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.

Collaborative positioning for swarms:A brief survey of vision,LiDAR and wireless sensors based methods

As positioning sensors,edge computation power,and communication technologies continue to develop,a moving agent can now sense its surroundings and communicate with other agents.By receiving spatial information from both its environment and other agents,an agent can use various methods and sensor types to localize itself.With its high flexibility and robustness,collaborative positioning has become a widely used method in both military and civilian applications.This paper introduces the basic fundamental concepts and applications of collaborative positioning,and reviews recent progress in the field based on camera,LiDAR(Light Detection and Ranging),wireless sensor,and their integration.The paper compares the current methods with respect to their sensor type,summarizes their main paradigms,and analyzes their evaluation experiments.Finally,the paper discusses the main challenges and open issues that require further research.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Full-domain collaborative deployment method of multiple interference sources and evaluation of its deployment effect

This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system(GNSS)and evaluates the deployment effect to enhance the ability to disturb the attacker and the capability to defend the GNSS during navigation countermeasures.Key evaluation indicators for the jamming effect of GNSS suppressive and deceptive jamming sources are first created,their evaluation models are built,and their detection procedures are sorted out,as the basis for determining the deployment principles.The principles for collaboratively deploying multi-jamming sources are developed to obtain the deployment structures(including the required number,structures in demand,and corresponding positions)of three single interference sources required by collaboratively deploying.Accordingly,simulation and hardware-in-loop testing results are presented to determine a rational configuration of the collaborative deployment of multi-jamming sources in the set situation and further realize the full-domain deployment of an interference network from ground,air to space.Varied evaluation indices for the deployment effect are finally developed to evaluate the deployment effect of the proposed configuration and further verify its reliability and rationality.

Optimization and Construction of Collaborative Office under the Background of Smart Campus in Universities

At present, with the continuous development of technologies such as the Internet, big data, and artificial intelligence, smart campuses in universities are being rapidly constructed. Improving the informatization level of administrative management work is also an important content. The collaborative office work in multiple departments requires more standardized, convenient, intelligent, and secure office systems. In response to this issue, this article analyzes the optimization and construction process of collaborative office systems based on the development of university informatization, summarizes the operational results, and explores the prospects of smart office.

Research on the Collaborative Governance of Social Responsibility in Online Audiovisual Enterprises

This paper aims to analyze the present conditions of the social responsibility ecosystem in online audiovisual enterprises in the digital age. It focuses on the governance of social responsibility in these enterprises and conducts an in-depth analysis of the problems and influencing factors related to the social responsibility aberrations of online audiovisual enterprises. Drawing upon social responsibility theory and collaborative governance theory, this research constructs a social responsibility guidance and governance system guided by the public, supported by the voluntary fulfillment of responsibilities by online audiovisual enterprises, and based on the collaborative participation of diverse stakeholders. It explores and optimizes the implementation pathways of this system, providing theoretical support and practical guidance for promoting the sustainable development of online audiovisual enterprises. Furthermore, it aims to contribute to the creation of a harmonious Internet ecosystem.

Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

Active Micro-Nano-Collaborative Bioelectronic Device for Advanced Electrophysiological Recording

The development of precise and sensitive electrophysiological recording platforms holds the utmost importance for research in the fields of cardiology and neuroscience.In recent years,active micro/nano-bioelectronic devices have undergone significant advancements,thereby facilitating the study of electrophysiology.The distinctive configuration and exceptional functionality of these active micro-nano-collaborative bioelectronic devices offer the potential for the recording of high-fidelity action potential signals on a large scale.In this paper,we review three-dimensional active nano-transistors and planar active micro-transistors in terms of their applications in electroexcitable cells,focusing on the evaluation of the effects of active micro/nano-bioelectronic devices on electrophysiological signals.Looking forward to the possibilities,challenges,and wide prospects of active micro-nano-devices,we expect to advance their progress to satisfy the demands of theoretical investigations and medical implementations within the domains of cardiology and neuroscience research.

