Multiple Related Variables Research Articles

Predicting the critical organizational behavior and culture of the Turkish construction industry's occupational groups for determining the success of the construction business

Organizational culture is connected to people's capacity to organize themselves internally and adapt to the outside environment. Contracting companies were asked to complete a questionnaire based on organizational culture characteristics. This study intends to make a substantial theoretical contribution by demonstrating a broader perspective on exposing the essential cultural features using a quantitative approach with the fewest possible questions. Exploratory factor analysis and Principal Component Analysis were used to identify the most critical variables and extract a more manageable number of components from multiple related variables. Twenty-eight variables were reduced to thirteen to eliminate statistical cross-loading. Furthermore, the organizational culture was simplified from eight to three dimensions. Thirteen factors were created based on cultural habits in the construction sector, with three inclusive dimensions: multiple motivational, one–bipolar, and many bipolar. This study provides a more succinct survey that quantifies cultural habits and their influence on sectoral performance.

Multivariate Alarm Monitoring for Non-Convex Normal Operating Zones Based on Search Cones

Normal operating zones (NOZs) are the variation spaces of multiple related variables under normal conditions. This paper proposes a search-cone-based method to establish NOZ models and design dynamic alarm thresholds for multivariate alarm monitoring. First, mathematical models are established by exploiting search cones from historical normal data points to describe non-convex NOZs of multiple related process variables. Second, dynamic alarm thresholds of each process variable are designed based on the established NOZ models for new data points being inside or outside the NOZs. The proposed method extends from convex NOZs to non-convex ones, and the main contribution is to establish mathematical models of NOZs for monitoring abnormal conditions and design dynamic alarm thresholds for generating alarms. Continuous stirred tank reactors (CSTR) and industrial examples are provided to illustrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Alarm systems play critically important roles for safe and efficient operation of industrial plants. Many process variables are related so that their normal variation ranges formulate high-dimensional geometric spaces referred to as normal operating zones (NOZs). Hence, it is desirable to adopt the NOZs for alarm monitoring. By contrast, if relationships among process variables are ignored, missed and false alarms may occur to deteriorate the performance of alarm systems. This paper proposes a method to establish mathematical models for NOZs and design dynamic alarm thresholds. The established models can describe non-convex or convex NOZs; the dynamic alarm thresholds can quantify distances between NOZ boundaries and operating points of a multivariate process

Multivariable identification of membrane fouling based on compacted cascade neural network

The system consists of three modules: data acquisition, feature extraction, and online prediction. The data acquisition module obtains the value of process variables from the sensor and transfers the acquired variables to the database via PLC. The feature extraction module is designed to filter the collected process data. In this module, multiple related variables are selected as the preselected variables. Then the PLS method is used to reduce the dimension of the preselected variables to obtain the feature variables with a high correlation with the predicted variables. Finally, the online prediction module based on a cascade neural network predicts membrane fouling, membrane integrity, and membrane life for clean decisions. • A multivariable identification model is construted. • Cascade neural network is proposed to predict multiple variables to avoid the interference of overlap inputs. • Unsupervised pretraining algorithm is used to initialize the structure of cascade neural network. • Because the parameters are affected by multiple errors, the hierarchical learning algorithm is used to improve the identification accuracy. The membrane fouling phenomenon, reflected with various fouling characterization in the membrane bioreactor (MBR) process, is so complicated to distinguish. This paper proposes a multivariable identification model (MIM) based on a compacted cascade neural network to identify membrane fouling accurately. Firstly, a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network, which could avoid the interference of the overlap inputs. Secondly, an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM. Thirdly, a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online. Finally, the proposed model was tested in real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.

“Masks do not work”: COVID-19 misperceptions and theory-driven corrective strategies on Facebook

PurposeOne of the most prolific areas of misinformation research is examining corrective strategies in messaging. The main purposes of the current study are to examine the effects of (1) partisan media (2) credibility perceptions and emotional reactions and (3) theory driven corrective messages on people's misperceptions about COVID-19 mask wearing behaviors.Design/methodology/approachThe authors used a randomized experimental design to test the hypotheses. The data were collected via the survey firm Lucid. The number of participants was 485. The study was conducted using Qualtrics after the research project was exempt by the Institutional Research Board of a large University in the US. The authors conducted an online experiment with four conditions, narrative versus statistics and individual versus collective. The manipulation messages were constructed as screenshots from Facebook.FindingsThe findings of this study show that higher exposure to liberal media was associated with lower misperceptions, whereas higher credibility perceptions of and positive reactions toward the misinformation post and negative emotions toward the correction comment were associated with higher misperceptions. Moreover, the findings showed that participants in the narrative and collective-frame condition had the lowest misperceptions.Originality/valueThe authors tested theory driven misinformation corrective messages to understand the impact of these messages and multiple related variables on misperceptions about COVID-19 mask wearing. This study contributes to the existing misinformation correction literature by investigating the explanatory power of the two well-established media effects theories on misinformation correction messaging and by identifying essential individual characteristics that should be considered when evaluating how misperceptions about the COVID-19 crisis works and gets reduced.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0600

