Design of an Interconnection Architecture and Sizing of Two (2) EPC Core Networks: The Case of Orange-Guinea

With the arrival of the 4G and 5G,the telecommunications networks have experienced a large expansion of these networks.That enabled the integration of many services and adequate flow,thus enabling the operators to respond to the growing demand of users.This rapid evolution has given the operators to adapt,their methods to the new technologies that increase.This complexity becomes more important,when these networks include several technologies to access different from the heterogeneous network like in the 4G network.The dimensional new challenges tell the application and the considerable increase in demand for services and the compatibility with existing networks,the management of mobility intercellular of users and it offers a better quality of services.Thus,the proposed solution to meet these new requirements is the sizing of the EPC(Evolved Packet Core)core network to support the 5G access network.For the case of Orange Guinea,this involves setting up an architecture for interconnecting the core networks of Sonfonia and Camayenne.The objectives of our work are of two orders:(1)to propose these solutions and recommendations for the heart network EPC sizing and the deployment to be adopted;(2)supply and architectural interconnection in the heart network EPC and an existing heart network.In our work,the model of traffic in communication that we use to calculate the traffic generated with each technology has link in the network of the heart.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Digital Object Architecture for IoT Networks

The Internet of Things(IoT)is a recent technology,which implies the union of objects,“things”,into a single worldwide network.This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices.Device identification is one of these challenges that becomes complicated with the increase of network devices.Despite this,there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications.In this regard,one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks.Many unique soft-ware and hardware solutions are used as a unique global identifier;however,such solutions have many limitations.This article proposes a novel solution,based on the Digital Object Architecture(DOA),that meets the requirements of identifying devices and applications of the IoT.This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks.We propose a model of an identification system based on the architecture of digital objects,which differs from the well-known ones.The proposed model ensures an acceptable quality of service(QoS)in the common architecture of the existing public communication networks.A novel interaction architecture is developed by introducing a Middle Handle Register(MHR)between the global register,i.e.,Global Handle Register(GHR),and local register,i.e.,Local Handle Register(LHR).The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented.The developed model is simulated for a wide-area network with allocated registers,and the results are introduced and discussed.

A UAV-Assisted V2X Network Architecture with Separated Data Transmission and Network Control

With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.

Exploration of Graduate Student Cultivation Mode of Landscape Architecture under the Background of“Artificial Intelligence+X”

Under the background of“artificial intelligence+X”,the development of landscape architecture industry ushers in new opportunities,and professional talents need to be updated to meet the social demand.This paper analyzes the cultivation demand of landscape architecture graduate students in the context of the new era,and identifies the problems by comparing the original professional graduate training mode.The new cultivation mode of graduate students in landscape architecture is proposed,including updating the target orientation of the discipline,optimizing the teaching system,building a“dualteacher”tutor team,and improving the“industry-university-research-utilization”integrated cultivation,so as to cultivate high-quality compound talents with disciplinary characteristics.

An evaluation method of contribution rate based on fuzzy Bayesian networks for equipment system-of-systems architecture

The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.

Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial intelligence.However,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally expen-sive.To tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)approach.Firstly,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN.Secondly,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate candidates.Thirdly,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost.The SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

Intelligent architecture modeling for multilevel fluvial reservoirs based on architecture interface and elements

