Combinations Of Input Values Research Articles

An Extended Algorithm using a Divide-and-Rule Approach for Boundary Value Analysis and Equivalence Class Partitioning

An extended algorithm using a divide-and-rule approach is proposed in this paper. Instead of generating test cases by only using Boundary Value Analysis and an experience-based technique for combination of input variables with functional dependency like the original algorithm, the extended algorithm applies both Boundary Value Analysis and Equivalence Class Partitioning techniques as well as various techniques for combination of input values. In addition, a new tool provides advantages when compared with the original tool. It provides users with more choices in generating test cases.

REPAIRS: Gaussian Mixture Model-based Completion and Optimization of Partially Specified Systems

Most system optimization techniques focus on finding the values of the system components to achieve the best performance. Searching over all component values gives the search methodology the freedom to explore the entire design space to determine the best system configuration. However, real-world systems often require searching in a restricted space over only a subset of component values while freezing some of the components to fixed values. Rather than optimizing from scratch to search over the subset of components, incorporating the past simulation logs (search performed when all components were allowed to vary) enables the optimization mechanism to utilize knowledge from past system behavior. In addition, when the system gives the same response over different combinations of input values, the designer may prefer one combination over another. Furthermore, real-world data often contain errors. To avoid catastrophic consequences of making decisions based on incorrect data points, we need a mechanism to identify and correct the resulting error. We propose REPAIRS, a methodology to complete/optimize partially specified systems. It also performs data integrity checks and identifies/corrects errors after detecting an anomaly in the data. We use a Gaussian mixture model to learn the joint distribution of the system inputs and the corresponding output response (objectives/constraints). We use the learned model to complete a partially specified system where only a subset of the component values and/or the system response is specified. When the system response exhibits multiple modes (e.g., same response for different combinations of input values), REPAIRS determines the combinations of input values that correspond to the several modes. Using past simulation logs, it searches over various subsets of system inputs to improve the performance of the reference solution. We also present a framework for verifying the integrity of a given data instance. When the integrity check fails, we provide a mechanism to identify the error location and correct the error. REPAIRS provides an explanation for the decision it makes for the different use cases described in this article. We provide results of REPAIRS in the context of completion, partial optimization, and data integrity check of real-world systems. REPAIRS achieves a hypervolume that is better than that obtained using a baseline method by up to 50%. It successfully identifies the error location and predicts the correct value of the erroneous feature with an error less than 0.2%. It detects error locations with a mean accuracy of up to 95% even when three feature values have an error.

Влияние технологических отклонений на пусковые свойства асинхронного двигателя при его производстве

Исследовано влияние технологических отклонений при производстве и сборке магнитопровода асинхронного двигателя на кратность пускового тока КПТ и кратность пускового момента КПМ. Получены уравнения регрессии для КПТ и КПМ асинхронного двигателя, исследованные на экстремум, что позволило определить координаты для минимального и максимального значения исследуемых функций. Построены поверхности отклика, которые дают полученные уравнения в факторном пространстве для различных сочетаний входных величин. Определены коэффициенты влияния и получены зависимости, связывающие отклонения кратностей пускового тока и пускового момента асинхронного двигателя и технологические отклонения.

Separability of stereoisomers by electrokinetic chromatography in presence of a neutral selector – Fundamental aspects assessed by computer simulation

The impact of the two essential parameters, the complexation constant and the mobility of the formed diastereomeric complex, on stereoisomer separation in presence of a neutral chiral selector was assessed by computer simulation for an electrokinetic chromatography configuration with a uniform background electrolyte and one with a cationic discontinues buffer system of isotachophoretic nature. With two enantiomers of norpseudoephedrine as model analytes, data for seven cases featuring complexation in free solution with various combinations of input values, complexation with an immobilized selector and no complexation were analyzed in a hitherto unexplored way. For the uniform buffer study, the determined differences of the effective mobilities and separation selectivities of the stereoisomers were found to be equal to those calculated with the well-known algebraic equations. For the isotachophoretic system with its Kohlrausch adjusted zones, separation is also based on differences in effective mobilities, but the mobility differences cannot be predicted with the same algebraic equation. In both techniques, chiral separations occur due to the presence of the selector and if there is inequality between the mobilities of the transient diastereomeric complexes and the mobility of the free, uncomplexed analyte. Separation of the stereoisomers is possible when complexation constants, complex mobilities or both of these parameters differ. In the isotachophoretic separation a migrating steady-state is formed in which analytes either establish consecutive zones with plateau concentrations or, if present in an insufficient amount, as a peak-like distribution that migrates within a moving steady-state boundary. Simulation data illustrate for the first time the use of a spacer compound that establishes an isotachophoretic zone between enantiomers and thereby provides complete separation of the enantiomers and the possibility of individual detection in peak-mode isotachophoresis. They demonstrate that such an approach could be employed to assess the enantiomeric purity of a chiral compound.

Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

PurposeGlobal sensitivity analysis increasingly replaces manual sensitivity analysis in life cycle assessment (LCA). Variance-based global sensitivity analysis identifies influential uncertain model input parameters by estimating so-called Sobol indices that represent each parameter’s contribution to the variance in model output. However, this technique can potentially be unreliable when analyzing non-normal model outputs, and it does not inform analysts about specific values of the model input or output that may be decision-relevant. We demonstrate three emerging methods that build on variance-based global sensitivity analysis and that can provide new insights on uncertainty in typical LCA applications that present non-normal output distributions, trade-offs between environmental impacts, and interactions between model inputs.MethodsTo identify influential model inputs, trade-offs, and decision-relevant interactions, we implement techniques for distribution-based global sensitivity analysis (PAWN technique), spectral clustering, and scenario discovery (patient rule induction method: PRIM). We choose these techniques because they are applicable with generic Monte Carlo sampling and common LCA software. We compare these techniques with variance-based Sobol indices, using a previously published LCA case study of geothermal heating networks. We assess eight environmental impacts under uncertainty for three design alternatives, spanning different geothermal production temperatures and heating network configurations.ResultsIn the application case on geothermal heating networks, PAWN distribution-based sensitivity indices generally identify influential model parameters consistently with Sobol indices. However, some discrepancies highlight the potentially misleading interpretation of Sobol indices on the non-normal distributions obtained in our analysis, where variance may not meaningfully describe uncertainty. Spectral clustering highlights groups of model results that present different trade-offs between environmental impacts. Compared to second-order Sobol interaction indices, PRIM then provides more precise information regarding the combinations of input values associated with these different groups of calculated impacts. PAWN indices, spectral clustering, and PRIM have a computational advantage because they yield stable results at relatively small sample sizes (n = 12,000), unlike Sobol indices (n = 100,000 for second-order indices).ConclusionsWe recommend adding these new techniques to global sensitivity analysis in LCA as they give more precise as well as additional insights on uncertainty regardless of the distribution of the model outputs. PAWN distribution-based global sensitivity analysis provides a computationally efficient assessment of input sensitivities as compared to variance-based global sensitivity analysis. The combination of clustering and scenario discovery enables analysts to precisely identify combinations of input parameters or uncertainties associated with different outcomes of environmental impacts.

Integrated Pairwise Testing based Genetic Algorithm for Test Optimization

Generation of Test cases in software testing is an important and a complex activity as it deals with diversified range of inputs. Fundamentally, test case generation is considered to be a multi-objective problem as it aims to cover many targets. Deriving test cases for the Web Applications has become critical to the most of the enterprises. In this paper, a solution for generating test cases for web applications is proposed; the solution uses the System Graph (consisting of links and data dependencies) considering that test cases were based on a combination of input values and data dependencies. Pairwise testing is used to derive the test cases to be executing from entire test cases and then a genetic algorithm is proposed to generate test cases specific to functional testing. The proposed approach was tested through two distinct experiments by measuring the code coverage at every generation and results show that genetic algorithm used increased the fitness value and code coverage. Overall, the results of the paper validate the proposed approach and algorithm, having potential in further construct an automated integrated solution for generating test cases for the entire process.

