Search Results (10,779)

Objectives: We aimed to explore whether incorporating a deliberate error-based learning activity, adapted from error management training (EMT), could enhance undergraduate dental students’ understanding of common intra-oral radiographic faults and their phantom-head imaging performance. Methods: This randomized two-arm educational study involved Year 2 dental undergraduates who completed pre- and post-intervention phantom-head imaging and a short multiple-choice test on radiographic errors. Students were allocated either to a brief, slide-based teaching session on imaging faults (conventional group) or to a hands-on activity in which they intentionally produced and corrected faulty images under supervision (EMT group). Main outcomes included MCQ scores, confidence ratings, overall imaging performance (percentage of maximum possible score), and distribution of diagnostic image quality categories. Two-sample t tests were used to check for inter-group differences in the change in the post–pre numerical scores. The Cochran–Mantel–Haenszel (CMH) linear-by-linear test was used to check for inter-group differences in the change in image distribution by diagnostic quality before and after the intervention. Results: Eighty-seven students contributed a total of 735 radiographs. Both groups showed improvements in MCQ scores and confidence within groups. Diagnostic acceptability of student radiographs was already high at baseline and remained so afterwards, with no significant differences between groups in MCQ gains, confidence changes, imaging scores, or shifts in diagnostic image quality distributions. Conclusions: Although the single, short EMT-style activity did not outperform conventional teaching on immediate outcomes, the study demonstrates that deliberate error-based learning is feasible in a pre-clinical radiography setting and well-received by students. The findings also help to clarify the circumstances under which EMT is most likely to yield benefits, suggesting that longer or more structured EMT sessions, particularly those involving metacognitive scaffolding or more challenging imaging scenarios, may be needed before performance differences can emerge. Full article

Automated defect detection on Printed Circuit Boards (PCBs) is essential for electronic product quality control, but it remains challenging because PCB defects are small, morphologically diverse, and easily submerged by dense circuit textures. Fine-grained edge cues are also weakened during feature downsampling. To address these challenges, this paper proposes Morphology-Adaptive YOLO (MA-YOLO), a lightweight PCB defect-detection model based on YOLO11n. First, a Morphology-Adaptive Normalized Wasserstein Distance (MA-NWD) loss is designed to reduce the excessive sensitivity of IoU-based regression losses to pixel-level offsets, thereby improving the stability of bounding box regression for small defects. Second, the High-Frequency Integrated C3k2 (HFI-C3k2) module is designed to enhance the representation of high-frequency features associated with subtle edge defects, such as burrs and minor notches, further improving defect boundary perception under complex background conditions. Finally, Morphology-Scale-Aware Exponential Moving Average Slide Loss (MS-EMASlideLoss) is proposed by introducing a morphological scale coefficient into the original sample quality evaluation mechanism. This loss alleviates the insufficient emphasis on tiny and elongated defects during classification training and assigns higher supervisory weights to hard-to-classify defect instances. Experiments on the public PCB defect benchmark DeepPCB show that MA-YOLO achieves an mAP50 of 97.5% and an mAP50–95 of 77.9%, outperforming the baseline YOLO11n by 1.2 and 4.6 percentage points, respectively. Meanwhile, MA-YOLO retains the lightweight characteristics of YOLO11n, with only 2.565 M parameters and 6.2 GFLOPs. Compared with representative detection methods, MA-YOLO achieves a better balance between detection accuracy, model complexity, and inference efficiency. These findings suggest that MA-YOLO provides a competitive and deployable solution for lightweight PCB defect detection in industrial inspection scenarios. Full article

