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.

Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

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.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry.There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch.In this study,a data-driven model of the uncertainty in the annual carbon price was created.Subsequently,a collaborative,robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation.The model is solved using the column-and-constraint generation algorithm.An operation and cost model of a carbon-capture power plant(CCPP)that couples the carbon market and the economic operation of the power system is also established.The critical,profitable conditions for the economic operation of the CCPP were derived.Case studies demonstrated that the proposed low-carbon,robust dispatch model reduced carbon emissions by 2.67%compared with the traditional,economic,dispatch method.The total fuel cost of generation decreases with decreasing,conservative,carbon-price-uncertainty levels,while total carbon emissions continue to increase.When the carbon-quota coefficient decreases,the system dispatch tends to increase low-carbon unit output.This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

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.

Analysis of the Application Effect of Family Collaborative Care Model on Elderly Patients with Type 2 Diabetes Mellitus and the Situation of Self-Care Ability

Objective: To study the application effect of the family collaborative care model on elderly patients with type 2 diabetes mellitus and its influence on self-care ability. Methods: The elderly type 2 diabetes mellitus patients (400 cases) treated in our hospital between March 2020 and July 2023 were divided into two groups by randomized grouping method;the control group received the conventional nursing program, while the observation group received the family collaborative nursing model. Blood glucose level, self-care ability, and quality of life were compared between the groups. Results: The blood glucose level of the observation group was lower than that of the control group (P < 0.05). The self- care ability and quality of life scores of the observation group were higher than those of the control group (P < 0.05). Conclusion: The family collaborative care model for elderly patients with type 2 diabetes mellitus can promote their self- care ability, improve the effect of glycemic control, and improve their quality of life, and is suitable for further promotion and application.

联合PBL和iSIM情景模拟教学在产儿团体合作培训中的应用

目的将基于问题的学习(problem based learning,PBL)联合优化情景模拟教学模式(improving simulation instructional methods,iSIM)应用于产儿科团队合作培训,探讨该教学模式在产儿科对临床医学本科实习医师的团队合作能力、新生儿急救实践技能和学生对教学模式的满意度的影响。方法将2022年7月—2023年6月在中山市人民医院产科和新生儿科轮转的四年级实习医师共72人,按工号末位奇偶数随机分为试验组(n=36)和对照组(n=36)。两组均实行小组教学,每6人为一个小组。所有人员前期均接受产科危重症、新生儿复苏的理论培训。其中,对照组采用传统授课+操作示范的教学模式;试验组采用PBL+iSIM情景模拟教学模式,期间穿插标准家属进行医患沟通,试验组和对照组每1个小组6个实习医师,利用Excel现场随机抽签确定所演练的角色分工。教学结束后比较两组实习医师理论、操作考核成绩、医患沟通能力和学生的满意度,并用团队合作评价工具(CSAT)进行评价,应用统计学方法,分析并评估PBL+iSIM情景模拟教学模式在产儿科团体合作培训与传统的教学模式培训教学效果差异。结果两组实习医师除了理论成绩和个人准备工作项目得分差异无统计学意义(P>0.05)外,操作考核成绩、医患沟通能力、学生的满意度以及CSAT评价指标总分和其他项目的组间差异均具有统计学意义(均P<0.05),结果提示PBL联合iSIM情景模拟教学模式优于传统的教学模式。结论PBL联合iSIM情景模拟教学模式应用在临床本科实习生的产儿科合作项目中有较大的优势,有利于提高临床医学本科实习生对产科和新生儿急救技能的掌握水平,提高教学效果和教学质量。

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.

