This study introduces the preparation process， product classification， physical and mechanical properties of calcined bauxite（CB） aggregate， and comprehensively reviews and summarizes the achievements and progress of worldwide researches on CB aggregate in the preparation of ultra-high performance concrete（UHPC）， improving the mechanical properties and volume stability of UHPCs， improving the abrasion and skid resistance of pavement， and preparing impact resistant cement-based materials the influence mechanism of CB aggregate on the properties of the materials is explained systematically. The results show that： ①CB aggregate is mainly composed of high strength and chemical-stable corundum and mullite， and its mechanical properties（strength， hardness， wear resistance） are significantly better than those of granite， limestone and other conventional aggregates. ②CB aggregate with lower alumina content has higher porosity， which is suitable for serving as internal curing agent to effectively reduce UHPC's shrinkage and cracking risk； the high strength of CB aggregate and the promotion of hydration degree due to internal curing jointly improve the mechanical properties of UHPC. ③CB aggregate with high alumina content（>80%） is suitable for the preparation of special road surface due to the aggregate's special properties of high hardness and high wear resistance， which can significantly improve the initial and long-term abrasion and skid resistance of the pavement， a thereby developed technology named as high friction surface treatment（HFST） has been widely used in foreign countries. ④The high strength and hardness characteristics of CB aggregate can effectively wear the impact projectile end or absorb higher impact damage energy， thus significantly improving the impact resistance of cement composites.

Existing trajectory prediction methods often overlook the interaction between vehicles and the map， resulting in trajectory predictions that do not conform to road topologies. To address this issue， this paper proposes a target-anchor-driven multimodal trajectory prediction method （TMTP） that couples the vehicle's motion trend. The proposed model efficiently incorporates prior knowledge of traffic scenarios into the algorithm through a graph model， allowing for precise description of heterogeneous interactions within traffic scenes. The model thoroughly considers the interaction between the vehicle's historical trajectories in the dynamic scene graph， the future trajectories of the ego vehicle， and the topological information of the vectorized map in the static scene graph. By utilizing an attention network， the model aggregates features from different nodes， achieving enhanced local-global feature fusion. Furthermore， TMTP represents driving intentions as target anchors， simplifying the complexity of the intention space. The proposed method was evaluated on the large-scale Argoverse motion forecasting benchmark. The results demonstrate thatthe model introduced in this paper outperforms the official benchmark model by 56.2% and 56.6% in metrics \mathrm{m}\mathrm{i}\mathrm{n}\mathrm{F}\mathrm{D}{\mathrm{E}}_{\mathrm{1}} and \mathrm{m}\mathrm{i}\mathrm{n}\mathrm{F}\mathrm{D}{\mathrm{E}}_{\mathrm{6}}， respectively， exhibiting an exemplary capability in accomplishing the task of trajectory prediction.

During the composite braking process of electric vehicles， there is a response speed difference between the motor and the hydraulic braking system， so the switching process between the two braking systems can cause impacts on the vehicle， affecting driving comfort. Therefore， an electro-hydraulic coordinated control strategy is proposed in this paper for braking mode switching process. A fuzzy PID control algorithm is used for the hydraulic braking system， and a model predictive control （MPC） algorithm is used for the motor braking system， and the seagull optimization algorithm is used to optimize the MPC weight coefficients to eliminate the dynamic response difference between the two braking systems. A semi-physical simulation platform was built to verify the designed control strategy. The results show that， under constant and variable braking intensity conditions， the impact degree during different mode switching processes was reduced by at least 13.9 m/s³， and the impact duration was reduced by at least 0.15 s. Therefore， the designed control strategy can achieve a smooth transition during the braking mode switching process and improve the stability and comfort of braking mode switching in the composite braking process of electric vehicles.

