Search Results (934)

This study investigated the dry sliding wear behaviour of Al₂O₃ particulates (2–6 wt.%) in Al6061 metal matrix composites produced by ultrasonic stir casting, examining mechanical and tribological behaviour. We modelled wear rate using machine learning. Optical studies confirmed uniform Al₂O₃ particle dispersion, minimal agglomeration, and strong interfacial bonding between the Al6061 and Al₂O₃ phases. With an increase in Al₂O₃ content, density and hardness increased by 1.3% and 33%, respectively. The Al6061–6 wt.% Al₂O₃ composite exhibited 40% higher wear resistance than the base alloy in dry-sliding conditions, with sliding distance varying between 0 and 10,000 m and load varying between 0 and 50 N. At lower loads and shorter sliding distances, abrasive wear dominated; as load and sliding distance increased, the dominant wear mechanism shifted to delamination and adhesion wear. Moreover, tribological testing at 6 wt.% reinforcement showed a 40% improvement in dry sliding wear resistance, with applied normal load and sliding distance varying between 10 and 50 N, and 1000 and 10,000 m, respectively. The specific wear rate was subsequently modelled and predicted using K-Nearest Neighbours (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANNs), Random Forests (RFs), and Gradient Boosting Machines (GBMs). Among these, the RF model achieved the highest accuracy (R² = 0.946). Based on feature importance analysis, applied normal load and sliding distance are the most influential factors in wear. As a result, in dry-sliding conditions, Al6061-Al₂O₃ MMCs show a stable wear response, thereby improving dataset homogeneity and model performance. Overall, this study used experimental insights and predictive analytics to predict MMC wear behaviour. By employing ML models, composite design and wear can be optimised. Additionally, feature importance analysis showed that applied normal load and sliding distance best predicted wear rate. Full article

Monitoring the long-term cumulative impacts of industrial mining and environmental changes in sensitive ecosystems remains a critical challenge for sustainable resource management. This study investigates 41 years (1984–2025) of land use and land cover (LULC) dynamics in the Athabasca oil sands region (Alberta, Canada) using the historical Landsat archive (TM, ETM+, and OLI). The methodology integrated temporal and spectral features through two complementary phases: automated spectral trajectory-based change detection and a Support Vector Machine (SVM) classification combining original bands with multiple spectral indices, including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), transformed difference vegetation index (TDVI), bare soil index (BSI), and Modified Normalized Difference Water Index (MNDWI). This framework quantified baseline LULC, mining expansion, tailings pond footprints, and reclamation operations across a 12,146-km² study area. The spectral trajectory analysis effectively tracked multiple temporal disturbances, revealing accelerating mining activities alongside forest harvesting, wildfire stress, and insect damage. Additionally, it captured progressive and significant spectral variations in the Athabasca River’s surface water, reflecting cumulative industrial runoff and long-term environmental pressure. Concurrently, the SVM classifications (Overall accuracy ≥ 96.8% and Kappa coefficient ≥ 94%) measured major LULC shifts. Dense coniferous forests declined by 1200 km², while mixed forests decreased from 4500 km² in 1984 to a minimum of 1200 km² in 2010, before recovering to 3040 km² by 2025 due to reclamation efforts. Conversely, active mining infrastructure and tailings ponds expanded from 95 km² (1984) to 900 km² (2025), occupying 7.41% of the landscape. Ultimately, this integrated remote sensing approach captured the complex interactions between accelerating industrial development, climate stressors, and the localized progress of land reclamation, providing a scalable framework for ecosystem monitoring in heavily disturbed landscapes. Full article

