Joint Management of Shared Resources, Issues and Conflicts of the Nso and Oku Tribes in the Ngongbaa Montane Forest Area, NWR of Cameroon

Wherever people live together in or near homelands that harbour joint natural resources such as forest, water, minerals, oil etc., they must collaborate, co-exist and share these resources irrespective of their genetic, social, cultural, and political peculiarities. This is not always the case when self-interest, ethnic divisions, elitist politics, economic interest and power tussle set in. The Nso and Oku people who reside in the Ngongbaa Forest area constitute an example of joint management of resources where at one period, their relationship over the management of the Oku Mountain Forest was cordial and at another, the relationship became conflictive. This study was designed to study the influence of the KIFP forest management policies on the Nso indigenous forest Management system in Ngongbaa Forest, and its implications. The data collected for the study was secured through the administration of a questionnaire to residents of 10 villages adjacent to the Ngongbaa Forest, and forest users who carried out livelihood activities there between 1963 and 2015. These forest user groups include beekeepers, rat trappers, grazers, mushroom gatherers, ground honey/tree honey harvesters, carvers, etc. The investigation also extended to the traditional leaders of Nso and Oku who are in charge of the land tenure systems. The findings show that the Oku Mountain Forest is jointly owned by the Nso and Oku people. The Nso own the eastern half of the forest known as Ngongbaa Forest while the Oku own the western half, known as the Kilum Forest. The cordial relationship that existed between these people ensured the conservation of the forest. But the conflicts that later set in, especially due to the creation of the Kilum Mountain Forest Project in 1987 later changed to the Kilum Ijim Forest Project (KIFP) in 1992 sidelined the local communities which was contrary to the dispositions of the new Cameroon forest law enacted in 1994. Thus, the indigenous conservation practices became ignored which the local population never digested well. This seriously jeopardized the wellbeing of the forest in general.

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.

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.

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

‘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.

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.

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.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.

Resource utilization of hazardous gypsum sludge in oxidation smelting of lead concentrate

Gypsum sludge,a hazardous waste generated by the non-ferrous smelting industry,presents a significant challenge for disposal and utilization.To investigate the feasibility of substituting gypsum sludge for limestone as a flux for smelting,the effects of calcium sulfate(CaSO_(4))and smelting conditions on oxygen-rich smelting of lead concentrate were studied.The interaction between CaSO_(4)and sulfides facilitates the conversion of CaSO_(4)into CaO,which is crucial for slag formation.The order of the influence of sulfide minerals on the conversion of CaSO_(4)is pyrite>sphalerite>galena.When using gypsum sludge exclusively as the calcium source,under optimal conditions with a CaO/SiO_(2)mass ratio of 0.8,an FeO/SiO_(2)mass ratio of 1.2,a melting temperature of 1150℃,an oxygen flow rate of 1.3 L/min,the recovery rates of Pb and Zn in the lead-rich slag reached 85.01%and 95.69%,respectively,with a sulfur content of 2.65 wt%.The As content in the smelting slag obtained by reduction smelting was 0.02 wt%.Resource utilization of gypsum sludge in lead smelting is a feasible method.

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.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

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.

Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency.To tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell.To fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent cells.To tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV position.Then,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation schemes.We further propose an iterative power allocation algorithm based on the sequential convex approximation theory.The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Resource management at the network edge for federated learning

Federated learning has been explored as a promising solution for training machine learning models at the network edge,without sharing private user data.With limited resources at the edge,new solutions must be developed to leverage the software and hardware resources as the existing solutions did not focus on resource management for network edge,specially for federated learning.In this paper,we describe the recent work on resource manage-ment at the edge and explore the challenges and future directions to allow the execution of federated learning at the edge.Problems such as the discovery of resources,deployment,load balancing,migration,and energy effi-ciency are discussed in the paper.

Ethics of Human Resources Management in the Cameroonian Health System, Medical Nomadism and the Ineffectiveness of the Fight against High Blood Pressure

Context/objectives: The fight against Chronic Non-Communicable Diseases (NCDs) is a long-term undertaking, which requires available, motivated and well-managed human resources (HR). The administrative management of skills on both qualitative and quantitative levels is one of the essential functions of a health system. To better implement policies of fight against High Blood Pressure (HBP) and other chronic diseases, it is important to establish strategies to retain health personnel. This loyalty requires favorable working conditions and consideration of the contribution-reward couple. Good working conditions are likely to reduce the phenomenon of medical nomadism;conversely, poor HR management can contribute to their exodus towards exotic “green pastures”, thus leading to an additional crisis in the Cameroonian health system. The fight against HBP is a complex, multifaceted and multifactorial reality that requires appropriate management model for all types of resources mainly HR. The main objective of this research is to show the impact of poor management of human resources in Cameroon health system on medical nomadism and the ineffectiveness of the fight against High Blood Pressure. Method: A cross-sectional descriptive survey among five hundred (500) health facilities in the center region of Cameroon has been conducted. A stratified probabilistic technique has been used, and the number of health facilities to be surveyed has been determined using the “sample size estimation table” of Depelteau. The physical questionnaires have been printed and then distributed to data collectors. After data collection, the latter were grouped during processing in Excel sheets. The Chi-square test was used for data with a qualitative value and that of Kolmogorov-Sminorf for data with a quantitative value to assess the normality and reliability of data. The Crochach’s Alpha reliability test allowed us to have a summary of the means and variances and then to search for intragroup correlations between variables. Descriptive analysis was possible with the XLSTAT 2016 software. Results: 43.60% of Health Facilities (HF) managers were unqualified. 82.20% of HF managers have staff in a situation of professional insecurity. They are mainly contractual (49.00), decision-making agents (24.40%), casual agents (08.80). The proportion of unstable personnel is average of 22.00% and very unstable, 12.00%.

Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

Unrelated parallel machine scheduling problem(UPMSP)is a typical scheduling one and UPMSP with various reallife constraints such as additional resources has been widely studied;however,UPMSP with additional resources,maintenance,and energy-related objectives is seldom investigated.The Artificial Bee Colony(ABC)algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources,among other factors.In this study,an energy-efficient UPMSP with additional resources and maintenance is considered.A dynamical artificial bee colony(DABC)algorithm is presented to minimize makespan and total energy consumption simultaneously.Three heuristics are applied to produce the initial population.Employed bee swarm and onlooker bee swarm are constructed.Computing resources are shifted from the dominated solutions to non-dominated solutions in each swarm when the given condition is met.Dynamical employed bee phase is implemented by computing resource shifting and solution migration.Computing resource shifting and feedback are used to construct dynamical onlooker bee phase.Computational experiments are conducted on 300 instances from the literature and three comparative algorithms and ABC are compared after parameter settings of all algorithms are given.The computational results demonstrate that the new strategies of DABC are effective and that DABC has promising advantages in solving the considered UPMSP.