At present,the architecture modeling method of fluvial reservoirs are still developing.Traditional methods usually use grids to characterize architecture interbeds within the reservoir.Due to the thin thickness of this type of the interlayers,the number of the model grids must be greatly expanded.The number of grids in the tens of millions often makes an expensive computation;however,upscaling the model will generate a misleading model.The above confusion is the major reason that restricts the largescale industrialization of fluvial reservoir architecture models in oilfield development and production.Therefore,this paper explores an intelligent architecture modeling method for multilevel fluvial reservoirs based on architecture interface and element.Based on the superpositional relationship of different architectural elements within the fluvial reservoir,this method uses a combination of multilevel interface constraints and non-uniform grid techniques to build a high-resolution 3D geological model for reservoir architecture.Through the grid upscaling technology of heterogeneous architecture elements,different upscaling densities are given to the lateral-accretion bedding and lateral-accretion bodies to simplify the model gridding.This new method greatly reduces the number of model grids while ensuring the accuracy of lateral-accretion bedding models,laying a foundation for large-scale numerical simulation of the subsequent industrialization of the architecture model.This method has been validated in A layer of X oilfield with meandering fluvial channel sands as reservoirs and B layer of Y oilfield with braided river sands as reservoirs.The simulation results show that it has a higher accuracy of production history matching and remaining oil distribution forecast of the targeted sand body.The numerical simulation results show that in the actual development process of oilfield,the injected water will not displace oil in a uniform diffusive manner as traditionally assumed,but in a more complex pattern with oil in upper part of sand body being left behind as residual oil due to the influences of different levels of architecture interfaces.This investigation is important to guiding reservoir evaluation,remaining oil analysis,profile control and potential tapping and well pattern adjustment.

Long-Term Impacts of Tree Architectures and Branch Configurations on Tree Growth, Yield, Fruit Quality Attributes, and Leaf Minerals in “Aztec Fuji” Apple

Canopy and branch architectures in high-density orchards can be crucial in production and fruit quality. The influence of two canopy orientations (Upright and Tilted) in combination with two arm (branch) architectures (Shortened or Overlapped) on tree growth, yield components, fruit quality, and leaf mineral nutrients in an “Aztec Fuji” apple (Malus domestica Bork.) high-density orchard was studied over five years. Tilted trees with shortened arm configuration (TilShArm) always had significantly larger trunk cross-sectional area (TCSA) than Upright trees with an Overlapped arm configuration (UpOverArm) every year from 2012 to 2016. Trees with a TilShArm system had more cumulative fruit per tree than those with an Upright orientation. Trees with a tilted canopy (TilShArm and TilOverArm) tended to have higher yield per tree and yield per hectare than those with an upright system. Trees with a TilShArm system were more precocious and had more yield per tree than those with an upright canopy orientation in 2012. When values were polled over five years, trees with an upright canopy-shortened arm system (UpShArm) treatment had a lower biennial bearing index (BBI) than those with an upright canopy-overlapped system (UpOverArm). Trees receiving an arm shortening (UpShArm or TilShArm) configuration often had larger fruits than those with overlapped arms (UpOverArm and TilOverArm). Fruit from trees receiving an UpOverArm had higher fruit firmness than those from trees with other canopy-branch arrangements at harvest due to their smaller size. Fruit from trees with a TilShArm and TilOverArm had significantly higher water core and bitter pit but lower sunburn than trees with an upright canopy (UpShArm and UpOverArm). Leaves from trees with an UpOverArm canopy-branch configuration had the lowest leaf Ca but the highest leaf K and Fe concentrations among all treatments.

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.

Timing effect of high temperature exposure on the plasticity of internode and plant architecture in maize

The occurrence of high temperature(HT)in crop production is becoming more frequent and unpredictable with global warming,severely threatening food security.The state of an organ’s growth and development is largely determined by the temperature conditions it is exposed to over time.Maize is the main cereal crop,and its stem growth and plant architecture are closely related to lodging resistance,and especially sensitive to temperature.However,systematic research on the timing effect of HT on the sequentially developing internode and stem is currently lacking.To identify the timing effect of HT on the morphology and plasticity of the stem in maize,two hybrids(Zhengdan 958(ZD958),Xianyu 335(XY335))characterized by distinct morphological traits in the stem were exposed to a 7-day HT treatment from the V6 to V17 stages(Vn presents the vegetative stage with n leaves fully expanded)in 2019-2020.The results demonstrated that exposure to HT during V6-V12 accelerated the rapid elongation of stems.For instance,HT occurring at V7 and V12 specifically promoted the lengths and weights of the 3rd-5th and 9th-11th internodes,respectively.Meanwhile,HT slowed the growth of internodes adjacent to the promoted internodes.Interestingly,compared with control,the plant height was significantly increased soon after HT treatment,but the promotion effect became narrower at the subsequent flowering stage,demonstrating a self-adjusting mechanism in the maize plant in response to HT.Importantly,HT altered the plant architectures,including a rising of the ear position and increase in the ear position coefficient.XY335 exhibited greater sensitivity in stem development than ZD958 under HT treatment.These findings improve our systematic understanding of the plasticity of internode and plant architecture in response to the timing of HT exposure.

