Change Propagation Analysis in Complex Technical Systems

Understanding how and why changes propagate during engineering design is critical because most products and systems emerge from predecessors and not through clean sheet design. This paper examines a large data set from industry including 41,500 change requests that were generated during the design of a complex sensor system spanning a period of 8 years. In particular, the networks of connected parent, child, and sibling changes are resolved over time and mapped to 46 subsystem areas of the sensor system. These change networks are then decomposed into one-, two-, and three-node motifs as the fundamental building blocks of change activity. A statistical analysis suggests that only about half (48.2%) of all proposed changes were actually implemented and that some motifs occur much more frequently than others. Furthermore, a set of indices is developed to help classify areas of the system as acceptors or reflectors of change and a normalized change propagation index shows the relative strength of each area on the absorber-multiplier spectrum between −1 and +1. Multipliers are good candidates for more focused change management. Another interesting finding is the quantitative confirmation of the “ripple” change pattern previously proposed. Unlike the earlier prediction, however, it was found that the peak of cyclical change activity occurred late in the program driven by rework discovered during systems integration and functional testing.

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Change Propagation Analysis in Complex Technical Systems

Volume 3: 19th International Conference on Design Theory and Methodology; 1st International Conference on Micro- and Nanosystems; and 9th International Conference on Advanced Vehicle Tire Technologies, Parts A and B ◽

10.1115/detc2007-34652 ◽

2007 ◽

Cited By ~ 1

Author(s):

Monica Giffin ◽

Olivier de Weck ◽

Gergana Bounova ◽

Rene Keller ◽

Claudia Eckert ◽

...

Keyword(s):

Change Management ◽

Large Data ◽

Relative Strength ◽

Change Propagation ◽

Data Set ◽

Propagation Analysis ◽

Complex Technical Systems ◽

Management Approaches ◽

Change Requests ◽

Testing Patterns

Understanding how and why changes propagate during engineering design is critical because most products and systems emerge from predecessors and not through clean sheet design. This paper applies change propagation analysis methods and extends prior reasoning through examination of a large data set from industry including 41,500 change requests, spanning 8 years during the design of a complex sensor system. Different methods are used to analyze the data and the results are compared to each other and evaluated in the context of previous findings. In particular the networks of connected parent, child and sibling changes are resolved over time and mapped to 46 subsystem areas. A normalized change propagation index (CPI) is then developed, showing the relative strength of each area on the absorber-multiplier spectrum between −1 and +1. Multipliers send out more changes than they receive and are good candidates for more focused change management. Another interesting finding is the quantitative confirmation of the “ripple” change pattern. Unlike the earlier prediction, however, it was found that the peak of cyclical change activity occurred late in the program driven by systems integration and functional testing. Patterns emerged from the data and offer clear implications for technical change management approaches in system design.

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Stratigraphy of architectural elements in a buried volcanic system and implications for hydrocarbon exploration

Interpretation ◽

10.1190/int-2016-0201.1 ◽

2017 ◽

Vol 5 (3) ◽

pp. SK141-SK159 ◽

Cited By ~ 23

Author(s):

Alan Patrick Bischoff ◽

Andrew Nicol ◽

Mac Beggs

Keyword(s):

