3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Local failure mechanism of sand-blocking fence in latticed dune along desert roads

The latticed dunes in the Tengger Desert are widely distributed,and the sand-blocking fence placed here are highly susceptible to local failure due to complex wind-sand activities,posing a serious threat to the safe operation of the highway.To explore the local failure mechanism of sand-blocking fence in the latticed dune area,the local failure of sand-blocking fence in the latticed dune areas along the Wuhai-Maqin Highway in China was observed.Taking the first main ridge of the latticed dune as the placement location,the structure of the wind-sand flow field of sand-blocking fence placed at top,the bottom and the middle of windward slope was analyzed by Computational Fluid Dynamics(CFD).The results show that when placed at top of the first main ridge,the wind speed near the sand-blocking fence is the highest,up to 15.23 m/s.Therefore,the wind load strength on the sand barrier is correspondingly larger,up to 232.61 N∙m-2.As the strength of material continues to decrease,the nylon net is prone to breakage.The roots of the angle steel posts are susceptible to hollowing by vortex action,which can cause sand-blocking fence to fall over in strong wind conditions.When placed at the bottom of windward slope,wind speed drop near sand-blocking fence is greatest,with the decrease of 12.48-14.32 m/s compared to the original wind speed.This is highly likely to lead to large-scale deposition of sand particles and burial of the sand-blocking fence.When placed in the middle of windward slope,sand-blocking fence is subjected to less wind load strength(168.61N∙m-2)and sand particles are mostly deposited at the bottom of windward slope,with only a small amount of sand accumulating at the root of sand-blocking fence.Based on field observations and numerical modelling results,when the sand-blocking fence is placed in latticed dune area,it should be placed in the middle of the windward slope of the first main ridge as a matter of priority.Besides the sand-blocking fence should be placed at the top of the first main ridge,and sand fixing measures should be added.

Exploring the Factors Associated with 12-Month Non-Return to Work among Motorcyclists Involved in Road Accidents

Introduction: Motorcyclists bear a disproportionate burden of morbidity and mortality from road accidents. In addition, the consequences of these accidents affect the ability of victims to return to work. This study aimed to determine the prevalence and factors associated with non-return to work among surviving motorcyclists involved in road accidents 12 months after the event. Materials and Methods: It was a cross-sectional study conducted using data from a cohort of motorcyclists involved in accidents and recruited in five hospitals in Benin from July 2019 to January 2020. The dependent variable was non-return to work 12 months after the accident (yes vs no). The independent variables were categorized into two groups: baseline and 12-month follow-up variables. Logistic regression was used to determine the factors associated with non-return to work at 12 months among the participants. Results: Among the 362 participants, 55 (15.19%, 95% CI = 11.84 - 19.29) had not returned to work 12 months after the accident. Risk factors for non-return to work identified were: smoking (aOR = 4.41, 95% CI = 1.44 - 13.56, p = 0.010), hospitalization (aOR = 2.87, 95% CI = 1.14 - 7.24, p Conclusion: The prevalence of non-return to work at 12 months was high among surviving motorcyclists involved in road accidents in Benin. Integrated support for patients based on identified risk factors should effectively improve their return to work.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

A Brief Discussion on the Development of the Silk-Weaving Industry Along the “Southern Silk Road” in Yunnan

