Search Results (11,085)

The physical environment of a neighborhood, such as its road networks and land-use distribution, appears to be closely related to its ambient population density, but this relationship is difficult to measure quantitatively. A previous study based on Japanese neighborhoods addressed this challenge by developing an XGBoost baseline model to estimate Average Hourly Ambient Population Density (AHAPD) at a neighborhood scale using 16 physical environment-related features, achieving 75.9% accuracy. This study evaluates that baseline model by comparing it with human evaluators and multimodal AI models in a spatial reasoning task based on AHAPD estimation. A questionnaire-based benchmark with 29 sets of questions was developed to compare the three evaluator types across accuracy, reasoning, and confidence. The online questionnaire was distributed from 8 June–22 September 2025. In total, 100 responses were analyzed, comprising 94 human evaluators, 5 free-tier versions of multimodal AI models, and 1 output from the baseline model. The baseline model scored 16 points, compared with a mean of 15.1 points for the human evaluators and 14.2 points for the tested AI models. Its performance was also comparable to human evaluators with domain expertise and contextual familiarity, who averaged 16.0 points. The comparison further revealed differences in feature importance, confidence patterns, and characteristic errors among the evaluator types. Selected discrepancy cases highlighted possible strengths and weaknesses of each approach. The main contribution of this study is a questionnaire-based diagnostic benchmark for comparing evaluator types with different characteristics in spatial reasoning tasks. Full article

Remote fault-diagnosis services in dynamic edge–cloud environments depend on timely monitoring-data upload, remote inference, and result delivery, making their communication layer sensitive to variations in available bandwidth, link latency, and packet loss rate. This study addresses the network-state forecasting layer that supports such services rather than the fault-classification model itself. We propose Horizon-Aware Transformer–Ridge Fusion (HATR-Fusion), which combines a nonlinear Transformer expert with a low-variance ridge-regression expert for joint short- and long-horizon forecasting. Historical available bandwidth, link latency, packet loss rate, mobility, and offered load are used as inputs. Preprocessing statistics are estimated using the training set only, and validation-calibrated convex fusion weights are frozen before test inference. Experiments on controlled synthetic trajectories from eight links sampled at 1-min intervals, using 10 neural-network initialization seeds and ten baselines including DLinear and iTransformer, show that HATR-Fusion reduces mean absolute error (MAE) relative to the standalone Transformer by 6.94–8.31% over the 10-min horizon and by 3.02–4.40% over the 60-min horizon, with all six paired improvements remaining significant after Holm correction. Against iTransformer, HATR-Fusion is significantly more accurate for short-horizon bandwidth and latency, whereas iTransformer is significantly more accurate for long-horizon latency and packet loss; short-horizon packet loss and long-horizon bandwidth are not significantly different after Holm correction. The six-task mean normalized mean absolute error (NMAE) is 0.07106 for HATR-Fusion and 0.07047 for iTransformer, indicating comparable overall accuracy with task-dependent differences between the two methods. Ablation results show complementary short- and long-range contributions from ridge regression and Transformer, while input-quality sensitivity analysis identifies a limitation of the fixed fusion weights under corrupted or missing history. The conclusions are therefore restricted to scenarios with relatively stable input quality and distribution shifts comparable to those evaluated in this study. Full article

Traditional fault diagnosis methods for gas-insulated switchgear (GIS) equipment primarily rely on offline detection and periodic maintenance, which suffer from limitations such as poor real-time performance and localization difficulties, thereby compromising the safe and stable operation of ultra-high-voltage power grids. To enhance the accurate identification and localization capabilities of defects within GIS equipment, this study first establishes a multi-physics coupled simulation model integrating temperature field, flow field, and concentration field to analyze gas diffusion characteristics under varying conditions of fault source locations, decomposition product types, and initial concentrations. Subsequently, a GIS busbar gas chamber experimental platform is constructed to validate the simulation model. Finally, a response time matrix, a peak concentration matrix, and a fault coverage index are developed, and a weighted comprehensive evaluation method is employed to optimize sensor placement schemes. The findings reveal that fault source location significantly influences concentration response speed and spatial distribution patterns; SO₂, HF, H₂S, and SOF₂ exhibit distinct diffusion characteristics due to their differing physical properties; and initial concentration primarily affects the non-uniformity during the early diffusion stage. The simulation results demonstrate good agreement with experimental data, with a maximum root-mean-square error of 3.936 × 10⁻⁴. Monitoring point M4 achieves the highest comprehensive score, making it the preferred location for single-sensor deployment. These results provide a theoretical foundation and technical guidance for GIS online monitoring and fault diagnosis. Full article

