Search Results (5,277)

The automated production of large-scale labeled datasets from concrete X-ray computed tomography (CT) images is a fundamental prerequisite for training and validating deep learning-based segmentation models. However, existing methods either require extensive manual annotation or rely on domain-specific deep learning models that themselves demand labeled data—a circular dependency. This paper presents a parameter-driven three-class segmentation framework that automatically classifies each pixel in a concrete CT slice into one of three material phases: void (air pores and cracks), coarse aggregate, and mortar matrix, generating annotation masks suitable for large-scale dataset production without manual labeling. The proposed method combines: (1) fixed-threshold void detection calibrated to concrete CT grayscale characteristics; (2) adaptive percentile-based initial segmentation responsive to image-specific statistics; (3) multi-criteria connected component scoring based on area, shape descriptors (circularity, solidity, compactness, extent, aspect ratio), intensity distribution, and boundary gradient; (4) material science-informed size constraints aligned with concrete phase volume fractions; and (5) a material continuity enforcement module that applies topological hole-filling and conditional convex-hull consolidation to eliminate internal contamination within accepted aggregate regions, reducing boundary roughness by 7.6% and recovering misclassified boundary pixels. All parameters are centralized in a configuration file, enabling reproducible batch processing of 224 × 224 pixel CT slices at 0.07–1.12 s per image. Evaluated on 1007 224 × 224 concrete CT patches cropped from 200 representative scan frames, the framework produces three-class segmentation masks with physically consistent void fractions (mean 3.2%), aggregate fractions (mean 32.4%), and mortar fractions (mean 64.4%), all within ranges reported in the concrete CT literature (used as a dataset-scale QC screen, not a validation metric). Primary outputs and the archived image–mask pairs for this work are provided as an 8-bit patch archive. For pixel-wise validation, we report IoU, Dice, and pixel accuracy on an independently labeled subset that can be unambiguously paired with the released predictions: averaged over 57 matched patches, mean pixel accuracy is 88.6%, macro-mean IoU is 74.7%, and macro-mean Dice is 84.9%. The framework provides a fully automated annotation pipeline for dataset production, eliminating manual labeling costs for concrete CT image collections. The generated datasets are suitable for training semantic segmentation networks such as U-Net and its variants. Full article

The development of More Electric Aircraft (MEA) necessitates that Electro-Hydrostatic Actuator (EHA) controllers achieve exceptional power density within rigorously constrained volumes. However, the compact layout design of these controllers constitutes a challenging NP-hard problem, characterized by strong multi-physics coupling—such as electromagnetic, thermal, and structural fields—and complex nonlinear constraints. Traditional meta-heuristic algorithms frequently suffer from premature convergence and struggle to balance global exploration with local exploitation. To address these challenges, the core contribution of this paper is the proposal of a novel Fractional-Order Anteater Foraging Optimization Algorithm (AFO), which is successfully applied to an established EHA controller layout optimization model. At the algorithmic level, by incorporating the Grünwald–Letnikov fractional derivative, the algorithm exploits the inherent memory property of fractional calculus to dynamically adjust the search step size and direction based on historical evolutionary information, thereby preventing stagnation in local optima. At the engineering application level, a high-fidelity mathematical model of the EHA controller is established, comprising 11 design variables and 10 critical physical constraints, including parasitic inductance minimization, thermal radiation efficiency, and electromagnetic interference (EMI) isolation. Extensive validation against the CEC2005 and CEC2022 benchmark functions demonstrates the superior convergence accuracy and stability of the AFO algorithm. In a specific EHA case study, the proposed method reduced the controller volume by 33.9% while strictly satisfying all multi-physics constraints, compared to traditional methods. Furthermore, a physical prototype was fabricated based on the optimized layout, and experimental tests confirmed its stable operation and excellent thermal performance. The results validate the efficacy of incorporating fractional calculus into bio-inspired algorithms to solve complex, high-dimensional engineering optimization problems. Full article

Hybrid renewable energy systems, which combine photovoltaic panels, wind turbines, batteries, generators, and grid connections, require careful sizing to balance economic performance, renewable integration, and supply reliability. In this context, this study proposes a deep reinforcement learning (DRL)-based sensitivity analysis framework in which the admissible energy contributions from the diesel generator and the grid are treated as explicit design-control parameters. The objective is to simultaneously minimize the levelized cost of energy, minimize the loss of power supply probability, and maximize the renewable energy fraction. A sensitivity analysis was conducted across different HRES configurations, load profiles, and tau/gamma values. The performance of the DRL approach was compared with that of multi-objective particle swarm optimization and the non-dominated sorting genetic algorithm II under the same study setting. The results indicate that DRL can identify competitive trade-offs, especially under standard load conditions, while also providing insight into how admissible backup-energy constraints reshape techno-economic and reliability compromises. The best trade-offs were observed around intermediate tau and gamma values, suggesting that moderate backup-energy margins are more favorable than extreme values. These findings should be interpreted within the scope of a simulation-based study and provide comparative design-oriented evidence rather than universally transferable design rules. Full article