Fuzz buttons are formed by interweaving and compacting fine metallic wires, resulting in a highly porous architecture with complex internal contact interactions. Their compressive behavior is governed by the evolution of wire–wire contacts, frictional sliding, local bending, and plastic deformation, which cannot be adequately captured by conventional homogenized models. To address this limitation, a process-informed finite element modeling approach based on virtual fabrication is proposed. First, the spatial trajectories of 24 beryllium copper wires are generated using a parametric three-dimensional weaving algorithm and smoothed by cubic spline interpolation to obtain continuous wire centerlines. The resulting preform is then virtually compacted to reconstruct the densified wire network and its contact topology. The model employs a globally controlled solid-element mesh, a penalty-based general contact algorithm, a Coulomb friction model, and an explicit quasi-static solution scheme. The size-dependent plastic response of the fine wires is further incorporated through a Nix–Gao-based correction to the constitutive relation. The model is validated against quasi-static compression experiments at compressive strains of 15%, 20%, and 25%. The relative errors in the predicted peak forces are 2.12%, 5.65%, and 6.81%, respectively, while the corresponding coefficients of determination for the force–displacement curves are 0.984, 0.970, and 0.973. The model successfully reproduces the nonlinear loading–unloading response and hysteretic energy dissipation over the investigated strain range. The proposed approach provides a physically grounded numerical framework for predicting the compressive behavior of fuzz buttons and investigating the mesoscopic mechanics of complex interwoven wire networks. Full article

Rotational friction welding offers an adhesive-free approach for producing wood dowel joints, but pull-out performance and process consistency are strongly affected by the welding parameters. This study investigated the effects of the hole-to-dowel diameter ratio, rotational speed, and plunging rate on rotationally friction-welded beech (Fagus sylvatica L.) dowel joints. An L9 orthogonal design was combined with supplementary testing, curve-based validity assessment, post-peak analysis, post-pull-out surface imaging, and independent validation. Range analysis ranked the parameter effects as plunging rate, hole-to-dowel diameter ratio, and rotational speed. Type III analysis of variance confirmed significant effects of the hole-to-dowel diameter ratio and plunging rate, whereas rotational speed was not significant within 1600–2000 rpm. The predicted combination was a ratio of 0.80, 1800 rpm, and 14 mm·s⁻¹. The validation group reached 2567.22 N, 34.96% above T3, but its coefficient of variation of 35.93% showed that considerable variability remained. All joints failed by complete dowel withdrawal; the exposed dowel surfaces indicated mixed interfacial separation, sliding, and localized wood-fiber tearing. Darkened regions occurred at different speed levels, without consistent evidence of extensive burning at 2000 rpm. High-capacity joints also showed more abrupt post-peak degradation, indicating a trade-off between capacity, consistency, and failure suddenness. Full article

In the highly volatile cryptocurrency market, trading decision support based on price prediction remains a challenging task. Although machine learning and deep learning techniques have been widely applied to cryptocurrency price prediction, many existing approaches rely on correlation-based black-box models, which limits interpretability and robustness. In this study, we employed a NOTEARS-Linear-based Prediction Model (NLBPM) that directly incorporated causal structures inferred through a causal discovery method as structural constraints within the prediction model. Unlike conventional approaches that focus primarily on minimizing prediction error, the NLBPM emphasized return maximization as its objective function, thereby prioritizing practical economic value. Using Bitcoin as a case study, we constructed a model to predict the direction of price movement four hours ahead and evaluated its performance using a rolling-window scheme with a one-month sliding window. Analysis of the inferred causal structures showed that the returns improved when trades were executed only during rolling-window trials in which specific directed edges to the target variable were detected. Based on this finding, we proposed a causal filter strategy that restricts trading to periods in which specific directed edges to the target variable are detected. In the data period analyzed in this study, the selected edge was the one from the opening price (Open) to the target variable. Backtesting experiments incorporating a transaction fee of 0.1% demonstrated that, while the benchmark LSTM model achieved a negative monthly average return of −3.20% and the NLBPM without filtering yielded −0.72%, the NLBPM with the Open filter attained a higher monthly average return of 10.35%. This study supports the usefulness of using inferred causal structure for cryptocurrency trading decision support. Full article

