畜菜生态系统中CO2和沼肥对黄瓜,番茄病害的生态防治研究

对畜菜生态系统中ＣＯ２和沼肥对温室黄瓜和番茄病害的生态防治作用进行了系统的对比研究,结果表明：ＣＯ２气体施肥和沼肥灌施均能提高植物抗病性,减轻发病程度,推迟发病时期。ＣＯ２气体施肥对减轻气传叶部病害作用明显,而沼肥灌施可有效降低土传根部病害的发病率。畜菜互补生态系统结合其它综合防治措施可望生产出无公害蔬菜。     

微型计算机温室环境监控管理系统

微型计算机温室环境监控系统采用了传感器技术，测控技术以及微机技术实现温室的管理自动化和科学化，它由单片机和系统微机组成，片机把传感器采集的与生物有关的参量，例如，温度、湿度、光照以及ＣＯ２等转换的电压进行存储与预处理，并通过总线同系统微机相联。     

天津北辰种菜用上物联网点鼠标就能浇水施肥

通过电脑实时了解蔬菜大棚中的温度、湿度、虫害情况,点击鼠标就可以为大棚中的蔬菜通风、浇水、施肥。天津北辰区设施农业物联网系统经过一年的运行,使今年蔬菜产量提高了15%以上。     

PbCrO4-V2O5系陶瓷湿敏电阻材料初探

报导了在体控型湿敏材料ＰｂＣｒＯ４中引入Ｖ２Ｏ５的研究结果。实验表明，随着Ｖ２Ｏ５含量的增加，材料在低温范围内感湿灵敏度下降，当ＰｂＣｒＯ４与Ｖ２Ｏ５的摩尔比为１：１时，材料几乎失去感湿特性，当比例为１：０．３时，在温度为４０￣８０℃相对湿度为３０％￣９０％ＲＨ的范围内，材料的湿阻特性几乎与温度无关，据此有可能制备不加温度补偿即可直接使用的湿度传感器。     

CO_2焊接基于模糊的电流波形控制系统研究

针对ＣＯ２焊接存在的问题，提出了ＣＯ２焊接基于模糊的电流波形控制方案。基于模糊控制理论和大量ＣＯ２焊接工艺实验结果，对ＣＯ２焊接基于模糊的电流波形控制系统进行了研究，并对其进行了８０９８单片微机程序设计；应用该系统对碳钢ＣＯ２焊接进行了应用研究，实验结果表明，达到了设计要求。     

玉米大斑病菌毒素的钝化反应及其机理研究

用１８种化合物对ＨＴ－毒素进行钝化反应试验，结果表明：代森锰锌、Ｎａ２ＭｏＯ４，ＮａＮＯ，ＺｎＳＯ４，ＫＭｎＯ４，ＫＩ及４种糖可钝化毒素，使其对玉米的毒性降低；而ＣｕＳＯ４，ＮａＣｌ，ＫＣＩ２Ｏ５，Ｈ２Ｏ２可使毒素毒性增强。毒素处理玉米叶片后细胞膜透性、细胞的Ｖｃ氧化酶，ＣＡＴ，ＰＡＬ，ＰＰＯ活性都发生了一定的规律性变化，而毒素中加入Ｈ２Ｏ２和代森锰锌可抑制或加强这种变化。     

保护地黄瓜对增施CO2的生理生态反应

本文探讨了保护地黄瓜在增加ＣＯ_２浓度条件下的生理生态反应。结果表明：增加ＣＯ_２浓度可促进黄瓜的光合作用，提高细胞ＣＯ_２浓度，降低气孔导度。在增施ＣＯ_２条件下，冬季保护地黄瓜叶片的净光合速率日变化曲线为单峰型，而正常条件下保护地内ＣＯ_２常出现亏缺，从而抑制光合碳同化，并引起所谓的“午休”现象。冬季保护地黄瓜的光合“午休”现象主要是由于ＣＯ_２亏缺所导致。     

