Search Results (9,617)

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

With the increasing demand for flexible electric vehicle charging and grid-interactive energy utilization, wireless power transfer (WPT) systems with high efficiency, bidirectional power flow capability, and controllable charging characteristics have attracted growing attention. However, existing WPT systems for electric vehicles still suffer from challenges including low adaptability to multiple operating modes, difficulty in achieving stable constant-current/constant-voltage output, and limited bidirectional power transfer capability under weak-coupling conditions. To address these issues, two relay-based four-coil WPT topologies, namely S-SS-LCC and LCC-SS-LCC, are proposed for electric vehicle charging and bidirectional energy transfer applications. Based on fundamental frequency analysis, frequency-domain models of the two topologies are established to reveal the relationships among resonant characteristics, output behavior, and power transfer direction. The results show that the S-SS-LCC topology can achieve constant-current and constant-voltage output in the forward grid-to-vehicle charging mode, as well as constant-voltage output in the reverse vehicle-to-grid mode. In contrast, the symmetrical LCC-SS-LCC topology can achieve bidirectional constant-current power transfer, making it suitable for vehicle-to-vehicle emergency charging scenarios. Under weak-coupling conditions (k = 0.1), the S-SS-LCC system delivers an output current of approximately 12 A at 85.2 kHz and an output voltage of about 612 V at 87.7 kHz, with a peak efficiency of 91.63%. The LCC-SS-LCC system achieves bidirectional constant-current output at 87.7 kHz with a maximum efficiency of 92.23%. Low-power experimental results further verify the predicted constant-current and constant-voltage characteristics. The proposed topologies provide a promising solution for efficient electric vehicle wireless charging and flexible bidirectional energy interaction in future smart charging systems. Full article

Linear assets (LAs), such as conveyor systems, road networks, pipelines, and power transmission lines, are typically maintained through localized, segment-based interventions. While such approaches effectively address spatially heterogeneous degradation, they often neglect the system-level consequences of repeated local actions. In particular, improvements in segment condition may be accompanied by increased structural complexity, leading to reduced reliability and higher lifecycle costs. This paper proposes a unified engineering framework that integrates segment-level condition assessment with system-level structural effects. The framework is based on a dual representation of asset condition, distinguishing between material state (MS) and structural state (SS), which correspond to material aging (MA) and structural aging (SA), respectively. A key contribution is the introduction of the fragmentation penalty (FP), capturing the negative impact of increasing segmentation and interface density on system performance. The framework incorporates multi-threshold decision logic, enabling differentiation between operational, refurbishment, and replacement regimes, and interprets maintenance actions as transformations affecting both condition and structure. A formal model is developed to represent the asset as a dynamic system of segments and interfaces. It provides a basis for future empirical calibration and structure-aware optimization. Although the model is developed using conveyor belt loops as a reference case, its broader relevance is discussed for other classes of linear assets with repeated local intervention and evolving structural heterogeneity. A simple worked example is included to demonstrate the operational meaning of the proposed fragmentation-aware perspective. The results show that maintenance decisions may change when structural side effects are considered together with local condition improvement, and they provide a basis for future empirical calibration and structure-aware optimization of maintenance strategies. Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article

Cystic echinococcosis (CE) is a zoonotic disease caused by the larval stage of the Echinococcus granulosus sensu lato (s.l.) complex. The disease is globally distributed, with particularly high prevalence in Central Asian countries, including Kazakhstan. Despite its significant impact on public health and livestock production, data on CE in sheep in Kazakhstan remain limited. This study investigated the prevalence and genetic diversity of Echinococcus granulosus sensu stricto (s.s.) in sheep across Kazakhstan, addressing an important zoonotic disease affecting both livestock and human health. Over the course of one year, a total of 31,389 sheep were examined, and cystic echinococcosis cysts were collected from the livers and lungs of 550 infected sheep across 14 regions of Kazakhstan. Molecular analyses targeting mitochondrial genes (nad1, cox1) were performed to determine genetic diversity. The results revealed a higher occurrence of CE in the southern regions of the country. Among the genotyped isolates (57), genotype G1 was dominant, accounting for 84.2% (48) of the samples, whereas genotype G3 (9) was detected at a lower frequency in three regions. A total of 11 distinct haplotypes were identified, indicating considerable genetic diversity among the isolates. Haplotype network analysis suggested gene flow among populations and revealed the widespread presence of the most common haplotype (EgKZ-2) across multiple regions. These findings highlight the need for continuous monitoring and targeted control strategies for cystic echinococcosis, emphasizing the importance of understanding parasite genetic diversity for public health interventions and livestock management in endemic areas. Overall, this study contributes to the understanding of the genetic diversity and transmission dynamics of E. granulosus s.s. in Central Asia. Full article

