Search Results (477)

Molybdenum tellurite compounds have attracted increasing interest as promising nonlinear optical (NLO) materials, yet their microscopic second-harmonic generation (SHG) mechanisms remain unclear. In this work, the electronic structures and SHG responses of ATeMoO₆ (ATM, A = Mg, Cd, Zn) are systematically investigated using first-principles calculations combined with atom response theory. The results show that the SHG responses are mainly governed by the occupied nonbonding O 2p states and the unoccupied Mo 4d and Te 5p states. Our atom response theory analysis reveals that a strong synergistic effect between stereochemically active lone pairs (SCALPs) on Te atoms and nonbonding O 2p states critically enhances the SHG response in ZnTM and MgTM. In contrast, the relative weaker Te SCALPs in CdTM fail to provide a comparable contribution, leading to its lower SHG performance. The structure group analysis reveals that MoO₄ units dominate the SHG response, while TeO₄ units provide secondary contributions. Moreover, group dipole moments are found to be insufficient to explain the SHG behavior. These findings provide microscopic insights into SHG origins and offer guidance for NLO material design. Full article

Southwest China’s karst region has developed a dam- and reservoir-dense pattern in which cascaded hydropower on mainstem rivers coexists with small hydropower on tributaries, forming a foundation for the region’s low-carbon energy supply. Under China’s “dual-carbon” targets and a strengthening ecological civilization agenda, it is urgent to clarify the mechanisms driving habitat quality (HQ) change under compound disturbances from cascaded hydropower, urbanization, and related pressures—especially the nonlinear pathway through which engineering disturbance propagates to ecological responses via land-use restructuring. To address this need, we develop a Cascade disturbance–Land restructuring–Habitat response chain framework and integrate an InVEST–IntPLUS–OPGD modeling approach to capture HQ dynamics in the Wujiang River Basin (1980–2020), attribute the interactive effects of coupled natural–social drivers, and project ecological responses under alternative 2035 scenarios. Results show that: (1) The basin maintained a stable ecological matrix, with forest land and cropland consistently >82.5% and forest cover near 50%, while construction land increased by 972.15 km² and water bodies by 354.23 km² (2) Mean HQ stayed high and declined by only 1.42%, with high and medium–high HQ dominating (>65%). HQ degradation is concentrated in urban expansion areas and reservoir shorelines, whereas most mountainous/forested regions remain stable; and (3) HQ spatial differentiation is mainly shaped by the synergy between forest structure and NDVI, while nonlinear urbanization edge effects impose stronger stress than hydropower development itself. Scenario simulations further indicate that a water protection pathway can enhance HQ by building integrated “water–forest” corridors that promote blue–green synergy. Overall, this study supports improved trade-off design between energy supply and ecological protection in vulnerable karst regions. Full article

Owing to their small size and surface hydrophobicity, microplastics (MPs) tend to act as vectors for various organic pollutants. However, in contrast to well-studied pollutants like polycyclic aromatic hydrocarbons, the sorption of benzene-series compounds on MPs has been seldom studied. To investigate the sorption process, the isotherms were determined for the sorption of three benzene-series sorbates by three polymers with different physicochemical properties. The linear sorption isotherms observed for PE indicate that sorbate uptake was dominated by partitioning into the bulk polymer. In contrast, the non-linear isotherms of PP and PVC imply that adsorption onto surfaces was the dominant mechanism. Sorption capacity of m-xylene and ethylbenzene increased in the following order: polyvinyl chloride (PVC) < polyethylene (PE) < polypropylene (PP). This order does not reflect the polarity or the crystallinity of the investigated MPs, suggesting the influence of additional factors (e.g., glass transition temperature, specific surface area) on the sorption of BTEX by MPs. In addition, the particle size and morphology of MPs are also factors affecting sorption capacity. The strong correlation between the sorption coefficients and sorbate hydrophobicity indicates that the hydrophobic interactions played a crucial role. Meanwhile, specific sorbate properties, such as electronic structure and molecular polarizability, are also significant factors that affect the sorption behaviors. Full article