To address the oversteering risk during aircraft ground towing with a towbarless towing vehicle, this study proposes a dual-LiDAR point-cloud detection and pose estimation method for aircraft rear-wheel targets. First, a complementary dual-LiDAR acquisition strategy is adopted to reduce rear-wheel point-cloud occlusion caused by the aircraft nose landing gear and towing mechanism. Second, considering the small size and distinctive local geometry of rear-wheel targets, vertical density enhanced encoding and a lightweight CNN-Transformer BEV backbone are introduced into the PointPillars framework. The vertical density enhanced encoding explicitly describes the normalized height-wise distribution of valid points within each pillar, thereby improving the representation of cylindrical wheel structures. The CNN-Transformer BEV backbone incorporates a window-based self-attention Transformer module into deep features to strengthen local contextual modeling in the BEV space. Based on the detected coordinates of the left and right rear wheels, the aircraft fuselage pose is then estimated in combination with the TLTV coordinate system. In three-seed experiments on the fixed validation split, the Full model achieves an mAP@0.5 of $0.8788\pm 0.0161$, which is 8.50 percentage points higher than the original PointPillars baseline. The model contains 4.1069 M parameters and runs at 38.0732 FPS. The towing-angle estimation error remains within the allowable engineering range. These results show that the task-specific adaptations improve rear-wheel detection while retaining a compact model and real-time processing capability. Full article

Artificially replicating the extraordinary adaptive potential of organisms remains difficult. Machine learning approaches based on big data pursue behavioral adaptation through generalization from training data, but often learn slowly and struggle with out-of-distribution situations. We propose that organisms may not adapt primarily through generalization alone but by rapidly eliminating infeasible solutions through online trial-and-error, effectively performing a search over the behavior space. If this search is complete, it is guaranteed to find an existing physically robust solution within a finite but unbounded time. For continuous behavioral domains that contain uncountably infinite behaviors, we introduce a mathematical framework for constructing a countably infinite dense subset of all behaviors using a mutable graph to segment behavior space, allowing any behavior to be progressively approximated arbitrarily well. Graph evolution is regulated by a heuristic feedback loop between outward growth and internal refinement; refinement is partially determined by a Bernoulli variance term related to binary entropy. Using this construction, we implement a proof-of-concept behavioral search algorithm and evaluate it on maze navigation and simple continuous control tasks. These preliminary results establish the practical feasibility of this approach in low-dimensional simulated environments while exposing unresolved limitations in terms of dimensional scaling and the incorporation of prior information. Full article

Vehicle-to-grid (V2G) technology lets a parked electric vehicle push power back to the network, so a fleet of cars can act as distributed storage. Many studies report large benefits from this, such as peak shaving and better use of renewable energy, but most describe their simulation only in words, name no test network, and compare a single charging behavior against a do-nothing case. It is then hard to separate what V2G delivers from what the control strategy delivers. This paper builds and compares dispatch strategies on one fully specified system: a price-following rule with no knowledge of the network, a stronger randomized time-of-use baseline, and a receding-horizon model predictive control (MPC) strategy that re-plans every hour with the distribution network’s per-line thermal limits and voltage bounds embedded directly in the optimization, not merely checked afterward. All run on the IEEE 33-bus feeder at low (10%), medium (30%), and high (50%) EV shares, with a full AC power flow solved every hour. The central result is a critical-penetration effect. At a 50% share, the naive price rule makes the system peak 23.7% worse than having no V2G at all, because the whole fleet reacts to one price signal at once, while the MPC cuts the peak by 18 to 28% in every case. Embedding the network limits removes the worst-line-loading side-effect a system-peak-only objective creates, bringing high-share worst-line loading down from 86.3% to 81.8% while preserving the full peak reduction. A workplace daytime-charging scenario shows renewable use becoming a real, separating metric (up to 2.8 MWh/day of EV demand met directly by rooftop solar, against zero for overnight charging), a quadratic wear cost smooths the profit-cycling frontier that a linear cost makes step-shaped, the controller is robust to forecast error up to 20%, and its solve time is set by network size rather than fleet size. Results are given as they came out of the model. Full article

