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Hybrid railway assets such as workshops and depots combine building, mechanical, electrical and plumbing (MEP)/industrial, and linear infrastructure domains, increasing coordination complexity and challenging continuity from the Project Information Model (PIM) to the Asset Information Model (AIM). Although Employer’s Information Requirements (EIR), Asset Information Requirements (AIR), and the BIM Execution Plan (BEP) prescribe deliverables and processes, a persistent gap remains between documentary prescriptions and the auditable evidence needed to support traceable decisions within the Common Data Environment (CDE). This paper proposes an ISO 19650-aligned governance framework that operationalizes the EIR/AIR → BEP → CDE transition by: (i) structuring the asset using Functional Units (FUs) as a stable anchor for PIM → AIM continuity; and (ii) implementing a pre-Published Quality Gate that separates control into three non-substitutable dimensions (spatial, semantic, and data). The approach is implemented as a tool-neutral, reproducible workflow (inputs → checks → outputs → publish) and produces a minimal, persistent evidence package in the CDE (file-level report, package summary, publish/hold decision record, and Nonconformity Report (NCR)/BIM Collaboration Format (BCF) traceability), with explicit roles governing the Shared→ Published transition. Across 22 Industry Foundation Classes (IFC), deliverables from two depot cases and multiple delivery states, All Gates Pass ranged from 25.0% to 44.4% depending on Case × State; overall, 14/22 deliverables (63.6%) would be held pending correction under the gate. Although validated on Spanish railway depots, the framework is grounded in ISO/openBIM standards and is designed for transferability to other international contexts and complex asset types where multidisciplinary federation and PIM → AIM continuity pose similar challenges. Full article

(This article belongs to the Special Issue Building Information Modeling (BIM) and Digital Twins for Sustainable and Resilient Construction)

19 pages, 3307 KB

Open AccessArticle

Towards Autonomous Powerline Inspection: A Real-Time UAV-Edge Computing Framework for Early Identification of Fire-Related Hazards

by Shuangfeng Wei, Yuhang Cai, Kaifang Dong, Chuanyao Liu, Fan Yu and Shaobo Zhong

Drones 2026, 10(3), 183; https://doi.org/10.3390/drones10030183 (registering DOI) - 6 Mar 2026

Abstract

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)

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15 pages, 898 KB

Open AccessArticle

Exploring Nonlinear Dynamics of the (3+1)-Dimensional Boussinesq-Type Equation: Wave Patterns and Sensitivity Insight

by Ejaz Hussain, Ali H. Tedjani and Muhammad Amin S. Murad

Axioms 2026, 15(3), 198; https://doi.org/10.3390/axioms15030198 (registering DOI) - 6 Mar 2026

Abstract

This study examines a nonlinear partial differential equation, namely the (3+1)-dimensional Boussinesq-type equation. To explore this model, three versatile analytical approaches are applied: the Exp-function method, the Kudryashov method, and the Riccati equation method. Using these techniques, a range of exact analytical solutions is derived, exhibiting diverse structural forms such as periodic, kink-type, rational, and trigonometric solutions. The analysis reveals the rich dynamical behavior of the equation and demonstrates its effectiveness in modeling a variety of nonlinear wave phenomena across different physical contexts. Several of the obtained solutions are illustrated through graphical representations for better interpretation. The results include hyperbolic, trigonometric, and rational function solutions, along with a sensitivity analysis. To highlight the physical relevance of the findings, suitable parameter values are selected, and the corresponding wave behaviors are visualized using three-dimensional and contour plots generated with Maple 2024. Overall, the study provides valuable insights into the mechanisms underlying the generation and propagation of complex nonlinear phenomena in fields such as fluid dynamics, optical fiber systems, plasma physics, and ocean wave transmission. Full article

(This article belongs to the Special Issue New Perspectives in Applied Mathematics with Nonlinear Equations and Dynamical Systems)

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26 pages, 2773 KB

Open AccessArticle

Boron Triggers Hepatic Ferroptosis: Unveiling the Dual-Pathogenic Nexus of Oxidative Stress and SLC7A11/GPX4 Dysregulation

by Ting He, Yumeng Li, Jiangli Huang, Weiqian Su, Siying Liu, Jinwen Quan, Gaolong Zhong, Zhonghua Liu, Dayou Shi and Wenlan Yu

Animals 2026, 16(5), 832; https://doi.org/10.3390/ani16050832 (registering DOI) - 6 Mar 2026

