Search Results (8,213)

Mechanical components are more difficult to detect at the initial state of failure. To solve this problem, this paper models and simulates the characteristics of an electrostatic oil-line sensor (OLS) wear particles carried in the lubricating oil path are detected. In this study, an OLS that monitors the charge in an oil line using the principle of electrostatic induction is modeled and simulated. The sensor characteristics are simulated and tested using finite element simulation. The sensor efficiency, spatial sensitivity, and length-to-diameter ratio are simulated based on the point charges at different locations. The simulation results show that the sensitivity exhibits different trends when the point charge is inside and outside the probe. The length-to-diameter ratio is proportional to the sensor efficiency, the spatial sensitivity distribution law of multiple charges is consistent with that of a point charge, and the relative deviation rate between the mathematically calculated values and the simulated values is less than 3% under the same conditions. In conclusion, the finite element simulation results of the electrostatic oil line sensor constructed in this study are consistent with the theoretical model calculations and can be used in future mechanical fault diagnosis. Full article

Driverless electric formula racing cars are affected by nonlinear vehicle characteristics, perturbations, and parameter uncertainties during races, which can cause problems such as low accuracy and instability in trajectory tracking. Aiming to address such problems, this paper proposes a control method combining a prescribed performance control with adaptive backstepping fuzzy control (PPC-ABFC) to solve the aforementioned issues and improve the trajectory tracking accuracy and stability of racing cars. This control method is achieved by constructing a combined error model and confining the error within a prescribed performance function. The nonlinear terms, disturbances, and unknown parameters of the model are approximated by a fuzzy logic system (FLS). An adaptive parameter update law is designed to update the learning parameters in real time. The virtual control law and the real control law were designed by using the backstepping method. The stability of the PPC-ABFC closed-loop system was rigorously proved by applying the Lyapunov stability theory. Finally, simulations were conducted to compare the proposed PPC-ABFC method with other algorithms at different speeds. The results demonstrated that the PPC-ABFC method effectively enhances the trajectory tracking performance of driverless electric formula racing cars. Full article

This study explores how different electrode shapes affect plasma flow in a non-transferred plasma torch. Various cathode geometries—including conical, tapered, flat, and cylindrical—were examined alongside stepped anode designs. A 2D axisymmetric computational model was employed to assess the impact of these shapes on plasma behavior. The results reveal that different cathode designs require varying current levels to maintain a consistent power output. This paper presents the changes in electric conductivity and electric potential for different input currents across the arc formation path (from the cathode tip to the anode beginning) and relating to Ohm’s law. Significant variations in plasma jet velocity and temperature were observed, especially near the cathode tip. The study concludes by evaluating thermal efficiency across geometry configurations. Flat cathodes demonstrated the highest efficiency, while the anode shape had minimal impact. Full article

Increasing wildfires are causing global concerns about ecosystem functioning and services. Although some wildfires are caused by natural ignitions, it is also important to understand how human ignitions and human-related factors can contribute to wildfires. While dynamic global vegetation models (DGVMs) have incorporated fire-related modules to simulate wildfires and their impacts, few models have fully considered various human-related factors causing human ignitions. Using global examples, this study aims to identify key factors associated with human impacts on wildfires and provides suggestions for enhancing model simulations. The main categories explored in this paper are human behavior and activities, socioeconomic background, policy, laws, regulations, and cultural and traditional activities, all of which can influence wildfires. Employing an integrated and interdisciplinary assessment approach, this study evaluates existing DGVMs and provides suggestions for their improvement. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

