Search Results (2,536)

The present study reports the design and physicochemical characterization of a hybrid nanoparticle system for the potential intranasal delivery of galantamine (GAL), aimed at improving its bioavailability. Carbon dots (CDs) were used to load GAL, enhancing its dissolution and stability, and were subsequently incorporated into a poly(lactic-co-glycolic acid)–curcumin (PLGA–Cur) conjugate matrix. The successful formation of the PLGA-Cur conjugate was verified via ¹H-NMR and FTIR spectroscopy, while the loading of GAL and its physical state in the CDs was assessed via FTIR and pXRD, respectively. The resulting GAL-CD/PLGA–Cur nanoparticles were spherical, with particle sizes varying from 153.7 nm to 256.3 nm, a uniform morphology and a narrow size distribution. In vitro release studies demonstrated a multi-phase sustained release pattern extending up to 12 days. Spectroscopic and thermal analyses confirmed successful conjugation and molecular interactions between GAL and the carrier matrix. This proof-of-concept hybrid system demonstrates promising controlled, multi-phase sustained galantamine release in vitro, highlighting the role of curcumin conjugation in modulating polymer structure and release kinetics and providing a foundation for future biological evaluation. Full article

With the increasing penetration of wind power, the transient synchronization stability of doubly fed induction generator (DFIG)-based wind turbines during grid faults has become a critical issue. While conventional fault ride-through methods like Crowbar protection can ensure safety, they compromise system controllability and worsen grid voltage conditions. Virtual resistance demagnetization control has emerged as a promising alternative due to its simple structure and effective flux damping. However, its impact on transient synchronization stability has not been revealed in existing studies. To fill this gap, this paper presents a comprehensive analysis of the transient synchronization stability of DFIG systems under virtual resistance control, introducing a novel fourth-order transient synchronization model that explicitly captures the coupling between the virtual resistance demagnetization control and phase-locked loop (PLL) dynamics. The model reveals the emergence of transient power and positive damping terms induced by the virtual resistance, which play a pivotal role in stabilizing the system. Furthermore, this work theoretically investigates how the virtual resistance and current loop’s proportional-integral (PI) parameters jointly influence transient stability, demonstrating that increasing the virtual resistance while reducing the integral gain of the current loop significantly enhances synchronization stability. Simulation results validate the accuracy of the model and the effectiveness of the proposed analysis. The findings provide a theoretical foundation for optimizing control parameters and improving the stability of DFIG-based wind turbines during grid faults. Full article

During liquid drainage from intermediate vessels in various industrial processes such as continuous steel casting, aircraft fuel supply, and chemical separation, free-surface vortices commonly occur. The formation and evolution of these vortices not only entrain surface slag and gas, but also lead to deterioration of downstream product quality and abnormal equipment operation. The vortex evolution process exhibits notable three-dimensional unsteadiness, multi-scale turbulence, and dynamic gas–liquid interfacial changes, accompanied by strong coupling effects between temperature gradients and flow field structures. Traditional macroscopic numerical models show clear limitations in accurately capturing these complex physical mechanisms. To address these challenges, this study developed a mesoscopic numerical model for gas-liquid two-phase vortex flow based on the lattice Boltzmann method. The model systematically reveals the dynamic behavior during vortex evolution and the multi-field coupling mechanism with the temperature field while providing an in-depth analysis of how initial perturbation velocity regulates vortex intensity and stability. The results indicate that vortex evolution begins near the bottom drain outlet, with the tangential velocity distribution conforming to the theoretical Rankine vortex model. The vortex core velocity during the critical penetration stage is significantly higher than that during the initial depression stage. An increase in the initial perturbation velocity not only enhances vortex intensity and induces low-frequency oscillations of the vortex core but also markedly promotes the global convective heat transfer process. With regard to the temperature field, an increase in fluid temperature reduces the viscosity coefficient, thereby weakening viscous dissipation effects, which accelerates vortex development and prolongs drainage time. Meanwhile, the vortex structure—through the induction of Taylor vortices and a spiral pumping effect—drives shear mixing and radial thermal diffusion between fluid regions at different temperatures, leading to dynamic reconstruction and homogenization of the temperature field. The outcomes of this study not only provide a solid theoretical foundation for understanding the generation, evolution, and heat transfer mechanisms of vortices under industrial thermal conditions, but also offer clear engineering guidance for practical production-enabling optimized operational parameters to suppress vortices and enhance drainage efficiency. Full article