Background: The hominoid loss of urate oxidase (uricase) represents a classic evolutionary trade-off, shifting uric acid (UA) from a metabolic waste product to a potent physiological modulator. In modern metabolic environments, however, this adaptation drives hyperuricemia and gout, transforming UA into a primary pathological substrate. Objective: This review aims to dissect the molecular architecture and biophysical networks of the renal and intestinal urate transportosome, delineate the dual intracellular/extracellular “urate paradox,” and synthesize genotype-based pharmacogenetic strategies to achieve personalized clinical management. Mechanistic Insights: During the Miocene epoch, inactivating pseudogenization of the UOX gene fixed a novel metabolic phenotype characterized by fructose-driven lipid deposition and enhanced antioxidant protection. Structurally, systemic urate homeostasis is strictly governed by a macromolecular interactome assembled by the four-domain scaffold protein PDZK1 on the epithelial apical membrane. Pathogenic gain-of-function variants in reabsorption facilitators (SLC22A12/URAT1, SLC2A9/GLUT9) or loss-of-function mutations in the efflux pump (ABCG2/BCRP) disrupt this delicate vector kinetics. Within the extracellular space, soluble urate acts as a critical hydrophilic radical scavenger. Paradoxically, upon URAT1/GLUT9-mediated internalization or intracellular supersaturation, intracellular urate triggers a pro-oxidant cascade mediated by NADPH oxidase (NOX4) activation and mitochondrial electron transport chain decoupling. This chronic cellular stress activates downstream p38 MAPK and NF-κB signaling pathways, driving localized endothelial injury and macrovascular inflammation, while crystalline monosodium urate (MSU) orchestrates NLRP3 inflammasome assembly in macrophages. Pharmacogenetic Implications: Striking ethno-geographic heterogeneity dictates immediate clinical stratification. The HLA-B*58:01 allele, an absolute molecular contraindication for allopurinol due to life-threatening severe cutaneous adverse reactions (SCARs), exhibits a critical genetic gradient in northern and eastern Eurasian populations, surging from under 1% in ethnic Caucasians to over 10% in indigenous populations of East/North Asian ancestry. Furthermore, structural defects in ABCG2 (such as the p.Q141K variant) alter the ATP-binding cassette domain, inducing standard allopurinol resistance and elevated statin exposure, which mandates a therapeutic pivot toward selective xanthine oxidase inhibitors (febuxostat) or precision uricosurics (benzbromarone, dotinurad) matched to the patient’s interactive network profile. Conclusions: Transitioning from generalized epidemiological guidelines to a comprehensive “transportosome genetic passport” is a fundamental prerequisite for predicting single-nucleotide polymorphism (SNP)-driven therapeutic responses and mitigating visceral complications. Full article

Contemporary research on facial aging is undergoing a profound paradigm shift, transitioning from macroscopic descriptive phenotyping to refined cellular-molecular mechanistic dissection. This review reveals these frontiers across multi-omics and structural dimensions, highlighting single-cell transcriptomic alterations (e.g., KLF6 and HES1 dysregulation), proteomic hubs (e.g., KNG1), epigenetic causal chains, and microbiome community succession within the cutaneous microenvironment. Concurrently, we map the progressive biomechanical failure of deeper tissues, including sequential adipose atrophy, SMAS-muscle complex degeneration, and sex-specific skeletal remodeling. Within the evolving landscape of regenerative aesthetics, calcium hydroxylapatite (CaHA) has emerged not merely as a passive space-occupying volumizer but as a dynamic biostimulatory material. We examine how CaHA’s morphology and rheological properties govern its biological behavior. Human histological studies provide evidence that CaHA is associated with fibroblast activity and temporal extracellular-matrix remodeling, particularly changes in type III and type I collagen expression. Changes in elastin- and angiogenesis-related markers have also been reported, whereas the mechanotransduction and immune-regulatory pathways proposed to underlie these responses remain supported predominantly by in vitro, ex vivo, and preclinical evidence and have not been fully validated in human facial tissue. Finally, we translate these material-driven interactions into advanced clinical strategies, including rheologically programmed dilution, layer-specific vectoring, and synergistic therapies. By bridging intricate aging biology with material-guided regeneration, this review provides a robust conceptual framework for individualized and precise rejuvenation. Full article

