Addressing the issue that the current energy method cannot accurately describe the time-varying meshing stiffness of the gears with tooth flank spalling，resulting in the imperfect dynamic modelling method for the fault of gear transmission system，an improved energy method was proposed.Considering various types of tooth frank spalling，a time-varying meshing stiffness formula that consi-dered the tooth frank spalling was established.The established time varying meshing stiffness was introduced into a six-degree-of-freedom transmission system，creating a dynamic model of the gear transmission system that could simulate the tooth frank spalling.The Newmark-β method was used for solving，the numerical simulation results were compared and analyzed with the experimental data to verify the accuracy of the established model.Based on this model，the influence of the main parameters of tooth frank spalling on the time-varying stiffness，the corresponding stiffness variation law and the frequency characteristics of the dynamic response were investigated.The results show that the secondary frequency peak with a spacing of 10 Hz occurs on both sides of the characteristic frequency of the gear transmission system with tooth frank spalling.

In response to the complexities of fault signal transmission path，instability and difficulties in extracting fault feature for aircraft engine main bearing，a fault recognition method was proposed based on the fusion of time-domain feature parameters，frequency-domain feature parameters and intrinsic mode function （IMF） energy moment feature parameters for dimensionality reduction.Firstly，60 groups of bearing rolling element fault，inner ring fault，outer ring fault and bearing without fault data were selected respectively then time-domain，frequency-domain and energy moment features were extracted from these instances.Addressing the issue of high dimensionality，extensive data and redundant information of the fusion vector composed of three parameters，principal component analysis （PCA） was employed to reduce the dimensionality of these data and effective principal components were extracted based on cumulative contribution rates of principal components.Finally，the dimensionality reduction feature vectors were input into the support vector machine （SVM） for pattern recognition to diagnose the types of bearing faults.The results demonstrate that compared to models employing single feature parameters，this method effectively extracts fault feature vectors from complex signals.Subsequently，it accurately identifies and classifies fault types using these feature vectors，achieving a fault recognition rate of 98.75%.

In order to solve the flight stability issue of small-sized single-person rotorcraft，considering the small-sized and heavy load of the entire aircraft，a stability-enhancing structure scheme was proposed and modal simulation analysis was conducted. Firstly，the center of gravity was determined to ensure that the design met the requirements of flight stability.Secondly， a stability-enhancing structure scheme was proposed to address stability issues such as sway and vibration.Finally，the damper scheme was determined through cockpit dynamics simulation with different damping values of damper and the mode simulation analysis was completed.The results show that the target aircraft can achieve stable flight and meet the practical application requirements through reasonable structural design and flight control strategies while maintaining a small size.

To improve the surface quality of TC4 alloy prepared by selective laser melting，the ultrasonic assisted magnetic abrasive finishing technology for polishing was adopted.Simulated the size and distribution of magnetic flux density under different magnetic pole dimensions and finishing areas.The cavitation bubble in the composite field force was analysed，the magnetic field on the cavitation effect on the influence mechanism was studied.The changes in surface morphology of the specimen before and after applying ultrasonic vibration were analyzed.The influence of amplitude，finishing rotate speed and finishing time on surface roughness were studied.The results show that as the diameter and height of the magnetic poles increase，the magnetic induction intensity increases and the distribution of magnetic flux density gradually changes from uneven to uniform.The finishing effect is better at the position of 80 mm，with an average magnetic field force of 17.12 mN and a surface roughness of 0.673 μm.After applying ultrasonic vibration，the original surface bumps and defects are completely removed，the surface exhibites fine scratches and the surface roughness is reduced to 0.243 μm.Compared with the magnetic abrasive finishing process，the surface roughness is reduced by 60%.The suppression effect of the magnetic field on the cavitation effect reduces the energy released when the cavitation bubble collap-ses， thus reducing the damage caused by the cavitation effect on the surface.

Under the cellular vehicle-to-everything （C-V2X） communication technology framework，the accuracy of basic safety messages （BSM） is crucial for ensuring road traffic safety.However，BSM data is susceptible to non-malicious factors such as sensor faults or environmental disturbances，leading to data anomalies that may misguide driving decisions.In response to this issue，two-phase learning strategy for correcting anomalies in BSM was proposed.In the first phase，an unsupervised hybrid generative model was used to learn the behavior patterns and distribution characteristics of normal BSM data and a memory module was introduced to construct a fine-grained prototype repository in the feature space for enhancing the model’s understanding of the diversity of normal behavior patterns.In the second phase，based on the network parameters obtained in the first phase，a self-supervised learning strategy was employed for data correction.Results show that the proposed solution exhibits good correction capability and significantly reduces the error in BSM.

