A novel multi-objective optimization method: Input values effect on responses (IVER)

In this study, a novel multi-objective optimization method based on the best effect of unique input (independent variable) values on responses (dependent variables) was proposed. The proposed method was compared with optimization using Derringer & Suich function that is still the most used. The comparison was made using the response values measured in real experiments and available in the literature. The advantages of the proposed method such as not needing the polynomial model aiming to predict the response values, no parameter selection problem, being able to offer optimum range instead of single optimum value, being suitable for use with existing experimental designs and being simple and interpretable were demonstrated as a result of comparison. It was also suggested how the proposed method will be effective according to experimental designs, and application for the users' application was presented.     

Bridging the Gap: Plant-Endophyte Interactions as a Roadmap to Understanding Small-Molecule Communication in Marine Microbiomes

Probing the composition of the microbiome and its association with health and disease states is more accessible than ever due to the rise of affordable sequencing technology. Despite advances in our ability to identify members of symbiont communities, untangling the chemical signaling that they use to communicate with host organisms remains challenging. In order to gain a greater mechanistic understanding of how the microbiome impacts health, and how chemical ecology can be leveraged to advance small-molecule drug discovery from microorganisms, the principals governing communication between host and symbiont must be elucidated. Herein, we review common modes of interkingdom small-molecule communication in terrestrial and marine environments, describe the differences between these environments, and detail the advantages and disadvantages for studies focused on the marine environment. Finally, we propose the use of plant-endophyte interactions as a stepping stone to a greater understanding of similar interactions in marine invertebrates, and ultimately in humans.     

Effect of height on the corrosion of steel and galvanized steel in nontropical coastal marine environments

In this study, the behavior of carbon steel and galvanized steel in nontropical coastal marine environments was evaluated. Evaluation was carried out with specimens with dimensions of 10 cm × 10 cm × 0.3 cm. These specimens were exposed to four testing stations (Iquique, Mejillones, Los Vilos, and San Vicente), where racks were installed both at ground level (ground), as well as in the upper zone of electrical transmission towers (tower). In each station, 24 specimens of A36 carbon steel and galvanized steel were placed (12 each). The corrosivity of the environment was measured using the ISO 9223, 9225, and 9226 standards. The specimens were evaluated on-site, monthly, through visual inspection and photographic record. Once withdrawn, the corrosion rate was determined and the corrosion products were analyzed through Raman and Fourier-transform infrared. The results show that, in all cases, the corrosion rate is greater in the tower than on the ground. However, even though the Los Vilos station is located farther from the sea (3,500 vs. ≈500 m), the corrosion rate of steel in the tower is the highest. This is caused by the generation of HCl from the transformation of lepidocrocite into goethite, in the presence of low chloride content, which acidifies the steel/corrosion product interface. In the case of galvanized steel, the corrosion rate is a function of the chloride content in the atmosphere, obtaining an excellent correlation between both parameters.     

Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

公民水素养评价表征因素模型构建与分析

针对我国水资源紧缺、水生态损害、水环境污染等相关水问题,以提升全民爱水惜水节水护水的意识和能力为目标,从水素养概念出发,通过扎根理论质性研究方法对公民水素养评价的表征因素进行筛选和提炼,最终得出包含水知识、水态度、水行为为主范畴的10个表征因素并运用解释结构模型(ISM)深入剖析各因素之间的内在联系。在此基础上建立了3级多层次递阶结构模型,进一步精确描述系统各要素两两之间的直接关系。结果表明:公民水素养评价的表征因素自上而下形成了表象层、中间层和根源层等3个层级,可直观、清晰地反映表征因素之间的递阶、层级关系;水态度在各表征因素中具有调节作用以及各表征因素之间存在内在逻辑关系。研究成果为提高公民水素养水平提供一定的科学依据和政策参考。     

振动监测系统的可调增益技术与实现

文章设计了一种具有可调增益功能的振动监测系统。首先从监测层和测量层两个方面提出了振动监测系统的整体框架,之后给出了硬件系统和软件系统的设计方法及软件实现代码,利用控制芯片和可调增益放大器,在监测系统中整合了一套可根据采集信号幅值大小调整增益的数据采集装置;利用模拟信号发生器对不加增益和设置可调增益之后的两种信号进行了对比分析,并在汽轮发电机组实际工况下进行了振动信号采集。结果表明,具备可调增益功能的振动监测系统可有效提高采集信号的信噪比,提高汽轮发电机组的振动监测能力。     

基于信息熵抑制的大数据特征隐匿性加密仿真

针对传统大数据特征加密方法中大数据精度较差、加密耗时较长的问题,提出基于信息熵抑制的大数据特征隐匿性加密方法。分析信息熵抑制相关理论,采用信息熵映射函数对大数据分类,将信息熵抑制过程转化为计算2个文件相似性的过程,并根据余弦相似度公式以及Hamming距离值计算数据相似度,完成数据消冗。在完成数据消冗的基础上,分析分组密码方法和ECC加密方法,结合两种方法得到大数据特征隐匿性加密处理计算公式,实现基于信息熵抑制的大数据特征隐匿性加密。实验研究结果表明,所提方法能够有效去除冗余数据,提升大数据精度,加快数据加密的处理速度,高效完成大数据加密过程。     

大数据环境下基于K中心点优化算法的Web服务组合

在当前Web服务海量增加、现有Web服务选择算法低效、用户匹配度差的基础上，针对K中心点算法存在的质点偏移、准确率低和容易发生畸变等问题，提出一种大数据环境下基于K中心点优化算法的Web服务组合方法。该方法是在大数据环境下，根据不同用户需求满意度及Web服务QoS参数，对基于优化初始聚类中心的K中心点算法的Web服务选择及最优Web服务组合进行研究。同时针对不同的选择方法对服务动态选择及组合的准确度、迭代更新次数、候选集选择时间及选择总时间进行实验分析，验证了本文研究方法的有效性和可靠性。     

基于Hadoop的电力运维审计系统的设计

国家电网公司信息化程度越来越高，单机运维审计系统产生的数据量日益增多，对海量数据高效率存储分析性能严重下降，系统稳定性降低。为满足国家电网当前对运维审计系统数据存储分析以及系统稳定性的需求，在Hadoop开源架构的基础上，本文提出基于Hadoop集群的海量数据分布式存储方法和基于Heartbeat的心跳检测技术，实现基于Hadoop的电力运维审计系统。〖JP2〗实验测试结果表明，基于Hadoop的电力运维审计系统相比单机系统可用性提高了8.42%，大大提升了存储分析海量数据的性能，具有系统工作稳定和服务不间断等优势。     

聚类混合型数据的密度峰值改进算法

聚类混合型数据，通常是依据样本属性类别的不同分别进行评价。但这种将样本属性划分到不同子空间中分别度量的方式，割裂了样本属性原有的统一性；导致对样本个体的相似性评价产生了非一致的度量偏差。针对这一问题，提出以二进制编码样本属性，再由海明差异对属性编码施行统一度量的新的聚类算法。新算法通过在统一的框架内对混合型数据实施相似性度量，避免了对样本属性的切割，在此基础上又根据不同属性的性质赋予其不同的权重，并以此评价样本个体之间的相似程度。实验结果表明，新算法能够有效地聚类混合型数据；与已有的其他聚类算法相比较，表现出更好的聚类准确率及稳定性。