Abstract:This study fabricates magnetorheological elastomer (MRE) samples with iron particle contents of 10%, 30%, 60%, and 75%, investigating how iron particle content influences the static and dynamic properties of MREs through experimental tests. Results show a positive correlation between increasing iron particle content and both tensile strength and elongation at break, although excessively high content leads to reduced mechanical properties. In terms of damping and hysteresis performance, higher iron particle content significantly improves maximum damping force, equivalent stiffness, storage modulus, and dissipated energy, displaying a distinct nonlinear trend. Additionally, the influence of loading frequency on material properties exhibited complex nonlinear and coupling effects. The study employes the Bouc-Wen model combined with a genetic algorithm to identify parameters of the material’s nonlinear hysteretic characteristics, achieving a strong fit. Comprehensive analysis identifies the sample with 60% iron particle content as having the best damping and energy dissipation performance across various conditions. These findings reveal the correlation between iron particle content and MRE properties, providing a theoretical foundation for the development and optimization of MREs.

Abstract:A thermal comfort evaluation model, based on energy balance principles, is developed for assessing indoor thermal comfort in a room equipped with a built-middle PV-Trombe wall during the heating season. The study also investigates how the installation location of the absorber plate affects indoor thermal comfort. Results show that while the built-middle PV-Trombe wall improves indoor thermal comfort, it maintains only a basic level of comfort overall. Between 9:00 and 13:00, the indoor environment remains cold, resulting in occupant discomfort. However, when solar radiation is low, the enclosed air layer formed by the photovoltaic panel and glass cover can still help maintain a more comfortable room temperature. Positioning the absorber plate on the outer surface of the massive wall enhances the indoor thermal environment, reducing discomfort duration by one hour without additional costs. The thermal comfort evaluation model constructed here allows for easy prediction of indoor thermal comfort variations over time, facilitating dynamic management of indoor temperature.

Abstract:As agricultural non-point source runoff is transported to a wastewater treatment plant by the sewage interception trunk around Dianchi Lake, the total phosphorus(TP) concentration decreases along the way, often falling below the designed influent concentration at the trunk’s endpoint. This reduction can result in non-compliance with discharge standards. To explore the influence of key design parameters on endpoint TP concentration and provide technical guidance for sewage interception trunk design, this study focuses on granular phosphorus from agricultural runoff transported by a well-designed sewage interception trunk on the eastern bank of Dianchi Lake. Using the three-dimensional simulation software FLUENT, a multiphase flow model was constructed to stimulate the fluid flow process within the trunk. The influences of section shape, roughness, hydraulic diameter, and slope on the endpoint TP concentration were analyzed. Results show that the endpoint TP concentration is lowest for circular cross sections and highest for trapezoidal sections. As roughness increases, TP concentration rises across all section shapes, with the smallest variation range in rectangular sections, and the largest in circular sections. Increasing hydraulic diameter or slope results in increased or decreased TP concentration for rectangular or trapezoidal sections, while in circular sections, TP concentration either decreases or shows minimal change.

Abstract:To facilitate the sustainable use and effective absorption of earth and stone byproducts from engineering construction projects, and to enhance the preservation and appreciation of natural resources, this study integrates GIS technology and survey big data into the management of earth and stone volumes for engineering construction in mountainous cities within a three-dimensional geographic information framework. A scientific method is proposed for managing these materials in abandoned construction areas. Through field observations, literature review and interviews, this paper explores methods to turn these “waste” materials into valuable resources. Using Chongqing’s Beibei District as a case study within territorial spatial planning, we investigate specialized resource utilization strategies for earth and stone byproducts, analyzing classification, value, and potential benefits. The study also addresses site selection for storage, incorporating ecological restoration of mined areas to maximize spatial planning’s role in guiding, restricting and guaranteeing the implementation of the project. This approach promotes the intensive and efficient use of earth and stone resources.

