Search Results (11,150)

The objective of this study was to evaluate the solar photocatalytic degradation of the disperse textile dye 2-(4-amino-2-nitrophenyl)-1,3-benzothiazole using titanium dioxide nanoparticles (TiO₂ P25) under a mean solar irradiance of 492 ± 58 W/m², evaluating the effects of pH, photocatalyst concentration, and continuous aeration on process efficiency. The degradation of the dye was determined by monitoring its concentration by UV–Visible spectrophotometry, while the mineralization was evaluated by chemical oxygen demand (COD). Likewise, kinetic behavior was analyzed using pseudo-first-order and pseudo-second-order models. The results showed that the degradation efficiency increased with the concentration of TiO₂, reaching the highest yield with 400 ppm of TiO₂ and continuous aeration, which confirms that both variables are determining operating factors for maximizing photocatalytic efficiency. pH exerted a significant influence on the activity of the system, obtaining the highest degradation efficiencies and the greatest reductions in COD under slightly alkaline conditions (pH 8–9), a behavior attributed to the greater colloidal stability of TiO₂ and the modification of its surface properties with respect to its point of zero charge (pHpzc ≈ 6.2). The pseudo-second-order model generally provided an adequate empirical description of the experimental data; however, the best-fitting model varied depending on the experimental condition. The simultaneous decrease in the concentration of the dye and the COD confirmed that the treatment produced not only the decolorization of the solution, but also the progressive oxidation of the organic matter. Integrating kinetic analysis with the simultaneous assessment of decolorization and COD enabled clear experimental differentiation between adsorption and photocatalysis, strengthening the interpretation of TiO₂-based solar photocatalytic systems. In conclusion, solar photocatalysis using TiO₂ and the optimization of operational variables constitute an effective treatment strategy that takes advantage of solar irradiation as the primary energy source, thereby reducing dependence on conventional artificial UV irradiation. Within the scope of this study, the combination of solar radiation, continuous aeration and appropriate operating conditions demonstrated promising potential for the treatment of textile wastewater containing persistent dyes. Full article

We propose an end-to-end differentiable framework for hedonic agricultural land pricing that integrates deep learning-based land cover classification with spatial econometric modeling into a single neural architecture. Traditional hedonic pricing approaches typically separate land cover extraction from price regression, leading to suboptimal feature representations that fail to capture the spatial spillover effects inherent to agricultural markets. In our system, a Swin Transformer-based semantic segmentation network extracts pixel-level land cover features from high-resolution multispectral imagery, which are then aggregated within parcel boundaries to produce composition vectors. These features are combined with static parcel attributes and fed into a graph isomorphism network that models spatial dependencies among neighboring parcels through message passing. The central methodological innovation is a differentiable Moran’s I operator that computes spatial autocorrelation from predicted parcel prices and incorporates this statistic into the training objective as a regularizing loss term. This constraint explicitly penalizes deviations from empirically observed target levels of positive spatial autocorrelation in agricultural land markets, thereby ensuring that the learned land cover features are optimized to explain spatial price clustering rather than generic class categories. The complete pipeline, including the segmentation backbone, graph neural network, and spatial autocorrelation computation, is fully differentiable, allowing gradients from the spatial loss to flow backwards and update pixel-level features. This design transforms land cover classification from a mere preprocessing step into an economically informed feature-learning process. The unified framework thereby produces parcel valuations that are both pixel-accurate and spatially coherent, capturing complex nonlinear dependencies such as irrigation network effects or soil-type continuity that conventional spatial econometric models cannot represent. By jointly optimizing segmentation features and their spatial spillover effects on market prices, our approach represents a significant departure from the two-stage hedonic pricing methodology. Full article

