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.

Density of excess modes below the first phonon mode in four-dimensional glasses

Glasses are known to possess low-frequency excess modes beyond the Debye prediction.For decades,it has been assumed that evolution of low-frequency density of excess modes D(ω) with frequency ω follows a power-law scaling:D(ω)~ω~γ.However,it remains debated on the value of γ at low frequencies below the first phonon-like mode in finitesize glasses.Early simulation studies reported γ=4 at low frequencies in two-(2D),three-(3D),and four-dimensional(4D)glasses,whereas recent observations in 2D and 3D glasses suggested γ=3.5 in a lower-frequency regime.It is uncertain whether the low-frequency scaling of D(ω)~ω^(3.5) could be generalized to 4D glasses.Here,we conduct numerical simulation studies of excess modes at frequencies below the first phonon-like mode in 4D model glasses.It is found that the system size dependence of D(ω) below the first phonon-like mode varies with spatial dimensions:D(ω) increases in2D glasses but decreases in 3D and 4D glasses as the system size increases.Furthermore,we demonstrate that the ω^(3.5)scaling,rather than the ω~4 scaling,works in the lowest-frequency regime accessed in 4D glasses,regardless of interaction potentials and system sizes examined.Therefore,our findings in 4D glasses,combined with previous results in 2D and 3D glasses,suggest a common low-frequency scaling of D(ω)~ ω^3.5) below the first phonon-like mode across different spatial dimensions,which would inspire further theoretical studies.

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.

Evaluation of mitral chordae tendineae length using four-dimensional computed

BACKGROUND Mitral valvuloplasty using artificial chordae tendineae represents an effective surgical approach for treating mitral regurgitation.Achieving precise measurements of artificial chordae tendineae length(CL)is an important factor in the procedure;however,no objective index currently exists to facilitate this measurement.Therefore,preoperative assessment of CL is critical for surgical planning and support.Four-dimensional x-ray micro-computed tomography(4D-CT)may be useful for accurate CL measurement considering that it allows for dynamic three-dimensional(3D)evaluation compared to that with transthoracic echocardiography,a conventional inspection method.AIM To investigate the behavior and length of mitral chordae tendineae during systole using 4D-CT.METHODS Eleven adults aged>70 years without mitral valve disease were evaluated.A 64-slice CT scanner was used to capture 20 phases in the cardiac cycle in electrocardiographic synchronization.The length of the primary chordae tendineae was measured from early systole to early diastole using the 3D image.The primary chordae tendineae originating from the anterior papillary muscle and attached to the A1-2 region and those from the posterior papillary muscle and attached to the A2-3 region were designated as cA and cP,respectively.The behavior and maximum lengths[cA(ma),cP(max)]were compared,and the correlation with body surface area(BSA)was evaluated.RESULTS In all cases,the mitral anterior leaflet chordae tendineae could be measured.In most cases,the cA and cP chordae tendineae could be measured visually.The mean cA(max)and cP(max)were 20.2 mm±1.95 mm and 23.5 mm±4.06 mm,respectively.cP(max)was significantly longer.The correlation coefficients(r)with BSA were 0.60 and 0.78 for cA(max)and cP(max),respectively.Both cA and cP exhibited constant variation in CL during systole,with a maximum 1.16-fold increase in cA and a 1.23-fold increase in cP from early to mid-systole.For cP,CL reached a plateau at 15%and remained elongated until end-systole,whereas for cA,after peaking at 15%,CL shortened slightly and then moved toward its peak again as end-systole approached.CONCLUSION The study suggests that 4D-CT is a valuable tool for accurate measurement of both the length and behavior of chordae tendineae within the anterior leaflet of the mitral valve.

