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Journal articles on the topic 'Change propagation'

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Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Clarkson, P. John, Caroline Simons, and Claudia Eckert. "Predicting Change Propagation in Complex Design." Journal of Mechanical Design 126, no. 5 (September 1, 2004): 788–97. http://dx.doi.org/10.1115/1.1765117.

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In redesign and design for customization, products are changed. During this process a change to one part of the product will, in most cases, result in changes to other parts. The prediction of such change provides a significant challenge in the management of redesign and customization of complex products where many change propagation paths may be possible. This paper reports on an analysis of change behavior based on a case study in Westland Helicopters of rotorcraft design; the development of mathematical models to predict the risk of change propagation in terms of likelihood and impact of change; and the development of a prototype computer support tool to calculate such information for a specific product. With knowledge of likely change propagation paths and their impact on the delivery of the product, design effort can be directed towards avoiding change to “expensive” sub-systems and, where possible, allowing change where it is easier to implement while still achieving the overall changes required.

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2

You, Chun-Fong, and Shen-Chou Yeh. "Engineering Change Propagation System using STEP." Concurrent Engineering 10, no. 4 (December 1, 2002): 349–56. http://dx.doi.org/10.1177/a030348.

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Reducing the time for engineering changes can greatly shorten a product's life cycle and improve the productivity of enterprises. This work proposes an approach to engineering change propagation between CAD and product data management (PDM) systems. A system for modeling and triggering changes of models of engineering data, geometries, and features, using STEP AP 214 and AP224, is developed. A change in CAD and PDM systems can propagate along a defined path and trigger a rule by which data of another system are changed. The proposed engineering change propagation (ECP) system provides a flexible, virtually integrated framework to enable engineering change. Affected items and propagation sequences of engineering changes are inferred with the help of an ECP network. Engineering change issued between a STEP-based solid modeling system and a PDM system is implemented and discussed to demonstrate the capability of the ECP system. Change in the feature parameters of a CAD system is propagated and the relevant data, defined in the ECP network, are triggered. Enterprises can use the ECP system to integrate various information systems, such as CAx, PDM, CAPP, ERP, and others, and facilitate collaborative engineering using a virtual framework, to promote competition.

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3

YU, LIGUO, and STEPHEN R. SCHACH. "APPLYING ASSOCIATION MINING TO CHANGE PROPAGATION." International Journal of Software Engineering and Knowledge Engineering 18, no. 08 (December 2008): 1043–61. http://dx.doi.org/10.1142/s0218194008004008.

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A software system evolves as changes are made to accommodate new features and repair defects. Software components are frequently interdependent, so changes made to one component can result in changes having to be made to other components to ensure that the system remains consistent; this is called change propagation. Accurate detection of change propagation is essential for software maintenance, which can be aided by accurate prediction of change propagation. In this paper, we study change propagation in three leading open-source software products: Linux, FreeBSD, and Apache HTTP Server. We use association rules-based data-mining techniques to detect change-propagation rules from the product version history. These rules are evaluated with respect to different training data sets and different test data sets. We discuss the applicability of using association-rule mining for change propagation, and several related issues. We find that a challenging issue in association-rule mining, concept drift, exists in software systems. Concept drift complicates the task of change-propagation prediction and requires special approaches, different from currently-used techniques for predicting change propagation.

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4

Kurt, Mehmet Necip, and Xiaodong Wang. "Multisensor Sequential Change Detection With Unknown Change Propagation Pattern." IEEE Transactions on Aerospace and Electronic Systems 55, no. 3 (June 2019): 1498–518. http://dx.doi.org/10.1109/taes.2018.2873067.

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5

Zhang, Hai-Zhu, Guo-Fu Ding, Rong Li, Sheng-Feng Qin, and Kai-Yin Yan. "Design Change Model for Effective Scheduling Change Propagation Paths." Chinese Journal of Mechanical Engineering 30, no. 5 (July 27, 2017): 1081–90. http://dx.doi.org/10.1007/s10033-017-0169-2.

