A new arc additive manufacturing process—droplet+arc additive manufacturing (DAAM) technology was applied to manufacture aluminum alloy samples in order to improve the quality and the efficiency of aluminum alloy. A new droplet generation system (DGS) was applied instead of the conventional wire feeding system, which makes the material addition and arc energy independent of each other. The formed material is 2219 aluminum alloy, and a trace amount of Mg element was added through the DGS. A thin-walled structure was deposited using the DAAM system at a significantly higher deposition rate (160 $ {\mathrm{m}\mathrm{m}}^{3}/\mathrm{s} $) than conventional wire and arc additive manufacturing techniques. The microstructure of the cross section of the thin-walled structure was observed and analyzed. Results showed that the grain morphology of the thin-walled structure was dominated by columnar crystals and exhibited a periodic distribution of inner-layer columnar crystals and inter-layer equiaxed crystals. The average tensile strengths in the horizontal and vertical directions were 455.4 MPa and 417.0 MPa after T6 heat treatment, while the yield strengths were 342.2 MPa and 316.4 MPa, respectively. The comparison results with the previous studies show that the addition of Mg element increases the yield strength of 2219 aluminum alloy, but leads to a corresponding decrease in elongation.

In order to portray the public emotion orientation caused by the public opinion on traffic incidents disseminated in short videos, a physiological feature graph was constructed by the text sentiment analysis and the multimodal physiological signal feature extraction. This work collected 136 highly-liked videos with 38 805 comments on TikTok. Considering all videos as a document set, with each video treated as a document and comments as words, the latent Dirichlet allocation topic model was adopted to obtain the distribution of comments under different topics and the distribution of topics under different videos. Naive Bayes-based SnowNLP was utilized to calculate the sentiment scores of comments and analyze the sentiment tendencies expressed by different opinion topics. Neuroscience experiments were carried out to collect multimodal physiological signals such as EEG, eye movement, ECG, and respiration as well as emotion ratings. Statistical test results show that videos with different sentiment tendencies induce different emotions, and the multimodal physiological features such as the relative spectral power of EEG, blinking frequency, respiration standard deviation, and the very low-frequency power of ECG are specific under different emotions. The emotional semantics embedded in the comments influence public emotion in various ways beyond that evoked by videos.

An unmanned aerial vehicle (UAV) small target detection algorithm based on YOLOv5, termed FDB-YOLO, was proposed to address the significant issue of misidentification and omissions in traditional target detection algorithms when applied to UAV aerial photography of small targets. Initially, a small target detection layer was added on the basis of YOLOv5, and the feature fusion network was optimized to fully leverage the fine-grained information of small targets in shallow layers, thereby enhancing the network’s perceptual capabilities. Subsequently, a novel loss function, FPIoU, was introduced, which capitalized on the geometric properties of anchor boxes and utilized a four-point positional bias constraint function to optimize the anchor box positioning and accelerate the convergence speed of the loss function. Furthermore, a dynamic target detection head (DyHead) incorporating attention mechanism was employed to enhance the algorithm’s detection capabilities through increased awareness of scale, space, and task. Finally, a bi-level routing attention mechanism (BRA) was integrated into the feature extraction phase, selectively computing relevant areas to filter out irrelevant regions, thereby improving the model’s detection accuracy. Experimental validation conducted on the VisDrone2019 dataset demonstrated that the proposed algorithm outperformed the YOLOv5s baseline in terms of Precision by an increase of 3.7 percentage points, Recall by an increase of 5.1 percentage points, mAP₅₀ by an increase of 5.8 percentage points, and mAP_50:95 by an increase of 3.4 percentage points, showcasing superior performance compared to current mainstream algorithms.

A road pavement distress situation forecasting method employing graph convolutional networks was introduced, addressing the prediction problem of road pavement distress generation and deterioration. Firstly, a topological network was established through clustering algorithms, selecting the main influencing factors of the target pavement distress during its evolution. Subsequently, to enhance the expressive capability of the graph neural network for distress information, a graph topology enhancement method was employed, constructing views related to distress information from both static and dynamic aspects. Finally, an enhanced graph neural network (GNN) architecture was applied, by incorporating attention mechanisms in the view dimension to adjust the influence of different views and utilizing Transformer and GRU modules in the temporal dimension to enhance the predictive performance of the model for pavement distress states over extended time sequences. The internal calibration tests of the model, including ablation studies, multi-sample testing, and hyperparameter control group validation, demonstrated the applicability and stability of the proposed model. For the large and sparse pavement disease dataset, the mean absolute error of this model converged within 4.0, which was better than the results of the traditional prediction algorithms in terms of comprehensive performance.