Lightning-induced forest fires represent a dominant natural ignition source in boreal and temperate ecosystems, yet their climatic and biophysical controls remain poorly understood. This study investigates the spatiotemporal patterns and environmental drivers of 646 lightning-induced forest fires across the Da Hinggan Ling region, Northeast China, during 2001–2024. Multi-source datasets from ERA5-Land, MODIS, and ETCCDI were integrated to quantify short-term meteorological variability, vegetation water status, and long-term climatic extremes. Using Random Forest and XGBoost models combined with SHAP interpretability analysis, we identified key predictors and nonlinear responses of burned area to environmental forcing. Results reveal pronounced interannual fluctuations in fire activity, with 2010, 2016, and 2022 emerging as compound extreme years characterized by co-occurring drought and heatwaves. Vegetation moisture index (NDMI), diurnal temperature range (DTR), and heatwave duration (HWDI) were the most influential variables controlling burned area variability. The total burned area and fire duration showed significant declining trends, while high burned-area fires exhibited spatial clustering in dry, low-LAI regions. These findings demonstrate that compound dry–hot conditions coupled with vegetation desiccation are the primary drivers of large lightning fires. The study provides a process-based understanding of climate–fuel–fire linkages and supports improved fire risk forecasting under a warming climate. Full article

The intensification of large-scale wildfires, driven by climate change, presents a critical threat to agricultural ecosystems, specifically during the vulnerable sowing season in March. Departing from the prevailing focus on urban air quality, this study elucidates the spatiotemporal dynamics of particulate matter (PM) in eight major Korean agricultural regions during the March 2025 wildfires. By employing a Generalized Additive Model (GAM), we characterized the complex non-linear interactions between PM concentrations and meteorological variables. The analysis reveals a substantial elevation in PM levels during the wildfire event relative to the pre-fire baseline. Most notably, the Sangju region experienced the most acute accumulation, with PM-10 and PM-2.5 concentrations surging by 74% and 46%, respectively; this intensification was significantly compounded by topographic trapping and surface inversion phenomena. Furthermore, GAM results identified temperature and relative humidity as the primary determinants of PM retention, whereas wind speed demonstrated a distinct non-linear, U-shaped effect, facilitating particulate resuspension at higher velocities. These findings quantitatively underscore the susceptibility of agricultural environments to wildfire-induced aerosols and highlight the imperative for establishing agriculture-specific monitoring networks and early warning protocols to safeguard crop productivity. Full article

Trouble sleeping has become a global public health challenge. However, the relationship between organophosphate flame retardant (OPFR) exposure and trouble sleeping remains unclear. This study integrated epidemiological analysis, network toxicology, molecular docking, molecular dynamics simulations, and adverse outcome pathway (AOP) construction to identify OPFRs linked to trouble sleeping and attempted to elucidate underlying molecular mechanisms. We analyzed cross-sectional data from the U.S. National Health and Nutrition Examination Survey (NHANES 2013–2018) involving 4585 eligible adults. Logistic regression confirmed dibutyl phosphate (DBuP) as significantly correlated with trouble sleeping. Restricted cubic splines (RCSs) revealed a significant non-linear, J-shaped relationship between dibutyl phosphate (DBuP) levels and trouble sleeping. Weighted quantile sum (WQS) analysis determined that DBuP accounted for the majority contribution (58.23%) to the observed effects within exposure mixtures. These findings indicated that DBuP, a metabolite of tributyl phosphate (TnBP), was closely related to trouble sleeping, suggesting that the environmental health risks of TnBP may be jointly contributed to by itself and DBuP. We used network analysis to identify five core target genes (PPARG, MMP9, PTGS2, APP, EGFR) that interact with DBuP and its parent compound TnBP. Molecular docking predicted binding poses of TnBP and DBuP toward these five core targets; all showed moderate binding affinity (ΔG ≤ −5.0 kcal/mol) except MMP9, which exhibited weak binding. Molecular dynamics simulations further supported this putative binding. Enrichment analysis highlighted inflammatory response pathways. Ultimately, we elucidated the process from molecular exposure to trouble sleeping by constructing an AOP framework. In conclusion, we proposed that TnBP and DBuP may contribute to trouble sleeping through multi-target interactions, primarily through PPARG-driven inflammatory dysregulation. These findings suggest a potential link between OPFR exposure and trouble sleeping, providing insights that warrant further mechanistic investigation. Full article

Drought is a slow-onset hazard whose economic impacts can propagate across sectors with multi-year delays. This study develops a non-linear autoregressive model with exogenous drought inputs (ARX) to assess whether U.S. drought severity, measured by the Drought Severity and Coverage Index (DSCI), contains incremental predictive information for monthly stock returns. Using weekly DSCI and stock price data from 2013 to 2023, we constructed monthly compound returns and multi-year drought lags spanning 1–5 years for four sector-representative firms: a water utility (American Water Works, AWK), two food service firms (Chipotle Mexican Grill, CMG; Starbucks, SBUX), and an industrial manufacturer (Tesla, TSLA). We compared regularized linear ARX baselines (Elastic Net, Ridge) with a non-linear Histogram Gradient Boosting Regressor (HGB) ARX model and used permutation importance to diagnose drought-relevant lag horizons. Results showed a clear, delayed drought signal for the water utility, with a dominant ~48-month drought lag, consistent with multi-year transmission through operations, regulation, and investment cycles. In contrast, drought lags added limited or unstable information for the food service firms and negligible information for TSLA, whose dynamics were dominated by non-drought drivers. Overall, the findings indicate that drought–return relationships are sector-specific and can emerge at multi-year lags, and that non-linear ARX models provide a flexible tool for detecting these delayed climate-risk signals. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