This study evaluates the effectiveness of integrating a Residual Shifting (ResShift)-based deep learning super-resolution (SR) technique with the Robust Dense Feature Matching (RoMa) algorithm for high-precision inter-satellite image registration. The key findings of this research are as follows: (1) Enhancement of Structural Details: Quadrupling image resolution via the ResShift SR model significantly improved the distinctness of edges and corners, leading to superior feature matching performance compared to original resolution data. (2) Superiority of Dense Matching: The RoMa model consistently delivered overwhelming results, maintaining a minimum of 2300 correct matches (NCM) across all datasets, which substantially outperformed existing sparse matching models such as SuperPoint + LightGlue (SPLG) (minimum 177 NCM) and SuperPoint + SuperGlue (SPSG). (3) Seasonal Robustness: The proposed framework demonstrated exceptional stability, maintaining registration errors below 0.5 pixels even in challenging summer–winter image pairs affected by cloud cover and spectral variations. (4) Geospatial Reliability: Integration of SR-derived homography with RoMa achieved a significant reduction in geographic distance errors, confirming the robustness of the dense matching paradigm for multi-sensor and multi-temporal satellite data fusion. These findings validate that the synergy between diffusion-based SR and dense feature matching provides a robust technological foundation for autonomous, high-precision satellite image registration. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

Coal-based humic acid waste residue is a solid waste generated during the production of humic acid products. The extraction of coal-based humin (NHM) from such residues presents an effective approach for solid waste resource recovery. In this study, a novel calcium-based humin (Ca-NHM) was synthesized via a low-temperature-assisted method. The material was characterized and its cadmium passivation mechanism was investigated using scanning electron microscopy (SEM), zeta potential analysis (Zeta), carbon nuclear magnetic resonance (¹³C-CPMAS-NMR), and X-ray photoelectron spectroscopy (XPS). Soil incubation experiments were conducted to determine the actual cadmium adsorption capacity of coal-based humin in soils and to evaluate the stability of cadmium passivation. Plant cultivation experiments were carried out to verify the effects of coal-based humin on migration and transformation in soil, as well as on cadmium bioefficiency. The results showed that Ca-NHM passivated soil cadmium through multiple mechanisms such as ion exchange, electrostatic adsorption, complexation reactions, and physical adsorption. Compared with NHM, Ca-NHM exhibited a 69.71% increase in passivation efficiency, and a 2.44% reduction in cadmium leaching concentration. In Ca-NHM treatments, the above- and below-ground biomass of pakchoi increased by 18.06%, and 80.95%, respectively, relative to the control group. Furthermore, Ca-NHM enhanced the cadmium resistance of pakchoi, reduced the enrichment coefficient, activity coefficient, and activity-to-stability ratio in the above-ground portion of pakchoi, and maintained a transfer coefficient below 1, thereby alleviating cadmium toxicity. In summary, this study provides a theoretical foundation for understanding the mechanisms by which coal-based humin mitigates cadmium toxicity in pakchoi. Full article

