Search Results (697)

In this study, three SnS₂ samples with different morphologies were synthesized using a one-step hydrothermal method, and the effect and mechanism of morphology on their adsorption ability toward methylene blue (MB) were studied. XRD and SEM revealed effective preparations of SnS₂ with flake, flower-like, and granular morphologies, as well as their size variations. The BET results indicate that the specific surface areas follow the order flower-like > granular > flake. Adsorption experiments demonstrated that the morphology of SnS₂ considerably impacts their MB adsorption ability. Kinetic investigations implied that the adsorption of MB on flower-like and granular SnS₂ followed a pseudo-second-order kinetic model, with adsorption rates in the order of flower-like > granular. MB adsorption on flake SnS₂ followed the Weber–Morris model. The adsorption of MB on all three SnS₂ structures followed the Langmuir isotherm model, with the flower-like SnS₂ exhibiting the highest maximum adsorption capacity of 33.1 mg/g, which is 28.8% and 27.8% higher than that of the flake structure (25.7 mg/g) and the granular structure (25.9 mg/g), respectively. Adsorption thermodynamics indicated that the ΔG^θ for MB adsorption on all morphologies was negative, suggesting their spontaneous adsorption process. Furthermore, ΔG^θ decreased with increasing temperature, indicating that higher temperatures promote MB adsorption. In addition, both the values of ΔH^θ and the ΔS^θ for the MB adsorption on SnS₂ were in the order of flower-like > granular > flake SnS₂, suggesting that MB is more easily adsorbed on the flower-like SnS₂ than granular SnS₂ and final flake SnS₂. DFT simulations confirmed that the distinct exposed facets of the flower-like morphology yielded the strongest adsorption energies, revealing the essential structure–property relationship for designing highly efficient 2D adsorbents. This work reveals the important effect of SnS₂ morphology on its adsorption behavior and gives essential insights for the design and development of adsorbents. Full article

Solving geometry problems is a critical challenge in education, for it demands the integration of textual semantic descriptions, mathematical formula logic and spatial graphical information, as well as rigorous geometric theorem application and stepwise logical deduction. These are core capabilities that underpin the realization of personalized intelligent tutoring and efficient educational resource allocation. Traditional geometry problem solving methods often suffer from deficiencies in accuracy and the fusion of text, formula and image features. Hence, this paper proposes a method of solving geometry problems based on a text–formula–image (TFI) multimodal parsing and fusion model. The TFI parser employs a self-attention multilayer Transformer to enhance the extraction of logical relations among geometric text expressions. Meanwhile, it parses formulas into tree structures to overcome the loss of formula structural features, which utilizes symbolic embedding and tree-structured encoding to preserve hierarchical logical information and yields unified formula representations via a multi-granularity fusion module. The TFI parser also leverages a Feature Pyramid Network (FPN) for the accurate detection of geometric and non-geometric instances, resolves the issues of blurred segmentation for slender geometric elements and the inaccurate localization of small-sized symbols through mask averaging and RoIAlign, and generates high-dimensional image features using DenseNet-121. The TFI multimodal fusion model integrates a contrastive learning mechanism and constructs fused feature representations by stacking self-attention and cross-attention layers. This design effectively narrows the semantic gap between text, formula, and image features, addressing the inadequacy of traditional fusion approaches in deep cross-modal feature alignment. An attention-augmented Gated Recurrent Unit (GRU) network processes the fused TFI features to produce target operation trees and geometry solutions, ensuring interpretable and precise reasoning performance. The proposed method is evaluated on the PGDP5K and GeoEval datasets, and it achieves an average accuracy of 59.63% in geometry problem solving, which validates its effectiveness. This paradigm offers a viable technical approach for uniformly modeling complex educational tasks, including geometry problem solving and timetable scheduling. Full article

Wet stirred-media milling (WSMM) is among the most widely used techniques for producing high-drug-loaded stable nanosuspensions, owing to its ease of scale-up, good repeatability, operational versatility and broad applicability. However, WSMM is also associated with high energy demand, substantial heat generation, and extended milling times. To reduce energy consumption, optimize the process and gain a deeper understanding of breakage kinetics, robust mechanistic models should be investigated. In this study, a microhydrodynamic (MHD) model framework is examined, and the first closed-form analytical solution for granular temperature $\theta$, a key parameter in the MHD model, is derived. In addition, an existing power consumption correlation from the literature is adopted and extended by introducing an additional parameter that accounts for bead-size effects, and the resulting improved formulation is embedded into the analytical framework. This integration facilitates continuous evaluation of power consumption, $\theta$ and the additional MHD parameters across the milling parameter space. With backward compatibility and high-quality fitting performance, the improved power consumption model enables robust, reliable, and systematic evaluation of sensitivities and trade-offs over diverse milling conditions, including varying stirrer speeds, bead loadings, and bead sizes. Full article

