Search Results (14,228)

To address the calibration challenge caused by the nonlinear variation in intrinsic parameters during continuous camera zooming in UAV electro-optical pods, this paper proposes an online calibration method based on IMU-visual fusion. Traditional offline calibration cannot adapt to dynamic scenarios, while existing self-calibration methods suffer from slow convergence and insufficient robustness. The proposed method aims to achieve real-time and accurate estimation of camera intrinsic parameters during zooming. Specifically, we first construct a unified state estimation framework that encodes the internal and external parameters of the camera and the 3D positions of scene feature points into a high-dimensional state vector, then establish a camera motion model based on IMU data, construct a visual observation model by combining the pinhole camera and second-order radial distortion model to establish a nonlinear mapping from 3D feature points to 2D pixel coordinates, and adopt an improved ORB algorithm for feature extraction and LK optical flow method to achieve high-precision cross-frame feature matching to enhance the stability of visual observation. Most importantly, we design a tight-coupling fusion strategy based on the Extended Kalman Filter (EKF) prediction-update iteration mechanism, which fuses IMU high-frequency motion constraints and visual geometric constraints in real time to suppress parameter drift induced by focal length changes. Finally, we recursively solve the state vector to complete the online dynamic estimation of intrinsic parameters. Monte Carlo simulation experiments and real UAV flight experiments confirm that the method has both high estimation accuracy and strong environmental adaptability, can meet the high-precision calibration needs of UAVs in dynamic scenarios, and provides reliable technical support for accurate target positioning. Full article

Accurate performance prediction of variable-speed centrifugal chillers is important for building energy optimization and the development of digital twins in HVAC systems. In practice, obtaining extensive operational data is costly, creating a prevalent “small-sample” dilemma under which conventional data-driven models are prone to overfitting with poor extrapolation capability. While recent Physics-Informed Neural Networks (PINNs) incorporate system-level thermodynamic constraints (e.g., COP definitions), they typically treat the centrifugal compressor as a thermodynamic black box, neglecting its inherent fluid dynamic characteristics; consequently, extrapolated predictions may be physically inconsistent or fall into unsafe operating regions such as compressor surge. To address this gap, this paper proposes an Aero-thermodynamic Physics-Informed Neural Network (Aero-PINN) that introduces three mechanisms into the PINN loss function: (1) dimensionless aerodynamic similarity mapping governed by affinity laws, (2) a surge boundary constraint that prevents non-physical extrapolations, and (3) an aerodynamic–electrical energy coupling validation. Experimental validation on 420 real-world variable-speed test records shows that the Aero-PINN achieves a COP RMSE of 0.04 and a COP MAPE of 0.3%, outperforming standard MLP and polynomial baselines. Moreover, 100% of the extrapolated operating points satisfy all fluid dynamic safety and energy efficiency constraints. This framework provides a reliable, physics-constrained small-sample learning approach, facilitating factory calibration and reduced-test digital modeling for chiller plants. Full article

Concrete is accelerating its transition towards green and low-carbon development, but its performance throughout its entire life cycle is significantly influenced by environmental changes, which remains a key technical challenge currently faced. The effects of early-age concrete tensile damage on thermal conductivity and moisture transport properties, as well as their coupling mechanism, remain unclear, leading to severe cracking. To explore the cracking mechanism of early-age concrete under the coupled conditions of environment and damage and to evaluate its performance throughout its lifecycle, this article conducts comparative experiments on the performance of concrete under high temperature, varying humidity, and damage conditions in the early age stage. The variation law of temperature, humidity, and strain of concrete is studied, and the evolution of microstructure and composition of concrete is explored. The response of porosity to ambient humidity exhibits opposite trends between restrained and unrestrained specimens, with rates of change of +0.0353%/RH and −0.0245%/RH, respectively. Furthermore, the study identified a critical turning point in ambient relative humidity (50% RH), which significantly alters the degree of hydration (Ca/Si ratio) of the concrete. The research results may provide theoretical and technical support for cracking risk assessment and crack control throughout the entire life cycle of concrete thin-walled structures. Full article