Abstract:Saline soils are widely distributed in the desert regions of western China, where road base deformation due to soluble sulfate poses a significant issue. Using multiphase flow theory, this study conducts a numerical simulation of water transport in unsaturated cement-based mixtures under infiltration conditions with Comsol Multiphysics software. The model considers capillary pressure as the driving force for water phase transport and saturated vapor pressure as the driver for both water and gas phase transport. Dynamic and evaporation governing equations for the water and wet air phases in unsaturated porous media are derived, forming the basis of a water transport model for partially-immersed unsaturated mixtures that accounts for water and wet air evaporation. Capillary absorption characteristics of three types of cement-stabilized materials are obtained through water transport testing, and model accuracy is verified against experimental results. Findings show that the capillary absorption coefficient is the highest for XM (0.09 g/cm²), followed by GK (0.085 g/cm²), and lowest for GM. In unsaturated porous media, fluid movement in pores is mainly driven by capillary water transport in the early stages, with later stages dominated by convective effects due to vapor pressure differences. These results highlight the critical influence of water capillary and evaporation on soluble salt transport in partially-immersed unsaturated cement-based mixtures. This research advances understanding of water transport in unsaturated media and provides a foundation for exploring water-salt transport in such materials and establishing numerical models.

Abstract:Assessing CO₂ storage capacity in saline aquifers is fundamental to supporting China’s carbon neutrality goals. The Sichuan Basin, an important oil and gas production area in southwest China, lacks in-depth quantitative research on CO₂ storage potential in its deep saline aquifers. The Leikoupo Formation(T₂l) is a high-quality brine reservoir within the Sichuan Basin, with its fourth(T₂l⁴), third (T₂l³), and first(T₂l¹) members identified as target reservoirs for CO₂ sequestration. This study utilizes an analytical solution to assess overpressure induced by multiwell simultaneous injection, and applies the MATLAB-based script CO2BLOCK to evaluate the CO₂ storage potential of deep saline aquifers in the Leikoupo Formation. Results show that under a 30-year continuous CO₂ injection scenario, the CO₂ storage capacity of the Leikoupo Formation reservoir in the Sichuan Basin is 0.83 Gt, with the fourth member showing the highest capacity and the first member the lowest. The maximum sustainable injection rates per well for the fourth, third and first members are 0.550 Mt/a, 0.051 Mt/a and 0.054 Mt/a, with corresponding maximum sustainable overpressures of 3.09 MPa, 5.67 MPa and 6.55 MPa, respectively. The optimal economic configurations (number of wells, well spacing in kilometers) for T₂l⁴, T₂l³ and T₂l¹ are (16, 17) (20, 17) and (16, 19), respectively, with storage capacities of 0.50 Gt, 0.07 Gt and 0.04 Gt under these configurations.

Abstract:This study presents a smart contract vulnerability detection method using MixStyle transfer to address challenges related to limited datasets and the detection of unknown vulnerabilities when new ones arise in smart contracts. The method first extracts the abstract syntax tree from the smart contract source code and uses a graph attention network to capture dependencies and information flow between nodes. Then, maximum mean discrepancy(MMD) is used to facilitate effective knowledge transfer from known vulnerabilities to emerging ones, thus expanding the dataset available for deep learning model training. Finally, the MixStyle technique is incorporated into the classifier to enhance model generalization and improve the accuracy of identifying novel vulnerability types. Experimental results show that this method outperforms BLSTM-ATT, BiGAS, and Peculiar methods in F1, ACC, and MCC metrics for detecting four types of vulnerabilities.

Abstract:Crack detection is crucial for assessing structural safety. Traditional image processing methods for crack detection in tunnels often suffer from high noise levels and low accuracy due to uneven lighting and severe noise pollution. To address these challenges, this study proposes a tunnel crack detection algorithm based on machine vision. First, tunnel images are filtered in the frequency domain and differentiated in the spatial domain to enhance texture features. Then, image segmentation is performed with area parameter T_v, saturation parameter T_s and special parameters Tv' and Ts' to remove background noise and reduce misdetections, facilitating complete crack detection. Finally, a lightweight crack-connection algorithm is designed to bridge discontinuities in crack images, based on the stability and development pattern of cracks in this application scenario. Experimental results show that the proposed method effectively extracts complete cracks, achieving an image recognition accuracy of 94%, and a recall rate of 98%, meeting the requirements of practical engineering applications.