Abstract

Boron compounds, classified as prohibited food additives due to their high toxicity, persist in pesticides and fertilisers, industrial processes, food supply chains, and consumer goods, perpetuating multisource exposure risks. Chronic ingestion may induce fatal hepatorenal injury; however, mechanistic insights and epidemiological surveillance remain critically lacking amidst sector-wide regulatory gaps. This study employed integrated cellular and organismal models to elucidate the relationship between boron-induced hepatotoxicity and ferroptosis. We demonstrate that dietary boron accumulation in chicken livers is associated with histopathological damage, mitochondrial cristae dissolution and atrophy (a hallmark of ferroptosis), and elevated serum biomarkers AST and ALT. Boron exacerbates oxidative damage in hepatocytes by elevating malondialdehyde (MDA) production while modulating the Nrf2/ARE antioxidant signaling pathway—specifically downregulating key genes (Nrf2, HO-1, GCLM, CAT). Concurrently, it inhibits critical antioxidant enzymes (SOD, T-AOC), thereby depleting cellular antioxidant defenses. Crucially, boron disrupts iron homeostasis and induces ferroptosis by dysregulating the SLC7A11-GPX4 pathway: upregulating pro-ferroptotic genes (ACSL4, TF, TFR) and downregulating cytoprotective genes (SLC7A11, GPX4, FTH1). Co-treatment with the ferroptosis inhibitor ferrostatin-1 (Fer-1) attenuated boron-induced oxidative damage, whereas the ferroptosis inducer Erastin potentiated toxicity. Collectively, we pioneer the dual-pathogenic mechanism of boron hepatotoxicity—oxidative stress and ferroptotic cell death—establishing the SLC7A11/GPX4 axis as a novel therapeutic target against boron toxicity. Full article

(This article belongs to the Section Poultry)

32 pages, 7690 KB

Open AccessArticle

FSSC-Net: A Frequency–Spatial Self-Calibrated Network for Task-Adaptive Remote Sensing Image Understanding

by Hao Yuan and Bin Zhang

Remote Sens. 2026, 18(5), 824; https://doi.org/10.3390/rs18050824 (registering DOI) - 6 Mar 2026

Abstract

Although recent studies have achieved remarkable progress in remote sensing image understanding by fusing spatial- and frequency-domain features to leverage their complementary strengths, they still face two key limitations: frequency modeling remains rigid due to static constraints, limiting adaptability, and spatial–frequency fusion often suffers from poor generalization and instability across tasks and network depths. Our experiments reveal that the relative importance of low- and high-frequency components varies dynamically across feature hierarchies and training stages, indicating that frequency information is inherently task-dependent and stage-aware. Motivated by these observations, we propose the Frequency–Spatial Self-Calibrated Network (FSSC-Net), a task-driven framework for adaptive frequency modeling and collaborative spatial–frequency fusion. FSSC-Net incorporates a lightweight, plug-and-play self-calibrated frequency modeling mechanism, comprising a Dynamic Frequency Selection Module and a Task-Guided Calibration Fusion Module. This mechanism adaptively modulates frequency responses via soft masks, enabling dynamic extraction of task-relevant low- and high-frequency components and effective alignment between spatial- and frequency-domain features. Moreover, we present a systematic analysis of frequency importance across tasks and training stages, providing quantitative evidence for the necessity of task-calibrated frequency modeling. Extensive experiments on various benchmarks demonstrate that FSSC-Net consistently outperforms state-of-the-art methods, exhibiting strong task adaptability and robust cross-task generalization. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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28 pages, 1602 KB

Open AccessSystematic Review

Variable Geometry Ejectors: A Systematic Review of Modulation Mechanisms, Actuation Strategies, Modeling Approaches, and Applications

by Masoud Arabbeiki, Mohsen Mansourkiaei, Domenico Ferrero and Massimo Santarelli

Energies 2026, 19(5), 1350; https://doi.org/10.3390/en19051350 (registering DOI) - 6 Mar 2026