High-moisture rice straw processed by whole-feed combine harvesters often exhibits high cutting resistance and uneven particle size distribution after comminution. To address these issues, a straw comminution device integrated with the straw outlet of the threshing and cleaning system was developed, and the shear fracture mechanism and operating parameters were investigated. The geometric characteristics, density, contact properties, and bending properties of rice straw cultivars Yongyou 7301 and Kenuigeng 1 were measured. A hollow flexible straw discrete element model was established using the Hertz–Mindlin with Bonding contact model, and its parameters were calibrated and validated through quasi-static shear cutting tests. The effects of shear cutting angle on maximum cutting force, bond failure evolution, and load transfer behavior were analyzed at shear angles of 30°, 45°, and 60°. Device-scale DEM simulations combined with field experiments were further conducted to optimize the guide plate angle and rotor speed. The results showed that the maximum cutting force under quasi-static single-stalk cutting conditions initially decreased and then increased with increasing shear angle. At a shear angle of 45°, the maximum cutting force was 78 N, representing a 44.8% reduction compared with that at 30°. Meanwhile, the fracture zone expanded along the blade sliding direction and stress concentration was alleviated. The DEM model effectively characterized the fracture behavior of rice straw, with an average relative error of 11.07% between simulated and experimental cutting forces. The optimized operating parameters under the tested conditions were a shear angle of 45°, guide plate angle of 55°, and rotor speed of 2500 r/min, resulting in average chopped lengths of 17.3 mm in simulation and 20.5 mm in field experiments, with a comminution qualification rate of 95.26%. These findings provide theoretical support for improving the fracture characteristics and chopping performance of straw comminution systems. Full article

This paper examines the dynamic relationship between innovation, total factor productivity (TFP), and economic growth in Ghana using annual data for the period 1965–2021. Although Ghana has recorded relatively strong economic growth, concerns remain regarding the sustainability of this performance in the absence of consistent productivity improvements. The study combines growth accounting techniques with time-series econometric methods, including the autoregressive distributed lag–unrestricted error correction model (ARDL–UECM), vector error correction modelling (VECM), Granger causality tests, and two-stage least squares estimation. The results provide robust evidence of a stable long-run equilibrium relationship among innovation, productivity, and output. Innovation exerts a positive and statistically significant effect on economic growth, primarily through productivity-enhancing channels, while TFP emerges as the dominant long-run driver of growth. Short-run dynamics reveal feedback effects between innovation, productivity, and economic growth. However, growth accounting results indicate substantial volatility in TFP growth, suggesting that Ghana’s expansion has been driven largely by factor accumulation rather than sustained efficiency gains. The findings offer policy-relevant insights for productivity-centred growth strategies in Sub-Saharan Africa. Full article

To address the challenges of susceptibility to interference and limited accuracy inherent in conventional gas flow rate measurement methods, this paper proposes and investigates a laser Doppler gas flow rate measurement method based on synchronous three-point velocity measurement. This method simultaneously measures the flow velocities at three characteristic points within the pipeline cross-section, subsequently fits and reconstructs the velocity distribution across the entire profile, and ultimately achieves high-precision flow measurement. The feasibility of selecting the center point, the quarter-width point, and the near-wall point as the three characteristic measurement positions is analyzed through computational fluid dynamics simulations, and the full-profile velocity distribution is fitted accordingly. A three-point synchronous velocity-flow rate measurement system is designed and constructed, employing a transmitting optical path based on the “three beam splitters and two mirrors” scheme and a receiving optical path based on the “multi-lens independent reception” scheme, and experimental validation is conducted. Experimental results demonstrate that the system operates stably with good repeatability. In contrast to the flow calculation method using a single-point Pitot tube combined with an empirical formula, the proposed system, which directly fits the velocity profile and integrates it for flow calculation, effectively avoids the significant model errors caused by using fixed empirical coefficients in non-circular pipe flows, and is inherently more universally applicable in principle. Through comparison with the TSI reference standard (3D LDV), the measurement results of the proposed system are in close agreement with the reference values, with relative errors all below −0.8%. This paper provides an effective solution for high-precision gas flow measurement in square pipelines. Full article