Intimate relationship stability is fundamental to human wellbeing, yet its quantitative assessment faces dual challenges: the inherent subjectivity of psychological constructs and the complexity of social ecosystems. Symmetry, as a fundamental structural feature of social interaction, plays a pivotal role in shaping relational dynamics. To address these limitations, this study proposes an innovative computational framework that integrates Fuzzy Set Theory with Social Network Analysis (SNA). The framework consists of two complementary components: (1) a psychologically grounded fuzzy assessment model that employs differentiated membership functions to transform discrete subjective ratings into continuous and interpretable relationship quality indices and (2) an enhanced Fuzzy C-Means (FCM) threat detection model that utilizes Weighted Mahalanobis Distance to accurately identify and cluster potential interference sources within social networks. Empirical validation using a simulated dataset—comprising typical characteristic samples from 10 couples—demonstrates that the proposed framework not only generates interpretable relationship diagnostics by correcting biases associated with traditional averaging methods, but also achieves high precision in threat identification. The results indicate that stable relationships exhibit greater symmetry in partner interactions, whereas threatened nodes display structural and behavioural asymmetry. This study establishes a rigorous mathematical paradigm—“Subjective Fuzzification → Multidimensional Feature Engineering → Intelligent Clustering”—for relationship science, thereby advancing the field from descriptive analysis toward data-driven, quantitative evaluation and laying a foundation for systematic assessment of relational health. Full article

Water resources constitute a foundational strategic resource, and the efficiency of their spatial allocation profoundly impacts national sustainable development. This study integrates multi-regional input–output modeling, complex network analysis, and exploratory spatiotemporal data analysis methods to systematically examine the patterns, network structures, and spatiotemporal evolution characteristics of virtual water flows across 30 Chinese provinces from 2010 to 2023. Findings reveal the following: Virtual water flows underwent a three-stage evolution—“expansion–convergence–stabilization”—forming a “core–periphery” structure spatially: eastern coastal and North China urban clusters as input hubs, while East–Northeast–Northwest China served as primary output regions; The virtual water flow network progressively tightened and segmented, evidenced by increased network density, shorter average path lengths, and enhanced clustering coefficients and transitivity. PageRank analysis reveals significant Matthew effects and structural lock-in within the network; LISA time paths indicate stable spatial structures in most provinces, yet dynamic characteristics are prominent in node provinces like Guangdong and Jiangsu. Spatiotemporal transition analysis further demonstrates high overall system resilience (Type₀ transitions accounting for 47%), while abrupt transitions in provinces like Hebei and Liaoning are closely associated with national strategies and industrial restructuring. This study provides theoretical and empirical support for establishing a coordinated allocation mechanism between physical and virtual water resources and formulating differentiated regional water governance policies. Full article

CRISPR-Cas9 systems have enabled unprecedented advances in genome engineering, particularly in developing treatments for human diseases, like cancer. Despite potential applications, limitations of Cas9 include its relatively large size and strict targeting requirements. Cas12j2, a variant ofCasΦ-2, shows promise for overcoming these limitations. However, its effectiveness in mammalian cells remains relatively unexplored. This study sought to develop an optimized CRISPR-Cas12j2 system for targeted knockout of the E6 oncogene in HPV-associated cancers. A combination of computational tools (ColabFold, CCTop, Cas-OFFinder, HADDOCK2.4, and Amber for Molecular Dynamics) was utilized to investigate the impact of engineered modifications on structural integrity and gRNA binding of Cas12j2 fusion constructs, in potential intracellular conditions. Cas12j2_F2, a Cas12j2 variant designed and evaluated in this study, behaves similarly to the wild-type Cas12j2 structure in terms of RMSD/RMSF profiles, compact Rg values, and minimal electrostatic perturbation. The computationally validated Cas12j2 variant was incorporated into a custom expression vector, co-expressing the engineered construct along with a dual gRNA for packaging into a viral vector for targeted knockout of HPV-associated cancers. This study provides a structural and computational foundation for the rational design of Cas12j2 fusion constructs with enhanced stability and functionality, supporting their potential application for precise genome editing in mammalian cells. Full article

