Conventional Regression Analysis Research Articles

PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature

This paper proposes a cuff-less systolic blood pressure (SBP) estimation method using partial least-squares (PLS) regression. Level-crossing features (LCFs) were used in this method, which were extracted from the contour lines arbitrarily drawn on the second-derivative photoplethysmography waveform. Unlike conventional height ratio features (HRFs), which are extracted on the basis of the peaks in the waveform, LCFs can be reliably extracted even if there are missing peaks in the waveform. However, the features extracted from adjacent contour lines show similar trends; thus, there is a strong correlation between the features, which leads to multicollinearity when conventional multiple regression analysis (MRA) is used. Hence, we developed a multivariate estimation method based on PLS regression to address this issue and estimate the SBP on the basis of the LCFs. Two-hundred-and-sixty-five subjects (95 males and 170 females [(Mean ± Standard Deviation) SBP: 133.1 ± 18.4 mmHg; age: 62.8 ± 16.8 years] participated in the experiments. Of the total number of subjects, 180 were considered as learning data, while 85 were considered as testing data. The values of the correlation coefficient between the measured and estimated values were found to be 0.78 for the proposed method (LCFs + PLS), 0.58 for comparison method 1 (HRFs + MRA), and 0.62 for comparison method 2 (HRFs + MRA). The proposed method was therefore found to demonstrate the highest accuracy among the three methods being compared.

Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.

ObjectivesThe aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.MethodsInformation of EOC patients from Samsung Medical Center (training cohort, n=1,128) was analyzed to optimize the prognostic model using GB. The performance of the final model was externally validated with patient information from Asan Medical Center (validation cohort, n=229). The area under the curve (AUC) by the GB model was compared to that of the conventional Cox proportional hazard regression analysis (CoxPHR) model.ResultsIn the training cohort, the AUC of the GB model for predicting second year overall survival (OS), with the highest target value, was 0.830 (95% confidence interval [CI]=0.802–0.853). In the validation cohort, the GB model also showed high AUC of 0.843 (95% CI=0.833–0.853). In comparison, the conventional CoxPHR method showed lower AUC (0.668 (95% CI=0.617–0.719) for the training cohort and 0.597 (95% CI=0.474–0.719) for the validation cohort) compared to GB. New classification according to survival probability scores of the GB model identified four distinct prognostic subgroups that showed more discriminately classified prediction than the International Federation of Gynecology and Obstetrics staging system.ConclusionOur novel GB-guided classification accurately identified the prognostic subgroups of patients with EOC and showed higher accuracy than the conventional method. This approach would be useful for accurate estimation of individual outcomes of EOC patients.

Finding the peak of dynamic oxygen uptake during fatiguing exercise in fish.

As fish approach fatigue at high water velocities in a critical swimming speed (Ucrit) test, their swimming mode and oxygen cascade typically move to an unsteady state because they adopt an unsteady, burst-and-glide swimming mode despite a constant, imposed workload. However, conventional rate of oxygen uptake (ṀO2 ) sampling intervals (5-20 min) tend to smooth any dynamic fluctuations in active ṀO2 (ṀO2active) and thus likely underestimate the peak ṀO2active Here, we used rainbow trout (Oncorhynchus mykiss) to explore the dynamic nature of ṀO2active near Ucrit using various sampling windows and an iterative algorithm. Compared with a conventional interval regression analysis of ṀO2active over a 10-min period, our new analytical approach generated a 23% higher peak ṀO2active Therefore, we suggest that accounting for such dynamics in ṀO2active with this new analytical approach may lead to more accurate estimates of maximum ṀO2 in fishes.

Revisiting the Effect of Visitation on Recidivism

Scholarship suggests that prison inmates who are visited may be less likely to recidivate. Questions exist, however, about whether the observed relationship is causal and, if so, whether it is consistent for different groups of inmates. To address these questions, this study employs two methodological approaches – first, conventional regression analyses and, second, instrumental variable (IV) analyses – to examine the effects of visitation in general and specifically for females, young inmates, and individuals incarcerated for the first time. Effect size estimates are similar across the two analytic approaches, but conventional regression analyses identify a statistically significant effect of visitation, whereas IV analyses do not. Subgroup analyses suggest differences between males and females and by age. Combined, the results raise questions about whether visitation exerts a causal effect on offending. Implications for theory, research, and policy of the divergent results and the potential for a generalized visitation effect are discussed.

