Risk assessment of oil and gas investment environment in countries along the Belt and Road Initiative

With the implementation of the Belt and Road Initiative, China is deepening its cooperation in oil and gas resources with countries along the Initiative. In order to better mitigate risks and enhance the safety of investments, it is of significant importance to research the oil and gas investment environment in these countries for China's overseas investment macro-layout. This paper proposes an indicator system including 27 indicators from 6 dimensions. On this basis, game theory models combined with global entropy method and analytic hierarchy process are applied to determine the combined weights, and the TOPSIS-GRA model is utilized to assess the risks of oil and gas investment in 76 countries along the Initiative from 2014 to 2021. Finally, the GM(1,1) model is employed to predict risk values for 2022-2025. In conclusion, oil and gas resources and political factors have the greatest impact on investment environment risk, and 12 countries with greater investment potential are selected through cluster analysis in conjunction with the predicted results. The research findings may provide scientific decisionmaking recommendations for the Chinese government and oil enterprises to strengthen oil and gas investment cooperation with countries along the Belt and Road Initiative.

基于ROAD的租车订购系统业务流程建模

基于业务架构为中心的企业架构开发思路,采用开放组架构框架(TOGAF)业务架构ACF元模型的划分,提出了一种ROAD架构迭代建模方法。针对目前ROAD元架构中业务活动模型采用IDEF0的活动模型导致的面向场景业务能力的不足,文中通过使用BPMN模型来覆盖IDEF0的活动建模,避免在描述复杂的业务过程时可能存在的局限性,并且更好地捕捉和表示所有的细节和关系,不会使得模型过于抽象。从而实现面向场景的系统建模和管理,提高业务架构设计的效率和精度。最后,文章中以旅游出行租车订购系统为例的方法,采用ROAD元架构方法建立场景化业务架构模型组,并对其进行讨论和改进,为现有架构体系提供一种面向场景的扩展思路。

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 Connection Between Medical Self-determination and the System of Adult Guardianship——The Institutional Construction of Medical Proxy

China has established the basic rules of informed consent in the medical field through Articles 1219 and 1220 of the tort liability part of the Civil Code of China to address the legality of medical conduct.Since patients’capacity to consent is the prerequisite,when the patient is a fully competent person,it is sufficient to give consent based on valid notification by the doctor.However,for those who are unable to give valid consent,especially adult patients with impaired capacity,resolving the legality of the doctor’s medical conduct remains an issue when it infringes on the patient’s body and health.To solve this issue,someone must give consent in place of the patient when the adult is unable to give valid consent.However,the personal and exclusive nature of the right to medical consent,which is informed consent,makes it impossible to simply delegate it to a guardian or other person to exercise it on behalf of the patient.In this paper,we borrow the concept of“medical proxy”proposed by Japanese scholar Teruaki Tayama,and for the first time,we discuss the construction of medical proxy from the perspective of adult guardianship by connecting the two systems from the standpoint of interpretive theory.

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.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

Toxicity Evaluation of Different Exposure Scenarios of Road Dust Using Daphnia magna and Artemia salina as Aquatic Organisms, and Prosopis cineraria and Vachellia tortilis as Native Plant Species

Particulate matter (PM10) deposited as road dust is considered an important source of contamination from atmosphere. However, there are limited studies on the toxicity of road dust as such on different organisms. This study evaluates the toxicity of road dust using different extraction scenarios on Daphnia magna and Artemia salina as aquatic organisms and also on Prosopis cineraria and Vachellia tortilis as local plant species. Chemical analysis of different extracts shows considerable amount of trace metals, however the trace metals in the dust extract associated with suspended sediment were not absorbed by the receptors. On the other hand, the concentration of trace metals in the artificial mixture was found bioavailable and absorbed causing a high percentage of mortality. In the plant assay, significant difference was obtained in the germination percentage between the control and three different extraction exposures in both plant species. The mean root length of P. cineraria and V. tortilis were higher in 20% and 50% extracts than the control probably due to the availability of nutrients from the dust extract. Interestingly however, the seedling vigor index was the opposite with higher index in the control and lower in dust extracts that contain heavy metals.

Unstructured Road Extraction in UAV Images based on Lightweight Model

There is no unified planning standard for unstructured roads,and the morphological structures of these roads are complex and varied.It is important to maintain a balance between accuracy and speed for unstructured road extraction models.Unstructured road extraction algorithms based on deep learning have problems such as high model complexity,high computational cost,and the inability to adapt to current edge computing devices.Therefore,it is best to use lightweight network models.Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes,such as blocks and strips,a TMB(Triple Multi-Block)feature extraction module is proposed,and the overall structure of the TMBNet network is described.The TMB module was compared with SS-nbt,Non-bottleneck-1D,and other modules via experiments.The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations.The comparison experiment,using multiple convolution kernel categories,proved that the TMB module can improve the segmentation accuracy of the network.The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.

