Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Cross-layer resource allocation based on equivalent bandwidth in OFDMA systems

A quality of service（QoS） guaranteed cross-layer resource allocation algorithm with physical layer, medium access control（MAC） layer and call admission control（CAC） considered simultaneously is proposed for the full IP orthogonal frequency division multiple access（OFDMA） communication system, which can ensure the quality of multimedia services in full IP networks.The algorithm converts the physical layer resources such as subcarriers, transmission power, and the QoS metrics into equivalent bandwidth which can be distributed by the base station in all three layers. By this means, the QoS requirements in terms of bit error rate（BER）, transmission delay and dropping probability can be guaranteed by the cross-layer optimal equivalent bandwidth allocation. The numerical results show that the proposed algorithm has higher spectrum efficiency compared to the existing systems.

Non-Convex Optimization of Resource Allocation in Fog Computing Using Successive Approximation

Fog computing can deliver low delay and advanced IT services to end users with substantially reduced energy consumption.Nevertheless,with soaring demands for resource service and the limited capability of fog nodes,how to allocate and manage fog computing resources properly and stably has become the bottleneck.Therefore,the paper investigates the utility optimization-based resource allocation problem between fog nodes and end users in fog computing.The authors first introduce four types of utility functions due to the diverse tasks executed by end users and build the resource allocation model aiming at utility maximization.Then,for only the elastic tasks,the convex optimization method is applied to obtain the optimal results;for the elastic and inelastic tasks,with the assistance of Jensen’s inequality,the primal non-convex model is approximated to a sequence of equivalent convex optimization problems using successive approximation method.Moreover,a two-layer algorithm is proposed that globally converges to an optimal solution of the original problem.Finally,numerical simulation results demonstrate its superior performance and effectiveness.Comparing with other works,the authors emphasize the analysis for non-convex optimization problems and the diversity of tasks in fog computing resource allocation.

The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory

In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for resource scheduling,however,resource providers have resource utilization requirements for cloud manufacturing platforms.In the process of resource optimization scheduling,the interests of all parties have conflicts of interest,which makes it impossible to obtain better optimization results for resource scheduling.Therefore,amultithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling.The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization,using the negotiation algorithm based on the Stackelberg game,the cloud manufacturing platformnegotiates andmediateswith the participants’agents,to maximize self-interest by constantly changing one’s own plan,iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform.Through multiple rounds of negotiation and calculation,we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task.Finally,through experimental simulation and comparative analysis,the validity and rationality of the model are verified.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Optimal resource allocation with spatiotemporal transmission discovery for efective disease control

Background:The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world,especially in highly populated regions.The healthcare capacity(especially the testing resources,vaccination coverage,and hospital capacity)is becoming extremely insufcient as the demand will far exceed the supply.To address this time-critical issue,we need to answer a key question:How can we efectively infer the daily transmission risks in diferent districts using machine learning methods and thus lay out the corresponding resource prioritization strategies,so as to alleviate the impact of the Omicron outbreaks?Methods:We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant.We collect the publicly available data from the ofcial website of the Hong Kong Special Administrative Region(HKSAR)Government and the study period in this paper is from December 27,2021 to July 17,2022(including a period for future prediction).First,we construct the spatiotemporal transmission intensity matrices across diferent districts based on infection case records.With the constructed cross-district transmission matrices,we forecast the future risks of various locations daily by means of the Gaussian process.Finally,we develop a transmission-guided resource prioritization strategy that enables efective control of Omicron outbreaks under limited capacity.Results:We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong,China.The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks,making it difcult for the public health authority to foresee the outbreaks and plan the responses accordingly.With the guidance of the inferred transmission risks,the developed prioritization strategy enables the optimal testing resource allocation for integrative case management(including case detection,quarantine,and further treatment),i.e.,with the 300,000 testing capacity per day;it could reduce the infection peak by 87.1% compared with the population-based allocation strategy(case number reduces from 20,860 to 2689)and by 24.2% compared with the case-based strategy(case number reduces from 3547 to 2689),signifcantly alleviating the burden of the healthcare system.Conclusions:Computationally characterizing spatiotemporal transmission patterns allows for the efective risk mapping and resource prioritization;such adaptive strategies are of critical importance in achieving timely outbreak control under insufcient capacity.The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants.Moreover,the investigation conducted in Hong Kong,China provides useful suggestions on how to achieve efective disease control with insufcient capacity in other highly populated countries and regions.

Distributed Algorithm for Robust Resource Allocation with Polyhedral Uncertain Allocation Parameters

This paper studies a distributed robust resource allocation problem with nonsmooth objective functions under polyhedral uncertain allocation parameters. In the considered distributed robust resource allocation problem, the（nonsmooth） objective function is a sum of local convex objective functions assigned to agents in a multi-agent network. Each agent has a private feasible set and decides a local variable, and all the local variables are coupled with a global affine inequality constraint,which is subject to polyhedral uncertain parameters. With the duality theory of convex optimization,the authors derive a robust counterpart of the robust resource allocation problem. Based on the robust counterpart, the authors propose a novel distributed continuous-time algorithm, in which each agent only knows its local objective function, local uncertainty parameter, local constraint set, and its neighbors＇ information. Using the stability theory of differential inclusions, the authors show that the algorithm is able to find the optimal solution under some mild conditions. Finally, the authors give an example to illustrate the efficacy of the proposed algorithm.

Distributed Chunk-Based Optimization for MultiCarrier Ultra-Dense Networks

In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.

Retrospect and reflection on the construction of large-scale technology platform of the State Key Laboratory of Natural and Biomimetic Drugs in 2018–2020

The State Key Laboratory of Natural and Biomimetic Drugs was approved for a funding of nearly 100 million yuan specifically aimed at the purchase and maintenance of equipment and instruments from 2018 to 2020,which is a record high.The Laboratory focuses on two major directions of scientific research,the"basic scientific problems of drug resistance of complex components of natural products"and the"key biomimetic scientific problems of endogenous substances therapeutic functions".The selection of scientific instruments and equipment,trial production,upgrading,as well as high level of technical and management personnel allocation and other aspects are critical to meet the development needs of the Key Laboratory and to maintain the advantages and leading role in these two major directions of scientific research.