Search Results (7,387)

Accurate energy prediction in organic molecular crystals, such as cyclotrimethylenetrinitramine (RDX) and cyclotetramethylenetetranitramine (HMX), is hindered by the scarcity of descriptors capable of encoding their hierarchical architecture. Furthermore, while reactive force field (ReaxFF) offers a pathway to simulating chemical reactions, its accuracy for polymorph stability remains suboptimal, and machine learning (ML) driven refinement of ReaxFF for molecular systems remains unexplored. Herein, we report the first construction of a Hierarchical “Center-Environment” (HCE) feature framework specifically tailored for molecular crystals. The HCE framework hierarchically decomposes structural complexity into intramolecular (ring vs. nitro groups) and intermolecular (central molecule vs. coordination shell) attributes, integrating physics-based priors with distance-weighted attention. Using this compact descriptor set, we present the first attempt to rectify ReaxFF predictions via ML, benchmarking against 5930 RDX and 3335 HMX DFT-calculated energies. Our models achieve a significant reduction in mean absolute error: from ~116.7 to 42.2 meV/atom for RDX (kernel ridge regression, KRR) and from 112.3 to 53.7 meV/atom for HMX (support vector regression, SVR). Crucially, HCE features enable robust cross-molecule transferability; a neural network pre-trained on RDX yields a validation error of 52.9 meV/atom on HMX, surpassing models trained exclusively on HMX data. This work not only establishes a pioneering paradigm for interpretable ML-driven force field refinement but also provides the first feature engineering solution incorporating chemical, physical, and structural information specifically designed for the machine learning of energetic molecular crystals. Full article

The Ming-style study served as the spatial embodiment of the lives of Chinese literati. Research into the patterns of furniture arrangement within these spaces has long relied on physical artifacts and textual records, lacking systematic quantitative analysis. This study collected data from visual sources, such as Ming Dynasty paintings and book illustrations, as well as textual records from historical documents and classical texts, to catalog Ming-style study furniture and construct a database of furniture arrangements. To address differences in the quantity and completeness of information across various sample sources, the study introduced the information entropy method to calculate source weights and incorporated these weights into the Apriori association rule mining process, thereby identifying strong association rules within Ming-style study furniture arrangements. Finally, eye-tracking experiments were conducted to examine the allocation of visual attention among furniture objects within selected representative combinations. The study ultimately identified three sets of patterns for Ming-style study furniture arrangements, while the eye-tracking experiment provided supplementary evidence regarding the visual attention hierarchy and visual organization of furniture objects in selected contemporary Ming-style study scenes. From a scientific perspective, this research offers theoretical references and practical pathways for the quantitative study of Ming-style study furniture arrangements and contemporary Chinese-style study design, providing a practical method that combines cultural and scientific elements for the transformation and development of traditional cultural resources. Full article

Soil organic carbon (SOC) is an important indicator of plantation-forest soil quality, but Vis-NIR models calibrated at one site may show systematic bias at another. This study used 370 Gaofeng samples as the source domain and 119 Yachang samples as the target domain to evaluate SOC-gradient-constrained spectral similarity weighting (SOC-SSW). Unlabeled target spectra guided source-sample weighting, while source SOC strata maintained calibration-gradient coverage. In Gaofeng-to-Yachang transfer, direct PLSR produced R² = 0.809, RMSE = 6.676 g kg⁻¹, and bias = −3.842 g kg⁻¹. SOC-SSW increased R² to 0.855, reduced RMSE to 5.800 g kg⁻¹, and decreased absolute bias by 75.42%. Paired bootstrap analysis confirmed lower RMSE and MAE, and paired absolute errors remained significantly lower after Holm correction (p = 0.0018). Similar accuracy was retained when weights were constructed from 10 or 40 target spectra, although the complete target set yielded the smallest absolute bias. Reverse transfer was unsuccessful, indicating direction-dependent applicability. SOC-SSW can therefore support conditional reuse of laboratory soil spectral datasets when target-site SOC labels are unavailable, provided that the source dataset is sufficiently representative. Full article

