Unveiling the adaptation strategies of woody plants in remnant forest patches to spatiotemporal urban expansion through leaf trait networks

Background:With the expansion of urban areas,the remnants of forested areas play a crucial role in preserving biodiversity in urban environments.This study aimed to explore the impact of spatiotemporal urban expansion on the networks of leaf traits in woody plants within remnant forest patches,thereby enhancing our understanding of plant adaptive strategies and contributing to the conservation of urban biodiversity.Methods:Our study examined woody plants within 120 sample plots across 15 remnant forest patches in Guiyang,China.We constructed leaf trait networks (LTNs) based on 26 anatomical,structural,and compositional leaf traits and assessed the effects of the spatiotemporal dynamics of urban expansion on these LTNs.Results and conclusions:Our results indicate that shrubs within these patches have greater average path lengths and diameters than trees.With increasing urban expansion intensity,we observed a rise in the edge density of the LTN-shrubs.Additionally,modularity within the networks of shrubs decreased as road density and urban expansion intensity increased,and increases in the average path length and average clustering coefficient for shrubs were observed with a rise in the composite terrain complexity index.Notably,patches subjected to‘leapfrog’expansion exhibited greater average patch length and diameter than those experiencing edge growth.Stomatal traits were found to have high degree centrality within these networks,signifying their substantial contribution to multiple functions.In urban remnant forests,shrubs bolster their resilience to variable environmental pressures by augmenting the complexity of their leaf trait networks.

A time-space network-based model for transportation service optimization of China Railway Express

This paper describes the development and optimization plans for the China Railway Express(CR Express).As a new type of international land transport organization,CR Express has emerged with the continuous expansion of China toward European investment and trade,and in particular,has expanded with the continuous progress of the One Belt and One Road(OBOR)initiative.In addition to improving the service quality of CR Express,the operating costs must be reduced for developing“smart railways”that serve“smart cities”.We propose a dualobjective-based function mathematical optimization model;the satisfaction of the cargo owner is considered,and the timeliness,transportation capacity,and goods category constraints of CR Express transportation are designed.Moreover,we present the normalized equivalent method of the two-objective function of the model.Finally,a case study is conducted against the background of certain trains in the western corridor of CR Express to validate the effectiveness of the model and research methods proposed in this study.

Dynamic Cell Range Expansion-based Interference Coordination Scheme in Next Generation Wireless Networks

Deploying Picocell Base Station(PBS) throughout a Macrocell is a promising solution for capacity improvement in the next generation wireless networks.However,the strong received power from Macrocell Base Station(MBS) makes the areas of Picocell narrow and limits the gain of cell splitting.In this paper,we firstly propose a Dynamic Cell Range Expansion(DCRE) strategy.By expanding the coverage of the cell,we aim to balance the network load between MBS and PBS.Then,we present a cooperative Resource block and Power Allocation Scheme(coRPAS)based on DCRE.The objective of coRPAS is to decrease interference caused by MBS and Macrocell User Equipments,by which we can expand regions of Picocell User Equipments.Simulation results demonstrate the superiority of our method through comparing with other existing methods.

On-Line Prediction of a Fixed-Bed Reactor Using K-L Expansion and Neural Networks

An on-line prediction scheme combining the Karhunen-Love expansion and a recurrent neural network for a wall-cooled fixed-bed reactor is presented.Benzene oxidation in a pilotscale,single tube fixed-bed reactor is chosen as a working system and a pseudo-homogeneous twodimensional model is used to generate simulation data to investigate the prediction scheme presentedunder randomly changing operating conditions.The scheme consisting of the K-L expansion andneural network performs satisfactorily for on-line prediction of reaction yield and bed temperatures.

Simultaneous synthesis of sub and above-ambient heat exchanger networks including expansion process based on an enhanced superstructure model

Synthesis of heat exchanger networks including expansion process is a complex task due to the involvement of both heat and work.A stream that expands through expanders can produce work and cold load,while expansion through valves barely affects heat integration.In addition,expansion through expanders at higher temperature produces more work,but consumes more hot utility.Therefore,there is a need to weigh work production and heat consumption.To this end,an enhanced stage-wise superstructure is proposed that involves synchronous optimization of expander/valve placement and heat integration for each pressure-change sub-stream in stages.A mixed-integer nonlinear programming(MINLP)model is established for synthesizing sub and aboveambient heat exchanger networks with multi-stream expansion,which explicitly considers the optimized selection of end-heaters and end-coolers to adjust temperature requirement.Our proposed method can commendably achieve the optimal selection of expanders and valves in a bid for minimizing exergy consumption and total annual cost.Four example studies are conducted with two distinct objective function(minimization of exergy consumption and total annual cost,respectively)to illustrate the feasibility and efficacy of the proposed method.

Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining,Natural language processing,Image processing,and Information retrieval etc.Word embedding has been applied by many researchers for Information retrieval tasks.In this paper word embedding-based skip-gram model has been developed for the query expansion task.Vocabulary terms are obtained from the top“k”initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query.The performance of the model based on mean average precision is 0.3176.The proposed model compares with other existing models.An improvement of 6.61%,6.93%,and 9.07%on MAP value is observed compare to the Original query,BM25 model,and query expansion with the Chi-Square model respectively.The proposed model also retrieves 84,25,and 81 additional relevant documents compare to the original query,query expansion with Chi-Square model,and BM25 model respectively and thus improves the recall value also.The per query analysis reveals that the proposed model performs well in 30,36,and 30 queries compare to the original query,query expansion with Chi-square model,and BM25 model respectively.

A traffic model of optical networks based on time-space complexity and traffic grooming

This paper researched the traffic of optical networks in time-space complexity,proposed a novel traf-fic model for complex optical networks based on traffic grooming,designed a traffic generator GTS(gener-ator based on time and space)with 'centralized+distributed' idea,and then made a simulation in Clanguage.Experiments results show that GTS can produce the virtual network topology which can changedynamically with the characteristic of scaling-free network.GTS can also groom the different traffic andtrigger them under real-time or scheduling mechanisms,generating different optical connections.Thistraffic model is convenient for the simulation of optical networks considering the traffic complexity.

Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

ZTE Corporation Selected for Telkom Indonesia's CDMA Network Expansion

August 30,2007,Shenzhen,China-ZTE Corporation ("ZTE"),a leading global provider of telecommunications equipment and network solutions,announced that it has recently signed an agreement with Telkom Indonesia ("Telkom"),the largest InfoCom company and full-service network provider in Indonesia,to help expand the CDMA2000 network capacity for national coverage. Under the contract,the network expansion will cover the four main cities of the eastern part of Sulawesi,Indonesia.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.

用于RFID网络规划的含Expansion关系的多目标云优化算法（英文）

给出了一个含有Expansion关系的多目标云优化算法,并用于求解RFID网络规划问题.该算法的思想源自于云模型,利用云模型理论估计优化过程的优势解区域和生成新解.在优化过程中,算法利用获得的信息建立优势解区域的云模型,并用反向云生成算法计算该云模型的3个数字特征.在此基础上,依据这3个数字特征,用正向云生成算法产生当前代的子代种群.为了引导寻优过程,Expansion关系被用于比较最好的一些个体,并对当前代种群和当前代子种群的并集用Expansion关系进行排序;之后,依据这个排序结果从中选择一些最好的个体组成下一代种群.该算法与其它一些算法就一组基准函数进行了模拟测试比较,并用于求解一个模拟RFID网络规划问题,代距准则(GD)和多样性(Δ)用于评价算法的性能,模拟结果显示该算法是有效的.

Minimum Cost of Capacity Expansion for Time-Limited Transportation Problem On-Demand

The minimum cost of capacity expansion for time-limited transportation problem on-demand (MCCETLTPD) is to find such a practicable capacity expansion transportation scheme satisfying the time-limited T along with all origins’ supply and all destinations’ demands as well as the expanding cost is minimum. Actually, MCCETLTPD is a balance transportation problem and a variant problem of minimum cost maximum flow problem. In this paper, by creating a mathematical model and constructing a network with lower and upper arc capacities, MCCETLTPD is transformed into searching feasible flow in the constructed network, and consequently, an algorithm MCCETLTPD-A is developed as MCCETLTPD’s solution method basing minimum cost maximum flow algorithm. Computational study validates that the MCCETLTPD-A algorithm is an efficient approach to solving the MCCETLTPD.

Chien-physics-informed neural networks for solving singularly perturbed boundary-layer problems

A physics-informed neural network(PINN)is a powerful tool for solving differential equations in solid and fluid mechanics.However,it suffers from singularly perturbed boundary-layer problems in which there exist sharp changes caused by a small perturbation parameter multiplying the highest-order derivatives.In this paper,we introduce Chien's composite expansion method into PINNs,and propose a novel architecture for the PINNs,namely,the Chien-PINN(C-PINN)method.This novel PINN method is validated by singularly perturbed differential equations,and successfully solves the wellknown thin plate bending problems.In particular,no cumbersome matching conditions are needed for the C-PINN method,compared with the previous studies based on matched asymptotic expansions.

Exact transverse solitary and periodic wave solutions in a coupled nonlinear inductor-capacitor network

Through two methods, we investigate the solitary and periodic wave solutions of the differential equation describing a nonlinear coupled two-dimensional discrete electrical lattice. The fixed points of our model equation are examined and the bifurcations of phase portraits of this equation for various values of the front wave velocity are presented. Using the sineGordon expansion method and classic integration, we obtain exact transverse solutions including breathers, bright solitons,and periodic solutions.

Adaptive Iterative Learning Control for Nonlinear Time-delay Systems with Periodic Disturbances Using FSE-neural Network

An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion （FSE） are combined into a new function approximator to model each suitable disturbed function in systems. The requirement of the traditional iterative learning control algorithm on the nonlinear functions （such as global Lipschitz condition） is relaxed. Furthermore, by using appropriate Lyapunov-Krasovskii functionals, all signs in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded, and the output of the system is proved to converge to the desired trajectory. A simulation example is provided to illustrate the effectiveness of the control scheme.