As a navigation solution with higher accuracy and better robustness compared to single navigation systems，GNSS/INS integrated navigation has been widely applied in various carriers.Aiming at the problem of the satellite navigation signal interruption caused by environmental obstruction or electromagnetic interference to reduce the accuracy of integrated positioning，GNSS/INS integrated navigation positioning method by a fully connected neural network （FCNN）was proposed.This method consisted of training and prediction modules.Under normal GNSS signal conditions，the me-thod utilized the position and velocity information calculated by INS and the position and velocity information output by the integrated navigation to train the FCNN model.When the GNSS signals were interrupted or fail，the pre-trained FCNN model was used to predict the navigation solutions.Experimental data was employed to validate the proposed method.The results indicate that the GNSS/INS based on FCNN integrated navigation method proposed in this study effectively suppresses the divergence of single INS positioning errors，thereby improving the accuracy and availability of GNSS/INS integra-ted positioning results when the GNSS signals are interrupted.

Taking a six degrees of freedom desktop upper limb rehabilitation robot （DULRR） as the research object，it was observed that traditional position control cannot meet the needs of patient rehabilitation training and may lead to secondary injuries during the rehabilitation process.To address this issue，a position closed-loop adaptive compliance control method was proposed.Firstly，based on the kinematic model of DULRR，a position controller based on fuzzy PID was constructed.Then，utilizing the impedance model’s ability to convert force signals into velocity and position signals，an adaptive compliance controller based on pressure sensors was proposed.Combined with the proposed fuzzy PID controller，a complete DULRR passive rehabilitation training control method was formed.Finally，the superiority of the adaptive compliance control method based on position closed-loop was verified through simulation analysis and prototype experiments.The experimental results show that compared with traditional PID controllers，fuzzy PID in the DULRR system has shorter response time and smaller steady-state error，demonstrating better trajectory tracking ability.Meanwhile，the controller exhibits good flexibility，meeting the needs of early passive rehabilitation training for patients and avoiding secondary injuries during the rehabilitation process.

In the context of the dual-carbon goal，the digital transformation of manufacturing enterprises is of great significance in promoting green transformation and achieving a balance between economic and environmental benefits.A-share listed companies in the manufacturing industry from 2011 to 2021 were taken as the research sample and empirically examined the impact of digital transformation on ESG responsibility performance of manufacturing enterprises.The results show that digital transformation positively promotes ESG responsibility performance of manufacturing enterprises and the conclusions are still valid after robustness test and endogeneity test.The mechanism test shows that digital transformation can promote the ESG responsibility performance of manufacturing enterprises by alleviating their financing constraints.The moderating effect shows that digital inclusive finance can increase the influence of digital transformation on the improvement of ESG responsibility performance of manufacturing enterprises.The heterogeneity analysis show that digital transformation has a more significant effect on ESG performance in manufacturing enterprises with high investments in innovation resources and in low-carbon pilot cities.The study conclusions provide countermeasures and suggestions for enhancing ESG responsibility performance from the government and enterprise levels.

The carbon emissions from the power industry remain a central focus of China’s current carbon reduction efforts.The production-related and consumption-related carbon emissions from electricity across 30 provinces in China were analysed and clarified the scale and pathways of spatial and sectoral transfers of carbon emissions between these provinces.The primary analysis was conducted using the multi-regional input-output （MRIO） model.The results indicate that carbon transfer is the primary driver of regional disparities in carbon emissions related to both production-related and consumption-related.Overall，the trend revealed that carbon emissions are being transferred from economically developed provinces to less developed provinces with surplus electricity supply.At the departmental level，the majority of power transfers are caused by electricity demand from the construction and service industries，which accounts for 57.89% of carbon emissions.It is effectively identified that the spatiotemporal characteristics and transfers of carbon emissions from electricity across various provinces，providing a scientific basis and theoretical foundation for the development of carbon reduction programs for the power industry at the provincial level.These efforts will contribute to achieving the goals of carbon peaking and carbon neutrality.

It was considered that a single-machine scheduling problem with job-rejection and common due-window，which the starting time and finishing time of the common due-window were decision variables.If the job was completed in the common due-window，no additional cost would be incurred，otherwise，advance or delay costs would be incurred.If the job was rejected，a corresponding rejection cost wonld be incurred.The goal was to find out which jobs were accepted and rejected， the sequence of accepted job set，the starting and finishing times of common due-window，so as to minimize the weighted sum of scheduling cost and rejection cost，which the weights were the position weights.Through theoretical analysis and algorithm design，it is proved that there is an optimal solution algorithm with lower time complexity.