Urban vitality (UV) is the core index to measure the quality and sustainability of urban development. Accurately analyzing the complex association mechanism between UV and built environment (BE) is critical to urban planning practice. Focusing on the main urban area of Chengdu, this study integrates eight categories of multi-source data, including nighttime light data, WorldPop population distribution data, street view images, and POI data, to construct a four-dimensional UV evaluation system and identify 26 BE factors. Firstly, the UV level is quantified by objective weighting methods. Secondly, an XGBoost model combined with a SHAP framework is adopted to investigate the nonlinear association between UV and BE factors. Finally, a spatial autocorrelation model, SHAP spatial visualization and clustering methods are employed to reveal the spatial pattern of UV and the spatial heterogeneity of the association between UV and BE. The results indicate: (1) Various elements of the BE show a significant nonlinear association and threshold effect for UV. Catering services and public transit services are the core factors for UV prediction, with their combined contribution accounting for 37.47%. (2) UV shows obvious spatial differentiation and agglomeration characteristics. It presents a spatial pattern with a gradual decline from the core to the periphery. (3) The association between UV and BE presents spatial heterogeneity, and the predictive contribution logic differs distinctly across different concentric rings. The study conclusions provide a scientific basis for UV improvement and BE optimization in Chengdu. Full article

This article proposes an improved Moss Growth Optimization (IMGO) algorithm to address the drawbacks of imbalanced exploration and exploitation and susceptibility to local optima in the original MGO. IMGO integrates three-dimensional guidance, elite guidance, adaptive search, and adaptive adversarial learning to achieve a dynamic balance between global exploration and local development. IMGO is compared with eight metaheuristic algorithms on CEC2017 and CEC2022 benchmarks. Results show IMGO ranks first in 30D and 50D CEC2017 with scores of 49 and 64, surpassing second-ranked CFDA (87 and 81). For 20D CEC2022, IMGO takes first place with a score of 27, while original MGO scores 66 and ranks seventh. The Friedman test validates its strong robustness with a statistic of 1.6897, lower than CFDA’s 3.0000. Furthermore, IMGO acquires the optimal average solutions for all six engineering design problems. Experiments verify that IMGO has comprehensive advantages in accuracy and robustness, providing a reliable method for complex optimization problems. Full article

Clarifying the relationships between built environment characteristics and urban fire risk is important for developing effective fire prevention and planning strategies. However, spatial dependence and nonlinear relationships between the built environment and fire risk remain insufficiently understood. Accordingly, this study presents a geographically enhanced machine learning (GE-ML) framework that incorporates spatial adjacency into machine learning models through spatially weighted feature construction. Specifically, contiguity-based spatial weight matrices were used to derive spatially weighted features from 2D and 3D built environment variables. The Optimal Parameter-based Geographical Detector (OPGD) was applied to assess scale sensitivity and compare the explanatory power and interactions of the original and spatially weighted features. Six candidate models, including Ordinary Least Squares (OLS), KNN, MLP, Random Forest (RF), LightGBM, and XGBoost, were then evaluated under different feature configurations, followed by SHapley Additive exPlanations (SHAP) analysis of the selected model. Results show that: (1) spatial weighting generally increased the explanatory power of major built environment factors and their interactions, with Queen contiguity yielding higher q-values than Rook contiguity; (2) spatially weighted features improved predictive performance across different models, and GE-XGBoost achieved the highest R² (0.7067) and lower residual spatial autocorrelation than GWR and GWRF; and (3) 2D and 3D built environment features accounted for 59.55% and 40.45% of the total SHAP importance, respectively, with Geo-TPD, Geo-BVD, Geo-PS, and Geo-LUI identified as the most important features. SHAP analysis further revealed nonlinear relationships and interactions between these features and predicted fire risk. These findings highlight the value of incorporating spatial adjacency information into fire risk modeling and support spatially differentiated fire risk management. Full article

From a social–ecological systems perspective, cold-region rural human settlements are human-centered spatial settings adapting to population shrinkage, declining density, and prolonged cold; existing assessments often compress change into single-period scores or endpoints and mix pressures with settlement states, obscuring evolutionary pathways and transformation needs. This study examines 13 prefecture-level units in Heilongjiang, China, from 2014 to 2024 by measuring settlement state, identifying multidimensional trajectories, interpreting their pressure contexts, and deriving differentiated transformation modes. Fifteen indicators measure livelihoods and production, social equity and public services, ecological conditions, and governance response; population shrinkage, density decline, and compound cold exposure are analyzed as structural pressure contexts. Fixed-boundary normalization, Theil–Sen trend estimation, dimensional imbalance measurement, and Ward clustering are combined to identify system trajectories. The settlement state index increased from 0.301 to 0.529 (Δ = 0.229), and all 13 units ended above their 2014 levels. However, improvement was asynchronous: after fluctuating gains, the ecological dimension returned to slightly below its baseline, indicating that ecological improvement had not been consolidated. Three pathways were identified: low-level catch-up with widening imbalance, medium-level steady improvement with structural lock-in, and high-level acceleration with convergence under pressure. They correspond, respectively, to livelihood–governance reinforcement and service network adaptation, governance upgrading with ecological–livelihood coordination, and resilience consolidation under ecological constraints. Within the complete Heilongjiang prefecture system, linking measured state pathways to pressure contexts and transformation priorities provides a transparent basis for shifting cold-region rural governance from uniform expansion toward differentiated optimization of existing assets and services, while wider transfer requires indicator recalibration and multiscale outcome validation. Full article