Abstract

Variable geometry ejectors (VGEs) offer passive, compact, and energy-efficient solutions for fluid transport and thermal management in applications such as refrigeration, hydrogen fuel cells, and solar-driven desalination. By adjusting internal geometries, VGEs maintain high performance under off-design and transient conditions, overcoming limitations of fixed-geometry ejectors. This systematic review synthesizes experimental, numerical, and hybrid research on VGEs published between 30 June 1995 and 1 July 2025. Peer-reviewed journal and conference papers were identified through structured searches of Scopus, Web of Science, and Google Scholar, followed by PRISMA-guided screening. Forty-eight studies were qualitatively synthesized with respect to modulation mechanisms, actuation and control strategies, working fluids, modeling approaches, validation practices, performance metrics, and Technology Readiness Levels (TRLs). Risk of bias was assessed using the Mixed Methods Appraisal Tool (MMAT), complemented by an engineering-specific extension for experimental and numerical studies. Results indicate a strong reliance on numerical modeling, predominantly 2D axisymmetric CFD, with limited high-fidelity experimental validation. Adjustable nozzle throats dominate current designs, while multi-variable geometries and real-time closed-loop control remain underexplored. Most studies cluster at TRLs 2–4, with only two demonstrating full system-level integration. Overall, VGEs show strong potential for energy-efficient operation, but progress toward deployment requires integrated geometry–control co-design, standardized benchmarking, uncertainty-aware validation, and scalable experimental demonstration. This review was not registered. Full article

(This article belongs to the Collection Current State and New Trends in Green Hydrogen Energy)

18 pages, 13734 KB

Open AccessArticle

Influences of Polishing Slurry Components on Material Removal and Surface Morphology of 4H-SiC C-Face Based on Fenton Reaction CMP

by Ying Wei, Ruhao Meng, Yongqi Huang, Guoyan Huo, Haitao Wu, Jiapeng Chen, Guizhong Guo, Yanan Peng, Nannan Zhu and Jianxiu Su

Crystals 2026, 16(3), 179; https://doi.org/10.3390/cryst16030179 (registering DOI) - 6 Mar 2026

Abstract

This study systematically investigates the effects of polishing slurry components on the material removal rate (MRR) and surface morphology of the C-face of 4H-SiC substrates during chemical mechanical polishing (CMP) based on the Fenton reaction. By regulating the particle size and concentration of colloidal silica abrasives, H₂O₂ concentration, and Fe₃O₄ catalyst content, the mechanisms of each component on MRR and surface roughness (Sa) were systematically analyzed. The results indicate that in an alkaline polishing slurry at pH = 9, Fe₃O₄ effectively catalyzes the decomposition of H₂O₂ to generate hydroxyl radicals (·OH), thereby significantly enhancing the material removal efficiency. When using colloidal silica with a particle size of 110 nm at a concentration of 8 wt%, H₂O₂ at 5 wt%, and Fe₃O₄ at 0.03 wt%, a maximum MRR of 701 nm/h was achieved along with a good surface quality of Sa = 0.79 nm. The study also found that the abrasive particle size and concentration, as well as the ratio of oxidant to catalyst, significantly influence the chemo-mechanical synergy. Excessively high H₂O₂ or Fe₃O₄ concentrations can trigger ·OH quenching reactions, thereby reducing polishing efficiency. This research provides a theoretical basis and process optimization direction for the application of heterogeneous Fenton reactions in SiC CMP under alkaline conditions. Full article

(This article belongs to the Section Inorganic Crystalline Materials)

27 pages, 8376 KB

Open AccessArticle

Graded SiC–Nanodiamond Coatings and Shallow De-Cobaltization for Spalling-Resistant PDC Cutters

by Lei Tao, Zhiyuan Zhou, Jiaju Chen and Liangzhu Yan

J. Compos. Sci. 2026, 10(3), 145; https://doi.org/10.3390/jcs10030145 (registering DOI) - 6 Mar 2026

Abstract

High-temperature, high-pressure (HTHP) hard-rock drilling frequently causes chamfer spalling of polycrystalline diamond compact (PDC) cutters, leading to ~20% loss in the rate of penetration (ROP) and large torque oscillations. We propose a surface-gradient chamfer comprising a thin SiC interlayer (t_SiC ≈ 0.7 μm) and a nanocrystalline diamond topcoat (t_D ≈ 5 μm, d_D ~100 nm), combined with shallow cobalt leaching (L_deCo ≈ 100 μm). The structure was verified by microscopy/spectroscopy and evaluated by scratch adhesion, SEVNB toughness, instrumented impact, thermal shock, 400 °C pin-on-disc wear, and bench-scale granite drilling with vibration/torque monitoring. A coupled thermo-mechanical finite-element model, calibrated with Raman stress maps and thermal measurements, was used to interpret failure trends. Relative to untreated cutters, the gradient design reduced peak tensile residual stress by ~45% and lowered high-temperature wear volume by ~40%. In the present impact dataset (limited cutters per condition), the observed spall incidence at 1.0 J decreased from 2/3 (baseline) to 1/5 (gradient-treated). Short bench drilling runs suggested improved signal separability between healthy and pre-spall states (ROC-AUC ≈ 0.85 vs. ~0.65 for baseline, evaluated using a leave-one-cutter-out protocol); these drilling results should be interpreted as trend-level evidence given the limited number of cutters. These gains arise from mitigated thermal mismatch and residual stresses at the chamfer. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Modeling and Simulation of Composite Materials, 2nd Edition)