Slope stability has consistently been a critical concern in mountainous road sections, with precipitation being the most significant factor precipitating slope instability. This study aims to elucidate the mechanism of slope instability under precipitation conditions and the extent of the impact of internal disaster-causing factors. To achieve this objective, a numerical simulation analysis method combining GeoStudio2018R2 and FLAC3D7.0 software was employed to conduct a comprehensive analysis of an unstable slope in Xinjiang. Regarding research methodology, cyclic precipitation and seasonal snowmelt were considered as external influencing factors. Initially, a two-dimensional model was constructed using GeoStudio software to analyze the spatial and temporal variations in pore water pressure and moisture content within the slope, elucidating their dynamic characteristics at different temporal and spatial scales. Subsequently, a three-dimensional numerical model was established using FLAC3D software to conduct a detailed analysis of the stress–strain state of the slope under various conditions, thereby obtaining disaster parameters such as displacement and sliding velocity in different directions. Through further comparison and verification of the overall stability analysis results of the slope obtained from both software packages, it was observed that they exhibited a consistent trend. The research findings indicate that under conditions of high-intensity short-term precipitation, the safety factor of the slope decreases to the lowest level, potentially leading to shallow landslides with smaller displacement but faster sliding velocity. Conversely, seasonal snowmelt and long-term localized precipitation have a more profound impact on the internal structure of the slope, with the sliding zone potentially penetrating into the deep bedrock. Although the occurrence frequency is low, the impact range is extensive. By combining two-dimensional and three-dimensional analyses, a comprehensive assessment of the different disaster-causing factors of the slope was conducted, enhancing the accuracy of the analysis results. The research findings provide a scientific basis and reference value for the formulation of subsequent slope protection and monitoring plans. Full article

Accurate electricity consumption forecasting is essential for the efficient planning and operation of modern power systems. The development of predictive models based on machine learning and deep learning strongly depends on the availability of well-documented and publicly accessible electricity consumption datasets. However, most existing databases are concentrated in Europe and North America and are typically focused on residential measurements obtained from smart meters, resulting in limited representation of equatorial regions. This work presents a structured review of public electricity consumption repositories, analyzing characteristics such as geographical coverage, temporal resolution, user type, and accessibility. Based on the limitations identified in the literature, a new electricity consumption dataset obtained from real measurements collected at distribution substations located in an equatorial region is presented. The dataset was organized through a systematic preprocessing workflow that included temporal standardization, construction of 48-hour sliding windows, normalization, and stratified partitioning into training, validation, and test subsets. The descriptive statistical analysis confirms the consistency of the generated subsets and reveals differences between working-day and non-working-day consumption patterns. The proposed dataset provides a reproducible resource for the development and evaluation of multi-horizon electricity demand forecasting models, as well as for load analysis and energy management studies in equatorial regions. Full article

This study extends the Absement Method to leaky-aquifer pumping-test analysis by time integrating the Hantush–Jacob governing equation and deriving four complementary operators. Time integrating the Hantush–Jacob equation yields S·s = T∇²A − C·A, with storativity S, drawdown s, transmissivity T, the time (t)-integrated drawdown A(t) (absement), and leakance C. The four operators, A(t), time-averaged A(t)/t, windowed ΔA/Δt, and the normalized absement derivative (NAD), are applied jointly across all available observation wells. In a homogeneous aquifer, the fitted operators and NAD diagnostic provide mutually consistent parameter and flow-regime signatures. In a heterogeneous aquifer, systematic differences between operators become part of the interpretation: T-related variation appears as changes in the ΔA/Δt sliding profile across wells, whereas the leakage factor B = √(T/C)-related variation is identified by divergent A(t)/t asymptotes and NAD type-curve crossing. Monte Carlo assessment under composite noise (N = 50) confirms near-zero parameter bias, with T and S standard deviations approximately 3–4 times smaller for A(t)/t and ΔA/Δt than for A(t). The three field cases are identified: a 14% outward T decline with spatially uniform B (sandstone aquifer); approximately homogeneous T with outward-declining B flagged by NAD type-curve crossing before fitting (sandy aquifer); and T–B coupling resolution through the windowed ΔA/Δt profile (medium-grained sandstone aquifer). The outputs supported sustainable-yield assessment directly from routine pumping-test records. Full article

