 99.47%. At the subject level, the selected AdaBoost classifier achieved the highest accuracy of 90.22% and the best F1-score reached 93.02%. Five-fold cross-validation further supported the robustness of the proposed representation, and leave-one-subject-out evaluation provided preliminary evidence of subject-independent generalization. Overall, FID-Gait provides an effective and interpretable framework for PD gait characterization and identification in offline experimental settings. Full article

Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in NM, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling. Full article

As a key component of ecosystems, the synergistic relationship between wetland carbon storage and habitat quality is vital for maintaining ecological functions, and its evolution is profoundly influence by changes in wetlands. This study focuses on wetlands in western Jilin Province. Based on four sets of land use data from 2010 to 2023 and utilizing the InVEST model, combined with methods such as spatial autocorrelation, the Coupled Coordination Degree Model, and the GeoDetector, the study analyzed the co-variation of carbon storage and habitat quality, as well as their response to landscape patterns. The study found that between 2010 and 2023, the wetland area increased by a net 858.13 km², and landscape fragmentation was generally alleviated, although local connectivity continued to degrade. Regional carbon storage increased by 68.1%, totaling 7.43 × 10⁶ Mg, while the habitat quality index exhibited high spatiotemporal stability, fluctuating marginally between 0.609 and 0.621. Spatially, high-value areas remained primarily concentrated within nature reserves. Results of bivariate spatial autocorrelation analysis revealed a strengthening of spatial positive autocorrelation between carbon storage and habitat quality, with Moran’s I increasing from 0.410 to 0.501. The coupled coordination degree model further confirmed that the level of synergy between the two services exhibited a pattern of higher values in the north and lower values in the south, and that areas of high coordination expanded significantly outward following restoration projects. GeoDetector analysis indicates that the largest patch index is the core factor driving the synergistic development of ecosystem services. The results also suggest that the integrity of core wetland patches and a heterogeneous landscape pattern can promote the synergistic improvement of carbon storage and habitat quality through boundary effects and habitat complementarity. Full article

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar offers significant advantages in mainlobe deceptive interference suppression, as its transmit steering vector contains both angle and range information, providing additional degrees of freedom beyond the angular dimension. However, conventional FDA-MIMO radar suffers from insufficient angle-range resolution, which limits its ability to suppress interferences located close to the target. Moreover, it lacks robustness under limited snapshots and parameter mismatch conditions. To address these issues, this paper proposes a robust beamforming method based on the FDA-MIMO radar model. A collocated sparse array with a sinusoidal element spacing offset and a logarithmic frequency offset is adopted to enhance beam resolution and resolve the periodic angle-range ambiguity problem. Based on this model, the interference-plus-noise covariance matrix is reconstructed using two-dimensional Capon spatial spectrum, and the steering vector is corrected via a joint objective function that combines MUSIC orthogonality and the flatness of the covariance residual spectrum. Simulation results demonstrate that, under conditions of near-target interferences, random range-angle errors, and frequency offset errors, the proposed method achieves a signal-to-interference-plus-noise ratio (SINR) close to the ideal value, exhibiting excellent mainlobe interference suppression performance and robustness. Full article

Adaptive beamforming based on polarization-sensitive arrays enables joint spatial–polarization filtering for mainlobe interference suppression, but mainlobe distortion and performance degradation occur when the received data include the desired signal or multiple mainlobe interferences. Accordingly, this paper proposes a mainlobe interference suppression method based on Polarimetric Sparse Iterative Covariance-based Estimation (POL-SPICE) and covariance matrix reconstruction. This method utilizes the POL-SPICE algorithm to accurately estimate the direction of arrival (DOA), polarization, and power parameters. It reconstructs the covariance matrix by nulling the corresponding source power and constructs a feature projection matrix to preprocess the received signal. These eliminate the impact of the desired signal and mainlobe interference components on subsequent joint spatial–polarization domain beamforming, ultimately achieving interference suppression and mainlobe shape preservation. Simulation results illustrate that the proposed method is applicable to scenarios with the coexistence of the desired signal and multiple mainlobe interferences, and its superiority over existing methods is verified. Full article