Hybrid electric vehicle (HEV) front-face styling is jointly constrained by functional requirements for engine intake, radiator cooling, and thermal management and by demands for brand identity and emotional expression. Existing Kansei engineering studies have largely focused on whole-vehicle exteriors or generic electrified vehicles, paying insufficient attention to the functional boundaries of HEV front grilles. Moreover, culturally informed automotive styling often relies on designers’ subjective associations and lacks a coherent design pathway. To address these gaps, this study proposes a rough-set-driven Kansei design method for HEV front faces under cultural-semantic constraints. First, an entropy-weighted neighborhood rough-set method is used to identify key Kansei requirements. A rough-set-induced hybrid-kernel prediction model is then constructed by combining rough-set indiscernibility relations with nonlinear similarity, thereby mapping discrete front-face morphological features to users’ Kansei evaluations and predicting the performance of different morphological combinations. Finally, the resulting design knowledge is integrated with the structural characteristics of traditional motifs to generate culturally oriented front-face concepts. Results identified power, premium quality, and approachability as the three key Kansei requirements for HEV front faces. The proposed rough-set-induced hybrid-kernel support vector regression (RSIHK-SVR) model achieved a mean coefficient of determination (R²) of 0.927 and a root mean square error (RMSE) of 0.157 on the test set. Compared with the optimized standard radial basis function (RBF) kernel models and the single rough-set-induced-kernel model, RSIHK-SVR achieved the highest predictive accuracy on the test set (R² = 0.927, RMSE = 0.157), improving R² by 0.8–13.3% and reducing RMSE by 3.1–35.9% across the comparator models, thereby confirming the effectiveness of the hybrid-kernel strategy. The model-predicted morphological configurations were then integrated with the structural characteristics of bronze animal-mask, ice-crackle lattice, and fangsheng motifs to develop three front-face concepts targeting power, premium quality, and approachability, respectively. User evaluations further showed that all three concepts effectively communicated their intended Kansei semantics and exhibited favorable cultural-semantic compatibility. The proposed method thus provides quantitative decision support for conceptual HEV front-face designs with cultural identity and differentiated styling. Full article

Achieving Zero-Defect Manufacturing (ZDM) in precision micro-injection molding requires continuous monitoring of non-linear interactions among continuous thermodynamic and kinetic sensor data (e.g., melt temperature, injection pressure) and discrete equipment states. The high-throughput production of optical lenses operates under stringent physical boundaries, limiting spherical power deviations to a ±0.25 Diopters (D) threshold. This study proposes a sensor-driven digital twin framework for virtual metrology (VM) and prescriptive decision support. The proposed hybrid framework operationalizes Knowledge-Informed Machine Learning (KIML) by imposing physical constraints during evolutionary optimization and auditing the learned internal interactions via a dual-level Explainable AI (XAI) protocol. Utilizing 175,089 sensor logs, a continuous surrogate is constructed via the Feature Tokenizer Transformer (FT-Transformer). Under randomized 5-fold cross-validation, the surrogate achieves a Root Mean Squared Error (RMSE) of 0.4211 D and a Coefficient of Determination (R²) of 0.97, demonstrating a 16.8% error reduction over the 1D-CNN baseline. To rigorously evaluate inter-machine generalization and mitigate batch-level data leakage, an equipment-isolated Leave-One-Machine-Out (LOMO) protocol confirms structural robustness with an R² of 0.8820. Furthermore, residual distribution analysis demonstrates that 76.19% of the validation samples actively fall within the ±0.25 D physical tolerance. Cross-verifying intrinsic Multi-Head Self-Attention (MHSA) weights against a global proxy statistically captures the underlying associative affinities between hardware and continuous sensor metrics. Integrating this differentiable surrogate with a real-valued Genetic Algorithm (GA) enables the autonomous generation of optimized process recipes, achieving an algorithmic convergence error below 0.001 D within the continuous latent space. While future physical validation remains necessary, this framework establishes a transparent, auditable foundation for prescriptive smart manufacturing. Full article