A Highly Compatible Circular-Shifting Network for Partially Parallel QC-LDPC Decoder

The conventional methodology for designing QC-LDPC decoders is applied for fixed configurations used in wireless communication standards, and the supported largest expansion factor Z (the parallelism of the layered decoding) is a fixed number. In this paper, we study the circular-shifting network for decoding LDPC codes with arbitrary Z factor, especially for decoding large Z (Z P) codes, where P is the decoder parallelism. By buffering the P-length slices from the memory, and assembling the shifted slices in a fixed routine, the P-parallelism shift network can process Z-parallelism circular-shifting tasks. The implementation results show that the proposed network for arbitrary sized data shifting consumes only one times of additional resource cost compared to the traditional solution for only maximum P sized data shifting, and achieves significant saving on area and routing complexity.

Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Heterogeneous Networks(HetNets)and cell densification represent promising solutions for the surging data traffic demand in wireless networks.In dense HetNets,user traffic is steered toward the Low-Power Node(LPN)when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency.However,because of the transmit power differences in different tiers of HetNets and irregular service demand,a load imbalance typically exists among different serving nodes.To offload more traffic to LPNs and coordinate the Inter-Cell Interference(ICI),Third-Generation Partnership Project(3GPP)has facilitated the development of the Cell Range Expansion(CRE),enhanced Inter-Cell Interference Coordination(eICIC)and Further enhanced ICIC(FeICIC).In this paper,we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe(LPS)approach.Our solution allows the separation of User Association(UA)functions at the User Equipment(UE)and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength(max-RSS)based UA scheme,where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system.The proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service regions.Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.

刑事抽样证明扩张适用的风险及防控

为应对网络犯罪海量证据运用与罪量证明难题,司法解释扩张了刑事抽样证明的适用范围。这种扩张适用主要表现为从法定的特殊犯罪拓展为具有海量证据的犯罪,从实物证据扩张至部分言词证据,从控诉方延伸至辩护方。然而,在相关规则尚不完备的情况下,易出现过度减轻控诉方的证明义务,不当加重辩护方的辩护负担,大幅增加审判方的裁判错误等风险。鉴此,应系统建构风险防控的技术与法律机制。在技术方面,抽样总体满足数量与同质性要求,抽样样本量采用数量和比例相结合的判定标准,抽样方法应当具有可靠性。在法律方面,建立健全刑事抽样证明适用必要性的司法审查机制,明确规定刑事抽样证明主体的抽样开示义务,严格要求刑事抽样证明必须达到排除合理怀疑标准。

一种基于蒙特卡洛方法的配电网概率可靠性快速计算方法

配电网的概率可靠性能够弥补传统可靠性指标的期望值仅从均值角度衡量系统可靠性的不足,但随着配电网规模扩大以及数据量的剧增,能够同时兼顾计算准确性与计算速度的概率可靠性计算方法是亟待研究的。为此,提出一种基于蒙特卡洛方法的配电网概率可靠性快速计算方法,利用改进三点估计法以及三阶多项式正态变换,在保留输入变量相关性的同时有效缩减输入样本点规模,并采用级数展开得到概率可靠性。首先采用改进三点估计法,在独立标准正态空间内选取样本点,再通过三阶多项式正态变换将其转换为原始变量空间的样本点;接着采用序贯蒙特卡洛方法,在考虑孤岛划分的情况下对样本点进行可靠性计算;最后通过Edgeworth级数展开,得到可靠性指标的概率分布。对改进的IEEE-RBTS Bus6的F4馈线进行算例分析,结果表明:该文所提方法与传统蒙特卡洛方法的配电网系统可靠性计算结果之间仅存在2.195%的最大偏差,而该文所提方法计算时间仅为传统蒙特卡洛方法的1.05%,证明该文所提方法在保证较高精度的同时可以显著提升计算速度。

数字化情境中的流通效率实现机制——基于匹配与扩张视角的案例研究

当前,快速发展的数字技术深刻影响了流通效率的实现。在数字化情境中探讨流通效率的微观实现机制,需要从流通的时空双重维度出发,将宏观层面的商品流通效率对应为企业在“匹配”和“扩张”的微观流通活动中的效率。采用追踪访谈的一手数据,基于韩都衣舍、酒仙网的案例,探讨了企业在数字化情境中的流通效率实现问题。研究发现:在“去中间化”路径中,数字化驱动了基于供应链整合的匹配效率实现和基于消费社群拓展的扩张效率实现,交叉网络效应与数据网络效应动态放大了这一作用;在“再中间化”路径中,数字化辅助了基于渠道纵向协作的匹配效率实现和基于店面连锁的扩张效率实现,用户网络效应与数据网络效应动态放大了这一作用。因此,承担流通活动的企业需结合自身对于流通渠道演化的路径选择,采取适配的数字化战略,以促进流通效率实现。