Search Results (1,383)

Precisely identifying the key drivers of regional carbon emissions and their spatiotemporal heterogeneity is critical for formulating differentiated strategies under China’s “Dual Carbon” goals. To address the limitations of traditional models in variable screening and handling non-stationarity, this study constructs an analytical framework that integrates a Random Forest (RF) model for preliminary variable screening, Geographically and Temporally Weighted Regression (GTWR) for spatiotemporal quantification, and the CRITIC method for multidimensional evaluation. Based on panel data from 30 Chinese provinces spanning 2005 to 2023, this study investigates the spatiotemporal evolution of carbon emission drivers. The findings reveal significant regional disparities. In the eastern region, the emission-increasing effect driven by population continues to intensify. Although economic growth shows signs of decoupling from emissions, the emission reduction benefits of industrial upgrading are diminishing. Notably, provinces such as Jiangsu have even experienced a rebound in energy consumption, which is potentially linked to the expansion of digital infrastructure. In the central region, a “pollution haven” effect has emerged due to the relocation of energy-intensive industries. Furthermore, the impacts of population, urbanization, and energy consumption structure exhibit an inverted U-shaped trend, with green urbanization beginning to yield initial emission reductions. In the western region, the suppressive effect of energy intensity on emissions continues to strengthen, particularly around Shaanxi. For northern energy-rich areas, economic growth acts as a prominent driver, while the impact of population displays a clear “positive in the south, negative in the north” spatial pattern. Moreover, northern provinces have successfully leveraged agglomeration effects to achieve emission reductions. Ultimately, these findings provide robust empirical support for constructing a spatially differentiated governance system to facilitate carbon neutrality. Full article

This study analyzes the evolution and spatial distribution of cervical cancer mortality. Furthermore, it develops a dynamic simulation model for estimating the evolution of the disease up to 2040. This manuscript details an ecological and retrospective study that analyzed official mortality, morbidity, and population data from the 58 municipalities that constitute the state of San Luis Potosi. We used Moran’s index, linear correlation, structural equation modeling, Excel predictions, and Vensim PLE x64 simulation software to conduct this study. The evolution of deaths from cervical cancer shows a downward trend; mortality follows a clustered distribution pattern, and it is not random. The structural model showed standardized regression coefficients of 0.68 between syphilis cases and cervical cancer cases, with a coefficient of 0.35 for deaths; candidiasis cases with cervical cancer at a coefficient of 0.25 and with deaths from the same disease at a coefficient of 0.46. The coefficients of determination for cervical cancer cases and deaths were 0.74 and 0.91, respectively. This shows that these co-infections—syphilis and candidiasis—are a risk factor for cervical cancer mortality. The estimated mortality rates per 100,000 inhabitants for 2025, 2030, 2035, and 2040 were 5.5, 5.1, 4.8, and 4.4, respectively. The prediction indicates an increase in the number of CC cases and deaths from this cause. Full article

Offloading remains the cornerstone of diabetic foot ulcer (DFU) management. This review traces the evolution of mechanical offloading from early plaster casting in South Asian leprosy clinics to modern removable walkers and emerging “SmartBoot” technologies. We examine the historical progression from total contact casting (TCC) through the era of randomized trials and instant TCC (iTCC), up to the current integration of wearable sensors and digital adherence tools. Contemporary evidence—including meta-analyses—is discussed to compare the effectiveness of offloading modalities (non-removable vs. removable devices, knee-high vs. ankle-high boots, therapeutic footwear, and adjunctive surgeries). Current challenges, such as patient adherence, frailty, and balance, are linked to technological responses like smart insoles, remote monitoring, and gamification strategies. Through this historical and evidence-based lens, we highlight how decades-old biomechanical principles are being reimagined with 21st-century innovations, aiming to improve healing rates and patient engagement in DFU care. Full article