Background: Main risk factors associated with the development of sarcopenia (coexistence of muscle mass loss and dysfunction) are a sedentary lifestyle coupled with obesity. Associated mitochondrial dysfunction leads to energy deficits and perturbations in the balance between protein synthesis and degradation, thereby triggering muscle dysfunction or atrophy. Aside from exercise, which is challenging to implement and maintain, particularly in women, treatments for diminishing sarcopenia are scarce. The objective of the present study was to evaluate the effect of the flavanol (−)-epicatechin (EC) in a hypercaloric diet-induced obese female rat model. Muscle strength and endurance, as well as relative mitochondrial DNA content in skeletal muscle, were assessed. Methods: Female rats were fed a hypercaloric diet to induce obesity, as evidenced by increases in body weight, Lee index, and lipid profile alterations, and by abdominal fat accumulation, and to promote a sarcopenic phenotype. Functional tests of grip strength and mobility (treadmill) were performed. Mitochondrial relative content was evaluated by measuring the ratio of mtDNA/nuclear DNA, and the expression of genes related to mitochondrial biogenesis (Pgc1-α, Tfam), fusion (Mfn1 and Opa1), fission (Drp1 and Fis1), and mitophagy (Pink1 and Pkn), and function; citrate synthase and Ucp3 were also evaluated. Results: A significant decrease in mobility and strength was observed in obese female rats, accompanied by reduced mitochondrial numbers, activity, and dynamics, but not by changes in muscle size or weight. Treatment with EC induced mitochondrial biogenesis and positive changes in mitochondrial dynamics (fission and fusion) and activity, as measured indirectly by changes in citrate synthase and Ucp3 expression. Discussion: Results reinforce the potential of EC as a modulator of mitochondrial function in dysfunctional conditions associated with obesity, thereby attenuating the mechanisms underlying sarcopenia. Full article

Hepatic ischemia–reperfusion injury (HIRI) is a significant clinical challenge in the field of liver surgery and transplantation, and its pathological mechanisms are complex. In recent years, ferroptosis, a novel form of iron-dependent programmed cell death, plays a central role in this injury process. G protein-coupled receptors (GPCRs), as the largest family of membrane receptors in the body, regulate cellular stress and death through extensive signaling networks. This review elucidates the specific molecular mechanisms by which GPCRs regulate ferroptosis in HIRI by affecting key pathways such as lipid peroxidation, iron metabolism homeostasis, and antioxidant defense. It further explores potential therapeutic strategies targeting specific GPCRs to modulate ferroptosis, thereby alleviating liver injury and improving postoperative outcomes, to provide new insights and a theoretical basis for clinical translation. Full article

Background: While neutrophil extracellular traps (NETs) have been shown to contribute to cancer progression, including that of lung adenocarcinoma, the mechanisms underlying NET formation within the tumor immune microenvironment (TIME) remain incompletely understood. Notably, neutrophil infiltration has been strongly linked to tumor necrosis factor alpha-inducible protein 6 (TNFAIP6) expression. Methods: In vitro and in vivo experiments were performed. DNA pulldown coupled with mass spectrometry, bioinformatics analyses, immunohistochemistry, and dual-luciferase reporter assays were conducted. DNA pulldown Western blotting, chromatin immunoprecipitation–quantitative PCR, and dual-luciferase reporter assays using truncated promoter constructs were also employed. Results: TNFAIP6 expressed by lung adenocarcinoma cells was shown to induce NET formation (a form of programmed cell death called NETosis). Mechanistically, TNFAIP6 interacted with CD44 in lung adenocarcinoma cells, leading to increased extracellular availability of secreted phosphoprotein 1 (SPP1) within the TIME and the subsequent promotion of NETosis. Additionally, nucleophosmin 1 (NPM1) significantly enhances the transcriptional activation of TNFAIP6 and associates with the −2000 to −1700 bp region of its promoter. Conclusions: These findings delineate a regulatory model in which lung adenocarcinoma cells directly stimulate NETosis through the NPM1–TNFAIP6–CD44–SPP1 axis, suggesting that therapeutic targeting of this pathway may attenuate tumor progression. Full article

Combine harvesters are core modern grain production equipment with high reliability, critical for food security. Yet their metal parts suffer severe grain-induced wear during operation, directly reducing efficiency, increasing grain loss, and raising maintenance costs and environmental burdens. This paper clarifies the grain-induced wear source characteristics and the dominant mechanisms and hazards for combine harvester metal surfaces, as well as summarizes the research progress of four key protection strategies: wear-resistant materials, surface engineering, structural and parameter optimization, and maintenance and remanufacturing. Based on the latest research data, the working principles, performance advantages and application scenarios of various protective technologies were analyzed. Current research faces several challenges: insufficient systematic wear data for multiple crops, unclear multi-factor coupled wear mechanisms, limited low-cost and long-lasting protective technologies, and the absence of online wear monitoring techniques. Finally, the directions for future research focus, such as the systematic research on the wear characteristics of multiple crops, the deepening of the wear mechanism of multi-factor coupling, the development of green, low-cost and long-term protection technologies, and the development of online wear monitoring and active control systems, are explored, providing theoretical support and technical reference for the transformation of wear control in combine harvesters, from passive maintenance to active protection throughout the entire life cycle. Such future work supports the high-quality development of agricultural mechanization and ensures food security. Full article