Assessing the causal role of body mass index on cardiovascular health in young adults: Mendelian randomization and recall-by-genotype analyses.

Body mass index (BMI) has been suggested to be causally related to cardiovascular health in mid-to-late life, but this has not been explored systematically at younger ages - nor with detailed cardiovascular phenotyping. Recall-by-Genotype (RbG) is an approach that enables the collection of precise phenotypic measures in smaller studies, whilst maintaining statistical power and ability for causal inference. In this study, we used a combination of conventional multivariable regression analysis, Mendelian randomization (MR) and sub-sample RbG methodologies to estimate the causal effect of BMI on gross-level and detailed cardiovascular health in healthy participants from the Avon Longitudinal Study of Parents and Children at age 17 (N=1420-3108 for different outcomes) and an independent sample from the same cohort (for RbG) study at age 21 (N=386-418). In both MR and RbG analyses, results suggested that higher BMI causes higher blood pressure (BP) and left ventricular mass index (LVMI) in young adults (e.g., difference in LVMI per kg/m2 using MR: 1.07g/m2.7; 95% CI: 0.62, 1.52; P=3.87x10-06 and per 3.58kg/m2 using RbG: 1.65g/m2.7 95% CI: 0.83, 2.47; P=0.0001). Additionally, RbG results suggested a causal role of higher BMI on higher stroke volume (SV: difference per 3.58kg/m2: 1.49ml/m2.04; 95% CI: 0.62, 2.35; P=0.001) and cardiac output (CO: difference per 3.58kg/m2: 0.11l/min/m1.83; 95% CI: 0.03, 0.19; P=0.01) but no strong evidence for a causal role on systemic vascular resistance or total arterial compliance. Neither analysis supported a causal role of higher BMI on heart rate. Complementary MR and RbG causal methodologies, together with a range of sensitivity analyses, suggest that higher BMI is likely to cause worse cardiovascular health, specifically higher BP and LVMI, even in youth. Higher BMI also resulted in increased CO in the RbG study, which appeared to be solely driven by SV, as neither MR nor RbG analyses suggested a causal effect of BMI on heart rate. These consistent results support efforts to reduce BMI from a young age to prevent later adverse cardiovascular health and illustrate the potential for phenotypic resolution with maintained analytical power using RbG.

Metabolic Syndrome as a Predictor of Adrenal Functional Status: A Discriminant Multivariate Analysis Versus Logistic Regression Analysis.

Patients harboring adrenal tumors are characterized by higher prevalence of metabolic syndrome (MetS) components and a higher incidence of cardiovascular complications, especially in cases of subclinical or overt hormonal hypersecretion. Early detection and referral of those patients in tertiary centers could prevent unfavorable outcomes. In this cross-sectional, retrospective study, we evaluated 111 consecutive patients with adrenal incidentalomas and 14 patients with known hypersecretory adrenal lesions (autonomous cortisol secretion, primary aldosteronism, and pheochromocytoma), who were investigated in our clinic. Based on the different distribution of MetS components in patients with non-functional and functional adrenal lesions we introduced a predictive model of hormonal hypersecretion using those components. We performed multivariate discriminant analysis and compared predictive results with conventional multiple logistic regression analysis. Diabetes, impaired glucose tolerance, impaired fasting glucose, hypertension, body mass index, HDL-cholesterol levels, triglyceride levels, drug treatment for lipid disorder (statins, fenofibrate, and fish oils, alone or in combination), and maximal adrenal lesion diameter were used as discriminating covariates. Multivariate discriminant function exhibited a sensitivity of 77.27% and specificity of 73.08% in predicting adrenal hormonal hypersecretion. Receiver operating characteristic curve of discriminant predictive function had an area under the curve value of 0.785, S.E. 0.04. Logistic function delivered comparable results. MetS components exhibit a good predictive feature of hormonal hypersecretion in patients with adrenal tumors. Predictive functions may help in the search for an easy and generally available algorithm to validly predict the functional activity of adrenal masses.

