Search Results (4,246)

Aiming at the industrial problems of traditional sulfonated drilling fluids in high-temperature drilling of deep oil and gas reservoirs at 180°C, including high-temperature degradation, poor environmental protection, and severe reservoir damage, this paper adopts a function-oriented research idea to construct a set of non-sulfonated water-based drilling fluid systems with excellent comprehensive performance and temperature resistance up to 180 °C. Strict screening criteria for single agents were established, and six core non-sulfonated treatment agents were selected from 18 candidate agents in four categories: viscosifiers, fluid loss reducers, inhibitors, and high-temperature stabilizers. The compounding synergistic effects of cross-category treatment agents were studied, and four core action mechanisms were revealed. The optimal formula was obtained through optimization. Tests show that after hot rolling at 180 °C for 16 h, the system has an apparent viscosity retention rate of ≥81%, a yield point retention rate of ≥76%, and a high-temperature and high-pressure filtration loss of ≤12.8 mL. It can resist 15% salt, 1.0% calcium, and 15% drill cuttings, and maintains stable performance under composite pollution. At 180 °C, the shale linear expansion rate is only 8.6%, and the cuttings rolling recovery rate reaches 92.4%. The core permeability recovery value is ≥90.2%, the biotoxicity EC₅₀ value is 42,600 mg/L, and the 28-day biodegradation rate is 68.3%. This system can replace traditional sulfonated drilling fluids and provide a green and feasible technical solution for safe and efficient drilling in deep high-temperature formations. Full article

Stepper-motor-driven Selective Compliance Assembly Robot Arms are susceptible to motion control challenges under short-stroke and high-frequency start–stop conditions, including high sensitivity to pulse timing, difficulty in multi-joint coordination, and insufficient trajectory smoothness. To address these issues, this paper proposes an optimized motion control method for smooth execution of obstacle-avoidance trajectories, integrating path smoothing, asymmetric S-curve velocity planning, and pulse-frequency-based multi-axis synchronization. First, piecewise cubic Hermite interpolation, Gaussian smoothing, and end-effector-based equidistant resampling are applied to post-process Rapidly-exploring Random Tree-generated paths, thereby eliminating polyline turning points and improving uniformity of waypoint distribution. Second, an asymmetric S-curve velocity planning method with nonzero boundary velocity constraints is developed, and multi-axis synchronization is achieved based on the maximum segment duration principle. Finally, instantaneous reference velocities are converted into per-axis pulse frequency commands via proportional mapping, enabling real-time stepper motor drive control. Experimental results show that the proposed method reduces the obstacle-avoidance path length by 8.52% and significantly decreases the dispersion of trajectory step sizes. In single-segment dynamic simulations, the proposed method reduces the peak dynamic output force by 62%. In real robot experiments, the average motion time across three obstacle-avoidance tasks is reduced by approximately 55.21%, while end-effector trajectory continuity and inter-joint coordination are improved, suggesting the effectiveness and preliminary engineering feasibility of the proposed method under the tested conditions. Full article

To clarify the transformation patterns and driving mechanisms of the explicit and implicit morphology of rural settlements in peri-urban areas of large mountainous cities in Southwest China, this study examines the central urban area of Chongqing. Using land-use, point-of-interest (POI), socio-economic and digital elevation model (DEM) data from 2008 to 2024, we constructed an evaluation system for explicit and implicit rural settlement morphology. Kernel density estimation, the Mann–Kendall test and the moving t-test were used to identify morphological evolution, while the coupling coordination degree model and structural equation modeling (SEM) were applied to examine coordination relationships and driving mechanisms. The results show that: (1) during the study period, explicit morphology showed continuous contraction, whereas implicit morphology exhibited fluctuating improvement and polarized differentiation, indicating an overall gradual transformation; (2) no statistically significant abrupt changes were detected in either morphology, while temporal changes in coupling coordination divided the process into three stages—stable coordination, intensified imbalance and weak recovery—reflecting structural adjustment; (3) the coupling coordination degree declined overall, shifting from primary coordination towards near imbalance and indicating an uncoordinated transformation characterized by advanced contraction of explicit morphology and lagged improvement of implicit morphology; and (4) SEM results indicate that transportation infrastructure is the core driver of morphological transformation, with a significant positive effect on explicit morphology and a significant negative effect on implicit morphology. Natural factors positively support both morphologies, socio-economic factors exert negative or weak effects, and public services and real estate negatively affect explicit morphology but significantly promote implicit morphology. These findings provide a scientific basis for optimizing the layout and improving the functions of rural settlements in mountainous cities. Full article