This study investigates the transient wheel–rail contact mechanics of welded joints in heavy-haul rails via a validated 3D finite element model, and analyzes the stick-slip behavior, dynamic response and elastoplastic characteristics in the base material zone, heat-affected zone and weld bead zone. Results show a distinct contact state transition from stick-slip in the base material to predominant slip within the welded zones, indicating higher wear susceptibility. Dynamic response analysis reveals the highest and lowest contact-point acceleration amplitudes in the base material and heat-affected zone, respectively, due to material heterogeneity. Plastic deformation consistently initiates at the rail surface, where stress and strain concentrate, establishing it as the primary site for damage nucleation. A systematic parametric study shows that plastic deformation can be effectively mitigated by increasing the yield strength and elastic modulus of the welded joint material, or reducing the wheelset velocity, unsprung mass and wheel–rail friction coefficient. In contrast, adjusting the primary suspension and fastener parameters exerts a negligible influence on plastic deformation control. These findings provide a mechanistic basis for optimizing the performance and maintenance of welded joints in heavy-haul rail operations. This study reveals the coupling law of multiple mechanisms among contact behavior, dynamic response and material failure during the damage initiation process of rail welded joints from the mechanistic perspective, which provides a theoretical basis for the structural optimization, condition assessment and maintenance of rail welded joints in heavy-haul railways. Full article

High-temperature carburization (HTC, 950–1050 °C) has emerged as a pivotal low-carbon, energy-efficient manufacturing technology for gear steels, accelerating carbon diffusion for reducing processing cycles by over 60% while achieving significant energy savings and emission reductions. However, the inherent contradiction between HTC efficiency and microstructural stability, specifically austenite grain coarsening, severely degrades mechanical properties (e.g., strength, toughness, fatigue resistance) and limits widespread application. This review systematically synthesizes recent advances in austenite grain size regulation during HTC of gear steels, focusing on the core scientific framework of “grain coarsening mechanism—regulation strategy—performance enhancement”. It elaborates on thermodynamic and kinetic mechanisms of austenite grain growth, ripening behavior of microalloying precipitates (Nb(C,N), Ti(C,N), AlN, etc.), and their synergistic grain-refining effects. Comprehensive coverage of regulatory strategies (microalloying design, pretreatment technologies, process optimization, and integrated regulation) and characterization techniques is provided, along with a quantitative correlation between grain size, microstructure, and surface performance (wear resistance, corrosion resistance, and fatigue life). Numerical simulation and predictive models (empirical, theoretical, multiphysics coupling, machine learning-based) are critically analyzed, and current challenges (temperature-grain stability trade-off, multifactor synergy understanding, industrial scalability) and future research directions (advanced microalloying systems, intelligent process optimization, cross-scale modeling, green technology integration) are proposed. This review aims to provide theoretical guidance and technical support for optimizing the HTC performance of gear steels, catering to the demands of high-power-density transmission systems in automotive, aerospace, and heavy machinery industries. Full article

Effective thermal management is critical for high-power lithium-ion batteries to mitigate excessive heat generation and ensure operational reliability. Failure to maintain a uniform temperature distribution can lead to accelerated capacity fading and severe safety risks, such as thermal runaway. In this study, a ferrofluid-based magnetohydrodynamic (MHD) microchannel cooling system was numerically investigated to elucidate the influence of magnetic intensity, magnet geometry, and electrical boundary conditions on flow behavior and heat transfer performance for battery cooling applications. A fully coupled multiphysics model incorporating electromagnetic, fluid flow, and heat transfer phenomena was developed and validated against experimental and numerical data from the literature. The results show that increasing the applied voltage enhances current density and Lorentz force almost linearly, leading to significant flow acceleration and improved convective heat transfer. Electrical insulation effectively suppresses current leakage into the channel walls, increasing the average current density by up to 222% and the Lorentz force by more than 300%. Compared with a cylindrical magnet, a rectangular magnet provides a more uniform magnetic field distribution and stronger near-wall Lorentz forcing, resulting in superior cooling performance. Under a 4C discharge condition, the insulated rectangular magnet reduces the maximum battery temperature by approximately 30% and increases the average Nusselt number by up to 103% relative to the non-insulated case. The findings reveal the critical roles of magnetic-field-controlled flow symmetry and near-wall forcing in MHD-driven microchannels, and provide practical design guidelines for battery cooling systems with no moving mechanical parts and active electromagnetic flow control. Full article

