The Climate Impact of Large-Scale Distributors:Acting on the Supply Chain

This work aims to analyse the actions that companies working in large-scale distribution carry along their value chains to minimise impacts on climate change.Companies operating in this field are aware that it is less effective to act directly on the core processes and need to involve the upstream value chain in their carbon reduction strategy.These businesses,in fact,need to focus on the indirect GHG(Greenhouse Gases)emissions and depend on how suppliers manage their impacts.In this sector,virtuous companies collaborate with their suppliers to get involved in a common path of quantifying and cutting said impacts together.This aspect is particularly relevant in the case of large-scale retailers.However,the process is not immediate since the supply chain is usually very dense and diverse,for instance,adopting various approaches that do not always coincide.In any case,the key aspect is mapping these suppliers:one of the tools mostly used for this purpose is the survey,as a quick instrument able to reach hundreds of suppliers at the same time,receiving a fast and standardized response,which can easily be processed to form a comprehensive and harmonized mapping of the results as the first step for the subsequent implementation of mitigation strategies.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

Antenna selection based on large-scale fading for distributed MIMO systems

An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.

Challenges and Considerations in Developing and Architecting Large-Scale Distributed Systems

This paper investigates large-scale distributed system design. It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems. Moreover, the paper investigates the challenges of designing, developing, deploying, and maintaining such systems, in regard to the features presented. Finally, the paper discusses aspects of available solutions and current practices to challenges that large-scale distributed systems face.

Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket

In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution.Addressing at this issue,we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket.We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm.Greedy algorithm is applied to initialize the population,and then hill-climbing algorithm is used to optimize individuals in each generation after selection,crossover and mutation.Our approach is evaluated on the dataset of BBG Supermarket which is one of the top 10 supermarkets in China.Experimental results show that our method outperforms some other methods in the field.

Using Quantile Regression to Detect Relationships between Large-scale Predictors and Local Precipitation over Northern China

Quantile regression（QR） is proposed to examine the relationships between large-scale atmospheric variables and all parts of the distribution of daily precipitation amount at Beijing Station from 1960 to 2008. QR is also applied to evaluate the relationship between large-scale predictors and extreme precipitation（90th quantile） at 238 stations in northern China.Finally, QR is used to fit observed daily precipitation amounts for wet days at four sample stations. Results show that meridional wind and specific humidity at both 850 h Pa and 500 h Pa（V850, SH850, V500, and SH500） strongly affect all parts of the Beijing precipitation distribution during the wet season（April–September）. Meridional wind, zonal wind, and specific humidity at only 850 h Pa（V850, U850, SH850） are significantly related to the precipitation distribution in the dry season（October–March）. Impacts of these large-scale predictors on the daily precipitation amount with higher quantile become stronger, whereas their impact on light precipitation is negligible. In addition, SH850 has a strong relationship with wet-season extreme precipitation across the entire region, whereas the impacts of V850, V500, and SH500 are mainly in semi-arid and semi-humid areas. For the dry season, both SH850 and V850 are the major predictors of extreme precipitation in the entire region. Moreover, QR can satisfactorily simulate the daily precipitation amount at each station and for each season, if an optimum distribution family is selected. Therefore, QR is valuable for detecting the relationship between the large-scale predictors and the daily precipitation amount.

Decentralized robust stabilization of discrete-time fuzzy large-scale systems with parametric uncertainties: a LMI method

Decentralized robust stabilization problem of discrete-time fuzzy large-scale systems with parametric uncertainties is considered. This uncertain fuzzy large-scale system consists of N interconnected T-S fuzzy subsystems, and the parametric uncertainties are unknown but norm-bounded. Based on Lyapunov stability theory and decentralized control theory of large-scale system, the design schema of decentralized parallel distributed compensation （DPDC） fuzzy controllers to ensure the asymptotic stability of the whole fuzzy large-scale system is proposed. The existence conditions for these controllers take the forms of LMIs. Finally a numerical simulation example is given to show the utility of the method proposed.