Laser–MIG hybrid welding is increasingly used in shipbuilding for medium-thick steel plates, where assembly-induced gaps and misalignment often compromise weld quality. In this study, a three-dimensional transient numerical model is established to simulate the molten pool dynamics during full-penetration laser–MIG hybrid welding of 10CrNiCu steel under varying gaps and misalignment conditions. The model incorporates coupled heat transfer, fluid flow, keyhole behavior, and droplet transfer and is validated against experimental weld profiles. The simulation results reveal that increasing gap size broadens the heat distribution, reduces keyhole depth, and decreases the bridging capacity of the filler metal. Larger misalignment enhances the step effect, promotes gravity-driven downward flow of liquid metal, and increases keyhole instability and porosity risk. The adaptability to assembly errors is further assessed. As welding current increases, the gap tolerance slightly improves from 1.61 mm to 1.65 mm, whereas the misalignment tolerance markedly decreases from 3.00 mm to 1.82 mm due to the earlier onset of porosity defects. These findings provide quantitative guidance for optimizing welding parameters to accommodate realistic assembly variations in marine steel fabrication. Full article

Battery prognosis is a critical component of battery management systems, enabling the prediction of end of life (EoL) and remaining useful life (RUL). However, obtaining sufficiently large labelled datasets for data-driven prognosis is challenging because battery ageing experiments are time-consuming and expensive. To address this data scarcity, we propose a conditional generative adversarial network (GAN) framework for targeted synthetic battery-data generation, in which degradation regime is explicitly used as conditioning information. The framework generates samples from three degradation regions—early, pre-knee, and post-knee—and is investigated using two representations of the same underlying battery data: direct time series and Gramian Angular Fields (GAFs). The generated data are evaluated using representation-specific quantitative metrics together with qualitative distributional analyses. As an additional validation of synthetic-data utility, GAN-generated samples are incorporated as unlabelled data in a Mean Teacher semi-supervised EoL prediction framework. Across 10 matched random-seed runs, augmentation reduces the mean EoL prediction error for both representations. For the time-series workflow, MAE and RMSE decrease by 6.37% and 3.66%, respectively, while the GAF-based workflow shows larger reductions of 15.93% and 16.20%. The improvements in both metrics are statistically significant for the GAF-based workflow, and after augmentation no statistically significant difference is detected between the aggregate EoL prediction errors of the GAF and time-series-based models. These findings demonstrate the potential of targeted conditional GANs for battery-data augmentation and highlight GAF-based generation as a promising complementary approach to conventional time-series-based augmentation for battery prognosis. Full article

Accurate calibration of Manning’s roughness coefficients is essential for reliable river hydraulic modelling, flood prediction, and water resources management, yet conventional calibration methods often struggle with high-dimensional parameter spaces and nonlinear hydraulic interactions. This study proposes and evaluates a hierarchical artificial intelligence framework for the inverse calibration of spatially distributed Manning’s roughness coefficients across three channel zones (left bank, main channel, and right bank), using a 48 km reach of the Tigris River in Baghdad as a case study. A one-dimensional HEC-RAS hydraulic model based on 30 measured cross-sections generated 18,360 simulations by systematically varying Manning’s roughness coefficients (0.02–0.045). Three calibration strategies were evaluated: (i) a simple Gradient Boosting Regression model based on a weighted composite roughness formula, (ii) conventional machine learning models (Random Forest, Gradient Boosting, and Multi-Layer Perceptron), and (iii) a deep learning framework combining a three-layer neural network (64 → 32 → 16 neurons), Differential Evolution optimisation, and cubic spline interpolation. Calibration accuracy increased with model complexity. The deep learning framework achieved the best performance, reducing the root mean square error by 96.6% (from 1.202 to 0.041 m), with R² = 0.992 and negligible bias (−0.004 m). Conventional machine learning models produced spatially variable Manning’s roughness distributions, with the calibrated main-channel roughness (mean n = 0.0512) being 34.0–57.5% higher than the corresponding bank values. The proposed framework provides an effective approach for calibrating spatially distributed roughness coefficients in one-dimensional hydraulic models, with strong potential to improve river hydraulic simulations and support future applications to flood modelling. Full article