Microplastics (MPs) are emerging contaminants in freshwater ecosystems, yet their ingestion by zooplankton remains poorly documented in large European lakes. This study provides the first evidence of MPs in zooplankton from Lake Como (Northern Italy), a major subalpine lake of ecological and socioeconomic relevance. Using high-resolution digital microscopy (detection limit: 2 µm), we quantified MPs across four sampling years (2016, 2017, 2018, 2025), capturing small size fractions typically overlooked by conventional methods. MPs were consistently detected, with mean concentrations of 0.06 ± 0.08 MPs ind.⁻¹ and 1.14 ± 1.22 MPs mg⁻¹ d.w., values comparable to those reported for freshwater zooplankton worldwide. No significant differences were observed between the lake’s two main branches, supporting a lake-wide interpretation of exposure. Clear seasonal patterns emerged, with higher MPs loads in autumn and winter. These findings highlight the potential for MPs to enter pelagic food webs and contribute to a lake-wide baseline for future harmonized monitoring and polymer-specific assessments. The main limitation of this study is the exclusive quantitative approach, which does not provide qualitative information on polymer composition. Overall, these results underscore the need to integrate zooplankton-based monitoring into freshwater microplastic risk assessment frameworks. Full article

This work presents a comparative study of micro- and nano-scale fillers in liquid composite molding processes, focusing on how particle size and morphology affect resin rheology, flow behavior, and filler filtration within fiber preforms. Glass microspheres and organo-modified montmorillonite were dispersed in epoxy resin and injected through glass-mat preforms at different fiber volume fractions (ranging from 0.27 to 0.47). Our study integrates rheological characterization, in situ flow-front tracking, unsaturated permeability analysis, thermogravimetric quantification of retained particles, and microstructural observations by SEM. Despite their smaller loading, nanoclay suspensions showed a markedly higher viscosity increase than microsphere systems, yet their permeability remained nearly unchanged. In contrast, microsphere-filled resins exhibited strong filtration at the flow inlet, density-driven settling near the lower tool face, and significant permeability loss. The results demonstrate that nano-fillers, although more viscous, maintain homogeneous distribution and flow continuity, whereas micro-fillers promote cake formation and local compaction. This controlled side-by-side comparison clarifies how filler size and shape govern filtration mechanisms in liquid composite molding (LCM), providing design guidelines for processing filled resin systems without compromising part quality. Full article

Basketball performance is influenced by cardiorespiratory fitness and body composition. However, evidence regarding the ability of maximal oxygen uptake (VO₂max) to distinguish between competitive levels remains inconsistent. This study aimed to examine differences in cardiorespiratory fitness and body composition between professional and amateur basketball players and to explore their contribution to variability in relative VO₂max. This pilot study also informed sample size estimation for future studies. A cross-sectional study was conducted with 12 professional (21.0 ± 2.3 years; BMI: 25.37 ± 3.04 kg/m²) and 12 amateur (22.6 ± 1.7 years; BMI: 26.83 ± 3.24 kg/m²) male basketball players. Absolute and relative VO₂max, ventilatory thresholds, and body composition (five-component fractionation) were assessed. Between-group comparisons were performed using Welch’s t-tests, effect sizes were estimated using Hedges’ g, and covariance analyses were adjusted for height and body fat percentage. No statistically significant differences were observed in relative VO₂max between groups. However, the absolute second ventilatory threshold was significantly higher in professional players, and absolute VO₂max showed a large effect size favoring this group. Professionals also showed lower body fat percentage and greater fat-free mass (p < 0.01; g ≈ 1.2). These findings suggest that body composition differences may partly explain variability in relative VO₂max between competitive levels. Full article