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6

Wang, Yang, and Gui Jiang Duan. "Engineering Change Propagation Analysis Based on Linkage Model in Complex Product Development." Applied Mechanics and Materials 271-272 (December 2012): 887–96. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.887.

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To handle change propagations during complex product development, an analysis framework based on linkage model was proposed. Firstly linkage was defined to encapsulate the relations among product characteristics, and methods of linkage identification from multi-dimensions and linkage model construction were given. Then how to identify change propagation paths step by step in open scene and how to identify change propagation paths with improved mouse maze algorithm in closed scene were discussed. In the following a quantitative evaluation method of change impact risk was advanced. Finally an example of carrier robot moving structure design change was provided to validate this method.

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7

Wang, Rongcun, Rubing Huang, and Binbin Qu. "Network-Based Analysis of Software Change Propagation." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/237243.

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The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system.

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8

Liu, Pei Zhi, Wei Yan Chai, and Jun Ji. "Engineering Change Propagation in Collaboration Environment." Applied Mechanics and Materials 271-272 (December 2012): 897–900. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.897.

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Engineering change is an important and complex activity for manufacturing enterprises. In order to improve the efficiency of engineering change, we proposed a method of EC propagation in collaboration environment. First, we analyzed the method of creating the knowledge database; then, studied the way knowledge sharing in collaboration; furthermore, proposed a algorithm change propagation in collaboration environment; finally, a created a system based on the method above and the experiment of managing a type of military vehicle engine was employed to validate our method and it showed the correctness of the proposed method.

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9

You, Chun-Fong, and Shen-Chou Yeh. "Engineering Change Propagation System using STEP." Concurrent Engineering 10, no. 4 (December 1, 2002): 349–56. http://dx.doi.org/10.1177/106329302129140241.

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10

Cheng, Yifeng, Lu Wang, and Tim Li. "Causes of Interdecadal Increase in the Intraseasonal Rainfall Variability over Southern China around the Early 1990s." Journal of Climate 33, no. 21 (November 1, 2020): 9481–96. http://dx.doi.org/10.1175/jcli-d-20-0047.1.

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AbstractThe southern China (SC) summer rainfall exhibits prominent intraseasonal variability, which exhibits a significant increase in the early 1990s with the turning point at 1993. The SC intraseasonal rainfall events could be divided into three categories according to different propagations, including the southward-propagating (SP) events, the northwestward-propagating (NWP) events, and the northward-propagating (NP) events. This study explores the causes of the observed interdecadal increase in the intraseasonal rainfall variability over SC by comparing the SC intraseasonal rainfall events of each category between the former decadal period (P1) and the later decadal period (P2). The result indicates that such interdecadal change is due to the more frequent NP events coming from the South China Sea (SCS). Based on the moisture and vorticity budget analysis, it is revealed that the summer mean southerly wind in the middle to lower troposphere is the dominant factor of the northward propagation over the SCS, as it could induce positive meridional moisture and vorticity advection anomalies ahead of the convection. A marked interdecadal enhancement of the summer mean southerly wind over the SCS is the cause of more frequent occurrence of NP events over SC, as it provides more favorable conditions for the northward propagation. The change of the atmospheric instability over the SCS where the NP convection perturbation originates was also investigated, but no significant change was found.

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11

JEONG, Duseok, and Kazuhiro AOYAMA. "1414 Design Change Planning with Deliberation on Change Effect Propagation." Proceedings of Design & Systems Conference 2013.23 (2013): _1414–1_—_1414–8_. http://dx.doi.org/10.1299/jsmedsd.2013.23._1414-1_.

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12

Ullah, Inayat, Dunbing Tang, Leilei Yin, Ishfaq Hussain, and Qi Wang. "Cost-effective propagation paths for multiple change requirements in the product design." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 9 (May 2, 2017): 1572–85. http://dx.doi.org/10.1177/0954406217707788.