Clinical characteristics, evolution, and treatment-related risk factors for mortality among immunosuppressed patients with influenza A (H1N1) virus admitted to the intensive care unit

PurposeInformation about immunocompromised patients infected with influenza A (H1N1) virus and requiring admission to the ICU is lacking. Our objective was to know the clinical characteristics of these patients and to identify treatment-related variables associated with mortality. Material and methodsA prospective multicenter observational cohort study was based on data from a Spanish registry (2009–2015) collected by 148 Spanish ICUs. All patients admitted to the ICU with the diagnosis of influenza A (H1N1) virus infection were included. Immunosuppression was clearly defined. Factors associated with mortality in immunocompromised patients were assessed by conventional logistic regression analysis and by a propensity score (PS) adjusted-multivariable analysis. ResultsOf 1899 patients with influenza A (H1N1) infection, 238 (12.5%) were classified as immunocompromised. Mortality was significantly higher in immunosuppressed patients. Four variables independently associated with mortality were identified: SOFA score, need of vasopressor, use of corticosteroids, and acute renal failure, AKIN 3 stage. In the PS-adjusted model, corticosteroid therapy remained as an independent factor associated with increased mortality (OR 2.25;95%CI, 1.15–4.38;p = 0.017). In the subgroup of hematological patients (n = 141), corticosteroid therapy was also associated with increased mortality (OR 3.12; 95%CI, 1.32–7.41; p = 0.010). ConclusionImmunocompromised individuals with influenza A (H1N1) admitted to the ICU have a poor outcome. In this population, the use of corticosteroids is strongly discouraged.

Thallium in flowering cabbage and lettuce: Potential health risks for local residents of the Pearl River Delta, South China

Thallium (Tl), a rare metal, is universally present in the environment with high toxicity and accumulation. Thallium's behavior and fate require further study, especially in the Pearl River Delta (PRD), where severe Tl pollution incidents have occurred. One hundred two pairs of soil and flowering cabbage samples and 91 pairs of soil and lettuce samples were collected from typical farmland protection areas and vegetable bases across the PRD, South China. The contamination levels and spatial distributions of soil and vegetable (flowering cabbages and lettuces) Tl across the PRD were investigated. The relative contributions of soil properties to the bioavailability of Tl in vegetables were evaluated using random forest. Random forest is an accurate learning algorithm and is superior to conventional and correlation-based regression analyses. In addition, the health risks posed by Tl exposure via vegetable intake for residents of the PRD were assessed. The results indicated that rapidly available potassium (K) and total K in soil were the most important factors affecting Tl bioavailability, and the competitive effect of rapidly available K on vegetable Tl uptake was confirmed in this field study. Soil weathering also contributed substantially to Tl accumulation in the vegetables. In contrast, organic matter might not be a major factor affecting the mobility of Tl in most of the lettuce soils. Fe and manganese (Mn) oxides also contributed little to the bioavailability of Tl. A risk assessment suggested that the health risks for Tl exposure through flowering cabbage or lettuce intake were minimal.

Interactive data visualization based on conventional statistical findings for antihypertensive prescriptions using National Health Insurance claims data

BackgroundInteractive visualization is an important approach to help to understand and to explain large amounts of data, particularly in light of decision support. Although data visualization have been introduced in healthcare and clinical fields, analytics has often been performed by data experts, focused on specific subjects, or insufficient statistical evidence. Therefore, this study suggests the procedures of effective and efficient visualization of big data for general healthcare researchers. Specifically, the procedure includes conventional regression analyses followed by interactive data visualization for prescription patterns of antihypertensive drugs. MethodsAs a large-scale nationally representative prescription data, the Korean National Health Insurance claims data were collected. Conventional descriptive and regression analyses were conducted for therapy decision and prescription patterns using the software R. Then, based on the statistically significant findings, dashboards were developed to visualize interactively the patterns of prescriptions using the software Tableau. ResultsMajor characteristics (genders, age groups, healthcare institutions, and comorbidities) explained the differences in therapy and the average number of drugs prescribed as well as differences among most commonly prescribed drug classes. Two interactive dashboards were created for visualizing prescription patterns with incorporation of horizontal bar charts, packed bubble charts, treemaps, filled maps, radar charts, box and whisker plots, and filters. ConclusionIn the current big data era, interactive data visualization offers substantial opportunities to have comprehensive view, extract insights and evidence from the flood of vast amounts of data. This study’s interactive visualizations can provide healthcare professionals insight into prescription patterns and demonstrate the value of creating interactive dashboards to support informed and timely decision-making. Exploring big data using interactive visualization is expected to deliver many future benefits in healthcare fields.