Ancient Yunnan was one of the most significant regions along China’s ancient“Southern Silk Road.”During the Nanzhao period(738–902)of the late Tang Dynasty,Yunnan’s silk-weaving industry underwent a qualitative leap as skilled silk craftsmen from the Bashu area migrated to Yunnan and introduced mulberry planting,silkworm breeding,and advanced silk-weaving techniques from Sichuan to the region.Consequently,people in Yunnan gradually acquired expertise in brocade weaving and embroidery.Many even mastered complex silk-weaving techniques.The development and progress of the silk-weaving industry in the ancient Yunnan region were intricately linked to the economic function and value of silk as both a commodity and currency along the“Southern Silk Road.”The local government in ancient Yunnan was greatly motivated by the economic interests brought by the development of silk-related industries and recognized the significance of developing the local silk industry.They even initiated a campaign to capture skilled silk craftsmen from Sichuan,aiming to foster the growth of the silk-weaving industry in Yunnan.After years of dedicated efforts from the local government in ancient Yunnan,the region emerged as a significant hub for silk production along China’s ancient“Southern Silk Road.”Despite the devastation caused by the wars in other parts of the country,Yunnan’s silk industry continued to thrive and provide ample silk products to sustain trade along this renowned route.In the contemporary era,amidst the decline of the silk-weaving industry in eastern China,Yunnan has proposed an industrial development strategy known as“relocating the silk-weaving industry from east to west.”This involves introducing advanced silk production techniques from the eastern regions into Yunnan to enhance and enrich its local silk industry,thereby establishing it as a traditional national sector and securing a competitive position within the global silk market.The historical experience of Yunnan’s silk industry demonstrated that economic development opportunities can only be seized through proactive endeavors rather than passive anticipation.The modern Yunnan silk industry,which upholds its historical traditions,continues to actively engage in international high-end technical cooperation,thus ensuring the enduring vitality of the ancient“Southern Silk Road.”

The coordinated evolution of ecological environment,public service,and tourism economy along the Silk Road Economic Belt,using the Dual-Carbon Targets

Carbon peak and carbon neutrality(dual-carbon)are important targets for the international response to climate change.The Silk Road Economic Belt is a strategic resource region and is important for future ecological environment and tourism development.Based on the“dual-carbon”targets,the Single index quantification,Multiple index synthesis,and Poly-criteria integration evaluation model were used in this study to measure the coordinated development index of the ecological environment,public service,and tourism economy along the Silk Road Economic Belt and to analyze its spatial and temporal evolution.Further,it explores the dynamic evolution and development trend of the three systems using the Kernel Density and Grey Markov Prediction Model.The results show that the coordinated development index along this region needs to be improved during the study period.Furthermore,the coordinated development index of the Southwest region is relatively higher than that of the Northwest region.From the development trend of the three systems,all of them develop in a stable manner;however,the tourism economy system is easily affected by external disturbances.The coordinated development index of the three systems changes dynamically and tends to be in a good state of coordination.There is a certain spatial and temporal heterogeneity.The gravity center of the coordinated development index has been in the Southwest region.During the forecast period,the coordinated development index along this region will improve significantly,while insufficient and unbalanced development will continue.

From“Car Priority”to“People Priority”: Exploring New Ideas for Refined Road Space Design

Roads are crucial public spaces in cities and serve as a window to showcase the city’s characteristics.They serve not only as a means of urban transportation but also as a crucial spatial carrier for urban communication activities and a significant location for meeting the increasing demands of people for a better quality of life.The current road infrastructure prioritizes the right of way for cars,neglecting the design of sidewalks and green belts within the road’s boundaries,and extension spaces between buildings and boundary lines of roads.There is a pressing need to improve street space in response to the demand for development transformation and the creation of a warmer city.This paper summarizes common problems in current road spaces,draws on the experience of excellent urban road spaces in foreign countries,discusses new ideas for the refined design of road spaces based on the transformation of road planning concepts,and suggests a reference standard for guiding detailed design.Simultaneously,the review of road construction will incorporate the detailed design of road space to enhance the role of planning in guiding and controlling the construction of road works.

‘Silk Road Artists’Rendezvous’ Exhibition

The"Silk Road Artists'Rendezvous"exhibition kicked off in Mengqi Town,Yuhang District,Hangzhou,where 152 artworks of 84 artists from 83 countries are on show.As a key sideline event of the Liangzhu Forum,the exhibition is hosted by the Ministry of Culture and Tourism of the People's Republic of China,People's Government of Zhejiang Province,and China International Culture Association(CiCA),and organized by the Bureau of International Exchanges and Cooperation of Ministry of Culture and Tourism of the People's Republic of China,Publicity Department of the CPC Zhejiang Provincial Committee,Zhejiang Provincial Department of Culture and Tourism,Hangzhou Municipal Government,and China Academy of Art(CAA)on December 3,2023 in Hangzhou,Zhejiang Province,China.

