Search Results (3,315)

This study investigated the development of a perlite waste-based geopolymer for stabilizing iron ore tailings byproduct (IOTB) for geotechnical applications. Mixtures containing 70/30 and 80/20 proportions of byproduct and geopolymer were produced using perlite waste as the precursor and NaOH as the alkaline activator through the one-part method. Raw and geopolymer-stabilized IOTB, air-cured for 7, 14, and 28 days, were evaluated by ICP-OES, XRF, pH, geotechnical characterization, compaction, permeability, SEM, and consolidated drained triaxial tests under confining stresses ranging from 250 to 2000 kPa. The selected mixture presented a maximum dry density of 1.8 g/cm³ and optimum moisture content of approximately 14%. XRD results indicated sodium aluminosilicate phases associated with geopolymerization, with mechanical characteristics comparable to feldspar-type structures, while the pH increased from 6.5 to 12.5. Triaxial tests indicated that elastoplastic behavior persisted regardless of the geopolymer addition; however, SEM images confirmed matrix–particle bonding at grain contacts without significant pore filling. The cohesive intercept increased from 0 kPa in the IOTB to 89.1 kPa and 179.2 kPa after 14 and 28 days of curing, respectively, while the friction angle showed a slight increase of up to 7.7%. Deviatoric stress at failure and energy absorption capacity also increased with curing time. Hydraulically, the permeability coefficient remained within the same order of magnitude (10⁻⁴ cm/s), varying from raw IOTB of 2.73 × 10⁻⁴ cm/s to 3.28 × 10⁻⁴ cm/s after 28 days. These results demonstrated that geopolymer stabilization enhanced mechanical performance without compromising drainage capacity, representing a technically viable and socio-environmentally sustainable solution. Full article

Magneto-fluorescent carbon quantum dots (CQDs) promise compact, dual-readout nanomaterials; however, achieving pronounced photoluminescence alongside magnetic functionality in a simple, scalable formulation remains difficult, especially for emerging doped CQDs. Here, we report Fe-doped carbon quantum dots (Fe-CQDs) as an emerging quantum-dot platform that integrates fluorescence with magnetic-resonance (MR) relaxometry within a single ultrasmall, carbonaceous nanostructure. To enable this, Fe-CQDs are prepared through a straightforward two-step, low-temperature route that uses a magnetic deep eutectic solvent precursor followed by mild carbonization in air at atmospheric pressure. Under UV excitation, the Fe-CQDs display bright blue emission centered at 439 nm, and their optical behavior is characterized by UV-Vis absorption, photoluminescence spectroscopy, and fluorescence microscopy. Meanwhile, dynamic light scattering indicates a narrowly distributed nanoscale hydrodynamic diameter, and X-ray diffraction together with FT-IR supports a carbonaceous framework enriched with oxygenated surface functionalities, consistent with aqueous dispersibility and environmentally responsive photophysics in water, while XPS supports Fe incorporation in an Fe(III)-dominated chemical environment. Importantly, Fe incorporation enables intrinsic MR relaxometric readout, establishing an intrinsic fluorescence/MR dual modality. As a proof-of-concept, Fe-CQDs were tested with a representative per- and polyfluoroalkyl substance (PFAS), showing parallel fluorescence and MR response trends at ppm levels in natural water matrices from Millerton Lake with Stern–Volmer analysis and a NaCl-based ionic strength control. Overall, these results position Fe-CQDs as a versatile magneto-fluorescent nanomaterial for dual-readout screening workflows and motivate future surface engineering and dopant tuning to improve selectivity and expand toward multi-modal readouts. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