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Design projects have been surrounded by tight schedule and cost overruns. Therefore, it is indispensable to resolve the design changes in an economical way. This work introduces an advanced technique to assess and optimize change propagation paths for multiple change requirements occurring simultaneously during the product development process. A novel multiple change requirement algorithm and a mathematical model considering the overall propagated risk are developed, to explore cost-effective change propagation paths in terms of lead time. The risk is quantified with regard to propagation likelihood and change impact, which results in re-work. Design structure matrix framework is used to capture the dependencies between components. It is revealed that the change requirements execution sequence has a significant effect on the total number of change propagation paths, change steps, distinct change components, and completion time. This approach is illustrated by a case study, which indicates that the proposed methodology can assist the designer in exploring and selecting optimal change propagation paths.

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13

Monteuuis, Olivier, and Doreen Kim Soh Goh. "Field growth performances of teak genotypes of different ages clonally produced by rooted cuttings, in vitro microcuttings, and meristem culture." Canadian Journal of Forest Research 45, no. 1 (January 2015): 9–14. http://dx.doi.org/10.1139/cjfr-2014-0339.

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Teak (Tectona grandis L. f.) clonal forestry has lately become a reality thanks to the development of efficient techniques for mass clonally propagating true-to-type teak trees of various ages. Field trials were set up to assess the influence of teak genotypes of different ages and three clonal propagation techniques on field growth performances of teak clones. Significant differences (P < 0.0001) in height (H, from 11.9 to 17.5 m), diameter at breast height (D, from 11.8 to 18.9 cm), and volume (V, from 67.9 to 194.7 dm3) were observed 5 years after planting for clones produced by microcuttings from 6-month-old to 70-year-old teak ortets, regardless of their age. After 6.5 years of testing, H, D, and V performances of clones produced by rooted cuttings and microcuttings from 7-year-old teak trees were similar, notwithstanding clone × propagation method interactions. Five years after planting, clones produced by meristem culture from 7-year-old ortets had larger diameters and volumes than clones produced by microcuttings, whereas H varied according to clone × propagation method interaction. The various propagation methods used had no significant effect on mortality (<10%). The pros and cons of these techniques for mass clonally propagating teak genotypes of different ages were discussed.

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14

Shirai, Osamu, and Kenji Kano. "Propagation of the Change in Membrane Potential." Review of Polarography 61, no. 2 (2015): 93–104. http://dx.doi.org/10.5189/revpolarography.61.93.

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15

Reddy, V. Koti, and Prof A. Ananda Rao. "Automation of Version Management and Change Propagation." International Journal of Science and Engineering Applications 6, no. 6 (June 1, 2017): 154–66. http://dx.doi.org/10.7753/ijsea0606.1002.

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16

Liu, Xiaojian, Shuyou Zhang, and Chunwen Zou. "Design Change Propagation in Assembly Joint Graph." Journal of Computer-Aided Design & Computer Graphics 22, no. 8 (September 6, 2010): 1300–1307. http://dx.doi.org/10.3724/sp.j.1089.2010.10940.

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17

Wang Chuang, and Meng Xiangwu. "Research on Software Change Impact Propagation Model." International Journal of Advancements in Computing Technology 3, no. 10 (November 30, 2011): 124–31. http://dx.doi.org/10.4156/ijact.vol3.issue10.16.

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18

Pan, Weifeng, Haibo Jiang, Hua Ming, Chunlai Chai, Bi Chen, and Hao Li. "Characterizing Software Stability via Change Propagation Simulation." Complexity 2019 (August 29, 2019): 1–17. http://dx.doi.org/10.1155/2019/9414162.

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Software stability means the resistance to the amplification of changes in software. It has become one of the most important attributes that affect maintenance cost. To control the maintenance cost, many approaches have been proposed to measure software stability. However, it is still a very difficult task to evaluate the software stability especially when software becomes very large and complex. In this paper, we propose to characterize software stability via change propagation simulation. First, we propose a class coupling network (CCN) to model software structure at the class level. Then, we analyze the change propagation process in the CCN by using a simulation way, and by doing so, we develop a novel metric, SS (software stability), to measure software stability. Our SS metric is validated theoretically using the widely accepted Weyuker’s properties and empirically using a set of open source Java software systems. The theoretical results show that our SS metric satisfies most of Weyuker’s properties with only two exceptions, and the empirical results show that our metric is an effective indicator for software quality improvement and class importance. Empirical results also show that our approach has the ability to be applied to large software systems.

