Efficient utilization of ecological and cultural-tourism resources:Taking Guangzhou Cultural Park as an example

Land space is precious resource and carrier of various economic activities.Ecosystem Product Value Realization and Cultural Tourism is new economic growth point for green and low-carbon development.It is necessary to study how to intensively and efficiently utilize ecological and cultural-tourism resources under"Three zones three lines for land use"(3Z3L),which is basic land space planning policy.However,there are few research and cases due to difficulties of interdisciplinary research.This article adopts the methods of policies research,graphic analysis,and visiting,to study efficient utilization of ecological and cultural-tourism resources.This study found that the development of land spaces of ecology,agriculture,and urban areas is 3D trend,multi-purpose resource utilization can be achieved by improving the utilization efficiency of 3D space and differentiating spatial utilization in different periods.Taking Guangzhou Cultural Park as an example,the research object has improved the efficiency of ecological and cultural-tourism resource utilization by its managements and technology,such as“house under the tree,tree inside the house”.However,there are shortcomings just as planning and protection policies with it.We proposed to optimize the spatial planning and regulations,and improve the protection of overlapping areas of 3Z3L by planning and regulations,improve the utilization efficiency of ecological and cultural-tourism space,meet the urban young people's multiple needs such as culture and ecology in 3D space,use new technologies to improve ecological services quality such as biological methods,strengthen the coordination of multiple management departments with the concept of nature based solutions,develop the“Reversed Transmission Effect”,keep the boundary of urban development strictly.

Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates continuously.This paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing environments.The motivation stems from the pressing issue of accommodating fluctuating levels of user demand efficiently.By adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy consumption.This reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing systems.To enhance the resource allocation process,we introduce a novel knowledge-based optimization algorithm.In this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly Algo-rithm.Our findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption reduction.This paper underscores the profound significance of resource allocation in the realm of cloud computing.By addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing systems.Our contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

Secure Computation Efficiency Resource Allocation for Massive MIMO-Enabled Mobile Edge Computing Networks

In this article,the secure computation efficiency(SCE)problem is studied in a massive multipleinput multiple-output(mMIMO)-assisted mobile edge computing(MEC)network.We first derive the secure transmission rate based on the mMIMO under imperfect channel state information.Based on this,the SCE maximization problem is formulated by jointly optimizing the local computation frequency,the offloading time,the downloading time,the users and the base station transmit power.Due to its difficulty to directly solve the formulated problem,we first transform the fractional objective function into the subtractive form one via the dinkelbach method.Next,the original problem is transformed into a convex one by applying the successive convex approximation technique,and an iteration algorithm is proposed to obtain the solutions.Finally,the stimulations are conducted to show that the performance of the proposed schemes is superior to that of the other schemes.

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be managed.Furthermore,the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users,CRs,and primary users,PUs,exist in the identical geographical area.Hence,this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarioswhile limiting interference to PUs to allowable threshold.Hence,this paper,compared to other frameworks proposed in the literature,proposes a two-step approach to reduce the complexity of the proposed mathematical model.In the first step,the subcarriers are assigned to the users of the CRN,while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second stage.The main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks,which has been achieved by including a pricing function in the proposed cost function.Finally,the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing(OFDM).Simulation results prove that the performance of the proposed algorithm is better than other algorithms,albeit with a lesser complexity of O(NM)+O(Nlog(N)).

Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system resources.It is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the Internet.In a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase efficiency.It is also known as a server pool or server farm.When a single node is overwhelmed,balancing the workload is needed to manage unpredictable workflows.The load balancer sends the load to another free node in this case.We focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling process.One of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at hand.Depending on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.

Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment

Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.

An Efficient Federated Learning Framework Deployed in Resource-Constrained IoV:User Selection and Learning Time Optimization Schemes

