In body area network(BAN)environments,traditional access control models face challenges such as single points of failure,rigid permission structures,and limited support for dynamic authorization.To address these challenges,this study proposes a blockchain-based dynamic trust delegation access control model for BANs.To effectively reduce storage and computational overhead,a lightweight two-layer blockchain architecture is designed,in which global policy management is maintained on the main chain,while specific service operations are processed on the subchain.In addition,a multi-smart-contract access control framework is developed to enable the automated management and execution of delegated authorization.To support dynamic permission adjustment,a trust evaluation mechanism integrating identity credibility,behavioral history,and real-time physiological context is further introduced.Experimental results show that the proposed model significantly reduces permission verification delay and emergency access latency,improve the success rate of delegation operations,and effectively reduce storage overhead.Overall,the model provides secure,efficient,and flexible access control support for resource-constrained body area network environments.
This study investigates the release patterns of major cations from coal and gangue collected from the Pingdingshan mining area under various conditions,including particle size and solution chemistry,with the aim of elucidating the underlying mechanisms controlling mine water mineralization.Laboratory experiments were conducted to analyze ion release characteristics and pH evolution.The results show that particle size plays a critical role in controlling ion release and solution pH.Finer particles enhance the dissolution of aluminosilicates and carbonate materials in coal-bearing strata,leading to enhanced release of key ions such as Si4+and Al3+.Gangue is identified as the primary source of Si4+and Al3+,whereas coal predominantly contributes Ca2+;moreover,the presence of gangue suppresses the release of Ca2+.Ion release patterns in actual mine water differ from those observed in deionized water,with Ca2+and Mg2+concentrations in mine water mainly governed by coal.Overall,the findings demonstrate that the interactions between coal and gangue significantly impacts the geochemical evolution of mine water,while the initial solution environment primarily regulates the dynamics of ion release and pH changes.
To address the low efficiency and accuracy of manual testing in railway computer interlocking systems,this study proposes a deep learning-based method for text localization and recognition in interlocking interface images.First,a text localization model based on the connectionist text proposal network(CTPN)is developed.By comparing multiple backbone networks(ResNet50,AlexNet,ZF and VGG16),VGG16 is selected as the feature extractor to enhance high-level semantic representation and improve the detection of small text regions.Second,the generalization ability and robustness of the CTPN model are improved through performance comparison with common object detection models and the incorporation of dropout.A projection-based segmentation method,combining horizontal and vertical projections,is further employed to address text adhesion issues in the interface.Finally,an improved AlexNet model is used for text recognition.Experimental results on a railway interlocking interface dataset in the TensorFlow environment show that the proposed method achieves a localization accuracy of 87.98%,a recall of 73.33%,and an F-score of 80.39%,while the recognition accuracy reaches 89%.These results demonstrate that the proposed approach can effectively locate and recognize interface text,providing reliable data support for automated routing and test result analysis in interlocking system testing.
With the continuous expansion of water resource allocation projects,accurate electricity consumption forecasting is crucial for energy conservation,cost control,and construction efficiency.Traditional forecasting methods,such as long short-term memory(LSTM)networks and Transformers,often struggle to capture both short-term and long-term dependencies in complex time-series data.To address this challenge,this paper proposes an xLSTM(extended long Short-term memory)model for multi-regional power consumption forecasting.The xLSTM model combines the short-term dependency modeling capability of sLSTM with the long-term dependency learning capacity of mLSTM,enabling effective analysis of power consumption data across multiple regions while considering temporal correlations among regions.Experimental results show that xLSTM achieve superior predictive performance,with a mean square error(MSE)of 0.0030 and a mean absolute error(MAE)of 0.035,outperforming competing models.The proposed model provides effective technical support for precise electricity demand forecasting and offers practical value for decision-making and intelligent scheduling management in large-scale water resource allocation projects.
