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.

How does network intermediary affect collaborative innovation?Evidence from Chinese listed companies

Purpose:This study aims to explore how network intermediaries influence collaborative innovation performance within inter-organizational technological collaboration networks.Design/methodology/approach:This study employs a mixed-method approach,combining quantitative social network analysis with regression techniques to investigate the role of network intermediaries in collaborative innovation performance.Using a patent dataset of Chinese industrial enterprises,the research constructs the collaboration networks and analyzes their structural positions,particularly focusing on their role as intermediaries,characterized by betweenness centrality.Negative binomial regression analysis is employed to assess how these network characteristics shape innovation outcomes.Findings:The study reveals that firms in intermediary positions enhance collaborative innovation performance,but this effect is nuanced.A key finding is that network clustering negatively moderates the intermediary-innovation relationship.Highly clustered networks,while fostering local collaboration,may limit the innovation potential of intermediaries.On the other hand,relationship strength,measured by collaboration intensity and trust among firms,positively moderates the intermediary-innovation link.Research limitations:This study has several limitations that present opportunities for further research.The reliance on quantitative social network analysis may overlook the complexity of intermediaries’roles,and future studies could benefit from incorporating qualitative methods to better understand cultural and institutional factors.Additionally,cross-country comparisons are needed to assess the consistency of these dynamics in different contexts.Practical implications:The study offers practical insights for firms and policymakers.Organizations should strategically position themselves as network intermediaries to access diverse information and resources,thereby improving innovation performance.Building strong trust helps using network intermediary advantages.For firms in highly clustered networks,it is important to seek external partners to avoid limiting their exposure to new ideas and technologies.This research emphasizes the need to balance network diversity with relationship strength for sustained innovation.Originality/value:This research contributes to the literature by offering new insights into the role of network intermediaries,presenting a comprehensive framework for understanding the interaction between network dynamics and firm innovation.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

Collaborative Charging Scheduling in Wireless Charging Sensor Networks

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, we studythe recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with theobjective of maximizing the number of surviving sensors, and further propose a collaborative charging schedulingalgorithm(CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors.TwoMCs can adaptively collaboratively charge the sensors based on the energy limit ofMCs and the energy demandof sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithmcan effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertaintyof node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.

Scheduling optimization for UAV communication coverage using virtual force-based PSO model

When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication services.Therefore,it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption.In this paper,we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization(AVF-PSO)strategy.In particular,we first introduce the gravity model into the traditional Particle Swarm Optimization(PSO)algorithm so as to increase the probability of full coverage.Then,the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced.Finally,in order to reduce the communication interference between UAVs,we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs.Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches.For example,in the target area of any size,the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%,and 2.0–56.1%,respectively.Moreover,the proposed scheduling algorithm is high adaptable to diverse execution environments.©2022 Published by Elsevier Ltd.

A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.

Pioneering role of machine learning in unveiling intensive care unitacquired weakness

In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.

Path Computing Scheme with Low-Latency and Low-Power in Hybrid Cloud-Fog Network for IIoT

With the rapid development of the Industrial Internet of Things(IIoT),the traditional centralized cloud processing model has encountered the challenges of high communication latency and high energy consumption in handling industrial big data tasks.This paper aims to propose a low-latency and lowenergy path computing scheme for the above problems.This scheme is based on the cloud-fog network architecture.The computing resources of fog network devices in the fog computing layer are used to complete task processing step by step during the data interaction from industrial field devices to the cloud center.A collaborative scheduling strategy based on the particle diversity discrete binary particle swarm optimization(PDBPSO)algorithm is proposed to deploy manufacturing tasks to the fog computing layer reasonably.The task in the form of a directed acyclic graph(DAG)is mapped to a factory fog network in the form of an undirected graph(UG)to find the appropriate computing path for the task,significantly reducing the task processing latency under energy consumption constraints.Simulation experiments show that this scheme’s latency performance outperforms the strategy that tasks are wholly offloaded to the cloud and the strategy that tasks are entirely offloaded to the edge equipment.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

A Zero-shot Universal Microscopic Image AI Processing Method

The team of Dr.LI Dong from the Institute of Biophysics(IBP)of the Chinese Academy of Sciences,in collaboration with the team of Dr.DAI Qionghai from the Department of Automation at Tsinghua University,published a research paper in Nature Communications on May 16.Based on the noise model of microscopic images and zero-sample learning theory,they proposed the zero-shot deconvolution networks(ZS-DeconvNet)and developed the corresponding one-click microscopic image processing software.

Development Methodologies for Network Softwarization: A Comparison of DevOps, NetOps, and Verification

This white paper explores three popular development methodologies for network softwarization: DevOps, NetOps, and Verification. The paper compares and contrasts the strengths and weaknesses of each approach and provides recommendations for organizations looking to adopt network softwarization.

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a revolutionary new perception in wireless communications.Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum.Centralized Cooperative Spectrum Sensing(CSS)is a kind of spectrum sensing.Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix(SCM)of the received signal.Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector(PRIDe),Hadamard Ratio(HR)detector,Gini Index Detector(GID),etc.This paper presents the simulation and comparative perfor-mance analysis of PRIDe with various other detectors like GID,HR,Arithmetic to Geometric Mean(AGM),Volume-based Detector number 1(VD1),Maximum-to-Minimum Eigenvalue Detection(MMED),and Generalized Likelihood Ratio Test(GLRT)using the MATLAB software.The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

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.

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.