Accelerated solvent extraction (ASE) is widely used for recovering bioactive compounds from hops; however, the extent to which global antioxidant assays reflect changes in molecular composition remains unclear. This study evaluated the relationship between global antioxidant parameters and targeted profiling of prenylated flavonoids in hop extracts obtained under different ASE conditions. Total antioxidant capacity (TAC), total phenolic content (TPC), and concentrations of xanthohumol, isoxanthohumol, and 8-prenylnaringenin were determined in extracts prepared using different solvents, extraction temperatures, and homogenization approaches. Global antioxidant parameters responded consistently to technological factors and exhibited a strong mutual correlation. In contrast, their correlations with individual prenylated flavonoids were moderate, indicating that global assays capture only part of the variability associated with specific bioactive compounds. Extraction temperature emerged as a key modulating factor, inducing compound-specific and partly non-linear responses that were not fully reflected by global antioxidant methods. Principal component analysis confirmed a shared chemical trend linking global and targeted parameters while separating extraction temperature as an independent technological driver. Overall, global antioxidant assays provide a robust but simplified assessment of hop extract quality. Their combination with targeted chromatographic analysis enables more accurate interpretation of extraction behavior and supports informed process optimization aimed at preserving and recovering bioactive compounds. Full article

The use of styrylium dyes as organic nonlinear optical materials in many photonics domains has been the subject of research for decades. It has been noted that over time, research has also looked into the biological activity of styrylium dyes, namely their antibacterial effects, as well as attempts to establish links between structure and property by choosing particular structural pieces. These investigations’ scope is still very limited. Therefore, our main goal was to synthesize a styrylium compound with antimicrobial potential. A novel styrylquinolinium compound (D) was synthesized using Knoevenagel condensation. Spectroscopic techniques, including IR, 1D and 2D NMR (COSY, HSQC, and HMBC), HRMS spectra, and X-ray analysis, were used to confirm its structure. The antimicrobial and anti-inflammatory activity of the compound was assessed. The compound was found to have very good antimicrobial activity against five Gram-positive strains, three Gram-negative strains, and fungi. The most pronounced effect of the compound was against Escherichia coli and Pseudomonas aeruginosa. The compound’s anti-inflammatory activity was evaluated through its ex vivo immunohistochemistry. DFT calculations, such as geometry optimization, Molecular Electrostatic Potential (MEP), HOMO–LUMO, reactivity parameters and molecular docking simulation were applied to investigate the electronic features of the compound and confirm the biological activity. The compound (D) demonstrated a promising antibacterial and immunomodulatory profile. Its ability to induce IL-1β and at the same time moderately reduce NOS3 can be considered as a controlled adaptation of the immune response, especially in cases requiring local immune activation. Docking simulation revealed that (D) binds effectively to the active site of the bacterial protein, supporting the experimental findings of the compound’s antibacterial activity. Full article

This manuscript provides a comprehensive review of the enzymatic hydrolysis of lignocellulosic biomass, emphasizing how chemical composition, structural features, inhibitory compounds, and process configurations collectively influence the conversion of structural polysaccharides into fermentable sugars. Variability among herbaceous, woody, and residual biomasses results in differences in cellulose, hemicellulose, lignin content, and crystallinity, which strongly affect enzyme accessibility. The review discusses key inhibitory mechanisms, including nonproductive cellulase adsorption onto lignin, interference from phenolic derivatives and pretreatment by-products, and inhibition caused by accumulating mono- and oligosaccharides. Process configurations such as SHF, SSF, PSSF, and consolidated bioprocessing are compared, with SSF often achieving superior performance by mitigating end-product inhibition. The manuscript also highlights the growing relevance of computational modeling and simulation tools, which support kinetic prediction, the evaluation of transport limitations, and the optimization of operating conditions in high-solids systems. Additionally, recent advances in artificial intelligence are presented as powerful approaches for modeling nonlinear hydrolysis behavior, estimating kinetic parameters, identifying rate-limiting steps, and improving predictive accuracy in complex bioprocesses. Overall, the integration of experimental insights with advanced modeling, simulation, and AI-based strategies is essential for overcoming current limitations and enhancing the technical feasibility and industrial competitiveness of lignocellulosic bioconversion. Full article