The Pennes bioheat equation is the most widely used model for describing heat transfer in living tissue during thermal exposure. It is derived from the classical Fourier law of heat conduction and assumes energy exchange between blood vessels and surrounding tissues. The literature presents various numerical methods for solving the bioheat equation, with exact solutions developed for different boundary conditions and geometries. However, analytical models based on this framework are rarely reported. This study aims to develop an analytical three-dimensional model using MATHEMATICA software, with subsequent mathematical validation performed through COMSOL simulations, to characterize heat transfer in biological tissues induced by laser irradiation under various therapeutic conditions. The objective is to refine the conventional bioheat equation by introducing three key improvements: (a) incorporating a non-Fourier framework for the Pennes equation, thereby accounting for the relaxation time in thermal response; (b) integrating Dirac functions and the telegraph equation into the bioheat model to simulate localized point heating of diseased tissue; and (c) deriving a closed-form analytical solution for the Pennes equation in both its classical (Fourier-based) and improved (non-Fourier-based) formulations. This paper investigates the nuanced relationship between the relaxation time parameter in the telegraph equation and the thermal relaxation time employed in the bioheat transfer equation. Considering all these aspects, the optimal thermal relaxation time determined for these simulations was 1.16 s, while the investigated thermal exposure time ranged from 0.01 s to 120 s. This study introduces a generalized version of the model, providing a more realistic representation of heat exchange between biological tissue and blood flow by accounting for non-uniform temperature distribution. It is important to note that a reasonable agreement was observed between the two modeling approaches: analytical (MATHEMATICA) and numerical (COMSOL) simulations. As a result, this research paves the way for advancements in laser-based medical treatments and thermal therapies, ultimately contributing to more optimized therapeutic outcomes. Full article

We present a thermodynamically consistent energetic variational model for active nematics driven by ATP hydrolysis. Extending the classical Toner–Tu framework, we introduce a chemo-mechanical coupling mechanism in which the self-advection and polarization dynamics are modulated by the ATP hydrolysis rate. The model is derived using an energetic variational approach that integrates both chemical free energy and mechanical energy into a unified energy dissipation law. The reaction rate equation explicitly incorporates mechanical feedback, revealing how active transport and alignment interactions influence chemical fluxes and vice versa. This formulation not only preserves consistency with non-equilibrium thermodynamics but also provides a transparent pathway for modeling energy transduction in active systems. We also present numerical simulations demonstrating the positive energy transduction under a specific choice of model parameters. The new modeling framework offers new insights into energy transduction and regulation mechanisms in biologically related active systems. Full article

Semantic modeling of legal reasoning is an important research direction in the field of artificial intelligence and law (AI and law), aiming to enhance judicial transparency, fairness, and the consistency of legal applications through structured semantic representations. This paper proposes a semantic judicial reasoning framework based on the “Data–Information–Knowledge–Wisdom–Purpose” (DIKWP) model, which transforms the conceptual expressions of traditional legal judgment into DIKWP graphs enriched with semantics. The framework integrates the objective content of legal norms with stakeholders’ subjective cognition through a DIKWP×DIKWP bidirectional mapping mechanism, achieving “semantic justice”. Specifically, we define a DIKWP-based legal knowledge representation method and design a mapping algorithm from traditional legal concepts to the DIKWP semantic structure. To validate the effectiveness of the framework, we use a real administrative law case as an example and construct DIKWP (normative content) and DIKWP (subjective cognition) graphs to model legal rules, evidence, and various perspectives. The results indicate that the intention-driven semantic transformation mechanism can harmonize legal reasoning with stakeholders’ cognitive backgrounds, thereby enhancing the interpretability and fairness of judicial interpretation. Case analysis further demonstrates that reasoning within the DIKWP semantic space can reveal underlying assumptions, bridge cognitive gaps, and promote judicial fairness by aligning legal intentions. This study provides new theoretical and methodological support for the explainable reasoning of intelligent judicial systems. Full article

The time-domain electromagnetic method has been widely applied in mineral exploration, oil, and gas fields in recent years. However, its response characteristics remain unclear, and there is an urgent need to study the response characteristics of the borehole-surface transient electromagnetic(BSTEM) field. This study starts from the time-domain electric field diffusion equation and discretizes the calculation area in space using tetrahedral meshes. The Galerkin method is used to derive the finite element equation of the electric field, and the vector interpolation basis function is used to approximate the electric field in any arbitrary tetrahedral mesh in the free space, thus achieving the three-dimensional forward simulation of the BSTEM field based on the finite element method. Following validation of the numerical simulation method, we further analyze the electromagnetic field response excited by vertical line sources.. Through comparison, it is concluded that measuring the radial electric field is the most intuitive and effective layout method for BSTEM, with a focus on the propagation characteristics of the electromagnetic field in both low-resistance and high-resistance anomalies at different positions. Numerical simulations reveal that BSTEM demonstrates superior resolution capability for low-resistivity anomalies, while showing limited detectability for high-resistivity anomalies Numerical simulation results of BSTEM with realistic orebody models, the correctness of this rule is further verified. This has important implications for our understanding of the propagation laws of BSTEM as well as for subsequent data processing and interpretation. Full article

