Search Results (1,719)

Based on experimental studies, a model of the control effect on the plastic deformation process under boundary asymmetric loading conditions has been developed. The regulating factor of plastic deformation unevenness δ, which determines the stress–strain state of the entire deformation zone and the boundary conditions, is presented. The boundary conditions, determined by additional compressive and tensile stresses along the height, generate shear stresses and specific loading regimes at the edges and within the deformation zone itself. The confirmed reduction in interaction, which coincides with the effect of plastic deformation occurring under conditions of force unevenness, is one of the criteria for the controlling effect. A distinctive feature of this approach is the recognition and proof of the existence of a controlling additional effect under conditions of complex force and deformation loading. Theoretical and experimental studies have revealed such effects under various loading conditions. Based on a closed-form problem in plasticity theory and the method of argument functions of a complex variable, a mathematical model of the control process exerted by the metal’s plastic flow zone has been developed. A key feature of the solution to this theoretical problem was the consideration of the interaction between zones under different force loads, represented by a finite-difference scheme in the mathematical model. The decisive influence of deformation unevenness from the working rolls on the force and deformation parameters of the process was demonstrated, with the deformation unevenness factor δ serving as a quantitative measure of this influence. The result obtained through theoretical justification was confirmed by numerical simulation and a comparison of calculated data with experimental data, ensuring the reliability of the result. Full article

Sustainable management of hydrogeological systems that supply water and exhibit high hydrologic complexity can be studied through pragmatic numerical modeling supported by field-constrained conceptualization. This study develops a local-scale three-dimensional groundwater flow numerical model using FEFLOW for the Barranca Lebrija settlement in Aguachica town, where the Lebrija River, the Musanda floodplain lake, and groundwater system converge. The numerical model incorporates: (i) the three-dimensional distribution of geological units and lithology; (ii) water level observations from the Musanda floodplain lake; (iii) stage records from the Lebrija River; (iv) boundary conditions and flux estimates inherited from a previous regional groundwater model; and (v) hydraulic heads from two monitoring wells and five community wells. Steady-state and transient conditions were calibrated, and a sensitivity analysis was performed to identify the parameters that most strongly control surface water–groundwater exchange. The simulations reproduce seasonal groundwater level trends and demonstrate the exchange pathways among the river, floodplain lake, and groundwater system. Results indicate dual behavior: during wet periods, flooding of the Musanda floodplain lake driven by high river levels seeps into the underlying aquifer, whereas in dry periods the floodplain lake reverses its role and becomes a principal discharge boundary. This local-scale, boundary-driven approach provides a computationally tractable framework to quantify SW–GW exchange in data-scarce tropical floodplains and supports monitoring design and water-supply management. Full article

Aviation accidents during the final approach phase of transport aircraft account for nearly half of all accidents in the flight stage, with unstable approaches being a notable contributing factor. In related accident analysis research, traditional single-parameter threshold monitoring methods have shown difficulty in capturing the complex coupling relationship between kinetic energy and potential energy. This weakness results in insufficient adaptability under variable meteorological disturbances and poor risk identification. To address this limitation, this study establishes an evaluation framework based on the concepts of “energy altitude” and “balance energy, shifting the analytical focus of aircraft state variations to energy evolution. A hybrid dynamic safety boundary function is further constructed by integrating flight mechanics principles with civil aviation regulatory constraints. This boundary integrates a height attenuation mechanism, enhancing adaptability to environmental disturbances. The study adopts QAR flight data of Boeing aircraft collected at an international airport from 2015 to 2020 as the database for machine learning modeling, and selects two additional independent flight datasets under calm-air and wind-shear conditions respectively for model verification. The research results indicate that this framework provides a robust theoretical foundation for the early identification of unstable approaches and provides actionable insights for optimizing energy control strategies, thus improving flight safety under complex operational conditions. Nevertheless, the verification only relies on two groups of typical flight cases under limited meteorological conditions, which restricts the generalizability of the research conclusions. Follow-up work will expand multi-type and multi-meteorological flight samples to carry out quantitative performance evaluation and further optimize the model’s practicality under diverse operational environments. Full article