Curvilinear Moderation—A More Complete Examination of Moderation Effects in Behavioral Sciences

In behavioral sciences, researchers often examine whether any linear moderations exist in their studies. That is, they evaluate the extent (i.e., magnitude, direction) to which a linear effect of a predictor X (e.g., cognitive ability) on a criterion Y (e.g., performance) may differ across the levels of a moderator M (e.g., gender). In that case, researchers often run a liner regression analysis for examining this moderation (e.g., gender by ability). Despite its popularity, linear moderation is insufficient for researchers to understand complex human phenomena. Curvilinear moderation is a data-analytic technique that identifies whether a predictor X and a criterion Y form a non-linear relationship, and how this relationship may differ across the levels of a moderator M. I describe eight common types of curvilinear moderation that are typically not addressed in the literature and propose an algorithm for detecting them. Using a Monte Carlo simulation, I show that the conventional linear regression analysis inappropriately and mistakenly flags a significant main effect of the moderator (M), but this effect is appropriately signaled as a significant curvilinear moderation effect (i.e., X by M) using my proposed algorithms. Misidentification of moderation effects poses serious threats to the accuracy of theory and model testing. Researchers can use curvilinear moderation analysis to avoid this problem and correctly detect curvilinear moderation in their studies.

The association between red cell distribution width and venous thromboembolism is not explained by myocardial infarction, stroke, or cancer

Unlabelled Box BackgroundRed cell distribution width (RDW) is a risk marker of venous thromboembolism (VTE), myocardial infarction (MI), stroke, and cancer. Due to interrelations between these diseases, the association between RDW and VTE may be explained by MI, stroke, or cancer. ObjectiveTo investigate whether the effect of RDW on VTE could be explained by intermediate development of MI, stroke, or cancer. MethodsRDW was measured in 24 363 participants of the Tromsø Study in 1994‐1995. Incident VTE, MI, stroke, and cancer were registered until December 31, 2010. Conventional and cause‐specific Cox‐regression models were used to estimate hazard ratios (HR) for VTE with 95% confidence intervals (CI) across categories of RDW. ResultsThere were 502 first VTEs during a median follow‐up of 16 years. In conventional Cox regression analysis, RDW in the highest quartile was associated with a 71% (HR 1.71, 95% CI 1.09‐2.67) and 27% (HR 1.27, 95% CI 0.88‐1.85) higher risk of VTE in men and women, respectively, compared to subjects in the lowest quartiles. The risk of VTE among subjects with RDW in the highest quartile was similar for men and women of postmenopausal age. In cause‐specific analysis, where each individual contributed with person‐time until the first occurring event only, the risk estimates were similar to those of the conventional Cox‐regression analysis. ConclusionOur findings suggest that the association between RDW and future risk of VTE is not explained by intermediate development of MI, stroke, or cancer.

Usefulness of multilevel modeling in sport management research: The case of voluntary roles in nonprofit sports clubs

ABSTRACTIn many areas of sport management, individual behavior is not only driven by individual characteristics but also by higher level contextual factors. For instance, voluntary engagement in nonprofit sports clubs is influenced by organizational characteristics. Such multilevel structures should not only be considered theoretically, as in ecological systems theory, but they also require appropriate statistical treatment. This study aims to demonstrate the usefulness of multilevel modeling using the case of voluntary roles (administrative, coach, referee) in nonprofit sports clubs. Survey data from German sports clubs (n = 228) and their members (n = 636) were collected and merged. The results of multilevel models show that organizational capacity is significantly associated with taking up different voluntary roles. However, the effects are lower and fewer organizational-level variables are significant compared with conventional regression analyses which neglect the hierarchical data structure. Hence, adequate statistical treatment is necessary for deriving accurate managerial implications.

Bayesian and LASSO Regressions for Comparative Permeability Modeling of Sandstone Reservoirs

In this research, Bayesian model averaging (BMA) and least absolute shrinkage and selection operator regression (LASSO) algorithms were adopted for the core permeability modeling as a function of well log and core measurements. More specifically, the core permeability (dependent factor) was modeled given the well log and core data (independent variables) and then predicted in non-cored intervals of a well in a sandstone formation. The BMA is a stochastic linear modeling and a Bayesian parameter selection. Among 50 linear models generated as a function of the independent variables and depicted in Occam’s window, the best model of the highest posterior probability is determined. The best model has the optimal subset parameters that most influences the response factor. The core permeability modeling was again conducted using the LASSO algorithm, which adopts a completely different way of modeling and subset selection by using the penalized least-squared equation. Both BMA and LASSO resulted in a very accurate prediction of the core permeability by achieving perfect measured and calculated permeability matching. Matches for the two algorithms were quantified by computing the adjusted R-squared and root-mean-squared prediction error. In addition, results of the BMA and LASSO were compared to the conventional multiple linear regression (LM). The LASSO algorithm led to an accurate matching similar to LM, but slightly better than BMA. Therefore, both BMA and LASSO represent integrated procedures for accurate permeability estimation similar to conventional regression analysis.

