Search Results (798)

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

The topic of environmental pollution by petroleum products is highly relevant due to rapid urbanisation, including industrial development, road infrastructure and fuel distribution. Potential threat areas include refineries, fuel stations, pipelines, warehouses and transshipment bases, as well as sites affected by accidents or fuel spills. This study aimed to determine whether organic and mineral materials could mitigate the effects of diesel oil pollution on the soil’s trace element content. The used materials were compost, bentonite and calcium oxide. Diesel oil pollution had the most pronounced effect on the levels of Cd, Ni, Fe and Co. The levels of the first three elements increased, while the level of Co decreased by 53%. Lower doses of diesel oil (2.5 and 5 cm³ per kg of soil) induced an increase in the levels of the other trace elements, while higher doses caused a reduction, especially in Cr. All materials applied to the soil (compost, bentonite and calcium oxide) reduced the content of Ni, Cr and Fe. Compost and calcium oxide also increased Co accumulation in the soil. Bentonite had the strongest reducing effect on the Ni and Cr contents of the soil, reducing them by 42% and 53%, respectively. Meanwhile, calcium oxide had the strongest reducing effect on Fe and Co accumulation, reducing it by 12% and 31%, respectively. Inverse relationships were recorded for Cd (mainly bentonite), Pb (especially compost), Cu (mainly compost), Mn (mainly bentonite) and Zn (only compost) content in the soil. At the most contaminated site, the application of bentonite reduced the accumulation of Pb, Zn and Mn in the soil, while the application of compost reduced the accumulation of Cd. Applying various materials, particularly bentonite and compost, limits the content of certain trace elements in the soil. This has a positive impact on reducing the effect of minor diesel oil pollution on soil properties and can promote the proper growth of plant biomass. Full article

This literature review explores the role of symmetry in thermal sciences, fluid dynamics, and energy applications, emphasizing the theoretical and practical implications. Symmetry is a fundamental tool for simplifying complex problems, enhancing computational efficiency, and improving system design across multiple engineering and physics domains. Thermal and fluid processes are applied in several modern energy use technologies, essentially involving the complex, multidimensional interaction of fluid mechanics and thermodynamics, such as renewable energy applications, combustion diagnostics, or Computational Fluid Dynamics (CFD)-based optimization, where symmetry is highly considered to simplify geometric parameters. Indeed, the interconnection between experimental analysis and the numerical simulation of processes is an important field. Symmetry operates as a unifying principle, its presence determining everything from the stability of turbulent flows to the efficiency of nuclear reactors, revealing hidden patterns that transcend scales and disciplines. Rotational invariance in pipelines; rotors of hydraulic, thermal, and wind turbines, and in many other cases, for instance, not only lowers computational cost but also guarantees that solutions validated in the laboratory can be effectively scaled up to industrial applications, demonstrating its crucial role in bridging theoretical concepts and real-world implementation. Thus, a wide range of symmetry solutions is exhibited in this research area, the results of which contribute to the development of science and fast information for decision making in industry. In this review, essential findings from prominent research were synthesized, highlighting how symmetry has been conceptualized and applied in these contexts. Full article

The object of this study is welded joints of AISI 321 and Ti-6Al-4V, obtained by diffusion welding on developed conical surfaces. The problem of creating bimetallic joints of AISI 321 and Ti-6Al-4V with developed conical contact surfaces, using diffusion welding through an intermediate Electrolytic Tough Pitch Copper (Cu-ETP) copper layer, was solved. The joints were studied using micro-X-ray spectral analysis, microstructural analysis, and mechanical tests. High mutual diffusion of copper and titanium, along with increased concentrations of Cr and V in copper, was detected. The shear strength of the obtained welded joints is 250 MPa and 235 MPa at 30 min and 15 min, respectively, which is higher than the copper layer’s strength (180 MPa). The obtained results are explained by the dislocation diffusion mechanism in the volume of grains and beyond, due to thermal deformations during welding. Under operating conditions of internal pressure and cryogenic temperatures, the strength of the connection is ensured by the entire two-layer structure, and tightness is ensured by a vacuum-tight diffusion connection. The obtained strength of the connection (250 MPa) is sufficient under the specified operating conditions. Analysis of existing solutions in the literature review indicates that industrial application of technology for manufacturing bimetallic adapters from AISI 321 stainless steel and Ti-6Al-4V titanium alloy is limited to butt joints with small geometric dimensions. Studies of the transition zone structure and diffusion processes in bimetallic joints with developed conical contact surfaces enabled determination of factors affecting joint structure and diffusion coefficients. The obtained bimetallic adapters, made of Ti-6Al-4V titanium alloy and AISI 321 stainless steel, can be used to connect titanium high-pressure vessels with stainless steel pipelines. Full article