Interactive Dynamics of Reaction-Diffusion and Adhesion Predict Diverse Invasion Strategies of Cancer Cells in Matrix-Like Microenvironments

The metastasis of malignant epithelial tumors begins with the egress of transformed cells from the confines of their basement membrane to their surrounding collagenous stroma. The invasion can be morphologically diverse, ranging from that of dispersed mesenchymal cells to multicellular collectives. When cancer cells are cultured within basement membrane-like matrix (BM), or Type 1 collagen, or a combination of both, they show collective-, dispersed mesenchymal-, and hybrid collective-dispersed (multiscale) invasion respectively. Here, we ask how distinct these invasive modes are with respect to the cellular and microenvironmental cues that drive them, and how can cancer cells transition between such states. A rigorous exploration of invasion was performed within an experimentally motivated Cellular Potts-based computational modeling environment. The model comprises of adhesive interactions between cancer cells, BM- and collagen-like extracellular matrix (ECM), and reaction-diffusion-based remodeling of ECM. The model output consisted of metrics cognate to dispersed- and collective- invasion. An exhaustive combination of input values gave rise to a spatial output distribution that comprised dispersed-, collective- and multiscale- invasion. K-means clustering of the output distribution followed by silhouette analysis revealed three optimal clusters: one signifying indolent invasion and two representing multiscale invasions, each of which encompass the purer dispersed- and collective invasions respectively. Principal component analyses of phenotypic outputs close to the separation of these two multiscale modes established specific input signatures: perturbing such signatures revealed the cues that can allow phenotypic transitions among these three clusters. Our systems-level analysis provides quantitative insights into the biases and constraints cancer cells face during their invasion through tissue microenvironments with distinct physicochemical properties.

Improving dynamic domain reduction test data generation method by Euler/Venn reasoning system

Test data adequacy is a major challenge in software testing literature. The difficulty is to provide sufficient test data to assure the correctness of the program under test. Especially, in the case of latent faults, the fault does not reveal itself unless specific combinations of input values are used to run the program. In this respect, detection of subdomains of the input domain that cover a specific execution path seems promising. A subdomain covers an execution path provided that each test data taken from the subdomain satisfies the path constraint. Dynamic Domain Reduction, or DDR in short, is a very well-known test data generation procedure, targeted at detection of the subdomains of the input domain that satisfy a given path constraint. In this paper, an improved version of DDR called Rapid Dynamic Domain Reduction, or RDDR in short, is introduced. RDDR is intended to explore subdomains of a program input domain, satisfying a given path constraint. For each feasible path, there is a distinct subdomain of the input domain that causes the program to execute the path. Hereby, we introduce a new metric named domain coverage, to qualify input data sets, in terms of the percentage of the subdomain of a feasible path covered by the data set. The main inspiration behind the domain coverage metric is to support test data adequacy. Our empirical results based on some well-known case studies confirms that RDDR significantly outperforms DDR in terms of speed and accuracy.

Automated detection of lineaments express geological linear features of a tropical region using topographic fabric grain algorithm and the SRTM DEM

The availability of the large volume of remote sensing data has allowed for the developing of several automated algorithms for detecting linear geological features and more reliable analysis. However, most of the algorithms focus on edge detection and tone change on a satellite image, which represents all geological and non-geological features. In this study, a topographic fabric algorithm, which calculates the slope and aspect at each point in a DEM, is applied for automatically geological linear features mapping in Bau Goldfield, Malaysia using the new version of the Shuttle Radar Topographic Mission (SRTM) DEM. A series of topographic fabric input parameters was tested using different combinations of input values in order to decide the optimal parameters that provided the suitable detection parameters, best fit and the highest accuracy. Comparison with the geological map demonstrated that the tested parameters made the algorithm able to automatically detect geological structures.

