根生群优化算法

针对全局优化问题,基于一类支持向量数据描述（SVDD）和已有的根系生长算法提出一种新的智能优化算法——根生群优化算法,将根系划分为主根群体和侧根群体。基于SVDD描述主根群体的生长行为,将土壤中养分浓度最高的位置作为全局优化的目标,构建了根系生长模型;分析了RGSO的数学模型,从理论上证明了RGSO的收敛性。在实验中,与当前最先进的其他三个算法进行综合比较,并观察了不同参数对优化效果的影响。实验结果验证了RGSO的收敛性和有效性,表明RGSO是一种解决全局优化问题的有效算法。     

基于群体Parrondo博弈的根分枝建模方法

针对采用序列模型实现根分枝建模中不能实现根分枝可塑性的问题,提出一种基于群体Parrondo博弈的根分枝仿真方法,用于异质根系生长环境下根分枝可塑性建模。该方法首先通过构建根原基为个体的根原基群体,然后采用Parrondo博弈实现环境影响下根原基群体间的交互,最后依据根原基群体交互结果,实现根原基个体中生长素更新,从而对根分枝过程实现建模。在四种不同的根系生长环境中实现了对根原基发展成根分枝概率的预测。通过与RootMap等其他根分枝建模方法相比的仿真结果表明,所提方法能够依据根系生长环境中资源空间和时间变化,实现对根原基发展为根分枝过程的建模,从而为根系建模与仿真研究提供模型支撑和分析手段。     

微根窗图像中根系的自动分割方法

植物根系的生长状况可以反映该地区的气候以及土壤特性,根系的发达与否也能够反应整株植物的健康情况。研究根系的生态参数是研究根系生长状况的基础,目前对于微根窗图像的处理都是通过人工手动方式实现的,费时费力。提出了一种微根窗图像根系的自动分割方法,使用基于脊线检测的方法获取微根窗图像中根系的中心线,计算中心线上所有像素点的方向,采用改进的定向局部对比度方法检测出中心线两侧的根系像素得到分割后的图像。实验结果表明,与传统分割方法相比,方法具有良好的分割效果。     

外源乙烯对黄瓜幼苗根系生长及内源多胺含量的影响

精确控制营养液溶氧浓度和外源乙烯浓度,研究了黄瓜幼苗根系在低氧水培时的生长情况以及PAs含量的变化。结果表明,外源乙烯对根际低氧逆境下黄瓜幼苗根系的生长有抑制作用,虽然根径增粗,含水量增加,但根系的总根数、总表面积、总体积、干重和鲜重都有所减少,随着乙烯浓度的增加,抑制作用增强;外源乙烯对根际低氧逆境下黄瓜幼苗根系PAs含量有着明显的拮抗作用,外源乙烯使根际低氧逆境下黄瓜幼苗根系的伤害症状加重。     

根系分泌物的化感作用及其对土壤微生物的影响

总结了根分泌化感物质的种类及其化感效应,分析了生物和非生物因素与根系分泌化感物质之间的关系。此外,根系分泌化感物质的研究手段对于所取得的研究结果至关重要,其中根分泌物收集系统是收集根分泌化感物质的常用而可行的方法之一。对于根分泌化感物质的分离鉴定技术有多种,可根据需要选择适宜的分离方法。还列举了一些作物根分泌的化感物质对根际微生物产生的影响,阐明了根分泌的化感物质所起的重要化感作用。根分泌物化感作用的研究已成为土壤生态学领域的热点与前沿课题,自然条件下原位收集鉴定植物根系分泌物中的化感物质等诸多问题是该领域今后的研究重点。     

植物生长环境与根系分泌物的关系

植物所处环境条件会影响植物根系分泌物的数量和组成,同时,植物根系分泌物也会影响根际环境条件。本文综述了植物种类(品种、生长发育阶段)和光温水、养分胁迫、重金属胁迫、微生物活动等环境条件对植物根系分泌物的影响;植物根系分泌物对根际微区土壤结构性、pH、CEC、养分活化和微生物区系等方面的影响,并提出了今后植物根系分泌物与环境条件研究中在研究方法、不同环境条件等方面值得关注的问题。     

大豆连作及其根茬腐解物对大豆根系分泌物中异黄酮类物质-黄豆甙元(daidzein)的影响

本试验通过收集正茬、重茬7年、重茬7年并经MB-生物制剂调控和加入根茬腐解物(20g kg-1土)的正茬、重茬、调控的大豆根系分泌物,采用高效液相色谱检测方法,对根系分泌物中的大豆异黄酮—黄豆甙元(daidze in)进行了检测。结果在正茬根系分泌物中没有检测到该物质,在重茬、调控根系分泌物中检测到了该物质,其检测出的量为重茬>调控。在加入根茬腐解物的各处理的根系分泌物中均检测到了大豆异黄酮,其检测出的量为重茬>正茬>调控。加入根茬腐解物的处理与不加根茬腐解物的相应的处理相比,在其根系分泌物中检测到的大豆异黄酮的量均有显著提高。     