In practical engineering, buildings are predominantly subjected to combined forces, and reinforced concrete (RC) beams serve as the primary load-bearing components of buildings. However, there is a paucity of research on the torsional effects of RC beams, particularly concerning the torsional failure mechanisms of large-size beams. To address this gap, this paper establishes a meso-scale numerical analysis model for RC beams reinforced with Carbon Fiber Reinforced Polymer (CFRP) sheets under combined bending and torsion pressures. The research analyzes how the fiber ratio and torsion-bending ratio govern torsion-induced failure characteristics and size effects in CFRP-strengthened RC beams. The results indicate that an increase in the fiber ratio leads to accumulated damage distribution in the RC beam, a gradual decrease in CFRP sheet strain, and an increase in peak load and peak torque, albeit with diminishing amplitudes; as the torsion-bending ratio increases, crack distribution becomes more concentrated, the angle between cracks and the horizontal direction decreases, overall peak load decreases, peak torque increases, and CFRP sheet strain increases; and the nominal torsional capacity of CFRP-strengthened RC beams declines with increasing size, exhibiting a reduction of 24.1% to 35.6%, which distinctly demonstrates the torsional size effect under bending–torsion coupling conditions. A modified Torque Size Effect Law is formulated, characterizing in quantitative terms the dependence of the fiber ratio and the torsion-bending ratio. Full article

The sedimentary characteristics and model of the shallow-water delta front are of great significance for the development of oil and gas reservoirs. At present, there are great differences in the understanding of the distribution patterns of estuary dams in the shallow-water delta front. Therefore, this paper reveals the distribution characteristics of estuary dams through the detailed dissection of the Qing 1 Member in the Daqingzijing area and establishes a completely new distribution pattern of estuary dams. By using geological data such as logging and core measurements, sedimentary microfacies at the shallow-water delta front are classified and logging facies identification charts for each sedimentary microfacies are developed. Based on the analysis of single-well and profile facies, the sedimentary evolution laws of the Qing 1 Member reservoirs are analyzed. On this basis, the sedimentary characteristics and model of the lacustrine shallow-water delta front are established. The results indicate that the Qing 1 Member in the Daqingzijing area exhibits a transitional sequence from a delta front to pro-delta facies and finally to deep lacustrine facies, with sediments continuously retrograding upward. Subaqueous distributary channels and estuary dams constitute the skeletal sand bodies of the retrogradational shallow-water delta. The estuary dam sand bodies are distributed on both sides of the subaqueous distributary channels, with sand body development gradually decreasing in scale from bottom to top. These bodies are intermittently distributed, overlapping, and laterally connected in plan view, challenging the conventional understanding that estuary dams only occur at the bifurcation points of underwater distributary channels. Establishing the sedimentary characteristics and model of the shallow-water delta front is of great significance for the exploration and development of reservoirs with similar sedimentary settings. Full article

Optical clearing agents (OCAs) offer a promising approach to enhance skin transparency by reducing scattering and improving photon transmission, which is critical for non-invasive optical diagnostics such as glucose sensing and vascular imaging. However, the complex multilayered structure of skin and anatomical variability across different regions pose challenges for accurately evaluating OCA performance. In this study, we developed a multilayer Monte Carlo (MC) simulation model integrated with a depth- and time-resolved diffusion model based on Fick’s law to quantitatively assess the combined effects of OCA penetration depth and refractive index change on optical clearing. The model incorporates realistic skin parameters, including variable stratum corneum thicknesses, and was validated through in vivo experiments using glycerol and glucose at different concentrations. Both the simulation and experimental results demonstrate that increased stratum corneum thickness significantly reduces blood absorption of light and lowers the clearing efficiency of OCAs. The primary influence of stratum corneum thickness lies in requiring a greater degree of refractive index matching rather than necessitating a deeper OCA penetration depth to achieve effective optical clearing. These findings underscore the importance of considering regional skin differences when selecting OCAs and designing treatment protocols. This work provides quantitative insights into the interaction between tissue structure and optical response, supporting improved application strategies in clinical diagnostics. Full article

