River width and depth as key factors of diurnal activity energy expenditure allocation for wintering Spot-billed Ducks in the Xin'an River Basin

Rivers are important habitats for wintering waterbirds.However,they are easily influenced by natural and human activities.An important approach for waterbirds to adapt to habitats is adjusting the activity time and energy expenditure allocation of diurnal behavior.The compensatory foraging hypothesis predicts that increased energy expenditure leads to longer foraging time,which in turn increases food intake and helps maintain a constant energy balance.However,it is unclear whether human-disturbed habitats result in increased energy expenditure related to safety or foraging.In this study,the scan sample method was used to observe the diurnal behavior of the wintering Spot-billed Duck(Anas poecilorhyncha) in two rivers in the Xin’an River Basin from October 2021 to March 2022.The allocation of time and energy expenditure for activity in both normal and disturbed environments was calculated.The results showed that foraging accounted for the highest percentage of time and energy expenditure.Additionally,foraging decreased in the disturbed environment than that in the normal environment.Resting behavior showed the opposite trend,while other behaviors were similar in both environments.The total diurnal energy expenditure of ducks in the disturbed environment was greater than that in the normal environment,with decreased foraging and resting time percentage and increased behaviors related to immediate safety(swimming and alert) and comfort.These results oppose the compensatory foraging hypothesis in favor of increased security.The optimal diurnal energy expenditure model included river width and water depth,which had a positive relationship;an increase in either of these two factors resulted in an increase in energy expenditure.This study provides a better understanding of energy allocation strategies underlying the superficial time allocation of wintering waterbirds according to environmental conditions.Exploring these changes can help understand the maximum fitness of wintering waterbirds in response to nature and human influences.

Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

The rapid advancement of Internet of Things(IoT)technology has brought convenience to people’s lives;however further development of IoT faces serious challenges,such as limited energy and shortage of network spectrum resources.To address the above challenges,this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation(SIATS)algorithm.First,an adaptive time slot consisting of periods for sensing,information transmission,and energy harvesting is designed to ensure that the minimum energy harvesting requirement ismet while the maximumuplink and downlink throughputs are obtained.Second,the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods,and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized.Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4%compared with that of the fixed time slot allocation(FTS)algorithm.In the case of a large noise variance,the SIATS algorithm has good noise immunity,and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7%compared with that obtained using the FTS algorithm.

A Machine-Learning Based Time Constrained Resource Allocation Scheme for Vehicular Fog Computing

Through integrating advanced communication and data processing technologies into smart vehicles and roadside infrastructures,the Intelligent Transportation System(ITS)has evolved as a promising paradigm for improving safety,efficiency of the transportation system.However,the strict delay requirement of the safety-related applications is still a great challenge for the ITS,especially in dense traffic environment.In this paper,we introduce the metric called Perception-Reaction Time(PRT),which reflects the time consumption of safety-related applications and is closely related to road efficiency and security.With the integration of the incorporating information-centric networking technology and the fog virtualization approach,we propose a novel fog resource scheduling mechanism to minimize the PRT.Furthermore,we adopt a deep reinforcement learning approach to design an on-line optimal resource allocation scheme.Numerical results demonstrate that our proposed schemes is able to reduce about 70%of the RPT compared with the traditional approach.

An RVT-based power allocation method for dynamic LEO-MEO links

Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite communication.A game theory power allocation method based on remaining visible time(RVT)of LEO-MEO satellites is proposed.Firstly,one LEO-MEO satellite network is classified as a cluster in which the RVT of LEO satellites is modeled.Secondly,the cost function of RVT concerning the character of orbit and throughput in each LEO satellite is mainly designed,which gives greater punishment of utility value to LEO satellites with less RVT and is an essential part of the reasonable utility function applied in diverse motion scenes.Meanwhile,the existence of Nash equilibrium for the proposed utility function in game theory area is proved.Thirdly,an off-cluster scheme for LEO satellites through the proposed threshold is raised to ensure the overall utility value of the whole LEO satellites in cluster.Finally,the performance improvement of the proposed algorithm to the baseline algorithm is verified through simulations in different scenarios.