The Midong Block is currently a primary target for coalbed methane (CBM) exploration and development in Xinjiang. However, fracturing operations in this region generally exhibit low flowback rates, which escalate the risk of reservoir damage and ultimately suppress daily gas production. To elucidate the impact of various geological and engineering factors on flowback efficiency and permeability damage, as well as their underlying mechanisms, this study conducted fracturing fluid flowback simulation experiments. The pulse-decay permeability measurement and weighing methods were employed to quantify the variations in flowback rates and permeability damage intensities under different conditions. Experimental results indicated that the permeability damage rate in the Xishanyao Formation coal samples ranged from 3.12% to 92.86% after flowback, with 92% of the samples exhibiting a flowback rate of less than 10%. This significant impairment was primarily attributed to the synergistic effects of stress-induced fracture closure, clay mineral hydration swelling, and coal fines migration. Specifically, elevated confining pressures and prolonged soaking times exacerbated reservoir damage. A low flowback pressure differential intensified the water locking effect, hindering fluid recovery. Notably, the flowback velocity displayed a U-shaped velocity sensitivity profile. In the low-temperature regime, damage characteristics fluctuated, controlled by competitive thermal–hydro–mechanical (THM) coupling mechanisms. Full article

Improving the energy efficiency of the building envelope is critical for global decarbonization, yet a gap remains in the comprehensive thermophysical characterization of carbon-enhanced Expanded Polystyrene (EPS). This study evaluates the impact of expansion ratios and moisture content on the thermal behavior of two commercial EPS grades, EPS-A (12.7 ± 0.5 kg/m³) and EPS-B (16.0 ± 1.1 kg/m³), investigating the counterintuitive role of graphite (1.4–1.8 wt.%) in enhancing the thermal insulation properties. Thermal conductivity and diffusivity were independently determined via Transient Plane Source (TPS) and Heat Flow Meter (HFM) methods across a 10–50 °C range, while specific heat capacity (c_p) was analyzed using HFM and Differential Scanning Calorimetry (DSC) through the sapphire comparison method and Temperature-Modulated DSC (TOPEM^®). Methodologically, it was found that standard HFM protocols are unsuitable for c_p determination in low-density foams, yielding an average relative error of ±29%; conversely, the sapphire comparison method provided the most reliable results in agreement with theoretical expectations. Results indicate that the efficacy of graphite as a radiative shield is closely coupled with cellular morphology, proving significantly more effective in the higher expansion grade (EPS-A, 70 wt.% open porosity) than in the denser EPS-B. Furthermore, 30-day water immersion tests revealed that the higher open porosity of EPS-A facilitates increased water uptake of 144 ± 17 wt.% (compared to 97 ± 7 wt.% for EPS-B), causing the geometric densities of the two grades to converge and fundamentally altering thermal transport mechanisms. The study concludes that accurate thermal modeling of carbon-enhanced insulation requires careful selection of testing parameters, particularly when accounting for moisture-induced degradation in high-porosity systems. Full article