This systematic review analyzes the evolution of Machine Learning and Big Data applications in agriculture from 2021 to 2025, with particular emphasis on how recent technological advances facilitate the transition from precision agriculture to Intelligent Agricultural Ecosystems. A comprehensive literature search was conducted across Scopus, Web of Science, IEEE Xplore, the ACM Digital Library, SpringerLink, and MDPI, following the PRISMA 2020 guidelines. After duplicate removal and a two-stage screening process (title/abstract screening followed by full-text assessment), eligible peer-reviewed studies were systematically extracted using a structured coding matrix encompassing six analytical domains: crops, soil, weather and water, land use, animal systems, and farmer decision-making. The findings reveal a substantial increase in ML-driven agricultural analytics. Although Random Forest and Convolutional Neural Networks remain widely adopted, recent studies demonstrate a marked shift toward advanced Deep Learning architectures, integrated cloud–edge–device infrastructures, Federated Learning frameworks for privacy-preserving collaboration, Explainable AI techniques to enhance transparency, and governance-oriented mechanisms to ensure interoperability. Notwithstanding these advances, several persistent challenges remain, including limited generalizability across diverse agroclimatic contexts, the high costs associated with high-quality data annotation, the integration of heterogeneous and multimodal datasets, and infrastructural constraints related to connectivity. These developments are synthesized within the IAE conceptual framework, underscoring governance- and lifecycle-aware orchestration MLOps as a critical differentiator that transcends purely technology-centric approaches. Full article

Background/Objectives: Decoding affective states from Electroencephalography (EEG) signals is fundamental to non-invasive Brain–Computer Interfaces. Despite recent advances, accurate recognition is impeded by the inherently non-stationary nature of physiological signals and the entanglement of spatio-temporal dynamics within high-dimensional recordings. While Transformers excel at global modeling, they often neglect the continuous dynamical properties of neural signals and suffer from quadratic complexity. Methods: In this paper, we propose the Spatio-Temporal Hybrid Mamba-Attention (STHMA), a framework designed to explicitly disentangle and model EEG dynamics via linear-complexity State Space Models. First, to incorporate domain knowledge, we introduce a Dual-Domain Physics-Aware Embedding module. This module fuses learnable temporal convolutions with explicit frequency-domain spectral features, ensuring fidelity to neurophysiological principles. Second, we propose a novel Decoupled Spatial–Temporal Scanning strategy. By dynamically reconfiguring the serialization of the data tensor, our model strictly separates the learning of instantaneous functional connectivity from the tracking of emotional state evolution, thereby preventing the structural collapse common in 1D sequence models. Results: Extensive experiments on the FACED and SEED-V datasets demonstrate that the STHMA achieves state-of-the-art performance, significantly exceeding the random chance baselines (11.11% for 9-class FACED and 20.00% for 5-class SEED-V). Conclusions: The results validate that combining Physics-Aware Embeddings with decoupled state-space modeling offers a scalable and effective paradigm for EEG emotion recognition. Full article

Reactive Powder Concrete (RPC) exhibits mechanical failure behaviors distinct from those of ordinary concrete. To investigate the mechanical properties and damage evolution characteristics of RPC during failure, uniaxial compression, axial compression, splitting tensile, and four-point bending tests were performed on RPC specimens integrated with Acoustic Emission (AE) technology. Subsequently, damage stage identification models were established using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms coupled with AE parameters—including ringing count (RC), energy, peak frequency, RA, and AF—and were optimized via the Ivy algorithm (IVY). Results indicate that RPC demonstrated the highest ductility and resistance to failure under four-point bending, compared to its weakest performance under axial compression. By integrating the evolution of AE ringing counts and energy, the damage process was divided into three stages: compaction-elastic, crack propagation, and failure. Under axial compression, AE activity peaked before reaching the peak stress, whereas splitting tension exhibited concentrated signal bursts during crack propagation, and bending failure was characterized by a sustained signal escalation. The proportion of high-frequency signals was highest in cubic compression specimens, while splitting tension was dominated by low-frequency signals. The RA-AF distribution revealed that steel fibers inhibited through-thickness tensile cracks, and a decrease in the b-value served as a precursor to unstable failure. Notably, the IVY-optimized XGBoost model achieved the best performance, with an accuracy improvement of 26%. Under compressive stress, AF was identified as the primary parameter, whereas peak frequency became critical under tensile-bending conditions, reflecting the distinct damage mechanisms associated with different loading modes. These findings provide a scientific basis for damage assessment and early warning strategies in RPC structures. Full article