In response to the problem of luffing vibration in automotive crane， firstly， this article analyzes the causes and mechanisms of luffing vibration based on the principle of luffing vibration hydraulic system and the dynamics of luffing mechanism. Then， on-site tests were conducted on normal and shaking hosts， and time-frequency analysis methods were used to reveal the differences in test signal characteristics under different amplitude changes. Quantitative evaluation indicators for amplitude change shaking phenomena were developed. Finally， the quantitative evaluation index of luffing vibration was extended to the oil cylinder factory inspection test bench， and the factory test method for luffing vibration was established. The research results indicate that excessive internal friction force in the luffing oil cylinder is the main cause of crane luffing vibration； the vibration acceleration signals of normal and shaking hosts have obvious characteristic differences， which can be used as the quantitative evaluation index for luffing vibration phenomenon； the improved oil cylinder factory inspection test bench has good consistency between the test results and the host test results， and can achieve pre detection of luffing vibration phenomenon.

To solve the problem of low-speed off-tracking and high-speed single lane change stability of tractor-semitrailer， this paper adopted the semitrailer steering control method. The steering angle for the wheels of the semitrailer were deduced according to the articulation angle. And the off-tracking problem could be solved by semitrailer steering control. At the same time， the steering angle was calculated by referencing its yaw rate feedback. And the lateral stability problem can be solved with semitrailer steering control under single lane change condition at high speeds. Finally， the dynamic model of tractor-semitrailer vehicle was used to do simulations under a circle case and a single lane change operation. The simulation results show that the semitrailer steering control methods reduce the off-tracking by 92.3% and the yaw rate of semitrailer by 33.4% respectively. This study proposes a solution for solving the off-tracking and lateral stability problems and provide a reference for the further development of long combination vehicle （LCV）.

Aiming at the long-term aging prediction problem of Proton exchange membrane fuel cell （PEMFC）， this paper proposes a PEMFC output voltage prediction method of 2D-grid long short term memory networks（2D-G-LSTM） by denoising through locally weighted scatterplot smoothing （LOWESS）. First， data reconstruction and smoothing are performed by LOWESS to obtain smoothed data after eliminating noise and spikes. Second， a 2D-G structure is used to optimize the LSTM to determine the optimal parameters， and a 2D-G-LSTM is constructed based on the optimal parameters to achieve long-term prediction of the PEMFC output voltage over the next several hundred hour intervals. Finally， the proposed method is tested and compared with five classical methods， namely， extended Kalman filter， long short term memory network， correlation vector machine， echo state network， and back-propagation neural network， under two sets of aging datasets representing static and dynamic operating conditions， respectively. The results show that the root mean square error and the mean absolute percentage error of the proposed method are reduced by 51.19%， 53.66% and 43.88% and 49.43%， respectively， compared with LSTM when the training duration of the static and dynamic condition datasets reaches 550 h and 700 h， respectively. Therefore， the proposed method predicts a smaller error and the long-term aging trend of PEMFC is closer to the real value， and it can improve the aging prediction accuracy of PEMFC to some extent.

This paper proposes a method for excavator pose recognition using synthetic image datasets for model training. Initially， virtual models and scenarios are established. Programming is utilized to randomize the excavator pose， virtual camera position， and scene parameters. Subsequently， keypoint coordinates and occlusion information are computed to construct synthetic image datasets. Finally， excavator key points estimation is performed using a monocular camera. Experimental results demonstrate that training with synthetic image datasets improves model recognition accuracy， with a normalized error of 0.005 6 and a percentage of correct keypoint of 97.64%. Therefore， this method can meet the practical application needs of monitoring excavator operation safety and work efficiency. It also avoids issues such as high safety risks， high time/economic costs， narrow working condition coverage， and low label accuracy associated with high-quality engineering dataset collection. This contributes to the application and deployment of deep learning and big data technologies in excavator work state recognition.