Within the domain of Earth observation, tracking terrestrial transitions via high-resolution optical data plays a fundamental role. Nevertheless, current methods face critical challenges, including the difficulty in collaborative modeling of local details and global features and the singularity of bi-temporal difference representation, along with insufficient cross-scale feature communication, thereby constraining both the precision and resilience of models when applied to complicated environments. To solve these problems, we propose LADENet (Locally Adaptive Mamba and Multi-scale Feature Enhancement Network), an innovative framework that synergizes CNN, Transformer, and Mamba paradigms. By leveraging customized local contextual refinement alongside sophisticated hierarchical fusion, this integration delivers highly precise and resilient detection performance. LADENet adopts a weight-sharing multi-level Transformer encoder combined with a sequence reduction mechanism to generate multi-scale global features, achieving precise alignment of bi-temporal features and global context modeling while reducing computational complexity. To realize accurate localization and local enhancement of changed regions, we design a dual spatiotemporal adaptive local feature marking module based on State-Space Scanning (SSS). This module screens high-saliency changed regions through an adaptive scanning strategy, realizes pixel-aligned spatiotemporal feature fusion via cross-temporal state-space scanning, and introduces a sliding window boundary calibration mechanism to alleviate boundary information loss caused by window segmentation. To strengthen the feature representation of changed regions, a dual-branch difference enhancement module is constructed, which collaboratively captures global change trends and fine-grained local features through an attention-enhanced difference branch and a multi-scale convolution concatenation branch, effectively suppressing background interference. To address the semantic gap between cross-scale features, a global cross-scale spatial feature fusion decoder is proposed, which balances local detail preservation and global context perception through the synergy of spatial attention and two-dimensional selective scanning, completing refined multi-scale feature fusion and spatial resolution recovery. To rigorously validate the proposed LADENet, comprehensive experiments were conducted across four widely adopted bi-temporal benchmarks: LEVIR-CD, WHU-CD, CLCD-CD, and GVLM-CD. The presented architecture establishes substantial superiority over existing cutting-edge methodologies across primary evaluation criteria. Specifically, it yields an F1-measure of 91.06% alongside an IoU of 85.28% in the LEVIR-CD tests, while registering 90.51% (F1) and 82.45% (IoU) for WHU-CD. Similarly, robust outcomes are delivered on CLCD-CD (82.15% F1, 72.83% IoU) as well as GVLM-CD (89.12% F1, 77.78% IoU). These results demonstrate that LADENet possesses excellent detection accuracy, boundary delineation capability and generalization performance in diverse and intricate bi-temporal observation environments. Full article

When a short-circuit fault occurs along the transmission line of a modular multilevel converter high-voltage direct-current (MMC-HVDC) grid, the sub-module capacitors discharge, causing the fault current to rapidly rise, posing a threat to the safe operation of the system. Therefore, this paper proposes a novel fault location method based on the Gram Angle Difference Field (GADF). The column corresponding to the maximum differential value in a sliding window is used to identify the fault moment and locate faults in MMC-HVDC transmission lines. In order to effectively distinguish normal fluctuations from fault mutations and avoid false alarms, a dynamic threshold is set based on the statistical characteristics of normal data. This method utilizes the unique feature extraction capability of the GADF matrix, the adaptive mechanism of the dynamic threshold, and the stability of line-mode voltage to achieve fast and accurate fault location. Finally, this method is validated using a simulation model. The results show that the proposed method can accurately locate faults in different conditions. Full article

This study investigates the influence of barrel filling ratio on the microstructure, phase composition, and tribological performance of detonation-sprayed Cr₃C₂–NiCr coatings. Coatings were deposited at barrel filling ratios of 43% and 53% under identical spraying conditions. Microstructural characterization revealed the formation of dense lamellar coatings with low porosity and uniform distribution of Cr₃C₂ carbide particles within the NiCr metallic matrix. Compared with the coating deposited at a barrel filling ratio of 43%, the coating deposited at 53% exhibited a denser microstructure. X-ray diffraction analysis confirmed that Cr₃C₂ and NiCr remained the dominant phases after spraying, while a minor amount of Cr₇C₃ formed due to partial decarburization of chromium carbide during thermal exposure. Tribological performance was evaluated under dry sliding conditions using a ball-on-disc configuration at normal loads of 10 and 15 N and sliding speeds of 5 and 10 cm/s. Wear volume was determined from the geometry of the wear track after testing, and wear rate was calculated accordingly. The coating produced at a barrel filling ratio of 53% demonstrated improved wear resistance under elevated loads despite exhibiting a higher coefficient of friction. The minimum wear rate reached 1.23 × 10⁻⁴ mm³/(m·N), which was associated with reduced porosity and enhanced structural integrity of the coating. The obtained results demonstrate that optimization of detonation spraying parameters significantly affects coating structure and tribological behavior. The developed Cr₃C₂–NiCr coatings are promising protective materials for components operating under severe friction and wear conditions, including industrial and high-temperature engineering applications. Full article

