Vehicle-road collaboration aims to achieve intelligent and efficient traffic management through information exchange and collaboration， in which accurate， lightweight， and easily deployable vehicle and pedestrian detection from the roadside perspective is crucial. To this end， a lightweight traffic object detection model based on improved YOLOv8 was proposed. Firstly， the FasterBlock from FasterNet was introduced to replace certain bottleneck components in the original C2f， thereby reducing Giga FLOating-Point operations （GFLOPs） and parameters effectively， thus reducing the overall model complexity. Secondly， the GSConv （Group Shuffle Convolution） that balanced speed and precision was adopted in the neck network of the model to replace the original convolutional kernel， and the SlimNeck feature fusion module was introduced， enabling each feature layer to consider the semantic information of deep features and the details of shallow features simultaneously. Thirdly， the MPDIoU （Minimum Point Distance based Intersection over Union） was used to replace the original loss function， so as to improve the bounding box regression performance of the model. Finally， the channel pruning was performed to remove redundant connections in the model network， thereby reducing the model size and improving the detection speed. Experimental results show that compared to the original YOLOv8s， the improved and pruned model has the precision increased by 1.0 percentage points， the mean Average Precision （mAP） increased by 1.2 percentage points， and the computational cost and parameters reduced by 70.1% and 69.4% respectively. Under the conditions of edge device Atlas 200I DK A2 （computing power 4 TOPS， power consumption 9 W）， the proposed model has a detection speed of 58.03 frame/s.

Blockchain 3.0 is the third stage of the development of blockchain technology and the core of building a value Internet. Its innovations in sharding， cross-chain and privacy protection have given it a wide range of application scenarios and research value. It is highly valued by relevant people in academia and industry. For the development， technologies and applications of blockchain 3.0， the relevant literature on blockchain 3.0 at home and abroad in the past five years were surveyed and reviewed. Firstly， the basic theory and technical characteristics of blockchain were introduced， laying the foundation for an in-depth understanding of the research progress of blockchain. Subsequently， based on the evolution trend of blockchain technology over time， the development process and various key development time nodes of blockchain 3.0， as well as the reasons of the division of different stages of development of blockchain using sharding and side-chain technologies as benchmarks， were given. Then， the current research status of key technologies of blockchain 3.0 was analyzed in detail， and typical applications of blockchain 3.0 in six major fields such as internet of things， medical care， and agriculture were summarized. Finally， the key challenges and future development opportunities faced by blockchain 3.0 in its development process were summed up.

Improving the image quality of IntraVascular Optical Coherence Tomography （IVOCT） through guidewire artifact removal can assist physicians in diagnosing cardiovascular diseases more accurately， which reduces the probabilities of misdiagnosis and missed diagnosis. Aiming at the difficulties of complex structure information and a large proportion of artifact areas in IVOCT images， a Structure-Enhanced Transformer Network （SETN） using Generative Adversarial Network （GAN） architecture was proposed for guidewire artifact removal of IVOCT images. Firstly， based on the ORiginal Image （ORI） backbone generation network for extracting texture features， the generator of GAN was combined with RTV （Relative Total Variation） image enhanced generation network in parallel to obtain image structure information. Next， during the artifact area reconstruction of ORI/RTV image， Transformer encoders focusing on the temporal/spatial domain information respectively were introduced to capture the contextual information and the correlation between texture/structure features of IVOCT image sequence. Finally， the structural feature fusion module was used to integrate the structural features of different levels into the decoding stage of the ORI backbone generation network， so that the generator was cooperated with the discriminator for completing the image reconstruction of the guidewire artifact area. Experimental results show that the guidewire artifact removal results of SETN are excellent in both texture and structure reconstruction. Besides， the improvement of IVOCT image quality after guidewire artifact removal is positive for both vulnerable plaque segmentation and lumen contour extraction tasks of IVOCT image.

Blockchain technology， which is originated from Bitcoin， is a disruptive and innovative technology with very broad development prospects. Facing the expansion of demand of blockchain platforms and application fields， the introduction of visualization technology can enhance users’cognitive ability， help users efficiently discover useful information from massive and complex data， and facilitate users’understanding and decision-making， which is one of the frontiers of blockchain technology research. In order to gain a deeper understanding of the visualization research based on blockchain technology and application， firstly， the basic theory of blockchain and visualization was introduced， and the existing literature on blockchain visualization was analyzed form multiple dimensions. Next， starting from the common key technologies， the visualization research methods in blockchain transaction processing， consensus mechanism， smart contract and network security were introduced. At the same time， the application status of blockchain visualization in various fields such as virtual currency， social livelihood and integrated innovation was outlined. Finally， the development trends of visualization research based on blockchain technology and application were summarized and prospected.

The Firefly Algorithm (FA) has a few disadvantages in solving the constrained global optimization problem, including that it is difficult to produce initial population, the size of relative attractiveness has nothing to do with the absolute brightness of fireflies, the inertia weight does not take full advantage of the information of objective function, and it cannot better control and constrain the mobile distance of firefly. Therefore an improved FA was proposed. Firstly, Genetic Algorithm (GA) was used to produce an initial population, which improved the production speed of initial population. Secondly, on the basis of the objective function, a dynamic self-adaptive inertia weight was added to FA to improve the convergence speed. Furthermore, a calculation method of relative attractiveness was given, and the size of relative attractiveness had something to do with the absolute brightness of fireflies. Finally, the compression factor was introduced into the location update formula of FA to control and constrain the movement distance of firefly, and thus improved the convergence speed of FA. The experimental results of four test functions show that, compared with standard FA and FA with inertia weight, the improved FA is more effective, which significantly improves computing speed and reduces iteration number.