Auxin-brassinosteroid crosstalk:Regulating rice plant architecture and grain shape

Rice(Oryza sativa)plant architecture and grain shape,which determine grain quality and yield,are modulatedby auxin and brassinosteroid via regulation of cell elongation and proliferation.We review the signaltransduction of these hormones and the crosstalk between their signals on the regulation of rice plantarchitecture and grain shape.

A 48-bp deletion upstream of LIGULELESS 1 alters rice panicle architecture

Panicle architecture is an agronomic determinant of crop yield and a target for cereal crop improvement.To investigate its molecular mechanisms in rice,we performed map-based cloning and characterization of OPEN PANICLE 1(OP1),a gain-of-function allele of LIGULELESS 1(LG1),controlling the spread-panicle phenotype.This allele results from a 48-bp deletion in the LG1 upstream region and promotes pulvinus development at the base of the primary branch.Increased OP1 expression and altered panicle phenotype in chimeric transgenic plants and upstream-region knockout mutants indicated that the deletion regulates spread-panicle architecture in the mutant spread panicle 1(sp1).Knocking out BRASSINOSTEROID UPREGULATED1(BU1)gene in the background of OP1 complementary plants resulted in compact panicles,suggesting OP1 may regulate inflorescence architecture via the brassinosteroid signaling pathway.We regard that manipulating the upstream regulatory region of OP1 or genes involved in BR signal pathway could be an efficient way to improve rice inflorescence architecture.

Apical meristem transcriptome analysis identifies a role for the blue light receptor gene GhFKF1 in cotton architecture development

Cotton architecture is determined by the differentiation fate transition of axillary meristem(AM),and influences cotton yield and the efficiency of mechanized harvesting.We observed that the initiation of flowering primordium was earlier in early-maturing than that in late-maturing cultivars during the differentiation and development of AM.The RNA-Seq and expression level analyses showed that genes FLAVIN BINDING,KELCH REPEAT,F-BOX1(GhFKF1),and GIGANTEA(GhGI)were in response to circadian rhythms,and involved in the regulation of cotton flowering.The gene structure,predicted protein structure,and motif content analyses showed that in Arabidopsis,cotton,rapseed,and soybean,proteins GhFKF1 and GhGI were functionally conserved and share evolutionary origins.Compared to the wild type,in GhFKF1 mutants that were created by the CRISPR/Cas9 system,the initiation of branch primordium was inhibited.Conversely,the knocking out of GhGI increased the number of AM differentiating into flower primordium,and there were much more lateral branch differentiation and development.Besides,we investigated that proteins GhFKF1 and GhGI can interact with each other.These results suggest that GhFKF1 and GhGI are key regulators of cotton architecture development,and may collaborate to regulate the differentiation fate transition of AM,ultimately influencing plant architecture.We describe a strategy for using the CRISPR/Cas9 system to increase cotton adaptation and productivity by optimizing plant architecture.