Traditional single-frame pose estimation methods in simultaneous localization and mapping(SLAM)often suffer from cumulative errors,map misalignment,and trajectory drift due to unreliable inertial measurement unit(IMU)data and sparse point cloud features.To address these issues,this study proposes an enhanced pose estimation method based on multi-frame data fusion and optimized front-end scan matching.The proposed approach performs multi-frame fusion of LiDAR data by exploiting pose transformation relationships between consecutive frames.A weighted LiDAR-IMU fusion strategy is employed for pose prediction.During scan matching,statistical filtering is introduced to remove point cloud noise,and pose estimation is further refined through a secondary matching process.Experimental results demonstrate that,compared to mainstream conventional algorithms,the proposed method improves localization accuracy in real-world scenarios by 28.4%,30.1%,and 65.3%,respectively,effectively reducing cumulative errors and enhancing trajectory estimation accuracy and mapping quality.This study provides a novel solution for enhancing pose estimation accuracy and mitigating cumulative errors in mobile robots mapping and self-localization tasks.
This study aims to elucidate the effects of dissolved oxygen(DO)on aerobic granular sludge(AGS)systems treating influent with different organic matter concentrations.Two AGS reactors,i.e.R1(low organic load)and R2(high organic load),were operated under DO ranges of 4 mg/L to 6 mg/L and 2 mg/L to 4 mg/L to investigate differences in pollutant removal performance,microbial community structure,and functional gene profiles.The results show that after reducing the DO concentration maintained high removal efficiencies of chemical oxygen demand(COD)and total phosphorus(TP)in both reactors.Meanwhile,the rates of endogenous denitrification coupled with simultaneous nitrification increased by 17.54%and 7.05%in R1 and R2,respectively,with corresponding increases of 9.84%and 6.11%in their contribution to total nitrogen removal.Lower DO levels also induced shifts in microbial community structure,enriching functional microorganisms associated with nitrogen and phosphorus removal.In addition,the abundance of genes related to denitrification and intracellular carbon utilization increased,promoting enhanced nutrient removal performance in the AGS systems.Furthermore,DO variation exerted a more pronounced effect on the low-organic system(R1),indicating that more accurate DO control is required in such conditions for optimal operation.
To address the issues of high computational complexity and large parameter size in convolutional neural network(CNN)-based image dehazing,this study proposes a lightweight dehazing network(LDNet).First,the atmospheric scattering model is reformulated to directly suppress haze noise,thereby reducing cumulative errors in intermediate variable estimation.Second,a reverse residual network module with an attention mechanism(RNAM)is designed to extract multi-scale features while emphasizing critical semantic information,effectively reducing model complexity and parameter size.Finally,a joint loss function combining L1 smoothing loss and multi-scale structure similarity(MS-SSIM)loss is used to improve reconstruction quality.The experimental results show that the proposed method outperforms existing approaches in terms of structural similarity and peak signal-to-noise ratio(PSNR)on synthetic datasets,while also achieving effective dehazing performance on real-world images.In addition,the model exhibits reduced parameter size and improved computational efficiency.
This study investigates the differences in roof caving structure and crack evolution between gob-side entry retaining and traditional coal pillar mining.Taking the 52605 and 52606 working faces of Daliuta Coal Mine as the engineering background,two sets of similar-material simulation experiments were conducted to reproduce the mining processes under both conditions.The movement and fracture evolution of the overlying strata were systematically recorded and analyzed.The results show that,under gob-side entry retaining with flexible formwork concrete walls,the crack development rate at the end of primary mining is lower than that during secondary mining.In contrast,under traditional coal pillar mining,the crack evolution patterns on both sides of the coal pillar are similar.Significant differences are observed between the two mining methods in terms of crack rate,crack type,caving range,and caving angle.Specifically,for gob-side entry retaining,the crack rate of overlying strata reaches 5.0756%,the caving range extends to within 50m,and the caving angle varies from 31° to 86.9°.For coal pillar mining,the crack rate is 2.8604%,the caving range is within 40 m,and the caving angle ranges from 50° and 52°,with shear cracks dominating along the caving direction.After mining with gob-side entry retaining,the roof strata on both sides of the concrete wall remain stable without sliding,forming a hinged structural system.In contrast,in coal pillar mining,the overlying strata on both sides of the pillar tend to fail together as a whole after extraction.These structural differences lead to distinct load transfer mechanisms,resulting in significant stress concentration effects on the concrete wall in gob-side entry retaining faces.