A finite element numerical model of the segregated foundation pit was established based on the case of a deep foundation pit in Hangzhou adjacent to an operating underground shield tunnel in order to analyze the influence of the construction sequence, the separation wall location and other factors on the deformation of deep and large foundation pits and adjacent facilities caused by the segregated-pit construction. The reasonableness of the parameters of the HSS model was verified by combining with the measured data. The influence of the construction sequence of the "platform" type segregated pit on the displacements of out-of-pit strata and existing adjacent tunnels were analyzed by combining with a simplified model based on the case. Results show that the displacements of strata and tunnels caused by the excavation of the segregated pit in Hangzhou soft soil are related to the construction sequence, the location of the separation wall, the thickness of the soft clay, and the relative position of the tunnel and the pit. The deformation of the close pit retaining wall, the surface settlement and the tunnel displacement will be greater with a wider far sub-pit when the close sub-pit is firstly constructed. An opposite finding is observed if the far sub-pit is firstly excavated, and the optimal control effect on the deformation of the retaining wall and adjacent tunnels is achieved by dividing the ratio of the far sub-pit width to the close one by 3.0 to 4.0 and the width of the close sub-pit by 15 m to 20 m. The deformation of the close pit retaining wall, the surface settlement and the tunnel displacement caused by the two sub-pit construction sequences will increase as the thickness of the soft clay layer increases. The concept of the displacement impact zone resulting from different sub-pit construction sequences was proposed, and the demarcation line of the zone can be simplified to be a straight line with an angle of 45° to the wall of the pit. The range of the displacement impact zone which is defined as the strata displacement caused by the close-first-then-far construction sequence is smaller than that of the far-first-then-close construction sequence gradually decreases with the increase of the width of the far sub-pit and the thickness of the soft clay layer. A parametric analysis was conducted to propose formula for fitting the demarcation line of the impact zones related to the location of the separation wall and the thickness of the soft soil layer.

The backgrounds are cluttered, the spot sizes of apple leaf disease are varying in complex environments, and the existing models have the problems of multiple parameters and a large amount of calculation. Thus, an apple leaf disease recognition network, ConvNext network based on attention and multiscale feature fusion (MA-ConvNext), was proposed. A multiscale spatial reconstruction and channel reconstruction block (MSCB) and a feature extraction block with triplet attention fusion (TAFB) were utilized to effectively extract the features at different scales and enhance the focus on leaf disease spots. Additionally, a stepwise relational knowledge distillation method was employed to fuse the "teacher" network (MA-ConvNext) with an "intermediate" network (DenseNet121) to guide the training of the "student" network (EfficientNet-B0) and achieve the model lightweighting. Experimental results showed that MA-ConvNext achieved a recognition accuracy of 99.38%, improving by 3.98 percentage points, 7.55 percentage points and 4.27 percentage points compared to ResNet50, MobileNet-V3, and EfficientNet-V2 networks, respectively. After the stepwise relational knowledge distillation, the recognition accuracy further improved by 1.76 percentage points, with a smaller network size and parameters of 1.56×10⁷ and 5.29×10⁶. respectively. The proposed method offers new insights and technical support for the precise detection of pests and diseases in agriculture.

A 3D target inversion algorithm based on multi-feature reconstruction was proposed in order to solve the problems of large memory occupation and time-consuming training in deep learning-based three-dimensional inversion methods. Four types of features, horizontal area, center depth, vertical thickness and residual density of the target were extracted by feature decomposition to realize the compression of the three-dimensional model and reduce the memory occupation. The multi-feature reconstruction of inversion network (MRNet) was designed to realize the prediction of the four types of target features by different Decoder, and the four types of features were used to reconstruct the three-dimensional model to realize the inversion of the 3D target. The gradient union was introduced at the input of the network to realize the enhancement of target boundary information. The CA attention mechanism was introduced at the cross-layer connection to realize the differentiation of Decoder’s prediction function and optimize the inversion effect. The simulation results showed that the local relative accuracy of MRNet was improved by more than 30% compared with 3D U-Net, reaching 88.91%, and the training time per round was only 1/13 of 3D U-Net. MRNet was applied to Vinton Salt Mound, and the distribution of caprocks was obtained more accurately, which verified that MRNet had certain generalizability.

