Search Results (728)

As product aesthetics increasingly drive consumer preference, quantitative evaluation remains hindered by subjective evaluation biases and the black-box nature of modern artificial intelligence. This study proposes an advanced machine learning framework incorporating sensitivity-aware morphological features for the aesthetic evaluation of industrial products, with automotive design as a representative case. An aesthetic index system and its quantitative formulations are first developed to capture the morphological characteristics of product form. Subjective weights are determined via grey relational analysis (GRA), while objective weights are calculated using the coefficient of variation method (CVM) integrated with the technique for order preference by similarity to an ideal solution (TOPSIS). A game-theoretic weighting approach is then employed to fuse subjective and objective weights, thereby establishing a multi-scale aesthetic evaluation system. Sensitivity analysis is applied to identify six key indicators, forming a high-quality dataset. To enhance prediction performance, a novel model—improved lung performance-based optimization with backpropagation neural network (ILPOBP)—is proposed, where the optimization process leverages a maximin latin hypercube design (MLHD) to enhance exploration efficiency. The ILPOBP model effectively predicts aesthetic ratings based on limited morphological input data. Experimental results demonstrate that the ILPOBP model outperforms baseline models in terms of accuracy and robustness when handling complex aesthetic information, achieving a significantly lower test set mean absolute relative error (MARE = 4.106%). To further enhance model interpretability, Shapley additive explanations (SHAP) are employed to elucidate the internal decision-making mechanisms, offering reverse design insights for product optimization. The proposed framework offers a novel and effective approach for integrating machine learning into the aesthetic assessment of industrial product design. Full article

The fracturing development of low-permeability and ultra-low-permeability oil and gas reservoirs urgently requires a fracturing fluid that combines high performance and low damage. To overcome this challenge, this study synthesized a novel polyamine boron crosslinker (PBC) suitable for 0.2% guar gum. The molecular structure was characterized by Fourier transform infrared spectroscopy (FT-IR) and nuclear magnetic resonance hydrogen spectroscopy (¹H NMR). Meanwhile, this study introduced the response surface methodology and established a second-order regression model to determine the optimal synthesis conditions (polyetheramine 10.8 g, n-butanol 7.4 g, and ethylene glycol 20.7 g) with a model prediction error of only 0.7%. The results indicated that PBC exhibited excellent performance in 0.2% guar gum. The viscosity of crosslinked gel fracturing fluid remained stable at approximately 100 mPa·s under 60 °C and 100 s⁻¹ shear. The wall forming filtration coefficient was 2.30 × 10⁻⁴ m/s^1/2, and the initial filtration was 1.30 × 10⁻³ m³/m². The static settling rate was 2.4 cm·min⁻¹, demonstrating good suspended sand capacity. Furthermore, the synergistic interaction between borate ester bond and polyetheramine in the PBC conferred dynamic reversible crosslinking and uniform network formation. This enabled high-strength, low-damage crosslinking effects at low concentrations. This study provides an efficient crosslinker solution for 0.2% guar gum, holding both theoretical and engineering significance for advancing the low-cost development of fracturing fluid. Full article

Servicification—defined as the services value added embodied in goods—has been studied mainly in manufacturing, but its role in agricultural exports is less understood. We measure servicification in agricultural exports and examine how it is associated with export performance, upstream linkages and upgrading-related proxies. Using trade-in-value-added accounting for 80 countries (1995–2022), we estimate two-way fixed-effects panel models with exporter-clustered standard errors. Higher servicification is associated with both larger and intermediate agricultural value-added exports within countries over time. Decompositions show that these relationships are driven by services produced domestically, which are a location-based measure that may include services supplied by foreign-owned affiliates operating locally. Foreign services value added is not systematically related to outcomes. Servicification is also associated with a smaller agriculture-to-economy value-added gap proxy, and embodied financial and Information and Communication Technology (ICT) services appear complementary. Labour-market results for a smaller subsample are suggestive of stronger links with skill-intensive employment shares at lower GDP per capita levels. Because reverse causality cannot be ruled out, the findings are interpreted as conditional associations that motivate future causal identification. Full article