Photocatalytic fuel cells (PFCs) integrate photocatalysis with fuel cell technology to enable simultaneous wastewater treatment and energy recovery. This review examines recent advances in PFC cathode materials, focusing on design strategies, reduction mechanisms, and applications. The cathode governs electron transfer and interfacial reactions, including oxygen reduction (4e⁻ or 2e⁻ pathways), direct pollutant electroreduction, and oxidant activation for radical generation. Cathodic materials including transition metal oxides/sulfides, carbon-based materials, metal–organic frameworks and their derivatives, are systematically summarized, evaluating their respective activities, stabilities and costs. Rational design via heterojunction engineering, defect modulation, and composite construction enables tunable reaction pathways and enhanced performance. Furthermore, representative applications are reviewed, with particular attention to the effective degradation of organic pollutants, and reduction of heavy metals and radionuclides in PFCs. Future efforts should prioritize long-term stability, scalable fabrication, and multi-functional cathode integration. Full article

Digital soil mapping (DSM) is an effective approach for assessing soil organic carbon (SOC) at regional scales. With the increasing availability of geospatial datasets, a growing number of environmental covariates have been incorporated into SOC mapping. However, the influence of covariate selection on model predictive performance remains poorly understood and has not been systematically quantified. In this study, 2865 observations of SOC stocks from 0–100 cm soil profiles and 147 environmental covariates were compiled across northwestern China. The extreme gradient boosting (XGBoost) algorithm was employed to evaluate the effects of covariate quantity on model performance and to generate a 30 m resolution SOC stock map. The results show that model performance increased rapidly with the inclusion of additional covariates before gradually reaching a plateau. The model achieved 90% of the maximum Lin’s Concordance Correlation Coefficient (LCCC) using 13 covariates and 90% of the maximum coefficient of determination (R²) with 26 covariates. Further increases in covariate numbers continued to improve error metrics, with root mean square error (RMSE) and mean absolute error (MAE) reaching stable levels. Considering multiple performance metrics, model accuracy reached a high and stable level when 26–47 covariates were included. In this study, 47 environmental covariates were selected to construct the SOC stock prediction model. Our SOC map outperformed existing SOC products in capturing the spatial heterogeneity of SOC stocks across northwestern China’s mountain–oasis–desert ecosystems, particularly over complex mountainous terrain. Furthermore, our model further enabled the identification of the regional-scale drivers of temperature and precipitation on the spatial distribution of SOC stocks, as well as the quantification of the local-scale effects of topographic factors, including elevation and slope aspect, on SOC stocks. This study establishes a methodological framework for high-resolution regional SOC stocks mapping, highlighting the importance of optimizing environmental covariate selection to balance predictive accuracy and model complexity. The findings provide practical guidance for efficient and reliable DSM and generate valuable high-resolution SOC stocks data for further carbon cycle studies in arid regions. Full article

Construction accident investigation reports provide rich narrative evidence for understanding why incidents occur, yet conventional text-mining studies in safety analytics often prioritize frequent patterns and may overlook low-frequency but high-consequence scenarios. This paper proposes a tail-risk profiling approach for construction accidents using text data. We transform accident narratives into semantic scene representations and organize reports into ten stable scene clusters (S1–S10) using spherical K-means with HDBSCAN-based robustness validation. Tail behavior is quantified at the scene level via quantile-based indicators, where P50 represents typical consequences and P90 represents extreme consequences; we further derive the Heavy-Tail Index (HTI = P90/P50) and the P90 exceedance rate to measure extreme-outcome tendency. A case study on 409 official accident reports shows that the proposed profiling can distinguish “tail-heavy” scenarios and support severity-sensitive scenario prioritization beyond frequency statistics. The results indicate that tail-risk profiling offers an interpretable and scalable basis for targeted safety interventions focusing on extreme-risk scenarios. Full article

Whether vegetation structural recovery is accompanied by improvement in soil properties remains a key question for evaluating karst forest restoration. We investigated a six-stage natural recovery sequence in the Maolan karst forests of southwestern China by analyzing 18 plots. This sequence was interpreted as a spatial gradient rather than a strict temporal succession. Six soil physicochemical variables, community-weighted leaf traits, and functional diversity indices were integrated to construct a relative soil multifunctionality proxy (SMF) and to evaluate associations between soil-property patterns and plant functional reorganization. Later woody stages generally had lower soil bulk density and higher soil moisture, organic carbon, nitrogen, phosphorus, and potassium contents, and the SMF was higher from the tree–shrub stage onward. Community-weighted leaf traits showed nonlinear reorganization along the leaf economic spectrum, indicating shifts in resource acquisition and tissue investment strategies. Functional diversity responded asynchronously: functional richness was positively associated with the SMF, whereas dispersion-related indices did not show consistent positive associations. These findings indicate that variation in the SMF is associated with measured soil properties, the reorganization of dominant leaf traits along the leaf economic spectrum, and the restructuring of functional trait space, rather than being attributable to any single trait or diversity index. Because this study used 18 plots and a space-for-time design, the observed relationships should be interpreted as spatial associations, not temporal or causal effects. Full article