Identifying genetic susceptibility to Aspergillus fumigatus infection using collaborative cross mice and RNA-Seq approach

Background:Aspergillus fumigatus(Af)is one of the most ubiquitous fungi and its infection potency is suggested to be strongly controlled by the host genetic back-ground.The aim of this study was to search for candidate genes associated with host susceptibility to Aspergillus fumigatus(Af)using an RNAseq approach in CC lines and hepatic gene expression.Methods:We studied 31 male mice from 25 CC lines at 8 weeks old;the mice were infected with Af.Liver tissues were extracted from these mice 5 days post-infection,and next-generation RNA-sequencing(RNAseq)was performed.The GENE-E analysis platform was used to generate a clustered heat map matrix.Results:Significant variation in body weight changes between CC lines was ob-served.Hepatic gene expression revealed 12 top prioritized candidate genes differ-entially expressed in resistant versus susceptible mice based on body weight changes.Interestingly,three candidate genes are located within genomic intervals of the previ-ously mapped quantitative trait loci(QTL),including Gm16270 and Stox1 on chromo-some 10 and Gm11033 on chromosome 8.Conclusions:Our findings emphasize the CC mouse model's power in fine mapping the genetic components underlying susceptibility towards Af.As a next step,eQTL analysis will be performed for our RNA-Seq data.Suggested candidate genes from our study will be further assessed with a human cohort with aspergillosis.

Research on the Path of Improving the Efficiency of Urban Community Governance from the Perspective of Collaborative Governance:A Case Study of Daqing City,China

Under the background of“co-construction,co-governance,and sharing,”the role of the grassroots level is becoming increasingly important.In the past,the governance body was single with blocked information,and the inefficient governance mode required gradual transformation.In order to achieve collaborative governance and break through the obstacles in grassroots governance,the key lies in how to play the role of the community,coordinate the relationship between citizens and the community,and allow the community to better play the role of grassroots governance.Under the guidance of the concept of collaborative governance and combined with relevant data,this paper discusses the problems and current situation of community governance in Daqing City,China,explores the path to improve the efficiency of urban community governance,and puts forward relevant constructive suggestions to better realize the role of community in grassroots governance.

Research on Practical Teaching of Ideological and Political Courses in Colleges and Universities and Collaborative Education in the Second Classroom

In today’s society,the need for talent training is of utmost importance due to rapid development.To achieve high-quality talent training,we need to focus on building a collaborative education mechanism.Practical teaching and the second classroom,which serve as important carriers of educational talents,must align their educational goals and complement each other.This article explores the collaborative education mechanism of the second classroom and practical teaching for the ideological and political courses in colleges and universities.It proposes reasonable construction measures to provide some guidelines for teaching work.

云南省R&D经费投入结构动态研究

为研究云南省“十二五”以来R&D经费投入结构变化,分析R&D经费投入现状,并与全国,东、中、西部地区及临近省份进行了对比;从资金来源和资金配置视角,运用Cobb-Douglas效用函数及其修正模型对R&D经费投入结构展开动态分析。结果表明:云南省R&D经费投入结构不尽合理,资金来源中政府资金占比较低且呈下降趋势,资金配置中基础研究和应用研究投入不足,与最优结构偏离程度较大。针对存在的问题,提出持续加大政府R&D经费投入力度、围绕产业链提升基础研究和应用研究比重的建议。

R&D资本化背景下我国TFP增长率再测算

研究与试验发展(R&D)资本对经济增长和科技创新作用日益凸显,但对于R&D资本的核算亟待完善。本文基于SNA2008和CSNA2016框架,将R&D支出作为资本形成,对我国各省份物质资本存量和R&D资本存量进行规范化测算,使用随机前沿分析法重新估算1991—2019年间的全要素生产率(TFP)增长率。研究发现:第一,我国R&D资本存量规模呈总体递增和地区聚集趋势;第二,1991—2019年间,我国广义TFP增长率年平均增幅为2.10%,对经济增长存在正向作用;第三,将TFP增长率分解为技术进步、前沿技术效率和规模效应,发现技术进步对TFP增长率的贡献最大,是推动我国TFP增长的主导力量,但却呈现逐年递减的趋势;第四,随着我国对R&D投入的不断增加与重视,R&D对我国广义TFP增长率的贡献呈现递增的趋势,R&D的贡献逐渐成为推动我国科技进步的主要力量。

