Search Results (6,965)

Surface cracks on mandrel bars affect product quality and production stability in seamless steel pipe manufacturing. Existing vision-based methods mainly rely on bounding-box detection, which is insufficient for precise crack delineation and geometric characterization. This study proposes a lightweight segmentation framework for online mandrel bar crack inspection using grayscale industrial images. Based on YOLO11n-seg, the framework incorporates single-channel input adaptation, lightweight network reconfiguration, and crack-oriented feature enhancement to improve the extraction of weak, thin, and irregular cracks while reducing computational cost. A dedicated industrial dataset and a sample-balancing strategy are introduced to alleviate severe crack–background imbalance. Based on the predicted pixel-level masks, crack area, projected length, maximum width, and average width are calculated for online evaluation. Experimental results show that the proposed method achieves a mask mAP@0.5 of 88.5%, a false negative rate of 1.72%, and real-time inference at 204 FPS with 3.01 GFLOPs. Field deployment further demonstrates the effectiveness of the proposed framework for online crack inspection and geometric parameter calculation of mandrel bars. Full article

This study investigates non-isothermal laminar flow of waxy crude oil in a pipe. Due to heat exchange with the surroundings, the flow cools along the pipe length, resulting in a gradual transformation of the oil rheology from Newtonian to viscoplastic behavior. The mathematical model is based on the generalized Navier–Stokes equations coupled with the Shvedov–Bingham rheological model and the effective viscosity approach. The governing equations were solved numerically using the control volume method in the velocity–pressure formulation. The numerical simulations produced velocity, temperature, and effective viscosity fields, as well as pressure-drop data characterizing the rheological state of the waxy crude oil throughout the pipe flow domain. It was established that, in the central region of the inlet flow, the oil retains Newtonian behavior, whereas viscoplastic behavior begins to develop near the pipe wall. Further downstream, the flow progressively transforms into a viscoplastic state over the entire pipe cross-section, accompanied by the formation of stagnant near-wall regions and a plug-flow core. Full article

Investments in urban water supply should be informed by resilience management frameworks that consider traditional reliability requirements, community preparedness during system disruptions, and sustainability goals in long-term planning. Grounded in a framework (WARATA) that integrates these aspects, this paper presents a stepwise, performance-based theoretical approach to resilience quantification, supported by explanations and practical guidance. For instance, in addition to the piped infrastructure components, emergency supply options and human resources should be incorporated within the system boundaries (Step 1), and water supplied to users is recommended as a single performance measure (Step 2). During disruptions, performance at user nodes is influenced by operational rules for resource allocation (Step 3), which must be implemented in the required computer model for simulating performance (Step 4). Equations for computing withstanding, absorptive, restorative, adaptive, and transformative capabilities as time-based metrics are proposed (Step 5), enabling the analysis of results from the bottom up (Step 6) to inform resilience management. Using illustrations of performance curves at individual system nodes, this paper advocates for extended system boundaries that bridge the gap between infrastructure and community resilience; discusses challenges with the modeling of dynamic, adaptive performances; and emphasizes the importance of assessing temporal distances to fail-safe and safe-fail thresholds during disturbances. Pending case study validation and integration into tools for predictive and real-time analyses of options, the quantification approach could support infrastructure and emergency response planning and management, ultimately ensuring sustainable system designs with equitable resilience outcomes. Full article

This study focuses on the design and development of the Double Purpose Bench Vise to address safety, efficiency, and adaptability challenges in welding and fabrication environments. The project responds to limitations of conventional vises that restrict precision and increase the risk of strain-related injuries when handling heavy, irregular, or vertically oriented workpieces. Through an engineering-based development approach involving analysis, design, fabrication, and performance evaluation, the study introduces a Double Jaw Vertical Bench Vise equipped with a dual-clamping system and an integrated hydraulic jack mechanism for precise vertical adjustment with minimal physical effort. The device is designed to securely hold various materials, including metal bars, pipes, and wooden components, during cutting, grinding, shaping, welding, and assembly operations. Evaluation results from functional testing and user feedback indicate improved clamping stability, alignment accuracy, and ergonomic performance compared to traditional models, although refinements in structural optimization, weight distribution, and user interface components are recommended. The study suggests further prototype enhancement, extended field testing, and integration of advanced ergonomic and safety features to maximize durability, usability, and overall productivity in professional workshops and technical training laboratories. Full article

