A new composite bent cap consisting of a shell made of steel plate and ultra-high-performance concrete (UHPC) and cast-in-place core normal concrete (NC) was proposed in order to realize the assembly and rapid construction of ultra-large-scale bent cap for urban viaducts or highway reconstruction and expansion projects. Parametric analysis of different UHPC and steel plate thickness was conducted in order to analyze the influence of the thickness of UHPC and steel mold plate on its stress performance. Results showed that the stiffness of the shell was affected by the thickness of UHPC and steel plate and their ratio together under the action of self-weight. The thicker the UHPC and steel plate are, the better the stress performance of the shell is, but the economy will be reduced when tensioning prestress and casting concrete. It is recommended to use UHPC thickness of 70 mm and steel plate thickness of 6 mm. A piece of 1∶2.5 scaled-down model was designed and static loading test was conducted in order to verify the feasibility and safety of this scheme. Results show that the new UHPC-NC composite bent cap has good force performance and high safety reserve, which can provide reference for the assembly construction of bent cap.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

The similar test of physical model was designed aiming at the diversion tunnel project of layered surrounding rock of San Gavan Hydropower Station. LSDYNA was used to analyze the propagation characteristics and distribution characteristics of stress wave in layered rock mass by considering the static load, dynamic load and the dip angle. The sensitivity of different factors to the peak stress and secondary equilibrium stress of surrounding rock was analyzed by orthogonal test. A stress prediction model under the influence of multiple factors was established based on the dimensional analysis in order to determine the safety load control range of surrounding rock. Results showed that there was initial stress concentration in the surrounding rock of tunnel under high ground stress. The dynamic load had a significant impact on the value of stress concentration. The stress wave front was discontinuously distributed due to the influence of bedding. The dynamic and static loads were positively linearly correlated with the peak stress and secondary equilibrium stress. The peak stress and secondary equilibrium stress showed '∧' type distribution with the increase of dip angle. The sensitivity order of different factors to the peak stress and secondary equilibrium stress was dynamic load>static load>dip angle. The static load limit values were 0.731, 0.555, 0.479 and 0.456 MPa respectively, and the dynamic load limit values were 0.624, 0.523, 0.477 and 0.463 MPa respectively when the dip angle was 90°(0°), 75°(15°), 60°(30°) and 45°.

Traditional computational intelligence technology was found to lack real-time capabilities and adaptability, and computational intelligence technology based on parallel computing made computational efficiency improve and addressed the issue of compatible processing of multimodal information. From three branches of computational intelligence: neural networks, evolutionary algorithms, and swarm intelligence algorithms, the current states were reviewed on the integration of computational intelligence and big data-parallel computing. Problems present in parallel computing intelligence were summarized, and some thoughts were given to the development direction of related studies.

A low-jitter high-speed charge-pump phase-locked loop (CPPLL) suitable for high-speed SerDes serial link was designed using 65 nm CMOS technology. Loop bandwidth and circuit structure of voltage-controlled oscillator (VCO), charge pump (CP), phase frequency detector (PFD) were optimized to reduce jitter caused by voltage ripple and internal noise. CPPLL can achieve a stable clock signal with the smaller jitter offset while meeting the wide frequency range and high speed requirements of SerDes link. Chip area including the entire pads is 0.309 mm². The measurement results show that CPPLL can generate a 10.9-12 GHz clock signal and exhibit a phase noise of ?111.47 dBc/Hz and a reference spur of ?25.14 dBc and a figure-of-merit (FoM) of ?223.5 dB at 10 MHz offset. It takes 600 μs to generate a stable 11.3 GHz clock signal, and its RMS jitter is 973.9 fs when the reference frequency is 706.25 MHz, which is approximately 0.065 UI. The power consumption is 47.3 mW at the supply voltage of 1.2 V. The proposed phase-locked loop (PLL) is suitable for high-speed communication link systems at 20 Gb/s and above.

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 multi-distortion type underwater image enhancement algorithm based on improved CycleGAN was proposed, aiming at the difficulties of underwater image blurring, low contrast and image distortion recognition caused by various factors such as scattering, absorption and color deviation. Firstly, in order to improve the image enhancement effect, Auto-Encoder+Skip-connection network structure was used in the generator of CycleGAN, and global color correction structure was added for global enhancement in terms of pixel as well as color, so as to better capture the color information in underwater images. Secondly, a multidimensional perceptual discriminator was designed to learn the global and local features of the image. This discriminator payed more attention to the local details of the image, effectively targeted scattering and color noise, perceived the image from a multidimensional space, and had a stronger ability to extract the features, thereby enhancing the accuracy of image discrimination. Finally, the experimental results on EUVP, UIEB and U45 datasets showed that the proposed method achieved better results, compared with other algorithms. In processing multi-distortion types of underwater images, the algorithm’s SSIM indicator was higher than that of the second place by an average of 1.57%, the PSNR indicator was higher by 1.836%, the UIQM indicator was higher by 1.324%, and the UCIQE indicator was higher by 1.086%. The proposed method performed well in processing color and noise details.

The spatio-temporal characteristic of mobile signaling data was analyzed to mitigate the impact of spatio-temporal uncertainty in the location information of mobile signaling data on trip identification. Area of interest (AOI) and base station locations were incorporated based on the spatio-temporal threshold-based method for identifying stay points. A method for identifying stay points using a variable-parameter sliding window was proposed. A trip chain model was established, and Bayesian multi-objective optimization was employed to determine the best parameters. The dynamic adjustment of spatio-temporal thresholds was realized to enhance recognition accuracy. Volunteers were organized to collect real travel GPS data and travel information labels serving as validation data and compared with the results after applying the model to the corresponding mobile phone signaling data in order to validate the effectiveness of above-mentioned method. The research results indicate that there are characteristic differences in the sampling of mobile signaling data between mobile and stationary states. The proposed method show reduced errors and improved recognition rates in terms of both generalization and optimal performance compared with the benchmark methods. There is an improvement ranging from 3% to 26% especially in recognition rate compared to other state-of-the-art algorithms.

A novel detection algorithm (efficient multi-scale attention (EMA) and small object detection based on YOLOv5, ES-YOLOv5) was proposed by targeting small ship targets in SAR scenes aiming at the issues of inconspicuous imaging features and low detection accuracy caused by arbitrary orientation of small targets in synthetic aperture radar (SAR) imaging. A small target detection layer was added to adjust the receptive field size, making it more suitable for capturing small target scale features and facilitating multi-scale fusion. An EMA mechanism was introduced to focus on key target information and enhance feature representation capability. The circular smooth label (CSL) technique was utilized to adapt to the periodicity of angles, achieving high-precision angle classification. The experimental results demonstrate that the proposed method achieves an average detection accuracy of 90.9% at an intersection over union (IoU) threshold of 0.5 on the RSDD-SAR dataset. The algorithm outperforms the baseline algorithm YOLOv5 by 6% in improving the precision of detecting small SAR ship targets, significantly enhancing the model’s detection performance.

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.