Background: Esophageal cancer remains one of the most aggressive and fatal malignancies worldwide. Historically, therapeutic strategies relied primarily on neoadjuvant chemotherapy or radiotherapy and surgery, but over the past two decades, randomized controlled trials have driven a major transformation in clinical practice. Methods: This narrative review synthesizes the evolution of evidence-based management from early cytotoxic regimens to modern, histology-specific treatment pathways. We examine landmark trials establishing the roles of neoadjuvant chemoradiation and chemotherapy, along with emerging data on immunotherapy—including checkpoint inhibitors. Persistent challenges include optimizing treatment selection based on molecular profiling, improving response prediction, and managing toxicity in an aging population. Conclusions: We conclude by highlighting key gaps in current evidence and outlining future directions and ongoing clinical investigations. Full article

Trade imbalances and equipment shortages are making it increasingly important to coordinate container slot allocation with empty container repositioning on liner services. This paper develops an integrated bi-objective mixed-integer model for voyage-level slot planning on a fixed cyclic route. The model jointly decides booking acceptance, inter-voyage shipment, and empty repositioning with port-level empty-inventory dynamics and leg-based vessel capacity constraints. We optimize two conflicting objectives: maximizing operational profit and minimizing empty container TEU-miles. To solve the model at practical scales, we propose a hybrid evolutionary framework, NSGA-II-RL, which uses a lightweight Q-learning controller to adapt operator and repair choices during NSGA-II evolution. Computational experiments on representative service route instances show that NSGA-II-RL produces diverse Pareto-efficient solutions and improves hypervolume relative to fixed-operator and random-control variants, revealing clear trade-offs between profitability and repositioning intensity. Full article

Hyperparameter optimization for cryptocurrency trading strategies encounters distinct challenges owing to continuous operation, volatility rates 3–4 times higher than equity indices, and price dynamics influenced by market sentiment. Bayesian optimization (Tree-Structured Parzen Estimator, TPE) and evolutionary algorithms (Differential Evolution, DE) are great for machine learning, but there are not many systematic comparisons for trading cryptocurrencies. This research evaluates Random Sampling, TPE, and DE through 36 factorial experiments, comprising 3 trading strategies (3, 4, and 5 hyperparameters) × 3 optimizers × 4 cryptocurrency pairs (BTC/USDT, ETH/USDT, INJ/USDT, SOL/USDT), resulting in 14,400 backtesting trials with walk-forward validation. TPE won 75% of strategy–asset pairs (9 of 12), reaching 90% of optimal performance within 13–17% of trial budgets. We find strategy-specific optimizer compatibility: mean-reversion strategies show DE underperformance independent of topology (−1% to −8%), whereas trend-following strategies show consistent DE competitiveness across assets (+13% to +37%). Most notably, for the same strategy, parameter space topology differs significantly between assets (trend following: 4.6% viable on BTC to 82% on ETH = 17.8×; mean reversion: 10.8% on ETH to 92% on SOL = 8.5×), indicating that topology results from strategy–asset interaction rather than intrinsic properties. Complete testing failures and widespread severe overfitting point to regime non-stationarity as a fundamental problem. Among the contributions are: (1) evidence shows that topological effects are dominated by optimizer–strategy compatibility (DE fails on mean-reversion strategies even in 92% viable spaces, but succeeds on trend-following strategies regardless of topology, spanning 13.6–82% viable spaces); (2) this is the first systematic Bayesian versus evolutionary comparison across 4 cryptocurrency assets; (3) parameter space topology emerges from strategy–asset interaction, varying up to 17.8-fold; and (4) single-period backtests inadequately identify parameter instability. Full article

Corrosion damage is a vital factor that causes strength weakening or even failure of offshore derrick. To study the influence of local corrosion on the derrick, a regular spherical pitting model was adopted to analyze the evolution mode of pitting corrosion damage and the corresponding pitting corrosion damage models with different morphology. The connection technique for the across-scale interface was discussed, a method for constructing multi-scale models of derrick with pitting corrosion damage was proposed. The pitting damage simulation and ultimate bearing capacity analysis are carried out for an offshore derrick in service. The results show that the interaction between meso-scale pitting corrosion damage and macro-scale structure can be effectively considered, the stress distribution of the pitting corrosion damage and its effect on stress concentration coefficient can be obtained, and the influence of local random pitting distribution location, style, and density on the ultimate bearing capacity can be determined. In addition, the ultimate bearing capacity can be predicted. It provides a new idea for bearing capacity prediction and safety assessment of large steel frame structures in service with local damage. Full article