Ensuring the structural safety of steel trusses in escalators is critical for the reliable operation of vertical transportation systems. While manual inspection remains widely used, its dependence on human judgment leads to extended cycle times and variable defect-recognition rates, making it less reliable for identifying subtle surface imperfections. To address these limitations, a novel context-aware, multi-scale deep learning framework based on the YOLOv5 architecture is proposed, which is specifically designed for automated structural defect detection in escalator steel trusses. Firstly, a method called GIES is proposed to synthesize pseudo-multi-channel representations from single-channel grayscale images, which enhances the network’s channel-wise representation and mitigates issues arising from image noise and defocused blur. To further improve detection performance, a context enhancement pipeline is developed, consisting of a local feature module (LFM) for capturing fine-grained surface details and a global context module (GCM) for modeling large-scale structural deformations. In addition, a multi-scale feature fusion module (MSFM) is employed to effectively integrate spatial features across various resolutions, enabling the detection of defects with diverse sizes and complexities. Comprehensive testing on the NEU-DET and GC10-DET datasets reveals that the proposed method achieves 79.8% mAP on NEU-DET and 68.1% mAP on GC10-DET, outperforming the baseline YOLOv5s by 8.0% and 2.7%, respectively. Although challenges remain in identifying extremely fine defects such as crazing, the proposed approach offers improved accuracy while maintaining real-time inference speed. These results indicate the potential of the method for intelligent visual inspection in structural health monitoring and industrial safety applications. Full article

The adoption of predictive maintenance in public transportation has gained increasing attention in the context of Industry 4.0. However, many urban bus fleets remain in early digital transformation stages, with limited historical data and fragmented infrastructures that hinder the implementation of data-driven strategies. This study proposes a reproducible Machine Learning pipeline tailored to such data-scarce conditions, integrating domain-informed feature engineering, lightweight and interpretable models (Linear Regression, Ridge Regression, Decision Trees, KNN), SMOGN for imbalance handling, and Leave-One-Out Cross-Validation for robust evaluation. A scheduled batch retraining strategy is incorporated to adapt the model as new data becomes available. The pipeline is validated using real-world data from hybrid diesel buses, focusing on the prediction of time spent in critical soot accumulation zones of the Diesel Particulate Filter (DPF). In Zone 4, the model continued to outperform the baseline during the production test, indicating its validity for an additional operational period. In contrast, model performance in Zone 3 deteriorated over time, triggering retraining. These results confirm the pipeline’s ability to detect performance drift and support predictive maintenance decisions under evolving operational constraints. The proposed framework offers a scalable solution for digitally emerging fleets. Full article

Coal gangue, a major by-product of coal mining, poses significant environmental challenges due to its large-scale accumulation, land occupation, and potential for air and water pollution. This manuscript presents a comprehensive overview of continuous chamber gangue storage technology as a sustainable mining solution for coal mines. The principles of this approach emphasize minimizing disturbance to overlying strata, enabling uninterrupted mining operations, and reducing both production costs and environmental risks. By storing the surface or underground gangue in continuous chambers, the proposed method ensures the roof stability, maximizes the waste storage, and prevents the interaction between mining and waste management processes. Detailed storage sequences and excavation methods are discussed, including continuous and jump-back excavation strategies tailored to varying roof conditions. The process flows for both underground and ground-based chamber storage are described, highlighting the integration of gangue crushing, paste preparation, and pipeline transport for efficient underground storage. In a case study with annual storage of 500,000 t gangue, the annual economic benefit reached CNY 1,111,425,000. This technology not only addresses the urgent need for sustainable coal gangue management, but also aligns with the goals of resource conservation, ecological protection, and the advancement of green mining practices in the coal industry. Full article