Parametric Transformation of Timed Weighted Marked Graphs:Applications in Optimal Resource Allocation

Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing systems.Due to the existence of multiplicities(i.e.,weights)on edges,the performance analysis and resource optimization of such graphs represent a challenging problem.In this paper,we develop an approach to transform a timed weighted marked graph whose initial marking is not given,into an equivalent parametric timed marked graph where the edges have unitary weights.In order to explore an optimal resource allocation policy for a system,an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent net.Finally,we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic approach.Simulation analysis shows that the developed approach is superior to the heuristic approach.

Efficient coding schemes with power allocation using space-time-frequency spreading

An efficient spaee-time-frequency （STF） coding strategy for multi-input multi-output orthogonal frequency division multiplexing （MIMO-OFDM） systems is presented for high bit rate data transmission over frequency selective fading channels. The proposed scheme is a new approach to space-time-frequency coded OFDM （ODFDM） that combines OFDM with space-time coding, linear precoding and adaptive power allocation to provide higher quality of transmission in terms of the bit error rate performance and power efficiency. In addition to exploiting the maximux diversity gain in frequency, time and space, the proposed scheme enjoys high coding advantages and low-complexity decoding. The significant performance improvement of our design is confirned by corroborating numerical simulations.

Design of cost allocation rule for joint replenishment with controllable lead time

To encourage retailers to form cooperative alliances to jointly replenish inventory,considering that the supplier provides a flexible lead time and quantity discount to retailers,a model of average total cost per unit time of periodic joint replenishment is constructed,and an approximate algorithm,which can satisfy the requirement of any given precision,is given.The cost allocation rule in the core of the joint replenishment game is designed based on the cooperative game theory.The numerical experiment results show that the proposed algorithm can quickly solve the joint replenishment problem when the item number is not greater than 640.The retailer's cost saving rate is always greater than 0,and it increases with the increase in quantity discount and fixed cost after adopting the given cost allocation rule.With the increase in the safety stock level,the retailer's cost saving rate increases first and then decreases;and the retailer's cost saving rate increases with the increase in the size of the alliance,but it decreases as the number of product category increases.The proposed cost allocation rule can reduce the retailer's cost up to 20%,which is conducive to forming a cooperative coalition.

Runtime Power Allocation Based on Multi-GPU Utilization in GAMESS

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize performance under a given power budget by distributing the available power according to the relative GPU utilization. Time series forecasting methods were used to develop workload prediction models that provide accurate prediction of GPU utilization during application execution. Experiments were performed on a multi-GPU computing platform DGX-1 equipped with eight NVIDIA V100 GPUs used for quantum chemistry calculations in the GAMESS package. For a limited power budget, the proposed strategy may deliver as much as hundred times better GAMESS performance than that obtained when the power is distributed equally among all the GPUs.

Performance of Cross-Layer Design with Power Allocation in Space-Time Coded MIMO Systems

A cross-layer design(CLD)scheme with combination of power allocation,adaptive modulation(AM)and automatic repeat request(ARQ)is presented for space-time coded MIMO system under imperfect feedback,and the corresponding system performance is investigated in a Rayleigh fading channel.Based on imperfect feedback information,a suboptimal power allocation(PA)scheme is derived to maximize the average spectral efficiency(SE)of the system.The scheme is based on a so-called compressed SNR criterion,and has a closed-form expression for positive power allocation,thus being computationally efficient.Moreover,it can improve SE of the presented CLD.Besides,due to better approximation,it obtains the performance close to the existing optimal approach which requires numerical search.Simulation results show that the proposed CLD with PA can achieve higher SE than the conventional CLD with equal power allocation scheme,and has almost the same performance as CLD with optimal PA.However,it has lower calculation complexity.

An Iterative Power Allocation Algorithm for Group-wise Space-Time Block Coding Systems

An iterative transmit power allocation (PA) algorithm was proposed for group-wise space-time block coding (G-STBC) systems with group-wise successive interference cancellation (GSIC) receivers. Group-wise interference suppression (GIS) filters are employed to separate each group's transmit signals from other interferences and noise. While the total power on all transmit symbols is constrained, all transmit PA coefficients are updated jointly according to the channel information at each iteration. Through PA, each detection symbol has the same post-detection signal to interference-and-noise ratio (SINR). The simulation results verify that the proposed PA algorithm converges at the equilibrium quickly after few iterations, and it achieves much lower bit error rates than the previous single symbol SIC PA and the fixed ratio PA algorithms for G-STBC systems with GSIC receivers .

