When a large number of containers arrive at a node during coal intermodal transportation, it can lead to problems such as container congestion and transfer delays during the transshipment process. Based on this, the criteria importance through intercrieria correlation (CRITIC) objective weighting method was adopted to quantify the risk of transfer delay in coal rail-road intermodal transportation, and then the risk was incorporated into the path optimization factors. Meanwhile, the improved activity-share-intensity-factor (ASIF) equation was introduced to measure the carbon emissions of transportation and transshipment node exchange processes. A carbon emission and transfer delay risk model for the entire freight transportation time was established. Based on the above model, a coal rail-road intermodal transportation path optimization model was proposed, with the objectives of minimizing carbon emissions, transportation costs, transportation time, and transfer delay risk. Through a case study, MATLAB programming was introduced, and the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) algorithm was designed to solve the example. The optimization results under different transportation decision conditions were simulated, respectively. By comparing the single-objective optimization results of minimum carbon emissions, minimum transportation costs, shortest transportation time, and minimum transfer delay risk with the multi-objective optimization results that comprehensively consider the above objectives, the advantages of multi-objective optimization in handling low-carbon path optimization for coal rail-road intermodal transportation were explored. The research results show that compared with single-objective optimization, the multi-objective optimization scheme can effectively reduce transportation costs, shorten transportation time, reduce comprehensive energy consumption, and lower the risk of transfer delay, achieving a comprehensive optimal combination of economy and safety in coal rail-road intermodal transportation. At the same time, it also provides a certain reference for the path optimization of rail-road intermodal transportation under different transportation demands and scenarios.

Aiming at the problem that it is difficult to directly use external heat sink to dissipate heat for high-power electronic devices with short-time operation in external insulation condition, the phase-change material with low melting point and high volume enthalpy value was adopted to optimize the design of energy storage structure and realize temperature control for electronic devices. Firstly, based on the constraints of electronic device volume, weight, external environment, thermal power, working time, etc., combined with the thermal performance of phase change materials, an integrated design of heat dissipation structure was carried out. Secondly, according to the characteristics of phase change material(PCM), an equivalent specific heat capacity thermal analysis method based on temperature feedback was proposed. Finally, the thermal conductivity of three PCM including paraffin, carbon composite and liquid metal, was analyzed by numerical simulation, and the heat dissipation performance of the three PCM in specificenergy-storage structures was evaluated by using heat source temperature rise and temperature equalization as indicators, and an optimal phase-change energy-storage structure of the electronic devices was determined. The results show that the PCM can significantly control the temperature rise in a certain period of time, which meets the temperature control requirements of electronic devices in small volume and external adiabatic environment. The volume enthalpy of PCM represents the energy storage capacity per unit volume of PCM. The higher the volume enthalpy, the smaller the volume of PCM required. Liquid metal can obtain better thermal properties because of its large enthalpy and high thermal conductivity.

In order to solve the problems of imperfect fire risk management system and subjective and one-sided risk assessment methods of civil aviation transport aircraft, a comprehensive risk assessment method based on random forest ensemble algorithm was proposed for the analysis and evaluation of risk factor indicators. Firstly, according to the man machine environment management(MMEM) theory, the risk index system was established based on the investigation of the causes of aircraft fire accidents in the past 20 years, and then the scores of scholars and experts in related fields on the correlation between the indicators were collected, and the subjective risk index weight results were obtained by using the analytic network process(ANP) method. The causes of major aircraft fire accidents in the database in the past 20 years were counted and their prior probabilities were calculated by classification, and the Bayesian network(BN) dynamic analysis method was used for reverse reasoning to obtain the probability distribution of each risk factor. The random forest regression model was established to obtain the predicted value and importance of the characteristic index, and put forward scientific and effective suggestions for the fire risk control of the operating unit. The results show that the five indicators of missing dangerous goods in security inspection, failure to eliminate hidden dangers in time, component failure, bird strike, and high surface temperature are the most critical risk factors in aircraft fire accidents.

In order to investigate the vortex characteristics and wave propulsion generated by the undulating pectoral fins of the flatworm, a simplified model of the flatworm's pectoral fin was re-established using the linear interpolation method in MATLAB. The relationship between thrust and kinetic energy, during the flatworm's swimming was derived, and the undulating posture of the flatworm was simulated using Fluent software. The results show that compared to similar MPF propulsion fish species such as rays and cownose rays, the flatworm, due to its narrow and elongated body structure, exhibits better stability, adaptability, and flexibility in water. When the Reynolds number is set to 1.05×10⁵, the pectoral fins of the flatworm demonstrates more stable thrust, effectively reducing flow separation and turbulence effects. At a frequency of 0.6 Hz and a wavelength of 2.5 m, the pectoral fins displays optimal undulating parameters, enhancing fluid mixing and energy transfer efficiency, thereby improving the flatworm's propulsion performance. It is concluded that, during the mid-phase of an undulation cycle, the pressure distribution on the pectoral fins changes significantly, with the lift efficiency being highest at the peak of the undulation.