State-of-the-art data-driven intelligent computations (DDICs) were comprehensively reviewed in order to effectively solve the increasingly complex and expensive optimization problems (EOPs) emerging in real-world applications, which can effectively reduce computing costs and improve solutions. The latest research achievements of DDICs were outlined from both algorithm and application perspectives. Various technical points in generalized DDICs and adaptive DDICs were summarized and categorized. The challenges and opportunities faced by DDICs in solving EOPs were analyzed. Future research potential trends were proposed, such as conducting deeper theoretical analyses, exploring novel learning paradigms, applying these methods in various practical fields, and so on. This aims to provide targeted references and directions for researchers, stimulating innovative ideas to more effectively address the complex EOPs encountered in real-world applications.

Foundational models in natural language processing, computer vision and multimodal learning have achieved significant breakthroughs in recent years, showcasing the potential of general artificial intelligence. However, these models still fall short of human or animal intelligence in areas such as causal reasoning and understanding physical commonsense. This is because these models primarily rely on vast amounts of data and computational power, lacking direct interaction with and experiential learning from the real world. Many researchers are beginning to question whether merely scaling up model size is sufficient to address these fundamental issues. This has led the academic community to reevaluate the nature of intelligence, suggesting that intelligence arises not just from enhanced computational capabilities but from interactions with the environment. Embodied intelligence is gaining attention as it emphasizes that intelligent agents learn and adapt through direct interactions with the physical world, exhibiting characteristics closer to biological intelligence. A comprehensive survey of embodied artificial intelligence was provided in the context of foundational models. The underlying technical ideas, benchmarks, and applications of current embodied agents were discussed. A forward-looking analysis of future trends and challenges in embodied AI was offered.

An improved non-dominated sorting genetic algorithm II (NSGA-II) was proposed in order to address the challenges of time-consuming manual airspace sectorization and the difficulty in comparing the quality of different sectorization schemes. A three-dimensional multi-objective optimization model for sectorization was established by using a grid-region-sector hierarchy in order to balance controllers’ workload within sectors and reduce workload differences between sectors. A fitness evaluation operator, a probability-adaptive combination crossover operator and a dynamic mutation operator were incorporated in the NSGA-II algorithm in order to enhance the number of feasible solutions, solution diversity and computational efficiency. A simulation was conducted for the automatic 3D sectorization of Xi'an high-altitude airspace. Results showed that the optimized scheme improved workload balance within sectors by 37% and reduced inter-sector workload by 24% compared with the current sectorization configuration. The proposed improved NSGA-II provided a broader range of options for decision-makers with varying preferences compared with traditional weighted multi-objective optimization algorithms.

The development and research status of YOLO algorithm in traffic object detection were systematically summarized from the perspective of the three core elements of 'people-vehicle-road' in order to comprehensively analyze the important role of YOLO (You Only Look Once) algorithm in improving traffic safety and efficiency. The commonly used evaluation indexes of YOLO algorithm were outlined, and the practical significance of these indexes in traffic scenarios was elaborately expounded. An overview of the core architecture of YOLO algorithm was provided, its development process was traced, and the optimization and improvement measures in each version iteration were analyzed. The research status and application scenarios of YOLO algorithm for traffic object detection were sorted out and discussed from the perspective of the three traffic objects 'people-vehicle-road'. The limitations and challenges of YOLO algorithm in traffic object detection were analyzed, and corresponding improvement methods were proposed. Future research focuses were anticipated, providing a research reference for the intelligent development of road traffic.

A method based on dynamic position encoding and multi-domain attention feature enhancement was proposed to fully exploit the positional information between the template and search region and harness the feature representation capabilities. Firstly, a position encoding module with convolutional operations was embedded within the attention module. Position encoding was updated with attention calculations to enhance the utilization of spatial structural information. Next, a multi-domain attention enhancement module was introduced. Sampling was conducted in the spatial dimension using parallel convolutions with different dilation rates and strides to cope with targets of different sizes and aggregate the enhanced channel attention features. Finally, a spatial domain attention enhancement module was incorporated into the decoder to provide accurate classification and regression features for the prediction head. The proposed algorithm achieved an average overlap (AO) of 73.9% on the GOT-10K dataset. It attained area under the curve (AUC) scores of 82.7%, 69.3%, and 70.9% on the TrackingNet, UAV123, and OTB100 datasets, respectively. Comparative results with state-of-the-art algorithms demonstrated that the tracking model, which integrated dynamic position encoding as well as channel and spatial attention enhancement, effectively enhanced the interaction of information between the template and search region, leading to improved tracking accuracy.