Rehabilitation care for pain in elderly knee replacement patients

BACKGROUND Total knee arthroplasty(TKA)is recognized as the most effective surgical intervention for relieving pain and improving joint mobility and deformity in patients with knee osteoarthritis and other synovial diseases.The application of accelerated postoperative rehabilitation(enhanced recovery after surgery)has demonstrated its efficacy in improving patient outcomes,and early postoperative joint function exercise has become a key prognostic factor in knee replacement.The unexpected appearance of limb pain and swelling hindered the patient’s tendency for early mobilization,leading in prolonged hospitalization,delayed functional recovery and negative psychological responses.A retrospective analysis was conducted on a cohort of 116 patients who under-went TKA at our hospital between July 2019 and July 2021.The patients were divided into two groups:A control group(n=58)receiving programmatic nur-sing,and an observed group(n=58)receiving programmed nursing combined with a collaborative nursing model.A pain management team consisting of attending physicians,head nurses,and responsible nurses was established.Outcome measures included visual analogue scale(VAS)scores,activities of daily living(ADL)scale scores,and functional scores.The ADL scores of patients in both groups exhibited a continuous increase.However,there was no statistically significant difference in the ADL scores between the two groups at 48 h and the 7th d post-surgery(P>0.05).Upon reexamination at the 3rd mo,the observation group demonstrated higher ADL scores compared to the control group(67.48±14.69 vs 59.40±16.06,P<0.05).The VAS scores of both groups significantly decreased,with no significant difference observed between the groups at each time point(P>0.05).The functional status of patients in both groups exhibited a gradual increase prior to intervention and at the 1st,2nd,and 3rd month following discharge(P<0.05).There was no statistically significant difference in knee joint function scores between the two groups at the 1st month after discharge(47.52 vs 45.81,P>0.05).However,the knee joint function scores of patients in the observation group were significantly higher than those in the control group at the 2nd(59.38 vs 53.19,P<0.05)and 3rd month(71.92 vs 64.34,P<0.05)following discharge.CONCLUSION The utilization of programmed pain nursing in conjunction with collaborative nursing for out-of-hospital care of TKA patients has demonstrated favorable outcomes,encompassing pain reduction,enhanced prognosis,and improved nursing quality for patients.

区域协同救治体系对急性ST段抬高型心肌梗死救治效率及预后影响的Meta分析

目的 通过Meta分析方法,探讨区域协同救治体系对急性ST段抬高型心肌梗死患者救治效率及疾病预后的影响。方法 计算机检索Web of science、PubMed、Embase、Ovid、Cochrane Library、CINAHL、中国生物医学文献数据库、中国知网、万方数据库及维普网等数据库中关于区域协同救治体系运行前后急性ST段抬高型心肌梗死患者救治效率及疾病预后的队列研究,检索时限为建库至2022年11月。由两名研究人员分别对检索结果进行文献筛选及数据提取、采用纽卡斯尔-渥太华量表进行文献质量评价。使用Revman 5.4及Stata16.0进行数据分析,评估研究的异质性并进行亚组分析。结果 本研究共纳入29篇文献,共包含研究对象51 953例,其中试验组26 110例,对照组25 843例。Meta分析结果显示,区域协同救治运行后ST段抬高型心肌梗死患者PCI医院入门至球囊扩张(D-to-B)时间较运行前缩短[MD=-42.14,95%CI:(-61.54,-22.74)](P<0.001),不同地区研究间异质性较大(I2=99%>50%);2012年前区域协同救治的运行与D-to-B时间无关[MD=50.67,95%CI:(-103.35,2.00)](P=0.060);区域协同救治运行后直接PCI患者比例得到提高[OR:1.78,95%CI:(1.23,2.57)](P=0.002)、整体死亡率下降[OR:0.77,95%CI:(0.71,0.82)](P<0.001);区域协同救治运行后ST段抬高型心肌梗死患者术后1个月[OR:0.40,95%CI:(0.25,0.64)]及6个月[OR:0.47,95%CI:(0.22,0.97)]主要不良心脏事件(MACE)发生率低于运行前(P<0.001;P=0.040),但随访12个月MACE发生率无明显差异[OR:0.61,95%CI:(0.32,1.16)](P=0.130)。结论 区域协同救治体系可缩短急性D-to-B时间并提高直接PCI患者比例,改善急性ST段抬高型心肌梗死患者短期预后,但对患者远期预后无明显影响。未来还需总结先进经验,促进各地区区域协同救治体系救护水平的均衡发展,加强社区居家心脏康复建设投入,降低冠心病患者病死率。

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.

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.