The high-temperature corrosion behavior of A1, T22, and T91 steels was investigated in molten nitrate salts at 400, 500, and 600 °C during 100 h of exposure. The combined influence of temperature and chromium content on corrosion kinetics and oxide-scale stability was evaluated using open-circuit potential (OCP), linear polarization resistance (Rp), electrochemical impedance spectroscopy (EIS), scanning electron microscopy, X-ray diffraction, and cross-sectional elemental mapping. OCP measurements showed a progressive shift toward more negative potential with increasing temperature, indicating enhanced oxidation tendency. Electrochemical measurements revealed a systematic decrease in Rp and impedance magnitude as temperature increased, confirming accelerated corrosion kinetics and reduced interfacial resistance. EIS spectra exhibited two characteristic time constants associated with the outer corrosion products and the inner metal/oxide interface. Significant differences in scale growth were observed depending on alloy composition. At 600 °C, oxide thickness reached approximately 700–800 μm for A1, ~100 μm for T22, and ~10 μm for T91. Chromium-containing steels promoted the formation of a compact Cr-rich inner oxide layer that improved scale adhesion and suppressed the exfoliation phenomena observed in A1 steel. Overall, temperature controls corrosion kinetics, whereas chromium content governs oxide-scale compactness and long-term stability in molten nitrate environments. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity of large-scale grids. To address these issues, this paper proposes ST-ResGAT, a spatio-temporal residual graph attention framework for nonlinear state estimation under heterogeneous sensing conditions. The proposed method models the problem on an augmented heterogeneous factor graph, employs a reliability-aware heterogeneous graph attention mechanism with residual propagation to adaptively fuse measurements of different quality, and further refines the graph-based estimates through a lightweight LSTM post-processing module that exploits short-term temporal continuity. All datasets are generated using pandapower on the IEEE 30-bus, IEEE 118-bus, and IEEE 1354-bus benchmark systems to ensure full reproducibility of the experimental pipeline. Experimental results show that the proposed method consistently achieves lower estimation errors than WLS, DNN, GAT, and PINN baselines across all three systems, while also exhibiting more compact node-level error distributions and stronger spatial consistency. Multi-seed ablation studies further indicate that residual propagation, reliability-aware attention, and temporal refinement play complementary roles across different system scales. Robustness experiments additionally show that, under random measurement exclusion as well as bias, Gaussian, and mixed corrupted-measurement settings, ST-ResGAT exhibits smooth and progressive degradation, including on the newly added large-scale IEEE 1354-bus benchmark. These results suggest that the proposed framework is a promising direction for data-driven state estimation under controlled mixed-measurement benchmark conditions. Full article

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

To address the limitations of insufficient feature representation, large model size, and high deployment cost in network traffic classification, a lightweight classification framework based on multi-teacher knowledge distillation is proposed. The framework consists of two heterogeneous teacher networks and a compact student network to enable end-to-end traffic classification under constrained computational resources. The teacher networks incorporate complementary spatio-temporal modeling strategies, including a bidirectional temporal convolutional network (BiTCN) enhanced with attention mechanisms and convolutional neural network (CNN), and a parallel spatio-temporal fusion architecture integrating bidirectional long short-term memory (BiLSTM) and CNN. Knowledge from the teacher ensemble is distilled into a lightweight CNN-based student network through soft-target supervision, leading to improved generalization capability with significantly reduced model complexity. Experimental results demonstrate that effective knowledge transfer is achieved while reducing model parameters by more than 80%, and performance gains of about 1–3% are obtained compared with baseline methods. These results indicate strong potential for practical deployment in resource-constrained network environments. Full article

The shrinkage deformation is a significant risk to self-compacting concrete (SCC)-filled steel tube structures. It was essential to understand the concrete autogenous shrinkage strain before being regulated in order to determine compensation shrinkage measures. In this study, A PSO-BPNN model was constructed, which is based on the Particle Swarm Optimization-Back Propagation Neural Networks (PSO-BPNN), and the autogenous shrinkage strain of SCC was predicted based on PSO-BPNN before being regulated. Moreover, some experiments about compensating for shrinkage by expansion and by a combination of expansion and contraction were investigated. Based on this prediction, a series of experiments was conducted on the regulation of the shrinkage deformation of SCC for an actual bridge project. The results indicated that a good consistency of PSO-BPNN between predicted and measured values, demonstrating that PSO-BPNN is a model with high accuracy in predicting concrete autogenous shrinkage strain before regulation, and as a guidance for regulation to compensate for shrinkage. The prediction error was less than 10% for 28-day self-shrinkage, and the experimental workload was reduced. The PSO-BPNN is a convenient tool for predicting the shrinkage of SCC, enabling the determination of dosages of expansion agent and reducing shrinkage agent to achieve SCC’s shrinkage regulation. Full article