In coastal saline soil regions, foundation instability frequently arises due to salt heave, dissolution-induced weakening and corrosion-driven degradation. To enhance the engineering performance of fine-grained saline soil, this study evaluates the effectiveness of Enzyme-Induced Carbonate Precipitation (EICP) treatment under varying salinity levels and curing solution concentrations. Mechanical properties, hydraulic behavior and water stability were examined through unconfined compressive strength (UCS), disintegration and permeability tests, complemented by microstructural analyses using XRD and SEM. The results indicate that EICP notably improves mechanical strength, water stability and reduced permeability. The UCS of treated specimens increased by 37–152% relative to untreated soil, and disintegration time was prolonged by 214–563%. The permeability coefficient was reduced by 45.8–95.7%, demonstrating effective suppression of seepage channels. The optimal stabilization performance was achieved at 0.02% salinity and curing concentrations of 1.0–1.3×. Excessive salinity distorted vaterite crystal morphology and weakened cementation. XRD and SEM analyses revealed that vaterite dominated the calcium carbonate polymorphs, while ionic complexity influenced crystal structure, ACC conversion and pore-filling performance. These findings confirm the feasibility of applying EICP for improving fine-grained coastal saline soils and provide practical engineering guidance for coastal subgrades, reclamation foundations and port infrastructures. Full article

Developmental Language Disorder (DLD) is associated with abnormalities in both intrinsic resting-state brain networks and task-evoked neural responses, yet direct electrophysiological evidence linking these levels remains limited. This study examined multi-level EEG markers in 21 typically developing children and 15 children with DLD across resting-state, a semantic matching task, and an auditory oddball task. Resting-state analyses revealed frequency-specific connectivity imbalances, reduced stability of intrinsic microstate dynamics, and atypical transitions between microstates in the DLD group. During the semantic matching task, DLD children showed weaker occipital P1 and N2 responses (100–300 ms) and lacked the right fronto-central difference wave (500–700 ms) observed in TD children. In the auditory oddball task, DLD children exhibited high-theta/low-alpha event-related desynchronization at left frontal electrodes (400–500 ms), in contrast to TD children. A machine learning framework integrating resting-state and task-based features discriminated DLD from TD children (test-set F1 = 70.3–80.0%) but showed limited generalizability, highlighting the constraints of small clinical samples. These findings support a translational neurophysiological signature for DLD, in which atypical intrinsic network organization constrains emergent neural computations, providing a foundation for future biomarker development and targeted intervention strategies. Full article

Accurate analysis and forecasting of short-term heavy rainfall (hourly rainfall ≥ 20 mm) are crucial for extending warning, enabling targeted preventive measures, and supporting efficient resource allocation. In recent years, machine learning techniques combined with atmospheric physical variables have offered promising new approaches for analyzing and predicting and forecasting short-term heavy rainfall. However, these methods often lack transparency, which hinders the interpretation of key atmospheric physical variables that drive short-term heavy rainfall and their coupling mechanisms. To address this challenge, the present study integrates the interpretable SHAP (SHapley Additive exPlanations) framework with machine learning to examine potential relationships between widely used atmospheric physical variables and short-term heavy rainfall, thereby improving model interpretability. CatBoost models were constructed based on multiple feature-input strategies using 71 physical variables across five categories derived from ERA5 reanalysis data, and their performance was compared with two benchmark algorithms, XGBoost and LightGBM. The SHAP method was subsequently applied to quantify the contributions of individual features and their interaction effects on model predictions. The results indicate that (1) the CatBoost model, utilizing all 71 physical variables, outperforms other feature combinations, with an AUC of 0.933, and F1 score of 0.930, and a Recall of 0.954, significantly higher than the XGBoost and LightGBM models; (2) Shapley value analysis identified 500 hPa vertical velocity, the A-index, and precipitable water as the most influential features on model performance; (3) The predictive mechanism for short-term heavy rainfall is fundamentally bifurcated: negative instances are classified through the discrete main effects of individual features, whereas positive event detection necessitates a sophisticated coordination of intrinsic main effects and synergistic interactions. Among the feature categories, the horizontal and vertical wind fields, stability and energy indices, and humidity-related variables exhibited the highest contribution ratios, with wind field features demonstrating the strongest interaction effects. The results confirm that integrating atmospheric physical variables with the CatBoost ensemble learning approach significantly improves short-term heavy rainfall identification. Furthermore, incorporating the SHAP interpretability framework provides a theoretical foundation for elucidating the mechanisms of feature influence and optimizing model performance. Full article

