Advances in liver transplantation allocation systems

With the growing number of patients in need of liver transplantation, there is a need for adopting new and modifying existing allocation policies that prioritize patients for liver transplantation. Policy should ensure fair allocation that is reproducible and strongly predictive of best pre and post transplant outcomes while taking into account the natural history of the potential recipients liver disease and its complications. There is wide acceptance for allocation policies based on urgency in which the sickest patients on the waiting list with the highest risk of mortality receive priority. Model for end-stage liver disease and Child-Turcotte-Pugh scoring system, the two most universally applicable systems are used in urgencybased prioritization. However, other factors must be considered to achieve optimal allocation. Factors affecting pre-transplant patient survival and the quality of the donor organ also affect outcome. The optimal system should have allocation prioritization that accounts for both urgency and transplant outcome. We reviewed past and current liver allocation systems with the aim of generating further discussion about improvement of current policies.

Agricultural Land Use Optimal Allocation System in Developing Area:Application to Yili Watershed, Xinjiang Region

In developing countries, land productivity involves little market, where the agricultural land use is mainly determined by the food demands as well as the land suitability. The land use pattern will not ensure everywhere enough land for certain cropping if spatial allocation just according to land use suitability. To solve this problem, a subzone and a pre-allocation for each land use are added in spatial allocation module, and land use suitability and area optimi- zation module are incorporated to constitute a whole agricultural land use optimal allocation （ALUOA） system. The system is developed on the platform .Net 2005 using ArcGIS Engine （version 9.2） and C# language, and is tested and validated in Yili watershed of Xinjiang Region on the newly reclaimed area. In the case study, with the help of soil data obtained from 69 points sampled in the fieldwork in 2008, main river data supplied by the Department of Water Resources of Xinjiang Uygur Autonomous Region in China, and temperature data provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences, land use suitability on eight common crops are evaluated one by one using linear weighted summation method in the land use suitability model. The linear pro- gramming （LP） model in area optimization model succeeds to give out land area target of each crop under three scenarios. At last, the land use targets are allotted in space both with a six subzone file and without a subzone file. The resuits show that the land use maps with a subzone not only ensure every part has enough land for every crop, but also gives a more fragmental land use pattern, with about 87.99% and 135.92% more patches than the one without, while at the expense of loss between 15.30% and 19.53% in the overall suitability at the same time.

Theory and model of water resources complex adaptive allocation system

Complex adaptive sys tem theory is a new and important embranchment of system science, which prov ides a new thought to research water resources allocation system. Based on the a nalysis of complexity and complex adaptive mechanism of water resources allocat ion system, a fire-new analysis model is presented in this paper. With t he description of dynamical mechanism of system, behavior characters of agents and the evaluation method of system status, an integrity research system is built to analyse the evolvement rule of water resources allocation system. A nd a brief research for the impact of water resources allocation in benefi cial regions of the Water Transfer from South to North China Project is conducted.

Power Allocation for SE Maximization in Uplink Massive MIMO System Under Minimum Rate Constraint

In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.

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.

Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Resource Allocation for Cognitive Network Slicing in PD-SCMA System Based on Two-Way Deep Reinforcement Learning

In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System ContainingWind-Storage Combined Unit

To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.

Bayesian network-based resilience assessment of interdependent infrastructure systems under optimal resource allocation strategies

Critical infrastructure systems(CISs)play a key role in the socio-economic activity of a society,but are exposed to an array of disruptive events that can greatly impact their function and performance.Therefore,understanding the underlying behaviors of CISs and their response to perturbations is needed to better prepare for,and mitigate the impact of,future disruptions.Resilience is one characteristic of CISs that influences the extent and severity of the impact induced by extreme events.Resilience is often dissected into four dimensions:robustness,redundancy,resourcefulness,and rapidity,known as the“4Rs”.This study proposes a framework to assess the resilience of an infrastructure network in terms of these four dimensions under optimal resource allocation strategies and incorporates interdependencies between different CISs,with resilience considered as a stochastic variable.The proposed framework combines an agent-based infrastructure interdependency model,advanced optimization algorithms,Bayesian network techniques,and Monte Carlo simulation to assess the resilience of an infrastructure network.The applicability and flexibility of the proposed framework is demonstrated with a case study using a network of CISs in Austin,Texas,where the resilience of the network is assessed and a“what-if”analysis is performed.