China's landscape in oncology drug research:perspectives from research collaboration networks

Objective： Better understanding of China＇s landscape in oncology drug research is of great significance for discovering anti-cancer drugs in future. This article differs from previous studies by focusing on Chinese oncology drug research communities in co-publication networks at the institutional level. Moreover, this research aims to explore structures and behaviors of relevant research units by thematic community analysis and to address policy recommendations. Methods： This research used social network analysis to define an institutions network and to identify a community network which is characterized by thematic content. Results： A total of 675 sample articles from 2008 through 2012 were retrieved from the Science Citation Index Expanded （SCIE） database of Web of Science, and top institutions and institutional pairs are highlighted for further discussion. Meanwhile, this study revealed that institutions based in the Chinese mainland are located in a relatively central position, Taiwan＇s institutions are closely assembled on the side, and Hong Kong＇s units located in the middle of the Chinese mainland＇s and Taiwan＇s. Spatial division and institutional hierarchy are still critical barriers to research collaboration in the field of anti-cancer drugs in China. In addition, the communities focusing on hot research areas show the higher nodal degree, whereas communities giving more attention to rare research subjects are relatively marginalized to the periphery of network. Conclusions= This paper offers policy recommendations to accelerate cross-regional cooperation, such as through developing information technology and increasing investment. The brokers should focus more on outreach to other institutions. Finally, participation in topics of common interest is conducive to improved efficiency in research and development （R＆D） resource allocation.

Global Collaboration in Artificial Intelligence:Bibliometrics and Network Analysis from 1985 to 2019

Purpose:This study aims to explore the trend and status of international collaboration in the field of artificial intelligence(AI)and to understand the hot topics,core groups,and major collaboration patterns in global AI research.Design/methodology/approach:We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science(WoS)and studied international collaboration from the perspectives of authors,institutions,and countries through bibliometric analysis and social network analysis.Findings:The bibliometric results show that in the field of AI,the number of published papers is increasing every year,and 84.8%of them are cooperative papers.Collaboration with more than three authors,collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns.Through social network analysis,this study found that the US,the UK,France,and Spain led global collaboration research in the field of AI at the country level,while Vietnam,Saudi Arabia,and United Arab Emirates had a high degree of international participation.Collaboration at the institution level reflects obvious regional and economic characteristics.There are the Developing Countries Institution Collaboration Group led by Iran,China,and Vietnam,as well as the Developed Countries Institution Collaboration Group led by the US,Canada,the UK.Also,the Chinese Academy of Sciences(China)plays an important,pivotal role in connecting the these institutional collaboration groups.Research limitations:First,participant contributions in international collaboration may have varied,but in our research they are viewed equally when building collaboration networks.Second,although the edge weight in the collaboration network is considered,it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implications:The findings fill the current shortage of research on international collaboration in AI.They will help inform scientists and policy makers about the future of AI research.Originality/value:This work is the longest to date regarding international collaboration in the field of AI.This research explores the evolution,future trends,and major collaboration patterns of international collaboration in the field of AI over the past 35 years.It also reveals the leading countries,core groups,and characteristics of collaboration in the field of AI.

Centralization and collaboration in 5G ultra-dense network architecture

An ultra-dense network scenario is a scene where a large number of people assemble in a limited area to generate centralized broadband data traffic requirements.Because ultra-dense networks generate enormous traffic pressure,traditional network capabilities are not enough to accommodate the user s needs.Based on the description of ultra-dense network architecture,we analyze millimeter wave radio spectrum,high gain beam forming,physical layer frame structure,resource concentration and edge computing technology.In addition,the cooperative technology required by overlay and interference symbiosis in the dense network architecture as well as the access control technology of centralized access is analyzed and discussed comprehensively.

Contextualized Analysis of Social Networks:Collaboration in Scientific Communities

Currently, the collaboration in scientific communities has been studied in order to explain, among other things, the knowledge diffusion. The quality of Graduate Programmes is often associated with the scientific collaboration. This paper discusses how scientific collaboration processes can be identified and characterized through social and complex networks. For this purpose, collaboration networks of bibliographic production, research projects, and committees of PhD theses and Masters’ dissertations by researchers from a graduate program in computational modeling were studied. The data were obtained from CAPES’ reports of the period from 2001 to 2009. Among the studied indices, centrality indices indicate the presence of prominent researchers who influence others and promptly interact with other researchers in the network. The indices of complex networks reveal the presence of the small-world (i.e. these networks are favorable to increase coordination between researchers) phenomenon and indicate a behavior of scale-free degree distribution (i.e. some researchers promote clustering more than others) for one of the studied networks.

A Collaboration Network Model with Multiple Evolving Factors

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of we11-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

Manufacturing enterprise collaboration network:An empirical research and evolutionary model

With the increasingly fierce market competition,manufacturing enterprises have to continuously improve their competitiveness through their collaboration and labor division with each other,i.e.forming manufacturing enterprise collaborative network(MECN)through their collaboration and labor division is an effective guarantee for obtaining competitive advantages.To explore the topology and evolutionary process of MECN,in this paper we investigate an empirical MECN from the viewpoint of complex network theory,and construct an evolutionary model to reproduce the topological properties found in the empirical network.Firstly,large-size empirical data related to the automotive industry are collected to construct an MECN.Topological analysis indicates that the MECN is not a scale-free network,but a small-world network with disassortativity.Small-world property indicates that the enterprises can respond quickly to the market,but disassortativity shows the risk spreading is fast and the coordinated operation is difficult.Then,an evolutionary model based on fitness preferential attachment and entropy-TOPSIS is proposed to capture the features of MECN.Besides,the evolutionary model is compared with a degree-based model in which only node degree is taken into consideration.The simulation results show the proposed evolutionary model can reproduce a number of critical topological properties of empirical MECN,while the degree-based model does not,which validates the effectiveness of the proposed evolutionary model.