Search Results (9,706)

Fir-tree slots provide the form-fit connection between turbine disks and blades in aerospace engines and therefore constitute highly stressed geometries in safety-critical components. These slots are manufactured by broaching, a machining process performed near the end of the manufacturing chain for turbine disks made from high-strength materials such as Inconel 718. The severe thermo-mechanical loads encountered during broaching result in pronounced wear of high-speed steel tools, potentially leading to cutting-edge chipping and, consequently, compromising component quality. Early and reliable detection of tool defects is therefore essential to ensure process stability and component quality. In this study, a data-driven process monitoring system based on internal machine signals was used to investigate the detectability of cutting-edge chipping of broaching tools with straight cutting edges, identifying relevant signal sources and features and evaluating different modeling approaches. Broaching experiments with intentionally induced defects were conducted, and features in the time and frequency domains were extracted from torque, current, and three-phase motor signals. These features were used to train and compare a support vector machine, a random forest, and a neural network. The results demonstrate that reliable classification is feasible, with the neural network and support vector machine achieving balanced accuracies of up to 98.96% and 99.13%, respectively. High-frequency motor signals showed the highest relevance, highlighting their potential for industrial applications. Full article

Machine learning (ML) provides new opportunities to model the nonlinear relationships among composition, processing, microstructure, defects, properties, and in-service degradation of structural steels. This structured critical review examines ML applications to materials and process design, microstructural characterization, mechanical-property prediction, corrosion, fire and elevated-temperature performance, fatigue, fracture, and remaining-life assessment. Literature published up to 31 July 2026 was searched primarily through the Web of Science Core Collection and Scopus. A total of 110 publications were retained based on their relevance to structural steels, transparency of data and modeling procedures, and availability of information on validation or engineering applicability. The reviewed studies show that model suitability depends strongly on data modality, sample independence, feature representation, and validation strategy rather than on algorithm family alone. ML has progressed from property prediction toward process optimization, inverse materials design, environmental degradation assessment, and fatigue- and crack-related prognostics. However, independent cross-manufacturer, cross-laboratory, production-scale, and field validation remains limited, while uncertainty quantification and applicability-domain assessment are still inconsistently reported. These limitations are particularly important for corrosion, fire, fatigue, and remaining-life applications, where internally validated models should not be interpreted as substitutes for established physical models or design provisions. Future research should prioritize standardized multimodal data, physics-informed and uncertainty-aware modeling, prospective validation, and rigorously evaluated closed-loop monitoring and digital-twin frameworks for structural-steel life-cycle management. Full article

High shrinkage-related defect rates in aluminum die casting reduce effective production capacity. They also create energy-intensive re-melting loops, which weaken production planning reliability and environmental performance. This study examines how Green Lean Six Sigma can stabilize a resource-constrained Small and Medium-Sized Enterprise (SME) die-casting process and translate quality improvement into measurable capacity recovery and Scope 2 electricity-related carbon savings. Based on a 10-month case study, the Define–Measure–Analyze–Improve–Control (DMAIC) framework was integrated with factorial ANOVA, the Response Surface Methodology (RSM), one-way analysis of variance (ANOVA) and statistical process control (SPC). These methods supported process parameter identification, operating-window development and shop-floor process stabilization. The analysis identified the filling speed and mold temperature as significant shrinkage drivers, developed a mold temperature control map, and determined the standardized filling speed at 800 mm/s. The intervention reduced the shrinkage defect rate from 7.19% to 1.46%, reduced the overall scrap rate from 7.60% to 2.71%, and improved the overall sigma level from 2.93 to 3.42. This yield improvement generated a 4.89 percentage-point yield-equivalent capacity gain, avoided 4401 kWh of re-melting electricity, reduced Scope 2 emissions by 2.36 t CO₂e, and generated gross annualized savings of RMB 249,214 (USD 35,783). Considering a one-time implementation cost of RMB 31,445 (USD 4515), the first-year net saving was RMB 217,769 (USD 31,268). The findings show that accessible statistical process control methods can provide SMEs with a resource-efficient pathway to improve process stability, capacity utilization and electricity-related environmental performance before investing in advanced digital technologies. Full article