Vessel trajectory prediction is fundamental to maritime navigation, safety, and operational efficiency, particularly as the industry increasingly relies on digital solutions and real-time data analytics. This study addresses the challenge of forecasting vessel movements using historical Automatic Identification System (AIS) data, with a focus on understanding the temporal behavior of deep learning models under different input and prediction horizons. To this end, a robust data pre-processing pipeline was developed to ensure temporal consistency, filter anomalous records, and segment continuous vessel trajectories. Using a curated dataset from the eastern Mediterranean, three deep recurrent neural network architectures, namely LSTM, Bi-LSTM, and Bi-GRU, were evaluated for short- and long-term trajectory prediction. Empirical results demonstrate that Bi-LSTM consistently achieves higher accuracy across both horizons, with performance gradually degrading under extended forecast windows. The analysis also reveals key insights into the trade-offs between model complexity, horizon-specific robustness, and predictive stability. This work contributes to maritime informatics by offering a comparative evaluation of recurrent architectures and providing a structured and empirical foundation for selecting and deploying trajectory forecasting models in operational contexts. Full article

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a hybrid framework that couples real-time perception and double-estimating reinforcement learning. A Convolutional Neural Network (CNN) classifies nine fatigue–skill combinations from synthetic physiological cues (heart-rate, blink rate, posture, wrist acceleration); its outputs feed a Double Deep Q-Network (DDQN) whose state vector also includes task-queue and robot-status features. The DDQN optimises a multi-objective reward balancing throughput, workload and safety and executes at 10 Hz within a closed-loop pipeline implemented in MATLAB R2025a and RoboDK v5.9. Benchmarking on a 1000-episode HRC dataset (2500 allocations·episode⁻¹) shows the hybrid CNN+DDQN controller raises throughput to 60.48 ± 0.08 tasks·min⁻¹ (+21% vs. rule-based, +12% vs. SARSA, +8% vs. Dueling DQN, +5% vs. PPO), trims operator fatigue by 7% and sustains 99.9% collision-free operation (one-way ANOVA, p < 0.05; post-hoc power 1 − β = 0.87). Visual analyses confirm responsive task reallocation as fatigue rises or skill varies. The approach outperforms strong baselines (PPO, A3C, Dueling DQN) by mitigating Q-value over-estimation through double learning, providing robust policies under stochastic human states and offering a reproducible blueprint for multi-robot, Industry 5.0 factories. Future work will validate the controller on a physical Doosan H2017 cell and incorporate fairness constraints to avoid workload bias across multiple operators. Full article

Wax deposition, driven by the crystallization of long-chain n-alkanes, poses severe challenges across industries such as petroleum, oil and natural gas, food processing, and chemical manufacturing. This phenomenon compromises flow efficiency, increases energy demands, and necessitates costly maintenance interventions. Wax inhibitors, designed to mitigate these issues, operate by altering wax crystallization, aggregation, and adhesion over the pipelines. Classic wax inhibitors, comprising synthetic polymers and natural compounds, have been widely utilized due to their established efficiency and scalability. However, synthetic inhibitors face environmental concerns, while natural inhibitors exhibit reduced performance under extreme conditions. The advent of nano-based wax inhibitors has revolutionized wax management strategies. These advanced materials, including nanoparticles, nanoemulsions, and nanocomposites, leverage their high surface area and tunable interfacial properties to enhance efficiency, particularly in harsh environments. While offering superior performance, nano-based inhibitors are constrained by high production costs, scalability challenges, and potential environmental risks. In parallel, the development of “green” wax inhibitors derived from renewable resources such as vegetable oils addresses sustainability demands. These eco-friendly formulations introduce functionalities that reinforce inhibitory interactions with wax crystals, enabling effective deposition control while reducing reliance on synthetic components. This review provides a comprehensive analysis of the mechanisms, applications, and comparative performance of classic and nano-based wax inhibitors. It highlights the growing integration of sustainable and hybrid approaches that combine the reliability of classic inhibitors with the advanced capabilities of nano-based systems. Future directions emphasize the need for cost-effective, eco-friendly solutions through innovations in material science, computational modeling, and biotechnology. Full article

