Variable Analysis Model Research Articles

A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics

Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N= 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p= 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.

Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.

A novel compliant assembly variation analysis with consideration of initial deviation and welding shrinkage

Welding, as the most common joining technique, causes welding deformations that reduce the geometrical accuracy of hull structures. In conventional variation analysis, welding distortion is not taken into account. In this paper, a compliant assembly variation modeling and analysis method is presented to investigate the impact of local weld-induced shrinkage on the propagation relation from the initial deviation to the final assembly deviation in thin plate assembly. First, welding deformations in the form of inherent strains are incorporated into variation analysis model based on Kirchhoff plate model. Additionally, part deviations are also introduced via equivalent forces in order to consider the effects of geometrical variation on compliant assembly. Then, other variation sources, such as fixture locating errors, are modeled as equality constraints of the mechanical variation model, which are then transformed into boundary conditions with Lagrange multiplier method. With an additional stiffness matrix, we describe concisely the coupling relationship between initial deviations and welding deformations caused by gaps and misalignments. In order to validate the effectiveness of the proposed method, numerical simulations and experimental verifications are carried out for two assembly cases, and the results show that the present method has high prediction accuracy and efficiency. This study provides a potential application for improving assembly dimensions considering the inevitable manufacturing deviations and welding deformations.

The boundary-spanning mechanisms of Nobel Prize winning papers.

The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on a scale that significantly alters the betweenness centrality distributions in existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of similar prize-winning properties that may have been overlooked among other regular publications.

Analysis of variability models: a systematic literature review

Dealing with variability, during Software Product Line Engineering (SPLE), means trying to allow software engineers to develop a set of similar applications based on a manageable range of variable functionalities according to expert users’ needs. Particularly, variability management (VM) is an activity that allows flexibility and a high level of reuse during software development. In the last years, we have witnessed a proliferation of methods, techniques and supporting tools for VM in general, and for its analysis in particular. More precisely, a specific field has emerged, named (automated) variability analysis, focusing on verifying variability models across the SPLE’s phases. In this paper, we introduce a systematic literature review of existing proposals (as primary studies) focused on analyzing variability models. We define a classification framework, which is composed of 20 sub-characteristics addressing general aspects, such as scope and validation, as well as model-specific aspects, such as variability primitives, reasoner type. The framework allows to look at the analysis of variability models during its whole life cycle—from design to derivation—according to the activities involved during an SPL development. Also, the framework helps us answer three research questions defined for showing the state of the art and drawing challenges for the near future. Among the more interesting challenges, we can highlight the needs of more applications in industry, the existence of more mature tools, and the needs of providing more semantics in the way of variability primitives for identifying inconsistencies in the models.

Evaluation of reanalysis and global meteorological products in Beas river basin of North-Western Himalaya

It is a great challenge to obtain reliable gridded meteorological data in some data-scarce and complex territories like the Himalaya region. Less dense observed raingauge data are unable to represent rainfall variability in the Beas river basin of North-Western Himalaya. In this study four reanalyses (MERRA, ERA-Interim, JRA-55 and CFSR) and one global meteorological forcing data WFDEI have been used to evaluate the potential of the products to represent orographic rainfall pattern of Beas river basin using hydrology model. The modeled climate data have compared with observed climate data for a long term basis. A comparison of various rainfall and temperature products helps to determine uniformity and disparity between various estimates. Results show that all temperature data have a good agreement with gridded observed data. ERA-Interim temperature data is better in terms of bias, RMSE (Root Mean Square Error), and correlation compared to other data. On the other hand, MERRA, ERA-Interim and JRA-55 models have overestimated rainfall values, but CFSR and WFDEI models have underestimated rainfall values to the measured values. Variable Infiltration Capacity (VIC), a macroscale distributed hydrology model has been successfully applied to indirectly estimate the performance of five gridded meteorological data to represent Beas river basin rainfall pattern. The simulation result of the VIC hydrology model forced by these data reveals that the discharge of ERA-Interim has a good agreement with observed streamflow. In contrast there is an overestimated streamflow observed for MERRA reanalysis estimate. JRA-55, WFDEI, and CFSR data underestimate the streamflow. The reanalysis products are also poor in capturing the seasonal hydrograph pattern. The ERA-Interim product better represents orographic rainfall for the Beas river basin. The reason may be the ERA-Interim uses a four-dimensional variational analysis model during assimilation. The major drawback of MERRA is the non-inclusion of observed precipitation data during assimilation and modeling error. The poor performance of JRA-55, CFSR and WFDEI is due to the gauge rainfall data assimilation error. This research finding will help for broader research on hydrology and meteorology of the Himalayan region.