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

The high renewable penetrated power system has severe frequency regulation problems.Distributed resources can provide frequency regulation services but are limited by com-munication time delay.This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service.Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation.We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance.Besides,we study communication resources allocation solution in high renewable energy penetrated power systems.We provide a case study based on the HRP-38 system.Results show communication time delay decreases distributed resources'ability to provide frequency regulation service.On the other hand,allocating more communication resources to distributed resources'communica-tion services improves their frequency regulation performance.For power systems with renewable energy penetration above 70%,required communications resources are about five times as many as 30%renewable energy penetrated power systems to keep frequency performance the same.Index Terms-Communication resources allocation,commun-ication time delay,distributed resource,frequency regulation,high renewable energy penetrated power system.

Rescue vehicle allocation problem based on optimal reliable path under uncertainty

Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research.Because of uncertain factors,such as the weather or OD(origin-destination)variations caused by traffic accidents,travel time is a random variable.In emergency situations,it is particularly necessary to determine the optimal reliable route of rescue vehicles from the perspective of uncertainty.This paper first proposes an optimal reliable path finding(ORPF)model for rescue vehicles,which considers the uncertainties of travel time,and link correlations.On this basis,it investigates how to optimize rescue vehicle allocation to minimize rescue time,taking into account travel time reliability under uncertain conditions.Because of the non-additive property of the objective function,this paper adopts a heuristic algorithm based on the K-shortest path algorithm,and inequality techniques to tackle the proposed modified integer programming model.Finally,the numerical experiments are presented to verify the accuracy and effectiveness of the proposed model and algorithm.The results show that ignoring travel time reliability may lead to an over-or under-estimation of the effective travel time of rescue vehicles on a particular path,and thereby an incorrect allocation scheme.

Dynamic Resource Allocation in TDD-Based Heterogeneous Cloud Radio Access Networks

To fulfill the explosive growth of network capacity, fifth generation(5G) standard has captured the attention and imagination of researchers and engineers around the world. In particular, heterogeneous cloud radio access network(H-CRAN), as a promising network paradigm in 5G system, is a hot research topic in recent years. However, the densely deployment of RRHs in H-CRAN leads to downlink/uplink traffic asymmetry and severe inter-cell interference which could seriously impair the network throughput and resource utilization. To simultaneously solve these two problems, we proposed a dynamic resource allocation(DRA) scheme for H-CRAN in TDD mode. Firstly, we design a clustering algorithm to group the RRHs into different sets. Secondly, we adopt coordinated multipoint technology to eliminate the interference in each set. Finally, we formulate the joint frame structure, power and subcarrier selection problem as a mixed strategy noncooperative game. The simulation results are presented to validate the effectiveness of our proposed algorithm by compared with the existing work.

Evaluation of the individual allocation scheme and its impacts in a dynamic global vegetation model

The strategies of plant growth play an important role not only in ecosystem structure,but also in global carbon and water cycles.In this work,the individual carbon allocation scheme of tree PFTs and its impacts were evaluated in China with Institute of Atmospheric Physics-Dynamic Global Vegetation Model,version 1.0（IAP-DGVM1.0）as a test-bed.The results showed that,as individual growth,the current scheme tended to allocate an increasing proportion of annual net primary productivity（NPP）to sapwood and decreasing proportions to leaf and root accordingly,which led to underestimated individual leaf biomass and overestimated individual stem biomass.Such biases resulted in an overestimation of total ecosystem biomass and recovery time of mature forests,and an underestimation of ecosystem NPP and tree leaf area index in China.

Optimal Resource Allocation and Quality of Service Prediction in Cloud

In the present scenario,cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients.Resources are in self-administration;consequently,clients can adjust their usage according to their requirements.Resource usage is estimated and clients can pay according to their utilization.In literature,the existing method describes the usage of various hardware assets.Quality of Service(QoS)needs to be considered for ascertaining the schedule and the access of resources.Adhering with the security arrangement,any additional code is forbidden to ensure the usage of resources complying with QoS.Thus,all monitoring must be done from the hypervisor.To overcome the issues,Robust Resource Allocation and Utilization(RRAU)approach is developed for optimizing the management of its cloud resources.The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS.The asset assignment calculation is heuristic,which is based on experimental evaluations,RRAU approach with J48 prediction model reduces Job Completion Time(JCT)by 4.75 s,Make Span(MS)6.25,and Monetary Cost(MC)4.25 for 15,25,35 and 45 resources are compared to the conventional methodologies in cloud environment.