High-fidelity acquisition of canopy phenotypic data is critical for the advancement of orchard Artificial Intelligence (AI). Yet, an improper Light Detection and Ranging (LiDAR) installation height (IH) frequently induces data occlusion and substantial measurement errors. To address this limitation, this study developed an information collection vehicle (ICV) integrated with a 16-channel three-dimensional (3D) LiDAR to determine the optimal LiDAR IH. Three representative LiDAR IHs (1.4 m, 2.0 m, and 2.6 m) were evaluated on spindle-shaped cherry trees under both forward and reverse driving strategies. Subsequently, a novel 12-zone refined evaluation framework was introduced to quantify localized errors that are conventionally obscured by traditional whole-canopy metrics. Results demonstrated a profound nonlinear relationship between IH and measurement accuracy. Specifically, the 2.0 m IH (approximating the canopy’s geometric center) emerged as the optimal setup, maintaining relative errors (REs) below 5% with minimal dispersion. Conversely, the 2.6 m IH caused lower-canopy volume REs to surge beyond 16% owing to restricted downward viewing angles. Additionally, reverse driving at higher IHs exacerbated mechanical vibrations via the “lever arm effect”, thereby significantly degrading point cloud registration accuracy. Ultimately, these findings underscore the critical necessity of aligning sensors with the canopy geometric center, supplying essential theoretical guidelines for the hardware design of future orchard robots. Full article

Compared with previous analytical designs for deep UBHE, the present study is new in three aspects: (1) a segmented FLS model combined with the virtual heat source method is applied to the full U-shaped path (injection, horizontal, and production wells) in a unified formulation; (2) equivalent thermal conductivity is introduced to account for groundwater seepage in porous media, avoiding the need for separate CFD or coupled numerical solvers; (3) the relationship between production well depth and the maximum effective insulation length is quantified and discussed. Deep U-shaped borehole heat-exchangers (UBHE) systems boast high heat-exchange efficiency, yet most analytical models are too simplistic, causing inaccuracies. This study proposes a segmented finite line source (FLS) model for UBHE using the virtual heat source method. Introducing equivalent thermal conductivity (k_equ), it treats rock-soil as a groundwater-saturated porous medium, coupling seepage’s dynamic heat-transfer impact. By comparing the simulation results of the same type of research within 720 h, the average temperature difference between the models was found to be 1.31 °C, with an error rate of 5.31%, which is 40.87 percentage points lower than the existing achievements, thereby demonstrating the accuracy of this model. In addition, based on this model, the influence trends of five main factors such as seepage velocity and geothermal gradient on the system’s heat exchange were drawn and analyzed. Among them, the laying length of the insulation layer was analyzed in detail. The results show that its maximum laying length should be in line with the depth node where reverse heat exchange occurs with the production well. Under the set conditions of this study, when the depth of the production well is 2500 m, the maximum laying length of the insulation layer is 1900 m. Full article

In the current numerical simulation study of high-strength steel welding, ignoring the phase transformation plasticity effect in the coupling analysis led to a significant deviation between the simulated value of residual stress and the experimentally measured value. To investigate the influence mechanism of the Welding Residual Stresses (WRSs) of 30MnCrNiMo armor steel, the transformation plasticity (TP) coefficient (7.81 × 10⁻⁵ MPa⁻¹) was measured via a Gleeble 3500, and a Finite Element Model (FEM) of thermal–metallurgical–mechanical coupling considering yield strength, volumetric strain and TP behavior in Solid-State Phase Transformation (SSPT) was developed. The results show that the volume expansion during the SSPT is the main factor for the shift in WRS from tensile to compressive. In contrast, the TP effect reduces the peak longitudinal tensile stress in the Heat-Affected Zone (HAZ) by 51 MPa. It also ultimately neutralizes the compressive component in this region. When the martensite fraction ranges from 0.12 to 0.45, transformation plastic strain becomes the dominant factor, leading to a characteristic evolution of longitudinal stress that initially decreases and subsequently increases. The FEM incorporating the TP effect successfully captures the dual reversals of residual stress in the HAZ. The average relative error between the simulated longitudinal stress and the experimental data obtained via X-ray diffraction (cosα method) is 8.8%. The TP coefficient database and the developed multi-field coupling model markedly enhance the predictive accuracy for WRS in 30MnCrNiMo steel, offering a robust theoretical foundation for the design of stress corrosion resistance and the service life assessment of welded joints in armored vehicles. Full article