The transition toward sustainable energy systems requires reliable, efficient, and computationally practical control strategies for renewable energy-based microgrids. Grid-connected DC microgrids provide an effective platform for integrating distributed renewable energy resources, while their sustainable operation requires robust regulation under load variations, nonlinear loads, and input disturbances. This study proposes a lightweight Polynomial Regression Controller (PRC) for voltage regulation in grid-connected DC microgrids. The proposed data-driven controller uses a second-order polynomial model to estimate the converter duty cycle from input voltage, voltage error, and load current. The model is trained offline using independently generated operating trajectories and evaluated under previously unseen operating conditions. The results demonstrate accurate DC bus voltage regulation and robust operation under linear, constant power, motor load, and grid-connected conditions. The PRC maintains the DC bus voltage close to its 50 V reference, with steady-state errors of 0.002–0.008% and a settling time of 0.001 s under fast transient responses. The proposed approach combines nonlinear mapping capability with a compact computational structure, supporting practical implementation on resource-constrained platforms. Overall, the proposed PRC contributes to reliable renewable energy integration, resilient microgrid operation, and the development of sustainable, efficient, and scalable smart energy systems. Full article

Accurate stereo visual–inertial measurement is essential for mobile robots operating in Global Navigation Satellite System (GNSS)-denied and structurally complex environments. In stereo visual–inertial odometry (VIO), pose and trajectory outputs depend strongly on the point measurements delivered by the visual front end before sensor-fusion update. In sparse-texture but structurally regular scenes, tracked point features may exhibit poor persistence, uneven spatial distribution, and local tracking noise, even when informative line structures are present. Existing point–line VIO methods can improve positioning accuracy by introducing line landmarks or line residuals, but they usually modify the estimator state, measurement model, and Jacobian treatment. We present a scene-adaptive line-aware visual measurement conditioning method for stereo VIO front ends with point-measurement updates. The method uses 2-D image-line segments as lightweight structural priors and applies bounded normal-direction conditioning to reliable point measurements before a fixed-interface VIO back-end update. A sparse pruning safeguard removes only highly inconsistent long-lived tracks under strong structural support, while a scene-level confidence gate attenuates the intervention when line evidence is weak or unstable. The method is instantiated and evaluated in an S-MSCKF pipeline. On the reported EuRoC MAV sequences, it reduces the sequence-averaged absolute trajectory error (ATE) RMSE by approximately 13% relative to S-MSCKF, with 3–27% reductions on machine-hall sequences. On three real-world robot measurement sequences with an RTK-aided inertial reference, the mean Sim(2)-aligned planar position error decreases from 8.72 m to 7.31 m, and the mean yaw error decreases from $8.{02}^{\circ }$ to $6.{76}^{\circ }$; an additional scale-preserving SE(2) evaluation reveals sequence-dependent planar behavior and residual metric-scale sensitivity. Candidate-level stereo-consistency diagnostics show subpixel mean and 95th-percentile image-domain perturbations without systematic vertical-stereo bias, while the final reliability-weighted primary-view update is analytically bounded by approximately 0.221 pixels in the reported implementation. Runtime profiling reports an average front-end time of 33.34 ms on the tested CPU platform, close to the 33.3 ms frame period of the 30 Hz stereo input, although the $\mu +3\sigma$ runtime of 47.23 ms exceeds a strict frame-by-frame 30 Hz budget. These results suggest that line-aware front-end conditioning can improve visual measurement quality in structured stereo visual–inertial sensing without modifying the evaluated back-end interface. Full article

Frame–beam thin-walled components are widely used in aerospace equipment owing to their lightweight characteristics and high specific strength. However, their low and spatially non-uniform stiffness makes them susceptible to tool vibration and workpiece deformation during machining, posing considerable challenges in controlling deformation magnitude and maintaining uniform deformation across the machined region. To address the limitations of conventional end mills in machining frame–beam thin-walled components, a helical variable-angle end mill tailored to the spatial variation in workpiece stiffness was designed and manufactured. The rake and clearance angles of the proposed tool gradually increase from the tool tip toward the shank, progressively enhancing cutting-edge sharpness and reducing the cutting forces generated during machining. This geometric variation is designed to match the gradual decrease in workpiece stiffness from the fixed end toward the free end. Single-factor comparative milling experiments were subsequently conducted on a VDL850 machining center to evaluate the machining performance of the proposed tool. The results show that, compared with a conventional end mill, the proposed helical variable-angle end mill reduces the cutting force by at least 9.50%, flank wear by 77.78%, machining vibration by 25.17%, and machining error by 24.13%. These improvements reduce workpiece deformation and promote a more uniform deformation distribution across the machined region, thereby improving both machining quality and its consistency. Full article