Temporal Distribution ◽

Large Data ◽

Sedimentary Basins ◽

Building Blocks ◽

Hydrocarbon Exploration ◽

Coarse Grained ◽

Data Set ◽

Volcanic System ◽

Architectural Elements ◽

Spatio Temporal

The interaction between magmatism and sedimentation creates a range of petroleum plays at different stratigraphic levels due to the emplacement and burial of volcanoes. This study characterizes the spatio-temporal distribution of the fundamental building blocks (i.e., architectural elements) of a buried volcano and enclosing sedimentary strata to provide insights for hydrocarbon exploration in volcanic systems. We use a large data set of wells and seismic reflection surveys from the offshore Taranaki Basin, New Zealand, compared with outcropping volcanic systems worldwide to demonstrate the local impacts of magmatism on the evolution of the host sedimentary basin and petroleum system. We discover the architecture of Kora volcano, a Miocene andesitic polygenetic stratovolcano that is currently buried by more than 1000 m of sedimentary strata and hosts a subcommercial discovery within volcanogenic deposits. The 22 individual architectural elements have been characterized within three main stratigraphic sequences of the Kora volcanic system. These sequences are referred to as premagmatic (predate magmatism), synmagmatic (defined by the occurrence of intrusive, eruptive, and sedimentary architectural elements), and postmagmatic (degradation and burial of the volcanic structures after magmatism ceased). Potential petroleum plays were identified based on the distribution of the architectural elements and on the geologic circumstances resulting from the interaction between magmatism and sedimentation. At the endogenous level, emplacement of magma forms structural traps, such as drag folds and strata jacked up above intrusions. At the exogenous level, syneruptive, intereruptive, and postmagmatic processes mainly form stratigraphic and paleogeomorphic traps, such as interbedded volcano-sedimentary deposits, and upturned pinchout of volcanogenic and nonvolcanogenic coarse-grained deposits onto the volcanic edifice. Potential reservoirs are located at systematic vertical and lateral distances from eruptive centers. We have determined that identifying the architectural elements of buried volcanoes is necessary for building predictive models and for derisking hydrocarbon exploration in sedimentary basins affected by magmatism.

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Change Propagation Analysis and Prediction in Process Choreographies

International Journal of Cooperative Information Systems ◽

10.1142/s0218843015410038 ◽

2015 ◽

Vol 24 (03) ◽

pp. 1541003 ◽

Cited By ~ 1

Author(s):

Walid Fdhila ◽

Stefanie Rinderle-Ma ◽

Conrad Indiono

Keyword(s):

Change Management ◽

Process Mining ◽

Change Propagation ◽

Process Redesign ◽

Multiple Partners ◽

Propagation Behavior ◽

Propagation Analysis ◽

Exploratory Data ◽

Change Requests ◽

Analysis Models

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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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181220130232 ◽

2019 ◽

Vol 21 (9) ◽

pp. 662-669 ◽

Cited By ~ 1

Author(s):

Junnan Zhao ◽

Lu Zhu ◽

Weineng Zhou ◽

Lingfeng Yin ◽

Yuchen Wang ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Regression Tree ◽

Large Data ◽

Thrombin Inhibitors ◽

Coagulation Cascade ◽

Gradient Boosting ◽

Support Vector ◽

Data Set ◽

Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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Correlation between the structure and skin permeability of compounds

Scientific Reports ◽

10.1038/s41598-021-89587-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ruolan Zeng ◽

Jiyong Deng ◽

Limin Dang ◽

Xinliang Yu

Keyword(s):

Large Data ◽

Qsar Model ◽

Coefficient Of Determination ◽

Support Vector ◽

Skin Permeability ◽

Data Set ◽

Test Set ◽

Svm Algorithm ◽

Svm Model ◽

Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

Publications of the Astronomical Society of Australia ◽

10.1017/pasa.2020.46 ◽

2020 ◽

Vol 37 ◽

Author(s):

Lior Shamir

Keyword(s):

Large Scale ◽

Spiral Galaxies ◽

Hubble Space Telescope ◽

Gravitational Interaction ◽

Large Data ◽

Sloan Digital Sky Survey ◽

Data Sets ◽

Dipole Axis ◽

Data Set ◽

The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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The Complete DNA Sequence of the Mitochondrial Genome of a “Living Fossil,” the Coelacanth (Latimeria chalumnae)

Genetics ◽

10.1093/genetics/146.3.995 ◽

1997 ◽

Vol 146 (3) ◽

pp. 995-1010 ◽

Cited By ~ 1

Author(s):

Rafael Zardoya ◽

Axel Meyer

Keyword(s):

Mitochondrial Genome ◽

Tandem Repeats ◽

Phylogenetic Analyses ◽

Large Data ◽

Molecular Data ◽

Phylogenetic Position ◽

Data Set ◽

Living Fossil ◽

Latimeria Chalumnae ◽

Relationship Of

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.

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