Resource allocation based on fairness and QoS provisioning for OFDMA-WLAN system

To satisfy different service requirements of multiple users in the orthogo nal frequency division multiple access wireless local area network OFDMA-WLAN system downlink transmission a resource allocation algorithm based on fairness and quality of service QoS provisioning is proposed. Different QoS requirements are converted into different rate requirements to calculate the QoSs atisfaction level.The optimization object is revised as a fairness-driven resource optimization function to provide fairness. The complex resource allocation problem is divided into channel allocation and power assignment sub-problems. The sub-problems are solved by the bipartite graph matching and water-filling based method.Compared with other algorithms the proposed algorithm sacrifices less data rate for higher fairnes and QoS satisfaction.The sim ulation results show that the proposed algorithm is capableo fp rovi ding QoS and fairness and performs better in a tradeoff among QoS fairness and data rate.

Adaptive resource allocation in downlink multi-user MC-CDMA systems

The joint channel and power allocation in the downlink transmission of multi-user multi-carrier code division multiple access（MC-CDMA） systems are investigated and the throughput maximization problem is considered as a mixed integer optimization problem. For simplicity of analysis, the problem is divided into two less complex sub-problems： power allocation and channel allocation, which can be solved by a suboptimal adaptive power allocation （APA）algorithm and an optimal adaptive channel allocation （ACA） algorithm, respectively. By combining APA and ACA algorithms, an adaptive channel and power allocation scheme is proposed. The numerical results show that the proposed APA algorithm is more suitable for MC-CDMA systems than the conventional equal power allocation algorithm, and that the proposed channel and power allocation scheme can significantly improve the system throughout performance.

Quantitative Allocation Analysis of "Raising by Planting" System Combining Fruits and Poultry for Mountainous Region in Headwaters of Dongjiang River

[Objective] The aim was to promote nutrient cycling and utilization in the mountain＇s system combining fruits and poultry and to control non-point source pollution produced from swine raising and navel orange planting in headwaters of Dongjiang River. [Method] The quantitative analysis was mainly conducted for the so called ＂raising by planting＂ which is about material cycle in ＂pig-methane-fruit-fish＂ model and energy cascade utilization, based on relationship between excreted amount by livestock and the utilized quantity in Xinlin Farm in Longtang Town, Dingnan County, Jiangxi Province. [Result] Considering N requirement by fruit trees, a navel orange orchard （1 mu） could support about 2.72 pigs, the equipped biogas pool （1.88 m3） could support 1 166.67 kg of duckweeds and a fish pond could support 25.57 grass carps. In contrast, a satsuma orchard （1 mu） could support about 1.96 pigs, the equipped biogas pool （1.35 m3） could support 841.53 kg duckweeds and the fish pond could support 18.44 grass carps. [Conclusion] The results provided scientific references for quantitative allocation of members in ＂pig-methane-fruit-fish＂ model when popularized in headwaters of Dongjiang River.

Improved reduced-complexity bit and power allocation algorithms for multicarrier systems

Based on the iterative bit-filling procedure, a computationally efficient bit and power allocation algorithm is presented. The algorithm improves the conventional bit-filling algorithms by maintaining only a subset of subcarriers for computation in each iteration, which reduces the complexity without any performance degradation. Moreover, a modified algorithm with even lower complexity is developed, and equal power allocation is introduced as an initial allocation to accelerate its convergence. Simulation results show that the modified algorithm achieves a considerable complexity reduction while causing only a minor drop in performance.

Cross-Layer Adaptive Resource Allocation Algorithm with Diverse QoS Requirements for Single-Cell OFDMA Systems

The orthogonal frequency division multiple access( OFDMA) based communication system has been considered as the main trend of next-Generation communication system. But the existing resource allocation algorithm designed for such system is always with high complexity thus hard to be realized. To solve such problem with the constraints of spectrum efficiency and buffer state,a novel cross-layer resource allocation algorithm( RAA) is proposed in this paper. The goal of our RAA is to maximize the system throughput while satisfying several practical constraints,such as fairness among services,head of line( Ho L) delay and diverse quality of service( Qo S) requirements. Due to these constraints,finding the optimal solution becomes a NPhard problem. Therefore in this paper a novel method to solve such problem with acceptable complexity is proposed within following steps: firstly,based on the link state we formulate the ideal subchannel allocation strategy as a convex optimization problem,which can be efficiently solved by our proposed lagrange multiplier technique subchannel allocation( LMTSA) algorithm; secondly,according to the obtained channel allocation matrix,a power allocation algorithm based on the water-filling power allocation( WPA) idea is deployed to get the optimal power allocation matrix combining with adaptive modulation and coding( AMC); finally,through a greedy algorithm,the ultimate subchannel and power allocation matrix can be obtained based on iterative method. The simulation results illustrate that we can achieve the higher throughput and better Qo S performance than the widely-used maximum throughput( MT) algorithm and round robin( RR) algorithm.