Research on symbiosis state between manufacturing and producer services industry

The producer services industry and the manufacturing industry are interconnected; subdivision industries of producer services industry have different effects on the manufacturing industry. In order to accurately determine the relationship among them, and to guide China Government to correctly make industrial planning, a variable analysis model is built based on theoretical analysis. From the viewpoint of time series, the first period state of their relation is compared with the whole period, the reasons for changes are analysed. In addition, the balance of industrial relation is forecasted. Using increased value of industry to calculate their interacting relation after data smoothness treatment based on Lotka-Volterra model and EViews software. The result shows that the relationship between manufacturing, technology services, and finance is predator-prey relation, while manufacturing and transportation and warehousing are in mutually reinforcing and symbiosis condition. After discovering their current relationship, recommendations for optimising industrial relations in order to achieve industrial cooperation and mutual development are brought forward.

Research on the Variation Analysis Model for CFRP Assembly

The conventional error analysis model is based on Key Characteristics (KC) tree [1] and the nominal 3D assembly model. Before the actual execution of assembly process, the potential interferences and out-of-tolerance problems are simulated and predicted. Then the tolerance and assembly schemes of the parts are optimized accordingly. This method requires statistical deviation data of parts and it can only give a rough probability for mass production case. In this paper, the common CAT technologies, variation analysis and optimization method are presented with an example of wing box model in 3DCS. Based on KC tree and optimizing theories a model analysis method is proposed to improve the performance in the case of multi-material part assembly processes.

Scalable learning and probabilistic analytics of industrial big data based on parameter server: Framework, methods and applications

With the ever increasing scale of industrial data, the computational burden for process modeling and analytics has becoming tremendous, particularly for large-scale processes. In this paper, a distributed parallel probabilistic learning framework based on scalable Parameter Server (PS) architecture is proposed for big process data. Under this framework, the traditional Variational Inference (VI) probabilistic model can be transformed to a scalable form through the Stochastic Variational Inference (SVI) algorithm, which can be further deployed on the PS architecture to make a distributed and parallel model. As an example, the traditional Variational Inference Mixture Factor Analysis (VIMFA) model is converted to the SVI-MFA model and deployed on the PS architecture for process big data modeling. Then it is utilized for process monitoring and quality prediction applications. A numerical case is first generated to validate the feasibility and efficiency of the SVI-MFA modeling algorithm, and then a TE benchmark process and a real Methanation Unit process demonstrate the effectiveness of the proposed framework for big process data modeling and analytics.

Performance of Three Reanalysis Precipitation Datasets over the Qinling-Daba Mountains, Eastern Fringe of Tibetan Plateau, China

Evaluation of different reanalysis precipitation datasets is of great importance to understanding the hydrological processes and water resource management practice in the Qinling-Daba Mountains (QDM), located at the eastern fringe of the Tibetan Plateau. Although the evaluation of satellite precipitation data in this region has been performed, another kind of popular precipitation product-reanalysis dataset has not been assessed in depth. Three popular reanalysis precipitation datasets, including ERA-Interim Reanalysis of European Centre for Medium Forecasts (ERA-Interim), Japanese 55-year Reanalysis (JRA-55), and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis-1 (NCEP/NCAR-1) were evaluated against rain gauge data over the Qinling-Daba Mountains from 2000 to 2014 on monthly, seasonal, and annual scales. Different statistical measures based on the Correlation Coefficient (CC), relative BIAS (BIAS), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE) were adopted to determine the performance of the above reanalysis datasets. Results show that ERA-Interim and JRA-55 have good performance on a monthly scale and annual scale. However, the NCEP/NCAR-1 has the least BIAS with the observed precipitation in annual scale in QDM. All reanalysis datasets performed better in spring, summer, and autumn than in winter. The advantages of involving more precipitation observation stations was probably the main reason of the different performance of three precipitation reanalysis products, and the benefit of a four-dimensional variational analysis model over a three-dimensional variational analysis model may be another reason. The evaluation suggested that ERA-Interim is more suitable for study the precipitation and water cycles in the QDM.