In this paper， a point array turbulence structure is designed at the leading-edge of the standard NACA0012 airfoil， and the bump array has obvious noise reduction effect under the condition of 20 m/s and 30 m/s at the 0° angle of attack of the incoming flow， and the speed of 20 m/s and 30 m/s. Compared with the standard airfoil， the noise reduction of the total sound pressure level of the lattice leading edge airfoil is 3.6 dB under the condition of 20 m/s incoming flow， and the noise spectrum curve of the point array airfoil is lower than that of the acoustic wind tunnel background noise at 30 m/s， while the total sound pressure level of the standard airfoil is 1.5 dB higher than that of the background noise， that is， the noise reduction of the total sound pressure level of the point array airfoil is at least 1.5 dB under the condition of 30 m/s incoming flow. The LES （Large eddy simulation）/FW-H （Ffowcs williams hawkings） acoustic comparison hybrid prediction method is used to illustrate the noise reduction mechanism of the leading-edge array airfoil from the perspective of the spread coherence of the source of airfoil surface pressure disturbance. In order to solve the problem that the acoustic wind tunnel test results under the 30 m/s incoming flow condition cannot fully reflect the real noise reduction effect of the lattice airfoil， the spectral characteristics of the sound pressure level under the 20 m/s and 30 m/s incoming flow conditions are compared by numerical methods， and it is found that the lattice structure has almost the same noise reduction effect at the two speeds. Finally， the influence of parameters such as height and number of rows on the noise reduction effect of the leading-edge array structure was systematically analyzed， which provided support for the engineering parametric design of the leading-edge array turbulence structure.

At present， fault diagnosis methods based on deep learning generally have the problems of large model parameters and long diagnosis times， and the diagnostic performance will be greatly reduced in noisy environments. This paper proposes a fast and lightweight intelligent diagnosis model for rolling bearing fault diagnosis. Firstly， the parameters of the variational mode decomposition （VMD） are optimized using the osprey optimization algorithm （OOA） to design a unique multi-frequency band grayscale feature map based on the intrinsic mode function （IMF） component. Then a residual attention mechanism module （RAM） is designed based on the efficient channel attention （ECA） module， which is integrated into the SqueezeNet model， and the K-nearest neighbor （KNN） method is used instead of the Softmax function to identify and classify the faults， and the RSqueezeNet-KNN model is established. Experimental results on two bearing datasets show that the model is able to achieve lightweight applications with excellent diagnostic performance compared to other methods in noisy environments.

This paper discusses the evaluation indices of the thoracic biofidelity of the Chinese anthropomorphic test device（ATD） and proposes a scaling method for the chest mechanical response that considers the effect of biological viscosity. Based on the viscoelastic biological characteristics of the human body， the concept of equivalent viscosity C_E is introduced to correct the current elastic scaling method for viscosity. By introducing the viscosity correction factor ξ and the velocity loss factor e， a nonlinear channel scaling expression is established. The average errors of the maximum impact force and maximum compression calculated by this method are 8.77% and 5.55%， respectively， which are 3.84% and 0.37% lower than those of the original method. The simulation results show that this method effectively corrects the impact force.

Aiming at the problem that long-term aging makes heating pipelines prone to leakage under internal or external forces， and that traditional pulse-based methods suffer large errors under electromagnetic interference， temperature variation， and soil-moisture conditions， we propose a multi-aperture leak-detection method based on acoustic signals. Install acceleration sensors in the heating pipeline network in an axisymmetric manner to collect real-time sound signals carrying leaked sound waves. By using wavelet packet transform to decompose the collected acoustic signals， useful feature information for leak detection can be extracted and used as input for the Duffing oscillator function. By adjusting the phase of the built-in driving force to match the phase of the acoustic signal to be detected， the Duffing oscillator can be excited to a large-scale periodic state， which helps to distinguish weak acoustic signal features in noisy backgrounds and achieve more efficient multi aperture leak detection in heating pipelines network. The experimental results show that the algorithm can accurately distinguish and detect leaks in the heating pipeline network of all apertures based on the characteristics of sound waves， and can control the time within 0.32 ms when detecting leaks with an aperture of 2 mm. This indicates that the method proposed in this paper can detect multi aperture leaks in the heating pipeline network in a timely manner， ensuring the stable operation of the heating pipeline network.