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain and is particularly valuable in medical imaging, where dense annotations are costly and acquisition conditions vary. Cross-modality segmentation remains challenging because modality-dependent intensity and texture shifts alter image appearance, while teacher-generated pseudo-labels are often unreliable near anatomical boundaries. We propose a fractional texture-guided and boundary-aware perturbation-learning framework within a student–teacher scheme. On the source side, soft histogram transfer introduces target-related low-order intensity shifts. A multi-order fractional Gram discrepancy between shallow features of the intensity-transferred source and target images then provides a gradient signal for generating magnitude-normalized, range-clipped perturbations. This discrepancy is used as a perturbation cue rather than a direct alignment loss, exposing the student to target-relevant texture and edge-transition variation while preserving source annotations. On the target side, teacher logits are perturbed only within predicted boundary bands to model local contour uncertainty. Box-counting fractal boundary complexity guides the boundary-band width and logit perturbation scale and, together with predictive entropy, regulates pseudo-label supervision. Across five adaptation tasks, the proposed method achieves three-seed mean ± standard deviation Dice scores of 89.24 ± 0.12% and 82.01 ± 0.10% for cardiac MR→CT and CT→MR, 88.65 ± 0.29% and 90.43 ± 0.22% for abdominal MR→CT and CT→MR, and 84.76 ± 0.25% for bSSFP→LGE adaptation. Within the protocol-aware benchmark comparisons, the proposed method attains the highest average Dice score on four of the five tasks and is within 0.07 percentage points of the highest reported value on abdominal CT→MR. Ablation and operator-replacement studies further indicate that the source- and target-side pathways provide complementary benefits. Because all auxiliary perturbation and reliability-weighting modules are used only during adaptation, deployment requires only the adapted segmentation network, without additional inference-time modules or parameters. Full article

Belt conveyors are essential to continuous material transport systems, and reliable surface defect detection is therefore critical for safe and stable operation. In real industrial environments, defects such as tears, punctures, and localized damage are often small, elongated, and characterized by weak boundary contrast. Complex background interference further increases the difficulty of accurate and reliable detection for real-time defect detectors. To address these challenges, this paper proposes SEDR-Net (Structure-Edge and Detail Reconstruction Network), a YOLOv11n-based network, for this task. The Structural-Edge Fusion Block (SEFBlock), Channel-Spatial Collaborative Attention (CSCA), and Efficient Up-Convolution Block (EUCB) respectively enhance structural-edge representation, suppress redundant background responses, and recover local structures and boundary details. On a public conveyor-belt defect dataset, SEDR-Net achieves 90.8% Recall, with an mAP@0.5 of 92.4% and an mAP@0.5:0.95 of 58.4%, yielding improvements of 4.7, 3.8, and 7.1 percentage points over YOLOv11n, respectively. Meanwhile, SEDR-Net uses 2.42 M trainable parameters and maintains an inference speed of 134.2 FPS, indicating a favorable accuracy–complexity trade-off for real-time inspection. An independent external industrial test set further verifies the cross-scenario robustness and practical applicability of the proposed method under real mining conveyor-belt conditions. Full article

Pearlitic wheel steel subjected to thermal–mechanical fatigue (TMF) during braking can undergo catastrophic fracture. This study clarifies the microstructural evolution governing the macroscopic cyclic hardening/softening behavior of pearlitic wheel steel under thermal–mechanical fatigue (TMF) with a constant mechanical strain range of −0.4% to +0.2%. At lower temperature amplitudes (200–500 °C), the geometrically necessary dislocation (GND) density reaches 20.4 × 10¹⁴/m² during initial cycles, corresponding to cyclic hardening due to dislocation pile-ups at cementite lamellae interfaces. With increasing cycles, the GND density decreases to 12.3 × 10¹⁴/m², concurrent with softening arising from lamellar bending/fracture, partial spheroidization, and dynamic recrystallization of ferrite. At higher temperature amplitudes (200–730 °C), the GND density decreases from 8.8 × 10¹⁴/m² to 3.5 × 10¹⁴/m², reflecting sustained cyclic softening dominated by thermally activated mechanisms, including cementite spheroidization and dislocation annihilation. The resulting softened microstructure consists of ferrite grains, intragranular dispersed cementite, and chain-like coarse cementite at boundaries. Unlike previous studies that focused on single loading conditions (e.g., thermal fatigue, rolling contact fatigue, or wear), the present work addresses the more complex TMF scenario and quantitatively elucidates the interplay between mechanical response and microstructural evolution in pearlitic steel. This work provides theoretical guidance for the development of a fatigue life prediction model for pearlitic wheels under braking. Full article

