Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy sharing.Conventional distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high latency.To address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage systems.This architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and access.The on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw data.The off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data operations.We provide a unified access interface for user-friendly system interaction.Extensive testing validates the system’s reliability and stable performance.The proposed approach significantly enhances storage capacity compared to standalone blockchain systems.Rigorous reliability tests consistently yield positive outcomes.With average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

Global collaboration of eye research--personal experience

On December 9,2023,I was privileged to be honored and participate in the Dr.Chi Chao Chan Symposium on Global Collaboration of Eye Research as the Global Eye Genetic Consortium(GEGC)session,which was held in the 16th Congress of the Asia-Pacific Vitreo-Retina Society(APVRS)in Hong Kong.Along with my talk on“Global collaboration of eye research:personal experience”,other prominent international speakers provided their own perspectives on opportunities for networking,collaboration,and exchange of ideas with global leaders and experts in ophthalmic practice,research,and education.

Impact of BRICS Trade Facilitation on China's Exports Using Cross-Border E-Commerce

With the rapid growth of the global digital economy, cross-border e-commerce, as an emerging form of trade, has gradually become a powerful engine to promote the development of global trade. BRICS is an important force in the global economy, and the progress of the BRICS countries' trade facilitation level has an important impact on the global trade environment. This paper conducts an in-depth study of the dynamic changes in BRICS trade facilitation from 2013 to 2022, and uses an extended gravity model to analyze the specific impact of this change on China's exports using cross-border e-commerce. The results show that although the BRICS countries have made some progress in trade facilitation, the overall level still needs to be improved, and there are obvious differences among member countries. However, the improvement of trade facilitation among BRICS countries has undoubtedly brought significant positive effects to China's exports using cross-border e-commerce.

Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Micro,Small,and Medium Enterprises’Cross-Border Business Strategies

This paper explores the operational strategies of cross-border micro, small, and medium enterprises (MSMEs). Against the backdrop of globalization and digitalization, cross-border trade has become one of the important pathways for many MSMEs to achieve growth and competitive advantage. Firstly, the paper outlines the concept and characteristics of cross-border MSMEs, as well as the analysis of their operational environment in the context of globalization, including political, economic, social, technological, environmental, and legal factors. Secondly, the paper proposes operational strategies for cross-border MSMEs, including international market selection and positioning, cross-border marketing strategies, supply chain management, cross-border financial management, and cross-border risk management. Finally, the paper summarizes the importance of effectively implementing these strategies for cross-border MSMEs to seize international market opportunities, reduce operational risks, and enhance competitiveness and profitability.

Human-Robot Collaboration Framework Based on Impedance Control in Robotic Assembly

Human–robot(HR)collaboration(HRC)is an emerging research field because of the complementary advantages of humans and robots.An HRC framework for robotic assembly based on impedance control is proposed in this paper.In the HRC framework,the human is the decision maker,the robot acts as the executor,while the assembly environment provides constraints.The robot is the main executor to perform the assembly action,which has the position control,drag and drop,positive impedance control,and negative impedance control modes.To reveal the characteristics of the HRC framework,the switch condition map of different control modes and the stability analysis of the HR coupled system are discussed.In the end,HRC assembly experiments are conducted,where the HRC assembly task can be accomplished when the assembling tolerance is 0.08 mm or with the interference fit.Experiments show that the HRC assembly has the complementary advantages of humans and robots and is efficient in finishing complex assembly tasks.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Global trends in international research collaboration, 1980-2021

Purpose:The aim of this study is to analyze the evolution of international research collaboration from 1980 to 2021.The study examines the main global patterns as well as those specific to individual countries,country groups,and different areas of research.Design/methodology/approach:The study is based on the Web of Science Core collection database.More than 50 million publications are analyzed using co-authorship data.International collaboration is defined as publications having authors affiliated with institutions located in more than one country.Findings:At the global level,the share of publications representing international collaboration has gradually increased from 4.7%in 1980 to 25.7%in 2021.The proportion of such publications within each country is higher and,in 2021,varied from less than 30%to more than 90%.There are notable disparities in the temporal trends,indicating that the process of internationalization has impacted countries in different ways.Several factors such as country size,income level,and geopolitics may explain the variance.Research limitations:Not all international research collaboration results in joint co-authored scientific publications.International co-authorship is a partial indicator of such collaboration.Another limitation is that the applied full counting method does not take into account the number of authors representing in each country in the publication.Practical implications:The study provides global averages,indicators,and concepts that can provide a useful framework of reference for further comparative studies of international research collaboration.Originality/value:Long-term macro-level studies of international collaboration are rare,and as a novelty,this study includes an analysis by the World Bank’s division of countries into four income groups.