Community detection on elite mathematicians’collaboration network

Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the field of mathematics.Design/methodology/approach:Two community detection algorithms,namely Greedy Modularity Maximization and Infomap,are utilized to examine collaboration patterns among mathematicians.We conduct a comparative analysis of mathematicians’centrality,emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness,Closeness,and Harmonic centrality.Additionally,we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields.Findings:The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles.The elite distribution across the network is uneven,with a concentration within specific communities rather than being evenly dispersed.Secondly,the research identifies a positive correlation between distinct mathematical sub-fields and the communities,indicating collaborative tendencies among scientists engaged in related domains.Lastly,the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community.Research limitations:The study’s limitations include its narrow focus on mathematicians,which may limit the applicability of the findings to broader scientific fields.Issues with manually collected data affect the reliability of conclusions about collaborative networks.Practical implications:This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles.Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions,potentially enhancing scientific progress in mathematics.Originality/value:The study adds value to understanding collaborative dynamics within the realm of mathematics,offering a unique angle for further exploration and research.

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.

NAVIGATING CHALLENGES TOGETHER 3rd AIWW calls for regional and global collaboration in the field of water resources

The 3rd Asia International Water Week (AIWW),themed"Enhancing Our Future Water Security,"concluded in Beijing on September 26.Delegates from the many Asian countries attending the event widelyrecognized the water governance philosophy proposed by Chinese President Xi Jinping:"prioritizing water conservation,balancing water distribution in time and space,and taking a systematic approach to water management with the synergy of government and market."

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.

Effects of nursing team communication and collaboration on treatment outcomes in intensive care unit patients with severe pneumonia

BACKGROUND Severe pneumonia is a common severe respiratory infection worldwide,and its treatment is challenging,especially for patients in the intensive care unit(ICU).AIM To explore the effect of communication and collaboration between nursing teams on the treatment outcomes of patients with severe pneumonia in ICU.METHODS We retrospectively analyzed 60 patients with severe pneumonia who were treated at the ICU of the hospital between January 1,2021 and December 31,2023.We compared and analyzed the respiratory mechanical indexes[airway resistance(Raw),mean airway pressure(mPaw),peak pressure(PIP)],blood gas analysis indexes(arterial oxygen saturation,arterial oxygen partial pressure,and oxygenation index),and serum inflammatory factor levels[C-reactive protein(CRP),procalcitonin(PCT),cortisol(COR),and high mobility group protein B1(HMGB1)]of all patients before and after treatment.RESULTS Before treatment,there was no significant difference in respiratory mechanics index and blood gas analysis index between 2 groups(P>0.05).However,after treatment,the respiratory mechanical indexes of patients in both groups were significantly improved,and the improvement of Raw,mPaw,plateau pressure,PIP and other indexes in the combined group after communication and collaboration with the nursing team was significantly better than that in the single care group(P<0.05).The serum CRP and PCT levels of patients were significantly decreased,and the difference was statistically significant compared with that of nursing group alone(P<0.05).The levels of serum COR and HMGB1 before and after treatment were also significantly decreased between the two groups.CONCLUSION The communication and collaboration of the nursing team have a significant positive impact on respiratory mechanics indicators,blood gas analysis indicators and serum inflammatory factor levels in the treatment of severe pneumonia patients in ICU.

Dynamic business process modeling and verification for inter-organizational collaboration

To achieve an on-demand and dynamic composition model of inter-organizational business processes, a new approach for business process modeling and verification is introduced by using the pi-calculus theory. A new business process model which is multi-role, multi-dimensional, integrated and dynamic is proposed relying on inter-organizational collaboration. Compatible with the traditional linear sequence model, the new model is an M x N multi-dimensional mesh, and provides horizontal and vertical formal descriptions for the collaboration business process model. Finally, the pi-calculus theory is utilized to verify the deadlocks, livelocks and synchronization of the example models. The result shows that the proposed approach is efficient and applicable in inter-organizational business process modeling.

Intercultural Collaboration in Group Work at Chinese Universities

The empirical work on intercultural communication issues at university settings have mainly focused on the minority cul-tural groups and the majority groups are usually neglected.Most Chinese scholars are engaged in the development of cross-cultural communication competence of the Chinese EFL learners,the few researches on intercultural relations in Chinese universities are also on the international students.To compare the attitudes and motivations of both the International Students in China(ISCs)and the Chinese Home Students(CHSs)in intercultural collaboration(IC)in the group work,group discussion and questionnaire survey areemployed.Survey of the 445 CHSs and 87 ISCs show that the IC environment at Chinese universities is far from satisfactory.Those who have IC experiences find they have encountered difficulties in adapting to each other's language,race,religion,agenda,etc.Chinese students'language difficulty is regarded both groups as the first obstacle in their collaboration,but personality is the first concern in their choice of a workmate.Neither language proficiency nor IC courses are significant factor in their motivation for IC involvement.What they want most is the real collaboration opportunities and programs.The findings may provide some reference for Chinese universities in their construction of international and intercultural campus environment.