Unidirectional glass fiber reinforced polymer (GFRP) composite insulators are widely used in extra-high voltage (EHV) and ultra-high voltage (UHV) transmission lines due to their outstanding electrical and mechanical performance. However, the accurate prediction of the critical buckling load is crucial to satisfy the high reliability requirement under complex operations. In this paper, an improved Perry–Robertson theory to predict the critical buckling loads of GFRP composite insulators with different slenderness is proposed. Firstly, initial imperfection is expressed as a function of the insulator strut length, which enables the critical load to be formulated as a function of slenderness explicitly. It also allows for convenient comparisons with other theories, such as Euler and Johnson’s, and easy calibration with the magnitude of initial imperfections. Secondly, the nonlinear material behavior of the GFRP composite insulator strut, resulting from changes in glass fiber orientation in relation to the loading direction during buckling, is considered to further enhance the prediction accuracy. The predicted results with current theory were validated through compression tests of GFRP composite insulators with solid and hollow struts and different slenderness and boundary conditions, which shows an accuracy of over 85%. Thus, the proposed improved Perry–Robertson theory can be also applied in other fiber-reinforced composite buckling analyses. Full article

Vehicle-to-Vehicle (V2V) cooperative object detection enhances environmental perception capabilities in complex traffic scenarios by sharing sensory information among vehicles, but limited transmission bandwidth and wireless channel noise can significantly affect the reliable transmission of cross-vehicle semantic features and lead to a degradation in detection performance at the receiver. Although existing semantic communication methods based on DeepJSCC can alleviate the cliff effect of traditional separated source–channel coding under low signal-to-noise ratio conditions, they typically rely on additional external autoencoder structures, which increase model complexity and the deployment burden on vehicular edge computing platforms. Meanwhile, under high compression ratios, these methods struggle to adequately preserve detection-related fine-grained information, such as object boundaries, spatial locations, and local structures. Motivated by these challenges, we develop Mamba-KGSC as a lightweight knowledge-guided semantic communication framework for robust V2V cooperative object detection. At the transmitter, Mamba-KGSC utilizes the internal time-scale parameters of the Mamba-YOLO-T backbone network to generate spatial semantic masks, realizing the sparse encoding and transmission of task-relevant features while avoiding the introduction of complex external codec networks. At the receiver, a multi-source knowledge base constraint verification module is constructed to refine the initial detection results by combining physical consistency screening with visual–physical spatial joint redundancy suppression, thereby suppressing physically inconsistent misdetections and repeated detections induced by channel noise. The experimental evaluation indicates that, under a 50% compression ratio, multiple SNR settings, and different channel models, the front-end semantic communication branch of Mamba-KGSC improves mAP@0.5:0.95 by an average of 1.90 percentage points over the DeepJSCC baseline. The multi-source knowledge base constraint verification module further reduces abnormal and duplicate candidate bounding boxes. Overall, Mamba-KGSC provides a balanced solution in terms of transmission cost, detection accuracy, model complexity, and physical consistency, offering a lightweight implementation scheme for robust V2V cooperative detection in challenging communication environments. Full article

Flood risk assessment in urban areas is becoming increasingly important due to climate change and rapid urbanization. This study presents an integrated flood risk modeling framework for the city of Volos, Thessaly, Greece, coupling the Soil and Water Assessment Tool (SWAT) with HEC-RAS 2D for comprehensive hydrological–hydraulic analysis. The study area is characterized by complex geomorphology, intense urban development and the presence of torrent streams (Xirias, Krausidonas and Anavros) with a total catchment area of 166.25 km². Geospatial and hydrological datasets, including land use, soil types and a Digital Elevation Model, were integrated within SWAT to generate 19-year daily discharge time series. These outputs were linked to HEC-RAS 2D boundary conditions to simulate flood extent, depth and velocity under two scenarios: Baseline (Manning n = 0.05) and Nature-Based Solutions (Manning n = 0.15). Results show that NBS interventions reduce flooded area by 25.3%, maximum depth by 20.4%, and affected buildings by 28.7%, with a Benefit–Cost Ratio of approximately 2.2. The methodology provides valuable input for flood risk management, spatial planning and civil protection strategies in Mediterranean urban environments. Full article