One of the most common problems in drilling operations is lost circulation, which can significantly increase well costs and lead to issues such as pipe sticking, blowouts, and even well closures. Identifying thief zones using analytical models is especially difficult, and there are no robust equations available in the literature due to a wide range of influential parameters, both controllable and uncontrollable. These parameters include operational factors, as well as the physical properties of the rock and drilling fluid. This study presents an artificial intelligence-based model designed to predict lost circulation zones. It investigates the underexplored potential of WV-curves for feature selection. Traditionally used to represent the spectral characteristics of training data, their role in feature selection has not been widely examined in the literature. The presentation of WV-curves is modified, and their effectiveness in identifying the optimal number of input and hidden neurons is evaluated. In this research study, a total of 15,000 data points were used and collected from oil wells in the Middle East. The artificial neural network (ANN) model exhibited a remarkable ability to accurately predict the locations of lost circulation zones based on the collected data, achieving an impressive accuracy of 94.5%. This is a significant achievement when compared to existing ANN models in the literature. The results highlight the strength of the ANN model in predicting lost circulation locations across a wide range of data collected from various wells in the Middle East. In addition, this model takes into account a diverse set of drilling operational parameters, as well as rock characteristics and fluid properties, offering a broader approach compared to other available ANN models. This advancement will also greatly facilitate future studies, enabling the prediction of lost circulation zones, and enabling advanced planning of appropriate prevention and remediation methods during the well planning phase to reduce the risk of lost circulation. Nevertheless, it should be noted that one limitation of the proposed methodology relates to data availability, as comprehensive formation parameters were not fully accessible; the inclusion of additional formation data may offer opportunities for further improvement in future studies. Full article

The majority of currently available hand kinematic databases have been gathered using expensive marker-based systems or are restricted to a particular gesture-recognition task, failing to capture the dynamic nature of joints when the hand is engaged with an object. To address this gap, we introduce the RGB-based Hand Joint Kinematics (RGB-HJK) dataset, a publicly available collection of continuous, frame-level 3D joint angle trajectories, recorded while ten healthy adults (six male, four female; age $25.8\pm 3.2$ years; BMI $22.8\pm 2.0$ kg/m²) performed five standardized object interaction grasps: Power Grasp (cylindrical bottle), Tripod Grasp (pen), Static Power Hold (smartphone), Precision Pinch (thin paper), and Lateral Pinch (book). Data were collected using a standard RGB camera and the MediaPipe Hands markerless pipeline at $26.95\pm 0.29$ Hz, a rate that was stable across all subjects. Each participant completed five trials for each grasp type. After filtering using active hold, 28,111 validated frames remained, with a 100% detection rate for all 250 trials. Intra-subject repeatability was good (mean SD $\le 7.9°$ across all joint grasp combinations) and inter-subject variability was within the range expected based on normal anatomical diversity. Importantly, kinematic validation of the Index Proximal Interphalangeal (PIP) joint (61.8° ± 18.4°) showed values consistent with ranges reported in previous studies using instrumented gloves and depth sensors. Principal Component Analysis (PCA) confirmed clear linear separability among the five grasp configurations. Unlike existing datasets, the RGB-HJK method does not compromise the natural sense of touch and is free of hardware occlusions, thereby providing an easily accessible ecological baseline. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

