Search Results (511)

Catalytic transfer hydrogenation (CTH) provides a practical and sustainable approach for reducing unsaturated compounds, serving as an alternative to high-pressure H₂ in laboratory and fine chemical contexts. This broad reaction class includes asymmetric transfer hydrogenation (ATH), a key strategy in enantioselective synthesis due to its operational simplicity, high stereocontrol, and compatibility with sensitive functional groups. A central variable governing CTH efficiency is the role of bases, which may function as essential activators, co-hydrogen donors, or be entirely absent depending on the catalytic system. This review provides a comparison of base-assisted, base-free, and base-as-co-hydrogen-donor CTH methodologies across diverse metal catalysts and substrates. We highlight how bases such as triethylamine, K₂CO₃, and NaOH facilitate catalyst activation, modulate hydride formation, and tune reactivity and selectivity. The dual function of bases in formic-acid-driven systems is examined alongside synergistic effects observed with mixed-base additives. In contrast, base-free CTH platforms demonstrate how tailored ligand frameworks, metal-ligand cooperativity, and engineered surface basicity can eliminate the need for external additives while maintaining high activity. Through mechanistic analysis and cross-system comparison, this review identifies the key structural, electronic, and environmental factors that differentiate base-assisted from base-free pathways. Emerging trends—including greener hydrogen donors, advanced catalyst architectures, and additive-minimized protocols—are discussed to guide future development of sustainable CTH processes. Full article

Increasingly frequent intense rainfall is increasing landslide occurrence and risk. In southern China in particular, steep slopes and thin residual soils produce frequent landslide events with pronounced spatial heterogeneity. Therefore, displacement prediction methods that function across sites and deformation regimes in similar settings are essential for early warning. Most existing approaches adopt a multistage pipeline that decomposes, predicts, and recombines, often leading to complex architectures with weak cross-domain transfer and limited adaptability. To address these limitations, we present CRAFormer, a causal role-aware Transformer guided by a dynamic-lag Bayesian network-style causal graph learned from historical observations. In our system, the discovered directed acyclic graph (DAG) partitions drivers into five causal roles and induces role-specific, non-anticipative masks for lightweight branch encoders, while a context-aware Top-2 gate sparsely fuses the branch outputs, yielding sample-wise attributions. To safely exploit exogenous rainfall forecasts, next-day rainfall is entered exclusively through an ICS tail with a leakage-free block mask, a non-negative readout, and a rainfall monotonicity regularizer. In this study, we curate two long-term GNSS datasets from Guangxi (LaMenTun and BaYiTun) that capture slow creep and step-like motions during extreme rainfall. Under identical inputs and a unified protocol, CRAFormer reduces the MAE and RMSE by 59–79% across stations relative to the strongest baseline, and it lowers magnitude errors near turning points and step events, demonstrating robust performance for two contrasting landslides within a shared regional setting. Ablations confirm the contributions of the DBN-style causal masks, the leakage-free ICS tail, and the monotonicity prior. These results highlight a practical path from causal discovery to forecast-compatible neural predictors for rainfall-induced landslides. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article

This paper examines whether financial development affects economic growth across different levels of carbon emissions in 30 Chinese provinces from 1990 to 2022. We employ a novel partially linear functional-coefficient model with latent factor structure. This approach relaxes the traditional assumptions of linearity and cross-sectional independence, allowing us to capture more flexible growth patterns. Our empirical findings reveal three key insights: (i) the positive effect of financial development on economic growth follows a nonlinear pattern—it initially strengthens as carbon emissions increase but declines rapidly after emissions reach a threshold; (ii) innovation and openness show limited impacts on economic growth; (iii) regional variations exist based on resource endowment. These findings offer important policy implications. Promoting green financial products could extend the beneficial range of carbon emissions for economic growth. Optimizing innovation structures and supervising foreign enterprises may help unlock growth potential while preventing pollution transfer. Regional strategies would benefit from accounting for resource disparities. Full article