Reinforced concrete beams with a fiber-reinforced polymer (FRP) plate bonded to their soffit, known as FRP-plated RC beams, commonly fail due to premature debonding of the FRP plate, limiting the utilization of the FRP strength. Inclined U-jacketing has been demonstrated to be effective as the end anchorage for mitigating debonding failures. The mechanism by which the inclined U-jacketing mitigates debonding failure remains unclear, and no design approach has been developed. Therefore, the present study has been conducted to investigate the mitigating effects of the key parameters of the inclined U-jacket through a series of four-point bending tests and systematic modeling. The test results indicate that both the inclination angle and the chamfer radius significantly affected the bond behavior of inclined U-jacket-to-concrete joints. Compared with the 45° configuration, reducing the inclination angle to 30° increased the peak load and peak displacement by 85.4% and 81.6%, respectively. In contrast, the effect of U-jacket side height became negligible once an effective bonded height had been reached, as increasing the side height from 75 mm to 120 mm changed the peak load by only 2.17%. In addition, a pre-peak parameter identification framework based on a power-function-type cohesive element constitutive relationship was proposed and validated. By analyzing the power-function parameters, namely the coefficient a and exponent b, the influences of U-jacket geometric variables on interfacial mechanical behavior were quantitatively characterized. The proposed approach provides experimentally verifiable parameterization to support the optimized design of inclined U-jacket anchorage systems. Full article

A robust procedure for compensating for inaccuracies caused by 3-D positioning errors in the measurement of the near-field (NF) data required by the non-redundant (NR) spherical near-to-far-field (NtFF) transformations for long antennas is presented in this article. These errors may arise from hardware defects and positioners’ controlling inaccuracies, which may cause the probe to deviate from the intended spherical scan surface and prevent it from reaching the NR sampling points required by either of the two NR representations for long antennas. To account for these errors, the method proceeds through two steps. The first step, called spherical wave correction, compensates for the phase shifts due to radial displacements from the intended scanning sphere. As a result of this correction, the NF samples belong to the intended scanning sphere, but at points different from those required by the adopted NR representation, thus impairing the subsequent NF reconstruction via the optimal sampling interpolation (OSI) algorithm. Such an algorithm enables one to efficiently build the iterative scheme used in the second step, which makes it possible to effectively retrieve the NF samples at the prescribed NR positions. Test results are shown to numerically validate the capability of the developed two-step compensation technique to correct even significant and pessimistic 3-D positioning errors affecting the collection of the NF data. Full article

Fused deposition modeling (FDM) is a material-extrusion additive manufacturing technology that is widely used in rapid prototyping, complex product modeling, and functional part fabrication. However, process-parameter drift and environmental disturbances may induce underfilling, overfilling, warping, delamination, and other defects, thereby reducing part quality or interrupting the manufacturing process. Since FDM is characterized by point-wise extrusion and layer-by-layer deposition, layer-surface images naturally contain both spatial morphology and temporal evolution information. Existing image-based diagnostic methods often treat layer images as independent samples, and the selection of the image-stream length is still insufficiently supported by experimental evidence. Moreover, spatial compression in spatiotemporal neural networks may remove local defect information that is important for distinguishing similar process-parameter drifts. This study provides a deployment-oriented analysis of FDM image-stream diagnosis by systematically examining how layer-window length, spatial feature preservation, and strict data partitioning influence process-parameter drift recognition. To address these issues, this paper studies ConvLSTM-based FDM image-stream process-parameter drift diagnosis. Continuous region-of-interest image streams are constructed for one nominal condition and six process-parameter drift conditions. In this paper, the time step T denotes the number of consecutive layer-surface images, or, equivalently, the number of consecutive printed layers, contained in one diagnostic image stream. A ConvLSTM-Flatten baseline is first developed to preserve complete spatial feature maps and to evaluate the effect of different time-step lengths. Then, a ConvLSTM model with adaptive spatial pooling and temporal attention (ASP-TA) is constructed to analyze the influence of spatial pooling granularity and temporal feature fusion. The experiments show that the ConvLSTM-Flatten model achieves the highest average test accuracy of 0.7288 at $T=9$, whereas $T=3$ is identified as a practical optimal time step when test accuracy, image-frame computation, diagnosis latency, and convergence behavior are considered together. The paired trial-wise accuracy difference between $T=9$ and $T=3$ is small and not statistically significant over ten repeated trials. Thus, the diagnostic window corresponding to $T=3$ covers three consecutive deposited layers; after the initial window is available, stride-one stream construction allows the diagnosis to be updated with each newly acquired layer image. ASP-TA with a pooling size of eight consistently outperforms ASP-TA with a pooling size of four, but both are lower than the Flatten baseline, indicating that preserving sufficient spatial information is essential for distinguishing FDM process-parameter drift states. The results reveal the non-monotonic influence of time-step length and clarify the tradeoff between spatial feature preservation and model compactness in FDM image-stream process-parameter drift diagnosis. Full article