Exhaust piping in diesel engines is subject to severe thermal stress arising from high-temperature, high-pressure gas flows, and spray cooling with atomizing nozzles has become a widely adopted method to safeguard structural reliability. However, at present, the understanding of the spray fragmentation mechanism of two-phase flow under low inlet pressure is still not comprehensive. This study establishes a three-dimensional model of a gas–liquid impinging-jet nozzle and applies a coupled Volume-of-Fluid to Discrete-Phase-Model (VOF–DPM) approach to resolve the liquid breakup process in detail. High-speed imaging experiments were carried out to validate the numerical results. Orthogonal tests were conducted at five pressure levels for both gas and water—0.28, 0.24, 0.20, 0.16, and 0.12 MPa—producing 25 data pairs of spray cone angle and Sauter Mean Diameter (SMD). Within the 0–0.3 MPa air inlet pressure range explored here, raising the pressure consistently reduced the SMD and widened the cone angle, although both trends weakened as the pressure increased. Water inlet pressure exhibited a nonlinear influence, with local extrema appearing in the higher-pressure region. The overall SMD reached a minimum of 34.12 μm and a maximum of 149.04 μm. Using these 25 data points, a genetic algorithm was employed to optimize the pressure ratio under the constraint of total hydraulic power, yielding optimization strategies for different power budgets. An additional outcome of the simulation was the identification of a structural weakness: by reshaping the original flat impingement surface into a full conical surface, atomization quality improved by 29.36% under identical boundary conditions. These findings clarify the atomization mechanism of gas–liquid impinging jets under low inlet pressure and offer practical guidance for nozzle optimization. Full article

The existing arresting efficiency evaluation method overlooks corrosion defects in its formulation. If directly applied to evaluate and design arrestors for corroded sandwich pipes, it often leads to conservative evaluations of arresting efficiency and unreasonably designed arrestors. Based on this, this paper first verifies the reliability of numerical simulation results through physical experiments. On this basis, the influence of the structural parameters and material parameters of the arrestor on the arresting efficiency of the integral arrestor is analyzed. The results show that an increase in the length, thickness and material strength of the arrestor not only affects the arresting efficiency of the arrestor but also changes the arresting crossing mode, from parallel crossing to orthogonal crossing. A chart of arresting efficiency suitable for engineering design is proposed. Finally, a systematic comparison is conducted of different modeling methods. The results show that, considering both prediction accuracy and training efficiency, the Genetic Algorithm–Back Propagation (GA-BP) model significantly outperforms the empirical model, the Whale Optimization Algorithm–Back Propagation (WOA-BP) model, and the Particle Swarm Optimization–Back Propagation (PSO-BP) model. The average prediction error is only 6.56%, and 94.42% of the data error is less than 20%. The model provides a theoretical basis for the arrestor design and failure assessment of sandwich pipes with corrosion defects and has clear engineering guidance value. Full article

Stable three-dimensional hand landmark reconstruction using low-cost RGB-D sensors is important for human–computer interaction, robot teleoperation, and vision-based motion analysis. RGB-based hand landmark detectors provide stable semantic 2D landmarks, but their depth output is not a metric measurement in the physical camera coordinate system. Stereo cameras can provide metric depth, but direct landmark-level back-projection is sensitive to invalid pixels, local depth holes, boundary noise, and partial occlusion. To address these problems, this paper presents a lightweight RGB-D sensing front-end that combines MediaPipe semantic hand landmarks with ZED2 stereo depth. The proposed pipeline detects 21 semantic hand landmarks in the RGB image, obtains landmark-level metric depth from the aligned ZED2 depth map using local median sampling, reconstructs 3D landmarks by camera back-projection, and further applies exponential moving average filtering and a bone-length consistency constraint. Experiments were conducted on a self-collected SVO dataset containing 13 hand actions and 26 recorded sequences, and an additional checkerboard-based reference-distance validation was performed to evaluate the metric depth sampling and 3D back-projection component. Compared with single-pixel sampling, the $5\times 5$ local median strategy slightly increased the valid-depth ratio from 0.9731 to 0.9738 and reduced the temporal smoothness metric from 1.7163 mm to 1.6902 mm. To further justify the temporal filtering choice, an additional comparison with the 1 Euro Filter was conducted using the reconstructed win5 trajectories. The 1 Euro Filter produced stronger smoothing, reducing the temporal smoothness metric to 0.196 mm, but also reduced the path-length ratio to 0.484, indicating substantial motion attenuation. EMA0.7 was therefore retained as a more balanced setting, reducing the temporal smoothness metric to 0.826 mm while maintaining a path-length ratio of 0.803. The BL0.5 bone-length constraint reduced the bone-length standard deviation from 2.0727 mm to 1.1995 mm with limited trajectory modification. The final configuration provides a practical low-cost RGB-D front-end for stable 3D hand landmark reconstruction under controlled indoor conditions. Full article