反应离子深刻蚀硅的研究

利用ＳＦ６／Ｃ２ＣｌＦ５，ＳＦ６／ＣＣｌ２Ｆ２，ＳＦ６／ＣＣＬ２Ｆ２／Ｏ２等氟氯族混合气体，在普通的平板型反应离子刻蚀系统上，进行了反应离子深刻蚀硅的研究。在Ｃｒ膜的掩护下，ＳＦ６／Ｃ２ＣｌＦ５刻蚀硅的速度，但刻蚀的方向性差；ＳＦ６／ＣＣ１２Ｆ２和ＳＦ６／ＣＣｌ２Ｆ２／Ｏ２都可获得各向异性的刻蚀工艺，刻蚀速率在２００－５００ｎｍ／ｍｉｎ之间。     

I~2C串行总线在单片机上的实现

介绍了Ｉ２Ｃ串行总线的性能和特点，以串行Ｅ２ＰＲＯＭ为例，提出了一种在８０３１上利用其Ｉ／Ｏ线实现Ｉ２Ｃ总线的方法，并给出了相应的汇编程序。     

番茄长季节栽培日光温室内CO2浓度变化的分析

本文分析了番茄长季节栽培日光温室内ＣＯ_２浓度的变化，结果表明：在不放风的条件下，日光温室内的ＣＯ_２浓度存在着垂直差异和水平差异；室内ＣＯ_２浓度的变化与番茄植株生育状态，特别是与番茄株高和叶面积关系密切，也与室内温度、光照度和土壤呼吸有关。因此番茄越冬生产时，如果土壤施入充足的有机质，在植株高度小于５０ｃｍ（或叶面积小于０．２２ｍ~２／株）时，土壤有机质分解释放的ＣＯ_２能够满足番茄光合的需要，不需增施ＣＯ_２，而应改善温度光照条件；只有植株较大、温度和光照条件好、见光到放风时间长时才需要增施ＣＯ_２ 。     

Multiple Objective Optimization Problems in Statistics

Many statistical problems can be viewed as optimization problems. A careful and detailed analysis of these procedures reveals that many of these problems are essentially multiple objective optimization problems. Furthermore, most of the standard statistical procedures aim at finding an efficient (non-dominated) solution. Our objective in this paper is to introduce some of the single sample statistical problems that can be formulated and solved as multiple objective optimization problems.     

Solving fuzzy assembly-line balancing problem with genetic algorithms

Assembly-line balancing problem is known as one of difficult combinatorial optimization problems. This problem has been solved with linear programming, dynamic programming approaches, but unfortunately these approaches do not lead to efficient algorithms. Recently, genetic algorithm has been recognized as an efficient and usefull procedure for solving large and hard combinatorial optimization problems, such as scheduling problems, travelling salesman problems, transportation problems, and so on. Fuzzy sets theory is frequently used to represent uncertainty of information. In this paper, to treat the data of real-world problems we use a fuzzy number to represent the processing time and show that we can get a good performance in solving this problem using genetic algorithms.     

遗传模拟退火算法在弹药装载中的应用研究

多约束条件下的弹药装载问题是一个复杂的组合优化问题,属于NP-完全问题,其求解是很困难的。本文在考虑弹药装载中各类约束条件的情况下,将模拟退火算法作了若干改进后,融入遗传算法,提出了一种遗传模拟退火算法来求解弹药装载问题。本文对该算法的遗传算子和冷却进度表进行了详细的阐述,给出了使用该算法求解弹药装载问题的具体实现方法。     

Implicit branching and parameterized partial cover problems

Covering problems are fundamental classical problems in optimization, computer science and complexity theory. Typically an input to these problems is a family of sets over a finite universe and the goal is to cover the elements of the universe with as few sets of the family as possible. The variations of covering problems include well-known problems like Set Cover, Vertex Cover, Dominating Set and Facility Location to name a few. Recently there has been a lot of study on partial covering problems, a natural generalization of covering problems. Here, the goal is not to cover all the elements but to cover the specified number of elements with the minimum number of sets. In this paper we study partial covering problems in graphs in the realm of parameterized complexity. Classical (non-partial) version of all these problems has been intensively studied in planar graphs and in graphs excluding a fixed graph H as a minor. However, the techniques developed for parameterized version of non-partial covering problems cannot be applied directly to their partial counterparts. The approach we use, to show that various partial covering problems are fixed parameter tractable on planar graphs, graphs of bounded local treewidth and graph excluding some graph as a minor, is quite different from previously known techniques. The main idea behind our approach is the concept of implicit branching. We find implicit branching technique to be interesting on its own and believe that it can be used for some other problems.     