We analyze the power of the recently proposed Lorentz quantum computer (LQC), a theoretical model leveraging hyperbolic bits (hybits) governed by complex Lorentz transformations. We define the complexity class BLQP (bounded-error Lorentz quantum polynomial-time) and demonstrate its equivalence to the complexity class ${\mathrm{P}}^{♯\mathrm{P}}$ (the class of problems solvable by a deterministic polynomial-time Turing machine with access to a $♯\mathrm{P}$ oracle). LQC algorithms are shown to solve NP-hard problems, such as the maximum independent set (MIS), in polynomial time, thereby placing NP and co-NP within BLQP. Furthermore, we establish that LQC can efficiently simulate quantum computing with postselection (PostBQP), while the reverse is not possible, highlighting LQC’s unique “super-postselection” capability. By proving BLQP $={\mathrm{P}}^{♯\mathrm{P}}$, we situate the entire polynomial hierarchy (PH) within BLQP and reveal profound connections between computational complexity and physical frameworks like Lorentz quantum mechanics. These results underscore LQC’s theoretical superiority over conventional quantum computing models and its potential to redefine boundaries in complexity theory. Full article

Under the vision of carbon neutrality, the global energy system urgently requires storable, transportable, and tradable zero-carbon carriers. Iron, due to its high crustal abundance, low cost, environmentally friendly reaction products, and ease of closed-loop cycling, is being reconsidered as a potential “metallic energy” alternative to fossil fuels. This paper systematically reviews the conceptual evolution, scientific lineage, and paradigm shift logic of iron-based energy within the framework of dual pathways: combustion and electrochemistry. On the combustion front, a multi-level understanding has been established—ranging from microscopic reaction mechanisms to macroscopic flame propagation, and from unit combustors to diversified thermal power systems—laying a methodological foundation for an integrated “solid fuel–thermal–power” approach. In parallel, the electrochemical pathway has developed both liquid and solid routes, integrating energy storage, pollution control, and resource recovery within a single device through multi-valent redox reversibility, thereby expanding the concept of generalized energy storage under the “battery-as-factory” paradigm. Current research is shifting its focus from single performance metrics toward synergistic optimization of efficiency, lifespan, cost, safety, and environmental impact, marking a transition in technological paradigm from “material trial-and-error” to “mechanism design.” Looking forward, to advance iron energy beyond the experimental validation stage, it is imperative to establish a cross-scale, closed-loop scientific characterization system, develop recycling strategies with low entropy and low energy consumption, and deeply integrate with renewable electricity, hydrogen, and high-temperature heat sources to form spatiotemporally transferable zero-carbon energy systems. In this way, iron may integrate into global energy trade as a “metallic energy in specific scenarios like ports/islands,” offering a scalable, hydrocarbon-independent technological option for achieving carbon neutrality. Full article

The design of broadband, high-efficiency solar absorbers remains challenging due to the complex and ill-posed inverse mapping from the target optical responses to the physical structures in inverse design optimization. To address this, we propose a joint forward–inverse deep learning framework that enables the rapid and accurate optimization of multilayer metamaterial absorbers. This method integrates an inverse network based on a Modified Swin Transformer with a Multilayer Perceptron forward proxy and performs end-to-end training in a consistency-driven cycle. This strategy reduces the one-to-many ambiguity in inverse design and improves the prediction accuracy, with normalized test mean squared errors of 7.2 × 10⁻⁵ (inverse) and 6.8 × 10⁻⁵ (forward). Using this framework, we optimized an absorber comprising W/SiO₂ hyperbolic metamaterial stacks and TiO₂/SiO₂ anti-reflection coatings, achieving 97.4% average absorptivity across the 400–1750 nm solar spectrum, along with polarization insensitivity and robust wide-angle performance up to 60° incidence. The outdoor solar heating tests showed that the fabricated absorber reaches a peak temperature of 86.3 °C under natural sunlight, with an irradiance peak of about 850 W/m² at noon. This work shows that combining forward and reverse deep learning provides a powerful and scalable paradigm for accelerating the intelligent design of high-performance solar thermal metamaterials. Full article