Autonomous process collaboration framework for C4ISR system interoperation

Interoperability plays an important role in the joint command, control, communication, computer, intelligence, surveillance, reconnaissance(C4 ISR) operations. Coordinating and integrating operational processes to fulfill a common mission are challenged by the ever-changing battlefield and hence requires a cross-organizational process management that produces an autonomous, flexible and adaptable architecture for collaborative process evolution. The traditional business process collaboration pattern is based on the predefined "public-view" perspective and cannot meet the requirement of the joint task operations. This paper proposes a flexible visibility control mechanism and a dynamic collaboration framework for modeling and generating collaborative processes. The mechanism allows collaborators to define a set of visibility rules to generate different views of the private processes for different collaborations, which gives a great flexibility for the collaboration initiator to decide on an appropriate collaboration pattern. The framework supports collaborators to dynamically and recursively add a new process or even a new organization to an existing collaboration. Moreover, a formal representation of the processes and a set of generation algorithms are provided to consolidate the proposed theory.

Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation

This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme （E4DVAR） benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence： the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.

Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction

A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.

Four-Dimensional Variational Data Assimilation Experiments for a Heavy Rain Case During the 2002 IOP in China

A heavy rainfall event along the mei-yu front during 22-23 June 2002 was chosen for this study. To assess the impact of the routine and additional IOP (intensive observation period) radiosonde observations on the mesoscale heavy rainfall forecast, a series of four-dimensional variational (4DVAR) data assimilation and model simulation experiments was conducted using nonhydrostatic mesoscale model MM5 and the MM5 4DVAR system. The effects of the intensive observations in the different areas on the heavy rainfall forecast were also investigated. The results showed that improvement of the forecast skill for mesoscale heavy rainfall intensity was possible from the assimilation of the IOP radiosonde observations. However, the impact of the IOP observations on the forecast of the rainfall pattern was not significant. Initial conditions obtained through the 4DVAR experiments with a 12-h assimilation window were capable of improving the 24-h forecast. The simulated results after the assimilation showed that it would be best to perform the intensive radiosonde observations in the upstream of the rainfall area and in the moisture passageway area at the same time. Initial conditions created by the 4DVAR led to the low-level moisture convergence over the rainfall area, enhanced frontogenesis and upward motion within the mei-yu front, and intensified middle- and high-level unstable stratification in front of the mei-yu front. Consequently, the heavy rainfall forecast was improved.

Real-Time Four-Dimensional Trajectory Generation Based on Gain-Scheduling Control and a High-Fidelity Aircraft Model

Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations.Moreover,autonomous taxiing is envisioned as a key component in future digitalized airports.However,state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases.The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions.This paper proposes a new approach for generating efficient four-dimensional trajectories(4DTs)on the basis of a high-fidelity aircraft model and gainscheduling control strategy.Working in conjunction with a routing and scheduling algorithm that determines the taxi route,waypoints,and time deadlines,the proposed approach generates fuel-efficient 4DTs in real time,while respecting operational constraints.The proposed approach can be used in two contexts:①as a reactive decision support tool to generate new trajectories that can resolve unprecedented events;and②as an autopilot system for both partial and fully autonomous taxiing.The proposed methodology is realistic and simple to implement.Moreover,simulation studies show that the proposed approach is capable of providing an up to 11%reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.