Scaffold architecture shapes tissue regeneration through the mechanical, topographical, and biochemical cues it presents to cells, yet geometrically elaborate scaffolds do not reliably produce more organized tissues, while comparatively simple architectures can exert strong organizational effects. We argue that scaffold performance is better understood by distinguishing geometric complexity from effective informational dimensionality: a relational property of the scaffold–cell system, defined as the number of independently manipulated architectural directions that produce distinguishable, above-noise changes in a jointly measured mechanotransductive response. Unlike structural entropy, fractal dimension, or feature-counting metrics, this construct depends on cellular accessibility, cue persistence, and non-redundancy. Mechanotransduction supplies its biological basis, integrating scaffold-derived cues through focal adhesions, cytoskeletal organization, nuclear deformation, and YAP/TAZ signaling, and we distinguish early resolvability from later organizational stabilization. We outline an operational strategy for estimating both from factorial scaffold libraries, common readout panels, and rank-based analysis of the response mapping, illustrated with selected experimental precedents rather than a systematic evidence sample. Positioned relative to biomimetic, mechanobiology-guided, and morphospace approaches, it yields testable predictions on dimensional compression, redundancy, and the resolvability–stability dissociation. Scaffold design is thus reframed from maximizing complexity or native resemblance toward engineering stable, cell-readable dimensions of organization. Full article

Based on three years of sustained personal observations and extensive on-site interviews, this study is the first to investigate the bird totem worship customs of the Dong ethnic minority in southeast Guizhou, China. The Dong-inhabited regions of southeast Guizhou possess a long-standing tradition of bird totem worship, which has permeated every aspect of local people’s daily life, serving as a living museum where ancient bird totem worship continues to thrive in the present day. Through investigation and research, we can clearly perceive that bird totem worship here is no longer merely evidence of primitive thinking as previously discussed in academic circles, nor is it an endangered intangible cultural heritage. Instead, it represents emotional resonance with a more modern spirit and open-ended identity construction. Faced with the era of the internet and new media, they have broken free from the constraints of totem concepts and taboos, instead embracing creative totem will, totem activities, totem behaviors, and intelligent totems, thereby endowing totems with new belief content and forms of expression. They have transcended the preconceived concepts and fixed thinking about totems in academic discourse, making it an important vehicle for maintaining community relations, strengthening ethnic solidarity, inheriting ethnic culture, and continuing local civilization. Through the contemporary reconstruction of collective behavior, the traffic empowerment of internet short videos, and complex functional transformations, they have constructed a brand-new totem value system in the context of the internet and new media era, organizing a multi-dimensional, comprehensive social ecological view and civilizational value system. It is precisely through this continuous evolution, expansion, and activation in response to changing times that totems have acquired sustained vitality and creativity, providing a vivid case study for the contemporary transformation and creative development of ancient totemic beliefs. This is also the significant importance of field investigation—it enables us to gain entirely new value cognition and different theoretical perspectives from the living site of totem practices. Full article

Hybridizing concentrated solar power (CSP) with residual biomass allows supercritical CO₂ (sCO₂) power cycles to deliver dispatchable low-carbon electricity, but it is unclear whether the extra equipment of the more efficient layouts adds a life-cycle burden that offsets their thermodynamic gain. This work reports what is, to the authors’ knowledge, the first unified energy, exergy, environmental and exergoenvironmental comparison of two combined sCO₂–organic Rankine cycle (ORC) configurations—a simple and a recompression Brayton layout, both with reheating, regeneration and a toluene bottoming ORC—driven by a solar tower and a coconut-shell-biomass furnace. Life-cycle impacts are quantified with Eco-indicator 99, a damage-oriented method that scores construction, operation and decommissioning damage in milli-points (mPts), and are allocated to the exergy streams through the exergoenvironmental balance. Both cycles are modelled in Python with CoolProp properties and validated against published sCO₂ analyses (efficiency deviation below 7.3%). The recompression layout reaches 54.3% thermal and 32.0% second-law efficiency and cuts the exergy destruction from 173 to 128 kW. Its larger construction impact (22.6 vs. 20.1 mPts/h) is negligible against the shared biomass reheater (429.4 mPts/h), so it is also marginally cleaner overall (459 vs. 472 mPts/h). Efficiency-oriented layout selection is therefore environmentally safe, and the remaining leverage lies in the biomass supply chain. Full article