Chromium slag, a chromium-bearing solid waste characterized by substantial environmental hazards yet with appreciable resource potential, has become a focal topic in solid-waste pollution control and the circular economy. Centered on the overarching logic of “evidence chain–system boundary–scalable and verifiable acceptance,” this review systematically synthesizes recovery technologies, industrial-scale utilization pathways, and the key challenges associated with the detoxification and resource utilization of chromium slag. From the perspective of recovery technologies, we examine pyrometallurgical and hydrometallurgical routes, solidification/stabilization (S/S), and bioelectrochemical coupling approaches, elucidating their fundamental principles, applicability boundaries, and critical nodes where environmental burdens may be transferred across media. We emphasize that process design should concurrently consider detoxification efficiency, resource recovery performance, and whole-process pollution control. Regarding utilization pathways, this review highlights three major routes with strong scale-up relevance—metallurgical process co-treatment (CAP–sintering–blast furnace), bulk utilization in construction materials, and high-value utilization—and analyzes their industrial potential and engineering constraints. Particular attention is given to the lack of long-term leaching and durability evidence, which represents a central bottleneck limiting product-side credibility. Furthermore, we discuss cross-cutting challenges including the long-term stabilization of Cr(VI), the verifiability of “green utilization” concepts, cost and economic feasibility, and standardized acceptance criteria. We propose that future research should shift from single-process optimization toward multi-objective, system-level evaluation, and establish a full-chain evidence system covering “speciation/mineral phases–process mechanisms–environmental behavior–risk assessment–engineering scale-up–standardized acceptance.” This review aims to provide a systematic analytical framework and practical reference for improving comparability across resource-utilization technologies and supporting engineering decision-making for chromium slag management. Full article

The alloy with a chemical composition of 64 at. % Fe and 36 at. % Ni is known as Invar36 and is characterized by a coefficient of thermal expansion (CTE) less than 2 × 10⁻⁶ °C⁻¹ below Curie temperature (about 250 °C). The conventional method of obtaining Invar36 alloys consists of melting and casting, followed by a series of heat treatments. In recent years, research has focused on unconventional technologies for Invar36 preparation such as the sintering of Fe and Ni elemental powders. Also, Invar36 in powder form can be synthesized by mechanical alloying (MA). The aim of this paper is the characterization of Invar36 compacts obtained by conventional sintering of mechanically alloyed Fe and Ni elemental powders. MA was performed in a high-energy planetary ball mill (Ar atmosphere). Mechanically alloyed powders were densified by conventional sintering (simple and double). The sintering parameters used are those specific to the sintering of ferrous parts. After simple sintering, the relative density was 74%. Re-pressing and double sintering lead to an increase in the relative density to 78.6%. The microstructure of Invar36 compacts consists of two phases. The coefficient of thermal expansion (CTE) was determined for Invar36 compacts obtained by both simple and double sintering at 1120 °C in endogas. The CTE values of Invar36 simple sintered (α = 0.6 × 10⁻⁶ °C⁻¹) and double sintered (α = 0.5 × 10⁻⁶ °C⁻¹) are very low, up to 195 and 225 °C, respectively. HV_0.05 values of the Invar-ss sample are lower than the values of the Invar-ds sample. Thus, the HV_0.05 value in areas where the γ phase predominates increases from 203 to 218, while in areas where the α phase is predominant it increases from 257 to 271. The results of this study have potential applicability in obtaining Invar parts by sintering under the specific conditions used for ferrous parts, without requiring any modification of the production flow. Full article

Background/Objectives: We evaluated whether resin infiltration treatment of demineralized enamel improves shear bond strength (SBS). Methods: Thirty permanent bovine incisor teeth were assigned randomly into three groups (n = 10 per group): control group, demineralized enamel pretreated with ICON^® resin infiltrant (Exp1 group), and demineralized enamel without pretreatment (Exp2). Demineralization was induced using a pH 4.5 solution for 21 days and was monitored using swept-source optical coherence tomography on days 0, 7, 14, and 21. The lesion depth (LD) was quantified and evaluated using ImageJ software. In the Exp1 group, ICON^® was applied prior to bracket bonding; no pretreatment was applied in the Exp2 group. In all groups, brackets were bonded using Super-Bond/Clear fluoride-free self-cure adhesive resin (4-META/MMA-TBB, Sun Medical) following Phosphoric acid (65%; Red Activator, Sun Medical). After debonding, enamel surfaces were evaluated to determine the adhesive remnant index (ARI). Results: No significant difference (p = 0.631) was noted in LD between Exp1 and Exp2 groups. The SBS values significantly differed (p < 0.05) between the control (4.1 ± 1.0 MPa) and Exp1 (5.5 ± 1.4 MPa) groups and between the Exp1 and Exp2 (3.8 ± 1.3 MPa) groups. However, SBS did not differ significantly between the control and Exp2 groups. Furthermore, ARI scores showed no significant difference between the control and Exp1 groups, whereas the Exp2 group recorded significantly elevated ARI scores relative to the control group (p = 0.0127). Conclusions: These findings suggest that resin infiltration with ICON^® may improve bracket adhesion on demineralized enamel. Full article