In order to overcome the disadvantages of the processing difficulties and high consumption of tungsten carbide cylinder， a novel split-type ultra-high pressure die（STD） is designed according to the principle of mass support and lateral support， which has the advantages of higher limit pressure capacity and larger cavity volume. The STD cylinder is composed of a plurality of cemented carbide sections， without using large-size cemented carbide， effectively reducing the circumferential stress of the cylinder and obtaining greater chamber pressure. The stress of STD cylinder is analysed by numerical simulation. The results show that the stress of STD cylinder is obviously less than that of belt type ultra-high pressure die （BTD） cylinder， and the stress decreases gradually with the increase of the number of split blocks. After the cylinder is split， the stress of the support ring does not change significantly， which can ensure that the pressure cylinder can obtain sufficient and effective pre-tightening. Based on the maximum distortion energy theory and the maximum shear stress theory， the ultimate pressure bearing capacity of the ultra-high pressure die was evaluated. The pressure bearing capacity of STD was obviously greater than that of BTD cylinder， and the more the number of split blocks of the cylinder， the stronger the pressure bearing capacity. The destructive experiment further verifies that the pressure bearing capacity of STD is better than that of BTD， and the split block of the pressure cylinder can be replaced separately after damage， which can effectively reduce the use cost.

Given the relatively limited development of analytical models for adhesive-rivet hybrid joints in automotive lightweight design， this paper proposes a load-bearing stiffness prediction model for carbon fiber-reinforced thermoplastic composite-to-metal adhesive-rivet hybrid joints based on spring-mass theory， aiming to deepen the understanding of stiffness in hybrid joint structures. The reliability of the spring-mass model was verified by quasi-static tensile test. Based on the proposed spring-mass model， the effects of adhesive layer thickness， lap length， and rivet hole diameter on the stiffness of the joint structure are discussed. The main conclusions are as follows： with an increase in the thickness of the adhesive layer， both the overall stiffness of the joint and the shear stiffness of the adhesive layer decrease. For every 0.01 mm increase， the shear stiffness of the adhesive layer decreases by about 1.94% on average. Increasing the lap length can improve the overall stiffness of the joint and the shear stiffness of the adhesive layer， but it numerically reduces the stiffness of the metal substrate significantly. As the diameter of the rivet hole increases， both the overall stiffness of the joint and the stiffness of the rivet increase， but the shear stiffness of the adhesive layer is reduced， which affects the joint's connection performance.

In shipbuilding， the laser-welding heat source is intrinsically narrow and deep； while this favors penetration depth and travel speed， it also promotes localized heat accumulation and residual-stress concentration. Focusing on AH36 high-strength steel， this study employs finite-element simulation to characterize the melt-pool geometry in laser welding. Ten moving-heat-source patterns are selected and defined； the cross-sectional shapes at steady-state welding are extracted for each model and compared with the actual melt-pool profile observed in laser welds， allowing the optimum heat-source model for butt-joint laser welding to be identified. Furthermore， thermo-mechanical coupling simulations are performed to predict the welding distortion and residual-stress distribution in laser-welded butt plates， and the predictions are validated against experimental measurements. Good agreement is achieved in both magnitude and trend， providing a reliable theoretical basis for subsequent optimization of laser-welding procedures and fatigue assessment of laser-welded structures.

To address the “empty-backhaul” problem that occurs after delivery vehicles unload at vehicle-assembly plants， this paper constructs a space-time network to depict the parts-allocation processes among city distribution centers and assembly plants. An optimization model for parts-allocation strategies is developed with the objectives of minimizing transportation， inventory， and total costs， and a numerical example is solved using real enterprise data. The results show that， compared with the existing allocation plan， the optimized solution reduces the resource waste caused by empty return hauls， improves vehicle utilization， and significantly lowers total cost.