Opinion-maximization methods often optimize an aggregate network response and may therefore distribute target-aligned intervention gains unevenly across groups. This issue is especially important in signed networks, where cooperative and antagonistic relations can make the same positive opinion seed increase the equilibrium opinion of one group while decreasing that of another. We study fairness-aware internal-opinion seeding in undirected signed Friedkin–Johnsen networks with heterogeneous anchoring strengths. The objective combines the whole-network average opinion gain with the minimum group-average gain through a tunable fairness parameter. We show that the equilibrium response of any seed set decomposes additively into candidate-wise group-gain vectors. This representation yields an equivalent mixed-integer linear program for exact optimization on tractable instances and an adjoint formulation that evaluates all candidate gains using only one shifted signed-Laplacian solve per group. Because the global averaging vector is a size-weighted combination of the group averaging vectors, no additional global solve is required. We further introduce a dynamically maintained Pareto frontier based on componentwise dominance and establish conditions under which pruning preserves the underlying greedy sequence. Theoretical results establish well-posedness and stability of the signed equilibrium, correctness of the gain and adjoint formulations, sufficient conditions for monotonicity, non-submodularity in general, residual-based numerical error bounds, and computational complexity. Experiments on 14 signed networks ranging from hundreds to more than four million nodes show that the adjoint implementation scales most robustly among the tested methods. The results further demonstrate that the fairness parameter and heterogeneous anchoring strengths can substantially affect both the selected seed sets and the fairness–efficiency balance. Against structural, random, and target-aware Top-K baselines, the proposed greedy method remains broadly competitive, and on small full-candidate instances it matches the solver-certified mixed-integer linear program (MILP) optimum in five of six tested configurations. Overall, the framework provides a scalable approach to group-aware opinion intervention in signed networks while explicitly distinguishing opinion seeding from clamped leader selection. Full article

Mineral fertilization strongly influences soil enzymatic activity, organic carbon dynamics, and microbial nutrient limitation in agroecosystems, yet a systematic synthesis isolating the effects of mineral fertilizers alone independent of organic amendments has been lacking. This meta-analysis compiled 118 observations from 48 peer-reviewed studies (2001–2025), following PRISMA guidelines, to evaluate how nitrogen, phosphorus, and potassium and their combinations affect soil extracellular enzyme activities, soil organic carbon (SOC), dissolved organic carbon (DOC), microbial biomass C/N/P, and microbial C, N, and P limitation using ecoenzymatic vectors and stoichiometric models. Overall fertilization significantly increased soil enzyme activities, SOC, DOC, and microbial biomass C/N/P (p < 0.05–0.001), with effects varying by fertilizer combination. Ecoenzymatic vector analysis indicated persistent microbial C limitation across all treatments (vector length > 0.61). Meanwhile, vector angle and stoichiometric models confirmed sustained N and P limitation with overall fertilization and NPK significantly reducing MPL; NP significantly reducing MNL; and MCL declined only under N fertilization alone. Standardized major axis regression showed that C:N, C:P, and N:P enzyme activity slopes deviated significantly from the theoretical 1:1:1 ratio in all cases, and fertilization significantly steepened these slopes relative to controls. The N addition produced the most pronounced shift, reversing negative control slopes to strongly positive values, indicating that fertilization synchronizes microbial C-, N-, and P-acquisition strategies. These findings indicate that mineral fertilization partially alleviates microbial nutrient limitation, most effectively when nutrients are applied in balanced combinations. Meanwhile, carbon limitation remains largely unresponsive, likely reflecting its dependence on plant-derived carbon inputs rather than direct fertilization. The results provide a mechanistic framework linking ecoenzymatic stoichiometry to microbial resource allocation and support the integration of balanced fertilization strategies for optimizing nutrient cycling and soil carbon sequestration in agroecosystems. Full article