Enhancing MXene-based supercapacitors:Role of synthesis and 3D architectures

MXene has been the limelight for studies on electrode active materials,aiming at developing supercapacitors with boosted energy density to meet the emerging influx of wearable and portable electronic devices.Despite its various desirable properties including intrinsic flexibility,high specific surface area,excellent metallic conductivity and unique abundance of surface functionalities,its full potential for electrochemical performance is hindered by the notorious restacking phenomenon of MXene nanosheets.Ascribed to its two-dimensional(2D)nature and surface functional groups,inevitable Van der Waals interactions drive the agglomeration of nanosheets,ultimately reducing the exposure of electrochemically active sites to the electrolyte,as well as severely lengthening electrolyte ion transport pathways.As a result,energy and power density deteriorate,limiting the application versatility of MXene-based supercapacitors.Constructing 3D architectures using 2D nanosheets presents as a straightforward yet ingenious approach to mitigate the fatal flaws of MXene.However,the sheer number of distinct methodologies reported,thus far,calls for a systematic review that unravels the rationale behind such 3D MXene structural designs.Herein,this review aims to serve this purpose while also scrutinizing the structure–property relationship to correlate such structural modifications to their ensuing electrochemical performance enhancements.Besides,the physicochemical properties of MXene play fundamental roles in determining the effective charge storage capabilities of 3D MXene-based electrodes.This largely depends on different MXene synthesis techniques and synthesis condition variations,hence,elucidated in this review as well.Lastly,the challenges and perspectives for achieving viable commercialization of MXene-based supercapacitor electrodes are highlighted.

WPA1 encodes a vWA domain protein that regulates wheat plant architecture

Plant height,spike,leaf,stem and grain morphologies are key components of plant architecture and related to wheat yield.A wheat(Triticum aestivum L.)mutant,wpa1,displaying temperaturedependent pleiotropic developmental anomalies,was isolated.The WPA1 gene,encoding a von Willebrand factor type A(vWA)domain protein,was located on chromosome arm 7DS and isolated by map-based cloning.The functionality of WPA1 was validated by multiple independent EMS-induced mutants and gene editing.Phylogenetic analysis revealed that WPA1 is monocotyledon-specific in higher plants.The identification of WPA1 provides opportunity to study the temperature regulated wheat development and grain yield.

Sedimentary architecture of submarine channel-lobe systems under different seafloor topography:Insights from the Rovuma Basin offshore East Africa

Seafloor topography plays an important role in the evolution of submarine lobes.However,it is still not so clear how the shape of slope affects the three-dimensional(3-D)architecture of submarine lobes.In this study,we analyze the effect of topography factors on different hierarchical lobe architectures that formed during Pliocene to Quaternary in the Rovuma Basin offshore East Africa.We characterize the shape,size and growth pattern of different hierarchical lobe architectures using 3-D seismic data.We find that the relief of the topographic slope determines the location of preferential deposition of lobe complexes and single lobes.When the topography is irregular and presents topographic lows,lobe complexes first infill these depressions.Single lobes are deposited preferentially at positions with higher longitudinal(i.e.across-slope)slope gradients.As the longitudinal slope becomes higher,the aspect ratio of the single lobes increases.Lateral(i.e.along-slope)topography does not seem to have a strong influence on the shape of single lobe,but it seems to affect the overlap of single lobes.When the lateral slope gradient is relatively high,the single lobes tend to have a larger overlap surface.Furthermore,as the average of lateral slope and longitudinal slope gets greater,the width/thickness ratio of the single lobe is smaller,i.e.sediments tend to accumulate vertically.The results demonstrate that the shape of slopes more comprehensively influences the 3-D architecture of lobes in natural deep-sea systems than previously other lobe deposits and analogue experiments,which helps us better understand the development and evolution of the distal parts of turbidite systems.

A family of quantum von Neumann architecture

We develop universal quantum computing models that form a family of quantum von Neumann architectures,with modular units of memory,control,CPU,and internet,besides input and output.This family contains three generations characterized by dynamical quantum resource theory,and it also circumvents no-go theorems on quantum programming and control.Besides universality,such a family satisfies other desirable engineering requirements on system and algorithm design,such as modularity and programmability,hence serves as a unique approach to building universal quantum computers.