Abstract:Electric vehicle wireless power transfer (EV-WPT) technology holds promising application potential. However, during vehicle parking, misalignment or angular deviation in the coupling mechanism can reduce charging power, efficiency, and may even cause charging failure. To address these issues, this paper presents a novel coupling mechanism with omnidirectional anti-offset capability. Additionally, the design method incorporates stray spacing to increase mutual inductance between energy coils, reduce the self-inductance of the transmitting coil, and minimize the wire required for coil winding. Leveraging the decoupling characteristics of dual D-type and Q-type coils, the receiving coil and the receiving-end compensation coil are magnetically integrated, saving space and reducing the amount of magnetic core material needed. Finally, a simulation model with 1.4 kW output power is developed to verify the system’s robust anti-offset capability and stable constant current output.

Abstract:Sentence sentiment classification is an important task for extracting emotional semantics from text. Currently, the best tools for sentence sentiment classification leverage deep neural networks, particularly BERT-based models. However, these models require large, high-quality labeled datasets to perform effectively. In practice, labeled data is usually limited, leading to overfitting on small datasets and difficulties in capturing subtle sentiment features. Although existing semi-supervised models utilize features from large unlabeled datasets, they still face challenges from errors introduced by pseudo-labeled samples. Additionally, once test data is labeled, these models often do not adapt by incorporating feature information from test data. To address these issues, this paper proposes a semi-supervised sentence sentiment classification model. First, a K-nearest neighbors-based weighting mechanism is designed, assigning higher weights to high confidence samples to minimize error propagation during parameter learning. Second, a two-stage training mechanism is implemented, enabling the model to correct misclassified samples in the test data. Extensive experiments on multiple datasets show that this method achieves strong performance on small datasets.

Abstract:In wireless power transfer(WPT) system design, incorporating multiple transmitters can improve power transmission capacity and reduce voltage and current stress on power conversion components. This paper proposes a dual-transmitter WPT system featuring two operational modes: standby and strengthening. When the receiver requires low power, one transmitter operates in standby mode, while the other provides power to reduce system losses. When the receiver’s power demand increases, the system switches from standby to strengthening mode, with both transmitters supplying power to the receiver. Additionally, to address energy cancellation caused by phase inconsistencies in the output voltages of the two transmitter inverters, a synchronization control method is introduced, ensuring synchronized operation and boosting system power capacity. Experimental results confirm the feasibility and effectiveness of the proposed method.

Abstract:This study proposes a physics-informed neural networks (PINN) approach to solve transient nonlinear heat conduction problems and estimate the temperature-dependent thermal conductivity. First, a loss function is formulated using the residuals of partial differential equation, initial conditions, and boundary conditions specific to heat conduction. Then, automatic differentiation is applied to compute the temperature’s partial derivatives within the equation. The heat conduction problem is solved by minimizing the loss function through a gradient descent algorithm, which updates the network parameters. The influences of varying the number of hidden layers, neurons and interior collection points on the results are also examined. Finally, the PINN is applied to identify temperature-dependent thermal conductivities by formulating a loss function that includes residuals from the governing equation, measured temperature, and computed temperature. The network parameters and thermal conductivity values are updated by gradient descent algorithm to approximate the true solution. Additionally, the influences of different measurement points and errors on the results are compared. The findings show that the proposed method effectively solves transient heat conduction problems and accurately estimates temperature-dependent thermal conductivity.

Abstract:Titanium dioxide (TiO₂)-based materials are commonly used in constructing photoelectrochemical(PEC) sensors due to their advantages, such as an optimal band gap, abundant surface-active sites, and environmental friendliness. These materials hold significant promise for the efficient enrichment and detection of environmental pollutants. However, high recombination rates of photogenerated charges and limited visible light utilization remain challenges that severely restrict the PEC performance of TiO₂-based materials. This paper provides a comprehensive overview of the mechanisms of PEC sensing, key factors affecting PEC sensor performance, as well as various modification strategies such as heterostructure construction, crystal plane adjustment, ion doping, and dye sensitization. Furthermore, the application of TiO₂-based PEC sensors in detecting trace levels of diverse environmental pollutants is discussed. Finally, the paper presents a summary and outlook on the advancements of TiO₂-based materials in environmental detection, serving as a reference for developing highly sensitive TiO₂-based PEC sensors.