面向低慢小无人机的SKDC-LDS技术

反无人机技术的发展很大程度上与无人机技术扩散带来的威胁有关,特别是在平时和战时环境中的低慢小廉价无人机。面对日益迫切的低慢小无人机威胁需求,全面分析威胁问题产生的根源和反制难点,系统总结国内外低慢小无人机探测和反制系统的产品和技术发展现状,提出架构统一、功能扩展、分层防御的SKDC-LDS(Soft-Kill Distributed Coordination Layer Defence System)系统解决方案,并分析定区侦察、定区干扰等急需突破的关键技术。

LARGE-SCALE VORTICAL STRUCTURES PRODUCED BY AN IMPINGING DENSITY JET IN SHALLOW CROSSFLOW

The large-scale vortical structures produced by an impinging density jet in shallow crossflow were numerically investigated in detail using RNG turbulence model. The scales, formation mechanism and evolution feature of the upstream wall vortex in relation to stagnation point and the Scarf vortex in near field were analyzed. The computed characteristic scales of the upstream vortex show distinguished three-dimensionality and vary with the velocity ratio and the water depth. The Scarf vortex in the near field plays an important role in the lateral concentration distributions of the impinging jet in crossflow. When the velocity ratio is relatively small, there exists a distinct lateral high concentration aggregation zone at the lateral edge between the bottom layer wall jet and the ambient crossflow, which is dominated by the Scarf vortex in the near field.

LD泵浦Nd∶GdVO_4晶体Cr^(4+)∶YAG被动调Q激光特性研究

考虑腔内光子数密度的空间高斯分布以及晶体热效应的影响,给出了LD泵浦Nd∶GdVO4晶体Cr4+∶YAG被动调Q1.06μm激光的耦合速率方程组.数值求解该方程组获得了输出激光的脉冲宽度、重复率、峰值功率以及单脉冲能量随泵浦功率的变化特性,其理论值与实验结果相符.

LD泵浦Nd∶YVO_4晶体GaAs被动调Q激光理论和实验研究

考虑LD泵浦光强和腔内振荡光强的空间高斯分布以及晶体热效应的影响,给出了$山东大学信息科学与工程学院!山东济南250100晶体GaAs被动调Q 1. 06μm激光的耦合速率方程组,数值求解该方程组获得了输出激光的平均输出功率、脉冲宽度、重复率随泵浦功率的变化特性,所得理论值与实验结果相符.

高功率LD阵列侧面泵浦激光晶体的增益分布研究

研究了LD Bar侧面半环形直接泵浦Nd∶YAG激光增益介质的泵浦光分布特点。建立了不同泵浦参数下棒状增益介质内的泵浦光能量分布数值模型,通过光线追迹方法,并利用Matlab软件进行了数值模拟计算,分析了几个泵浦参数对泵浦光分布的影响,总结了一般的影响规律,分析了产生差异的原因,提出了选取泵浦参数时需注意的问题。可为激光二极管环绕泵浦固体激光器的泵浦结构设计提供理论上的指导。

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

LD泵浦Nd:GdVO_4晶体Cr^(4+):YAG被动调Q激光脉宽控制

采用LD泵浦技术,实现了Nd:GdVO4、Cr4+:YAG被动调Q 1.06μm激光运转。通过改变泵浦光束在激光晶体内的分布以及Cr4+:YAG晶体在腔内的位置,实现了LD泵浦Nd:GdVO4晶体Cr4+:YAG被动调Q激光器的脉宽控制。根据由ABCD矩阵传输理论计算得出的腔内模式半径沿腔轴的变化关系,考虑激活介质热效应以及泵浦光强和腔内振荡光强的空间高斯分布,给出了LD泵浦Nd:GdVO4晶体Cr4+:YAG被动调Q激光的耦合速率方程组,数值求解该方程组,获得了脉冲宽度随饱和吸收体在激光腔内位置和泵浦光在激活介质内分布的变化关系,理论计算与实验结果相符。

LD端面泵浦薄片激光器的温度和热应力分布研究

为了分析薄片激光器的热效应,建立了LD端面泵浦薄片激光介质的数值模型。考虑到介质与空气的对流换热和介质材料的热力学参数随温度的变化,根据经典热传导方程和热弹性方程,运用有限单元法,得出了介质内温度和热应力的时空分布,分析了温度和热应力与泵浦功率、换热系数和时间的变化规律。模拟结果表明:热破坏主要为前表面光斑外侧的拉伸破裂;温度和应力的上升时间和热恢复时间随泵浦功率的变化不是很明显,随换热系数的增大而减小,但随着换热系数的增加,温度和应力的变化越来越小。

LD泵浦固体激光介质内增益分布与输出特性研究

通过改变二极管的泵浦方式,观察和研究不同泵浦方式对激光输出的影响。用MAT-LAB软件模拟增益介质内的增益分布以及平-平腔条件下的静态输出光斑,并分别与实验所得结果进行比对,来进一步分析研究不同二极管泵浦阵列条件下激光的输出特性。