Social media platforms host abundant and timely descriptions of medication experiences that can complement traditional pharmacovigilance systems. Yet the linguistic informality of these data presents a major challenge for mapping adverse drug event (ADE) expressions to standardized medical terminology. In this study, we developed BERT-based language models to classify ADE mentions from social media into MedDRA System Organ Classes (SOCs). Using the SMM4H and CADEC corpora, as well as their combination, we performed 20 iterations of 20% holdout validation for 3-, 6-, 22-, and 25-SOC classification tasks with a selected fixed training configuration (learning rate, batch size, and training epochs) based on training-loss convergence. The models achieved accuracies ranging from 75% to 94%, demonstrating strong performance for SOC-level classification of noisy and informal ADE expressions under the evaluated settings. These results are based on a controlled mention-level evaluation using deduplicated adverse drug event strings and do not establish document-level or real-world deployment generalization. This work provides a systematic evaluation of BERT-based models for SOC-level classification of ADEs and demonstrates consistent performance within the evaluated datasets and label granularities. While direct comparison with prior studies is limited by differences in datasets and evaluation protocols, the results demonstrate that transformer-based models can effectively classify ADEs into SOCs. These findings support the use of transformer-based normalization for SOC-level aggregation of user-reported adverse events and their integration into large-scale social media pharmacovigilance pipelines as a downstream component under controlled conditions. Full article

3D multi-object tracking (MOT) for autonomous driving remains challenging due to frequent identity switches in crowded scenes, trajectory fragmentation during occlusions, and the difficulty of adapting association strategies to varying scene complexities. While existing methods rely on fixed geometric or appearance-based associations, they struggle to handle ambiguous cases and detection failures. We present an adaptive multi-level 3D MOT framework that achieves robust tracking through three key innovations: (1) multi-granularity temporal modeling that captures both fine-grained short-term motion and coarse long-term trends via dual-scale spatio-temporal attention, enabling accurate motion prediction across different object dynamics; (2) Transformer-based Appearance Association that employs cross-attention to model global inter-object relationships, resolving ambiguous associations in crowded scenarios where geometric cues alone fail; and (3) scene-adaptive learned thresholds that automatically adjust association strictness based on object density, motion complexity, and occlusion levels, avoiding the one-size-fits-all limitations of fixed thresholds. Our hierarchical four-level tracking strategy progressively handles cases from easy geometric matching (Level 1) to complex interval-frame recovery (Level 4), with SOT-based virtual detection generation bridging detector failures. Extensive experiments on the nuScenes benchmark demonstrate state-of-the-art performance. Full article

Emerging technologies (ETs), such as AI and reality techniques, are reshaping educational measurement. However, existing studies remain dispersed and are rarely synthesized in ways that clarify how ETs participate in the evidentiary work of educational measurement. Guided by PRISMA 2020, we systematically reviewed 933 empirical studies published between 2016 and 2025 in formal educational settings. We coded studies by (a) grain size (micro, meso, macro), (b) Constructing Measures Theory building blocks (construct map, item design, outcome space, measurement model), and (c) ET category. Results showed a strong concentration at the micro level (88.88%) and in outcome space and measurement model work (86.80% combined), indicating that ET-enabled innovation has focused primarily on transforming performances into indicators and modeling those indicators for interpretation and decision-making. Learning analytics and educational data mining, machine learning and deep learning, and automated scoring and feedback systems were the dominant ET clusters. These findings point to an uneven development of ET-enabled educational measurement. Included studies also indicating recurring concerns about transparency, fairness, and governance are linked to the field’s main areas of ET-enabled concentration. We therefore argue for closer alignment among construct claims, evidence, modeling, and intended use, and offer implications for developers, researchers, and education practitioners. Full article