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19

Reddi, Krishna R., and Young B. Moon. "A framework for managing engineering change propagation." International Journal of Innovation and Learning 6, no. 5 (2009): 461. http://dx.doi.org/10.1504/ijil.2009.025060.

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20

Lee, Jihwan, and Yoo S. Hong. "Bayesian network approach to change propagation analysis." Research in Engineering Design 28, no. 4 (March 1, 2017): 437–55. http://dx.doi.org/10.1007/s00163-017-0252-9.

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21

Dowse, J., and B. Skews. "Area change effects on shock wave propagation." Shock Waves 24, no. 4 (March 18, 2014): 365–73. http://dx.doi.org/10.1007/s00193-014-0501-z.

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22

Huffman, Gregory W. "Propagation through endogenous investment-specific technological change." Economics Letters 84, no. 2 (August 2004): 191–97. http://dx.doi.org/10.1016/j.econlet.2004.02.001.

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23

Stitt-Bergh, Monica, and Thomas Hilgars. "Program Assessment: Process, Propagation, and Culture Change." Across the Disciplines 6, no. 1 (2009): 1–14. http://dx.doi.org/10.37514/atd-j.2009.6.1.07.

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24

Steigemann, Martin, and Maria Specovius-Neugebauer. "On the change of energy caused by crack propagation in 3-dimensional anisotropic solids." Mathematica Bohemica 139, no. 2 (2014): 401–16. http://dx.doi.org/10.21136/mb.2014.143865.

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25

Kester, D. E., T. M. Gradziel, K. A. Shackel, and W. C. Micke. "063 Biological Basis for Noninfectious Bud-failure in Almond (Prunus dulcis)." HortScience 34, no. 3 (June 1999): 452A—452. http://dx.doi.org/10.21273/hortsci.34.3.452a.

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Noninfectious bud-failure (BF) is a genetic disorder in almond, associated with nursery source selection. Previously (Kester, PASHS, 1968), the latent potential for BF (BFpot) was shown to be heritable but its phenotypic expression (BFexp) varied among individual seedlings of a populations as a function of age. Vegetative propagation perpetuates BFpot of individual propagules (Kester and Asay, JASHS, 1978b) but the subsequent age of BFexp within individual plants is a function of accumulated exposure to high summer temperature and growth (Kester and Asay, JASHS 1978a). A recent 7-year “somatic heritability” study of 12 commercial nursery sources (Kester et al., HortScience 1998abst) portrays the total range of variability of BFpot and BFexp within the entire `Carmel' almond clonal population and includes a pattern of BF increase in consecutive vegetative propagation cycles that mimics patterns produced by phase change (i.e., juvenile > mature) phenomena (Hartmann et al., 1997). Although phase change potential is heritable in seedling populations, phase change expression is not (Kester, HortScience 1983). Furthermore phase changes can be reversed under particular conditions during consecutive vegetative propagations (Hartmann et al., 1997). In contrast, evidence shows that BF produces permanent changes in genotype that are heritable and irreversable. High correlations exist between BFpot of individual source blocks, individual trees and individual budsticks and the age and severity of BFexp in progeny trees. The apparent continuous change in BFpot and BFexp within clones appears to be the pattern of expression of different populations of increasingly defective (?) somatic cells that result from consecutive sequences of change during annual cycles of growth and generations of vegetative propagation.

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26

Gupta, Chetna, and Varun Gupta. "Software Change Impact Analysis." International Journal of Systems and Service-Oriented Engineering 5, no. 2 (April 2015): 44–55. http://dx.doi.org/10.4018/ijssoe.2015040103.

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This paper presents an approach to prioritize program segments within the impact set computed using functional call graph to assist regression testing for test case prioritization. The presented technique will first categorize the type of impact propagation and then prioritize the impacted segments into higher and lower levels based on propagation categorization. This will help in saving maintenance cost and effort by allocating higher priority to those segments which are impacted more within the impacted set. Thus a software engineer can first run those test cases which cover segments with higher impacted priority to minimize regression test selection.