To solve the problems in Internet of Things (IOT), such as high coupling between levels, low reusability and massive data processing difficulty, this paper proposed a Web service-based middleware model of IOT. It used XML file to configure and implement logical separation, achieved function modularization by the application of "object" in IOT, distributedly processed massive data by load balance, and ensured information security by role permissions setting. The experimental results show that, the middleware model reduces reliance on the type of gateway, improves the reusability of the program and achieves ten-thousand data processing. According to the application of middleware system developed by the model in the vehicle monitoring, the cost of development is reduced, the efficiency of development is improved and the system has a flexible configuration.

Concerning the lack of refined and quantized real-time evaluation of rainstorm disaster risk level, this paper applied meteorological data, historical disaster data, height and distance from the sea of Guangxi, and confirmed the identification technique and data sequence building method of hazard-formative factors of rainstorm of Guangxi based on hazard-bearing body, the hazard-formative environment, hazard-formative factors, and anti-disaster capability. Real-time evaluation model and grade index of rainstorm disaster risk level based on risk, subsequently environment fragile degree, vulnerability and anti-disaster capability were constructed for different hazard-bearing body, such as agriculture and social economy. And then the rainstorm risk level of real-time evaluation system was developed, with real-time evaluation model as the core. By using the Geographic Information System (GIS) secondary development techniques, the operating process of rainstorm risk level real-time evaluation was simplified and standardized. By using the proposed system to evaluate the violent typhoon named Neuchatel on September 29, 2011, the experimental results show that it is consistent with disaster condition.

Image quality detection technology can automatically detection the image abnormality in order to replace manual inspection methods for the monitoring system. It can accurately analyze abnormalities of the video, and alarm the system in order to ensure normal running of the expanding network video surveillance system. Noise detection technology based on the spatial domain use the image information of the field characteristics, profile and orientation distributions of various kinds of abnormal noise in spatial domain, and take advantage of OpenCV-based image processing technology achieving detection of noise points, snowflakes and stripes. The noise detection algorithm in spatial domain proposed, is consistent with human visual perception and can be used to monitoring video for real-time detection.

Based on the research in ultrasound CT image principle, the authors explored the array detection method. By improving the layout of sensors, the amount of data required for imaging increased. Due to the least squared criterion, the formula of simultaneous iterative reconstruction techniques was deduced. The matrix of the coefficient correlation was obtained through algorithm for scanning four edges. The velocity matrix had been achieved through correcting error to approach the real data constantly. The result of simulation shows that the method can reduce error effectively, and the effect of image reconstruction is satisfactory.

An automatic multi-view point cloud merging algorithm for structure light measure system was presented. Firstly, an improved region identification integrated with least-squares algorithm was used to compute the center of the reference point accurately, and the 3D coordinate of the reference point was ascertained through the theory of machine vision. Then, based on the invariability of spatial character, a rapid reference point matching algorithm was put forward to acquire the corresponding relationship between the reference points with different visual angles. Lastly, the rotation matrix and translation matrix were computed by quaternion decompose algorithm to carry out the automatic multi-view point cloud merging algorithm. The result of the experiments shows the validity and great practicability of the algorithm.

Concerning the limitations of Chain-of-Thought （CoT） prompting technology， such as insufficient integration of human strategies and poorly performance for small-scale Large Language Models （LLMs）， a CoT prompting framework based on the 6W2H （Why， What， Which， When， Where， Who， How， How much） problem decomposition strategy， WH-CoT （6W2H Chain-of-Thought）， was proposed. Firstly， the task dataset was clustered， sampled and divided into training and test datasets by using the Sentence-BERT model. Then， in the training dataset， all samples were subjected to element extraction， problem decomposition， answer paragraph construction， and answer generation to form the CoT， thereby constructing a task-specific corpus. Finally， during the reasoning stage， demonstration samples were extracted adaptively from the corpus and added to the prompts， allowing the model to combine the prompts to generate answers to test questions. For the Qwen-turbo model， on arithmetic reasoning task， the average accuracy of WH-CoT is improved by 3.35 and 4.27 percentage points respectively compared with those of the mainstream Zero-Shot-CoT and Manual-CoT； on multi-hop reasoning task， compared with Zero-Shot-CoT and Manual-CoT， WH-CoT has the total performance improvement ratio on EM （Exact Matching ratio） increased by 36 and 111 percentage points respectively. In addition， for the Qwen-14B-Chat and Qwen-7B-Chat models， the total performance improvement ratios of WH-CoT are higher than those of Zero-Shot-CoT and Manual-CoT on both EM and F1. It can be seen that by further integrating human strategies with machine intelligence， WH-CoT can improve the reasoning performance of LLMs of different sizes effectively on both arithmetic reasoning and multi-hop reasoning tasks.