Permanent ground deformation caused by fault movement threatens the safe operation of buried pipelines. Accurate fragility assessment of buried pipelines subjected to faulting is essential for pipeline design and risk management. However, buried pipelines exhibit nonlinear mechanical responses due to the coupled effects of multiple factors. Moreover, the effects of key parameters remain insufficiently quantified, limiting the accuracy and engineering applicability of existing fragility assessments. In this study, a three-dimensional finite element model incorporating large deformation and nonlinear pipe–soil interaction is developed and validated against representative experimental data. Using this model, numerical simulations are performed for 352 parameter combinations covering fault type, dip angle, burial depth, soil type, and pipe material. Nonlinear regression of the simulation results yielded predictive models for pipeline peak axial strain under normal-slip and reverse-slip faulting. A fragility framework is then established with fault displacement as the intensity measure, and fragility curves are derived for both faulting modes. The predicted peak axial strains agree with the finite element results: 78.6% (normal-slip) and 72.5% (reverse-slip) of predictions fall within ±20% error. The fragility curves enable quantitative estimation of fault-displacement thresholds. In the case study, the intact-to-damage displacement threshold is approximately 0.6 m for normal-slip faults but approximately 0.2 m for reverse-slip faults, indicating a higher failure likelihood under reverse-slip faulting. Within the investigated parameter ranges, the fault dip angle is the most significant factor affecting the pipeline failure probability for both normal-slip and reverse-slip faulting. Sandy soil and greater burial depth substantially increase the probability of moderate-to-severe damage, whereas higher steel grade increases the displacement threshold for transition from intact to failure. This study provides a rapid quantitative tool and a theoretical basis for pipeline design and risk quantification of buried pipelines in fault zones. Full article

Accurate time series forecasting of sea surface temperature (SST) is essential for understanding the ocean climate system and large-scale ocean circulation, yet it remains challenging due to regime-dependent variability and correlated errors across heterogeneous prediction models. This study addresses these challenges by formulating SST ensemble time series forecasting aggregation as a stochastic, sample-adaptive weighting problem. We propose a diffusion-conditioned ensemble framework in which heterogeneous base forecasters generate out-of-sample SST predictions that are combined through a noise-conditioned weighting network. The proposed framework produces convex, sample-specific mixture weights without requiring iterative reverse-time sampling. The approach is evaluated on short-horizon global SST forecasting using the Global Ocean Data Assimilation System (GODAS) reanalysis as a representative multivariate dataset. Under a controlled experimental protocol with fixed input windows and one-step-ahead prediction, the proposed method is compared against individual deep learning forecasters and conventional global pooling strategies, including uniform averaging and validation-optimized convex weighting. The results show that adaptive, diffusion-weighted aggregation yields consistent improvements in error metrics over the best single-model baseline and static pooling rules, with more pronounced gains in several mid- to high-latitude regimes. These findings indicate that stochastic, condition-dependent weighting provides an effective and computationally practical framework for enhancing the robustness of multivariate time series forecasting, with direct applicability to global SST prediction from large-scale geophysical reanalysis data. Full article

The aerodynamic behavior of small-scale UAV propellers operating under non-axial inflow conditions poses a significant prediction challenge due to the presence of strong azimuthal asymmetries, inherently unsteady flow phenomena, and Reynolds number effects that dominate forward flight conditions. Although numerical models based on the Moving Reference Frame (MRF) formulation combined with steady RANS solvers are widely used in engineering practice because of their low computational cost, the precise limits of their applicability in crossflow configurations remain poorly defined. This work conducts a comprehensive numerical investigation that systematically compares steady RANS–MRF predictions against time-accurate URANS simulations across a wide range of advanced ratios and rotor tilt angles. Rigorous validation of the computational framework against experimental data in axial and near-axial regimes demonstrates excellent agreement, with deviations below 5% in propulsive efficiency. The results clearly identify the operational envelope within which MRF-based steady models remain valid under non-axial inflow. In particular, the steady approach exhibits robust performance for low-to-moderate advance ratios, where global errors in thrust and power remain below 10% for $\mu =0.40$. However, the fidelity of the method deteriorates sharply under extreme edgewise-flight conditions ($\mu =0.70)$, in which the crossflow component dominates the aerodynamic field, the “frozen-rotor” assumption progressively loses mathematical consistency, and the solver may converge toward steady solutions that no longer represent a physically meaningful flow state. The URANS analysis further reveals two critical phenomena that cannot be captured by steady-state models. First, at high advance ratios, the retreating blade encounters an extensive region of reverse flow, which induces negative sectional thrust and strongly anharmonic load waveforms. This behavior has direct implications for structural design: the peak-to-peak amplitude of thrust oscillation in edgewise flight can exceed the mean thrust level, implying extreme cyclic loading and a high risk of high-cycle fatigue. Second, the simulations quantify the emergence of off-axis parasitic moments (pitching and rolling), which are negligible in vertical flight but reach magnitudes comparable to the total aerodynamic torque in forward-flight conditions. Taken together, these findings highlight the need for a hybrid-fidelity strategy in UAV propulsion analysis: employing steady RANS–MRF within the validated domain for energetic assessments, while relying on time-accurate URANS for mandatory evaluation of structural loading, vibration, and control logic in critical high-speed regimes. Full article