The Influence of Shopping Characteristics and Socio-Demographic Factors on Selected In-Store Buying Practices in Different Socio-Economic Regions

The aim of this paper is to ascertain the usage of price comparisons and avoidance of impulse purchases regarding in-store food-buying practices among consumers who reside in low, middle and high socio-economic status (SES) regions. The article will also focus on the effect of shopping characteristics and socio-demographic factors on in-store food-buying practices. A quantitative consumer intercept survey was conducted among 400 low, 400 middle and 400 high SES region consumers in retail stores. Statistical analysis of in-store food-buying practices was conducted via a generalised linear model analysis of variables, utilising the Wald’s Chi-square statistic distribution. Consumers who live in the low SES region exhibited the largest price comparison usage tendency, whereas the high SES region consumers showed the highest predisposition to make impulse purchases. Several shopping characteristics and socio-demographic factors resulted in significant associations in terms of price comparisons and the avoidance of impulse purchases. Food manufacturers, marketers and retailers should consider implementing the study’s primary results in their marketing activities among consumers in the different SES regions. Additionally, shopping characteristics and socio-demographic factors of in-store food-buying practices require further research to add to the limited theoretical discourse.Â

Compliant assembly analysis including initial deviations and geometric nonlinearity, part II: Plate structure

Part I of this paper (Liu et al., “Compliant assembly analysis including initial deviations and geometric nonlinearity, part I: Beam structure”) has studied the variation propagation of beam structures with consideration of initial deviations and geometric nonlinearity. In practices, plate structures are more commonly used in manufacturing fields, and the attempt of this paper is to expand previous methodology for the assembly process of orthotropic composite plate structures. Similarly, initial deviations and von Kármán-type geometric nonlinearity are introduced into variation analysis model, with Mindlin plate theory accounting for shear effect. The analyzed plates are set as orthotropic composite materials, which also preserve the compatibility with isotropic metal materials. Governing equations and corresponding finite element expressions can be obtained by applying the principle of virtual work. Also, a linearized model or noninitial model can be regarded as a degradation of origin governing equations. A variation analysis approach for plate structures is proposed to make more refined assembly variation predictions with consideration of initial deviations, fixture errors, and matching deviations. The verification of the developed method is implemented with case studies on springback prediction of two composite plates assembly.

A Variation Analysis Method for Linkage Mechanism with Consideration of Joint Clearance and Deformation

As typical transmission mechanisms, linkage mechanisms are widely applied in mechanical engineering field, and accuracy prediction of them has received much more attentions especially from some high-precision application fields. For a certain linkage mechanism system, external forces will also cause linkage deformations and deterministic oriented-drift of joint clearances, which become additional variation sources besides component tolerances. In order to perform a more accurate prediction of linkage mechanism, a variation analysis method is proposed in this study, in which component tolerances, joint clearances and deformations are considered. With an equivalent method, positional tolerance and joint clearances are taken into the present variation analysis model, and serve as circular tolerances. Based on classical Euler-Bernoulli beam theory, linkage deformations are disposed as equivalent deviations and contribute to assembly deviations. A decomposing method of percentage contribution, which contains different variation sources, is presented as well. A case study of four-bar linkage mechanism is illustrated to validate the accuracy of the present method by corresponding FEA simulation and experimental test. Moreover, a case study of a three-loop mechanism is also analyzed for the accuracy and percentage contribution of different variation sources with the present method.

보육교사의 조직헌신도에 영향을 미치는 관련 변인들의 구조모형 분석

보육교사의 조직헌신도에 영향을 미치는 관련 변인들의 구조모형 분석

A probabilistic self-validating soft-sensor with application to wastewater treatment

In the wastewater treatment plants (WWTPs), soft sensors are viewed as a simple signal estimator for hard-to-measure quantities. However, the presence of unreliable data, coupled with increasing demands for measurement quality assurance, has rendered inadequate such a simplistic view. In this paper, a probabilistic self-validating soft-sensor is proposed with the capability of performing self-diagnostics, self-reconstruction and online uncertainty measurement. In this framework, data collecting for soft-sensor modeling (easy-to-measure data) is validated by a Variational Bayesian Principal Component Analysis (VBPCA) model before performing a soft-sensor model construction. By integrating Relevant Vector Machine (RVM) as a predictive model, not only prediction values are obtained, but also the credibility of information for easy-to-measure and hard-to-measure quantities can be generated. The performance of the proposed soft-sensor is validated through two simulation studies of WWTPs with different process characteristics. The results suggest that the proposed strategy significantly improves the prediction performance.