As autonomous driving moves toward large-scale deployment, controllable and efficient simulation testing has become a primary means of ensuring system safety. However, in open-world environments, existing scenario catalogs often fail to cover the full spectrum of potential traffic situations, while rare yet high-risk critical scenarios are even harder to obtain. This scarcity renders traditional random sampling and parameter-sweeping strategies ineffective for identifying unknown risks. This study addresses two core challenges: (1) incomplete scenario catalogs hindering unknown critical scenario recognition and (2) insufficient critical samples, where generated scenarios struggle to balance physical realism and edge case coverage. To tackle the first challenge, we propose an open-world recognition method integrating transformers, random forests, and extreme value theory for precise unseen sample detection. Outlier and validity filtering ensure clustering reliability, and random forest activation patterns cluster unknown samples into meaningful groups to expand the scenario catalog. Experiments show the overall F1_macro improved by 2.3 percentage points over SOTA MDENet, with its clustering accuracy surpassing iterative-AutoNovel by 6.2 percentage points. For the second challenge, we introduce a reinforcement-learning-based maneuver-level generation method. It extracts maneuver semantics from trajectories, constructs a low-dimensional parameter space, and models parameter correlations via a multivariate multimodal distribution. A dual-layer LSTM agent with a composite reward iteratively optimizes policies toward high-risk edge scenarios. The results outperformed RLBE; longitudinal and lateral reconstruction errors were reduced by 32.7% and 15.3%, respectively, while high-risk time steps and the collision rate increased by 4.3% and 5.1%, respectively. Finally, we develop a CARLA-based scenario-driven simulation framework, integrating recognized and generated scenarios into closed-loop testing on high-risk road segments. CAS failure cases validate the generated scenarios’ physical feasibility and extreme challenge. Targeted augmentation of scarce critical scenarios enriches the test library and ensures broader coverage of real-world driving conditions. Full article

Cube-shaped automated storage and retrieval systems (AS/RS) enhance storage density by organizing inventory in a three-dimensional grid. However, they face two operational bottlenecks: (1) digging—the temporary removal and restacking of upper bins to access a target bin—and (2) inefficient idle staging and return policies in multi-AGV operations. We proposed a demand-based digging and bin-placement strategy and a waiting-point (staging) selection policy that considers AGV positions and remaining task times. These control policies are implemented in both rule-based and multi-agent reinforcement learning (MARL) variants. Their performance is evaluated using a Discrete Event System Specification (DEVS) simulation framework. In a 30 × 30 × 4 grid, three experiments demonstrated that deploying five AGVs achieved the best performance within the tested configuration; the demand-based digging and placement strategy achieved a 6.2% reduction in makespan, and the rule-based and MARL staging policies achieved additional reductions of 2.5% and 1.1%, respectively. These results highlight the benefits of jointly optimizing digging and multi-AGV staging and provide practical guidance for control-policy design in cube-shaped AS/RS. Full article

Clinical prediction rules often suffer from a translation gap, balancing high-dimensional statistical accuracy against practical bedside interpretability. This study presents the Surgical Complication Risk Index (S-CRI), an explainable, data-decoupled risk-stratification framework designed to predict post-operative complications using multi-center electronic health registry records (N = 19,965). To ensure strict validation integrity, data partitioning (70% development, n = 13,975; 30% independent holdout testing, n = 5990) was executed before any engineering or risk-tier group isolation. A parsimonious multivariate logistic regression model was fitted within the development cohort, utilizing five predictors: length of stay (LOS) accrued up to the morning of assessment, two institutional categorical groupings, and two historical entry-diagnosis empirical risk tiers. To bridge the translational gap, all fractional regression coefficients were scaled by the baseline anchor and rounded to the nearest whole integer, yielding a simple bedside scorecard where 1 point = 1 inpatient day. On the completely blinded independent holdout cohort, the whole-integer S-CRI demonstrated robust discriminative performance with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.8741 (95% CI: 0.864–0.884) and a Precision–Recall AUC of 0.5785. Setting a baseline operational threshold ≥ 0 yielded an accuracy of 88.18%, a specificity of 96.43%, and a sensitivity of 35.43%, while an optimized integer screening cutoff score of ≥−4 maximized screening capacity (sensitivity: 63.95%; specificity: 91.68%). By enforcing strict temporal landmark constraints to eliminate reverse causality and removing all out-of-sample data leakage, the S-CRI provides an objective, transparent, and interpretable clinical decision support mechanism for early inpatient risk stratification, designed as a supplementary clinical decision-support aid, rather than as a definitive diagnostic replacement for independent clinical judgment. Full article