Background/Objectives: Precise identification of seizure onset zones (SOZs) and their propagation pathways is essential for effective epilepsy surgery and other interventional therapies and is typically achieved through invasive electrophysiological recordings such as intracranial electroencephalography (EEG). Previous research has demonstrated that analyzing information flow patterns, particularly in high-frequency oscillations (>80 Hz) using parametric and Wilson algorithm (WL)-based nonparametric Granger causality (GC), is valuable for SOZ identification. In this study, we analyzed scalp EEG recordings from epilepsy patients using an alternative nonparametric GC approach based on spectral density matrix factorization via the Janashia–Lagvilava algorithm (JLA). The aim of this study is to evaluate the effectiveness of JLA-based matrix factorization in nonparametric GC for noninvasively identifying seizure onset zones from ictal EEG recordings in patients with drug-resistant epilepsy. Methods: Two regions of interest (ROIs) in pairs were isolated across different time epochs in six patients referred for presurgical evaluation. To apply the nonparametric Granger causality (GC) estimation approach to the EEG recordings from these regions, the cross-power spectral density matrix was first computed using the multitaper method and subsequently factorized using the JLA. This factorization yielded the transfer function and noise covariance matrix required for GC estimation. GC values were then obtained at different prediction time steps (measured in milliseconds). These estimates were used to confirm the visually suspected seizure onset regions and their propagation pathways. Results: JLA-based spectral factorization applied within the Granger causality framework successfully identified SOZs and their propagation patterns from scalp EEG recordings, demonstrating alignment with positive surgical outcomes (Engel Class I) in all six cases. Conclusions: JLA-based spectral factorization in nonparametric Granger causality shows strong potential not only for accurate SOZ localization to support diagnosis and treatment, but also for broader applications in uncovering information flow patterns in neuroimaging and computational neuroscience. Full article

Studying the thermodynamic properties of soft rocks is critical for geothermal energy extraction, as it elucidates their temperature-dependent mechanical behaviors and heat transfer mechanisms, thereby optimizing reservoir stimulation, enhancing extraction efficiency, and ensuring long-term operational stability. Owing to the intricate geothermal settings and interconnected physicochemical processes, the thermodynamic properties exhibit pronounced spatial heterogeneity and interdependencies. Concurrently, constraints imposed by technical and economic limitations result in scarce practical field survey and experimental data on these properties, severely hampering comprehensive assessments of geothermal energy potential and exploitation feasibility. To evaluate the spatial variability of thermodynamic properties for heterogeneous soft rock using limited data, the thermal conductivity (TC), heat capacity (HC), and thermal diffusivity (TD) were measured. A new Copula statistical method is used to analyze thermodynamic properties under limited measurement data. Spatial variability in heterogeneous soft rocks is quantified using random field theory. The methodology’s reliability is confirmed through cross-validation against theoretical predictions, empirical measurements, and simulation outputs. The analysis framework of thermodynamic variability characteristics has been presented by stability point analysis and linear regression analysis processes. The variance reduction function, scale of fluctuation, autocorrelation distances, and autocorrelation structure of thermodynamic properties for heterogeneous soft rock are analyzed and discussed. This study can provide scientific data for thermal energy analysis and geothermal reservoir modification specifically applicable to soft rock formations with diagenetic and tectonic histories similar to those investigated in the Weishan Lake area. Full article

Background: Artificial intelligence (AI) emerges as a powerful tool to assist breast cancer screening; however, its integration into different mammographic modalities remains insufficiently explored. Digital Mammography (DM) is widely accessible but presents limitations in dense breast tissue, whereas Contrast-Enhanced Spectral Mammography (CESM) provides functional information that enhances lesion visualization. Understanding how deep learning models behave across these modalities, and determining whether their decision-making patterns remain consistent, is essential for equitable clinical adoption. Methods: This study evaluates three convolutional neural network (CNN) architectures, ResNet-18, DenseNet-121, and EfficientNet-B0, for binary classification of breast lesions using DM and CESM images from the public CDD-CESM dataset (2006 images, three diagnostic classes). The models are trained separately on DM and CESM using three classification tasks: Normal vs. Benign, Benign vs. Malignant, and Normal vs. Malignant. A 3-fold cross-validation scheme and an independent test set are employed. Training uses transfer learning with ImageNet weights, weighted binary cross-entropy (BCE) loss, and SHapley Additive exPlanations (SHAP) analysis to visualize pixel-level relevance of model decisions. Results: CESM yields higher performance in the Normal vs. Benign and Benign vs. Malignant tasks, whereas DM achieves the highest discriminative ability in the Normal vs. Malignant comparison (EfficientNet-B0: AUC = 97%, Accuracy = 93.15%), surpassing the corresponding CESM results (AUC = 93%, Accuracy = 85.66%). SHAP attribution maps reveal anatomically coherent decision patterns in both modalities, with CESM producing sharper and more localized relevance regions due to contrast uptake, while DM exhibits broader yet spatially aligned attention. Across architectures, EfficientNet-B0 demonstrates the most stable performance and interpretability. Conclusions: CESM enhances subtle lesion discrimination through functional contrast, whereas DM, despite its simpler acquisition and wider availability, provides highly accurate and explainable outcomes when combined with modern CNNs. The consistent SHAP-based relevance observed across modalities indicates that both preserve clinically meaningful information. To the best of our knowledge, this study is the first to directly compare DM and CESM under identical preprocessing, training, and evaluation conditions using explainable deep learning models. Full article

Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R² = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement. Full article