Monitoring Climate Change and Air Quality Project Initiative: A Case Study of the Collaboration between ECSTAR and TeroSpace in Thailand

In Thailand, the alteration of weather patterns has resulted in an increase in instances of irregular rainfall, contributing to the occurrence of droughts. The decline of water levels in dams, due to the combined effects of climate change and prolonged droughts, has had a significant impact on agricultural productivity. Drought has a profound effect on the terrestrial biosphere and the atmospheric water cycle and can also contribute to air pollution. Researchers have found a strong correlation between air pollution and drought severity. In response to this pressing issue, the Excellence Center of Space Technology and Research (ECSTAR) at King Mongkut’s Institute of Technology Ladkrabang has joined forces with TeroSpace company to launch an initiative aimed at promoting sustainable growth in Chiang Rai, a province in Thailand known for its rich biodiversity. ECSTAR and TeroSpace’s partnership on the sustainable growth initiative in Thailand’s Chiang Rai province focuses on expanding their collaboration to include international organizations such as the Centre National d’Etudes Spatiales (CNES), which will provide access to satellite imagery and climate and weather information to improve decision-making in various areas of development. CNES is a French organization in charge of space-related activities in France. The collaboration between France and Thailand for this project, in the context of the France-Thailand Year of Innovation 2023, will be crucial for the successful initiation and execution of this research project. The project aims to explore the relationship between air pollution and climate change through the deployment of air quality monitoring devices in designated locations, connected to a global data-sharing network. The results of this research will be valuable to policymakers as they consider the interplay between air pollution and climate change and make efforts to address these challenges.

Cross-border Urban Networks Based on Manufacturing Global Value Chain:A Study of Listed Companies in Western China

Cross-border investment is essential for western China’s globalization.Global value chain(GVC)forms cross-border investment networks between industries in western China and overseas cities.Focusing on GVC,this study uses the social network analysis method,entropy method,multi-index comprehensive evaluation method,and quadratic assignment procedure analysis method to examine the characteristics and influencing factors of the urban networks of research and development(R&D),production,and sales formed as a result of the overseas investments of listed manufacturing companies in western China.Results showed that the three types of investment networks involved multiple industry types and multiple central cities with differentiated diversity and multicentrality.The R&D urban network’s leading sub-industries were the mechanical equipment and instruments,medicine and biological products,and metal and nonmetal industries.The destination cities were mostly those home to educational and scientific research centers.The production urban network’s leading sub-industries were the mechanical equipment,instrument,and food and beverage industries.The destination cities were mostly regional central cities in developing countries.The sales urban network’s leading sub-industries were the mechanical equipment and instrument,metal and nonmetal,and petrochemical and plastics industries.The destination cities were numerous and scattered.In addition,the R&D urban network easily formed specialized clusters,core nodes easily controlled the production urban network,and individual nodes did not easily control the sales urban network.Technological and economic system advantages greatly impacted the three network types.Considering the different influencing factors,this study suggests optimizing the institutional investment environment to narrow the institutional gap,adjusting and optimizing the investment layout to expand overseas markets,and increasing R&D funds to stimulate technological progress and overseas investments in western China.

View Types and Visual Communication Cues for Remote Collaboration

Over the last several years,remote collaboration has been getting more attention in the research community because of the COVID-19 pandemic.In previous studies,researchers have investigated the effect of adding visual communication cues or shared views in collaboration,but there has not been any previous study exploring the influence between them.In this paper,we investigate the influence of view types on the use of visual communication cues.We compared the use of the three visual cues(hand gesture,a pointer with hand gesture,and sketches with hand gesture)across two view types(dependent and independent views),respectively.We conducted a user study,and the results showed that hand gesture and sketches with the hand gesture cueswerewell matchedwith the dependent viewcondition,and using a pointer with the hand gesture cue was suited to the independent view condition.With the dependent view,the hand gesture and sketch cues required less mental effort for collaborative communication,had better usability,provided better message understanding,and increased feeling of co-presence compared to the independent view.Since the dependent view supported the same viewpoint between the remote expert and a local worker,the local worker could easily understand the remote expert’s hand gestures.In contrast,in the independent view case,when they had different viewpoints,it was not easy for the local worker to understand the remote expert’s hand gestures.The sketch cue had a benefit of showing the final position and orientation of the manipulating objects with the dependent view,but this benefit was less obvious in the independent view case(which provided a further view compared to the dependent view)because precise drawing in the sketches was difficult from a distance.On the contrary,a pointer with the hand gesture cue required less mental effort to collaborate,had better usability,provided better message understanding,and an increased feeling of co-presence in the independent view condition than in the dependent view condition.The pointer cue could be used instead of a hand gesture in the independent view condition because the pointer could still show precise pointing information regardless of the view type.

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Daya Bay Collaboration Awarded 2023 High Energy and Particle Physics Prize by European Physical Society

China’s Daya Bay collaboration and the RENO collaboration of South Korea were jointly awarded the 2023 High Energy and Particle Physics Prize of the European Physical Society(EPS)at a ceremony in Hamburg,Germany on August 21.Prof.WANG Yifang,initiator of the experiment in China and current director of the Institute of High Energy Physics(IHEP)of the Chinese Academy of Sciences(CAS),accepted the award on behalf of the Daya Bay collaboration.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

An Analysis of US-European Strategic Collaboration in Economic Competition Against China

Europe as an important part of the US-led global strategic alliance has been a target for US attempt to include in its strategy as a counterweight against China in the context of intensifying ChinaUS competition and the prolonged Russia-Ukraine conflict,so as to form the US global strategic collaboration architecture to contain China through the echoing of the US-European strategic collaboration with the QUAD and the AUKUS painstakingly managed by the US.

Digital Development Rights in Developing Countries:Where the Governance Rules for Cross-Border Data Flows

The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.