34 pages, 2269 KB

Open AccessReview

Systemic Integrative Mechanisms and Intervention Strategies in Exercise-Induced Skeletal Muscle Damage: Evidence from Animal, Clinical, and Multi-Omics Studies

by Tianhang Peng, Zike Zhang, Ju Wei, Ni Ding, Wanyuan Liang and Xiuqi Tang

Int. J. Mol. Sci. 2026, 27(5), 2451; https://doi.org/10.3390/ijms27052451 (registering DOI) - 6 Mar 2026

Abstract

Exercise-induced muscle damage (EIMD) has classically been attributed to localized mechanical disruption following eccentric contractions. Emerging evidence, however, indicates that EIMD represents a systems-level failure of stress integration within skeletal muscle rather than a purely mechanical lesion. Mechanical loading initiates disturbances in intracellular Ca²⁺ homeostasis, which interact with metabolic stress, redox imbalance, and immune activation to form self-reinforcing feedback loops. When compensatory capacity is exceeded, transient injury may shift toward maladaptive remodeling marked by mitochondrial dysfunction, ferroptosis, chronic inflammation, and impaired regeneration. Recent studies identify reactive oxygen species accumulation, iron-dependent lipid peroxidation, dysregulated energy sensing, and aberrant immune polarization as key molecular tipping points governing injury reversibility. Beyond their regenerative role, satellite cells act as integrators of metabolic history and epigenetic memory, linking repetitive injury to reduced muscle adaptability, age-related sarcopenia, and heightened metabolic disease risk. Here, we synthesize evidence from animal models, clinical studies, and multi-omics analyses to establish a systems biology framework for EIMD. We delineate the spatiotemporal interactions among mechanical, metabolic, oxidative, immune, and regenerative modules; identify regulatory nodes that determine adaptive repair versus pathological outcomes; and critically evaluate current nutritional, physical, pharmacological, and regenerative interventions from a mechanism-oriented perspective. Finally, we discuss how multi-omics, digital monitoring, and individualized rehabilitation may enable precision management of EIMD and advance understanding of muscle stress resilience and adaptive limits. Full article

(This article belongs to the Special Issue Molecular Mechanisms Related to Exercise)

34 pages, 10497 KB

Open AccessArticle

Stress-Doped Interface Synergy: Unraveling the Atomic-Scale Corrosion Initiation of Al/Al₂Cu Interfaces with Fe–Si Additions in Chloride Environments

by Shuang Li, Wenyan Wang, Jingpei Xie, Aiqin Wang, Zhiping Mao, Wendong Qin and Qingyuan Guo

Materials 2026, 19(5), 1026; https://doi.org/10.3390/ma19051026 (registering DOI) - 6 Mar 2026

Abstract

In this study, first-principles calculations were employed to systematically investigate the adsorption of Cl⁻ on Al₂Cu(110) surfaces, clean Al(111)/Al₂Cu(110) interfaces, and Fe/Si-doped interfaces, as well as the influence of strain on interfacial electronic structure and corrosion activity. When Cl⁻ is adsorbed on Al sites, the bonding between Cl and Al exhibits strong ionic characteristics with localized charge transfer, while adsorption on Cu sites is characterized by more delocalized, covalent interactions. This competition dictates the site-dependent stability of adsorption. Through geometric–electronic synergy, the interface functions as both a “Cl⁻ enrichment zone” and an “activity source,” significantly favoring Cl⁻ adsorption at high-activity anodic sites such as Al-hole and Al-bridge. Conversely, Cu-top sites maintain a high work function and an inert cathodic nature, facilitating the formation of efficient micro-galvanic couples across the interface. Moreover, Fe/Si doping further modulates the interfacial electronic landscape: Si serves as an effective strengthening element due to its low substitution energy and high stability, while Fe primarily forms a solid solution on the Al side, potentially introducing galvanic corrosion risks. Stress analysis indicates that tensile strain systematically enhances surface activity by lowering the work function, while compressive strain non-monotonically influences corrosion through a three-stage mechanism involving the “densification–cracking–plastic relaxation” of the passive film. These findings elucidate the atomistic origins of corrosion initiation at Cu–Al composite interfaces and provide a theoretical foundation for enhancing corrosion resistance through alloy design and strain engineering. Full article