Reference Values for Spirometry Derived Using Lambda, Mu, Sigma (LMS) Method in Korean Adults: in Comparison with Previous References.

BackgroundThe present study aimed to update the prediction equations for spirometry and their lower limits of normal (LLN) by using the lambda, mu, sigma (LMS) method and to compare the outcomes with the values of previous spirometric reference equations.MethodsSpirometric data of 10,249 healthy non-smokers (8,776 females) were extracted from the fourth and fifth versions of the Korea National Health and Nutrition Examination Survey (KNHANES IV, 2007–2009; V, 2010–2012). Reference equations were derived using the LMS method which allows modeling skewness (lambda [L]), mean (mu [M]), and coefficient of variation (sigma [S]). The outcome equations were compared with previous reference values.ResultsPrediction equations were presented in the following form: predicted value = e{a + b × ln(height) + c × ln(age) + M − spline}. The new predicted values for spirometry and their LLN derived using the LMS method were shown to more accurately reflect transitions in pulmonary function in young adults than previous prediction equations derived using conventional regression analysis in 2013. There were partial discrepancies between the new reference values and the reference values from the Global Lung Function Initiative in 2012.ConclusionThe results should be interpreted with caution for young adults and elderly males, particularly in terms of the LLN for forced expiratory volume in one second/forced vital capacity in elderly males. Serial spirometry follow-up, together with correlations with other clinical findings, should be emphasized in evaluating the pulmonary function of individuals. Future studies are needed to improve the accuracy of reference data and to develop continuous reference values for spirometry across all ages.

Modeling the North American vertical datum of 1988 errors in the conterminous United States

Abstract A large systematic difference (ranging from −20 cm to +130 cm) was found between NAVD 88 (North AmericanVertical Datum of 1988) and the pure gravimetric geoid models. This difference not only makes it very difficult to augment the local geoid model by directly using the vast NAVD 88 network with state-of-the-art technologies recently developed in geodesy, but also limits the ability of researchers to effectively demonstrate the geoid model improvements on the NAVD 88 network. Here, both conventional regression analyses based on various predefined basis functions such as polynomials, B-splines, and Legendre functions and the Latent Variable Analysis (LVA) such as the Factor Analysis (FA) are used to analyze the systematic difference. Besides giving a mathematical model, the regression results do not reveal a great deal about the physical reasons that caused the large differences in NAVD 88, which may be of interest to various researchers. Furthermore, there is still a significant amount of no-Gaussian signals left in the residuals of the conventional regression models. On the other side, the FA method not only provides a better not of the data, but also offers possible explanations of the error sources. Without requiring extra hypothesis tests on the model coefficients, the results from FA are more efficient in terms of capturing the systematic difference. Furthermore, without using a covariance model, a novel interpolating method based on the relationship between the loading matrix and the factor scores is developed for predictive purposes. The prediction error analysis shows that about 3-7 cm precision is expected in NAVD 88 after removing the systematic difference.

Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders

Objectives This study aimed to identify return-to-work (RTW) trajectories among workers with work-related musculoskeletal disorders (MSD) and examine the associations between different MSD and these RTW trajectories. Methods We used administrative workers' compensation data to identify accepted MSD lost-time claims with an injury date between 2010-2012 in British Columbia, Canada. Cox regression analyses were used to investigate differences in time to RTW between MSD. Validated day-to-day calendar measures of four RTW states (sickness absence, modified RTW, RTW, and non-RTW) were grouped into RTW trajectories spanning a one-year period using sequence analysis. RTW trajectories were clustered using decision rules that identified a shared trajectory structure. Poisson regression with robust standard errors was used to estimate relative risk ratios (RR) with 95% confidence intervals (CI) between MSD and RTW trajectory clusters. Results In a cohort of 81 062 claims, 2132 unique RTW trajectories were identified and clustered into nine RTW trajectory clusters. Half of the workers sustainably returned to work within one month. Workers with back strains were most likely to have trajectories characterized by early sustained RTW, while workers with fractures or dislocations were more likely to have prolonged sickness absence trajectories (RR 4.9-9.9) or non-RTW trajectories (RR 1.4-7.6). Conclusion This is the first study that has characterized different types of RTW trajectories of workers with MSD using sequence analysis. The application of sequence analysis and the identification of RTW trajectories yielded a number of key insights not found using conventional cox regression analysis.

