Information-Defined Networks:A Communication Network Approach for Network Studies

The research of complex networks facilitates the progress of various disciplines,including biology,chemistry,social science,computer,and communication engineering.Recently,it is popular to utilize complex networks to study the communication networks,such as designing efficient routing strategies and robust communication networks.However,exploiting the advantages of communication networks to investigate networks in various disciplines beyond telecommunications is still in infancy.Because of this situation,this paper proposes an information-defined network(IDN)framework by which a complex network can be abstracted as a communication network associated with multiple intelligent agents.Specifically,each component and dynamic process in this framework can be defined by information.We show that the IDN framework promotes the research of unsolved problems in the current complex network field,especially for detecting new interaction types in realworld networks.

Real traffic-data based evaluation of vehicular traffic environment and state- of-the-art with future issues in location-centric data dissemination for VANETs

Extensive investigation has been performed in location-centric or geocast routing protocols for reliable and efficient dissemination of information in Vehicular Adhoc Networks （VANETs）. Various location-centric routing protocols have been suggested in literature for road safety ITS applications considering urban and highway traffic environment. This paper characterizes vehicular environments based on real traffic data and investigates the evolution of location-centric data dissemination. The current study is carded out with three main objectives： （i） to analyze the impact of dynamic traffic environment on the design of data dissemination techniques, （ii） to characterize location-centric data dissemination in terms of functional and qualitative behavior of protocols, properties, and strengths and weaknesses, and （iii） to find some future research directions in information dissemination based on location. Vehicular traffic environments have been classified into three categories based on physical characteristics such as speed, inter-vehicular distance, neighborhood stability, traffic volume, etc. Real traffic data is considered to analyze on-road traffic environments based on the measurement of physical parameters and weather conditions. Design issues are identified in incorporating physical parameters and weather conditions into data dissemination. Functional and qualitative characteristics of location-centric techniques are explored considering urban and highway environments. Comparative analysis of location-centric techniques is carded out for both urban and highway environments individually based on some unique and common characteristics of the environments. Finally, some future research directions are identified in the area based on the detailed investigation of traffic environments and location-centric data dissemination techniques.

How can artificial intelligence and humans work together to fight against cancer?

This editorial will focus on and discuss growing artificial intelligence(AI)and the utilization of AI in human cancer therapy.The databases and big data related to genomes,genes,proteins and molecular networks are rapidly increasing all worldwide where information on human diseases,including cancer and infection resides.To overcome diseases,prevention and therapeutics are being developed with the abundant data analyzed by AI.AI has so much potential for handling considerable data,which requires some orientation and ambition.Appropriate interpretation of AI is essential for understanding disease mechanisms and finding targets for prevention and therapeutics.Collaboration with AI to extract the essence of cancer data and model intelligent networks will be explored.The utilization of AI can provide humans with a predictive future in disease mechanisms and treatment as well as prevention.

Evaluating the resource management and profitability efficiencies of US commercial banks from a dynamic network perspective

The central concept of strategic benchmarking is resource management efficiency,which ultimately results in profitability.However,little is known about performance measurement from resource-based perspectives.This study uses the data envelopment analysis(DEA)model with a dynamic network structure to measure the resource management and profitability efficiencies of 287 US commercial banks from 2010 to 2020.Furthermore,we provide frontier projections and incorporate five variables,namely capital adequacy,asset quality,management quality,earning ability,and liquidity(i.e.,the CAMEL ratings).The results revealed that the room for improvement in bank performance is 55.4%.In addition,we found that the CAMEL ratings of efficient banks are generally higher than those of inefficient banks,and management quality,earnings quality,and liquidity ratios positively contribute to bank performance.Moreover,big banks are generally more efficient than small banks.Overall,this study continues the current heated debate on performance measurement in the banking industry,with a particular focus on the DEA application to answer the fundamental question of why resource management efficiency reflects benchmark firms and provides insights into how efficient management of CAMEL ratings would help in improving their performance.

大数据技术在网络招聘中的应用研究——以K企业为例

信息时代,网络招聘已成为企业广泛应用的招聘手段,但随之产生的海量数据并未得到充分的重视和应用,反而造成候选人筛选效率下降与人岗匹配度降低,大数据的价值被大大歪曲和忽视。本文以K企业网络招聘过程所面对的大数据挑战为例,应用大数据处理技术优化其网络招聘流程,促进信息化工具与人力资源管理实务的有效融合进而提高企业网络招聘及人力资源管理系统的运行效率。