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27

Li, Yuliang, Wei Zhao, and Yongsheng Ma. "A shortest path method for sequential change propagations in complex engineering design processes." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, no. 1 (June 9, 2015): 107–21. http://dx.doi.org/10.1017/s0890060415000311.

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AbstractEngineering design changes constantly occur in complex engineering design processes. Designers need appropriate measures to handle the numerous design changes in order to realize consistent and completely validated product models so that successful product development is assured. In this paper, a time-based mathematic model is presented to characterize the sequential change propagation process, and then the shortest path algorithm is given to find the most timesaving routes for changes to propagate to other dependent design tasks. An analysis method is introduced to compute the sensitivities of change impacts on the affected design tasks, which indicates that the more time consumed by a change to take its effect, the more sensitive the change impacts on those downstream dependent tasks. A case study of change propagations in motorcycle engine design process was presented to demonstrate the proposed method.

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28

Mo, Rong, Zhong Wei Gong, Hai Cheng Yang, and Xin Zhang. "The Detection of Avalanche Propagation in Engineering Change." Applied Mechanics and Materials 121-126 (October 2011): 3578–82. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3578.

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In order to avoid avalanche propagation in engineering change that is high-risk and high-cost, two methods for the detection of avalanche propagation were proposed. Firstly, analyzed the reasons for the avalanche propagation and its main features: constraint conflict and/or a great many affected elements. Then, the methods for the detection of those two features based on directed graph and complex networks respectively were proposed. Finally, the experiment of managing a type of transmission was employed to validate our method and it showed the correctness of the proposed method.

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29

Cheng, Yusi, Jingfeng Yuan, Lei Zhu, and Wei Li. "Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach." Complexity 2020 (November 30, 2020): 1–12. http://dx.doi.org/10.1155/2020/8854609.

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Construction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and to analyze the effects on the risk propagation through numerical simulations. First, construction projects are represented by activity-on-node (AON) networks. A model of risk propagation is then built based on a susceptible-infected (SI) model considering the effects of the nodal characteristics on the propagation process. Next, the model is tested on a real-world project to examine cascading failures with varying parameters. The experimental results demonstrate that the model is effective in identifying the activities most capable of affecting a project schedule and evaluating the impact of schedule change risk propagation. This study will provide a basis for enhancing the robustness of AON networks and controlling the propagation of schedule change risks.

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30

Chua, D. K. H., and M. A. Hossain. "Predicting Change Propagation and Impact on Design Schedule Due to External Changes." IEEE Transactions on Engineering Management 59, no. 3 (August 2012): 483–93. http://dx.doi.org/10.1109/tem.2011.2164082.

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31

Fdhila, Walid, Stefanie Rinderle-Ma, and Conrad Indiono. "Change Propagation Analysis and Prediction in Process Choreographies." International Journal of Cooperative Information Systems 24, no. 03 (September 2015): 1541003. http://dx.doi.org/10.1142/s0218843015410038.

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Business process collaborations among multiple partners require particular considerations regarding flexibility and change management. Indeed, each change or process redesign originated by a partner may cause ripple effects on other partners participating in the choreography. Consequently, a change request could spread over partners in an unexpected way with relevant costs due to its transitivity (e.g. in supply chains). In order to avoid costly negotiations or propagation failures, understanding this behavior becomes critical. This paper focuses on analyzing the behavior of change requests in process choreographies, i.e. the change propagation behavior. The input data might be available in two different formats, i.e. as change logs or change propagation logs (CPs). In order to understand the data and to explore potential analysis models and techniques, we employ exploratory data analysis as well as analysis techniques from process mining and change management to simulation data. The results yield the requirements for designing a mining algorithm that derives the propagation behavior behind change logs. This algorithm is a memetic algorithm that is based on different heuristics. Its feasibility is shown based on a comparison with the other mining techniques.

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32

Fang, Yong Ze, Xiao Xi Guo, and Jian Hai Chen. "Research of Engineering Change Analysis." Advanced Materials Research 542-543 (June 2012): 285–88. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.285.