In order to investigate the factors influencing urban residents’ transfer intention and their interaction mechanisms under the transit transfer preferential （TTP） policy， this paper integrates behavioral habits based on the theory of planned behavior （TPB） and technology acceptance model （TAM）， and develops a residents' transfer intention model under the TTP using the structural equation model（SEM）. The questionnaire was designed using the revealed preference（RP） method. A total of 365 valid samples were obtained through an online survey. The path relationships between psychological latent variables were explored， and the mediating effect of the model was examined using the Bootstrap method. Additionally， a one-way analysis of variance（ANOVA） was conducted to investigate the heterogeneity of latent variables across different groups. The results show that attitude， subjective norms， perceived usefulness， and behavioral habits have a direct significant impact on transfer intention. Attitude， perceived usefulness， and perceived ease of use are mediating variables， but they do not play a mediating effect in all paths. The total effect values of latent variables on transfer intention， in descending order， are behavioral habits （0.457）， perceived usefulness （0.366）， attitude （0.326）， subjective norms （0.312）， and perceived ease to use （0.096）. Significant differences were found between groups with different socio-economic attributes and travel characteristics on the latent variables. The findings of this study not only contribute to the theoretical framework of behavioral intention research， but also provide theoretical support for urban transportation management authorities in formulating the TTP policy.

Aiming at the problems of high operation and maintenance costs and the lack of scientific basis for strategies during the whole life cycle of freeway electromechanical equipment， an economic operation and maintenance measure selection method based on quality evaluation results is proposed. Firstly， the fuzzy comprehensive evaluation method is used to evaluate the quality status of the equipment in real-time. Secondly， based on this， the total cost of equipment usage is dynamically calculated， and then the most economical maintenance time point to implement different degrees of operation and maintenance measures is selected through cost comparison. Finally， the data from the Cihuling Tunnel in Wenzhou， Zhejiang Province was selected as an example to verify the feasibility and validity of this method.

Aiming at the problem of insufficient consideration of the time-varying characteristics of hidden spatial associations of road network nodes in the existing traffic flow prediction studies， this paper proposes a traffic flow prediction model based on the feedback of spatio-temporal dynamic constraint graph. First， the temporal features are extracted by GRU， generates a dynamic constraint graph characterising the neighbourhood relationship of the road network at the current moment by using a spatio-temporal graph generator and a spatio-temporal fusion constraint matrix within the STC-GCL component， and then realises spatial feature extraction by using a multilayer graph structure convolution operation. Second， the multi-scale gated convolution unit is used to dynamically adjust the information flow of important features to complete the fine screening of key features. Finally， the consistent extraction of spatio-temporal features is achieved by embedding STC-GCL into GRU. The experiments are tested on the public datasets of high-speed road network PeMSD4， PeMSD8， and Chengdu-DDT， and the results show that compared with the current mainstream spatio-temporal prediction methods for traffic flow FGI， the MAE of the proposed model in this paper reduced by 2.69%， 1.88%， and 0.92% in the three datasets， respectively.

To pinpoint bottleneck sections in the port-city transportation network and delineate the physical boundaries between urban zones and port zones， this paper analyzes the characteristics of the port-city transportation network based on percolation theory. Firstly， the Van-Genuchten soil permeability model was applied to transportation research for the first time， formulating a road section seepage probability model tailored for port-city transportation networks. Secondly， based on actual port-city transportation network data and penetration model parameters， the bottleneck roads and periodic characteristics of the network were analyzed， which categorized the road network into three characteristic regions： urban， port， and fusion areas. Finally， an optimization strategy for the port-city transportation network based on the characteristics of zonal network is established. Results revealed that the partition optimization strategy consistently surpassed the overall optimization strategy， achieving a higher average critical threshold when enhancing bottleneck capacity by 5%-30%. Notably， the partition strategy demonstrated remarkable improvements even with a 5% capacity boost， whereas the overall strategy's benefits became evident only at a 15% increase. This paper offers theoretical guidance for alleviating traffic congestion in port-city regions and shaping effective regional traffic enhancement plans.