氮素水平对粳稻根系形态及其活力的影响

以秋光和辽粳5号为试材，采用盆栽方法，研究了不同供氮水平对根系的影响。研究结果表明：生育前期各处理的水稻根数、最长根长、根体积、根干重及其根系活力差异不显；在拔节期，高氮素水平有利于促进根系生长，提高根系活力；抽穗期是水稻一生中根系生长和活力变化的转折点，抽穗期后根系各性状呈现大幅度下降趋势，但高氮水平下根系各性状降幅较小，这说明适当提高氮素供应水平有利于维持后期根系活力，延缓根系衰老。     

不同土壤层次供应水分和养分对玉米幼苗生长和吸收养分的影响

本试验模拟滴灌方法 ,在不同土壤层次进行灌水和施用氮磷养分的盆栽玉米试验 ,旨在探讨在不同土壤层次供应水分和养分对夏玉米幼苗生长、根系空间构型及玉米对养分吸收的影响。试验表明 :在土壤深层进行滴灌可以有效降低土面蒸发 ,提高水分和养分的利用效率 ,从而显著提高玉米幼苗的生长量 ;在不同层次施肥灌水 ,对玉米根系在土壤中的空间构型影响很大 ,进行深层滴灌可以极大促进根系在较深土壤中的发育 ;在不同土层施肥灌水对玉米幼苗吸收N、P、K三种养分的能力也有影响 ,深层施肥灌水提高了玉米对 3种养分的吸收量     

镉胁迫下小麦根系的生理生态变化

本文通过水培和砂培两种方法 ,研究了镉胁迫下小麦 (TriticumaestivmL .)根系的生理生态变化。通过研究镉对小麦根系生长发育状况 ,根系活力 ,根系对矿质元素的吸收 ,探讨镉胁迫下植物根系的生理生态效应。研究结果表明 :镉影响根系的长度、生物量、体积和根系活力。Cd2 在低浓度 (处理浓度低于 5mg/L)作用下 ,随处理浓度的升高 ,刺激小麦根系的长度、生物量、体积相应地升高 ;当处理浓度高于相应浓度时 ,根长度、生物量、体积相应随浓度升高而降低。镉胁迫下根系活力受到抑制。水培和砂培中 ,镉对根系的影响趋势一致 ,但是影响幅度有差异。砂培好于水培。镉影响小麦根对矿质元素的吸收 ,Ca、Cu、Fe、K、Mg、Mn、Na、Zn吸收情况不太一致。Ca、Cu、Fe、Mg、Mn、Na的吸收量随Cd2 浓度升高而增加 ,K、Zn的吸收量随Cd2 浓度升高而减少     

空间微重力下植物栽培水分养分控制研究

通过"天宫2号"空间实验室搭载的空间植物栽培装置,研究了微重力下植物栽培水分养分控制技术.针对空间微重力特点,设计了植物栽培水分养分传导结构,并验证了其可行性和合理性.生菜在空间实验室微重力环境下的顺利萌发、生长发育,证明了水分养分传导结构的合理性和可行性,为后续开展更大规模的空间植物栽培奠定了基础.通过实验,了解了空间微重力下水分养分的传导和分布规律,为研究空间植物栽培水分养分供应方法和测试方法提供了依据.     

新型包膜肥在早熟禾上的试验研究

通过自制长效包膜肥与常规施肥在草地早熟禾盆栽上的试验研究表明,在同一施肥量的情况下作基肥一次施入,长效包膜肥在整个试验期间可以满足早熟禾整个试验期对养分的需求,但常规施肥则出现了缺肥症状。包膜肥加工过程及加入缓释剂对N素养分和P、K养分的释放规律有所不同:缓释剂的加入有利于提高N肥效但却加速了肥料中P、K的释放;包膜却对N、P、K均有延缓释放的作用。     

Role of additive manufacturing in medical application COVID-19 scenario: India case study

This paper reviews how the Additive Manufacturing (AM) industry played a key role in stopping the spread of the Coronavirus by providing customized parts on-demand quickly and locally, reducing waste and eliminating the need for an extensive manufacturer. The AM technology uses digital files for the production of crucial medical parts, which has been proven essential during the COVID-19 crisis. Going ahead, the 3D printable clinical model resources described here will probably be extended in various centralized model storehouses with new inventive open-source models. Government agencies, individuals, corporations and universities are working together to quickly development of various 3D-printed products especially when established supply chains are under distress, and supply cannot keep up with demand.     