A new assembly variation analysis model based on the method of power balance for auto-body parts

Purpose – The purpose of this paper is to propose a new assembly variation analysis model to analyze assembly variation for sheet metal parts. The main focus is to analyze assembly processes based on the method of power balance. Design/methodology/approach – Starting with issues in assembly variation analysis, the review shows the critical aspects of tolerance analysis. The method of influence coefficient (MIC) cannot accurately analyze the relationship between part variations and assembly variations, as the welding point is not a point but a small area. Therefore, new sensitivity matrices are generated based on the method of power balance. Findings – Here two cases illustrate the processes of assembly variation analysis, and the results indicate that new method has higher accuracy than the MIC. Research limitations/implications – This study is limited to assembly variation analysis for sheet metal parts, which can be used in auto-body and airplane body. Originality/value – This paper provides a new assembly variation analysis based on the method of power balance.

Assembly Operation Optimization Based on Social Radiation Algorithm for Autobody

Assembly dimensional quality affects wind noise and driving steady and whole external appearance. The quality can be improved by reducing part tolerance and fixture tolerance and optimizing key control characteristics (KCCs). However, reducing tolerance should largely increase manufacturing costs, and then the paper assembly tolerance is decreased by selecting optimal KCCs. In this work, a fitness function is presented to evaluate assembly operations based on the linear assembly variation analysis model. Afterwards, a new social radiation algorithm (SRA) is proposed to optimize KCCs, and some test functions are used to evaluate optimum performance between the genetic algorithm (GA) and SRA, and the results show that the performance of SRA is better than that of GA. Finally two cases are used to illustrate process of assembly operation optimization by SRA, and the results show that the SRA has higher precision and efficiency than that of GA.

Assembling Variation Analysis Based on Power Balance Method for Sheet Metal Parts

Sheet metal parts are widely used in auto body and airplane body. As the method of influence coefficient cannot accurately analyze the relationship between part deviations and assembly variations, this paper proposes a new variation analysis model based on a method of power balance for sheet metal parts. The simple metal planes are loaded to fixtures according to the principle of “4-2-1”; and then variety of part deviations is analyzed during assembly process. The analysis shows that the welding guns do same work during welding process. Based on the above, this work establishes sensitive coefficient matrices between part deviations and assembly variations. Finally, two Z-shape sheet metal parts illustrate the assembling variation analysis process and the results indicate the accuracy of the method of power balance.

DXA-based Model for Advanced Analysis of Body Shape Variation

The aim of this study was to measure the effective dose on an anthropomorphic phantom undergoing lumbar and femoral dual energy X-ray absorption (DXA) examinations, using 3 different scan modalities (fast-array [FA], array [A], high-definition [HD]), and assess the differences in the lifetime attributable risk (LAR) of cancer due to radiation. An anthropomorphic phantom was used. Thermoluminescent dosimeters were placed over 12 anatomic phantom regions and outside the room (to measure background radiation). Fifty scans on the femur and spine were performed for each mode. The dose relative to a single DXA scan for each dosimeter was measured (mean over the 50 scans) and the background radiation was then subtracted. The equivalent dose per organ was obtained. The total body effective dose was calculated by adding the equivalent doses. We estimated the lifetime dose absorption and LAR for cancer for a male and a female patient undergoing 36 DXA studies (18 lumbar, 18 femoral) every 21 months for 32 years. The effective dose for lumbar scans was FA = 17.79 μSv, A = 32.88 μSv, HD = 31.08 μSv; for femoral scans, FA = 5.29 μSv, A = 9.55 μSv, HD = 7.54 μSv. LAR estimation showed a minimal increase in cancer risk (range 4.55 × 10−4% [FA, femoral, male] to 4.02 × 10−3% [A, lumbar, female]). The lifetime dose absorption and LAR for cancer for a male and a female patient undergoing 36 DXA studies (18 lumbar, 18 femoral) every 21 months for 32 years were 0.756 mSv, 3.82 × 10−3% and 0.756 mSv, 5.11 × 10−3%, respectively. DXA examinations cause radiation levels that are comparable to the background radiation. Regardless of the scan modality or the anatomic site, a patient undergoing DXA scans for a lifetime has a negligible increased risk of developing cancer.

Assembling Variation Analysis Based on Power Balance Method for Sheet Metal Parts

Sheet metal parts are widely used in auto body and airplane body. As the method of influence coefficient cannot accurately analyze the relationship between part deviations and assembly variations, this paper proposes a new variation analysis model based on a method of power balance for sheet metal parts. The simple metal planes are loaded to fixtures according to the principle of “4-2-1”; and then variety of part deviations is analyzed during assembly process. The analysis shows that the welding guns do same work during welding process. Based on the above, this work establishes sensitive coefficient matrices between part deviations and assembly variations. Finally, two Z-shape sheet metal parts illustrate the assembling variation analysis process and the results indicate the accuracy of the method of power balance.