政府R&D资助对OFDI逆向绿色创新的影响机制研究

应用中国分省区层面数据并采用面板门槛模型,实证考察了政府R&D资助影响OFDI逆向绿色创新的动态效应。研究发现,OFDI对国内绿色创新产生了显著的驱动效应,但这种影响呈现出明显的先负后正的“U”型动态演化特征;政府R&D资助会正向调节OFDI逆向绿色创新溢出,且存在最适宜于OFDI逆向绿色创新的政府R&D资助区间(0.366,0.428],过高或过低强度的政府R&D资助均会在一定程度上造成OFDI逆向绿色创新的红利损失;政府R&D资助调节下OFDI对绿色创新的动态影响具有显著时空异质性,即在时空维度上存在“激励效应”与“挤出效应”交替演化的鲜明特征。

2021年副省级城市R&D经费投入分析

2017—2021年,副省级城市的R&D经费投入都保持持续增长的态势。深圳的R&D经费投入在副省级城市中一直处于遥遥领先的地位,西安由于科研院所和高校众多,2017年R&D经费投入总量在副省级城市中排名第四,2021年排名第七,被资源禀赋相似的南京、成都、武汉等城市超越。西安由于GDP总量不高,R&D经费投入强度一直保持在5%左右,2020年被深圳反超。基于数据的可获得性,本文从2021年副省级城市的R&D经费投入总量、R&D经费投入强度等方面进行解读和分析,并结合分析结果提出相关建议,以供参考。

Dynamic regulation of the irrigation-nitrogen-biochar nexus for the synergy of yield,quality,carbon emission and resource use efficiency in tomato

Integrated water and fertilizer management is important for promoting sustainable development of facility agriculture,and biochar plays an important role in guaranteeing food production,as well as alleviating water shortages and the overuse of fertilizers.The field experiment had twelve treatments and a control(CK)trial including two irrigation amounts(I1,100%ETm;I2,60%ETm;where ETm is the maximum evapotranspiration),two nitrogen applications(N1,360 kg ha^(−1);N2,120 kg ha^(−1))and three biochar application levels(B1,60 t ha^(−1);B_(2),30 t ha^(−1)and B3,0 t ha^(−1)).A multi-objective synergistic irrigation-nitrogen-biochar application system for improving tomato yield,quality,water and nitrogen use efficiency,and greenhouse emissions was developed by integrating the techniques of experimentation and optimization.First,a coupled irrigation-nitrogen-biochar plot experiment was arranged.Then,tomato yield and fruit quality parameters were determined experimentally to establish the response relationships between irrigation-nitrogen-biochar dosage and yield,comprehensive quality of tomatoes(TCQ),irrigation water use efficiency(IWUE),partial factor productivity of nitrogen(PFPN),and net greenhouse gas emissions(NGE).Finally,a multi-objective dynamic optimization regulation model of irrigation-nitrogen-biochar resource allocation at different growth stages of tomato was constructed which was solved by the fuzzy programming method.The results showed that the application of irrigation and nitrogen to biochar promoted increase in yield,IWUE and PFPN,while it had an inhibitory effect on NGE.In addition,the optimal allocation amounts of water and fertilizer were different under different scenarios.The yield of the S1 scenario increased by 8.31%compared to the B_(1)I_(1)N_(2) treatment;TCQ of the S2 scenario increased by 5.14%compared to the B_(2)I_(2)N_(1) treatment;IWUE of the S3 scenario increased by 10.01%compared to the B1I2N2 treatment;PFPN of the S4 scenario increased by 9.35%compared to the B_(1)I_(1)N_(2) treatment;and NGE of the S5 scenario decreased by 11.23%compared to the B_(2)I1N1 treatment.The optimization model showed that the coordination of multiple objectives considering yield,TCQ,IWUE,PFPN,and NGE increased on average from 4.44 to 69.02%compared to each treatment when the irrigation-nitrogen-biochar dosage was 205.18 mm,186 kg ha^(−1)and 43.31 t ha^(−1),respectively.This study provides a guiding basis for the sustainable management of water and fertilizer in greenhouse tomato production under drip irrigation fertilization conditions.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