Timing Error Aware Register Allocation in TS

Timing speculative(TS)architecture is promising for improving the energy efficiency of microprocessors.Error recovery units,designed for tolerating occasional timing errors,have been used to support a wider range of voltage scaling,therefore to achieve a better energy efficiency.More specifically,the timing error rate,influenced mainly by data forwarding,is the bottleneck for voltage down-scaling in TS processors.In this paper,a new Timing Error Aware Register Allocation method is proposed.First,we designed the Dependency aware Interference Graph(DIG)construction to get the information of Read after Write(RAW)in programs.To build the construction,we get the disassemble code as input and suppose that there are unlimited registers,the same way as so-called virtual registers in many compilers.Then we change the disassemble codes to the SSA form for each basic block to make sure the registers are defined only once.Based on the DIG construction,registers were real-located to eliminate the timing error,by loosening the RAW dependencies.We con-struct the DIG for each function of the program and sort the edge of DIG by an increasing weight order.Since a smaller weighted-edge value means that its owner nodes have more frequent access in instruction flows,we expect it in different registers with no read-write dependency.At the same time,we make sure that there are no additional new spill codes emerging in our algorithm to minimize the rate of spill code.A high rate of spill code will not only decrease the performance of the system but also increase the unexpected read-write dependency.Next,we reallocate the reg-isters by weight order in turn to loosen the RAW dependencies.Furthermore,we use the NOP operation to pad the instructions with a minimal distance value of 2.Experiment results showed that the average distance of RAW dependencies was increased by over 20%.

Optimal Power Allocation Strategy for TBLAST Based 4G Systems

There is a big demand for increasing number of subscribers in the fourth generation mobile communication systems. However, the system performance is limited by multi-path propagations and lack of efficient power allocation algorithms in conventional wireless communication systems. Optimal resource allocation and interference cancellation issues are critical for the improvement of system performance such as throughput and transmission reliability. In this paper, a turbo coded bell lab space time system (TBLAST) with optimal power allocation techniques based on eigen mode, Newton and convex optimization method and carrier-interference-and-noise ratio (CINR) are proposed to improve link reliability and to increase throughput with reasonable computational complexity. The proposed scheme is evaluated by Monte-Carlo simulations and is shown to outperform the conventional power allocation scheme.

Utility-based and fairness-aware radio resource allocation in OFDMA cellular relay networks with traffic prioritization

The combination of orthogonal frequency division multiple access(OFDMA) with relaying techniques provides plentiful opportunities for high-performance and cost-effective networks.It requires intelligent radio resource management schemes to harness these opportunities.This paper investigates the utility-based resource allocation problem in a real-time and non-real-time traffics mixed OFDMA cellular relay network to exploit the potentiality of relay.In order to apply utility theory to obtain an efficient tradeoff between throughput and fairness as well as satisfy the delay requirements of real-time traffics,a joint routing and scheduling scheme is proposed to resolve the resource allocation problem.Additionally,a low-complexity iterative algorithm is introduced to realize the scheme.The numerical results indicate that besides meeting the delay requirements of real-time traffic,the scheme can achieve the tradeoff between throughput and fairness effectively.

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.

碳减排“时-空-效-益”统筹理论:I.时间统筹

在气候变化应对过程中,面临一种纵向的代际统筹,即当前人类应当或者愿意付出多大代价来避免未来可能发生的气候变化损失。对这一问题的回答关乎当代人与后代人的利益,需要权衡当前减缓气候变化的成本和未来气候变化带来的损失。然而,当前国际上对贴现率和跨期均衡路径的探讨暂未得出明确方案,亟需形成统筹长期与短期的碳减排理论,以指导相关减排路径设计。作为碳减排“时-空-效-益”统筹理论系列论文的组成部分,聚焦于碳减排的时间统筹,构建了跨期均衡路径,并设置了积极但不激进的贴现率,从而统筹当前减缓气候变化的成本和未来气候变化带来的损失,回答碳减排的跨期责任分担与成本分配等问题,应用于CEEP-BIT自主开发的中国气候变化综合评估模型(C3IAM)等气候环境治理与碳减排路径的研究中,为统筹长期减排与短期减排提供方法基础。