Accurate estimation of atmospheric environmental capacity is essential for optimizing industrial structure and assessing the development potential of industrial parks;however,single models often lack predictive accuracy.To address this issue,this study develops a smoke-integral multi-dimensional multi-box model based on site-specific environmental parameters and applies it to an industrial park in Chongqing.Results indicate that the calculated atmospheric environmental capacity exceeds current pollutant emissions,confirming potential for further development under existing emission controls.Comparison with the modified A-value method demonstrates minimal discrepancies,revealing the reliability and accuracy of the proposed model.Furthermore,by combining model results with current emission characteristics,targeted optimization strategies for industrial park planning and development are proposed.
The performance of the driving circuit is critical for the stable and high-precision operation of micro piezoelectric ultrasonic motors.To meet the low-voltage drive and high-speed operation requirements of a 1 mm micro cylindrical piezoelectric ultrasonic motor,a transformer-inverter booster push-pull driving circuit based on the digital signal processing(DSP)28335 chip is proposed.The DSP 28335 generates four pulse-width modulation(PWM)signals,which alternately drive four MOSFET switches.The inverter boosts and amplifies the voltage through a transformer,and an LC matching circuit filters the output to produce sinusoidal waves with equal amplitude and 90° phase difference,driving the motor.Detailed hardware and software design analyses are presented.Simulation and experiment results show that the output voltage is adjustable from 0 V to 100 V and the frequency from 15 kHz to 50 kHz.The no-load speed of the motor increases linearly with excitation voltage,reaching 480 r/min at 22 kHz and 50 V,satisfying the driving requirements of the 1mm cylindrical piezoelectric ultrasonic motor.
Traditional rotor position detection schemes typically mount Hall sensors on the stator or motor base to measure the air-gap magnetic field or permanent magnet leakage.However,armature reaction significantly influences detection accuracy.This study proposes mounting Hall sensors on printed circuit boards(PCBs)external to the rotor of an external rotor permanent magnet synchronous motor(PMSM).With the permanent magnet slightly extending beyond the rotor yoke,rotor position is determined by detecting the magnet's field,which is minimally influenced by armature reaction.Theoretical and experimental analyses reveal that when two Hall sensors are placed at a 90° interval on the PCB,the fundamental phases of their signals are not orthogonal,resulting in position errors.To resolve this,it is shown that orthogonal fundamental phases are achieved when the sensors are spaced at an interval of 90°∙Pr/(Pr+1).The theoretical predictions are validated through ANSYS Maxwell finite element simulations and physical motor experiments,confirming the feasibility of the proposed position detection scheme.
Gas supply systems are generally not allowed to operate under leakage conditions;pipeline networks with extensive leakage after earthquakes must be shut down immediately for inspection and repair.However,under low seismic intensity or minor pipeline damage,complete shutdown is often impractical due to urban gas demand,and the system may operate under slight leakage conditions.To evaluate the reliability of gas supply networks under such conditions,this study integrates post-earthquake pipeline failure states with a leakage model for buried gas pipelines and establishes a hydraulic analysis model that accounts for leakage.A three-state failure probability model is coupled with Monte Carlo method to randomly generate post-earthquake pipeline failure states.The functional reliability of the gas supply network under specified seismic conditions is then evaluated using a"pressure-driven"hydraulic analysis method,and the service status of user nodes after the earthquake is quantified.Based on this framework,the functional reliability of a low-pressure gas supply network under different seismic intensities is analyzed through 1 000 Monte Carlo simulations.The case study results show that the system maintains high seismic reliability under Ⅵ and Ⅶ earthquakes,while reliability decreases significantly under Ⅷ earthquakes.These findings provide a scientific basis for prost-earthquake reliability assessment and repair planning of urban gas supply pipeline networks.