Mini-roundabouts are increasingly implemented as compact, low-cost alternatives to conventional roundabouts and signalized intersections, especially at low-speed, space-constrained urban locations where safety is a concern. Their design emphasizes speed management, reduced conflict severity, and operational simplicity, contributing to safer mobility for all road users. This paper reviews U.S., German, and UK design guidelines and synthesizes empirical safety evidence from before-and-after studies of mini-roundabout conversions. In terms of design, the U.S. practice typically relies on a single large design vehicle and more permissive geometry, whereas the German guidance adopts a multi-vehicle approach with tighter curvature and stronger compactness to enforce lower speeds, affecting crash risk and driver behavior. The UK guidance is distinguished by its flush or slightly domed central marking and flexible application approach. Conversions from two-way stop-controlled (TWSC) or one-way stop-controlled (OWSC) intersections yield substantial reductions in injury and severe crashes, with total crash reductions of 17–42%. Conversions from all-way stop-controlled (AWSC) intersections present more variable outcomes, including increases in total crashes, because drivers are still reacting based on the previous control and may not adjust their expectations quickly. Modular roundabouts are also examined as alternative compact interventions for constrained or high-risk sites, with early evidence indicating reductions in severe crashes and improved speed control while minimizing construction costs and right-of-way impacts. Full article

Electrophoresis titration sensors are widely used for biomarker detection. However, traditional methods rely on a visible boundary for signal readout. Although conventional capacitively coupled contactless conductivity detection avoids indicator dependence, its single-point detection method suffers from long measurement times, large amounts of redundant data, and the inability to dynamically monitor the moving reaction boundary. To address these issues, we developed a novel contactless scanning capacitively coupled conductivity-detection method for microchip electrophoretic titration sensors. This method enables the rapid tracking and monitoring of the boundary within the microfluidic channel through dynamic scanning. The spatial distribution of conductivity during electrophoretic titration was theoretically analyzed. To evaluate the method, glucose was chosen as a model analyte. Quantitative detection was achieved over the linear range of 0.2–50 mM, with the limit of detection of 0.1 mM. The method exhibited satisfactory stability with relative standard deviation values ranging from 0.9% to 4.3% (n = 3). While the detection limit is higher than optical methods (0.02 mM), the results confirmed that the novel method offers merits, such as compact size, low cost and label-free operation. Moreover, it demonstrated strong potential for portable, quantitative analysis of target analytes across a wide range of applications. Full article

Real-time facial emotion recognition remains challenging due to the high dimensionality and computational cost of dense facial representations, which limit their applicability in resource-constrained and real-time scenarios. This study proposes a compact, anatomically informed angular facial representation for efficient, interpretable emotion recognition under real-time constraints. Facial landmarks are first extracted using a standard landmark detection framework, from which a reduced facial mesh of 27 anatomically selected points is defined. Internal geometric angles computed from this mesh are analyzed using temporal variability and redundancy criteria, resulting in a minimal set of eight angular descriptors that capture the most expressive facial dynamics while preserving geometric invariance and computational efficiency. The proposed representation is evaluated using multiple supervised machine learning classifiers under two complementary validation strategies: stratified frame-level cross-validation and strict Leave-One-Subject-Out evaluation. Under mixed-subject stratified validation, the best-performing model (MLP) achieved macro-averaged F1-scores exceeding 0.95 and near-unity ROC–AUC values. However, subject-independent evaluation revealed reduced generalization performance (average accuracy ≈55%), highlighting the influence of inter-subject morphological variability embedded in absolute angular descriptors. These findings indicate that a minimal angular geometric encoding provides strong intra-subject discriminative capability while transparently characterizing its cross-subject generalization limits, offering a practical and interpretable alternative for data- and resource-constrained real-time scenarios. Full article