In this article,an efficient federated learning(FL)Framework in the Internet of Vehicles(IoV)is studied.In the considered model,vehicle users implement an FL algorithm by training their local FL models and sending their models to a base station(BS)that generates a global FL model through the model aggregation.Since each user owns data samples with diverse sizes and different quality,it is necessary for the BS to select the proper participating users to acquire a better global model.Meanwhile,considering the high computational overhead of existing selection methods based on the gradient,the lightweight user selection scheme based on the loss decay is proposed.Due to the limited wireless bandwidth,the BS needs to select an suitable subset of users to implement the FL algorithm.Moreover,the vehicle users’computing resource that can be used for FL training is usually limited in the IoV when other multiple tasks are required to be executed.The local model training and model parameter transmission of FL will have significant effects on the latency of FL.To address this issue,the joint communication and computing optimization problem is formulated whose objective is to minimize the FL delay in the resource-constrained system.To solve the complex nonconvex problem,an algorithm based on the concave-convex procedure(CCCP)is proposed,which can achieve superior performance in the small-scale and delay-insensitive FL system.Due to the fact that the convergence rate of CCCP method is too slow in a large-scale FL system,this method is not suitable for delay-sensitive applications.To solve this issue,a block coordinate descent algorithm based on the one-step projected gradient method is proposed to decrease the complexity of the solution at the cost of light performance degrading.Simulations are conducted and numerical results show the good performance of the proposed methods.

Novel interface engineering of LDH-based materials on Mg alloy for efficient photocatalytic systems considering the geometrical linearity of condensed phosphates

This study presents a facile and rapid method for synthesizing novel Layered Double Hydroxide(LDH)nanoflakes,exploring their application as a photocatalyst,and investigating the influence of condensed phosphates'geometric linearity on their photocatalytic properties.Herein,the Mg O film,obtained by plasma electrolysis of AZ31 Mg alloys,was modified by growing an LDH film,which was further functionalized using cyclic sodium hexametaphosphate(CP)and linear sodium tripolyphosphate(LP).CP acted as an enhancer for flake spacing within the LDH structure,while LP changed flake dispersion and orientation.Consequently,CP@LDH demonstrated exceptional efficiency in heterogeneous photocatalysis,effectively degrading organic dyes like Methylene blue(MB),Congo red(CR),and Methyl orange(MO).The unique cyclic structure of CP likely enhances surface reactions and improves the catalyst's interaction with dye molecules.Furthermore,the condensed phosphate structure contributes to a higher surface area and reactivity in CP@LDH,leading to its superior photocatalytic performance compared to LP@LDH.Specifically,LP@LDH demonstrated notable degradation efficiencies of 93.02%,92.89%,and 88.81%for MB,MO,and CR respectively,over a 40 min duration.The highest degradation efficiencies were observed in the case of the CP@LDH sample,reporting 99.99%for MB,98.88%for CR,and 99.70%for MO.This underscores the potential of CP@LDH as a highly effective photocatalyst for organic dye degradation,offering promising prospects for environmental remediation and water detoxification applications.

Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

‘Partly'globalized networks and driving mechanism in resource-based state-owned enterprises:A case study of J Group

In the context of economic globalization,while multinational enterprises from developed countries occupy a high-end position in the global value chain,enterprises from developing countries are often marginalized in the world market.In China,resource-based state-owned enterprises(SOEs)are tasked with the mission of safeguarding resource security,and their internationalization development ideas and strategic deployment are significantly and fundamentally different from those of other non-state-owned enterprises and large multinational corporations.This study provides ideas for the globalization policies of enterprises in developing countries.We consider J Group in western China as a case and discuss its productive investment and global production network development from 2010 to 2019.We found that J Group was‘Partly'globalized,and there are multiple core nodes with the characteristics of centralized and decentralized coexistence in the production network;in addition,the overall layout centre shifted to Southeast Asia and China;however,its global production was restricted by the enterprise's investment security considerations,support and restrictions of the home country,political security risk of the host country,and sanctions from the West.These findings provide insights for future research:under the wave of anti-globalization and'internal circulation as the main body',resource SOEs should consider the potential risk of investment,especially keeping the middle and downstream industrial chain in China as much as possible.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Defining Cities by Water: Addressing Capital Misallocation in a Race to Conserve Resources

As part of its efforts to promote a sustainable and high-quality development,China has pledged to reduce water consumption and create a water-efficient society.On the basis of identifying the institutional root causes of excessive capital allocation and excessive water consumption in China’s water-intensive industrial sectors,this study elaborates how the national water-efficient cities assessment contributes to optimized capital allocation.Our research shows that national water-efficient cities assessment has motivated local governments to compete for water efficiency.To conserve water,local governments regulated the entry and exit of water-intensive enterprises,discouraged excessive investments in water-intensive sectors,and phased out obsolete water-intensive capacities within their jurisdictions.This approach has resulted in mutually beneficial outcomes,including improved allocation of capital,enhanced water efficiency,and reduced emissions.This paper offers policy recommendations for establishing a water-efficient society throughout the 14^(th) Five-Year Plan(2021-2025)period by presenting empirical evidence on the policy effects of resource efficiency evaluation.