Three super-span composite box girders with corrugated steel webs （CSW） under combined bending and torsion were designed and experimentally investigated in this paper， and the failure modes， torque-twist curves， bending moment-displacement curves， and strain distribution laws were obtained. Based on the verified finite element analysis （FEA） method， a parametric analysis was carried out. The bending-torsional relationship curves and the simplified calculation method for capacity were proposed according to the failure modes. The results show that the failure modes of the composite box girders are the bending and torsional failure， with crushing of the concrete top slab and spalling of the concrete bottom slab， respectively. Global buckling of bending failure occurs in the bending-shear-torsional section， whereas that of torsional failure is concentrated in the bending-torsional section. The bending moment enhances the torsional strength of the composite box girders within a certain range. The proposed bending-torsional relationship curves and the simplified calculation method both provide high accuracy， with a maximum error of less than 6%.

To solve the problems of multi-modal data and low recognition accuracy in road pavement distress detection， an improved pavement multi-distress recognition algorithm based on the YOLOv8 model enhanced with the non-parametric attention mechanism simAM is proposed. Utilizing the self-owned pavement distress dataset， Res2Net is embedded into the YOLOv8 structure to enhance multi-scale feature extraction capabilities while maintaining similar computational loads. The simAM module is employed to further adjust the weights of feature maps at different scales， improving the detection of targets. Genetic algorithm is used to increase the speed of automatic parameter searching for the model， and image enhancement techniques such as HSV and Mosaic are employed to expand the small sample distresss. Experimental results show that the improved simAM-YOLOv8 algorithm significantly improves accuracy and recall rates for various pavement distresss such as cracks， broken panels， repairs， etc.， on asphalt， cement， and other road surfaces. Specifically， the precision rate has increased by 15.3% and the recall rate has increased by 13.1% compared to the original network， demonstrating excellent intelligent recognition performance， and playing an important role in automated detection of highway conditions.

The semantics of Chinese vocabulary have a certain degree of ambiguity. In Chinese text， there are some features that have low relevance to named entity recognition. The same vocabulary has different meanings in different contexts， and different vocabulary and phrases have different contributions to named entity recognition. If weighting or masking operations are not performed， these features will interfere with the recognition accuracy of the model. To this end， a Chinese named entity recognition （CNER） algorithm with soft attention mask embedding is studied. Establish a multi-level CNER model， in the word vector representation layer of the model， use jieba technology to perform segmentation processing on the Chinese text passed from the input layer， and use Word2Vec method to obtain the word vectors of each vocabulary， forming a sequence of word vectors. In the BiLSTM layer， bidirectional long short-term memory processing is applied to the sequence of word vectors to obtain feature vectors that fuse contextual information for each word vector. Embedding a soft attention mask module after the BiLSTM layer， using the soft attention mechanism of this module to perform weighted and masked operations on the feature vectors output by the BiLSTM layer， focusing on features that contribute significantly to entity recognition， removing and suppressing unimportant features， and improving recognition accuracy. Label and decode the feature vectors processed by the soft attention mask module in the CRF layer to obtain the optimal entity label sequence， which is the Chinese named entity recognition result. The experiment shows that the algorithm can accurately recognize Chinese named entities， and has good performance in entity label annotation coverage and F1 value.

In smart-meter based non-intrusive load disaggregation with low-frequency sampling， load switching events are sparse and the class distribution is imbalanced. The shortage of minority-class and boundary samples tends to cause missed detections and misclassifications during load ON states and transition stages. Existing oversampling methods still have limitations in precisely controlling the number of synthesized samples， selecting nearest neighbors， and defining boundary samples， and random interpolation strategies may further introduce redundant samples or cross-class mixed samples. To address these issues， this paper proposes an improved algorithm that combines K-means clustering with Borderline-SMOTE （KB-SMOTE）： minority boundary samples are first extracted and clustered， and then centroid-guided within-cluster interpolation is performed to generate new samples， thereby reducing redundancy and enhancing boundary separability. At the model level， to overcome the limited capability of conventional sequence networks in capturing key transient and local features， a Bi-LSTM based load disaggregation model embedded with a convolutional block attention module is designed. By jointly leveraging channel and spatial attention， the model adaptively reweights features and strengthens key information relevant to load operating states. Experiments on the UK-DALE dataset show that， compared with baseline models including DAE， Seq2point， and the basic Bi-LSTM， the proposed method achieves better performance on multiple evaluation metrics， validating its effectiveness in imbalanced-load scenarios.