Foam cells are central to atherosclerotic plaque development, but their pathological significance in advanced lesions extends beyond lipid accumulation. Under chronic exposure to oxidized lipoproteins, cholesterol crystals, inflammatory cytokines, hypoxia, lysosomal stress, and mitochondrial injury, lipid-loaded foam cells of macrophage and vascular smooth muscle cell (VSMC) origin may acquire maladaptive stress phenotypes. In this focused review, we propose a redox-threshold model, defined as the transition point at which mitochondrial antioxidant and metabolic buffering capacity is exceeded, allowing lipid peroxide accumulation to shift senescence-like foam cells toward ferroptosis susceptibility and defective plaque resolution. Progressive mitochondrial reactive oxygen species (ROS) accumulation, impaired NADPH-dependent antioxidant buffering, defective glutathione and thioredoxin recycling, mitophagy impairment, and reduced GPX4-mediated lipid peroxide detoxification may converge to promote iron-dependent lipid peroxidation. If lipid-peroxidized or dying foam cells are not efficiently cleared, oxidized lipids, cellular debris, and inflammatory signals accumulate, promoting secondary necrosis, necrotic core expansion, plaque non-resolution, and instability. We explicitly distinguish established processes from mechanistically supported inferences and hypothesis-generating links, emphasizing that the complete senescence-like stress-to-ferroptosis-to-non-resolution sequence remains a testable framework rather than an established linear pathway. This focused framework suggests that advanced plaque stabilization may require strategies that preserve mitochondrial redox resilience, limit ferroptotic lipid peroxidation, and enhance efferocytosis-mediated resolution. Full article

Large-scale administrative hospital discharge data is a key resource for secondary health systems research, yet reuse is constrained by barriers of access, quality, interoperability, and semantic comparability. This paper presents and validates a reproducible, declarative, loss-aware harmonization framework for public discharge data that avoids full migration to a comprehensive common data model. The framework comprises a lightweight 14-field canonical model, versioned JSON crosswalks, a shared execution engine, a resilient file reader, schema validation, idempotency tests, value-domain checks, and an information-loss map. It was evaluated on public record-level discharge data from five jurisdictions on three continents (Korea, Brazil, Mexico, Chile, and New York State), comprising 561,966,231 harmonized records from 2001 to 2025. Validation demonstrated conformance to the declared source profiles for 82 of 99 files and full canonical conformance for 39, idempotency across all 99 files, 99.99% conformance with permitted value domains under an explicitly stated aggregation, and detection of source-level defects such as truncated files, malformed rows, and completeness anomalies. A marker-condition query for ischemic stroke (ICD-10 I63) showed that a single case definition executes consistently on the four sources retaining raw ICD-10 codes. The results show that, for heterogeneous administrative data, the key value lies not in scale alone but in the auditability of transformations, explicit loss documentation, and reproducibility of the harmonization process. Full article

Insulator types and materials vary substantially across power system inspection scenarios, while damage defects occur infrequently; consequently, defect images that match a target insulator type and operating environment are often difficult to obtain. Existing open-source insulator image datasets provide limited coverage of equipment types, scene backgrounds, and defect morphologies. Their direct use for detector training may therefore cause domain mismatch and poor generalization. To address these limitations, this study proposes a scene-domain-adaptive sample expansion method for few-shot damaged-insulator detection. The method adapts a general-purpose pretrained diffusion model to the insulator inspection domain and incorporates three-dimensional (3D) structural constraints to generate targeted samples of damaged insulators. First, low-rank adaptation (LoRA) is used for scene-domain adaptation, enabling the generation model to learn the characteristic geometry, appearance, and material properties of insulators. Second, a 3D model of the target insulator is constructed, and physical damage simulation and edge extraction are applied to obtain geometric guidance maps containing shed boundaries and fracture contours. These maps constrain the locations and shapes of the generated defects. Finally, the geometric guidance is injected into the diffusion process to synthesize damaged-insulator images, which are combined with limited real samples to train detectors for damaged-insulator instances, which were evaluated exclusively on real validation images. Experimental results show that adding a moderate number of generated samples enriches the scarce defect features in the real dataset and improves detector performance. In the mixture-ratio experiment using YOLOv8, mAP@0.5 increased by 8.2 percentage points. Additional experiments with multiple detectors yielded performance gains of varying magnitudes, demonstrating that the generated samples provide an effective supplement to limited, real-world data. The proposed method alleviates the scarcity of insulator defect samples and offers a practical data-augmentation strategy for intelligent inspection of power equipment. Full article