Sensitivity Analysis of the Main Factors Controlling Floor Failure Depth and a Risk Evaluation of Floor Water Inrush for an Inclined Coal Seam

The main factors controlling floor failure depth are highly consistent with those of floor water inrush. In this paper, a mechanical model was developed to calculate the maximum failure depth of the upper and lower sides of a mining working face floor along the coal seam’s inclination. The results indicated that the main factors controlling floor failure depth are mining thickness, working face slanting length, coal seam dip angle, mining depth, water pressure, cohesion, and internal friction angle. The floor failure depth of the 3303 working face in the Yangcheng coal mine was calculated using FLAC3D numerical simulation software. Based on matrix and variance analyses, the sensitivity of each of these factors with respect to floor failure depth followed the order: working face slanting length (extremely significant) > mining depth (highly significant) > cohesion (significant) > mining thickness (not significant) > coal seam dip angle (not significant) > water pressure (not significant) > internal friction angle (not significant). Also, the optimal plan for orthogonal simulation is A1 B1 C1 D1 E2 F1 G1, in which the floor failure depth is minimized. Finally, the average accuracy of logistic regression analysis of the principal component was up to 90.4% accurate, about 10% better than conventional logistic regression analysis.

Determination of the height of destressed zone above the mined panel: An ANN model

The paper describes an artificial neural network (ANN) model to predict the height of destressed zone (HDZ) which is taken as equivalent to the combined height of caved and fractured zones above the mined panel in longwall mining. For this, the suitable datasets have been collected from the literatures and prepared for modeling. The data were used to construct a multilayer perceptron (MLP) network to approximate the unknown nonlinear relationship between the input parameters and HDZ. The MLP proposed model predicted values in enough agreements with the measured ones in a satisfactory correlation, in which, a high conformity (R2=0.989) was observed. To approve the capability of proposed ANN model, the obtained results are compared to the results of the conventional regression analysis (CRA) method. The calculated performance evaluation indices show the higher level of accuracy of the proposed ANN model compared to CRA. For further evaluation, the ANN model results are compared with the results of available models and in-situ measurements reported in literatures. Comparative results present a logical agreement between ANN model and available methods. Obtained results remark that the proposed ANN model is a suitable tool in HDZ estimation. At the end of modeling, the parametric study shows that the most effective parameter is unit weight whereas elastic modulus is the least effective parameter on the HDZ in this study.

The impact of extracurricular activities participation on youth delinquent behaviors: An instrumental variables approach

The purpose of this study was to examine the association between extracurricular activities (EA) participation and youth delinquency while tackling an endogeneity problem of EA participation. Using survey data of 12th graders in South Korea (n = 1943), this study employed an instrumental variables approach to address the self-selection problem of EA participation as the data for this study was based on an observational study design. We found a positive association between EA participation and youth delinquency based on conventional regression analysis. By contrast, we found a negative association between EA participation and youth delinquency based on an instrumental variables approach. These results indicate that caution should be exercised when we interpret the effect of EA participation on youth delinquency based on observational study designs.

Performance optimization of integrated resilience engineering and lean production principles

Performance optimization of integrated resilience engineering and lean production.An intelligent algorithm based ANN, DEA and statistical methods.Previous studies neglected the combined impacts of both factors.Data envelopment analysis is used for validation of results.Considers a real-world pipe manufacturer as case study. This paper conducts performance assessment from integrated resilience engineering (IRE) and lean production points of view. This is the first study that evaluates the impact of integrated resilience engineering (IRE) on lean production principles. Second, this study considers integrated impact of lean production by a unique intelligent algorithm. The proposed algorithm is composed of radial basis function (RBF), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). Moreover, the algorithm is capable of handling both crisp and fuzzy data due to the existence of intelligent approach. The proposed algorithm is equipped with verification and validation mechanism through conventional regression, statistical methods and data envelopment analysis. To demonstrate the applicability of the study, a real-world pipe manufacturer is considered as our case study. The results showed that pull system and fault tolerant among lean and IRE factors, respectively have been implemented inappropriately, while other factors are either suitably executed or ineffective.