Background: This pilot study examined whether oxidative–nitrosative stress is associated with clinical status in amyotrophic lateral sclerosis (ALS). We analyzed associations between plasma markers of oxidative–nitrosative imbalance and ALSFRS–R, disease duration, survival, and routine biochemical parameters. Methods: Twenty-nine ALS patients fulfilling the Gold Coast diagnostic criteria were enrolled. Plasma levels of 3-nitrotyrosine (3–NT), 8-oxo-2′-deoxyguanosine (8–oxodG), malondialdehyde (MDA), glutathione (GSH), non-protein thiols (NP–SH), and non-protein disulfides (NP–SS–NP), as well as creatinine, urea, uric acid and BMI, were measured. Associations with ALSFRS–R and disease duration were evaluated using non-parametric correlation analyses and second-order polynomial regression (adjusted R²), while survival was explored using Kaplan–Meier analysis and multivariable Cox regression. Given the modest sample, we considered statistical power and applied Benjamini–Hochberg false discovery rate (FDR) correction within marker families. Results: At the uncorrected significance level, 3–NT showed a positive correlation with ALSFRS–R and a negative correlation with disease duration, and NP–SH correlated negatively with disease duration; however, these associations did not remain significant after FDR correction (FDR-adjusted p ≥ 0.099). Other oxidative–nitrosative markers and biochemical parameters showed no robust relationships with clinical measures. In Cox models, 3–NT was not significantly associated with survival (HR 3.44 per 1 nM, 95% CI 0.25–47.97, p = 0.358), whereas older age predicted higher mortality (HR 1.05 per year, 95% CI 1.00–1.10, p = 0.036). Conclusions: 3–NT and NP–SH exhibited the strongest trends among the investigated markers, but their clinical associations in this small cross-sectional cohort remain exploratory and require confirmation in larger longitudinal studies. Full article

Solid-state electrolytes (SSEs) are essential for achieving long-term stability and fast-charging performance in secondary batteries. Although Li_1.3Al_0.3Ti_1.7(PO₄)₃ (LATP) offers high ionic conductivity, its practical application is restricted by high-temperature sintering requirements and interfacial reduction at the lithium anode. In contrast, Li-based oxide electrolytes can be sintered below 600 °C, offering improved compatibility with conventional electrodes such as graphite and silicon. In this study, a Li₂O–LiCl–B₂O₃–Al₂O₃ (LCBA)/LATP composite SSE was fabricated via hot-press co-sintering at 600 °C. Composites with LCBA:LATP weight ratios of 8:2, 7:3, 6:4, 5:5, 3:7, and 2:8 were prepared to identify the optimal composition. The 3:7 composite achieved a sintered density of 2.40 g/cm³ and an ionic conductivity of 2.5 × 10⁻⁴ S/cm. Phase evolution and sintering behavior were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Compared to single-phase LCBA or LATP, the composite electrolyte exhibited improved interfacial stability and lower interfacial resistance against lithium metal. Full article

Gold nanospheres (AuNPs) and gold nanoflowers (AuNFs) are widely used as platforms for DNA aptamer functionalization, while conjugation behavior and colloidal tolerance remain important factors affecting subsequent sensing-oriented optimization. In this study, 82-nt thiolated DNA aptamer constructs bearing either 3′-SH or 5′-SH terminal modification were immobilized onto citrate-stabilized AuNPs and AuNFs under matched stepwise salt-aging conditions. Apparent nanoparticle-associated DNA output was estimated by Qubit-based measurement of unbound ssDNA in the supernatant and expressed as mass-based loading output (ng). Under the tested stock-dispersion conditions, AuNP samples showed higher apparent conjugation output than AuNF samples. Specifically, the apparent conjugation yields for AuNPs were 80.65 ± 1.64% (3′-SH) and 84.76 ± 1.98% (5′-SH), whereas those for AuNFs were 66.64 ± 3.36% (3′-SH) and 73.65 ± 1.36% (5′-SH). The corresponding apparent DNA loading outputs were 2329.7 ± 47.4 ng and 2448.7 ± 57.1 ng for AuNPs, and 1925.1 ± 97.0 ng and 2127.4 ± 39.3 ng for AuNFs. DLS size increases and zeta potential shifts toward more negative values were consistent with the formation of a DNA-associated interfacial layer, while TEM images supported morphology retention after conjugation. A qualitative visual salt-challenge assessment indicated that aptamer-functionalized nanoparticles displayed improved resistance to salt-induced aggregation relative to bare particles under the tested conditions. Because the commercially supplied AuNP and AuNF dispersions were not normalized to identical particle number or accessible surface area, the reported values should be interpreted as comparative apparent outputs rather than intrinsic loading capacities. Within this scope, the present study provides a convenient preliminary materials-level evaluation of thiolated aptamer conjugation behavior and may support future glyphosate aptasensor optimization. Full article