HPMC, regulating slurry properties, is widely used in cement-based materials. Research on the application of HPMC in gangue slurry is still in its early stages. Moreover, the interactive effects of various factors on gangue slurry performance have not been thoroughly investigated. The work examined the effects of slurry concentration (X₁), maximum gangue particle size (X₂), and HPMC dosage (X₃) on slurry performance using response surface methodology (RSM). The microstructure of the slurry was characterized via scanning electron microscopy (SEM) and polarized light microscopy (PLM), while low-field nuclear magnetic resonance (LF-NMR) was employed to analyze water distribution. Additionally, industrial field tests were conducted. The results are presented below. (1) X₁ and X₃ exhibited a negative correlation with layering degree and slump flow, while X₂ showed a positive correlation. Slurry concentration had the greatest impact on slurry performance, followed by maximum particle size and HPMC dosage. HPMC significantly improved slurry stability, imposing the minimum negative influence on fluidity. Interaction terms X₁X₂ and X₁X₃ significantly affected layering degree and slump flow, while X₂X₃ significantly affected layering degree instead of slump flow. (2) Derived from the RSM, the statistical models for layering degree and slump flow define the optimal slurry mix proportions. The gangue gradation index ranged from 0.40 to 0.428, with different gradations requiring specific slurry concentration and HPMC dosages. (3) HPMC promoted the formation of a 3D floc network structure of fine particles through adsorption-bridging effects. The spatial supporting effect of the floc network inhibited the sedimentation of coarse particles, which enhanced the stability of the slurry. Meanwhile, HPMC only converted a small amount of free water into floc water, which had a minimal impact on fluidity. HPMC addition achieved the synergistic optimization of slurry stability and fluidity. (4) Field industrial trials confirmed that HPMC-optimized gangue slurry demonstrated significant improvements in both stability and flowability. The optimized slurry achieved blockage-free pipeline transportation, with a maximum spreading radius exceeding 60 m in the goaf and a maximum single-borehole backfilling volume of 2200 m³. Full article

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%. Full article

Copper-based substances have historically been shown to exhibit antimicrobial properties, but the mechanisms by which they interact with bacterial biofilms in pipeline systems remain unclear. This study investigates the effects of copper sulfate and copper nitrate on the growth, biofilm formation, and antibiotic resistance profiles of Escherichia coli and Enterococcus faecalis. Across a wide concentration range (0.00005–100 mg/mL), both salts demonstrated strong antimicrobial activity at higher concentrations, while sublethal levels produced more nuanced effects. Higher concentrations exhibited potent antimicrobial activity, while sublethal concentrations paradoxically enhanced biofilm resistance, particularly in E. faecalis. Growth kinetic assays and spectroscopic analysis were used to better understand how copper interacts with microbes on a biochemical and physical level. Surprisingly, prolonged exposure to sublethal copper concentrations altered the antibiogram profiles of E. faecalis, developing resistance to ceftazidime. The findings confirm the bimodal activity of copper salts as antimicrobial and biofilm-controlling agents, highlighting the critical need for precise concentration optimization in wastewater treatment. This current study contributes to the collective knowledge pool of metal–microbe interactions, shedding light on selecting materials for industrial and environmental applications. Full article

Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (${10}^{-2}$ probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments. Full article