A genetic algorithm-based solution methodology for modular design

Combinatorial optimization problems usually have a finite number of feasible solutions. However, the process of solving these types of problems can be a very long and tedious task. Moreover, the cost and time for getting accurate and acceptable results is usually quite large. As the complexity and size of these problems grow, the current methods for solving problems such as the scheduling problem or the classification problem have become obsolete, and the need for an efficient method that will ensure good solutions for these complicated problems has increased. This paper presents a genetic algorithm (GA)-based method used in the solution of a set of combinatorial optimization problems. A definition of a combinatorial optimization problem is first given. The definition is followed by an introduction to genetic algorithms and an explanation of their role in solving combinatorial optimization problems such as the traveling salesman problem. A heuristic GA is then developed and used as a tool for solving various combinatorial optimization problems such as the modular design problem. A modularity case study is used to test and measure the performance of the developed algorithm.     

递阶优化问题理论及其算法研究与进展

递阶优化问题用来描述具有层次结构的决策问题,两级优化问题是最基本的递阶问题,又称为两层规划或静态Stackelberg问题。这里比较系统地介绍两级以及多级优化问题的模型以及其特征,并探讨了该领域有待研究的几个方向。     

包装容器造型人机分析

对包装容器造型设计进行了人机分析及应用,分析了包装容器造型设计中的人体测量尺寸问题、握力问题、使用动作和功能以及容器材料和色彩问题。通过包装容器造型人机分析为人机工程学在包装容器造型设计中的运用提供了研究方向。     

Large scale evolutionary optimization using cooperative coevolution

Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectiveness and advantages when applied to large and complex problems, e.g., those with high dimensions. Although cooperative coevolution has been proposed as a promising framework for tackling high-dimensional optimization problems, only limited studies were reported by decomposing a high-dimensional problem into single variables (dimensions). Such methods of decomposition often failed to solve nonseparable problems, for which tight interactions exist among different decision variables. In this paper, we propose a new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems. A random grouping scheme and adaptive weighting are introduced in problem decomposition and coevolution. Instead of conventional evolutionary algorithms, a novel differential evolution algorithm is adopted. Theoretical analysis is presented in this paper to show why and how the new framework can be effective for optimizing large nonseparable problems. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 1000 dimensions. The results show clearly that our framework and algorithm are effective as well as efficient for large scale evolutionary optimisation problems. We are unaware of any other evolutionary algorithms that can optimize 1000-dimension nonseparable problems as effectively and efficiently as we have done.     

OLD TRENDS AND NEW TRENDS IN GENERAL SYSTEMS RESEARCH†

Ludwig von Bertalanffy. as well as many of the other pioneers of systems theory, was motivated in large part by the problems or theoretical biology. This fact reflects the unique role which biology plays in the sciences. The character of biological explanations, and the relation of organic problems to other realms of human activity, is reviewed in this context, and some of the fundamental remaining problems are pointed out. It is suggested that these problems will play a dominant role in systems research in the future, as they have in the past.     

A complete and equal computational complexity classification of compaction and retraction to all graphs with at most four vertices and some general results

A very close relationship between the compaction, retraction, and constraint satisfaction problems has been established earlier providing evidence that it is likely to be difficult to give a complete computational complexity classification of the compaction and retraction problems for reflexive or bipartite graphs. In this paper, we give a complete computational complexity classification of the compaction and retraction problems for all graphs (including partially reflexive graphs) with four or fewer vertices. The complexity classification of both the compaction and retraction problems is found to be the same for each of these graphs. This relates to a long-standing open problem concerning the equivalence of the compaction and retraction problems. The study of the compaction and retraction problems for graphs with at most four vertices has a special interest as it covers a popular open problem in relation to the general open problem. We also give complexity results for some general graphs. The compaction and retraction problems are special graph colouring problems, and can also be viewed as partition problems with certain properties. We describe some practical applications also.