A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning

Federated Edge Learning(FEL),an emerging distributed Machine Learning(ML)paradigm,enables model training in a distributed environment while ensuring user privacy by using physical separation for each user’s data.However,with the development of complex application scenarios such as the Internet of Things(IoT)and Smart Earth,the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands.Therefore,joint resource optimization may be the key solution to the scaling problem.This paper simultaneously addresses the multifaceted challenges of computation and communication,with the growing multiple resource demands.We systematically review the joint allocation strategies for different resources(computation,data,communication,and network topology)in FEL,and summarize the advantages in improving system efficiency,reducing latency,enhancing resource utilization,and enhancing robustness.In addition,we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements,indirectly.This work not only provides theoretical support for resource management in federated learning(FL)systems,but also provides ideas for potential optimal deployment in multiple real-world scenarios.By thoroughly discussing the current challenges and future research directions,it also provides some important insights into multi-resource optimization in complex application environments.

AMAD:Adaptive Mapping Approach for Datacenter Networks,an Energy-Friend Resource Allocation Framework via Repeated Leader Follower Game

Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility objectives.Yet,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs too.Thus,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling solutions.Resource utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their resources.Service providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients first.In this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client tenants.Through this,the providers seek to retrieve those leased unused resources from their clients.Cooperation is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s premises.Hence,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned resources.Moreover,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each client.Compared to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.

Starlet:Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things.To guarantee the network's overall security,we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit.Firstly,we propose the method for dynamic setting of node defense resource thresholds to obtain the defender(attacker)benefit function of edge servers(nodes)and distribution.Secondly,we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes.Subsequently,we use the decomposability and Lipschitz conti-nuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically.Finally,experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter(CSIT).A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CsI feedback stage.In fact,the downlink communication generally includes three stages,i.e.,pilot training,CsI feedback,and data transmission.These three stages are mutually related and jointly determine the overall system performance.Unfortunately,there exist few studies on the reduction of csIT acquisition overhead from the global point of view.In this paper,we integrate the Minimum Mean Square Error(MMSE)channel estimation,Random Vector Quantization(RVQ)based limited feedback and Maximal Ratio Combining(MRC)precoding into a unified framework for investigating the resource allocation problem.In particular,we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio(SNR)based on the deterministic equivalence theory.Then the three performance metrics(the spectral efficiency,energy efficiency,and total energy consumption)oriented problems are formulated analytically.With practical system requirements,these three metrics can be collaboratively optimized.Finally,we propose an optimization solver to derive the optimal partition of channel coherence time.Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.

Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

Geological reservoir and resource potential(10^(13)m^(3))of gas hydrates in the South China Sea

A detailed understanding of the distribution and potential of natural gas hydrate(NGHs)resources is crucial to fostering the industrialization of those resources in the South China Sea,where NGHs are abundant.In this study,this study analyzed the applicability of resource evaluation methods,including the volumetric,genesis,and analogy methods,and estimated NGHs resource potential in the South China Sea by using scientific resource evaluation methods based on the factors controlling the geological accumulation and the reservoir characteristics of NGHs.Furthermore,this study compared the evaluation results of NGHs resource evaluations in representative worldwise sea areas via rational analysis.The results of this study are as follows:(1)The gas hydrate accumulation in the South China Sea is characterized by multiple sources of gas supply,multi-channel migration,and extensive accumulation,which are significantly different from those of oil and gas and other unconventional resources.(2)The evaluation of gas hydrate resources in the South China Sea is a highly targeted,stratified,and multidisciplinary evaluation of geological resources under the framework of a multi-type gas hydrate resource evaluation system and focuses on the comprehensive utilization of multi-source heterogeneous data.(3)Global NGHs resources is n×10^(15)m^(3),while the NGHs resources in the South China Sea are estimated to be 10^(13)m^(3),which is comparable to the abundance of typical marine NGHs deposits in other parts of the world.In the South China Sea,the NGHs resources have a broad prospect and provide a substantial resource base for production tests and industrialization of NGHs.

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.