Air pollution, particularly fine particulate matter (PM_2.5) and ozone (O₃) pollution, poses serious challenges to environmental quality and sustainable development. The Tibetan Plateau, often described as the “Third Pole,” functions as a key ecological shield for China and exerts wide-reaching influence on global climate systems, hydrological cycles, and cross-regional pollution transport. To better clarify the driving mechanisms of air pollution in this sensitive region, we propose an integrated MRG–HSW framework, which, for the first time, systematically couples statistical modeling and trajectory analysis by combining multivariate regression, residual-based screening, and HYSPLIT–WCWT trajectory analyses. Taking Qinghai Province as a case study, ERA5 and GDAS1 reanalysis products were coupled with in situ monitoring to identify the relative contributions of local emissions and long-range atmospheric transport. The results show that, in low-elevation zones, PM_2.5 levels are largely governed by local anthropogenic activities (R² = 0.631–0.803), whereas O₃ concentrations respond more strongly to meteorological variability (R² = 0.529–0.779). At higher elevations, however, local explanatory factors weaken, and long-range transport from the Hexi Corridor, Qaidam Basin, and even South Asia becomes the dominant influence for both pollutants. Additional sensitivity tests confirm that the framework performs robustly under diverse meteorological and seasonal conditions. Collectively, this work not only establishes a transferable methodology for source attribution in plateau environments but also underscores the pivotal role of the Tibetan Plateau in sustaining regional air quality and global environmental stability. Full article

This work describes the straightforward synthesis of a novel heterogeneous palladium catalyst immobilized on magnetic Janus-type silica particles coated with an amphiphilic ionic liquid (IL) layer. The material was prepared via a one-pot process wherein TEOS (tetraethoxysilane) and a bis(triethoxysilane) IL precursor are combined to form hollow shells. The IL motifs are selectively located on the outer surface of the hollow particles and serve as centers for the immobilization of palladium species on the material’s surface. The outer surface also hosts magnetic nanoparticles in close proximity to the palladium sites. Thanks to the uniform coverage of the surface with the amphiphilic IL functionality, the material exhibits a well-balanced wettability with reaction components of different polarities. The catalyst’s activity was tested in the Sonogashira cross-coupling reaction of terminal acetylenes and iodobenzene derivatives in water as the solvent. The results show that the mixed palladium–iron oxide catalyst exhibits higher activity than materials containing either immobilized palladium or iron oxide alone, suggesting a synergistic effect in this reaction. Additionally, the reaction proceeds well in the absence of expensive organic ligands and commonly employed additives such as copper co-catalysts or phase transfer catalysts. Furthermore, the material was also used in the oxidative Sonogashira coupling reaction of phenylboronic acid and phenylacetylene. The catalyst can be easily separated using an external magnet and can be reused several times. The feasibility of producing diphenylacetylene on a gram scale via the Sonogashira cross-coupling reaction was also investigated. Full article

The self-excited oscillation pulsed waterjet (SOPW) offers simplicity and effective pressure source separation, making it widely utilized. This study investigates the oscillation characteristics and flow field effects of SOPW generated by a Helmholtz nozzle. A transfer function model for the nozzle is established, and the natural frequency is found to correlate with structural parameters such as the oscillation chamber’s cross-sectional area, length, and downstream nozzle diameter. Numerical simulations reveal optimal structural parameters that closely match experimental results, with errors under 15%. Notably, submerged pulsed jets exhibit faster velocity decay compared to non-submerged jets. Additionally, the study examines the effect of area discontinuity at the nozzle inlet on axial velocity, showing that the area enlargement or contraction enhances velocity at lower pressures but inhibits it at higher pressures. This work advances the understanding of nozzle design and the flow field behavior of SOPW. Full article

Aiming to tackle the challenge of feature transfer in cross-domain fault diagnosis for rolling bearings, an enhanced domain adaptation-based intelligent fault diagnosis method is proposed. This method systematically combines multi-layer multi-core MMD with adversarial domain classification. Specifically, we will extend alignment to multiple network layers, while previous work typically applied MMD to fewer layers or used single core variants. Initially, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract features from both the source and target domains, thereby enhancing the diagnostic model’s cross-domain adaptability through shared feature learning. Subsequently, to address the distribution differences in feature extraction, the multi-layer multi-kernel maximum mean discrepancy (ML-MK MMD) method is employed to quantify the distribution disparity between the source and target domain features, with the objective of extracting domain-invariant features. Moreover, to further mitigate domain shift, a novel loss function is developed by integrating ML-MK MMD with a domain classifier loss, which optimizes the alignment of feature distributions between the two domains. Ultimately, testing on target domain samples demonstrates that the proposed method effectively extracts domain-invariant features, significantly reduces the distribution gap between the source and target domains, and thereby enhances cross-domain diagnostic performance. Full article