(This article belongs to the Special Issue Corrosion Mitigation and Protection of Metals and Alloys)

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23 pages, 2046 KB

Open AccessArticle

Carbon Price Forecasting via a CNN-BiLSTM Model Integrating VMD and Classified News Sentiment

by Xiyun Yang, Han Chen, Xiangjun Li and Xiaoyu Liu

Big Data Cogn. Comput. 2026, 10(3), 82; https://doi.org/10.3390/bdcc10030082 (registering DOI) - 6 Mar 2026

Abstract

Accurate carbon price forecasting is vital for risk management but is hindered by high volatility and sensitivity to external shocks. Existing multivariate models typically overlook unstructured news sentiment, failing to capture irrational fluctuations driven by market public opinion. To address this, this paper proposes VBN-Net, a hybrid model integrating carbon-specific news sentiment with Variational Mode Decomposition (VMD). Two core innovations are presented: First, a multi-modal input mechanism combines structured financial data with unstructured carbon news sentiment to effectively capture policy-driven shocks. Second, a Sequential Beluga Whale Optimization strategy is designed to adaptively optimize feature engineering in steps. Unlike conventional approaches, the VBN-Net first employs VMD for denoising and frequency decomposition, and then optimizes the fusion weights of news sentiment across different frequency components derived from multi-source news. This strategy effectively overcomes the subjectivity of manual parameter selection, providing high-quality features for a fixed CNN-BiLSTM backbone. By integrating VMD-based denoising with optimized multi-source news fusion, the model achieves consistent performance improvements across multiple evaluation metrics. The empirical findings validate the effectiveness of the proposed model in enhancing forecasting performance, thereby providing a reliable analytical tool for participants in the carbon market. Full article

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19 pages, 3896 KB

Open AccessArticle

LGA-YOLO: A Light Weight and High-Performance Network for Bubble Detection

by Wenda Luo, Yongjie Li and Siguang Zong

Appl. Sci. 2026, 16(5), 2560; https://doi.org/10.3390/app16052560 (registering DOI) - 6 Mar 2026

Abstract

Precise bubble detection is fundamental to process control in chemical engineering and oceanography, yet deploying heavy deep learning models on edge devices is often impractical due to hardware constraints. To bridge this gap, we present LGA-YOLO, a streamlined evolution of YOLO11n optimized for high-speed industrial inspection. By synergizing ghost convolutions with channel shuffling and incorporating a novel Triplet Attention-based feature enhancement, our design systematically eliminates redundancy without compromising discriminative power. The architecture integrates four specialized components—LightShuffleGhostConv, C3k2Ghost, LSPPF, and LightC2fPSA—to maximize efficiency. On our custom side-illuminated bubble dataset, LGA-YOLO maintains a high mAP@50 of 89.0% and mAP@50–95 of 67.0%, with a precision of 90.1% and recall of 85.1%. Crucially, it slashes the parameter count by 67% and FLOPs by 56% compared to the baseline, establishing itself as a viable, high-performance solution for real-time embedded monitoring. Full article

(This article belongs to the Special Issue AI in Object Detection)

18 pages, 450 KB

Open AccessArticle

Building Resilience Through ESG: Evidence from Employees’ Stress and Innovation

by Jeong Won Lee

Sustainability 2026, 18(5), 2609; https://doi.org/10.3390/su18052609 (registering DOI) - 6 Mar 2026