Carbon inclusivity (CI) mechanisms face weak low-carbon adoption despite strong consumer intention in China. Prior studies explain low-carbon behavior mainly from individual psychological perspectives yet rarely investigate the intention–behavior gap in the digital–physical scenarios of CI mechanisms. Furthermore, single theoretical lenses such as the Motivation–Opportunity–Ability (MOA) framework or Social Practice Theory (SPT) have clear limitations in interpreting this gap. This study integrates the MOA framework with SPT to construct a novel “Motivation–Intention–Context–Practice–Behavior” (MICPB) model, framing behavioral change as a dynamic interplay of internal motivation, external opportunities, and evolving social practices. Different from studies adopting MOA or SPT in isolation, the MICPB model bridges individual psychological processes and routine social practices to unpack the intention–behavior gap. Adopting grounded theory to analyze 42 interviews from China’s CI pilot regions, we identify three core dimensions of the gap: limited internal competencies and emotional conflicts, fragmented external incentives and platform defects, and misalignment between low-carbon values and daily routines. The MICPB model reveals that the gap stems from misalignment across motivation, context and practice, and enriches MOA theory by incorporating practice-based dynamics. This integrated framework deepens understandings of barriers to consumer low-carbon participation under CI schemes. This study suggests coordinated policy and corporate efforts to restructure social practices, bridge the intention–behavior gap, and align digital governance with low-carbon transitions. Full article

The development of sustainable, biocompatible scaffolds with enhanced structural stability remains a primary challenge in bone tissue engineering. In this study, structurally reinforced nanocomposite scaffolds were successfully fabricated by integrating green-synthesized carbon quantum dots (CQDs) into a gelatin (G) matrix, offering an innovative platform that mimics the organic–inorganic interfaces of natural bone tissue. The CQDs were derived from oatmeal via a sustainable, green hydrothermal route, serving simultaneously as zero-dimensional reinforcing fillers and bioactive agents within the biopolymer network. To ensure an additive-free fabrication process that avoids toxic chemical cross-linkers, dehydrothermal (DHT) treatment was employed, successfully modulating the interfacial and chemical cross-linking interactions between the gelatin chains and the oxygen-rich surface groups of the CQDs. Structural characterization confirmed the uniform dispersion of CQDs (average diameter 7–8 nm) within the porous gelatin framework. The incorporation of CQDs significantly improved the physicochemical properties of the scaffolds; the G/CQD 5% formulation emerged as the optimal composition, exhibiting a 118% increase in compression modulus compared to pristine gelatin. The composite demonstrated tuned swelling kinetics and a significantly reduced degradation rate, restricting mass loss after 14 days of incubation to approximately 24% compared to 40% for pristine gelatin, which is essential for maintaining a structural template during the initial stages of tissue formation. Bioactivity assays in simulated body fluid (SBF) confirmed the rapid, biomimetic induction of a crystalline hydroxyapatite layer with a natural Ca/P ratio of 1.61 within 14 days. Furthermore, preliminary in vitro assessments using Human Skin Fibroblasts (HSFs) confirmed excellent general cytocompatibility, with cell viability exceeding 90%. This study highlights the unique potential of utilizing biomass-derived carbon nanostructures and clean manufacturing processing to engineer multifunctional scaffolds with enhanced structural stability and intrinsic bioactivity for potential bone defect repairs. Full article

Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%. Full article

In industrial production processes, accurate and fast detection of band sawtooth deformations is of critical importance. Traditional detection approaches are not sustainable at industrial scale due to their high human attention requirements and low efficiency. In this study, we present RTC-DETR234, a task-specific configuration of the RT-DETR architecture designed for detecting small and low-contrast band sawtooth fractures. The proposed model uses the P2–P4 scales to provide higher-resolution feature extraction instead of the P3–P5 scaling structure of the baseline RT-DETR model. Furthermore, CBAM modules are integrated into the HGBlock-based backbone to refine channel- and spatial-level feature responses associated with small and low-contrast fracture regions. The proposed model, the baseline YOLO models, and the baseline RT-DETR model are trained and compared on a dataset consisting of band sawtooth defects. The proposed model achieved an average mAP of 0.9938 for tooth detection and 0.9178 for fracture detection across multiple independent runs. Full article

The Antigen Processing and Presentation Machinery (APM) is essential for immune surveillance by enabling the presentation of antigenic peptides to T lymphocytes and facilitating the elimination of infected or transformed cells. In cancer, the integrity of this process influences cancer immune responsiveness (CIR), defined as a tumour’s capacity to be recognised by the immune system and respond to immunotherapy. Tumours with intact antigen presentation pathways are more likely to generate effective antitumour responses, whereas APM defects promote immune escape and therapeutic resistance. Cancer cells frequently evade immune detection through altered antigen processing or reduced expression of major histocompatibility complex (MHC) class I molecules, limiting tumour antigen presentation to cytotoxic T lymphocytes. These alterations are increasingly recognised as determinants of response to immune checkpoint inhibitors and potential predictive biomarkers. APM defects may be reversible or irreversible. Interferon-mediated signalling can restore MHC class I expression and T-cell cytotoxicity in some tumours, whereas permanent genomic alterations affecting human leukocyte antigen (HLA) class I genes, β2-microglobulin (β2-m), or interferon-γ (IFN-γ) pathway components can severely impair antigen presentation. Emerging evidence highlights four mechanistic levels of APM perturbation: peptide generation, peptide loading, MHC class I integrity, and epigenetic regulation. Each contributes to distinct patterns of immune evasion. This review examines how MHC class I alterations influence CIR and contribute to immune evasion and immunotherapy resistance in gastrointestinal malignancies, while discussing therapeutic strategies to restore or bypass APM deficiencies. Full article

Taking the repair of prefabricated hole defects in 2024 aluminum alloy thin-walled components
by friction stir spot welding (FSSW) as the research object, the evolution laws
of microstructure and mechanical properties of FSSW-repaired joints of thin-walled components
were clarified through process experiments and numerical simulations. The
collaborative effect of the temperature field and material flow field during the FSSW repair
process and their regulation laws on the microstructure and properties were revealed. The
results show that as the repair speed increases, the macroscopic surface quality of the FSSW
joint improves. When the repair speed reaches 2000 r/min, a high-quality repaired joint
with a smooth and flat surface and no porosity defects can be obtained. Meanwhile, within
the repair speed range of 800 to 2000 r/min, the grains undergo dynamic recrystallization
(DRX) due to the combined effect of heat and mechanical forces, eventually forming a
uniform equiaxed grain structure in the weld core area. ABAQUS 2023 simulation verifies
the temperature distribution during the FSSW repair process. When the repair speed is
2000 r/min, the maximum temperature obtained from the simulation is 431.1 ◦C, which
agrees with the measured value from the experiment. The simulation results further reveal
that when the repair speed increases from 1200 r/min to 2000 r/min, the material fluidity
significantly enhances, and the flow velocity on the advancing side is always higher than
that in other areas. At the rotational speed of 2000 r/min, the plastic material flows continuously
from the periphery and eventually fills the defect area completely. The fracture
mode of the FSSW-repaired joint is mainly ductile fracture. With the increase in the repair
speed, the number of dimples at the fracture surface increases significantly. When the
rotational speed reaches 2000 r/min, the joint achieves the best mechanical properties, and
the FSSW-repaired joint reaches the maximum tensile strength of 169 MPa. Full article