Quantitative assessment of mitral regurgitation (MR) in dogs with myxomatous mitral valve disease (MMVD) is influenced by the method used to estimate left ventricular volume. This study aimed to evaluate the impact of different left ventricular volume estimation methods on quantitative MR assessment, using the modified Simpson’s method of discs (Disc method) as a reference. Echocardiographic data from 167 dogs with MMVD and 19 healthy control dogs were analyzed. Regurgitant volume (RVol), body size-normalized RVol, and regurgitant fraction (RF) were calculated using diameter-based methods (Cube, Gibson, Meyer, and Teichholz) and compared with values obtained using the Disc method. All diameter-based methods showed significant positive correlations with the Disc method. However, Bland–Altman analyses demonstrated wide limits of agreement and systematic bias. Between-method discrepancies increased with advancing disease stage, with diameter-based methods tending to overestimate RVol and RF, particularly in dogs classified as American College of Veterinary Internal Medicine (ACVIM) stages B2 and C/D. Although relative trends in regurgitant indices were consistent across methods, substantial differences were observed in absolute values. These findings indicate that diameter-based methods are not interchangeable with the Disc method for absolute quantification of MR severity in dogs with MMVD, especially in advanced disease stages. Full article

Fine particulate matter PM_2.5 continues to pose a critical public health risk in Northern Thailand, particularly in Chiang Mai, where traditional filtration methods often face limitations in cost and efficiency for large-scale applications. This study introduces a novel “Evaporator Condensation Growth Particle Scrubber (ECGP)” designed to enhance the collection efficiency of sub-micron particles by enlarging their physical size through a pressure-driven growth mechanism. The ECGP system utilizes synergistic effects between solid nuclei, high relative humidity, and mechanical pressure modulation. The ECGP system integrates solid nuclei, ~95% relative humidity and mechanical pressure modulation within a single chamber. Using incense smoke as a PM surrogate, the process utilizes controlled adiabatic cycles to induce stable heterogeneous condensation. The results indicate that the integrated process effectively shifts particle size distribution, reducing the PM_2.5/PM₁₀ mass ratio from 1.00 to 0.83. This indicates that approximately 17.5% (with a standard deviation < 1% across 10 trials, p < 0.05) of the fine mass successfully transitioned into the larger, more filterable PM₁₀ fraction and exhibited high physical stability and resistance to re-evaporation, effectively overcoming the low-efficiency threshold (typically <10%) of standard mechanical scrubbers and cyclones for sub-micron dust. This study concludes that ECGP technology offers a promising, cost-effective alternative for improving indoor air quality in large public infrastructures by leveraging particle inertia for enhanced removal, providing a scalable solution to the persistent smog crisis. Full article

Intensive nitrogen (N) fertilization in greenhouse vegetable systems degrades soil structure and accelerates soil carbon (C) mineralization. Biochar application can alleviate these adverse effects by enhancing aggregate stability and mediating microbially driven nutrient cycling, yet its effects across aggregate fractions remain poorly understood. Here, we investigated how biochar (0, 20, 40 t ha⁻¹) and N interact to affect aggregate stability, C mineralization, nutrient status, and microbial properties in bulk soil and four aggregate classes (large macroaggregates: LMA, > 2000 μm; small macroaggregates: SMA, 250–2000 μm; microaggregates: MA, 53–250 μm; silt + clay: S + C, < 53 μm) in vegetable soil after a 60-day incubation. Results showed that biochar–N co-application increased mean weight diameter by 27.4–30.5% and elevated soil total organic C (TOC) in LMA by 9.11–12.0% and in MA by 8.77–20.2% relative to the N-only treatment. It also reduced β-glucosidase and oxidase activities, as well as fungal and G⁻-bacterial abundance. Biochar amendment suppressed TOC mineralization by 2.7–24.6% in bulk soil and aggregate fractions, while boosting potentially mineralizable C pools by 12.5–155.7%, and thereby increasing overall mineralization potential. Structural equation modeling revealed the size-dependent regulatory mechanisms underlying these observations. Aggregate stability directly inhibited CO₂ emissions in bulk soil and SMA, while the effects in MA and S + C fractions were mediated by shifts in nutrient stoichiometry and hydrolase activities. Our findings clarified the size-dependent mechanisms by which biochar–N co-application promoted soil C sequestration, providing a theoretical basis for the sustainable management of intensive vegetable systems. Full article