Integrating functional perovskites on an amorphous microchannel plate (MCP) glass faces challenges regarding the lack of ordered nucleation sites and stringent thermal budgets. Herein, we propose a surface energetics-based atomic layer deposition (ALD) strategy to achieve template-assisted oriented BaTiO₃ growth via a (101)-oriented anatase TiO₂ seed layer. Systematic investigation of the TiCl₄/O₃ process reveals a kinetic-to-thermodynamic transition at 300 °C, triggering a singular (101) preferred orientation. Combined DFT calculations and Wulff construction elucidate that this texture evolution is governed by a thermally activated surface energy minimization mechanism, driven by the intrinsic stability of the (101) facet. Crucially, the optimized seed layer acts as a multifunctional template: it not only transforms BaTiO₃ growth from random polycrystalline morphology to a singular (100) orientation with suppressed bulk carbonate impurities but also ensures excellent conformality and uniformity throughout the high aspect ratio microchannels. This study clarifies the thermodynamic mechanism of oriented growth on amorphous substrates, providing a versatile surface engineering pathway for constructing high-performance MCP-based heterojunction devices. Full article

Bivalves possess a diverse array of photoreceptive organs that are significant for their evolutionary success and systematic classification. Giant clams are the largest bivalve mollusks, with mantle tissue permanently extended in nature to maintain symbiosis with zooxanthellae and perceive environmental cues. Eyes serve as critical sensory organs for these organisms, yet the structural and functional characteristics of tridacnine eyes remain inadequately understood. This study systematically investigated the ocular traits and visual resolution of three ecologically distinct giant clam species (Tridacna crocea, T. squamosa, T. maxima) using morphometric analysis, hematoxylin-eosin (HE) staining, transmission electron microscopy (TEM), and grating stimulation assays. Significant interspecific differences were observed in eye count, diameter, and pupil-to-eye ratio (PER): T. maxima exhibited the highest mean eye count (221 ± 8), T. squamosa the largest mean eye diameter (0.490 ± 0.082 mm), and T. crocea the highest mean PER (0.363 ± 0.041). Eyes were numerically symmetric on the left and right mantles but positionally asymmetric, showing random distribution patterns along the mantle margin without fixed corresponding locations across species. All three species possessed typical pinhole eyes lacking lenses and retinas, primarily composed of filler cells, receptor cells, and sparse neurons, with symbiotic zooxanthellae distributed in the surrounding mantle tissue. Grating stimulation assays revealed resolvable stripe periods of 5.82–11.64° (T. crocea), 8.62–13.16° (T. squamosa), and 10.15–12.26° (T. maxima), confirming T. crocea as the species with the highest visual resolution. These ocular variations are inferred to reflect adaptive evolution driven by ecological niches and habitat-specific factors (water depth or light intensity), while the simplified pinhole morphology is consistent with their sedentary lifestyle and metabolic dependence on symbiotic zooxanthellae. These ocular variations provide potential morphological markers for the systematic classification of Tridacninae and offer valuable insights for researchers studying the evolutionary plasticity of bivalve visual systems. Full article

The Zhunuo ore district, at the western end of the Gangdese porphyry Cu belt, hosts significant Cu mineralization and newly recognized W mineralization dominated by scheelite. However, the genetic relationship between scheelite and porphyry mineralization, and the evolution of ore-forming fluids remain poorly constrained. To address this, scheelite samples from multiple locations were analyzed for major elements (EMPA), in situ trace elements (LA-ICP-MS), and internal textures (cathodoluminescence, CL). These data, combined with machine learning methods, were used to determine scheelite genetic types and reconstruct fluid evolution. REE patterns and CL textures reveal three scheelite generations in Yalongri (early Sch I c, middle Sch I b, late Sch I a), two in Zhigunong (early Sch II a, late Sch II b), and one in Xiongbaxi (Sch III). Low Na (0–329 ppm) and Nb (3.9–39 ppm) relative to high ΣREE + Y-Eu (16–3857 ppm), indicate that the dominant substitution mechanism is 3Ca²⁺ = 2REE³⁺ + □Ca (□Ca = Ca vacancy). δEu values > 1 in Sch I a, Sch I b, Sch II a, and Sch II b indicate reducing fluids, whereas δEu < in Sch I c and Sch III reflects oxidizing conditions. Variations in REE, Mo, and Sr contents suggest that ore-forming fluids in Yalongri evolved from oxidizing to reducing conditions, with late-stage scheelite undergoing dissolution–reprecipitation. Zhigunong records two reducing stages: an early REE-rich-Mo-poor stage and a later REE-poor-Mo-rich stage. Xiongbaxi records a single oxidizing, REE-rich, Mo-rich stage. Scheelite exhibits low-to-moderate Sr/Mo ratios (0.02–6.10), consistent with a magmatic–hydrothermal origin, and relatively uniform Y/Ho ratios (12–59) indicating stable crystallization conditions. A Random Forest model classifies scheelite into orogenic, porphyry, skarn, and greisen types. Overall, the results indicate that ore-forming fluids evolved from oxidizing to reducing conditions, favoring metal transport and enrichment. Integrated geochemical and machine learning evidence suggest, strong potential for porphyry-type Cu-W(-Mo) mineralization in Yalongri and Zhigunong, and skarn-type W-Mo mineralization in Xiongbaxi, providing important guidance for future exploration in the western Gangdese metallogenic belt. Full article