Accurate and large-scale crop mapping is crucial for global food security, yet its performance is often hindered by domain shift when models trained in one region are applied to another. This is particularly challenging in cross-continental scenarios where variations in climate, soil, and farming systems are significant. To address this, we propose PLCM (PSAE-LTAE + Class-aware MMD), an unsupervised domain adaptation (UDA) framework for crop classification using Sentinel-2 satellite image time series. The framework features two key innovations: (1) a Pixel-Set Attention Encoder (PSAE), which intelligently aggregates spatial features within parcels by assigning weights to individual pixels, enhancing robustness against noise and intra-parcel heterogeneity; and (2) a class-aware Maximum Mean Discrepancy (MMD) loss function that performs fine-grained feature alignment within each crop category, effectively mitigating negative transfer caused by domain shift while preserving class-discriminative information. We validated our framework on a challenging cross-continental, cross-year task, transferring a model trained on data from the source domain in the United States (2022) to an unlabeled target domain in Wensu County, Xinjiang, China (2024). The results demonstrate the robust performance of PLCM. While achieving a competitive overall Macro F1-score of 96.56%, comparable to other state-of-the-art UDA methods, its primary advantage is revealed in a granular per-class analysis. This analysis shows that PLCM provides a more balanced performance by particularly excelling at identifying difficult-to-adapt categories (e.g., Cotton), demonstrating practical robustness. Ablation studies further confirmed that both the PSAE module and the class-aware MMD strategy were critical to this performance gain. Our study shows that the PLCM framework can effectively learn domain-invariant and class-discriminative features, offering an effective and robust solution for high-accuracy, large-scale crop mapping across diverse geographical regions. Full article

Early detection of plant diseases is key to ensuring food production, reducing economic losses, minimizing the use of agrochemicals, and maintaining the sustainability of the agricultural sector. Citrus plants, an important source of vitamin C, fiber, and antioxidants, are among the world’s most significant fruit crops but face threats such as canker and Huanglongbing (HLB), incurable diseases that require management strategies to mitigate their impact. Manual diagnosis, although common, I s imprecise, slow, and costly; therefore, efficient alternatives are emerging to identify diseases from early stages using Artificial Intelligence techniques. This study evaluated four deep learning models, specifically convolutional neural networks. In this study, we evaluated four convolutional neural network models (DenseNet121, ResNet50, EfficientNetB0, and MobileNetV2) to detect canker and HLB in citrus leaf images. We applied preprocessing and data-augmentation techniques; transfer learning via selective fine-tuning; stratified k-fold cross-validation; regularization methods such as dropout and weight decay; and hyperparameter-optimization techniques. The models were evaluated by the loss value and by metrics derived from the confusion matrix, including accuracy, recall, and F1-score. The best-performing model was EfficientNetB0, which achieved an average accuracy of 99.88% and the lowest loss value of 0.0058 using cross-entropy as the loss function. Since EfficientNetB0 is a lightweight model, the results show that lightweight models can achieve favorable performance compared to robust models, models that can be useful for disease detection in the agricultural sector using portable devices or drones for field monitoring. The high accuracy obtained is mainly because only two diseases were considered; consequently, it is possible that these results do not hold in a database that includes a larger number of diseases. Full article

Nanoplastics have emerged as widespread environmental contaminants with toxicological properties that differ from those of microplastics. While existing reviews often examine their effects on specific organisms, they rarely provide direct comparisons with microplastics. This review aims to comprehensively assess the toxic effects of nanoplastics on animals, with a comparative perspective highlighting their distinctions from microplastics. In mammals, nanoplastics cross the blood–brain barrier and induce oxidative stress, neuroinflammation, mitochondrial dysfunction, and synaptic disruption, with consequences ranging from cognitive impairment to Parkinson’s disease-like neurodegeneration. They also impair liver, kidney, intestinal, pancreatic, and reproductive function, with evidence of transgenerational toxicity. In aquatic organisms such as fish, crustaceans, bivalves, and aquatic invertebrates, nanoplastics compromise growth, immunity, reproduction, and metabolism, while in terrestrial invertebrates they cause gut toxicity, mitochondrial damage, immune suppression, and heritable defects. Across taxa, the dominant mechanisms involve oxidative stress, apoptosis, inflammation, and interference with metabolic and signaling pathways. Comparisons with microplastics reveal that while both particle types are harmful, nanoplastics generally exert stronger and more systemic effects due to higher bioavailability, cellular uptake, and molecular reactivity. Microplastics primarily impose mechanical stress, whereas nanoplastics disrupt cellular homeostasis at lower exposure levels, often acting at the subcellular level. Evidence also indicates size-, surface chemistry-, and concentration-dependent effects, with smaller and functionalized nanoplastics exhibiting heightened toxicity. Despite growing knowledge, significant gaps remain in cross-size comparative studies, long-term and multigenerational assessments, trophic transfer analyses, and investigations involving environmentally derived nanoplastics. Addressing these gaps is critical for advancing ecological risk assessment and developing mitigation strategies against plastic pollution. Full article