The purpose of this narrative review was to provide a conceptual and applied framework that distinguishes resistance training methods, strategies, and systems and clarifies how these constructs organize established training variables within contemporary recommendations for exercise prescription. The historical, conceptual, and theoretical synthesis was not restricted by publication date and included seminal studies and previous evidence syntheses. In addition, a focused descriptive mapping of primary experimental studies published between January 2021 and April 2026 was conducted using PubMed, Scopus, and Web of Science. This mapping comprised 40 studies assessing acute responses or chronic adaptations to methods including drop sets, rest–pause, pyramidal loading, eccentric training, blood flow restriction, cluster sets, and velocity-based training. Several studies reported no statistically significant between-method differences in selected strength or hypertrophy outcomes; however, their generally small samples and the absence of equivalence or non-inferiority analyses preclude conclusions of true equivalence. Individual studies also reported context-specific differences in session efficiency, fatigue distribution, movement velocity, regional hypertrophy, and contraction-specific adaptations. Given the heterogeneity of the mapped studies and the absence of formal risk-of-bias or certainty-of-evidence assessments, these observations should be interpreted as descriptive and exploratory rather than as evidence of general comparative superiority. Taken together with the broader historical and synthesized literature, the findings suggest that resistance training adaptations are primarily governed by foundational prescription variables, including load, volume, frequency, effort, density, and exercise selection. The principal contribution of this review is therefore to clarify how methods, strategies, and systems can be selected and organized according to specific goals, individual characteristics, and practical constraints. Full article

Soil erosion and the nutrient loss it causes are core issues threatening sustainable land use in arid and semi-arid regions. In this study, our aim was to reveal the spatiotemporal differentiation characteristics of soil erosion and soil nutrients in an ecologically fragile area of eastern Inner Mongolia—Tuquan County and clarify the relationship between them in order to provide a scientific basis for the precise management of water and soil resources and ecological construction in this region. Based on four sets of remote sensing images and ground observation data from 2012, 2016, 2020, and 2024, the Revised Universal Soil Loss Equation (RUSLE) was used to evaluate the dynamics of soil erosion, statistical methods were employed to analyze the spatial distribution and grade characteristics of soil nutrients (organic carbon, SOC; total nitrogen, TN, total phosphorus, TP) and pH values, and correlation analysis was conducted to explore their association with environmental factors (rainfall erosivity, R; soil erodibility, K; slope length, LS; vegetation cover and management factor, C). Our results demonstrate the following: (1) From 2012 to 2024, the intensity of soil erosion in the study area showed an increasing trend, with the average annual soil erosion modulus increasing from 551.4 t/(km²·a) to 859.6 t/(km²·a), and the high-intensity erosion areas were mainly distributed in the northwest. (2) The soil nutrient content was generally at medium to low levels, with the SOC and TN in the study area mainly categorized as “deficient” and “adequate”. The SOC ranged from 5.8 to 33.8 g·kg⁻¹, with an average content of about 23.5 g·kg⁻¹, while the TN content ranged from 0.45 to 4.63 g·kg⁻¹, with an average content of about 1.50 g·kg⁻¹, and was significantly affected by soil type. (3) There was a significant negative correlation between the soil erosion modulus and the SOC and TN content (p < 0.05), which was a key driving factor for nutrient loss. This conclusion suggests that soil erosion in Tuquan County is intensifying: the risk of nutrient loss is severe, and its spatial pattern is jointly restricted by topography, vegetation cover, and soil background characteristics. Therefore, future ecological engineering should focus on high-intensity erosion areas and combine the prevention of soil and water loss with the conservation of soil fertility in order to achieve sustainable land use in the region. Full article

Digital technologies are reshaping the spatial organization of economic activities, expanding cities’ access to external innovation resources across geographical boundaries, and creating new pathways for sustainable urban development. Using panel data for 279 prefecture-level cities in China from 2011 to 2022, this study constructs an urban digital network based on the cross-city organizational linkages of listed digital firms and their subsidiaries and measures virtual agglomeration by combining network linkages with urban digital foundations. The results show that virtual agglomeration significantly promotes urban innovation, and this finding remains robust to instrumental-variable estimation, alternative measures, and lagged specifications. Virtual agglomeration also strengthens the ability of latecomer cities to translate innovation gaps into subsequent growth. Both local digital foundations and external digital linkages contribute to this effect, while knowledge-related variety serves as an important transmission channel. The positive relationship is more pronounced in resource-based, third-tier, non-urban-agglomeration, and western cities. Further analysis shows that virtual agglomeration significantly promotes urban green innovation, indicating that its innovation effect extends to environmentally oriented technological development. Overall, virtual agglomeration improves access to innovation resources, supports innovation catch-up in less-developed cities, and, by promoting green technological innovation, offers a digital-network-based pathway toward more coordinated and sustainable urban development. Full article