LD侧泵浦Nd∶YAG板状介质温度特性的理论研究

利用有限差分法对LD侧面泵浦Nd∶YAG板状激光器介质的二维温度分布进行了数值计算。与用热像仪实际测得的结果比较 ,温度变化趋势吻合。根据计算结果重新设计激光晶体的冷却方案 ,改善了温度的分布 ,光束质量因子M2 从 6 .7减小到 3,提高了激光输出的光束质量。

Collaborative Planning of Distributed Wind Power Generation and Distribution Network with Large-scale Heat Pumps

With the advancement of clean heating projects and the integration of large-scale distributed heat pumps into rural distribution networks in northern China,power grid companies face tremendous pressure to invest in power grid upgrades,which bring opportunities for renewable power generation integration.The combination of heating by distributed renewable energy with the flexible operation of heat pumps is a feasible alternative for dealing with grid reinforcement challenges resulting from heating electrification.In this paper,a mathematical model of the collaborative planning of distributed wind power generation(DWPG)and distribution network with large-scale heat pumps is developed.In this model,the operational flexibility of the heat pump load is fully considered and the requirements of a comfortable indoor temperature are met.By applying this model,the goals of not only increasing the profit of DWPG but also reducing the cost of the power grid upgrade can be achieved.

Distribution and features of landslides in the Tianshui Basin, Northwest China

Landslides in Tianshui Basin, Gansu Province, Northwest China, severely affect the local population and the economy;therefore,understanding their evolution and kinematics is of great interest for landslide risk assessment and prevention. However, there is no unified classification standard for the types of loess landslides in Tianshui.In this study, we explored the landslide distribution and failure characteristics by means of field investigation,remotesensinginterpretation,geological mapping, drilling exploration and shearwave velocity tests, and established a database of Tianshui landslides. Our analysis shows that shear zones in mudstone usually develop in weak intercalated layers. Landslides occur mainly along the West Qinling faults on slopes with gradients of 10° to 25° and on southeast-and southwest-facing slopes.These landslides were classified into five types: loess landslides, loess–mudstone interface landslides, loess flow-slides, loess–mudstone plane landslides and loess–mudstone cutting landslides. We discussed the evolution and failure process of each landslide type and analyzed the formation mechanism and motion characteristics of large-scale landslides. The analysis results show that the landslides in the study area are characterized by a gentle slope, long runout and high risk. The relationship between the runout L and the vertical drop H of the large-scale landslides in the study area is L > 4 H. There are good correlations between the equivalent friction coefficient of largescale landslides and their maximum height, runout,area and volume. The sliding zone of large-scale landslides often develops in the bedrock contact zone or in a weak interlayer within mudstone. From microstructure analysis, undisturbed mudstone consists mainly of small aggregates with dispersed inter-aggregate pores, whereas sheared clay has a more homogeneous structure. Linear striations are well developed on shear surfaces, and the clay pores in those surfaces have a more uniform distribution than those in undisturbed clay.

Pattern control for large-scale spacecraft swarms in elliptic orbits via density fields

Space swarms,enabled by the miniaturization of spacecraft,have the potential capability to lower costs,increase efficiencies,and broaden the horizons of space missions.The formation control problem of large-scale spacecraft swarms flying around an elliptic orbit is considered.The objective is to drive the entire formation to produce a specified spatial pattern.The relative motion between agents becomes complicated as the number of agents increases.Hence,a density-based method is adopted,which concerns the density evolution of the entire swarm instead of the trajectories of individuals.The density-based method manipulates the density evolution with Partial Differential Equations(PDEs).This density-based control in this work has two aspects,global pattern control of the whole swarm and local collision-avoidance between nearby agents.The global behavior of the swarm is driven via designing velocity fields.For each spacecraft,the Q-guidance steering law is adopted to track the desired velocity with accelerations in a distributed manner.However,the final stable velocity field is required to be zero in the classical density-based approach,which appears as an obstacle from the viewpoint of astrodynamics since the periodic relative motion is always time-varying.To solve this issue,a novel transformation is constructed based on the periodic solutions of Tschauner-Hempel(TH)equations.The relative motion in Cartesian coordinates is then transformed into a new coordinate system,which permits zero-velocity in a stable configuration.The local behavior of the swarm,such as achieving collision avoidance,is achieved via a carefully-designed local density estimation algorithm.Numerical simulations are provided to demonstrate the performance of this approach.