Against the backdrop of China’s dual-carbon goals—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060—capacity optimization in the steel industry can no longer rely solely on aggregate output reduction. Instead, governance must shift toward a multidimensional approach that combines scale control, structural adjustment, and coordinated regional allocation. This study develops a quantifiable and interpretable assessment model for capacity-adjustment pressure. Monthly provincial crude steel output is used as a high-frequency proxy for capacity utilization and production adjustment. Additive time-series decomposition is applied to extract three components from monthly output—trend, residual, and volatility—which respectively represent structural evolution, short-term deviations, and exposure to shocks. The model further incorporates multidimensional variables, including downstream steel demand, resource and transport constraints, scrap steel ratio, and policy constraints. On this basis, a two-stage hybrid assessment framework is developed. In the first stage, extreme gradient boosting (XGBoost) is used to learn nonlinear relationships and derive data-driven feature importance. In the second stage, a composite pressure index is constructed and transformed into a standardized 0–100 score through a robust rank-based mapping mechanism. Dual thresholds are then used to generate three policy recommendations: maintaining current capacity, structural optimization, and capacity reduction. The results show that production trends and volatility intensity are the primary drivers of capacity-adjustment pressure, while pronounced spatial heterogeneity requires highly localized strategies. The classification assigns 25 provinces to maintaining current capacity, 3 to structural optimization, and 3 to targeted capacity reduction. Finally, integrating SHapley Additive exPlanations (SHAP) enhances model interpretability and provides a quantitative basis for shifting from indiscriminate capacity suppression toward differentiated, region-specific capacity governance, thereby supporting the sustainable low-carbon development of the global steel industry. Full article

The incidental discovery of focal liver lesions (FLLs) has reached unprecedented levels due to the widespread use of high-resolution imaging, yet unenhanced ultrasound often lacks the specificity required for definitive characterization. This review identifies Contrast-Enhanced Ultrasound (CEUS) as a transformative diagnostic pillar for common benign focal liver lesions (BFLLs). By utilizing strictly intravascular second-generation microbubble agents, CEUS enables continuous, real-time visualization of microvascular hemodynamics. This high temporal resolution allows clinicians to identify pathognomonic vascular signatures, such as the “iris-diaphragm” centripetal fill-in of hemangiomas and the rapid centrifugal “spoke-wheel” hyperperfusion characteristic of focal nodular hyperplasia (FNH). Critically, this review highlights the unparalleled safety profile of CEUS. Because microbubbles are not excreted by the kidneys and carry no risk of nephrotoxicity, CEUS is an essential diagnostic option for patients with renal failure. Furthermore, the lack of ionizing radiation and the ability to perform examinations bedside provide a safe and versatile solution for pregnant patients and those with contraindications to traditional cross-sectional imaging. This review serves as a comprehensive informational framework, equipping clinicians with detailed pathological, clinical, and radiological insights into common entities like hemangiomas, FNH, and hepatocellular adenomas (HCA). It correlates fundamental histological features—such as the “map-like” glutamine synthetase staining in FNH—with essential clinical management strategies. While multiparametric MRI remains the premier tool for complex subtyping and molecular mapping—particularly for differentiating hepatocellular adenoma variants—CEUS is established as a cost-effective and radiation-free initial diagnostic solution. This review equips clinicians with a comprehensive framework of pathological, clinical, and radiological insights to optimize the management of common benign hepatic incidentalomas Full article