The concentrations of DIN and DIP are important indicators in an offshore ecosystem; although they do not have optical activity, their concentrations are affected by optically active substances, such as sediment, chlorophyll, and dissolved organic matter, an association that is especially close in coastal waters. This study aimed to identify this relationship to provide a theoretical basis for using remote sensing to monitor DIN/DIP concentrations. This study first used correlation analysis to analyze the relationship between water quality indicators and the field-measured spectrum in the Sheyang estuary. The results show a strong positive correlation between the DIN and DIP concentrations and spectrum in near-infrared range, similar to that between the suspended sediment concentrations and spectrum; this indicates a close relationship between DIN/DIP concentrations and sediment concentration in this sea area. Traditional regression models for DIN and DIP concentrations were constructed using the sensitive bank factors of a Hyperion image. By comparing the physical meaning of the factors and the precision and stability of the models, the quadratic model established by the ratio factor of 45th and 10th bands was selected as the DIN concentration inversion model, the quadratic model established by the ratio factor of the 45th and 9th bands was selected as the DIP inversion model, and the inversion results of the image conformed to the actual distribution pattern of DIN and DIP concentrations. In order to fully utilize the spectral information of the Hyperion data, the model coupled using partial least squares (PLS) and support vector machine (SVM) was used to construct regression models of DIN and DIP concentrations. By comparing the standardized coefficients of PLS regression, the 8~16th bands and 37~57th bands of the Hyperion image were selected; all these bands were extracted as two orthogonal components to construct the SVM regression model. Finally, the parameter combinations of radial basis model with C = 10, γ = 0.05, and ε = 0.1 and C = 1, γ = 0.1, and ε = 0.001 were determined as the inversion models for DIN and DIP concentrations, respectively. The prediction accuracy of the models was significantly improved compared to the traditional regression models, and the inversion results were superior to those of the traditional regression models, demonstrating the potential of this algorithm in hyperspectral image modeling. Full article

Highway road debris is often small, irregular, weakly textured, and poorly contrasted against complex backgrounds, while limited real-world samples constrain model generalization. This study constructs a Synthetic-Debris dataset by compositing three representative debris categories—brick, paper box, and rock—onto highway scenes to support small-object and low-contrast detection. FBENet (Frequency-Aware Bidirectional Feature Fusion and Efficient Attention Enhancement Network) is developed from YOLO11n using a task-oriented, stage-coupled design: FreqFusion (Frequency-aware Feature Fusion) is embedded at two top-down cross-scale fusion stages to preserve boundary details and improve cross-resolution consistency before learnable bidirectional aggregation by BiFPN (Bi-directional Feature Pyramid Network), while EMA (Efficient Multi-Scale Attention) is applied only to the final high-resolution P3 feature before detection. The contribution lies in the stage-specific organization of established operations rather than in proposing new primitive modules. On Synthetic-Debris, FBENet achieved an mAP@0.5 of 0.862, an mAP@0.5:0.95 of 0.664, and an F1-score of 0.811, with 2.43 M parameters and 6.8 GFLOPs. In the fixed synthetic-to-real split, FBENet exceeded YOLO11n by 5.0 and 2.8 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively. Five-fold cross-validation showed modest mean AP gains and lower overall AP variance, although transfer behavior remained class-dependent. Runtime evaluation showed that FBENet incurred additional latency relative to YOLO11n. Under PyTorch FP32, the network inference latency increased from 8.07 to 12.79 ms, while the end-to-end throughput decreased from 79.15 to 53.97 FPS. Under TensorRT FP16, FBENet achieved 126.59 FPS compared with 137.64 FPS for YOLO11n. These results indicate that FBENet improves detection accuracy at the cost of additional runtime, representing an accuracy–latency trade-off rather than an improvement in inference efficiency. Overall, frequency-aware bidirectional fusion and selective high-resolution attention are useful for improving highway debris detection under limited real-data conditions. Full article