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An engineering change is costly and time consuming. Therefore, the research of effective methods and software is highly significant to engineering change analysis. This paper has conducted research on the engineering change analysis method and decision support environment. The characteristics of an engineering change were demonstrated; the data model was set up for supporting engineering change analysis; the method for analyzing engineering change propagation was proposed. The multi-user dynamic analysis of an engineering change and storage of the engineering change scheme with reusability were achieved in this paper. According to the results, the research shows more flexible and efficient to analyze engineering changes and further assure the completeness of changed data.

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Gupta, Chetna, Yogesh Singh, and Durg Singh Chauhan. "A Dynamic Approach to Estimate Change Impact using Type of Change Propagation." Journal of Information Processing Systems 6, no. 4 (December 31, 2010): 597–608. http://dx.doi.org/10.3745/jips.2010.6.4.597.

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34

Spiller, Marc. "Measuring adaptive capacity of urban wastewater infrastructure – Change impact and change propagation." Science of The Total Environment 601-602 (December 2017): 571–79. http://dx.doi.org/10.1016/j.scitotenv.2017.05.161.

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35

Piccolo, S. A., S. Lehmann, and A. M. Maier. "MODULARITY, ROBUSTNESS, AND CHANGE PROPAGATION: A MULTIFACETED RELATION." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 2335–44. http://dx.doi.org/10.1017/dsd.2020.17.

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AbstractIncreased system robustness is one of the promises of modularity. However, research on the topic has provided conflicting findings. By generating more than 2000 system architectures, this paper shows that the relation between modularity and robustness is multifaceted: Modularity decreases topological robustness, increases robustness to change propagation, and provides economic benefits. Results here confirm the importance of modularity, enable reconciliation of opposing findings from prior research, and guides researchers and practitioners in the selection of appropriate robustness measures.

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36

Mirdamadi, Shirin, Sid-Ali Addouche, and Marc Zolghadri. "A Bayesian approach to model change propagation mechanisms." Procedia CIRP 70 (2018): 1–6. http://dx.doi.org/10.1016/j.procir.2018.03.309.

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37

Gratus, Jonathan, and Robin W. Tucker. "Topology change and the propagation of massless fields." Journal of Mathematical Physics 36, no. 7 (July 1995): 3353–76. http://dx.doi.org/10.1063/1.531035.

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38

ZHANG, Jingxiong, and Yunwei TANG. "Misclassification error propagation in land cover change categorization." Geo-spatial Information Science 15, no. 3 (August 28, 2012): 171–75. http://dx.doi.org/10.1080/10095020.2012.715874.

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39

Tang, Dun-Bing, Lei-Lei Yin, Qi Wang, Inayat Ullah, Hai-Hua Zhu, and Sheng Leng. "Workload-based change propagation analysis in engineering design." Concurrent Engineering 24, no. 1 (November 3, 2015): 17–34. http://dx.doi.org/10.1177/1063293x15608510.

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40

Tamarin-Brodsky, Talia, and Yohai Kaspi. "Enhanced poleward propagation of storms under climate change." Nature Geoscience 10, no. 12 (November 13, 2017): 908–13. http://dx.doi.org/10.1038/s41561-017-0001-8.

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41

Griffin, Timothy, and Bharat Kumar. "Algebraic change propagation for semijoin and outerjoin queries." ACM SIGMOD Record 27, no. 3 (September 1998): 22–27. http://dx.doi.org/10.1145/290593.290597.

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42

Hinkel, Georg, and Erik Burger. "Change propagation and bidirectionality in internal transformation DSLs." Software & Systems Modeling 18, no. 1 (August 21, 2017): 249–78. http://dx.doi.org/10.1007/s10270-017-0617-6.

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M. Ferreira, Kecia A., Mariza A. S. Bigonha, Roberto S. Bigonha, Bernardo N. de Lima, Bárbara M. Gomes, and Luiz Felipe O. Mendes. "A model for estimating change propagation in software." Software Quality Journal 26, no. 2 (February 6, 2017): 217–48. http://dx.doi.org/10.1007/s11219-017-9358-6.