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation. Full article

The reliable joining of graphite and Cu holds significant promise for applications in electronic heat dissipation and sliding electrical contacts. However, the substantial differences in their physicochemical properties, poor wettability, and mismatch in coefficients of thermal expansion often result in low joint strength. In this study, a two-step joining strategy combines surface modification with low-temperature sintering, and this is proposed for fabrication of Cu/graphite joints. First, the graphite surface is modified using an AgCuTi active filler alloy under vacuum conditions. Ti preferentially segregates at and reacts with the graphite interface, leading to the formation of an Ag-Cu eutectic modified layer on the graphite surface. Subsequently, low-temperature joining of the modified graphite to a Cu substrate is achieved via a hot-pressing sintering process using a Ag paste. In the sintered joint, the Ag sintered layer forms sound metallurgical bonds with both the Cu substrate and the graphite-modified layer. When the sintering temperature is 250 °C, the joint exhibits a shear strength of 30 MPa, which is significantly higher than that of a directly brazed joint. This strategy effectively reduces thermal residual stress in the joint during cooling and shifts the failure location from the brittle graphite substrate to the ductile Ag sintered layer, thereby substantially enhancing the mechanical performance. Full article

Vehicular platoons under realistic scenarios are prone to actuator saturation, model uncertainties, and external disturbances, which degrade transient tracking and spacing stability. Conventional prescribed-performance control (PPC) strictly requires initial errors to lie within a predefined envelope, while finite/fixed-time schemes cannot directly assign the settling-time bound. To resolve these limitations, this paper proposes a practical predefined-time sliding-mode adaptive platoon control strategy under input saturation constraints. Specifically, a smooth hyperbolic-tangent approximation combined with a mean-value-theorem-based gain formulation is utilized to handle saturation nonlinearity and simplify stability analysis. A novel initial-error transformation is developed to eliminate the stringent envelope constraint on the original initial tracking error. Furthermore, a predefined-time sliding variable and an adaptive compensation mechanism are synthesized to guarantee that tracking errors converge into a bounded neighborhood of the origin within a user-specified time. Numerical simulations and comparisons with predefined-time sliding-mode and PID controllers demonstrate that the proposed strategy eliminates initial error restrictions and suppresses chattering. Compared to the alternative schemes, the proposed method restricts the maximum tracking error within 0.05 m—representing reductions of approximately 77% and 91%, respectively—and shortens the settling time to within 2 s. These results validate its effectiveness for robust cooperative platoon control. Full article

This paper presents an adaptive neural-network-based algorithm for chattering mitigation in sliding mode control (SMC) of parallel multicellular DC/AC power converters. Although conventional SMC provides strong robustness against parameter uncertainties, external disturbances, and load variations, its discontinuous control action often generates chattering, resulting in excessive switching activity and reduced converter performance. To address this limitation, a computationally efficient adaptive neural network is integrated into the SMC framework to approximate the discontinuous switching term and generate a smooth control signal. The proposed algorithm updates neural network parameters online through an adaptive learning mechanism, enabling real-time compensation of modeling uncertainties while preserving the inherent robustness of SMC. The resulting adaptive neural network sliding mode control (ANN-SMC) algorithm is formulated to ensure accurate output voltage tracking, balanced operation of converter cells, and reduced switching oscillations. Extensive simulation studies are conducted under different operating scenarios, including load variations and system disturbances. The performance of the proposed method is evaluated against classical SMC using quantitative indicators related to tracking accuracy, dynamic response, robustness, and chattering suppression. The results demonstrate that the ANN-SMC algorithm significantly reduces high-frequency oscillations while improving transient behavior and maintaining robust operation. These findings indicate that the proposed adaptive learning-based control algorithm constitutes an effective and scalable solution for advanced power conversion systems operating under uncertain conditions. Full article