Machine learning-based network intrusion detection systems (NIDSs) remain vulnerable to adversarial manipulation, but the robustness literature for tabular NIDS data is still dominated by single-model, single-dataset, and non-adaptive evaluations. In this paper, we reposition the manuscript as a comparative robustness study of a four-component defense pipeline rather than as a claim of a universal defense primitive. We evaluate XGBoost, LightGBM, TabNet, and Residual MLP on RT_IOT2022 and Web_IDS23 under standard attacks, representative constrained/adaptive attacks, component-wise ablations, sample-fraction sensitivity, repeated-run significance tests, per-class F1 analysis, and computational-overhead measurements. The results show strong dataset and architecture dependence. On RT_IOT2022, tree-based models close most of the robustness gap under strong attacks but often only after large clean-accuracy reductions; Residual MLP achieves a more favorable balance, while the full defense stack over-regularizes TabNet. On Web_IDS23, aggregate robustness-gap reduction remains positive, yet simpler baselines such as adversarial-training-only or ensemble-only configurations frequently outperform the full four-stage pipeline in absolute clean/attack accuracy. Across both datasets, median filtering is the most fragile component: larger filter windows substantially degrade both clean and attacked accuracy, whereas contamination rate, anomaly-mixing weight, and ensemble size are comparatively stable. Representative constrained/adaptive evaluations reduce performance only modestly relative to standard FGSM/PGD, but per-class and overhead analyses show that minority-class collapse and training cost remain important deployment limitations. These findings support a more cautious conclusion: adversarial defense for tabular NIDS is validation driven and dataset specific, and the full defense stack should not be treated as a universal default. Full article

Thermal processing of biomass can induce chemical transformations that lead to the formation of fluorescent carbonaceous products. In this study, six β-glucan-rich medicinal mushrooms, Ganoderma lucidum, Cordyceps sinensis, Inonotus obliquus, Lentinula edodes, Grifola frondosa, and Hericium erinaceus, were subjected to mild pyrolytic treatment (200 °C for 3 h) to investigate the formation of water-soluble fluorescent fractions. Physicochemical characterization of aqueous extracts was performed using high-performance liquid chromatography size-exclusion chromatography (HPLC-SEC), fluorescence emission spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and β-glucan quantification. Fluorescence emission spectra revealed species-dependent differences in emission intensity, with the most pronounced signals observed for G. lucidum and C. sinensis. HPLC-SEC analysis showed only minor changes in molecular weight distribution after thermal treatment, suggesting limited polymer degradation. FTIR spectra indicated moderate structural modifications consistent with partial carbonization and chemical rearrangement within the mushroom matrices. Despite the mild processing conditions, measurable increases in fluorescence intensity were observed in several species, indicating the formation of fluorescent carbon-rich molecular structures. These findings demonstrate that moderate thermal treatment of β-glucan-rich fungal biomass can generate water-soluble fluorescent carbonaceous fractions without extensive breakdown of the original polysaccharide matrix. The results provide new insights into thermally induced photophysical changes in medicinal mushrooms and contribute to understanding the formation of fluorescent carbonaceous products from natural biomaterials. Full article

In the present work, the elastic modulus of several types of polymer nanocomposites has been analyzed with a semiempirical model which takes into consideration agglomerate formation and their impact on the nanocomposites’ mechanical performance. The nanocomposites under investigation were either hybrids with a combination of graphene oxide (GO) with multi-walled carbon nanotubes (MWCNTs) or carbon nanofibers (CNFs) at various loadings, or monofillers with varying nanoparticle sizes, at a constant nanofiller loading. In addition, the effect of the type of polymeric matrix on the same nanofiller combinations has been examined. The basic assumption of two phases, namely a matrix with finely dispersed nanoparticles coexisting with agglomerates, was analyzed. The elastic stiffness of the first phase was calculated by the Mori–Tanaka model, and hereafter a semiempirical model was utilized for the estimation of the agglomerates’ stiffness. Within the context of this model, it was shown that the agglomerates’ volume fraction, combined with the nanoparticles’ density, namely the nanoparticles’ volume fraction in the agglomerates and consequently the inclusions’/agglomerates’ enhanced modulus, may cause a substantial improvement in the Young’s modulus, which cannot be explained by conventional mechanical models. These results apply to both nanocomposite types, hybrids at various nanofiller loadings and monofillers with varying particle sizes. Full article

The nominal maximum aggregate size (NMAS) plays a critical role in determining the mechanical performance of cement-stabilized macadam (CSM), yet its meso-mechanical influence mechanism remains insufficiently understood. In this study, three skeleton-dense CSM mixtures with different NMAS values were designed, and a combined experimental–numerical approach was adopted to investigate the macro- and meso-scale mechanical behavior. Uniaxial compression tests and aggregate crushing value tests were conducted to evaluate strength development and load-transfer characteristics, while a three-dimensional discrete element method (DEM) model incorporating realistic aggregate morphology was established to analyze the evolution of contact forces and crack propagation. The results show that increasing NMAS significantly improves the mechanical performance of CSM. Compared with CSM-30, the 7-day compressive strength of CSM-40 and CSM-50 increased by approximately 10.3% and 37.3%, respectively. The stress–strain response indicates that mixtures with larger NMAS exhibit higher stiffness and a higher strain. At the meso-scale, a larger NMAS promotes the formation of a more efficient force-chain network dominated by coarse aggregates. Strong contacts were predominantly carried by aggregates larger than 9.5 mm, and in CSM-50, the proportion of strong contacts in the 37.5–53 mm fraction exceeded 90%, indicating that the largest particles likely form the primary load-bearing skeleton. In addition, increasing NMAS delayed crack initiation, reduced crack propagation rate, and decreased the total number of cracks at failure. These findings demonstrate that macroscopic strength improvement is closely associated with meso-scale optimization of the aggregate skeleton and enhanced load-transfer efficiency. This study provides a mechanistic basis for NMAS selection and gradation optimization in semi-rigid base materials. Full article