A dead band compensation strategy for CTPS control was proposed aiming at the problem that the occurrence of return power as well as the failure of soft switching was caused after the addition of bridge arm dead band to the triple phase-shift cooperative control (CTPS) of dual active bridge (DAB) converter. The coupling relationships between different mode shift ratios and the power transfer model and the switching conditions of CTPS control modes were corrected based on the principle of return power generation by analyzing the changes of transformer primary and secondary side voltages and leakage currents caused by the dead band of the bridge arm in different modes of CTPS control. Then the effective control of the impact of dead band on CTPS control was realized. The proposed compensation scheme suppressed the return power caused by the dead band, restored the soft-switching performance of the CTPS control, and had better current stress than before compensation. Experiments before and after dead band compensation were conducted separately to verify the analysis of the dead band effects and the proposed compensation strategy.

An enhanced genetic algorithm was proposed to address the challenge of area coverage path planning for a tilt-rotor unmanned aerial vehicle (TRUAV) amidst multiple obstacles. A preliminary coverage path plan for the designated task area was devised, utilizing the minimum spanning and back-and-forth path generation algorithms. The area coverage dilemma was transformed into a traveling salesman problem to optimize the sequence of the coverage path. A fishtail-shaped obstacle avoidance strategy was proposed to circumvent obstacles within the region. The nearest neighbor algorithm was introduced to generate a superior initial population than a genetic algorithm. A three-point crossover operator and a dynamic interval mutation operator were adopted in the genetic processes to improve the proposed algorithm's global search capacity and prevent the algorithm from falling into local optima. The efficacy of the proposed algorithm was rigorously tested through simulations in polygonal areas with multiple obstacles. Results showed that, compared to the sequential path coverage algorithm and the genetic algorithm, the proposed algorithm reduced the length of the coverage path by 7.80%, significantly enhancing the coverage efficiency of TRUAV in the given task areas.

The DDCNet-YOLO algorithm model was proposed based on the deformable convolution and YOLOv5, aiming at the complex lithium battery electrode surface with multiple small object defects and large aspect ratio object defects at the same time. The deformable downsampling convolution network (DDCNet) was constructed in the backbone. The context augmentation module (CAM) was introduced in the feature fusion part and the deformable convolution block (DCB) was used to replace the C3 module. AD-Head, a decoupling head with an attention mechanism, was designed in the head part. The RIoU method was proposed to optimize the loss calculation for different aspect ratio objects. Experiments showed that the DDCNet-YOLO model improved the mAP50 by 6.2 percentage points compared to YOLOv5s model and by 3.7 percentage points compared to YOLOv5m model. The lightweight model DDCNet-YOLOs, constructed by DDCNet and a decoupling head with an attention mechanism. The DDCNet-YOLOs improved the mAP50:95 by 8.9 percentage points and reduced the number of parameters by 7.2 percentage points, compared with the YOLOv5s model. In addition, both models were deployed based on the C++. The two algorithmic models focus on accuracy and speed respectively, but both can achieve high accuracy under the condition of meeting the actual detection speed requirement.

A spatio-temporal graph attention network for vehicle multimodal trajectory prediction (STGAMT) was proposed to address the challenges of predicting manually-driven vehicle trajectories and investigating their impact on autonomous driving decisions. The temporal and spatial characteristics were modeled based on the historical information about the vehicle. A two-dimensional convolutional neural network was employed to identify transverse and longitudinal lane change states, which were then combined with the output from the spatio-temporal dynamic interaction module to form transverse and longitudinal motion characteristics. The Softmax function was used to determine the vehicle’s driving intention. The multi-mode trajectory output was achieved by using a GRU network based on Gaussian conditional distribution. Experimental results showed that, in short-term predictions, the STGAMT model reduced the average error by 63.8% and 41.0% compared to the other five classic models on HighD and NGSIM datasets, respectively. In long-term predictions, the STGAMT model reduced the RMSE by 62.5% and 19.1% compared to the average RMSE of the other five classic models on HighD and NGSIM datasets, respectively. Results indicated that the STGAMT model could effectively improve the accuracy of manually-driven vehicle trajectory prediction.