With the increasing penetration of behind-the-meter photovoltaic generation and distributed energy storage, distribution system operators usually observe only the net load at the point of common coupling, while the actual user load and energy storage charging/discharging power are difficult to measure directly. To address this problem, this paper proposes a mode-aware constrained inverse optimization method for behind-the-meter distributed energy storage power estimation under fixed time-of-use tariffs. The proposed method uses net load, photovoltaic power, and tariff information as inputs and estimates the hidden user load, storage power, SOC trajectory, and dominant storage arbitrage mode. A mode-aware joint representation model is developed by introducing single-cycle and dual-cycle charge–discharge templates, daily action intensity factors, mode weights, and local correction terms. In addition, power limits, SOC dynamics, SOC bounds, daily energy balance constraints, tariff-response consistency, and mode selection penalty are incorporated into the inverse optimization framework to improve the physical feasibility and interpretability of the estimation results. Case studies are conducted using a 40-day hybrid dataset with a 1 h sampling interval and a 70%/30% training/testing split. The dataset is constructed from park-level user load and photovoltaic data, while the storage power profile is reconstructed according to typical time-of-use arbitrage operation. For the main dual-cycle testing case, the NRMSEs of storage power, user load, and net load are 14.75%, 3.90%, and 3.76%, respectively. The results show that the proposed method can recover the main variation trend of hidden storage power under the studied fixed time-of-use tariff scenario and provides a preliminary basis for park-level storage monitoring and flexible resource perception. Full article

Accurately assessing the spatiotemporal evolution of ecological environment quality (EEQ) on the Loess Plateau of Northern Shaanxi is of great significance for consolidating the ecological security barrier of the Yellow River Basin. Most of the existing research focuses on a single ecological theme, which does not reflect the overall ecological status of the region. In this study, a remote sensing ecological index (RSEI) model was constructed to systematically assess the EEQ from 2000 to 2024. The Theil–Sen estimator, Mann–Kendall test, and Hurst exponent were jointly employed to detect change significance and predict future trends, while the Geodetector model was applied to explore driving factors. The results were as follows: (1) EEQ exhibited a fluctuating but overall upward trend, with the mean RSEI rising from 0.376 in 2000 to 0.545 in 2024—an average annual increase of approximately 0.00569. (2) Spatially, a distinct pattern of “higher in the south, lower in the north and the lowest in the northwest” was observed. Over the 25-year period, the combined proportion of “excellent” and “good” grades increased by roughly 20 percentage points, and the “moderate” grade expanded from 13.61% to 47.12%. (3) Areas showing an improving trend accounted for 91.21% of the total area and highly overlapped with those projected to improve in the future. (4) Single-factor detection revealed that geomorphological type exerted the greatest influence on the spatial heterogeneity of EEQ, with a multi-year mean q-value of 0.701. Interaction detection further indicates that the geomorphology–land use interaction may continue to shape the regional EEQ’s spatial distribution. These findings provide a scientific basis for precise ecological restoration planning and spatial optimization on the Loess Plateau of Northern Shaanxi. Full article