Aerial Silk Road Soars The“Silk Road in the Air”promoted by China Eastern Airlines will reach around the world to bring people in BRI partner countries even closer together

Two thousand years ago,the Silk Road connecting Xi’an to Rome was a long journey that took more than a year.Today,China Eastern Airlines makes the journey in just over 10 hours with the world’s most advanced wide-body airliner.The power of civil aviation has made the Silk Road more convenient than ever before.Over the last decade of the Belt and Road Initiative(BRI),the“Aerial Silk Road”has connected more and more destinations around the world.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.

Study on the Protection of Sites of“Maritime Silk Road”in Haikou from the Perspective of World Cultural Heritage

The sites of Maritime Silk Road in Haikou City are composed of sites of navigation and trade,religious temples and stone monuments,urban construction and coastal defense scattered on the south bank of the Qiongzhou Strait.They played a key role in the formation and shaping of the settlements in Haikou City,recorded the process of intercommunication and integration between Chinese civilization and other regional cultures,and witnessed the germination,flourishing and inheritance of Haikou’s unique marine culture.The mixture of points,lines and planes blends with the urban area and coastline of Haikou City in spatial distribution.In this paper,from the perspective of world cultural heritage,some suggestions for the protection planning of sites of Maritime Silk Road in Haikou City were proposed based on the analysis of historical and geographical background,comparison of domestic and foreign similar sites,and evaluation of cultural heritage value.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

Colorectal cancer’s burden attributable to a diet high in processed meat in the Belt and Road Initiative countries

BACKGROUND Colorectal cancer(CRC)plays a significant role in morbidity,mortality,and economic cost in the Belt and Road Initiative(“B and R”)countries.In addition,these countries have a substantial consumption of processed meat.However,the burden and trend of CRC in relation to the consumption of a diet high in processed meat(DHPM-CRC)in these“B and R”countries remain unknown.AIM To analyze the burden and trend of DHPM-CRC in the“B and R”countries from 1990 to 2019.METHODS We used the 2019 Global Burden of Disease Study to collate information regarding the burden of DHPM-CRC.Numbers and age-standardized rates(ASRs)of deaths along with the disability-adjusted life years(DALYs)were determined among the“B and R”countries in 1990 and 2019.Using joinpoint regression analysis,the average annual percent change(AAPC)was used to analyze the temporal trends of age-standardized DALYs rate(ASDALR)from 1990 to 2019 and in the final decade(2010–2019).RESULTS We found geographical differences in the burden of DHPM-CRC among“B and R”countries,with the three highest-ranking countries being the Russian Federation,China,and Ukraine in 1990,and China,the Russian Federation,and Poland in 2019.The burden of DHPM-CRC generally increased in most member countries from 1990 to 2019(all P<0.05).The absolute number of deaths and DALYs in DHPM-CRC were 3151.15[95%uncertainty interval(UI)665.74-5696.64]and 83249.31(95%UI 15628.64-151956.31)in China in 2019.However,the number of deaths(2627.57-2528.51)and DALYs(65867.39-55378.65)for DHPM-CRC in the Russian Federation has declined.The fastest increase in ASDALR for DHPM-CRC was observed in Vietnam,Southeast Asia,with an AAPC value of 3.90%[95%confidence interval(CI):3.63%-4.16%],whereas the fastest decline was observed in Kyrgyzstan,Central Asia,with an AAPC value of-2.05%(95%CI:-2.37%to-1.73%).A substantial upward trend in ASR of mortality,years lived with disability,years of life lost,and DALYs from DHPM-CRC changes in 1990-2019 and the final decade(2010-2019)for most Maritime Silk Route members in East Asia,South Asia,Southeast Asia,North Africa,and the Middle East,as well as Central Europe,while those of the most Land Silk Route members in Central Asia and Eastern Europe have decreased markedly(all P<0.05).The ASDALR for DHPM-CRC increased more in males than in females(all P<0.05).For those aged 50-74 years,the ASDALR for DHPM-CRC in 40 members exhibited an increasing trend,except for 20 members,including 7 members in Central Asia,Maldives,and 12 high or high-middle social development index(SDI)members in other regions(all P<0.05).CONCLUSION The burden of DHPM-CRC varies substantially across“B and R”countries and threatens public health.Relevant evidence-based policies and interventions tailored to the different trends of countries in SDIs or Silk Routes should be adopted to reduce the future burden of CRC in“B and R”countries via extensive collaboration.