Abstract

Organizations increasingly rely on environmental, social, and governance (ESG) practices as a core element of sustainable management, yet little is known about how ESG affects employees during periods of crisis. Despite the growing ESG literature, limited research has examined how firm-level ESG performance influences employee psychological mechanisms and innovative behavior under crisis conditions through multi-level pathways. Drawing on corporate reputation theory and conservation of resources (COR) theory, this study examines how corporate ESG performance shapes employee experiences and behaviors under crisis conditions. This study conceptualizes ESG performance as a reputation-based organizational resource that buffers employees against psychological stress, thereby enabling innovative behavior that is critical for business sustainability. In addition, team cohesion as a contextual social resource was proposed to strengthen the stress-buffering effect of ESG. Using multi-level data from 980 employees nested within 51 large Korean firms, combined with objective ESG ratings collected prior to the crisis, this study tests the proposed model through multi-level structural equation modeling. The results show that higher corporate ESG performance is associated with lower employee psychological stress, which in turn promotes innovative behavior. Moreover, team cohesion amplifies the negative relationship between ESG performance and employee stress. By revealing a micro-level pathway through which ESG enhances employee well-being and innovation during crises, this study advances research on the economic and business aspects of sustainability. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

30 pages, 7453 KB

Open AccessArticle

Interfacial Transition Zone Strengthening in Aeolian Sand Concrete via ssDNA Anchored CNTs on Alkali-Activated Surface Layer

by Yi Zhou, Taotao Cai, Xingu Zhong, Chao Zhao, Tianye Luo, Kunlong Tian and Yuanyuan Li

Materials 2026, 19(5), 1023; https://doi.org/10.3390/ma19051023 (registering DOI) - 6 Mar 2026

Abstract

The use of aeolian sand as a fine aggregate in concrete production provides a sustainable pathway to valorize abundant aeolian resources while alleviating the global shortage of natural construction aggregates. However, the high ultrafine particle content of aeolian sand results in the formation of highly porous interfacial transition zones (ITZ) between sand particles and cement paste, which is the primary cause of the inherent brittleness and inferior mechanical performance of aeolian sand concrete. To overcome this critical limitation, an alkali-activated surface layer (ASL) was constructed on aeolian sand via 4 mol/L KOH activation. This process induced the surface micro-dissolution of minerals to create high-density active ion sites (specifically Ca²⁺, K⁺, Na⁺, and Fe³⁺). These sites facilitated the precise anchoring of carbon nanotubes (CNTs) through the chemical coordination of single-stranded deoxyribonucleic acid (ssDNA). The influence of the ASL and the ssDNA/CNTs nanocomposite on the ITZ was elucidated through macro-mechanical testing and multi-scale microstructural characterization. Experimental results demonstrated that compressive strength, flexural strength, and compressive energy dissipation increased by 48%, 67%, and 42%, respectively. Microstructurally, the modification promoted a pore refinement mechanism, reducing the proportion of harmful (pores > 0.1 μm) from 51% to 20% and narrowing the ITZ width from 20–40 μm to 10–15 μm (a 67% reduction). The observed performance enhancement is attributed to the synergistic effect of the ASL and ssDNA/CNTs, which transforms the inherently weak ITZ into a chemically reinforced interfacial phase via molecular-scale coordination bonding and optimized stacking of cement hydration products. Full article

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18 pages, 4361 KB

Open AccessArticle

The Study of Improved YOLOv13-Based Method for Detection of Industrial Surface Defects

by Yiqing Yang, Song Chen and Jing Li

Symmetry 2026, 18(3), 457; https://doi.org/10.3390/sym18030457 (registering DOI) - 6 Mar 2026

Abstract

Surface defect detection is a core of industrial product quality control, vital for ensuring product reliability and production efficiency. However, due to diverse types and significant size variations of industrial surface defects—especially minute or complex ones—accurate feature extraction and efficient detection remain major challenges, and existing You-Only-Look-Once (YOLO) methods struggle to meet high-precision demands. This paper proposes a symmetry-aware YOLOv13-based industrial surface defect detection network. First, a Multi-level Feature Enhancement Module (MFEM) is designed, combining a star-shaped architecture with the CBAM attention mechanism to enhance defect feature discriminability via multi-branch feature interaction and nonlinear expression, while compensating for detail loss from multi-layer depth-wise separable convolutions (DSConv). The symmetric dual-branch structure in MFEM improves feature balance and structural consistency. Second, the Spatial Pixel Global Attention (SPGA) module is introduced to supplement detail information during feature pyramid transmission and enhance multi-scale feature fusion efficiency, while maintaining symmetric feature distribution. Third, the HyperACE module is improved using a multi-branch hypergraph structure to enhance long-range dependency modeling and local feature representation. On the GC10-DET dataset, the improved model achieved 69.6% Precision, 66.1% Recall, and 67.0% mAP@50, demonstrating superior performance while maintaining real-time capability. Full article

(This article belongs to the Section Engineering and Materials)

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