Surface roughening is a common defect in plastic deformation processing, directly affecting product surface quality and service performance. This study investigates the mechanisms of surface roughness evolution during plastic deformation by considering both intrinsic and extrinsic factors. A macro–meso-scale modelling framework is developed by coupling crystal plasticity finite element modelling, fluid–solid interaction modelling, and macro–meso boundary conditions. The crystal plasticity model incorporates a constitutive model based on crystal plasticity theory, a Voronoi-based geometric model, and a real rough-surface topography model to capture non-uniform grain-scale plastic deformation. Fluid–solid interaction modelling is introduced to analyze the influence of liquid lubricant on the deforming solid material. Boundary interpolation and continuous displacement theories are then used to transfer macro-scale boundary constraints to the meso scale. The proposed framework is numerically implemented and applied to the aluminum alloy tube drawing process. The effects of intrinsic factors, including grain size, grain orientation, and initial surface roughness, and extrinsic factors, including deformation path, strain rate, and lubrication condition, are systematically examined. From a practical point of view, effective strategies to improve surface quality are by reducing grain size, lowering initial surface roughness, decreasing the strain rate and using low-viscosity lubricants. Full article

This study considers multi-class visual detection for processing tomato sorting under controlled laboratory conditions. In densely arranged images, occlusion and visual similarity can weaken the local boundary and texture cues needed to distinguish ripe tomatoes, unripe tomatoes, defective tomatoes, and soil clods. We propose SG-RTDETR, an RT-DETRv2 adaptation that combines detail-preserving downsampling, saliency-guided token encoding with residual spatial refill, context-aware feature organization, and adaptive cross-scale fusion. A four-class dataset was constructed from 1000 multi-object images and 400 single-object images used for supplementary representation learning. Across three independent training runs under controlled laboratory evaluation, SG-RTDETR achieved $87.8\pm 0.4$% ${\mathrm{mAP}}_{50:95}$, $92.4\pm 0.4$% ${\mathrm{mAP}}_{50}$, and $95.5\pm 0.3$% ${\mathrm{mAR}}_{50:95}$ (mean ± sample standard deviation). Relative to RT-DETRv2, the mean ${\mathrm{mAP}}_{50:95}$ increased by 3.4 percentage points, while total FLOPs remained at 61.17 G and forward-pass inference throughput decreased from 110.5 to 103.6 FPS. These results indicate an accuracy-oriented trade-off under the laboratory evaluation protocol; validation under realistic postharvest sorting conditions remains necessary. Full article

Benefiting from excellent high-frequency characteristics and superior radiation tolerance, InP is an indispensable material for next-generation high-speed communications, widely applied in optical communication, 6G radio frequency chips, AI optical interconnection, and aerospace radiation-hardened electronics. Although ion implantation greatly promotes the performance optimization of InP-based devices, it inevitably induces lattice displacement defects that degrade device reliability. Hence, quantitative evaluation of the threshold displacement energy (TDE) and dominant defect configurations in InP is essential. Our calculations reveal that the average threshold displacement energy is 18.20 eV for In atoms and 18.94 eV for P atoms. The E_d distributions for both In and P atoms predominantly lie below 30 eV and rarely exceed 40 eV. From 150 K to 900 K, In and P have a large mass difference and exhibit distinct temperature-dependent trends. The threshold displacement energy of In decreases with increasing temperature, whereas that of P rises as temperature increases. Based on the structural analysis of Frenkel pairs formed by displaced atoms, the dominant interstitial configurations are identified. These results provide detailed insights for damage evaluation and defect structure characterization in InP, benefiting ion implantation process optimization and radiation-hardening design of InP electronic devices. Full article