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44

Sasaki, Yoshi N., Shoshiro Minobe, and Niklas Schneider. "Decadal Response of the Kuroshio Extension Jet to Rossby Waves: Observation and Thin-Jet Theory*." Journal of Physical Oceanography 43, no. 2 (February 1, 2013): 442–56. http://dx.doi.org/10.1175/jpo-d-12-096.1.

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AbstractThis study examines interannual to decadal variability of the Kuroshio Extension (KE) jet using satellite altimeter observations from 1993 to 2010. The leading empirical orthogonal function (EOF) mode of sea level variability in the KE region represents the meridional shift of the KE jet, followed by its strength changes with a few month lag. This shift of the KE jet lags atmospheric fluctuations over the eastern North Pacific by about three years. Broad sea level anomalies (SLAs) emerge in the eastern North Pacific 3–4 years before the upstream KE jet shift, and propagate westward along the KE jet axis. In the course of the propagation, the meridional scale of the SLAs gradually narrows, and their amplitude increases. This westward propagation of SLAs with a speed of about 5 cm s−1 is attributed to the westward propagation of the meridional shift of the jet, consistent with the thin-jet theory, whose importance has been suggested by previous numerical studies. In addition, the westward-propagating signals tend to conserve their quasigeostrophic potential vorticity anomaly, which may explain the characteristic changes of SLAs during the propagation. After the westward-propagating signals of positive (negative) SLAs reach at the east coast of Japan, the upstream KE jet strengthens (weakens) associated with the strength changes of the northern and southern recirculation gyres. Interestingly, this strength change of the KE jet propagates eastward with a speed of about 6 cm s−1, suggesting an importance of advection by the current.

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Li, Yuliang, Wei Zhao, Yongsheng Ma, and Lichen Hu. "Scheduling of Changes in Complex Engineering Design Process via Genetic Algorithm and Elementary Effects Method." Advances in Mechanical Engineering 6 (January 1, 2014): 169097. http://dx.doi.org/10.1155/2014/169097.

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Engineering design changes constantly occur in a complex engineering design process. Designers have to put an appropriate procedure in place to handle these changes in order to realize successful product development in a timely and cost-effective manner. When many change propagation paths are present, selection of the best change evolution paths and distribution of change results to downstream tasks become critical to the progress management of the project. In this paper, based on the available change propagation simulation algorithm, a global sensitivity analysis method known as elementary effects (EE) is employed to rank the importance of each potential propagation path with those involved design dependencies in the process. Further, an EE-based heuristic design dependency encoding method is applied to the genetic algorithm which is then adopted to schedule the change updating process. Finally, the optimal results obtained by the complete search and the heuristic dependency encoding methods are compared to illustrate the improvements and effectiveness of the latter method.

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Zhu, Yixin, Fengli Zhang, and Wenqiang Guo. "An Immunization Strategy Based on Propagation Mechanism." Discrete Dynamics in Nature and Society 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/971497.

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With the ubiquity of smart phones, wearable equipment, and wireless sensors, the topologies of networks composed by them change along with time. The immunization strategies in which network immune nodes are chosen by analyzing the static aggregation network topologies have been challenged. The studies about interaction propagations between two pathogens show that the interaction can change propagation threshold and the final epidemic size of each other, which provides a new thinking of immunization method. The eradication or inhibition of the virus can be achieved through the spread of its opposite party. Here, we put forward an immunization strategy whose implementation does not depend on the analysis of network topology. The immunization agents are randomly placed on a few of individuals of network and spread out from these individuals on network in a propagation method. The immunization agents prevent virus infecting their habitat nodes with certain immune success rate. The analysis and simulation of evolution equation of the model show that immune propagation has a significant impact on the spread threshold and steady-state density of virus on a finite size of BA networks. Simulations on some real-world networks also suggest that the immunization strategy is feasible and effective.

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CAMPOS, L. M. B. C., and P. M. V. M. MENDES. "On the effects of viscosity and anisotropic resistivity on the damping of Alfvén waves." Journal of Plasma Physics 63, no. 3 (April 2000): 221–38. http://dx.doi.org/10.1017/s0022377899008259.