To address the issues of computational redundancy and communication burden in model predictive control(MPC)of indoor thermal environments,this study proposes an integral-type event-triggered control strategy.First,a simplified building resistant-capacitance(RC)thermal network model is established using an equivalent circuit method,incorporating the influence of adjacent thermal zones,and its accuracy is verified.Then an integral-type event-triggered mechanism(ITETM)based on state errors is introduced.Building on this mechanism,an integral-type event-triggered MPC method grounded in the RC thermal network model is formulated.Finally,the performance of the proposed control method is verified by co-simulation experiments using EnergyPlus and MATLAB.The results show that the proposed control strategy effectively reduces computational effort and communication frequency in the optimization process,while lowering building energy consumption and maintaining indoor thermal comfort.
Previous studies have indicated that the sudden increase in jacking force and pipe sticking in the Guanjingkou rock pipe jacking project is closely associated with mud cake formation from crushed debris.To address this issue,acidic and alkaline solutions are applied on-site to corrode the mud cake and restore jacking progress.However,during the rainy season,these solutions may be transported backward by rainwater,potentially affecting subsequent pipe sections.To investigate whether the coexistence of karst water and acid and alkaline cleaning solutions influences the frictional characteristics of subsequent pipe sections,this study combines indoor direct shear tests,scanning electron microscopy(SEM),and field monitoring.The frictional characteristics at the pipe-rock interface under different pH conditions and debris mixing scenarios are systematically investigated,and the effects of key factors on the friction coefficient are analyzed.By comparing predictions from the experimental model with field monitoring data,the reliability of the results is verified.The findings provide a theoretical foundation and practical guidance for friction control and pipe sticking mitigation in karst environments.
AC excitation motors offer advantages such as constant frequency under variable speed and decoupled power in steady-state operation,making them suitable for applications like pumped storage and flywheel energy storage.However,these applications demand rapid emergency braking under high-inertia loads,which traditional mechanical braking strategies fail to meet.This study proposes a flexible braking strategy and parameter optimization method for high-inertia AC excitation motors.First,based on rotor structural characteristics,the strategy connects the rotor to a DC excitation source and the stator to multi-stage resistors,and derives the equivalent braking circuit.Second,a braking parameter optimization model is established,with multi-stage braking resistance,rotor excitation current,and resistor switching speed as variables;motor ratings and resistor power limits serve as constraints;and the shortest braking time is set as the objective.The model is solved using a genetic algorithm.Finally,multi-stage resistance braking results and influencing factors are analyzed via Matlab/Simulink simulations,and a 7 kW AC excitation motor platform is used to verify the simulations.Results show that the proposed multi-stage flexible braking strategy effectively reduces braking time,and optimized parameters achieve minimal braking duration while satisfying system power constraints,balancing braking efficiency and device economy.
This study applies machine learning techniques to predict the international roughness index(IRI)of asphalt pavement using structural,performance,environmental,and traffic-related variables.Data were obtained from the long-term pavement performance(LTPP)database and Chinese pavement datasets,with 3 066 asphalt pavement sections(construction number=1)selected for analysis.Model parameters were optimized using cross-validation combined with grid search.Considering the selected factors,three machine learning models,namely artificial neural networks(ANN),support vector machines(SVM),and XGBoost,were employed to predict IRI.Their performance was evaluated using R²,root mean square error(RMSE)and mean absolute error(MAE).The results show that XGBoost achieved the best predictive performance(R²=0.96,RMSE=0.08,MAE=0.05).Feature importance analysis based on XGBoost indicates that the initial IRI is the most influential factor.These results show that XGBoost can accurately predict asphalt pavement IRI and provide a reference model for pavement management systems.