Broken gangue in goafs exhibits complex mechanical deformation and seepage evolution under coupled loading and hydraulic action, which directly affects the hydraulic stability and water-hazard prevention of mining engineering. In this study, a systematic investigation was carried out to elucidate the evolution of seepage characteristics in a granular broken-rock assemblage under coupled hydraulic–mechanical loading. Four mono-sized specimen groups with particle-size ranges of 5–10 mm, 10–15 mm, 15–20 mm, and 20–25 mm were prepared. Using a modified rock triaxial–hydraulic testing system, nominal uniaxial compression tests, triaxial compression tests under different moisture conditions, and staged axial loading–seepage coupling tests were conducted. The results indicated pronounced particle-size effects: with increasing particle size, the nominal uniaxial compressive strength decreased (maximum reduction of 41.26%), while the crushing ratio increased (from 0.99% to 28.89%). The compression–densification process exhibited a staged evolution characterized by “slow increase–rapid increase–stable increase.” Water-induced deterioration intensified with increasing water content, and the compressive strength reduction reached 29.8% under saturated conditions. The evolution of seepage behavior was jointly governed by loading rate and particle size. Both pore pressure and pore-pressure gradient increased with loading rate. The permeability–porosity relationship was nonmonotonic, with an inflection occurring at a porosity of approximately 0.30–0.32, accompanied by an order-of-magnitude variation in the Darcy-flow deviation factor, indicating a progressive nonlinear deviation from Darcy behavior. These observations reflected a competitive mechanism involving “compaction-induced flow resistance increase–fragmentation and rearrangement–local channel regeneration.” Numerical simulations performed in COMSOL6.2 further confirmed, at the microscopic level, that the development of preferential local seepage channels and the expansion of stagnant-water zones were the fundamental causes of locally enhanced seepage capacity under an overall compaction background. The findings provide a theoretical basis for understanding water–rock interaction mechanisms in goafs and offer reference for mine water-hazard mitigation and groundwater resource protection. Full article

Granular systems confined in a shallow box and subjected to vertical vibration provide an attractive geometry for studying fluidized granular media. In this configuration, grains acquire kinetic energy in the vertical direction through collisions with the confining walls, and this energy is subsequently transferred to the horizontal degrees of freedom via interparticle collisions. In recent years, the so-called $\mathsf{\Delta }$-model has been introduced as a simplified yet effective description of the dynamics of granular systems in such geometries. This review presents the results obtained from kinetic theory for the granular $\mathsf{\Delta }$-model. To model the energy transfer mechanism, a fixed velocity increment $\mathsf{\Delta }$ is added to the normal component of the relative velocity during collisions. In this way, the vertical motion is effectively integrated out while retaining the collisional energy injection characteristic of the confined setup. This mechanism compensates for the energy loss due to inelastic collisions and leads to stable homogeneous steady states that can be analyzed within the framework of kinetic theory. The Enskog kinetic equation is formulated for this model and first analyzed in homogeneous steady states, yielding the stationary temperature and the equation of state. The dynamics of inhomogeneous states is then investigated using the Chapman–Enskog method, from which the Navier–Stokes transport coefficients are derived. The theory is further extended to granular mixtures, in which particles may differ in mass, size, restitution coefficient, or in the value of $\mathsf{\Delta }$. In this case, the phenomenology becomes richer; for example, energy equipartition is violated even in homogeneous steady states. The mixture dynamics is studied through the corresponding Navier–Stokes equations, and the associated transport coefficients are obtained in the low-density regime. The analysis of the hydrodynamic equations shows that, in agreement with simulations, the homogeneous state is linearly stable. Moreover, the intrinsically nonequilibrium nature of the model leads to the violation of Onsager reciprocity relations in granular mixtures. The theoretical predictions exhibit in general good agreement with both molecular dynamics simulations and direct simulation Monte Carlo results. Full article

Harmful algal blooms (HABs) pose a serious threat to public health, aquaculture, and coastal ecosystems, making the development of tools for their rapid and specific detection a high priority. Laser-induced fluorescence (LIF) spectroscopy enables the assessment of characteristic photosynthetic pigments, offering a pathway to automated, high-throughput monitoring systems. Here, we investigate the temperature dependency of LIF spectra in the range of 20–80 °C to establish stable fluorescence fingerprints for the harmful microalgae Alexandrium catenella. Critically, we demonstrate that the relationship between temperature and both fluorescence intensity and spectral position remains consistent over 35 days of cultivation, independent of culture age. We performed complementary flow cytometric and pigment analyses (HPLC) to characterize the culture’s physiological state. Over the 35-day period, cell concentration increased 20-fold, while cell size, granularity, and fluorescence spectra remained stable. A transient decrease in fluorescence intensity observed on day 10 coincided with a drop in peridinin concentration, confirming the link between the spectral signal and pigment composition. Obtained results validate the use of this fluorescence fingerprint for the reliable identification of A. catenella without prior knowledge of the culture’s age—a key advantage for field applications. Furthermore, these fingerprints remained clearly distinguishable even when the culture was diluted with seawater to just 3% of its original volume, underscoring the potential sensitivity of this approach for early warning systems. Full article