Measuring bite force is essential for assessing the masticatory system and diagnosing oral disease. Existing measurement devices have low spatial resolution and susceptibility to electromagnetic interference. This paper presents a machine learning (ML)-assisted distributed fiber optic sensing system based on Scattering Level Multiplexing (SLMux) for high-resolution bite force analysis. Enhanced backscattered data were acquired through optical backscattered reflectometry from 88 sensing points along the dental arch. Measured data were reconstructed into a two-dimensional map of bite force and analyzed through an ML pipeline. Sector classification across 4 regions and weight prediction were processed by an end-to-end fine-tuned ResNet-18 Convolutional Neural Network (CNN) and classical ML approaches. ResNet-18 is compared with Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost (Extreme Gradient Boosting), Extra Trees, and k-Nearest Neighbors (kNN) trained on handcrafted features. On sector classification, Logistic Regression achieved the best performance (98.71% accuracy). On the weight prediction task, formulated as a 12-class problem, the end-to-end ResNet-18 CNN substantially outperformed all classical models, reaching 51.28% accuracy and a mean absolute error of 78 g, versus 124 g for the best classical model. A regression-based ResNet-18 variant was also trained on the wavelength-shift and weight data, resulting in a mean absolute error of 62.6 g. The results indicate that integrating ML with distributed fiber-optic sensing has the potential to enhance dental diagnostics and treatment planning. Full article

Cost settlement for temporary repair of ship equipment is characterized by lengthy ex post audits and the lack of a directly quotable pricing benchmark. Given the intertwined effects of tight schedules, holiday wage premiums, and fluctuating resource availability—and the consequent need for historical memory and foresight in the model—an LSTM–PPO comprehensive unit price estimation model is developed. Because a standard Markov decision process cannot distinguish different historical paths or exploit forward-looking information, the problem is formulated as a finite-horizon partially observable Markov decision process (FH-POMDP), with a corresponding observation vector and a composite reward function. To capture time-varying holiday rates and the path dependence of historical trajectories, an LSTM encodes the full observation sequence and, through its gating mechanism, fuses historical trajectories with temporal changes in holiday windows in the hidden state, enabling the policy to anticipate rate shifts and allocate labor input in advance. For the hybrid action space of daily mode selection and intensity adjustment, hybrid entropy regularization is introduced to discourage premature collapse onto a single mode preference early in training and to improve robustness across diverse scenarios. Experiments show that the proposed method produces benchmark unit prices with smaller deviations from actual settlement prices than the baselines on the test set, and that it can discriminate holiday windows with different rate multipliers and resource conditions, thereby providing technical support for the ex post settlement of emergency support funds. Full article

An optical vector analysis (OVA) based on serrodyne modulation is proposed and numerically verified by simulations. In the proposed OVA, serrodyne modulation is implemented using a dual-parallel dual-drive Mach–Zehnder modulator to generate asymmetric optical double-sideband (ODSB) signals including a frequency-shifted optical carrier. The generated signals subsequently propagate through the optical device under test (ODUT). Owing to the asymmetric ODSB structure, the proposed OVA is inherently immune to errors induced by the nonlinearity of the electro-optic modulator (EOM). Consequently, the responses of the ODUT can be accurately obtained by processing the frequency-shifted photocurrent, which is converted from the frequency-shifted carrier and the two desired sidebands. Furthermore, the proposed approach overcomes the limitation of conventional optical single-sideband-based OVA in characterizing bandpass responses. Through numerical simulations, the frequency responses of a uniform fiber Bragg grating, a Fabry–Perot cavity, and a bandpass filter are obtained within a bandwidth of 20 GHz. The proposed OVA provides an approach for characterization of optical devices and integrated microwave photonics systems. Full article