To overcome the limitations of conventional constant-speed control methods in extreme mountainous conditions,this paper proposes a variable-speed coordinated longitudinal-lateral control strategy based on the coupling characteristics of vehicle dynamics.The strategy adopts a hierarchical control structure.The upper layer develops a steady-state evaluation model to provide decision support for subsequent control layers.The middle layer primarily utilizes a two-level model predictive control(MPC)framework to coordinate potential conflicts among longitudinal four-wheel slip rates,lateral active front steering(AFS)and direct yaw control(DYC),and outputs the total driving torque and yaw moment.The lower layer employs a weighted least squares method to optimally distribute torque based on the vehicle's operating state.A simulation model is constructed using Simulink and CarSim to evaluate performance under various complex road conditions.Results demonstrate that the proposed strategy significantly improves the driving stability of distributed electric vehicles under variable speed extreme conditions.
To study the influence of L-shaped flow deflectors on the vortex-induced vibration(VIV)characteristics of steel box girders,a series of 20 test cases was designed.A sectional model wind tunnel test was carried out under a+5° wind attack angle to analyze the VIV response patterns associated with variations in horizontal plate width and vertical plate height.In parallel,computational fluid dynamics(CFD)simulations were performed,and the vortex structures were extracted using the Ω vortex identification method to reveal the underlying VIV evolution mechanism.The results show that increasing the horizontal plate width significantly enhances the suppression of vertical bending VIV,while shifting the VIV lock-in region toward higher wind speeds.In contrast,the vertical plate height strongly influences torsional VIV;larger heights tend to induce torsional vibration in the high wind speed regime.Flow field analysis shows that widening the horizontal plate reduces the spanwise extent of vortex structures,thereby improving flow uniformity.Conversely,increasing the vertical plate height promotes the formation and development of vortex clusters,leading to a more complex flow field.Effective vibration mitigation is achieved only when the vortices generated by the attached components are of a comparable scale to those shed from the main girder,enabling interference with the dominant vortex-shedding process.
Cycle mining can help people deeply understand the structure and function of complex networks,which is of great significance for practical application fields such as road traffic networks,bioprotein networks,financial and economic networks,etc.However,the massive data in the information age makes cycle mining extremely challenging.In response to the problem of large data volumes but relatively limited available data that cannot mine complete cycles,the concept of approximate cycle(AC)is defined,and the approximate cycle detection algorithm(ACD)and its optimization algorithm(IACD)are proposed.Both algorithms are divided into three stages:first,calculate hotpoints through vertex degree calculation;secondly,perform forward and backward searches on the dataset based on hotpoints to obtain hotpoints and their neighbors,and use this to construct an index(H-Index);finally,calculate the tightness coefficient and average tightness coefficient between different vertices based on H-Index,the path between vertex pairs with a tightness coefficient greater than the average tightness coefficient is an approximate cycle.The IACD algorithm has been optimized in two aspects based on the ACD algorithm.On the one hand,it increases the deduplication of vertices in the acquisition of hotpoints and their neighbors,while reducing the number of searches for data.On the other hand,it uses function vectorization instead of cyclic modification in the construction of indexes.The experimental data used are all real datasets of SNAP public website.The experimental results show that both algorithms can run smoothly on larger datasets and have good scalability and efficiency.The efficiency of the IACD algorithm is about 25%higher than that of the ACD algorithm.
As a major temporary facility in railway construction,the carbon emissions generated by track-laying bases constitute a significant source of embodied carbon during the materialization stage.In this study,a carbon emission measurement model for the life cycle of railway track-laying bases is established using the carbon emission factor method.The characteristic emission contributors are then extracted as potential influencing factors,and key factors are identified by feature-importance ranking.Furthermore,an interpretable machine learning model is used to visualize the contribution of these key factors and to analyze their impact mechanisms on carbon emissions.The results show that the total life cycle carbon emissions of a track-laying base range from 4 825.134 t to 15 122.059 t.Carbon emissions from building materials in the production stage account for the largest share(72%to 86%).According to the importance ranking,the five key influencing factors are base area,foundation treatment method,road hardening method,mechanical track length,and stock track length.The influence of these key factors on carbon emissions are further analyzed by SHAP(Shapley additive explanations)summary plots and dependency scatter plots.The findings provide a theoretical basis for promoting carbon reduction strategies in the construction and operation of railway track-laying bases.