Dynamic resource allocation for high-speed railway downlink MIMO-OFDM system

The dynamic resource allocation problem in high-speed railway downlink orthogonal frequency-division multiplexing（OFDM） systems with multiple-input multiple-output（MIMO） antennas is investigated.Sub-carriers,antennas,time slots,and power are jointly considered.The problem of multi-dimensional resource allocation is formulated as a mixed-integer nonlinear programming problem.The effect of the moving speed on Doppler shift is analyzed to calculate the inter-carrier interference power.The optimization objective is to maximize the system throughput under the constraint of a total transmitted power that is no greater than a certain threshold.In order to reduce the computational complexity,a suboptimal solution to the optimization problem is obtained by a two-step method.First,sub-carriers,antennas,and time slots are assigned to users under the assumption of equal power allocation.Next,the power allocation problem is solved according to the result of the first-step resource allocation.Simulation results show that the proposed multi-dimensional resource allocation strategy has a significant performance improvement in terms of system throughput compared with the existing one.

Power Allocation and Performance Analysis of the Collaborative NOMA Assisted Relaying Systems in 5G

Serving multiple cell-edge mobile terminals poses multifaceted challenges due to the increased transmission power and interferences, which could be overcome by relay communications. With the recent advancement of 5G technologies, non-orthogonal multiple access(NOMA) has been used at relay node to transmit multiple messages simultaneously to multiple cell-edge users. In this paper, a Collaborative NOMA Assisted Relaying(CNAR) system for 5G is proposed by enabling the collaboration of source-relay(S-R) and relay-destination(R-D) NOMA links. The relay node of the CNAR decodes the message for itself from S-R NOMA signal and transmits the remaining messages to the multiple cell-edge users in R-D link. A simplified-CNAR(S-CNAR) system is then developed to reduce the relay complexity. The outage probabilities for both systems are analyzed by considering outage behaviors in S-R and R-D links separately. To guarantee the data rate, the optimal power allocation among NOMA users is achieved by minimizing the outage probability. The ergodic sum capacity in high SNR regime is also approximated. Our mathematical analysis and simulation results show that CNAR system outperforms existing transmission strategies and S-CNAR reaches similar performance with much lower complexity.

Joint Subcarrier and Power Allocation for Multi-UAV Systems

This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.

A Bandwidth-Link Resources Cooperative Allocation Strategy of Data Communication in Intelligent Transportation Systems

The bandwidth resources allocation strategies of the existing Internet of Vehicles(IoV) are mainly base on the communication architecture of the traditional 802.11 x in the wireless local area network(WLAN). The traditional communication architecture of IoV will easily cause significant delay and low Packet Delivery Ratio(PDR) for disseminating critical security beacons under the condition of high-speed movement, distance-varying communication, and mixed traffic. This paper proposes a novel bandwidth-link resources cooperative allocation strategy to achieve better communication performance under the road conditions of intelligent transportation systems(ITS). Firstly, in traffic scenarios, based on the characteristic to predict the relative position of the mobile transceivers, a strategy is developed to cooperate on the mobile cellular network and the Dedicated Short-Range Communications(DSRC). Secondly, by adopting the general network simulator NS3, the dedicated mobile channel models that are suitable for the data interaction of ITS, is applied to confirm the feasibility and reliability of the strategy. Finally, by the simulation, comparison, and analysis of some critical performance parame-ters, we conclude that the novel strategy does not only reduce the system delay but also improve the other communication performance indicators, such as the PDR and communication capacity.

Joint Optimization of Satisfaction Index and Spectrum Efficiency with Cache Restricted for Resource Allocation in Multi-Beam Satellite Systems

Dynamic resource allocation(DRA) is a key technology to improve system performances in GEO multi-beam satellite systems. And, since the cache resource on the satellite is very valuable and limited, DRA problem under restricted cache resources is also an important issue to be studied. This paper mainly investigates the DRA problem of carrier resources under certain cache constraints. What's more, with the aim to satisfy all users' traffic demands as more as possible, and to maximize the utilization of the bandwidth, we formulate a multi-objective optimization problem(MOP) where the satisfaction index and the spectrum efficiency are jointly optimized. A modified strategy SA-NSGAII which combines simulated annealing(SA) and non-dominated sorted genetic algorithm-II(NSGAII) is proposed to approximate the Pareto solution to this MOP problem. Simulation results show the effectiveness of the proposed algorithm in terms of satisfaction index, spectrum efficiency, occupied cache, and etc.