Anthocyanins are water-soluble flavonoid pigments that contribute to leaf coloration, tea quality, and have potential health-promoting properties. Purple-leaf tea plants have attracted significant interest due to their high anthocyanin levels; however, cultivar-specific evidence linking light intensity with pigment accumulation, enzyme activity, and transcriptional regulation in purple-leaf tea remains limited. In this study, the purple-leaf tea cultivar ‘Ziyan’ was exposed to three light intensity treatments: low (LL, 100 μmol·m⁻²·s⁻¹), medium (ML, 200 μmol·m⁻²·s⁻¹), and high (HL, 400 μmol·m⁻²·s⁻¹). Increasing light intensity resulted in deeper purple coloration and significantly higher anthocyanin accumulation. The contents of delphinidin, cyanidin, pelargonidin, and total anthocyanins under HL increased by 124.2%, 63.4%, 57.2%, and 105.9%, respectively, compared to the control. Activities of major biosynthetic enzymes (CHS, CHI, F3H, F3′H, F3′5′H, DFR, ANS) were enhanced under ML and HL, whereas ANR activity decreased. Transcriptome sequencing identified 1349 differentially expressed genes (DEGs) (404 up-regulated, 945 down-regulated) in the LL vs. HL comparison. Notably, TFBS motif enrichment analysis revealed that up-regulated DEGs were dominated by a single cohesive SPL/SBP regulatory module, while down-regulated DEGs partitioned into independent NAC, HSF, and EIL modules, suggesting a multi-layered transcriptional regulatory network governing light-responsive anthocyanin biosynthesis. Several transcription factors, including MYB44, MYB75, bHLH162, and WRKY40, were also identified as candidate regulators. These results indicate that increasing light intensity is associated with enhanced anthocyanin accumulation, accompanied by coordinated changes in phenotype, flavonoid metabolism, enzyme activities, and gene expression, providing evidence for a possible regulatory mechanism of light-responsive pigmentation in purple-leaf tea and a basis for cultivation optimization and molecular breeding. Full article

The sponge construction serves as a fundamental design framework for hash functions and authenticated encryption algorithms. In differential cryptanalysis of large-state permutations underlying such algorithms, conventional approaches are constrained by state size and diffusion speed. The feature extraction capability of deep learning offers a potential alternative to mitigate these limitations. To improve the distinguishing performance of differential neural distinguishers against sponge-based algorithms, a methodology integrating data construction and network architecture optimization is proposed. Specifically, a multi-sample triplet input format is designed to preserve differential characteristics, and a convolutional block attention module is introduced to capture long-range dependencies along both the channel and spatial dimensions within the large-state permutation. Experimental evaluations were conducted on the Keccak and Ascon algorithms. For Keccak, the maximum distinguishable round number was identified as 3. At this round number, Keccak-p achieved full distinguishability (100% accuracy), while the sponge-based SHA-3-256 attained a distinguishing accuracy of 99.99%, improving upon the previous best result by 0.95 percentage points. For Ascon, the maximum distinguishable round number was 4, where Ascon-p achieved an accuracy of 54.85% with 64 sample pairs—the highest reported accuracy for this setting—while delivering comparable performance at the matched 32-pair setting (53.40% vs. 53.54% in prior work) with approximately one-twelfth of the training epochs; under the same setting, the sponge-based Ascon-Hash256 achieved an accuracy of 53.06%. These findings demonstrate the effectiveness of the proposed framework in enhancing neural distinguisher accuracy against sponge-based algorithms and offer an analytical approach for empirical security evaluation, with results qualitatively consistent with the indifferentiability bound of the sponge construction. Full article

The Newton Downhill Optimizer (NDO) combines a derivative-free downhill relation with population differences. However, its Hybrid-Guided Operator uses a random reference and persistent best-solution guidance, which can cause directional fluctuations and premature population contraction. This study proposes the Adaptive Fitness–Distance-Guided Newton Downhill Optimizer (AFDNDO). Fitness–Distance Balance selection identifies guiding individuals that account for both solution quality and spatial diversity. Stage protection, elite protection, and historical success-rate feedback regulate activation of the improved branches. A tripodal heavy-tailed update and a wave-weighted masked differential update reconstruct the two original branches. A non-uniform mutation is also triggered for low-quality individuals when the global best value stagnates. Across 30 independent runs on CEC2017, CEC2020, and CEC2022, AFDNDO attained the lowest mean rank in all six formal benchmark configurations. Its mean ranks on the 10-, 30-, and 50-dimensional CEC2017 tests were 1.172, 1.241, and 1.276, respectively. Dynamic path-planning environments included rigid obstacles, three levels of soft-risk regions, and moving obstacles. In the single-target environments, AFDNDO–DWA achieved a 100% execution success rate without collisions. In the multi-target environments, it reduced the mean objective value by 4.46–6.64% relative to NDO. It also increased the success rate from 63.33% to 70.00% in the most constrained environment. These findings indicate that AFDNDO improves cross-landscape optimization performance while retaining the basic NDO framework. They also support its use as a global planner within the tested dynamic multi-task environments. Full article