In the present study, room temperature (RTR) and cryogenic (CR) rolling of electrolytic tough pitch copper (ETP-Cu) was performed to elucidate how deformation temperature and reduction ratio (40% and 80% thickness reductions) control dislocation storage, local stored energy (SE), and self-annealing. Correlated SEM/EDS and EBSD analyses were used to (i) locate Cu₂O particles, (ii) quantify local misorientation, and (iii) map the SE for self-annealing. Point EDS confirms that the intermetallic particles are copper oxides (Cu₂O), with apparent O content varying with particle size and EDS interaction volume. RTR80 (80% rolled) exhibits systematically higher KAM values and a larger area fraction of high SE than RTR40 (40% rolled), explaining the greater frequency and spatial density of self-annealed grains at higher reduction. Cryogenic rolling produces more severe fragmentation and a higher fraction of subgrains than RTR at equivalent reductions. CR80 shows the high KAM structures and locally highest SE regions among all conditions, and a higher fraction of self-annealed grains. Nevertheless, the mapped average SE for CR80 (2.93 × 10⁶ J/m³) was lower than for RTR80 (3.34 × 10⁶ J/m³) due to rapid post-deformation dislocation annihilation/self-annealing upon warming at RT. In all conditions, Cu₂O particles and bulged/irregular grain boundaries concentrate dislocations and SE and act as dominant particle-stimulated nucleation (PSN) sites and RT recrystallization, respectively. These results demonstrate that deformation temperature and reduction jointly determine the spatial distribution of SE and hence the propensity for self-annealing in ETP Cu. Full article

Ensuring the durability and safety of modern infrastructure critically depends on the quality and strength of concrete. The Ultrasonic Pulse Velocity (UPV) method is a widely used non-destructive testing technique for evaluating concrete properties; however, factors such as aggregate size distribution, compaction methods, and surface quality can significantly influence UPV results and their correlation with compressive strength. This study investigates the effects of different aggregate sizes and an innovative vibration-assisted compaction method—developed using piezoelectric (PZT) transducers—on the mechanical, ultrasonic, and surface properties of concrete. Four distinct aggregate size distributions were employed to produce sixteen concrete specimens with constant mix proportions. Unlike conventional low-frequency, high-power vibration practices, a high-frequency (40 kHz), low-power (120 W) vibration protocol was applied through PZT elements placed within the molds to enhance compaction and reduce entrapped air. Experimental results indicated that the heaviest specimen (7.13 kg) was the medium-aggregate sample compacted using tamping and rodding methods. The highest UPV value (4143 m/s) was obtained from the coarse-aggregate specimen subjected to three minutes of vibration. In contrast, the best compressive strength performance (22.73 MPa) was observed in the medium-aggregate specimen without any vibration treatment. The findings revealed that both aggregate size and advanced vibration techniques have significant effects on the mechanical properties, ultrasonic response, and surface quality of concrete. In addition, a proof-of-concept portable surface-finishing prototype consisting of a steel plate instrumented with multiple PZT transducers was developed, and preliminary trials qualitatively suggested improved surface leveling when applied in contact with the concrete surface. Surface roughness was quantified via image processing (Light Map 150 and Specular Map 150). The rough-area fraction decreased from ~29.8% in the untreated specimen to ~4.3% after ultrasonic application, indicating a marked improvement in surface leveling and overall surface quality. The results indicate that the applied PZT vibration protocol did not improve compressive strength; in several cases, particularly under prolonged excitation, a reduction in strength was observed. In contrast, a significant improvement in surface quality was achieved, with the rough-area fraction decreasing from approximately 29.8% to 4.3%. However, due to the limited number of specimens, the findings should be interpreted as preliminary. Overall, the method appears more promising as a surface enhancement technique rather than a direct alternative to conventional compaction methods. Full article