The non-lethal electric shock weapon is a research hotspot in the field of non-lethal weapons. In recent years, the research on wireless long-range electric shock bullets has become a key issue within this field. Therefore, aiming at the design of non-lethal long-range low-velocity electric shock bullet empennage, three airfoils of Clark Y, Eppler 387 and NACA-66 with good aerodynamic characteristics at low velocity were selected. The simulation results of NACA0012 airfoil at 30 m/s using CFD software were compared with the literature simulation results and wind tunnel test results to verify the algorithm's effectiveness. Subsequently, the aerodynamic characteristics of the three airfoils at low velocity were simulated, and the lift coefficient, drag coefficient and lift-drag ratio corresponding to different angles of attack at 30, 35 and 40 m/s were obtained respectively. The results show that at the same flight velocity, the lift coefficient and drag coefficient of the three airfoils gradually increase with the increase of the angle of attack, but the growth rate of the lift coefficient gradually decreases, and the growth rate of the drag coefficient gradually increases. The lift-to-drag ratio increases first and then decreases with the increase of the angle of attack. After comparison, it is found that the aerodynamic performance of the airfoil Eppler 387 is better than that of the other two airfoils. The velocity of 40 m/s and the angle of attack between 4° and 6° are the best working conditions, which can not only meet the structural design requirements of non-lethal long-range low-velocity electric shock bullets, but also produce less drag while providing as much rolling moment as possible.

The laminectomy robot is an auxiliary surgical robot developed for laminectomy in recent years. In order to explore the differences in reducing orthopedic surgeons' mental workload between laminectomy robot techniques and traditional laminectomy methods, a multimodal evaluation incorporating an electrocardiogram, eye tracking, and the NASA-TLX scale was utilized to assess the mental workload of orthopedic surgeons undergoing both surgical procedures. Through simulated surgical trials, 12 orthopedic surgeons performed laminectomies employing both the robot-assisted and the conventional techniques, collecting multimodal data for both variance and correlation analyses. The findings indicate significant differences in subjective mental workload between the laminectomy robot and traditional techniques (P<0.05). However, in terms of electrocardiogram indicators such as average heart rate, the low frequency/high frequency ratio (LF/HF), and the standard deviation of NN intervals (SDNN), no significant differences are noted. Significant differences are observed in eye movement indicators, including pupil diameter (P<0.05), fixation rate (P<0.05), and saccade rate (P<0.01). Further correlation analysis underscored a notably significant relationship between pupil diameter and levels of subjective mental workload in both surgery techniques. In conclusion, compared to traditional laminectomy methods, the use of laminectomy robots can alleviate the mental workload on orthopedic surgeons, with both pupil diameter and subjective mental workload levels providing effective reflections of the orthopedic surgeons’mental workload.

On-line and efficient monitoring of leakage faults in district heating network can effectively increase the quality of heat transmission and reduce energy consumption. However, the data feature extraction ability of conventional leakage fault diagnosis method is limited, and it is difficult to deal with the high dimensional nonlinear pressure flow monitoring data for complex heating network, which makes its diagnostic performance weak. Therefore, a fault diagnosis model of heating network leakage based on convolutional neural network (CNN) and Transformer was proposed. The proposed CNN-Transformer diagnostic model combines CNN and Transformer network to realize joint learning of different time scales and spatial features. The CNN network was used to extract local features, and the Transformer network was used to capture global features. The validity of the model was verified by simulating the fault data set of the annular heating pipe network system. The results show that the proposed CNN-Transformer diagnosis model based on multi-stage feature extraction and fusion mechanism of fault features significantly improves the accuracy of leak diagnosis. The CNN-Transformer method has the highest accuracy on the test set, with an accuracy increase of 13.21%, 7.49%, 6.1% and 4.62%, respectively, compared to other fault diagnosis methods including long short-term memory network, gate recurrent network, CNN and Transformer.

Machine learning technology is a hot research topic at present. It is widely used in various prediction, recognition and classification tasks with its strong learning ability and high versatility. The application of machine learning in computational structural mechanics was discussed, with emphasis on its role in material property prediction, structural damage analysis, improvement of traditional methods, constitutive equation establishment and differential equation solving. Through literature review, the advantages of machine learning algorithms such as neural networks, support vector machines and random forests in improving computational efficiency and design process optimization were summarized. It is pointed out that the combination of machine learning and classical computing methods provides a new way to solve engineering problems. Future research will focus on algorithm optimization, model improvement and interdisciplinary technology integration.

Aiming at the problem that the unordered charging of large-scale electric truck (ET) increases the peak load of the grid and affects the power quality, a cluster division and two-tier optimal scheduling strategy for peak load balancing scenarios were proposed. Firstly, the demand response model of ET participating in power grid peak regulation was established considering real-time road flow and multi-energy consumption factors. With logistics factors as characteristic quantities, ET was divided into day-ahead clusters by an improved fuzzy clustering algorithm. Secondly, based on the clustering results, combined with the different interests of power grid dispatching and enterprise users, a two-tier scheduling model was established under the framework of master-slave game considering the flexible time window to solve the charging and discharging power of pure electric heavy duty card in the cluster in real time. Finally, particle swarm optimization based on Kriging model was used to speed up the solving of the model. The simulation results of ET data in a logistics area show that the two-tier scheduling strategy based on cluster division and flexible time window can better smooth the load curve and reduce the scheduling deviation of clusters. At the same time, Kriging optimization algorithm is more fast in solving the two-tier optimization model.

Second order selective harmonic repetitive control(SOSHRC) strategy with good frequency adaptability and dynamic performance, is widely used in grid-connected inverter control. To address the problems of the traditional SOSHRC strategy, such as the difficulty of using the stability analysis method and the conservative stability criterion, a novel second order selective harmonic repetitive control and proportional control(SOSHRC-PC) was proposed. Firstly, the structure and principle of the novel SOSHRC were designed, the novel SOSHRC-PC control strategy was introduced. Then, the stability and parameter design methods of the novel SOSHRC-PC controller were analyzed. Finally, a three-phase grid-connected inverter based on the novel second-order (6k ± 1) harmonic repetitive control and proportional control was constructed, and the simulation results show that the proposed control strategy has great steady state and dynamic performance.