Achieving dynamic stability in bipedal locomotion against sustained external disturbances remains a significant challenge in humanoid robotics. Traditional methods, such as zero moment point (ZMP) preview control, often lack the reactive compliance and predictive planning necessary for robust performance on uneven terrain or under continuous force. This paper proposes a novel control framework that synergistically integrates a resistance torso compliance controller with a nonlinear model predictive control (NMPC)-based walking pattern generator. The compliance controller actively modulates the torso’s dynamics via impedance control, creating a virtual mass–spring–damper system that absorbs impacts and generates counterforces to resist sustained pushes. Concurrently, the NMPC module reformulates gait generation as a real-time optimization problem, simultaneously determining optimal footstep positions and orientations while respecting nonlinear constraints derived from centroidal momentum dynamics. Simulation results demonstrate that this integrated approach reduces the maximum ZMP error by 34.1% and the RMS ZMP error by 37.3% compared to traditional ZMP preview control, with a 38.9% improvement in settling time after a disturbance. This work establishes that the tight coupling of reactive impedance control and predictive optimization provides a foundational framework for enhancing the robustness and adaptability of bipedal locomotion. Full article

Tax evasion is a dynamic process reflecting continuous interaction between taxpayers and regulatory institutions rather than a static deviation from fiscal equilibrium. This study introduces a predator-prey model of tax evasion that translates the Lotka-Volterra framework from biology into budgetary dynamics. The model captures the feedback between the volume of tax evasion and the intensity of regulation, incorporating nonlinearity, implicit reactive lag, and adaptive response. Theoretical derivation and numerical simulation identify three dynamic regimes—stable equilibrium, limit-cycle oscillation, and instability—that arise through a Hopf bifurcation. Bifurcation maps in the (r, a), (r, b), and (r, c) parameter spaces reveal how control efficiency, institutional inertia, and behavioral feedback jointly determine fiscal stability. Results show that excessive enforcement may destabilize the system by inducing regulatory fatigue, while weak control enables exponential growth in evasion. The model provides a dynamic analytical tool for evaluating fiscal policy efficiency and identifying stability thresholds. Its findings suggest that adaptive, feedback-based regulation is essential for maintaining long-term tax discipline. The study contributes to closing the research gap by providing a unified dynamic framework linking micro-behavioral decision-making with macro-fiscal stability, offering a foundation for future empirical calibration and behavioral extensions of fiscal systems. Full article

Geostructures, such as foundations, embankments, retaining structures, bridge abutments, and both natural and engineered slopes, interact with the ground to ensure structural safety and functionality. One significant factor influencing these systems is climate, which continuously affects soil conditions through dynamic processes. Over the past century, climate change has intensified, increasing uncertainties regarding the safety of both existing and planned geostructures. While the impacts of climate change on geostructures are evident, effective methods to address them remain uncertain. This paper presents an approach for mitigating and adapting to climate change impacts through a stepwise geomechanical analysis and geotechnical design framework that incorporates expected climatic conditions. A novel framework is introduced that systematically integrates projected climate scenarios into geomechanical modeling, enabling climate-resilient design of geostructures. The concept establishes an interface between climate effects and geomechanical data, capturing the causal chain of climate hazards, their effects, and potential consequences. The proposed interface provides a practical tool for integrating climate considerations into geotechnical design, supporting adaptive and resilient infrastructure planning. The approach is demonstrated across different geostructure types, with a detailed slope stability analysis illustrating its implementation. Results show that the interface, reflecting processes such as water infiltration, soil hydraulic conductivity, and groundwater flow, is often critical to slope stability outcomes. Furthermore, slope stability can often be maintained through simple, timely implemented nature-based solutions (NbS), whereas delayed actions typically require more complex and costly interventions. Full article