Panaxadiol (PD) is a bioactive dammarane triterpenoid with limited antiproliferative potency, and systematic optimization of its C3 and C12 positions remains underexplored. Here, a stepwise, site-differentiated strategy was established by combining selective C3 esterification with late-stage C12 diversification through a chloroacetyl–piperazine linker, affording 23 PD derivatives and enabling complementary C3/C12 structure–activity analysis. Antiproliferative screening in AGS gastric cancer cells identified compounds 6, 11, and 17 as the most active analogues, with IC₅₀ values of 8.41, 5.08, and 6.78 μM, respectively, all outperforming 5-fluorouracil under identical conditions. Compound 6 provided a relatively favorable balance between low-micromolar activity and preservation of non-malignant GES-1 cells within a defined concentration range and was therefore selected for mechanistic investigation. Transcriptomic profiling identified 298 differentially expressed genes associated with cellular stress, cytokine signaling, epithelial growth regulation, and extracellular remodeling, whereas metabolomic analysis revealed marked perturbation of glycerophospholipid, choline, polyunsaturated fatty acid, and glutathione metabolism. Integrated analysis highlighted membrane-lipid remodeling, redox dysregulation, and ferroptosis-related processes as central features of the response. These findings establish a modular platform for PD optimization and identify compound 6 as a promising lead for further anti-gastric cancer development. Full article

Background/Objectives: Overlapping clinical and imaging features complicate the early differentiation between spontaneous spinal cord infarction (SCI) and seronegative acute myelitis (SAM). We aimed to compare the clinical and laboratory characteristics of SCI and SAM and, as an exploratory analysis, to develop and internally validate multivariable diagnostic models evaluating whether routinely available laboratory parameters provide additional discriminatory information beyond selected clinical features. Methods: We retrospectively analyzed 75 patients (34 SCI, 41 SAM) treated between January 2017 and June 2025. Between-group laboratory comparisons were adjusted for multiple testing using the Benjamini–Hochberg false-discovery-rate procedure. Penalized logistic regression analyses were performed using clinical variables, laboratory variables, and their combination, with candidate variables defined on clinical and data-quality grounds rather than by univariate statistical significance. Model discrimination was evaluated using repeated nested cross-validation. Bootstrap optimism correction was additionally performed for the full-data combined model, and decision curve analysis was conducted as an exploratory secondary analysis. Results: SCI patients were older, more often male, and had higher prevalence of hypertension and diabetes. Radicular pain was markedly more common in SCI. After Benjamini–Hochberg correction, five laboratory parameters remained significantly different between groups: CRP, triglycerides, monocyte percentage, and absolute monocyte count were higher in SCI, whereas HDL cholesterol was lower. In the combined penalized analysis, age, hypertension, radicular pain, CRP, triglycerides, absolute monocyte count, and absolute eosinophil count were retained. The clinical, laboratory, and combined models yielded AUCs of 0.821 (95% CI 0.718–0.910), 0.756 (95% CI 0.633–0.865), and 0.871 (95% CI 0.776–0.947), respectively. Compared with the clinical model, the combined model showed a modest increase in discrimination (ΔAUC 0.050, 95% CI 0.005–0.101, p = 0.030). Bootstrap internal validation of the full-data combined model yielded an optimism-corrected AUC of 0.902, compared with an apparent AUC of 0.937, with a mean optimism of 0.035. Exploratory decision curve analysis showed a potential net benefit of the combined model across a range of threshold probabilities. Conclusions: Spontaneous SCI and SAM showed distinct clinical and laboratory profiles in this retrospective cohort. Routine laboratory parameters provided modest additional discriminatory information beyond clinical features, and the combined model maintained discrimination during internal validation. These findings are exploratory and require validation in larger, independent cohorts before clinical application. Full article