Ground vibration testing (GVT) plays a key role in the validation of numerical models and the assessment of aeroelastic stability in lightweight aircraft structures. This study presents an experimental and numerical investigation of a full-scale composite flying wing unmanned aerial vehicle (UAV) intended for vertical take-off and landing operations. Due to its low structural mass and highly integrated configuration, the aircraft exhibits increased sensitivity to modeling assumptions, boundary conditions, and measurement uncertainties. A finite element model was developed in Ansys, incorporating detailed laminate definitions and the internal sandwich structure. Experimental modal testing was performed under free-free boundary conditions using an electrodynamic shaker and a distributed measurement consisting of 94 response locations. Frequency Response Functions (FRFs), coherence analysis, and the Complex Mode Indication Function (CMIF) were employed to identify the dominant structural modes. Particular attention was given to the bending and torsional modes that govern aeroelastic behavior. Comparison of experimental and numerical results showed good agreement in mode shapes, while discrepancies in natural frequencies ranged from 10.4% to 20.1%. The results demonstrate that the model adequately captures the dynamic behavior of the aircraft and provides a reliable basis for future aeroelastic and flutter analyses of lightweight composite flying wing. Full article

Physical tempering is widely used to enhance the mechanical strength and thermal stability of glassware. Traditional numerical studies commonly adopt the uniform heat transfer coefficient assumption, which significantly deviates from the actual non-uniform jet cooling conditions, especially for glassware with complex three-dimensional curved surfaces. In this work, a fluid–thermal–structure sequential coupling numerical model for low-borosilicate glassware was developed using STAR-CCM+. The Realizable k-ε turbulence model, temperature-dependent thermophysical properties of glass and air, and transient non-uniform convective heat transfer boundaries were employed. Flow characteristics, heat transfer behavior, and residual stress distribution during air cooling were systematically investigated. The simulation results were verified using a polarizing stress instrument. Results indicate that obvious flow separation and vortices occur at the curved regions, resulting in highly non-uniform heat transfer. Temperature uniformity first decreases and then rebounds, while stress uniformity finally stabilizes above 90%. The through-thickness stress exhibits a parabolic profile with surface compression and internal tension. The maximum relative error between simulation and experiment is below 6%, demonstrating the reasonable engineering accuracy of the sequential coupling framework. Ultimately, these numerical observations quantify the fluid–thermal–structural interactions and underscore the critical importance of integrating realistic non-uniform aerodynamic boundaries. Full article

To address the structural complexity, limited detection sensitivity, and environmental susceptibility of the stable operating point in conventional fiber-optic current transformers for low-current detection, this study proposes a fiber-optic current transformer based on the coupling of an extrinsic Fabry–Perot interferometer (EFPI) and a lead zirconate titanate piezoelectric ceramic (PZT). In the proposed sensor, a toroidal magnetic core and an induction winding are used as the current pickup unit to convert the measured alternating current into an induced voltage. This induced voltage directly drives the PZT to generate axial displacement, causing periodic variations in the length of the air Fabry–Perot cavity formed between the fiber end face and the coated quartz diaphragm. As a result, the current signal is converted into an optical interference intensity signal. To prevent the static operating point from deviating from the optimal linear region during EFPI intensity demodulation, a DC-component-feedback-based operating point control method is proposed. By adjusting the driving voltage of the fiber Fabry–Perot tunable filter, the center wavelength of the incident narrowband demodulation light can track the optimal operating point of the interference spectrum, thereby improving the stability of the intensity demodulation process. Experimental results show that the fabricated sensor can generate a stable reflected interference spectrum and exhibits a relatively flat frequency response within the range of 0–7 kHz, indicating its potential for power-frequency current detection under the present laboratory conditions. When the measured current is 0.13 mA, the sensor can still produce a distinguishable sinusoidal output signal. When the measured current increases to 75 mA, obvious nonlinear distortion appears in the output signal, indicating that the sensor is approaching the boundary of its linear detection range. Within the linear operating region, the output peak-to-peak value shows good linearity with the measured current. The results indicate that the proposed EFPI-PZT fiber-optic current transformer has the advantages of a relatively simple structure, clear low-current response, and adjustable structural parameters, providing a reference for the miniaturized design and further development of new fiber-optic current sensors. Full article