ChatModeler:基于大语言模型的人机协作迭代式需求获取和建模方法

需求获取和建模是需求工程中的关键步骤,影响后续系统设计与实现.传统的需求获取和建模方法通常由需求提供者、需求分析师等多类干系人共同协作、反复迭代完成,需要耗费大量的人力.如何减轻需求提供者与需求分析师的负担、提高获取和建模的效率有着重要意义.现有工作中有的使用知识库来提供更多知识,以辅助获取或者建模,有的利用自然语言处理等技术对获取或者建模过程进行自动化,但是它们并没有减轻需求提供者的负担.利用大语言模型(large language models,LLMs)的生成能力,提供了一种人机协作的迭代式需求获取和建模框架ChatModeler.具体来说,根据真实世界中需求团队的分工及协作关系,将部分需求提供者、需求分析师等角色的工作由大语言模型承担,而需求提供者只需要进行确认.为大语言模型扮演的各种角色进行了提示词设计,该提示词会随需求的元模型而变化.ChatModeler在7个需求案例上与3种需求模型的自动建模方法进行了14组对比实验,证明了ChatModeler在降低需求提供者的负担和生成高质量需求模型2个方面上的优越性.

基于ERAS理念的多学科协作对Stanford A型主动脉夹层患者I期心脏康复的应用效果研究

目的探讨基于加速康复外科(ERAS)理念的多学科协作对Stanford A型主动脉夹层患者心脏康复的应用效果研究。方法选取河南省胸科医院在2021年1月至2022年11月期间收治的80例Stanford A型主动脉夹层患者,随机分为对照组及观察组,各40例。观察组采用基于ERAS理念的多学科协作护理,对照组予以常规护理。比较两组患者围手术期指标、心功能、心理状况、疼痛评分以及生活质量水平。结果观察组的气管插管时间、ICU停留时间、术后住院时间、下床活动时间短于对照组,差异有统计学意义(P<0.05)。护理前,两组患者的心功能水平比较,差异无统计学意义(P>0.05);护理后,两组患者心功能水平均优于护理前,且观察组心功能优于对照组,差异有统计学意义(P<0.05)。护理前,两组心理状态与疼痛程度比较,差异无统计学意义(P>0.05);护理后,两组患者心理状态及疼痛程度均有所改善,且观察组改善程度优于对照组,差异有统计学意义(P<0.05)。护理前,两组生活质量评分比较,差异无统计学意义(P>0.05);护理后,两组患者生活质量水平均升高,且观察组高于对照组,差异有统计学意义(P<0.05)。结论基于ERAS理念的多学科协作方案可改善Stanford A型主动脉夹层手术患者心功能,缓解患者不良情绪及疼痛程度,提高患者生活质量,缩短住院时间,可促进患者术后恢复。

Multi-Stream Temporally Enhanced Network for Video Salient Object Detection

Video salient object detection(VSOD)aims at locating the most attractive objects in a video by exploring the spatial and temporal features.VSOD poses a challenging task in computer vision,as it involves processing complex spatial data that is also influenced by temporal dynamics.Despite the progress made in existing VSOD models,they still struggle in scenes of great background diversity within and between frames.Additionally,they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration.We propose a multi-stream temporal enhanced network(MSTENet)to address these problems.It investigates saliency cues collaboration in the spatial domain with a multi-stream structure to deal with the great background diversity challenge.A straightforward,yet efficient approach for temporal feature extraction is developed to avoid the accumulative noises and reduce time consumption.The distinction between MSTENet and other VSOD methods stems from its incorporation of both foreground supervision and background supervision,facilitating enhanced extraction of collaborative saliency cues.Another notable differentiation is the innovative integration of spatial and temporal features,wherein the temporal module is integrated into the multi-stream structure,enabling comprehensive spatial-temporal interactions within an end-to-end framework.Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on five benchmark datasets while maintaining a real-time speed of 27 fps(Titan XP).Our code and models are available at https://github.com/RuJiaLe/MSTENet.