This research targets prediction of power consumption change (PCC) in the branch of electrical distribution grid between a sum meter and consumer meter in response to consumer load change. The problem is relevant for power preservation law-based event-driven methods aiming for detection of anomalies like meter errors, electricity thefts, etc. The PCC in the branch is due to the change of technical (wiring) losses as well as change of power consumption of loads connected to the same distribution branch. Using synthesized dataset set a data-driven model is built to predict PCC in the branch. Model performance is assessed using root mean squared error (RMSE), mean absolute, and mean relative error, together with their standard deviations. The preliminary experimental verification using a test bed confirmed the potential of the method. The accuracy of the PCC in the branch prediction is influenced by the systematic error of the meters. Therefore, the error of the consumer meter and the PCC in the branch cannot be evaluated separately. It was observed that the absolute error of the estimate of power measurement gain error was observed to be within ±0.3% and the relative error of PCC in the branch prediction was within ±10%. Full article

The present study was motivated by the need to enhance indoor air quality and reduce airborne disease transmission in dense urban environments where high-rise residential buildings face challenges in achieving effective natural ventilation. The problem lies in the lack of scalable and convenient tools to optimize natural ventilation rate, particularly in urban settings with varying building heights. To address this, the scientific technique developed with an innovative metric, the urbanized natural ventilation effectiveness factor (uNVeF), integrates regression analysis of wind direction, velocity, air change rate per hour (ACH), window configurations, and building height to quantify ventilation efficiency. By employing a field measurement methodology, the measurements were conducted across 25 window-opening scenarios in a 13.9 m² residential unit on the 35/F of a Hong Kong public housing building, supplemented by the Hellman Exponential Law with a site-specific friction coefficient (0.2907, R² = 0.9232) to estimate the lower floor natural ventilation rate. The results confirm compliance with Hong Kong’s statutory 1.5 ACH requirement (Practice Note for Authorized Persons, Registered Structural Engineers, and Registered Geotechnical Engineers) and achieving a peak ACH at a uNVeF of 0.953 with 75% window opening. The results also revealed that lower floors can maintain 1.5 ACH with adjusted window configurations. Using the Wells–Riley model, the estimation results indicated significant airborne disease infection risk reductions of 96.1% at 35/F and 93.4% at 1/F compared to the 1.5 ACH baseline which demonstrates a strong correlation between ACH, uNVeF and infection risks. The uNVeF framework offers a practical approach to optimize natural ventilation and provides actionable guidelines, together with future research on the scope of validity to refine this technique for residents and developers. The implications in the building industry include setting up sustainable design standards, enhancing public health resilience, supporting policy frameworks for energy-efficient urban planning, and potentially driving innovation in high-rise residential construction and retrofitting globally. Full article

Grasping objects with a high degree of anthropomorphism is a critical component in the field of highly anthropomorphic robotic grasping. However, the accuracy of contact maps and the irrationality of the grasping gesture become challenges for grasp generation. In this paper, we propose a reasonably improved generation scheme, called Diffangle-Grasp, consisting of two parts: contact map generation based on a conditional variational autoencoder (CVAE), sharing the potential space with the diffusion model, and optimized grasping generation, conforming to the physical laws and the natural pose. The experimental findings demonstrate that the proposed method effectively reduces the loss in contact map reconstruction by 9.59% in comparison with the base model. Additionally, it enhances the naturalness by 2.15%, elevates the success rate of grasping by 3.27%, reduces the penetration volume by 11.06%, and maintains the grasping simulation displacement. The comprehensive comparison and qualitative analysis with mainstream schemes also corroborate the rationality of the improvement. In this paper, we provide a comprehensive account of our contributions to enhancing the accuracy of contact maps and the naturalness of grasping gestures. We also offer a detailed technical feasibility analysis for robotic human grasping. Full article