In thin-walled cylinder turning, wall thickness decreases with each pass. It reduces workpiece stiffness and shifts the stability limit of the cutting process. In this study, wall thickness and axial segment position are established as important factors that affect chatter occurrence in this geometry. To the best of the authors’ knowledge, there are no prior studies that used these geometric features as machine learning input. The objective of this study is to present a physics-informed framework for chatter detection in the thin-walled cylindrical turning of 1.4301 austenitic stainless steel. Five physics-informed features were extracted per segment: RMS resultant force, kurtosis, dominant non-harmonic frequency, normalized segment position and wall thickness. Four classical classifiers—Random Forest, Logistic Regression, Support Vector Machine (SVM) and Neural Network (NN)—are evaluated on 135 segments of 15 machining passes. For validation, Leave-One-Pass-Out (LOPO) cross-validation was performed. It holds out segments of each pass to reflect deployment conditions. Random Forest and Logistic Regression achieved above 95% recall and accuracy, exceeding the 90% safety threshold. A feature ablation study was performed to measure the importance and impact of individual input features; the results suggest that using geometric features achieves 100% recall with three classifiers. In addition, three validation schemes, LOPO, forward chaining, and fixed split, were used to evaluate the generalizability of the developed model. The non-tree classifiers ranked wall thickness and RMS cutting force as strong predictors, whereas the tree-based ensembles relied mostly on RMS. The results suggest that physics-informed feature engineering with classical ML is a promising approach for chatter detection in thin-walled cylindrical turning. Full article

The Jiangsu coastal zone has experienced substantial land use/land cover (LULC) changes under intensified anthropogenic activities, while spectral confusion caused by land–sea interactions increases the difficulty of accurate LULC classification. This study investigated LULC dynamics in the Jiangsu coastal zone in 2000, 2008, 2016, and 2024 using Landsat imagery and the Google Earth Engine platform. A total of 33 features, including spectral bands, spectral indices, texture, topographic variables, and nighttime light data, were extracted. To improve classification accuracy and model generalization, a Pearson-SHAP-RFE feature selection framework was developed by integrating Pearson correlation analysis, SHapley Additive exPlanations, and recursive feature elimination. Four classification algorithms, namely Random Forest, Extreme Gradient Boosting, Classification and Regression Trees, and Support Vector Machine, were compared. Feature selection improved classification accuracy across the four algorithms, with improvements ranging from 4.05% to 5.15% compared with the spectral feature baseline. Random Forest achieved the highest overall accuracy of 92.28%, with a Kappa coefficient of 0.9019. The long-term classification results showed that LULC changes mainly occurred among forest–grassland, construction land, and cultivated land. Water bodies showed the smallest centroid shift, whereas cultivated land exhibited the greatest spatial displacement. Full article

Ecoenzymatic stoichiometry links extracellular enzyme allocation to microbial resource-acquisition patterns, yet its behavior in managed subtropical soils remains poorly resolved. This uncertainty is especially important where strong edaphic heterogeneity may modify within-season enzyme patterns. We compared Pre (before cover-crop planting) and Post (after cover-crop termination) soils in two contrasting Florida agroecosystems: a calcareous South Florida Summer system (six farms; n = 104) and a sandy North Florida Winter system (four farms; n = 90). Each system was analyzed independently because season, geography, soil order, and cover-crop assemblage were confounded. In the Summer system, BG and NAG were higher Post than Pre, whereas ACP and AS did not differ significantly; vector length was lower Post, while circular vector-angle summaries remained within the P-acquisition domain. In the Winter system, BG, NAG, ACP, and microbial biomass C were lower Post, whereas AS was higher; C:N and N:P enzyme ratios did not differ significantly, and only C:P declined. Circular statistics showed that apparent shifts based on arithmetic vector-angle means could result from angular wraparound at ±180°. Variance partitioning indicated a larger pure Phase fraction in Summer than Winter, but these fractions are descriptive within-system associations rather than evidence of a climatic effect. Overall, ecoenzymatic responses differed between Pre and Post phases in a system-specific manner, and circular treatment of vector angles together with soil-specific interpretation is necessary for robust subtropical soil-health assessment. Full article