This study evaluated the influence of scan rate (4.23 mm/s [S10] and 6.35 mm/s [S15]) on the localized corrosion and tribocorrosion behavior of a laser engineered net shaping (LENS)-produced stainless steel 316L (SS316L) in a phosphate-buffered saline (PBS) solution. Electrochemical impedance spectroscopy (EIS) was performed by applying an AC signal from 10⁵ to 10⁻² Hz and cyclic potentiodynamic polarization (CPP) was performed by sweeping from −150 mV to +1.5 V (vs. open circuit potential) and back to characterize passivation and pitting susceptibility. Potentiostatic tribocorrosion tests were conducted using a reciprocating tribometer integrated with a potentiostat to probe material response in passive and cathodic regimes. S15 exhibited manufacturing-related defects that served as preferential pit initiation sites, with pits in both S10 and S15 showing evidence of cell-interior dissolution. Electrochemical results indicated that the charge transfer resistance was reduced by 66% for S15 and that the repassivation potential decreased by 35% compared to S10. Under tribocorrosion, material degradation was dominated by mechanical wear for both samples. However, sliding significantly accelerated electrochemical dissolution in S15, with the corrosion rate affected by wear (V_c-w) increasing by 46.8%. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) of wear scars revealed plastic deformation, abrasive grooves, and bio-tribofilm formation composed primarily of phosphates. Micro-pits associated with processing defects were observed exclusively in S15. Overall, lower scan rate processing (S10) produced a more defect-resistant microstructure with improved resistance to localized corrosion and tribocorrosion in PBS. Full article

Selective laser melting (SLM) offers significant potential for fabricating lightweight 316L stainless steel lattice structures (LSs), while forming defects and microstructural heterogeneity remain challenging, especially in fine struts. In this study, response surface methodology (RSM) and analysis of variance (ANOVA) were employed to quantify the coupled effects of geometric parameters (forming angle, FA; rod diameter, RD) and processing parameters (laser power, LP; scanning speed, SS; hatch spacing, HS) on the mass deviation (MD) of fine struts. The results show that FA and RD are the dominant factors affecting MD within the investigated parameter range, whereas LP and SS exhibit comparatively weaker effects. Representative samples with different FA and RD were further characterized by SEM, XRD, and EBSD to examine the associated microstructural evolution. The observations indicate that changes in FA and RD are accompanied by variations in solidification morphology, defect distribution, crystallographic texture, and GND density. Higher FA is associated with lower MD and stronger texture alignment along the building direction, whereas larger RD tends to promote columnar growth and enhanced texture intensity. These results suggest that geometric parameters can serve as effective design variables for tailoring forming deviation and representative microstructural characteristics of fine struts in SLM-fabricated 316L lattice structures. Full article

Impact-based acoustic inspection provides a rapid non-destructive approach for screening metallic components by analyzing the sound radiated after a controlled mechanical excitation. However, the limited availability of labeled data from defective parts remains a major challenge for deploying deep learning classifiers in production. This paper proposes a complete pipeline that converts raw impact-response audio recordings into magnitude log-spectrogram images and trains a semi-supervised Wasserstein GAN with gradient penalty (SS-WGAN-GP) designed to operate under extreme data scarcity. The architecture couples a shared convolutional backbone with two output heads: a Wasserstein critic for unsupervised discrimination between real and generated samples, and a binary classification head for supervised quality labeling, jointly optimized through a combined loss that balances Wasserstein distance, gradient penalty, and cross-entropy. A key property of the design is that the generator acts as a source of synthetic training samples, producing progressively more realistic spectrograms as training advances. These samples, in turn, enrich the feature representations learned by the shared backbone and improve the performance of the classification head. The classification head of the trained discriminator is deployed directly as the quality classifier, without requiring external data or post hoc retraining. Full article