Shared bikes are frequently parked in disorder, resulting in low efficiency of manual collection and transfer and heavy workload for maintenance staff. Random parking across various areas forces shared bikes to occupy sidewalks and fire exits, damaging urban landscapes and disrupting traffic order. To tackle these industrial pain points, this paper develops an integrated intelligent robot system equipped with functions of multi-pose grasping, automatic transfer and fixed-point delivery of shared bikes, which can effectively address the drawbacks of low efficiency and high labor costs in traditional manual maintenance. This paper focuses on the completion of the robot’s overall mechanical structure design, stiffness–precision collaborative optimization model construction, finite-element static simulation verification, 1:7 scaled prototype development and performance testing. Firstly, the overall layout design of the multi-posture adaptive floating clamping mechanism, transfer-bearing frame, and Mecanum wheel omnidirectional mobile chassis is completed, and the structural parameters and assembly benchmarks of the core components are clarified. Secondly, a stiffness–precision coupling optimization model is established, and the static analysis under extreme load conditions is carried out through Abaqus finite-element software, which verifies the rationality of 45# carbon steel material selection and the safety of structural strength. Subsequently, a 1:7 scaled principle prototype is developed, and repetitive grabbing and transfer tests are carried out to verify the system operation feasibility, stability and grabbing accuracy. Finally, the statistical analysis of the test data and the horizontal comparison of similar schemes are completed. The test and simulation results show that the maximum stress of the system under extreme working conditions is 131.21 MPa, which is far lower than the allowable stress of 355 MPa of 45# steel, and the safety factor reaches 2.71. The maximum total deformation is 4.0552 mm, which is concentrated at the end of the front-end clamping mechanism, and is within the allowable stiffness deviation range of the transfer system. The average value of the single clamping positioning error of the scaled prototype is 0.476 mm, with a 95% confidence interval of 0.457–0.495 mm, which is converted to a positioning error of ≤3.4 mm for the full-scale prototype, which is far better than similar industry solutions. The average time of a single complete grabbing and transfer operation is 12.38 s, which is more than 45% higher than the traditional manual mode. The structural design, grabbing accuracy and operation stability of the robot designed in this paper all meet the requirements of actual working conditions of urban sidewalks, which can effectively reduce the intensity of manual labor and improve the operation and maintenance efficiency of shared bicycles. It has strong engineering application value and can provide reference for the design and manufacturing of intelligent collection and transfer systems for shared two-wheelers. Full article

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. From comb-surface images of 20 A. mellifera colonies, 3400 region-of-interest images were processed through 12 preprocessing pipelines combining deblurring, histogram normalization, morphology-preserving resizing, and non-morphological resizing. Nineteen CNN architectures, including VarroaNet — a custom lightweight model with configurable channel attention — were screened across all pipelines, and the top six further evaluated at four feature-map resolutions (7 × 7 to 56 × 56); the two stages together comprised 1,548 classification training runs across 516 configurations. Resizing consistently improved classification accuracy, whereas histogram normalization degraded it. VarroaNet (r = 8) achieved the highest mean accuracy across configurations (97.28%) with the lowest cross-configuration variability (CV = 1.47%). The 28 × 28 resolution was jointly optimal for classification and localization at minimal computational overhead, whereas 56 × 56 degraded performance. Notably, classification accuracy and localization quality did not always coincide—the highest-accuracy configuration (ShuffleNet-V2-x1.0 at 14 × 14, 97.34%) achieved an IoU@30 of only 0.160, underscoring the need for explicit localization evaluation. Morphology-preserving resizing achieved higher localization efficiency with zero morphological distortion. The recommended configuration—VarroaNet (r = 8) at 28 × 28 with deblurred MR preprocessing—achieved the highest localization performance (Pointing Game = 0.927), indicating correct attention to the mite region in 92.7% of infested test images. Full article

This paper introduces a methodology for evaluating the voltage stability margin in high-voltage direct-current (HVDC) systems, which analyzes the singularity of the power flow Jacobian matrix—computed via the Newton—Raphson method—and identifies saddle-node bifurcations. The continuation power flow method is employed to model progressive load increases, enabling the continuous tracking of power flow solutions and the determination of voltage collapse points. Within this framework, the system’s behavior is analyzed under contingency conditions, particularly transmission line outages, assessing its capability to maintain secure operating conditions under increasing demand scenarios. The main objective is to identify the most critical line in the system, defined as that which leads to the greatest reduction in loadability when unavailable, prior to voltage collapse. This approach allows for the early identification of structural vulnerabilities, supporting decision-making processes aimed at risk mitigation and operating cost optimization. The proposed methodology is validated using two systems: the six-terminal CIGRE-B4 HVDC system and an 11-node HVDC test feeder. Full article

This paper proposes a novel approach for multiple-view $L_2$ triangulation, a key problem in computer vision which consists of estimating a scene point from its estimated image projections on two or more cameras and from the estimated projection matrices of the cameras by minimizing the reprojection error in the $L_2$ norm. In the proposed approach, the estimated image projections are allowed to be uncertain in admissible regions described by polynomial inequalities and equalities, and an estimate of the scene point is obtained by solving a linear matrix inequality (LMI) problem built with matrix decompositions, polynomial multipliers, and the Gram matrix method. It is proven that the optimal estimate can always be achieved by using multipliers with sufficiently large degree. Moreover, a simple test is provided in order to establish the optimality of the obtained estimate. As shown by some examples with real and synthetic data, the proposed approach presents key advantages with respect to several existing methods of a different nature, which may fail to find the optimal estimate, may not allow one to establish the optimality of the found estimate, or may require a larger computational burden. Full article