Background: Acute lung injury/acute respiratory distress syndrome (ALI/ARDS), a common complication of sepsis, is critically characterized by disruption of the alveolar–capillary barrier. The use of mesenchymal stem cells (MSCs) has emerged as a promising therapeutic strategy for ARDS owing to their potent paracrine effects. Wingless-type MMTV integration site family member 5A(Wnt5a) is a secreted protein with context-dependent effects on angiogenesis. This study aimed to investigate whether Wnt5a contributes to bone marrow-derived MSC (BMSC)-mediated repair of endothelial injury and whether Wnt5a-overexpressing BMSCs improve outcomes in an ALI animal model. Methods: Lipopolysaccharide (LPS) was used to induce endothelial cell (ECs) injury in vitro. EC proliferation, migration, tube formation and permeability, together with the expression of the junctional proteins zonula occludens-1 (ZO-1) and vascular endothelial cadherin (VE-cadherin) and the apoptosis-related proteins Bax and Bcl-2, were assessed after coculture with genetically modified BMSCs. In vivo, ALI was induced in mice by intraperitoneal administration of LPS. Lung histopathology, the lung wet-to-dry weight ratio, cytokine concentrations in bronchoalveolar lavage fluid (BALF) and serum, Evans blue extravasation, and the expression of junctional and apoptosis-related proteins were evaluated. PI3K/AKT signaling was examined as a potential pathway associated with the observed effects. Results: Compared with the vector-BMSCs, Wnt5a-overexpressing BMSCs increased the proliferation, migration, and tube formation of LPS-injured ECs; reduced endothelial permeability; preserved junctional protein expression; and shifted apoptosis-related protein expression towards an anti-apoptotic profile. Wnt5a knockdown attenuated these responses. In vivo, treatment with Wnt5a-overexpressing BMSCs was associated with less histological lung injury, lower inflammatory cytokine concentrations and a similar shift in apoptosis-related protein expression after LPS challenge. These protective effects were accompanied by enhanced PI3K/AKT signaling. Conclusions: Wnt5a overexpression optimized the protective effects of BMSCs against ALI in vivo and protected against LPS-induced EC barrier dysfunction in vitro, accompanied by enhanced PI3K/AKT signaling. Full article

Feature distributions shift under variable-speed gearbox operation, which can cause a model trained only on healthy samples to misclassify normal operating changes as anomalies. A semi-supervised anomaly detection method is proposed in this study. Using healthy data, the method first fits speed-dependent trends for the selected time- and frequency-domain statistics, and the deviations from these trends form the statistical residuals. Computed order tracking then converts the vibration signal to the angular domain, where five mechanism features describe meshing energy, harmonic structure, sideband modulation, and order-spectrum entropy. Removing the corresponding healthy speed trends yields the mechanism residuals. Robust Bounded Health-Consistency Weighting (RB-HCW) weights these residuals according to their variability in healthy data before they are fused with the statistical residuals and modeled by Deep Support Vector Data Description (DeepSVDD). The Sequential Bayesian Queue-Based Alarm (SBQA) module then confirms whether abnormal decisions persist across successive windows. Across the four fault types under the two separately modeled load conditions, the proposed method achieved macro-averaged true positive rate (TPR), accuracy (ACC), and F1-score values of 93.31%, 92.58%, and 94.02%, respectively. Full article

Synthetic aperture radar (SAR) provides high-resolution observations of radial sea surface currents. By combining radial currents observed from different viewing directions, the trajectory-crossing method can reconstruct the sea surface current vector field. In practice, observations from ascending and descending passes are acquired at different times, so the spatial structure of the surface current field may evolve between acquisitions. Directly combining multitemporal observations can therefore introduce spatial mismatch and reduce reconstruction accuracy. This study proposes a trajectory-crossing method for reconstructing sea surface current vectors from multitemporal Sentinel-1 Doppler observations. Maximum cross-correlation (MCC) is applied to gradient images of the radial current fields to estimate displacement and spatially register observations acquired at different times before vector reconstruction. The method is evaluated using Sentinel-1 data acquired over the Gulf Stream region and compared with geostrophic currents from the Copernicus Marine Environment Monitoring Service (CMEMS), Surface Water and Ocean Topography (SWOT) observations, and Global Drifter Program (GDP) drifter measurements. Results show that the proposed method reduces mismatch effects and improves the accuracy and stability of sea surface current vector reconstruction. It provides a practical approach for deriving surface current vector fields from multitemporal SAR Doppler observations. Full article