This study investigates the characteristics of water and sediment evolution under the influence of the Datengxia Water Control Hub Project by analyzing its affected area, with a focus on the driving mechanisms of human activities on these processes. Utilizing hydrological data (1993–2022) from the Wuxuan and Dahuangjiangkou Stations, along with meteorological, land use, and population data, we applied the M–K (Mann–Kendall) trend test, Pettitt change point test, double mass curve method, and a random forest model. These methods were used to quantify the contributions of rainfall and human activities and to identify the dominant controlling factors. Model reliability was verified by comparing predicted and observed P-III (Pearson Type III distribution curve), enabling an assessment of water–sediment changes before and after the project’s construction. The results indicate that (1) both stations showed a non-significant declining trend in runoff and sediment load, with a human activity-induced change point detected in 2003; (2) human activities accounted for 93.18% and 92.38% of the reduction in runoff and sediment load at Wuxuan Station, and 74.44% and 54.33% at Dahuangjiangkou Station, respectively; (3) population density was the dominant factor for water–sediment changes at Wuxuan Station (influence weight: 0.41), while grassland area (0.41) and population density (0.40) primarily controlled runoff and sediment changes, respectively, at Dahuangjiangkou Station; (4) following project construction, the trend of the decreasing flood inundation extent with increasing frequency became more pronounced, and sediment deposition was concentrated mainly in the reservoir area and downstream reaches. The study confirms the dominant role of human activities in the basin’s water–sediment dynamics, and the established methodological framework provides a scientific basis for integrated watershed management and ecological conservation. Full article

Background: The accurate prediction of response to neoadjuvant therapy (NAT) is crucial for optimizing breast cancer management. Conventional breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) radiomics typically relies on single post-contrast phases and may not fully capture temporal enhancement patterns related to tumor heterogeneity. This study evaluated a machine learning model based on time-dependent radiomic features extracted from pre-treatment DCE-MRI for predicting NAT response in breast cancer patients. Methods: Breast DCE-MRI examinations of women scheduled for NAT, acquired on 1.5 T scanners from three different vendors, were retrospectively collected from two centers. Tumors were automatically segmented on the third post-contrast DCE image using a 3D nnUNet model trained on 30 lesions. All DCE phases were registered to the reference image, and radiomic features were extracted from a consistent tumor region of interest across all phases. Time-dependent radiomic features were computed using linear regression modeling of feature evolution over time. A random forest classifier integrating static and time-dependent radiomic features was developed to predict pathological complete response (pCR), partial response (pPR), and non-response (pNR). Model performance was evaluated using internal validation (Center 1) and an independent external test cohort (Center 2). Results: A total of 212 patients were included (173 from Center 1 and 39 from Center 2), comprising 103 pCR, 103 pPR and 6 pNR cases. Among 759 extracted features, 30 showed significant differences across response groups. Several time-dependent texture features related to intratumoral heterogeneity were significantly associated with pNR. The model achieved AUC values of 0.80, 0.81, and 0.95 in the internal validation cohort and 0.75, 0.74, and 0.86 in the external test cohort for predicting pCR, pPR, and pNR, respectively. Conclusions: Time-dependent radiomic features derived from pre-treatment breast DCE-MRI enable the accurate prediction of response to NAT, with particularly strong performance in identifying non-responders. This approach may support imaging-based risk stratification and contribute to more personalized treatment. Full article