Four-dimensional flow magnetic resonance imaging incirrhosis

Since its introduction in the 1970’s,magnetic resonance imaging(MRI)has become a standard imaging modality.With its broad and standardized application,it is firmly established in the clinical routine and an essential element in cardiovascular and abdominal imaging.In addition to sonography and computer tomography,MRI is a valuable tool for diagnosing cardiovascular and abdominal diseases,for determining disease severity,and for assessing therapeutic success.MRI techniques have improved over the last few decades,revealing not just morphologic information,but functional information about perfusion,diffusion and hemodynamics as well.Four-dimensional(4D)flow MRI,a time-resolved phase contrast-MRI with three-dimensional(3D)anatomic coverage and velocity encoding along all three flow directions has been used to comprehensively assess complex cardiovascular hemodynamics in multiple regions of the body.The technique enables visualization of 3D blood flow patterns and retrospective quantification of blood flow parameters in a region of interest.Over the last few years,4D flow MRI has been increasingly performed in the abdominal region.By applying different acceleration techniques,taking 4D flow MRI measurements has dropped to a reasonable scanning time of 8 to 12 min.These new developments have encouraged a growing number of patient studies in the literature validating the technique’s potential for enhanced evaluation of blood flow parameters within the liver’s complex vascular system.The purpose of this review article is to broaden our understanding of 4D flow MRI for the assessment of liver hemodynamics by providing insights into acquisition,data analysis,visualization and quantification.Furthermore,in this article we highlight its development,focussing on the clinical application of the technique.

Clustering approach based on hierarchical expansion for community detection of scientific collaboration network

This study presents a clustering algorithm based on hierarchical expansion to solve the problem of community detection in scientific collaboration network. The characteristics of achievements information related to scientific and technological domains are analyzed,and then an ontology that represents their latent collaborative relations is built to detect clusters from the collaboration network. A case study is conducted to collect a data set of research achievements in the electric vehicle field and better clustering results are obtained. A hierarchical recommendation framework that enriches the domain ontologies and retrieves more relevant information resources is proposed in the last part of this paper. This work also lays out a novel insight into the exploitation of scientific collaboration network to better classify achievements information.

Spatial Structure,Hierarchy and Formation Mechanisms of Scientific Collaboration Networks:Evidence of the Belt and Road Regions

Scientific collaboration has become an important part of the people-to-people exchanges in the Belt and Road initiative,and remarkable progress has been made since 2013.Taking the 65 countries along the Belt and Road(BRI countries)as the research areas and using collaborated Web of Science(WOS)core collection papers to construct an international scientific collaboration matrix,the paper explores the spatial structure,hierarchy and formation mechanisms of scientific collaboration networks of 65 countries along the Belt and Road.The results show that:1)Beyond the Belt and Road regions(BRI regions),Central&Eastern Europe,China and West Asia&North Africa have formed a situation in which they all have the most external links with other countries beyond BRI regions.China has the dominant role over other BRI countries in generating scientific links.The overall spatial structure has changed to a skeleton structure consisting of many dense regions,such as Europe,North America,East Asia and Oceania.2)Within the Belt and Road regions,Central&Eastern Europe has become the largest collaboration partner with other sub-regions in BRI countries.The spatial structure of scientific collaboration networks has transformed from the‘dual core’composed of China and the Central&Eastern Europe region,to the‘multi-polarization’composed of‘one zone and multi-points’.3)The hierarchical structure of scientific collaboration networks presents a typical‘core-periphery’structure,and changes from‘single core’to‘double cores’.4)Among the formation mechanisms of scientific collaboration networks,scientific research strength and social proximity play the most important roles,while geographical distance gradually weakens the hindrance to scientific collaboration.

Generating one-,two-,three-and four-scroll attractors from a novel four-dimensional smooth autonomous chaotic system

A new four-dimensional quadratic smooth autonomous chaotic system is presented in this paper, which can exhibit periodic orbit and chaos under the conditions on the system parameters. Importantly, the system can generate one-, two-, three- and four-scroll chaotic attractors with appropriate choices of parameters. Interestingly, all the attractors are generated only by changing a single parameter. The dynamic analysis approach in the paper involves time series, phase portraits, Poincare maps, a bifurcation diagram, and Lyapunov exponents, to investigate some basic dynamical behaviours of the proposed four-dimensional system.