Against the backdrop of the in-depth adjustment of the global energy pattern and the accelerated advancement of the energy transition, coupled with the frequent occurrence of extreme climate events and the continuous intensification of risks such as supply fluctuations and external shocks faced by urban energy systems, improving urban energy resilience has become a core measure for all countries to address the vulnerability of energy systems and promote urban sustainable development. As a core technical carrier of intelligent manufacturing, the enabling role of industrial robots (IRs) in enhancing urban energy resilience (UER) has also become an important research topic in the field of the energy economy. This paper takes 280 prefecture-level and above cities in China from 2009 to 2023 as research samples and empirically examines their impact effects by constructing a Double Machine Learning (DML) model, transmission mechanism, and moderating effect of IRs on UER and ensures the reliability of conclusions through various robustness tests. The research findings indicate that IRs significantly promote the improvement of UER; industrial structure upgrading and green technology innovation are the main mediating paths, verifying how IRs affect UER from two different aspects and both environmental regulation (ER) and science expenditure (SE) positively moderate the promoting effect of IRs on UER, with the coefficients of the interaction terms being significantly positive. Robustness tests show that the core conclusions are highly reliable. This study fills the research gap in the transmission mechanism between IRs and UER and provides empirical evidence for the formulation of relevant policies. Accordingly, it is proposed that governments should strengthen the policy support for the application of industrial robots in high-energy-consuming industries, optimize the synergy mechanism between environmental regulation and scientific and technological expenditure, guide the deep integration of industrial robots with industrial structure upgrading and green technology innovation, and formulate differentiated promotion strategies based on regional energy resilience characteristics and industrial development foundations, so as to fully release the energy-resilience-improvement effect of industrial robots. Full article

Deep marine shale reservoirs are controlled by multi-factor coupling effects, and the genetic mechanism of “sweet spots” exhibits strong complexity, leading to prominent difficulties in quantitative prediction and precise evaluation of sweet spots. Aiming at the problems of an unclear lithofacies-controlled sweet spot evolution law and insufficient accuracy of multi-parameter quantitative evaluation in traditional evaluation methods, this paper takes the Wufeng Formation and Long1 member of the Longmaxi Formation in the LZ block, Southern Sichuan, as the research object. Innovatively integrating machine learning (ML), grey correlation analysis (GRA), and three-dimensiona (3D) geological modeling technologies, a refined prediction model for reservoir sweet spot evaluation indicators under lithofacies constraint conditions is established, and a multi-parameter fusion quantitative evaluation method for deep marine shale gas sweet spots with high prediction accuracy is proposed. The results demonstrate that the LightGBM-based prediction model for sweet spot evaluation indicators achieved excellent performance. Based on a total of 380 preprocessed samples divided into training and test sets in a 7:3 ratio, the coefficient of determination (R²) of the model exceeded 0.9 in both the test and validation datasets. The “sweetness index”, a comprehensive evaluation index of reservoir sweet spots constructed via GRA-based multi-factor fusion, shows a correlation coefficient of 0.91 with respect to actual gas well production, presenting a high fitting degree. The 3D sweet spot geological model reveals that Class I sweet spots are mainly developed in the 1st to 3rd sub-layers of the Long1 member, while Class II sweet spots are distributed in the 5th and 6th sub-layers, which is highly consistent with the actual development law of the gas field. This study breaks through the limitations of single evaluation methods and weak lithofacies control consideration in traditional sweet spot evaluation and forms a set of innovative technical process integrating “precision prediction—multi-factor fusion—3D characterization”. It provides a new technical approach for efficient and accurate evaluation of deep marine shale reservoir sweet spots and has important guiding significance for the efficient development of shale gas. Full article

Mobile Laser Scanning (MLS) integrated with Simultaneous Localization and Mapping (SLAM) has emerged as a key technology for digitizing GNSS-denied environments, such as underground mines. However, the automated interpretation of unstructured, high-density point clouds into semantic engineering models remains challenging due to extreme geometric anisotropy in point distributions and severe class imbalance inherent to narrow tunnel environments. To address these issues, this study proposes a highly automated scan-to-model framework for precise semantic segmentation and vectorized two-dimensional (2D) profile reconstruction. First, an enhanced hierarchical deep learning network tailored for point clouds is introduced. The architecture incorporates a context-aware sampling strategy with an expanded receptive field of up to 10 m to preserve axial continuity, coupled with a spatial–geometric dual-attention mechanism to refine boundary delineation. In addition, a composite Focal–Dice loss function is employed to alleviate the dominance of wall points during network training. Experimental validation on a field-collected dataset comprising 16 mine tunnels demonstrates that the proposed model achieves a mean Intersection over Union (mIoU) of 85.15% (±0.29%) and an Overall Accuracy (OA) of 95.13% (±0.13%). Building on this semantic foundation, a robust geometric modeling pipeline is established using curvature-guided filtering and density-adaptive B-spline fitting. The reconstructed profiles accurately recover the geometric mean surface of the tunnel wall, yielding an overall filtered Root Mean Square Error (RMSE) of 4.96 ± 0.48 cm. The proposed framework provides an efficient end-to-end solution for deformation analysis and digital twinning of underground mining infrastructure. Full article