Sensitivity analysis of building form and BIPVT energy performance for net-zero energy early-design stage consideration

This study examines the influence of building form – comprised of plan shape and roof slope – on the net annual energy performance of solar net-zero energy (NZE) buildings. The renewable energy system used is the building-integrated photovoltaics with thermal heat recovery (BIPVT). BIPVT is a relatively new enhancement of the building-integrated photovoltaics (BIPV) system which others have studied for the effects of building form on building energy demand and energy generation. Previous sensitivity analyses of building form have typically not considered BIPV or BIPVT. When they have, it has been limited to simple, prismatic forms; it is unclear how more complex or compound building forms impact building energy demand and BIPVT generation. Therefore this study analyzes the relationship between building form and BIPVT performance to provide guidance for medium-sized NZE commercial and institutional building design at the early design stage. The main input parameters are the building form: compound or complex plan shapes, and BIPVT roof slope. Also included are: building orientation, window to wall ratio (WWR), and U-value. These parameters are evaluated in a heating dominated climate to determine the parameters sensitive to the net energy demand – the balance between annual energy loads including heating/cooling energy demand and PV electricity generation and useful thermal heat recovery. A customized BIPVT model is used in TRNSYS simulations. A variance-based ANOVA global sensitivity analysis method is used for both main and interaction effects. Results show that each of the form variations studied is able to reach NZE using different combinations of input values. The most sensitive input parameters for the NZE output target are the BIPVT roof tilt angle, the azimuth, WWR (South), plan shape, and WWR (North). Each one is quantified. This information may provide building designers guidance at the early design stage.

Need for mobile application for disability assessment in India.

Introduction:Disability can be by birth or by any tragic event in life or medical illness. Such differently abled persons can become a valuable asset to the nation rather than a liability if we properly quantify their disabilities and help them rehabilitate into jobs best suited for them. The aim of this study was to develop a mobile application for easy quantification of disability of a differently abled person according to the latest Government of India guidelines.Material and Methods:The Government of India Gazette notification of January 2018 spells out revised guidelines approved by the Ministry of Social Justice and Empowerment for calculation of disabilities of differently abled persons. These were first converted into a spreadsheet, workflow, and interface modules. Subsequently, they were coded into a mobile application using a simple web language.Results:The result is an android App that can be downloaded from Google Play Store for free use. The rigorous testing of the App with all possible combination of input values was done to weed out errors. The results through the App were compared against manual calculation used as a control for validation purposes.Conclusion:To bring about uniformity, objectivity, ease in disability calculation for a large population of differently abled persons, a mobile App is truly justified.

Analytical and numerical study of the stability of shallow underground circular openings in cohesive ground

This paper addresses the problem of assessing the stability conditions of shallow excavations of circular shape, namely circular sections of long cylindrical tunnels or spherical cavities, in purely cohesive ground by means of a scalar factor of safety as it is traditionally done for the case of slopes in the practice of geotechnical engineering. The paper presents a closed-form statically admissible solution for cavities in dry ground that allows a conservative estimation of the scalar factor of safety to be made. The effects of the geometry of the excavation, whether a section of cylindrical tunnel or a spherical cavity, the depth of the cavity in relation to its radius, and the effect of the support pressure and ground surcharge load in relation to the cohesion of the ground, on the factor of safety are examined. A quantitative analysis for determining how conservative the results from the analytical solution are with respect to results obtained with a commercial software implementing the finite-difference method is presented. The analysis indicates that the values of factors of safety predicted with the analytical solution are within 80% of the values of factors of safety predicted with the numerical (finite-difference) solution. A quantitative comparison of values of factors of safety and required support pressure obtained with the analytical solution and with other published solutions is presented. Factors of safety obtained with Terzaghi (1943) limit equilibrium model are shown to be both conservative and non-conservative depending on the combination of input values considered for the model. Values of required support pressure predicted with the proposed analytical solution are shown to be similar to the ones predicted numerically by Davis et al. (1980). An extension of the proposed statically admissible model to account for water in the ground is also presented. Cases of shallow cavities of circular shape in saturated cohesive ground with a water surface at or above the ground surface, and limiting conditions of flooded and dry openings are considered. Stability conditions for flooded openings in saturated ground are shown to generally improve, while stability conditions for dry openings in saturated ground are shown to worsen, in comparison with the same equivalent cavities in dry ground.