Although artificial intelligence (AI) is reshaping music education, outcome-oriented quantitative syntheses remain relatively limited. This mixed-methods review examined the effects of AI-assisted music learning on learning motivation, self-regulated learning (SRL), and creative performance, while identifying learner-, task-, and time-related boundary conditions and clarifying how AI support is implemented and experienced. A three-level meta-analysis was used for quantitative integration, complemented by a qualitative synthesis of implementation pathways and learner experiences. Results showed positive trends across all three core domains, with different levels of statistical support and substantial heterogeneity. Learning motivation showed the most robust evidence (g = 1.28), creative performance showed a larger but highly heterogeneous effect (g = 1.21), and SRL showed a preliminary positive trend (g = 0.57). The task complexity × prior ability interaction provided tentative, directional evidence for learner–task fit, mainly for motivational outcomes. Dose-related analyses suggested a possible asynchronous pattern: motivational gains may emerge rapidly in the short term, whereas gains in higher-order cognition may become more evident under sustained intervention. Qualitative synthesis identified three AI implementation pathways—evaluative feedback, generative support, and adaptive personalization—suggesting that effectiveness depends less on technological complexity itself than on aligning AI roles with task demands and learner needs. Future research should strengthen long-term designs, deepen SRL-related evidence, and examine the adaptive effects of different AI roles across diverse music learning contexts. Full article

With the rapid advancement of artificial intelligence (AI), large language models exhibit nearly universal problem-solving capabilities yet cannot autonomously comprehend human intentions. As the externalization of human thinking, prompt engineering embodies human unique core value in the intelligent era. Text-to-image (T2I) research has largely focused on prompt-surface optimization, while the prior question of how user intent should be structurally encoded remains underexplored. We investigate whether 5W3H/PPS (Prompt Protocol Structure), an eight-dimension intent encoding framework previously studied in text generation, retains protocol-level relevance in neural image generation. Using a frozen baseline pilot and two follow-up studies evaluated on three commercial Chinese T2I systems under Chinese-language task specifications, we examine three issues: cross-modal protocol transfer, functional differentiation within the Why dimension, and task-dependent density boundaries in structured intent encoding. We find evidence that PPS supports a task-conditioned intent protocol in image generation, rather than functioning as a uniformly superior prompting method. In the baseline pilot, the structural gap—defined as the difference between dimensional recovery and intent fidelity—persists across all pilot task–condition aggregates, indicating that structurally plausible images can still fail to preserve user-specific intent. A Why-decomposition study shows that purpose-oriented formulations outperform audience-oriented or mixed formulations in high-complexity tasks, whereas audience specification is more useful in lower-complexity settings. A density study further shows that protocol density is non-monotonic: some tasks benefit from full eight-dimension specification, whereas others collapse under over-specification. Taken together, these findings suggest that structured intent encoding in T2I is better understood as a task-calibrated protocol variable than as a uniformly beneficial prompting strategy. All findings are established on three Chinese commercial T2I systems under Chinese-language specifications and should be read as evidence for a task-calibrated intent protocol within this setting, not as a claim that generalizes to other languages, model families, or image-generation systems. Full article

Continuous alumina (Al₂O₃) fibers are critical reinforcement materials for ceramic matrix composites (CMCs) utilized in extreme high-temperature environments. While their baseline thermal and mechanical properties are well-documented, their long-term service reliability in complex, multi-field environments—specifically coupled thermal, hygral, and atmospheric conditions—remains insufficiently quantified. This study systematically investigates the degradation mechanisms of alumina ceramic fiber ropes subjected to simulated engine exhaust atmospheres and cyclic rain exposure. By integrating macroscopic tensile testing with rigorous multi-scale microstructural characterizations (SEM, XRD, TGA, and advanced surface chemical state analyses via EDS and XPS), a comprehensive degradation model is proposed. Our findings reveal a pronounced two-stage mechanical degradation behavior: an initial catastrophic strength collapse followed by a stabilization phase. We elucidate that the initial embrittlement is governed not merely by thermal damage, but fundamentally by the hydrothermal volatilization and depletion of the surface amorphous SiO₂ binder, which annihilates the inter-fiber cooperative load-sharing capability. Concurrently, quantitative XPS and XRD analyses strongly suggest that the internal amorphous grain-boundary films undergo rapid structural rearrangement and crystallization, effectively homogenizing the microstructure and shifting the fracture mechanics from energy-dissipative crack deflection to unhindered brittle cleavage. After the preferential depletion of the amorphous silicate phase, the exposed α-Al₂O₃ core dictates a stabilized mechanical response. This research provides critical theoretical frameworks and experimental evidence for the life-cycle assessment and microstructural optimization of advanced oxide ceramic fibers in next-generation aerospace applications. Full article