A dynamic marketing network with monopsonistic acquisition and perfectly competitive disposition

The marketing networks of third-world countries are large systems having many markets and many agents. The dynamic behavior of one form is modeled in this paper by examining the actions of the traders. Recursive analysis yields time series in all prices and commodity flows. The network has a unique equilibrium state. Under certain conditions on supply and demand elasticities, that equilibrium state is locally asymptotically stable.     

Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.     

Orderly algorithms for graph generation

The existence of orderly analogues of graph generators proposed by Heap and Farrell is established. The modifications to these algorithms supply practical methods enabling one to generate exhaustive lists of graphs and locally restricted graphs; moreover, the difficulty involved in ensuring that no duplications occur in the list is greatly reduced.     

Homoclinic and Heteroclinic Bifurcations in an Overlapping Generations Model with Credit Market Imperfection

We analyze a nonlinear OLG model with credit market imperfection and endogenous labor supply. When the investors’ protection is perfect, the model reduces to the standard one sector growth model proposed by Reichlin (JET 40:89–102, 1986), while the model reduces to the one studied by Matsuyama (Econometrica 72:853–884, 2004) when the agents’ labor supply is exogenous. Our goal is to highlight that the local analysis of the perfect foresight equilibrium may lead to misleading conclusions because the local analysis neglects the occurrence of different global bifurcation scenarios. In particular, the existence of a heteroclinic connection or the occurrence of a homoclinic bifurcation may be associated with global indeterminacy even when all steady states are locally determinate.     

Self-regulating supply–demand systems

Supply–demand systems in Smart City sectors such as energy, transportation, telecommunication, are subject of unprecedented technological transformations by the Internet of Things. Usually, supply–demand systems involve actors that produce and consume resources, e.g. energy, and they are regulated such that supply meets demand, or demand meets available supply. Mismatches of supply and demand may increase operational costs, can cause catastrophic damage in infrastructure, for instance power blackouts, and may even lead to social unrest and security threats. Long-term, operationally offline and top-down regulatory decision-making by governmental officers, policy makers or system operators may turn out to be ineffective for matching supply–demand under new dynamics and opportunities that Internet of Things technologies bring to supply–demand systems, for instance, interactive cyber–physical systems and software agents running locally in physical assets to monitor and apply automated control actions in real-time. e.g. power flow redistributions by smart transformers to improve the Smart Grid reliability. Existing work on online regulatory mechanisms of matching supply–demand either focuses on game-theoretic solutions with assumptions that cannot be easily met in real-world systems or assume centralized management entities and local access to global information. This paper contributes a generic decentralized self-regulatory framework, which, in contrast to related work, is shaped around standardized control system concepts and Internet of Things technologies for an easier adoption and applicability. The framework involves a decentralized combinatorial optimization mechanism that matches supply–demand under different regulatory scenarios. An evaluation methodology, integrated within this framework, is introduced that allows the systematic assessment of optimality and system constraints, resulting in more informative and meaningful comparisons of self-regulatory settings. Evidence using real-world datasets of energy supply–demand systems confirms the effectiveness and applicability of the self-regulatory framework. It is shown that a higher informational diversity in the options, from which agents make local selections, results in a higher system-wide performance. Several strategies with which agents make selections come along with measurable performance trade-offs creating a vast potential for online adjustments incentivized by utilities, system operators and policy makers.     

Modelling Australian land use competition and ecosystem services with food price feedbacks at high spatial resolution

In a globalised world, land use change outlooks are influenced by both locally heterogeneous land attributes and world markets. We demonstrate the importance of high resolution land heterogeneity representation in understanding local impacts of future global scenarios with carbon markets and land competition influencing food prices. A methodologically unique Australian continental model is presented with bottom-up parcel scale granularity in land use change, food, carbon, water, and biodiversity ecosystem service supply determination, and partial equilibrium food price impacts of land competition. We show that food price feedbacks produce modest aggregate national land use and ecosystem service supply changes. However, high resolution results show amplified land use change and ecosystem service impact in some places and muted impacts in other areas relative to national averages. We conclude that fine granularity modelling of geographic diversity produces local land use change and ecosystem service impact insights not discernible with other approaches.     

基于自适应近邻参数的局部线性嵌入

局部线性嵌入算法是一种有效的非线性降维方法。文中提出一种自适应的局部线性嵌入方法。该方法通过分析数据集中任意样本所在局部区域的线性重构误差,确定该局部区域的近似线性块,然后根据位于此局部线性块上的样本来选择局部线性嵌入的近邻参数。实验结果表明,在不同的数据集上,采用多个评价标准,自适应的局部线性嵌入方法相比普通的局部线性嵌入方法,取得更好的结果。