An approach based on Support Vector Machines and a K-D Tree search algorithm for identification of the failure domain and safest operating conditions in nuclear systems

The safety of a Nuclear Power Plant (NPP) is verified by analyzing the system responses under normal and accidental conditions. This is done by resorting to a Best-Estimate (BE) Thermal-Hydraulic (TH) code, whose outcomes are compared to given safety thresholds enforced by regulation. This allows identifying the limit-state function that separates the failure domain from the safe domain.In practice, the TH model response is affected by uncertainties (both epistemic and aleatory), which make the limit-state function and the failure domain probabilistic.The present paper sets forth an innovative approach to identify the failure domain together with the safest plant operating conditions. The approach relies on the use of Reduced Order Models (ROMs) and K-D Tree.The model failure boundary is approximated by Support Vector Machines (SVMs) and, then, projected onto the space of the controllable variables (i.e., the model inputs that can be manipulated by the plant operator, such as reactor control-rods position, feed-water flow-rate through the plant primary loops, accumulator water temperature and pressure, repair times, etc.). The farthest point from the failure boundary is, then, computed by means of a K-D Tree-based nearest neighbor algorithm; this point represents the combination of input values corresponding to the safest operating conditions.The approach is shown to give satisfactory results with reference to one analytical example and one real case study regarding the Peak Cladding Temperature (PCT) reached in a Boiling Water Reactor (BWR) during a Station-Black-Out (SBO), simulated using RELAP5-3D.

FOREPOST: finding performance problems automatically with feedback-directed learning software testing

A goal of performance testing is to find situations when applications unexpectedly exhibit worsened characteristics for certain combinations of input values. A fundamental question of performance testing is how to select a manageable subset of the input data faster in order to automatically find performance bottlenecks in applications. We propose FOREPOST, a novel solution, for automatically finding performance bottlenecks in applications using black-box software testing. Our solution is an adaptive, feedback-directed learning testing system that learns rules from execution traces of applications. Theses rules are then used to automatically select test input data for performance testing. We hypothesize that FOREPOST can find more performance bottlenecks as compared to random testing. We have implemented our solution and applied it to a medium-size industrial application at a major insurance company and to two open-source applications. Performance bottlenecks were found automatically and confirmed by experienced testers and developers. We also thoroughly studied the factors (or independent variables) that impact the results of FOREPOST.

LDA-SVM-based EGFR mutation model for NSCLC brain metastases: an observational study.

Epidermal growth factor receptor (EGFR) activating mutations are a predictor of tyrosine kinase inhibitor effectiveness in the treatment of non-small-cell lung cancer (NSCLC). The objective of this study is to build a model for predicting the EGFR mutation status of brain metastasis in patients with NSCLC. Observation and model set-up. This study was conducted between January 2003 and December 2011 in 6 medical centers in Southwest China. The study included 31 NSCLC patients with brain metastases. Eligibility requirements were histological proof of NSCLC, as well as sufficient quantity of paraffin-embedded lung and brain metastases specimens for EGFR mutation detection. The linear discriminant analysis (LDA) method was used for analyzing the dimensional reduction of clinical features, and a support vector machine (SVM) algorithm was employed to generate an EGFR mutation model for NSCLC brain metastases. Training-testing-validation (3 : 1 : 1) processes were applied to find the best fit in 12 patients (validation test set) with NSCLC and brain metastases treated with a tyrosine kinase inhibitor and whole-brain radiotherapy. Primary and secondary outcome measures: EGFR mutation analysis in patients with NSCLC and brain metastases and the development of a LDA-SVM-based EGFR mutation model for NSCLC brain metastases patients. EGFR mutation discordance between the primary lung tumor and brain metastases was found in 5 patients. Using LDA, 13 clinical features were transformed into 9 characteristics, and 3 were selected as primary vectors. The EGFR mutation model constructed with SVM algorithms had an accuracy, sensitivity, and specificity for determining the mutation status of brain metastases of 0.879, 0.886, and 0.875, respectively. Furthermore, the replicability of our model was confirmed by testing 100 random combinations of input values. The LDA-SVM-based model developed in this study could predict the EGFR status of brain metastases in this small cohort of patients with NSCLC. Further studies with larger cohorts should be carried out to validate our findings in the clinical setting.