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.

Resilience assessment and optimization method of city road network in the post-earthquake emergency period

The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.

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.

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.

Optimized Binary Neural Networks for Road Anomaly Detection:A TinyML Approach on Edge Devices

Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.

Innovation Analysis of Rural Agricultural Economic Management Model in the Context of the Belt and Road Initiative

[Objectives]To explore innovative strategies for rural agricultural economic development in the context of the Belt and Road Initiative.[Methods]This research adopts the method of literature review and field research,systematically combs the research results related to the Belt and Road Initiative and rural agricultural economic management,and deeply understands the actual situation of rural agricultural economic management.[Results]The research shows that in the context of the Belt and Road Initiative,the rural agricultural economy is facing new development opportunities and challenges,but there are also some problems.In the new period,we should accelerate the innovative development of rural agricultural economy by promoting market-oriented reform,strengthening scientific and technological innovation,optimizing the industrial structure,deepening international cooperation and other measures.The Belt and Road Initiative provides an important opportunity for the innovation of rural agricultural economic management.Rural agricultural economic management should be actively integrated into the Belt and Road construction to improve the level of rural agricultural economic management.[Conclusions]This study provides useful exploration and reference for the innovation of rural agricultural economic management models,which helps to promote the healthy development of rural agricultural economy and achieve the goal of rural revitalization.

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.

Three-dimensional thermohaline structure estimation derived from HY-2 satellite data over the Maritime Silk Road and its applications

Estimated ocean subsurface fields derived from satellite observations provide potential data sources for operational marine environmental monitoring and prediction systems.This study employs a statistic regression reconstruction method,in combination with domestic autonomous sea surface height and sea surface temperature observations from the Haiyang-2(HY-2)satellite fusion data,to establish an operational quasi-realtime three-dimensional(3D)temperature and salinity products over the Maritime Silk Road.These products feature a daily temporal resolution and a spatial resolution of 0.25°×0.25°and exhibit stability and continuity.We have demonstrated the accuracy of the reconstructed thermohaline fields in capturing the 3D thermohaline variations through comprehensive statistical evaluations,after comparing them against Argo observations and ocean analysis data from 2022.The results illustrate that the reconstructed fields effectively represent seasonal variations in oceanic subsurface structures,along with structural changes resulting from mesoscale processes,and the upper ocean’s responses to tropical cyclones.Furthermore,the incorporation of HY-2 satellite observations notably enhances the accuracy of temperature and salinity reconstructions in the Northwest Pacific Ocean and marginally improves salinity reconstruction accuracy in the North Indian Ocean when compared to the World Ocean Atlas 2018 monthly climatology thermohaline fields.As a result,the reconstructed product holds promise for providing quasi-real-time 3D temperature and salinity field information to facilitate fast decisionmaking during emergencies,and also offers foundational thermohaline fields for operational ocean reanalysis and forecasting systems.These contributions enhance the safety and stability of ocean subsurface activities and navigation.

Application and Adaptability of Ornamental Grasses in Road Flower Borders in Hefei, Anhui Province

In-depth research was conducted on the types,seasonal patterns,spatial levels,overwintering,summering,drought resistance,and disease and pest resistance of ornamental grasses in the road flower borders of Hefei,Anhui Province.Through field investigations,a total of 36 ornamental grasses were identified,with Liliaceae having the most species and the Lamiaceae the least.The results show that there are more warm season ornamental grasses,of 23 species;Acorus gramineus and Melinis minutiflora have poor overwintering ability;Arrhenatherum elatius,Festuca glauca,and Stipa tenuissima have poor summer tolerance;most ornamental grasses have strong drought resistance and resistance to pests and diseases.The comprehensive investigation shows that ornamental grasses have good application prospects and adaptability in the road flower borders in Hefei city.

The Launching Ceremony of the Silk Road People-to-People Connectivity Donation Activity"Care for the Future"Held in Yangon

The launching ceremony of the Silk Road Peopleto-People"Connectivity donation activity"Care for the Future was jointly held by China Foundation for Peace and Development and the Myanmar-Chinese Cooperation&Communication Centre in Yangon on January 16.The Yangjin Middle School in Yangon Province of Myanmar received football,volleyball,badminton,notebooks,LED lights and other school utensils and sporting goods.