The psychological benefits of waterfront public spaces have become an important topic in environmental design and architectural research. However, existing studies have primarily focused on the direct relationship between physical environmental attributes and user satisfaction, with limited attention to the psychological mechanisms through which architectural design influences residents’ well-being. This study examines waterfront concert halls as a type of cultural architectural space and develops a theoretical model integrating environmental restoration theory and place attachment theory. In this model, waterfront design perception is conceptualized as a multidimensional construct including water visibility, water accessibility, water harmony, and water interactivity, while perceived restorativeness and place attachment are treated as mediating variables, and residents’ well-being as the outcome variable. Based on questionnaire data collected from 1345 urban residents across six Chinese cities and seven waterfront concert hall cases, and analyzed using covariance-based structural equation modeling, the results show that waterfront design perception has a significant positive effect on residents’ well-being. Perceived restorativeness and place attachment both play mediating roles and jointly form a sequential pathway through which environmental perception is translated into psychological and emotional benefits. These findings extend the understanding of waterfront design from objective spatial attributes to subjective experiential processes and provide empirical support for the design of waterfront cultural architecture aimed at enhancing the well-being of urban residents. Full article

This article proposes modified test statistics for six blind covariance-based detectors used in data fusion cooperative spectrum sensing, where the full Hermitian sample covariance matrix (SCM) of the received signal is replaced by a symmetric real-valued partial sample covariance matrix (PSCM). This substitution results in a substantial reduction in overall computational complexity compared to the original SCM-based formulations, while preserving or improving detection accuracy under realistic conditions that include non-uniform noise powers, time-varying distance-dependent path loss, spatially correlated shadowing, and multipath fading with a random Rice factor. The computation of the PSCM requires $50\%$ fewer floating-point operations than the full SCM and offers a hardware-friendly structure due to its reliance on real-valued arithmetic. On the test statistic side, the adoption of the PSCM leads to computational costs ranging from $3.37\%$ to $61.9\%$ of those incurred by the corresponding SCM-based test statistics. Full article

Background: Glioblastoma exhibits profound intratumoral heterogeneity, with anatomically distinct tumor zones characterized by divergent molecular programs that drive therapy resistance. Whether magnetic resonance imaging (MRI)-derived radiomic features can capture these regional transcriptomic differences remains unknown. We aimed to determine whether subcompartment-level radiomic features associate with transcriptomic pathway enrichment scores derived from biologically approximate tumor zones. Methods: We matched 28 patients (mean age 58.5 years; 13/28 MGMT methylated) across the IvyGAP RNA-seq atlas and the IVYGAP-RADIOMICS datasets. Single-sample GSEA (ssGSEA) pathway scores were computed for 24 gene sets. Radiomic features (3920 per subcompartment) were reduced to 597. Nested leave-one-patient-out cross-validation (LOPO-CV) with Elastic Net served as the primary predictive analysis; linear mixed-effects models (LMM) provided exploratory associational analysis. Analyses used a biologically motivated but spatially non-co-registered zone-to-subcompartment mapping; all reported associations are zone-approximate. Results: Twenty-one of 24 pathways showed no predictive signal (R²_cv ≤ 0). Inflammatory Response (R²_cv = 0.185, 95% CI [0.071, 0.355], p = 0.008) was the only pathway supported by both the nested CV (FDR = 0.096) and the exploratory LMM (FDR = 0.024, ΔR² = 0.214 beyond subcompartment effects) analyses; the LMM association was robust to clinical covariate adjustment (likelihood ratio test p = 0.004). Angiogenesis (R²_cv = 0.209, 95% CI [0.028, 0.353], p = 0.006) reached nested CV significance (FDR = 0.096) but was not corroborated by the LMM (FDR = 0.445); it is therefore reported as a tentative single-framework signal requiring independent validation. T2-derived texture features were selected in 100% of folds for both pathways. Conclusions: Inflammatory Response is the only pathway supported by both analytical frameworks; Angiogenesis is a tentative nested-CV-only signal pending independent validation. The absence of signal for 21 of 24 pathways should not be interpreted as evidence of biological inaccessibility: at N = 28 (vs. N ≈ 240 required by Riley criteria), severe underpowering, attenuation from the non-spatial zone-to-subcompartment mapping, and methodological constraints each independently suffice to suppress real associations. Five of the 24 gene sets (the IvyGAP zone modules) are non-independent from the outcome data and cannot be interpreted as discovery. All reported associations are zone-approximate and may partly reflect macro-compartment (between-subcompartment) effects; validation in larger cohorts with spatially precise co-registration is essential. Full article