As China transitions from rapid urbanization to high-quality development, the competition for population among cities has intensified, characterized by a shift from labor-intensive migration to multi-dimensional lifestyle choices. However, traditional migration models often assume global linearity, failing to capture the complex non-linear thresholds and spatial non-stationarity inherent in migration decisions. This study employs a novel Geographically Weighted Random Forest (GWRF) model to analyze net migration flows across 278 Chinese cities using high-granularity mobile signaling data from the 2020 Spring Festival travel rush. The results reveal that GWRF significantly outperforms traditional OLS, GWR, and global Random Forest models, effectively handling spatial heterogeneity and non-linearity. Wage levels are the dominant global driver, exhibiting a distinct “S-curve” non-linear threshold, while population scale shows a significant U-shaped effect, highlighting the transition from agglomeration economies to congestion costs. Migration drivers exhibit profound spatial heterogeneity: western inland cities are “wage-driven,” the Pearl River Delta is “employment-structure driven,” and the northeastern “Rust Belt” is increasingly sensitive to “innovation investment” (technology expenditure). These findings challenge the “one-size-fits-all” approach to population policy, offering precise, spatially targeted strategies for urban planners to mitigate population shrinkage and enhance urban vitality. Full article

Dynamic instability of water-saturated granular coal in tectonic stress zones is a critical safety issue in coal mining. This study adopts raw coal granules from the Daping Coal Mine to investigate the dynamic response and instability mechanisms under coupled confining pressure, median particle size (d₅₀), and water saturation via dynamic impact tests, 2D equivalent modeling, and theoretical analysis. The results indicate that confining pressure and median particle size jointly regulate the dynamic mechanical properties of coal, with liquid bridge volume serving as a key mediating variable. The study reveals a dual-path coupling instability mechanism of “liquid bridge softening and confining pressure strengthening”: a critical confining pressure of 12 MPa divides the dominant force from liquid bridge to friction. Small-particle units show a stronger strengthening effect, and large-particle units have a slightly higher critical confining pressure. Field observation validates the theoretical patterns, identifying areas near faults as high-risk zones for dynamic instability. Accordingly, a three-tier prevention and control strategy of “tectonic stress unloading, flexible support, grouting modification” is proposed. The research findings enhance the theory of water-saturated granular coal instability and provide theoretical and engineering foundations for disaster prevention and control in tectonic stress zones of coal mines. Full article

Against the backdrop of dual-carbon goals and resource constraints, the high-value utilization of recycled fine aggregates (RFAs) remains limited, leading to inconsistent engineering performance and insufficient durability. Enzyme-induced carbonate precipitation (EICP) represents a promising low-carbon cementation method, yet its deposition uniformity and cementation efficiency are influenced by the pore structure of granular media and associated mass transfer pathways. This study employs a two-stage experimental design to investigate the synergistic effects of particle size distribution characteristics, represented primarily by d₅₀, and fiber addition on EICP-cemented RFA. Phase I (fiber-free; d₅₀ = 0.67–1.14 mm) results indicate that, across the tested gradation schemes, the CaCO₃ content generally decreased from 9.49% to 7.72% as the representative d₅₀ increased, while the dry density changed only slightly (1.637–1.617 g/cm³). However, the unconfined compressive strength (UCS) decreased from 1000 kPa to 541 kPa (45.9% reduction), indicating that strength is primarily governed by the connectivity of the cementation network rather than solely by the degree of densification. In Phase II, glass fiber (GF), polypropylene fiber (PPF), and jute fiber (JF) were incorporated into the ERFA4 gradation scheme selected for fiber modification. All three systems exhibited a unimodal optimum pattern: the peak CaCO₃ contents reached 10.71% (GF 0.5%), 10.11% (PPF 0.7%), and 11.46% (JF 0.7%), corresponding to peak UCS values of 1917, 1874, and 2450 kPa, respectively. Microscopic analysis suggested that fiber bridging coupled with CaCO₃ deposition may contribute to the formation of a “fiber-CaCO₃-particle” stress-transfer network, which is consistent with the observed enhancements in load-bearing capacity, ductility, and post-peak stability. Full article