Otorhinolaryngological (ORL) and Cervicofacial Injuries Resulting from Road Accidents within the Otorhinolaryngology (ORL) Department of the Regional Hospital of Mamou

Introduction: Otorhinolaryngological (ORL) and cervicofacial traumas encompass injuries affecting the neck, face, and ORL region, resulting from external influence. The objective of this study was to conduct an epidemiological and clinical analysis of ORL and cervicofacial injuries observed in victims of road accidents treated at the Otorhinolaryngology (ORL) department of the Regional Hospital of Mamou. Materials and Methods: This prospective and descriptive study was conducted over a period of 12 months, from January 1st to December 31st, 2021, within the Otorhinolaryngology (ORL) department of the Regional Hospital of Mamou. It included patients hospitalized for ORL and/or cervicofacial traumas resulting from road accidents during the study period. Results: A total of 348 cases were included, representing 36.22% of all traumas related to road accidents (AVP) within the Regional Hospital of Mamou. The average age of the patients was 26.44 years, with a sex ratio of 3.83, favoring males. The dominant socio-professional category was students, encompassing 32.47% of cases. Road accidents were mainly associated with motorcycle use (31.32% motorcycle-to-motorcycle, 24.43% motorcycle skidding, 13.51% motorcycle-pedestrian). Facial region injuries were the most common, accounting for 69.03% of cases, followed by rhinological injuries (17.28%) and otological injuries (13.69%). Associated injuries were predominantly closed head traumas (52.55%) and lower limb fractures (20.41%). Imaging was underutilized. Regarding surgical interventions, reconstructive surgery was the most predominant, accounting for 52.51% of cases, with a management timeframe of 30 minutes to 12 hours for 99.72% of patients. Conclusion: Road traffic accidents remain a serious public health issue, primarily affecting young males. Emphasis should be placed on road safety measures to reduce the frequency of these accidents.

On the Road To Development

Uganda’s President Yoweri Museveni has lamented that his country’s bad roads get easily damaged by heavy rains,while also expressing his confidence in the new roads recently built by Chinese construction companies.

Analysis of the Impact of Road Traffic Safety Facilities on Traffic Safety

The development of the national economy is closely tied to infrastructure construction.In recent years,China has seen a significant increase in the number and scale of road construction projects,aimed at facilitating the flow of goods and enabling convenient travel for the masses.However,this surge in road construction also raises concerns about road traffic safety.Road traffic safety facilities play a crucial role in warning and protecting against traffic accidents.To ensure their effective implementation,this paper analyzes the essence of road traffic facilities and their impact on traffic safety.By identifying challenges in the application of traffic safety facilities and adhering to safety facility application principles,suggestions are proposed to enhance traffic safety management.

Design Method for Road Reconstruction and Expansion

This article analyzes the method for designing routes in road reconstruction and expansion projects,using an actual engineering project as an example.This includes an overview of a specific road reconstruction and expansion project,an analysis of the preexisting road,the basic principles of the design road project,and an analysis of the design methods and steps.This study aims to offer some guidance for road reconstruction and expansion design.

Discourse Analysis of the Belt and Road Initiative From the Perspective of Central Asian Countries: A Case Study of the Republic of Kazakhstan

This study employs Norman Fairclough’s Critical Discourse Analysis(CDA)three-dimensional model,using the Republic of Kazakhstan as a case study,to delve into the discourse construction of China’s Belt and Road Initiative(BRI)in Central Asian countries.Through detailed analysis of policy documents,media reports,and public discussions in Central Asian countries,this paper reveals how the BRI constructs specific social practices,discourse events,and textual meanings within these nations.The findings indicate that through this global development strategy,China has not only strengthened its economic ties with Central Asian countries but has also exerted profound influences on political,cultural,and social levels.