A Comparative study of Test Exit Criteria in Software Testing

Software Testing refers to the set of activities carried out on the software with the intent of finding errors. It is one to ascertain that the software meets all its specifications. For any software a very large number of scenarios and input value combinations exist and testing the software for each of these combinations is not possible. And, also the time and resources for testing are limited so the challenge of testing is to test maximum functionality in the available time period. Hence, Test Exit criteria or when to stop testing, is of utmost importance. In this paper we will discuss the different Test Exit criteria and focus on their comparison to determine which one of them is the best which covers maximum functionality of the software. General Terms Software Testing

INNOVATIVE APPROACH TO ORGANIZATION OF TAX CONTROL OVER ENTREPRENEURIAL ACTIVITY

Modern economists point out to up-to-datedness of the problem of better tax control. In the same time the indicators of efficiency are rather declarative and refer rather to tax system as a whole than to tax control. Classical approaches to the notion “tax policy” is analyzed in the article. Analysis of the reasons which determine the necessity to increase tax control, the needs of management and information bodies to work with non-payers of taxes, investigation of practical experience of tax offices of the Perm territory in carrying out of inspections and generalization of the results, identification of group patterns of the combination of in-house and on-site audits allowed the author to identify statistical indicators which can characterize in complex way the results of control for current period and in dynamics for the number (series) of investigated periods. Targets of each stage of tax control are identified; the key role is fulfilled by the systems of technological support because efficiency of taxation depends on use of targeted, functional, institutional technologies of technical means. The importance of the method of tax revenues simulation is outlined. The results of calculation of simulation model are the forecasts on expected object and the structure of tax revenues for every combination of input values. Investigations of inspections of specific objects allowed to find out the necessity of mobilization of the staff for on-site inspection, success of which depends on special training. DOI: http://dx.doi.org/10.12731/2218-7405-2013-10-23

Supply chain modelling and analysis: an application of Latin square to a repeated coupling of non-linear differential equations

In the last 50 years, Forrester’s system dynamics techniques have been adopted to analyse problems and find solutions for global supply chains. An important topic in production-inventory system modelling is the design of experiment. The aim of this paper is to present an application of a statistical technique of design of experiment, the Latin Square Design, to set a combination of input values for the initial-value problem of non-linear repeated coupling of first-order differential equations modelling a production-inventory system. This design permits to reduce the number of experiments while allowing statistical analysis for testing the significance of the studied parameters.

Artificial neural network modeling for fission gas release in LWR UO 2 fuel under RIA conditions

A fission gas release (FGR) model was developed by using an artificial neural network method to predict fission gas release in UO 2 fuel under reactivity initiated accident (RIA) conditions. Based on the test data obtained in the CABRI test reactor and nuclear safety research reactor, the model takes into account the effect of the five parameters: pellet average burnup, peak fuel enthalpy, the ratio of peak fuel enthalpy to pulse width, fission gas release during base-irradiation, and grain size of a fuel pellet. The parametric study of the model, producing a physically reasonable trend of FGR for each parameter, shows that the pellet average burnup and the ratio of peak fuel enthalpy to pulse width are two of the most important parameters. Depending on the combination of input values for the five parameters, the application of the model to a fuel rod under typical RIA conditions of light water reactor produces 1.7–14.0% of FGR for the pellet average burnup ranging from 20 to 70 MW d/kg U.

Steering and visualization: Enabling technologies for computational science

Computational steering is an investigative paradigm whereby the parameters of a running program can be altered according to what is seen in the currently visualized results of the simulation. For certain problems, interactive computation brings specific benefits: parameter sweeps can be completed more efficiently by quickly identifying combinations of input values that yield nonsensical results; ‘what-if’ studies may elucidate a number of related hypotheses without computing all of them from scratch; even simply tracking the development of a computation on-line may allow its early termination (with consequent resource savings) if it turns out its set-up was flawed for some reason. Having first come to prominence in the 1990s, the take-up of steering has accelerated in recent years: high performance computer (HPC) facilites have become more widely available, usage of the Grid is increasing, and visualization is experiencing take-up beyond the walls of the pioneering graphics laboratories that first nurtured it. All these factors combine to expand the range and difficulty of the scientific problems that can be tackled by steering. However, this benefit comes at the cost of increasing hardware and software complexity which itself may defeat further progress. This paper discusses these issues and, in the light of some users’ recent experiences, charts the challenges that still face us.