Addressing the inefficient distribution of materials in workshops, a multi-objective optimization model with the shortest distribution path and the smallest time window penalty value was established. A hybrid optimization algorithm, INSGA-Ⅱ, based on a fast non-dominated sorting genetic algorithm (NSGA-Ⅱ) was proposed. Density peak clustering (DPC) was adopted to initialize the population and reduce the problem size. To avoid falling into local optimums, the differential evolution (DE) algorithm was used in the genetic operation stage of NSGA-Ⅱ. The differential operation of mutation vectors was used with partial mapped crossover to accelerate the iteration speed and improve the population diversity. Different benchmark functions were solved with different sizes of arithmetic cases, and the results showed that the improved algorithm had better Pareto front compared to the traditional NSGA-Ⅱ algorithm. Meanwhile, the results of the proposed algorithm had better uniformity and diversity, and the solution time was shorter. Experimental results showed that the proposed algorithm generated , compared with the NSGA-Ⅱ and the multi-objective particle swarm optimization (MOPSO), the total distribution distance could be reduced by up to 26.65% and the total time window penalty could be reduced by up to 32.5%. The new method can effectively improve the distribution efficiency of workshop material.

A model based on improved YOLOv5m was proposed for wolfberry pest detection in a complex environment. The next generation vision transformer (Next-ViT) was used as the backbone network to improve the feature extraction ability of the model, and the key target features were given more attention by the model. An adaptive fusion context enhancement module was added to the neck to enhance the model’s ability to understand and process contextual information, and the precision of the model for the small object (aphids) detection was improved. The C3 module in the neck network was replaced by using the C3_Faster module to reduce the model footprint and further improve the model precision. Experimental results showed that the proposed model achieved a precision of 97.0% and a recall of 92.1%. The mean average precision (mAP50) was 94.7%, which was 1.9 percentage points higher than that of the YOLOv5m, and the average precision of aphid detection was improved by 9.4 percentage points. The mAP50 of different models were compared and the proposed was 1.6, 1.6, 2.8, 3.5, and 1.0 percentage points higher than the mainstream models YOLOv7, YOLOX, DETR, EfficientDet-D1, and Cascade R-CNN, respectively. The proposed model improves the detection performance while maintaining a reasonable model footprint.

The gas reservoir volume of a cryogenic loop heat pipe (CLHP) is usually 30 to 100 times the total volume of the other components, and its weight accounts for the largest proportion. To realize the lightweight design of CLHPs and improve the utilization rate of satellite payload resources, research was conducted on the mechanism of the influence of gas reservoir volume on the startup and steady-state operating characteristics of CLHPs. A start-up model and a steady-state failure model of CLHPs were established, and theoretical and experimental validation studies were carried out on the influence of gas reservoir volume on key parameters of the condensation temperature, evaporation temperature of the secondary evaporator and heat transfer thermal resistance. Results showed that, by increasing the design value of the evaporation temperature of the secondary evaporator, the CLHP experimental prototype started up smoothly with a gas reservoir volume only 11 times the total volume of the other components. The effect of different gas reservoir volumes on the heat transfer thermal resistance of CLHPs was negligible when the primary heat load was high. When the volume of the primary compensation chamber was certain, the regulating ability of the primary compensation chamber could be enhanced by decreasing the volume of the gas reservoir, thereby expanding the range of primary heat load for stable operation of the CLHPs.

The latent variables affecting travelers’ intention to low-carbon travel were comprehensively considered based on the theory of planned behavior and the theory of value-belief-norm in order to analyze travelers’ low-carbon travel behaviors and internal mechanisms. A multi-cause and multi-indicator model was constructed by using the empirical data of Hangzhou, and the values of latent variables were calibrated. A hybrid choice model and a Logit model without latent variables were constructed to compare and analyze travelers’ weekday mode choice behavior. A binary hybrid choice model was constructed to analyze the travel behavior shift of high carbon people under carbon incentives. Results show that the latent variables such as attitude, perceived behavioral control, and view of incentive significantly affect the travelers' willingness to travel in a low-carbon way and the choice of travel modes. The hybrid choice model has a better goodness-of-fit than the Logit model without latent variables, and the prediction accuracy improves by 4.6%. 64.4% of the high-carbon individuals tends to choose low-carbon modes under carbon incentives, showing that the carbon incentives can effectively promote travel modes shift behavior. There is some heterogeneity of the variables attitude and travel time in travel mode shift behavior.