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The equations of magnetohydrodynamics (MHD) are written for non-uniform viscosity and resistivity – the latter in the cases of Ohmic and anisotropic resistivity. In the case of Ohmic (anisotropic) diffusivity, there is (are) one (two) transverse components of the velocity and magnetic field perturbation(s), leading to a second-order (fourth-order) dissipative Alfvén- wave equation. In the more general case of dissipative Alfvén waves with isotropic viscosity and anisotropic resistivity, the fourth-order wave equation may be replaced by two decoupled second-order equations for right- and left-polarized waves, whose dispersion relations show that the first resistive diffusivity causes dissipation like the viscosity, whereas the second resistive diffusivity causes a change in propagation speed. The second resistive diffusivity invalidates the equipartition of kinetic and magnetic energy, modifies the energy flux through the propagation speed, and also changes the ratio of viscous to resistive dissipation. If the directions of propagation and polarization are equal (i.e. for right-polarized upward-propagating or left-polarized downward-propagating waves), the magnetic energy increases relative to the kinetic energy, the resistive dissipation increases relative to the viscous dissipation, and the total energy density and flux increase relative to the case of isotropic resistivity; the reverse is the case for opposite directions of propagation, i.e. upward-propagating left-polarized waves and downward-propagating right-polarized waves, which can lead to the existence of a critical layer. The role of the viscosity and first and second resistive diffusiveness on the dissipation of Alfvén waves is discussed with reference to the solar atmosphere.

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Song, Hong Wei, Zhen Luo, San San Ao, and Rui Wang. "Propagation Character of the Stress Wave in Weld Joints." Advanced Materials Research 199-200 (February 2011): 801–4. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.801.

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Stress wave test technique has broad prospects because of the long distance, large scale and relatively low cost. This paper studies the stress wave propagation characteristics in the butt weld joints in the case of external symmetrical impact. The stress wave propagating before and after the joints is measured by dynamic strain gauge. Then model analysis and simulation is applied to research stress wave. The results show the loss of stress wave is small. After spectrum analysis, the change of propagating stress wave can be detected.

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Sailov, Rahib Aqaqul, Fazil Ali Veliev, and Qusni Kadir Kerimov. "DETERMINATION OF THE HEAT DISTRIBUTION IN THE RAW COTTON PACKED IN THE COIL." EUREKA: Physics and Engineering 3 (May 31, 2017): 3–11. http://dx.doi.org/10.21303/2461-4262.2017.00359.

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As a result of experimental studies, a special mathematical model of raw cotton is developed. The effect of density change on the thermal conductivity coefficient is determined. A nonlinear differential equation of heat propagation in coils is obtained. The dependence of the density of raw cotton on the coil height is determined experimentally. The heat flux is intense propagating from denser layers of raw cotton to less dense ones. In a saturated form, the effect of density changes on the propagation of heat is less than in the coils. Pocket spontaneous heating occurs locally with sharp boundaries. An expression is found, which is the general solution of the mathematical model of heat propagation in raw cotton in coils, on the basis of which a number of physical real models can be constructed. The model allows to preliminarily give an estimation of the likely picture of the temperature field in the given microvolumes of raw cotton.

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Isnikurniawan, Ahmad, Yasuhiro Fujita, Sachio Tanimoto, and Tatsuo Sawada. "Investigation of Cluster Formation in MR Fluid under Compression Using Ultrasonic Measurement." Materials Science Forum 792 (August 2014): 147–52. http://dx.doi.org/10.4028/www.scientific.net/msf.792.147.

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This paper reports measurement results of ultrasonic propagation velocity in MR fluid under compression. Experiments were conducted by applying different pressures in MR fluid at constant magnetic flux density. At low magnetic flux densities (100 and 200 mT), the ultrasonic propagation velocity in MR fluids changes when subjected to pressure. This change is related to cluster formation in MR fluid. The ultrasonic propagation velocity change is smaller when higher pressures are applied, indicating that cluster size in MR fluid becomes thinner under higher pressures. However, at higher magnetic flux densities (300 and 400 mT), ultrasonic propagation velocities under different pressures are nearly similar. These results indicate that at higher magnetic flux densities, pressures do not affect cluster formation in MR fluids.

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