This study utilizes a large-scale numerical simulation model to investigate the hydrodynamic behavior and particle transport characteristics of gas–liquid–solid three-phase flow in vertical wellbores featuring multi-source confluence and curved geometries. Simulation results indicate that increasing flow velocity shifts the dominant control mechanism from surface tension to inertial forces, transitioning the flow pattern from slug flow to churn flow. In curved pipe sections, centrifugal phase separation and geometric shielding effects cause significant flow asymmetry and maintain large bubble stability at the inner wall. Additionally, the multi-inlet structure induces shear rate gradients that result in the spatial coexistence of two distinct bubble scales. Furthermore, localized gas concentrations exceeding 70% at the upper inlet can trigger severe gas-locking phenomena and intense pressure pulsations. Full article

This paper presents a comprehensive augmented reality (AR)-based rehabilitation system for upper-limb recovery that integrates AR-assisted art therapy, automated markerless goniometry, and the interval mathematical modeling of rehabilitation dynamics. The proposed platform combines four interconnected subsystems: a Python-based markerless video analysis module utilizing three stationary IP cameras, MediaPipe Pose Landmarker, and Kalman filtering; an AR art-therapy application developed for the Magic Leap 2 headset using Unity/OpenXR; a server-side subsystem implemented in NestJS/TypeScript; and (iv) a physiotherapist-oriented web application developed in React. The primary objective of the study is the real-time automated assessment of shoulder joint kinematics during AR-assisted rehabilitation sessions, including flexion (160–180°), extension (50–60°), and abduction (up to 180°). To describe and forecast rehabilitation dynamics, interval mathematical models based on recurrent difference equations were developed, enabling the prediction of subsequent joint angle values using the previous 3–4 observations. Structural and parametric identification of the interval models was performed using the artificial bee colony optimization algorithm. Experimental validation was conducted on rehabilitation data collected from five patients with different clinical diagnoses, including bursitis, epicondylitis, capsulitis, osteoarthritis, and fracture-related impairments. Under the considered experimental conditions, the proposed approach demonstrated promising predictive performance, with an angular prediction error below 5° and a correlation exceeding 95% between predicted and measured rehabilitation trajectories. The developed system implements a unified rehabilitation cycle of “execution–measurement–prediction–adaptation”, enabling the continuous monitoring of recovery dynamics, adaptive adjustment of rehabilitation scenarios, and estimation of the rehabilitation duration required to achieve target motor outcomes. The proposed approach contributes to the development of intelligent AR-based rehabilitation systems by combining markerless motion analysis, predictive interval modeling, and adaptive art-therapy mechanisms within a single clinical framework. Full article