Granular deposition and grading strongly influence pore-space topology and hence hydraulic conductivity in natural and engineered porous media, yet quantitative links between deposition sequence, particle-scale morphology, pore-network descriptors, and permeability anisotropy remain incomplete. Here, we develop a process-based digital porous-media framework that couples discrete element method (DEM) deposition with pore-network characterization and Darcy-scale permeability evaluation. Two deposition sequences—normal grading (coarse-to-fine) and reverse grading (fine-to-coarse)—are simulated using bi-disperse particle sets with controlled size ratios. To further isolate the role of particle morphology, particle irregularity is parameterized by a Perlin-noise-based shape perturbation factor and incorporated into the DEM-generated packings. For each packing, pore networks are extracted and quantified in terms of pore/throat size distributions and connectivity, while pore-space complexity is measured via box-counting fractal dimension. Single-phase flow is solved under imposed pressure gradient, and intrinsic permeability is computed along three orthogonal directions to evaluate anisotropy. Results show that increasing size contrast reduces porosity, shifts pore and throat distributions toward smaller characteristic radii, increases pore-space fractal dimension, and yields a monotonic permeability reduction. For identical size ratios, reverse grading consistently yields higher permeability than normal grading, suggesting that deposition sequence exerts a strong control on the continuity and efficiency of effective flow pathways at the sample scale. Increasing particle irregularity decreases permeability and systematically modifies permeability anisotropy, transitioning from weak horizontal anisotropy toward near-isotropy and, at strong irregularity, toward preferential vertical permeability. The proposed framework provides a reproducible route to relate depositional history and particle morphology to pore-network structure and directional permeability, offering implications for filtration, packed-bed design, and sedimentary reservoir characterization. Full article

Airborne electromagnetic (AEM) surveys map subsurface electrical structures by deploying transmitter and receiver coils on an airborne platform. However, platform-induced vibrations are transmitted to the sensors, generating strong motion-induced noise that severely degrades signal quality. To mitigate such noise, this study proposed the use of granular materials as a cushioning medium. An impact model based on the Discrete Element Method (DEM) was developed and validated against drop-weight experiments. Both granular material properties and impactor characteristics were investigated. The study examined the cushioning effects on both the base plate and the impactor under impact loading, and the sensitivity of key parameters was evaluated. The results showed that granular properties had minimal influence on the impactor peak force. Increasing particle Young’s modulus, density, or friction coefficient led to higher peak forces on the base plate, with Young’s modulus and density having significantly stronger effects than friction coefficient. Additionally, both the impactor size and velocity correlate positively with the peak forces transmitted to the base plate and experienced by the impactor. Under thin layer conditions, the impactor force was more sensitive to impact parameters, while in thick layers it was mainly determined by particle rearrangement and energy dissipation mechanisms. These findings reveal the mechanisms governing granular cushioning and provide a theoretical basis for vibration isolation design in AEM systems to preserve high-fidelity signals. Full article

Sulfamethoxazole (SMX) is one of the most frequently detected antibiotics in aquatic environments and is difficult to remove by conventional biological treatment because of its persistence, potential toxicity to microbial communities, and associated risk of antibiotic resistance selection. Aerobic granular sludge membrane bioreactors (AGMBRs), which combine the compact and stratified structure of aerobic granular sludge with membrane-based solid–liquid separation, have emerged as a promising platform for SMX-contaminated wastewater treatment because they provide high biomass retention, decoupled sludge retention time (SRT) and hydraulic retention time (HRT), and stable effluent quality. This review systematically summarizes recent advances in AGMBRs for SMX removal, with emphasis on how operating parameters (e.g., dissolved oxygen, hydraulic retention time, organic loading rate, C/N ratio, and sludge retention time) and membrane-related factors (e.g., membrane flux, aeration-induced shear, membrane type, and pore size) affect treatment performance and process stability. The main SMX attenuation pathways in AGMBRs are discussed from three perspectives: sorption and partitioning within granules and extracellular polymeric substances (EPSs), microbial biodegradation and co-metabolism, and membrane retention that prolongs effective contact time and shapes microbial ecology. Particular attention is given to the dual role of EPS and soluble microbial products (SMPs), which contribute to granule stability and SMX tolerance but also accelerate membrane fouling through cake-layer formation, pore blocking, and transmembrane pressure increase. Current challenges include incomplete understanding of transformation products, ARG- and MGE-related risks, long-term fouling–biodegradation interactions, and the lack of pilot-scale validation. Future research should therefore focus on mechanism clarification, integrated control of removal and fouling, energy-efficient operation, and scale-up of AGMBRs for practical antibiotic wastewater treatment. Full article