We investigate wheat lodging segmentation from UAV RGB imagery acquired over real production fields rather than controlled experimental sites. Besides pixel-level accuracy, our evaluation also emphasizes robustness under heterogeneous farmland conditions and deployment-oriented efficiency. We propose CASGNet, an edge-oriented segmentation network with a content-aware spatial gating mechanism that reweights intermediate features according to local structural variation. Instead of uniformly aggregating features, the module suppresses responses in homogeneous regions while preserving activation in structurally complex areas. In practice, this improves the continuity of irregular lodging shapes and reduces spurious responses in relatively homogeneous backgrounds. The dataset spans 46 farms across Jiaozuo, Jiyuan, and Luoyang, covering progressively fragmented farmland. Under a stricter mission-level data-isolation protocol, CASGNet achieves 94.4% mIoU and 90.38% IoU for the lodging class on the combined dataset. Under sequential regional adaptation, performance remains relatively stable in continuous parcels, and degradation is less severe than most compact baselines in highly fragmented landscapes. On Jetson Nano, CASGNet achieves 1.94 FPS embedded inference under the 5 W mode. Smaller networks achieve higher speed but show reduced structural continuity in complex scenes. The results indicate that CASGNet provides a favorable balance between structural fidelity and computational cost, while its robustness remains constrained by scene complexity. Full article

Axial Flux Permanent Magnet Synchronous Motors (AFPMSMs) are gaining increasing attention for their application in electric vehicle (EV) drive systems. Their high torque density and compact axial geometry make them attractive for high-performance EV drive systems. However, cogging torque remains a major challenge, degrading low-speed drivability, noise performance, and control stability. This article proposes a magnet skew on rotor modulation structure using a genetic algorithm (GA) to reduce cogging torque in AFPMSMs utilizing a 12/15 non-integer pole/slot arrangement. The objective of optimization is to simultaneously reduce cogging torque under identical electromagnetic constraints. A complete three-dimensional finite element model (3D-FEM) incorporating nonlinear magnetic material properties has been developed to evaluate the electromagnetic field distribution and torque components. The results indicate that a 12/15 non-integer pole/slot arrangement improves harmonic distribution and extends the operating range with lower cogging torque compared to integer pole/slot designs. Combined with GA-optimized skew angles, this reduces peak-to-peak cogging torque to less than 50%. This design is ideally suited for the traction requirements of electric vehicles, including premium electric vehicles where smooth operation at low speeds is critical. Full article

To investigate the structural characteristics of Ir_n clusters (n = 9–30) and their interaction with NH₃, the CALYPSO structure-prediction method was employed to identify the lowest-energy configurations. The Lennard–Jones potential was then used to compute the binding energy and average binding energy, thereby evaluating size-dependent stability. The results show that Ir_n clusters evolve from relatively open motifs to compact three-dimensional frameworks as n increases. Meanwhile, the average binding energy increases overall and exhibits several locally stable size regions, indicating a pronounced size effect. Based on slab and cluster models, NH₃ adsorption was further examined on the Ir₁₃ cluster as a representative system due to its high structural stability as a “magic-number” cluster. The calculated adsorption energies demonstrate that the Ir₁₃ cluster exhibits substantially stronger adsorption than the bulk Ir surface, with low-coordinated Ir atoms playing a key role in strengthening the interaction and enhancing adsorption activity. Adsorption-configuration analysis indicates that NH₃ preferentially binds to active surface sites via the N lone pair. These findings clarify the relationship between structural stability and adsorption performance of Ir clusters and provide theoretical support for Ir-based materials in NH₃ catalytic conversion and high-sensitivity gas detection, and offer insights relevant to improving NH₃ monitoring in underground coal mine environments. Full article