For the obstacle avoidance control problem of truck platoons， this paper proposes a hierarchical control method with upper-layer trajectory planning and lower-layer trajectory tracking. In the upper layer， a leader truck trajectory planning method based on model predictive control is designed using a particle filter algorithm， which transforms the optimization problem into a probabilistic estimation problem， thereby improving the solution efficiency. In the lower layer， considering the strong nonlinearity of the coupled longitudinal and lateral dynamics of trucks， a distributed model predictive controller based on an integrated longitudinal-lateral dynamics model is developed to enhance control accuracy. Co-simulation results using TruckSim and Matlab/Simulink demonstrate that the designed two-layer control architecture ensures obstacle-avoidance driving for truck platoons， and in various road scenarios， the proposed distributed model predictive controller effectively tracks the planned trajectory.

Because the satellite communication network link is unstable， the satellite beam has the jumping characteristic. In order to ensure the continuity of communication， the user terminal sharing the same channel needs to switch between different satellites， and the link between the new satellite and the ground station and the user terminal needs to be re-established and adjusted， which leads to the delay mutation problem. However， when the delay increases suddenly， the sending window is reduced too conservatively， which makes the round-trip delay balance in congestion control poor. Therefore， a multi-modal dynamic congestion control algorithm for satellite communication based on link state is proposed. By improving the traditional TCPVegas algorithm， the length of the satellite link is calculated， the round-trip delay is corrected according to the length of the satellite link， and the change of the congestion window caused by the sudden change of the round-trip time is solved. The bandwidth of satellite transmission is judged， and the problem of blind reduction of the congestion window caused by other algorithms is solved. The multi-mode dynamic congestion control of satellite communication is realized. The experimental results show that the algorithm has maximum data throughput and good round-trip delay balance. After changing α and β parameters， the average throughput of the algorithm will be improved obviously， but there is a certain limit. The algorithm can dynamically adjust the size of the congestion window to adapt to the congestion environment and ensure the smooth flow of satellite communication.

This study employs a simple spinning（dry-jet wet spinning） method to spin graphene and carbon black mixed with cellulose into temperature-responsive fibers， proposing a novel approach for fabricating wearable temperature sensors using graphene and carbon black fibers. The characteristics of this sensor include high tensile strength， short response time（6.5 s）， and good temperature recovery time（25.5 s）. After multiple mechanical deformations of the sensor， its performance remains stable. The wearable temperature sensor is mixed into fabrics through textile technology and can be worn by people to monitor skin temperature and has strong anti-interference properties. This research indicates that wearable temperature sensor based on conductive nano-modified textile fiber materials has great value and future in application.

In response to the problem of feeding blockage caused by the clustering of membrane debris mixture， this paper designs a membrane debris shredding device with feeding regulation function that meets the palatability requirements of white star flower beetle larvae. By conducting force analysis on the feeding and shredding process of the membrane mixture， the structural and working parameters of the main components were determined. In order to verify the reliability and operational performance of the machinery， a single factor experiment was conducted to determine the reasonable range of values for the feeding roller speed， dynamic and fixed blade clearance， and chopper roller speed. The feeding roller speed， dynamic and fixed blade clearance， and chopper roller speed were used as experimental factors， and the cotton stem shredding length qualification rate， residual film shredding length qualification rate， and power consumption were used as evaluation indicators for a three factor three-level response surface test. A regression model was established to analyze the influence of each factor and its interaction on the operational performance of the machinery， and optimize each factor. The optimization results showed that the optimal operating effect was achieved when the feeding roller speed was 24.8 r/min， the dynamic and fixed blade clearance was 0.4 mm， and the shredding blade roller speed was 256.4 r/min. The optimized results were verified through experiments， and the average qualified rate of cotton stem shredding length was 89.99%， the average qualified rate of residual film shredding length was 90.53%， and the average power consumption was 0.97 kW. The research results can provide reference for the development of film mixed shredding technology.