Electroencephalography (EEG) provides a non-invasive and high-temporal-resolution modality for decoding cognitive states, but high-density recordings remain challenging for Transformer-based models because self-attention scales quadratically with the number of channels. In addition, conventional Euclidean representations do not fully capture the intrinsic geometry of EEG covariance features, which may limit robustness in cross-subject settings. To address these issues, we propose EEG-RCformer, a Riemannian geometry-informed channel clustering Transformer for EEG decoding. The model first computes per-channel symmetric positive definite (SPD) covariance matrices from windowed EEG features and uses the affine-invariant Riemannian metric (AIRM) to identify trial-specific functional hubs. These hubs are then integrated with capacity-constrained spatial clustering to generate anatomically plausible and computationally efficient channel groups, which are encoded as tokens for a Transformer classifier. We evaluated EEG-RCformer on the MODMA and SEED datasets under both subject-dependent and -independent paradigms, achieving area under the curve (AUC) values of 0.9802 and 0.7154 on MODMA and 0.8541 and 0.8011 on SEED, respectively. Paired statistical tests further showed significant gains for MODMA in both the subject-dependent and -independent settings and for SEED in the subject-dependent setting, while SEED still showed a positive but non-significant mean improvement in the subject-independent setting. Full article

Atrial septal defect (ASD) has become increasingly common in the USA and now affects 1 in 11.3 children in some places, but space–time analysis has not been applied to this emerging trend. ASD rate (ASDR) data were obtained from the National Birth Defects Prevention Network 2003–2020. Substance (cigarettes, alcohol, cannabis, analgesics, cocaine) use data were obtained from the National Survey of Drug Use and Health. Income data were obtained from the US Census. Analysis was limited to the Non-Hispanic White population by technical factors. Time-sequential univariate and bivariate maps were prepared for both covariates and outcomes and their combinations. Spatial regression of the ASDR was performed using the R package splm. A total of 7.6% of data was interpolated by linear regression. A total of 110,107 ASD cases were identified amongst 17,751,437 live births in 27 US states across 10 reporting periods. Time series maps showed that ASDR showed concordant patterns with indices of cannabis use rather than other substances. This was confirmed by multivariate spatial regression where cannabis and cannabinoids alone were found to significantly relate to ASDR, with p = 0.00002 for cannabidiol. Cannabis legal status similarly tracked with ASDR. Compared to states where cannabis was not legal, ASDR was more prevalent in cannabis-legal states (OR = 2.73 (2.66, 2.80); E-Value 4.90 (lower C.I. 4.76)). Twenty-seven of 34 (79.4%) E-values were >9 (high range) and 34/34 were > 1.25 (causal threshold). Data show that cannabis, including cannabis legalization, is driving the US ASD epidemic. While most high-ASDR states have high rates of cannabis use, Midwestern states where cannabis is farmed, such as Kentucky, Tennessee and Missouri, do not, suggesting other routes of exposure, potentially implicating environmental contamination. ASD is a bellwether marker for cannabinoid teratogenicity, indicating that communities should carefully control cannabinoid exposure and limit transgenerational cannabinoid genotoxicity more generally. Full article

In the USA, agriculture is the largest consumer of freshwater resources, and precision irrigation (PI) can conserve water significantly while maintaining crop yield. Current approaches to soil volumetric water content (VWC) mapping for PI rely on installing a costly soil moisture sensor within each of 4–5 management zones per field. Although this strategy provides temporally dense data, it is spatially sparse. Alternatively, spatially dense remotely sensed data require calibration with in situ soil moisture measurements, which are expensive and labor intensive to obtain. Previous research indicates that soil VWC zones must be regularly reassessed, a process that is impractical without low-cost soil VWC sensors. In anticipation of deploying dense networks of inexpensive soil moisture sensors for PI in large turfgrass fields, we investigate the mapping errors and optimal sampling density required for accurate soil VWC mapping using random forests (RFs) and z-score calibration in two turfgrass sports fields in Utah. Dense sampling of soil VWC was undertaken at 101 and 103 points in each field with a theta probe. These data were systematically sub-sampled to quantify errors in z-score soil moisture maps generated with varying sample sizes. A jack-knife procedure was employed to determine the optimum number of sensors required to produce accurate RF-based soil moisture maps. The RF approach also allows identification of the most influential covariates for soil VWC prediction. For RFs, 21–79 samples were needed to characterize changing spatial patterns in fields with mean absolute errors (MAEs) of 1.39–9.71%, but for most dates only 25–40 samples were needed. The z-score calibration produced MAEs of 1.38–10.44% with as few as 10–15 samples, but the spatial patterns remain static and only the magnitude of values changes. Therefore, using RFs with 40–60 sensors was recommended to allow for accurate mapping despite dropped signals and broken sensors. Full article

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article