Within the framework of green chemistry and wild fruit valorization, this study optimizes the extraction of bioactive compounds from Prunus spinosa L. fruits using glycerin-based ultrasound-assisted extraction (UAE). Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were comparatively employed to model the process. Significant improvements in extraction efficiency were achieved, with total phenolic content increasing from 9.28 to 23.22 mg GAE/g DW, total flavonoid content from 6.53 to 21.65 mg CE/g DW, and antioxidant activity (DPPH assay) from 57.04% to 86.34%. While both models performed well, ANN demonstrated slightly higher predictive accuracy, highlighting its potential for capturing complex, non-linear relationships in the extraction process. We identified the optimal extraction conditions as 9 min extraction time, 100% ultrasonic amplitude, and 40% water in glycerin, and these conditions were experimentally validated. UHPLC-DAD-HRMS/MS profiling revealed a rich phytochemical fingerprint dominated by phenolic acids, caffeoylquinic acid derivatives, and flavonol glycosides, and revealed largely overlapping qualitative phytochemical profiles between hydroglyceric and ethanolic extracts. Comparative extraction using 70% ethanol under identical conditions resulted in lower TPC, TFC, and antioxidant activity, indicating the improved efficiency of glycerin under the investigated conditions. Overall, the optimized glycerin-based UAE provides a sustainable, food-safe approach for extracting bioactive compounds from underutilized P. spinosa fruits. These results support its application in functional foods and in nutraceutical and cosmetic formulations. Full article

This study aimed to explore the evolution of quality and flavor characteristics of braised pork during the cooking process and clarify the underlying formation mechanisms. Texture analysis revealed that shear force and hardness initially increased during blanching but decreased substantially with an extended stewing time. Low-field NMR indicated a progressive shift in water distribution from immobilized to free states, correlating with cooking loss and tenderness development. GC-MS and E-nose analyses showed significant increases in volatile compound diversity and concentrations, with aldehydes and ketones identified as dominant contributors to the evolving aroma profile. Throughout the processing, an enhancement in protein oxidation and nucleotide degradation was observed. Notably, significant increases were detected in the umami amino acids aspartic acid and glutamic acid, as well as in the umami nucleotide inosine monophosphate (IMP). These changes collectively contributed to the development of the characteristic taste profile. These findings indicate that the superior eating quality evolution and flavor development of braised pork during cooking are governed by the coordinated changes in texture, water distribution, lipid oxidation, and taste-active compounds. The interplay between these factors occurs at different stages of processing, leading to the complex, non-linear enhancement of flavor and texture. Full article

Shape memory alloy (SMA) actuators possess unique advantages for aerospace applications, including significant deformation, a high work-to-weight ratio, and structural simplicity. However, SMA actuators exhibit inherently strongly saturated and asymmetric hysteresis characteristics, which cause significant hysteresis in the output response. These hysteresis nonlinearities, compounded by process and measurement noise, severely degrade control precision. To overcome these issues, this study proposes a Smoothed Generalized Prandtl–Ishlinskii (SGPI) model to characterize such hysteresis behavior. Based on the SGPI model, we developed a state-space representation for the SMA actuator. Furthermore, an Extended Kalman Filter (EKF) is employed to estimate unmeasurable internal hysteresis states, and these estimates are subsequently utilized as input states for Model Predictive Control (MPC). The simulation results demonstrate that the proposed EKF-MPC approach achieves both rapid dynamic response and high-precision tracking control, effectively compensating for hysteresis nonlinearity while rejecting noise disturbances. Full article

Sintering is a key processing route to consolidate nuclear fuel powders into dense compacts, yet the atomic-level mechanisms governing the sintering of actinide compounds remain poorly understood. Herein, the sintering kinetics and structural evolution of uranium mononitride (UN) nanoparticles are investigated using molecular dynamics (MD) simulations. A three-stage sintering mechanism is revealed based on the symmetrical dual nanoparticle models: initial surface diffusion and neck formation, followed by interface amorphization driven by shear stress, and finally, lattice reconstruction and recrystallization, which peak during the cooling process. By studying the effect of sintering temperature, we find that near-complete densification with good structural integrity is achieved at 1900 K, whereas further increasing the temperature (to 2000 K) led to microstructural instability and near-overburning. In addition, holding time exhibits a clear saturation effect, with variations in holding time showing no significant impact on sintering morphology or density. Therefore, sintering temperature is the dominant factor determining sintering quality. The atomic level insights provided by this work reveal the nonlinear temperature dependence and time saturation effect of UN nanoparticle sintering, and provide a theoretical basis for the prediction, design, and optimization of nuclear fuel sintering process. Full article