During heavy oil development, the gathering and transportation of low-temperature wellhead-produced fluids are often accompanied by high viscosity, pipe-wall deposition, and high flow resistance, threatening the continuous and stable operation of gathering systems. Existing studies on wellhead heating mainly focus on overall steady-state heating performance, while variable-flow heat transfer and start–stop control in local heating systems remain insufficiently explored. This study aims to evaluate the steady-state heating capacity, transient thermal response, and start–stop control performance of a localized electric heating section under variable-flow conditions. A 3D fluid–solid-coupled heat-transfer model of the heating element, pipe wall, and internal fluid was developed using COMSOL Multiphysics. The steady-state temperature field, transient heating and cooling behavior, and start–stop control characteristics were analyzed under different flow rates. The results show that, at a heating power of 15 kW and a flow rate of 20 m³/d, the maximum outer-wall temperature reached 564 K, and the average outlet fluid temperature reached 308.83 K, indicating effective heating performance. As the flow rate increased from 10 m³/d to 30 m³/d, the maximum pipe-wall temperature and fluid temperature rise both decreased, whereas the average fluid-side heat-transfer coefficient increased from approximately 700 W/(m²·K) to 1800 W/(m²·K), demonstrating enhanced convective heat transfer. Under a dual-threshold control strategy of 463.15–483.15 K, the system maintained the target temperature near 473.15 K under all tested conditions, while the load factor increased from 37.83% to 86.15%. These findings provide theoretical references and engineering support for optimizing power configuration and improving temperature control strategies in local heating systems for wellhead-produced fluids. Full article

Chinese lacquerware is a multi-layered natural polymer composite whose characterization is complicated by burial degradation, organic–inorganic mixing, and the overlap of signals from lacquer, drying oils, proteins, polysaccharides, waxes, and pigments. This review evaluates analytical strategies for Chinese lacquerware by distinguishing three complementary levels of evidence: morphological and elemental observation, chemically specific molecular fingerprinting, and biomolecular source recognition. Microscopy, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and scanning electron microscopy–energy dispersive spectroscopy (SEM-EDS) are useful for identifying stratigraphy, pigments, fillers, and functional groups, but they are often insufficient for assigning degraded organic matrices and trace additives independently. Pyrolysis–gas chromatography/mass spectrometry provides more specific molecular evidence through diagnostic marker classes, including alkyl catechols, alkyl phenols, nitrogen-containing pyrolysis products, anhydrosugars, long-chain aliphatics, aldehydes, and ketones. Immunological assays based on lacquer glycoproteins further complement chemical analysis by supporting biological source differentiation, although their reliability depends on protein preservation, extraction efficiency, and antibody specificity. Representative case studies, including a seventeenth-century Swedish lacquered pipe, the Nanyue Kingdom lacquered ear cup, and a Tang Dynasty lacquered leather artifact, show that robust interpretation requires cross-validation among stratigraphic, elemental, spectroscopic, chromatographic, immunological, and archaeological evidence. The review concludes that integrated analytical workflows can improve material identification, clarify manufacturing sequences, assess degradation uncertainty, and provide more reliable evidence for conservation decision-making and the reconstruction of historical lacquer craftsmanship. Full article

A pipeline intelligent plugging robot (PIPR) is an important tool in subsea pipeline maintenance and emergency repair. Precise position-tracking control is crucial for the in-pipe plugging operation of a PIPR. The anchoring module is the key component responsible for fixed-point braking, which faces the challenges of insufficient structural adaptability within a narrow space. Additionally, traditional PID control may lead to poor robustness under fluctuating working conditions and load disturbances. To address these issues, this study designs a novel anchoring module combining screw transmission, an eccentric crank–slider mechanism, and a parallelogram linkage. To achieve adaptive tracking control, the improved deep deterministic policy gradient (DDPG) algorithm is introduced to optimize the parameters of the PID controller. A reward function with mechanical constraint penalties and a dual-phase strategy is proposed for dynamic parameter optimization. All control performances are analyzed and verified through simulations. The results indicate that the proposed method outperforms traditional PID control as regards response speed, overshoot, and robustness, which can achieve precise anchoring. This study provides a theoretical foundation for ensuring the precision of the plugging process. Full article