Accurate identification of concealed coal seam structures, such as folds or faults, is crucial for safe and effective production in the coal mining industry. In-seam seismic exploration serves as a promising technique for advanced detection of coal seam structures, but traditional numerical simulation methods easily produce errors when coping with irregular interfaces. This study uses the curvilinear grid finite-difference method (FDM) for modeling the 3D channel wave propagation. The body-fitted grids are utilized to conform to undulating interfaces, while the DRP/opt MacCormack difference scheme and the fourth-order Runge–Kutta algorithm are applied for the spatial and temporal derivative approximation, in that order. The forward and backward extrapolation for in-seam waves are implemented in the curvilinear coordinates. The roofs and floors of coal seams and special structures are imaged by reverse-time migration (RTM) using an excitation amplitude imaging condition. Numerical results show that compared with conventional methods, the curvilinear grid method effectively reduces spurious scattering caused by the staircase approximation, improves the modeling accuracy of channel waves, and enhances the continuity and interpretability of imaged coal-seam interfaces and structural boundaries. The proposed method has the potential to enhance the accuracy of channel wave exploration under complex geological conditions, supporting advanced hazard detection in coal mines. Full article

Accurate and reliable current measurement is a key prerequisite for ensuring the safe operation of power systems. Conventional through-core and wound current transformers require power outage for installation or modification of line structures, which are plagued by high installation difficulty and cost, and fail to meet the digital development needs of smart grids. To address the demand for non-intrusive installation of current transformers, this paper proposes a non-intrusive current transformer with PCB coils in reverse-series connection. First, a magnetic coupling current calculation model is established to design a reverse-series double-layer coil structure, and a mathematical model of the equivalent circuit for the sensing and measurement system is constructed. The influence of circuit parameters on the output response is analyzed, yielding an optimization method for the system operating state and completing the hardware circuit design. Subsequently, a simulation model of the reverse-series double-layer coil is built to calculate and analyze the amplitude-frequency characteristics, steady-state and transient performance, as well as anti-interference capability of the transformer. The results demonstrate that the designed transformer, combined with an active integrating circuit, achieves an upper cutoff frequency of 13,169 Hz and a lower cutoff frequency approaching 0 Hz, which satisfies the requirements of wide-frequency measurement while ensuring high sensitivity and anti-interference capability. Finally, a current-sensing experiment platform is built for comparative verification with conventional invasive current transformers. Experimental results show that after correction with a proportional coefficient of 1.317, the fitting squared error is only 0.0038. The linearity remains excellent under different conditions with a wide dynamic measurement range, and the phase error is less than 15°. Within the range of 2–120% of the rated current, the ratio error is less than 0.9%, indicating high measurement accuracy. This study provides a new high-precision and convenient method for current measurement in smart grids. Full article

Highway slope-mounted photovoltaic (HSPV) systems are increasingly deployed along expressways, yet wind loads on panel arrays can be strongly modified by slope-induced topographic effects. This study establishes a full-scale CFD framework (ANSYS Fluent, RANS with the SST k–ω model) to quantify the evolution of roadside wind profiles over embankments and the resulting wind loads on HSPV arrays. The inlet boundary layer, mesh independence, and surface pressure distributions were validated against theoretical profiles (errors < 5%), mesh refinement, and wind-tunnel data from the literature. Seven slope geometries (H = 2–10 m, i = 1:1–1:1.75) were analyzed to characterize wind-profile deviation and recovery height, followed by simulations of a 3 × 40-module array to evaluate shape and moment coefficients. Topographic effects are concentrated in the near-ground layer from the slope toe to crest, producing toe deceleration and mid-to-upper-slope acceleration; increasing H markedly enlarges the affected height range. For arrays, the slope ratio governs wake superposition and drives strong row-wise differentiation, with the rear row consistently yielding the most unfavorable net pressure and bending moment. Steep slopes can reverse the moment sign, with the moment coefficient varying approximately from −0.15 to +0.15 across the investigated cases, whereas gentler slopes amplify positive moments in the rear rows, suggesting that design checks should prioritize rear-row modules over single-row references. Full article