A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete

The replacement of natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) in concrete mixtures offers various advantages, including conservation of natural resources, reduction of CO2 emissions, and cost reduction. However, multiple related variables and objectives (e.g., mechanical, economic, and environmental objectives) need to be considered when optimizing mixtures of recycled aggregate concrete (RAC). This cannot be achieved through traditional laboratory- or statistics-based methods. This study proposes a hybrid intelligent system based on artificial intelligence (AI) and metaheuristic algorithms for designing optimal mixtures of RAC. To verify the proposed model, a data set containing 344 different RAC mixtures was collected from previous literature. A semi-supervised cotraining algorithm using two k-nearest neighbor (kNN) regressors with different distance metrics is developed to label the unlabeled data in the collected dataset. Different AI models are incorporated into the system for modeling the relationship between RAC strength and its influencing variables. A multi-objective optimization (MOO) model based on AI algorithms and on a multi-objective firefly algorithm is used to search for optimal mixtures of RAC. The results show that kNN-based semi-supervised cotraining can effectively exploit unlabeled data to improve the regression estimates. In the test set, A Random Forest and Backpropagation Neural Network achieve the best prediction accuracy for predicting, respectively, uniaxial compressive strength and splitting tensile strength of RAC, indicated by the highest correlation coefficients (0.9064 and 0.8387, respectively) and lowest root-mean-square errors (6.639 MPa and 0.5119 MPa, respectively). The Pareto fronts of the multi-objective mixture optimization problem are successfully obtained by the MOO model. The proposed system can also be used to optimize mixture proportions of other cementitious materials in civil engineering.

Analysis of organizational power networks through a holistic approach using consensus strategies

Power is one of the most complex organizational attributes to understand due to the multiple related variables and dimensions in which it appears. The ownership and use and of power are reflected in the interpersonal relationships within an organization, as a result, modeling its structure and interactions can lead to knowledge about the power networks that shape it. The objective of this study was to identify the behavior of organizational networks based on existing sources of power, using a consensual analysis of the different topologies present in these networks. The study was carried out in a private production company in Ecuador, which has representation at a domestic level. To this end, a 12-question personalized questionnaire was designed with the aim of identifying specific networks and was applied to 1190 workers in the company. The results were obtained using organizational network analysis and a consensus strategy to integrate the centralities found in multiple networks into one. This study can serve as a reference to organizations, so they can know the relationships between people within it, as part of their management process. In this way, the identification of people within power networks is useful for knowing the “key” actors in the promotion of organizational changes, as well as for the development of career plans based on the position that people occupy in the organizational system.

Compound Autoregressive Network for Prediction of Multivariate Time Series

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time‐series impact on each other to make the prediction more difficult. Then, a solution of time‐series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment‐monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.

Neutrosophic Soft Sets Forecasting Model for Multi-Attribute Time Series

Traditional time series forecasting models mainly assume a clear and definite functional relationship between the historical values and the current/future values of a dataset. In this paper, we extended the current model by generating multi-attribute forecasting rules based on the consideration of combining multiple related variables. In this model, neutrosophic soft sets (NSSs) are employed to represent historical statues of several closely related attributes in stock market, such as volumes, stock market index, and daily amplitudes. Specifically speaking, the status of up, equal, and down in historical stock index can be represented by truth, indeterminacy, and false, respectively, by neutrosophic sets (NSs) and NSSs can build mappings of different related attributes to NSs. The advantages of the proposed model are: 1) using NSSs to enclose different historical characteristics in time series to preserve inherent complexity of a dataset with mapping adequate features and 2) with the existing researches of NSSs, it is efficient with using Euclidean distance to find the optimal rules, and thus, the model can avoid incomplete of rules due to the limited sample dataset. To evaluate the performance of the model, we explored the closing prices of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major parameter that we forecast and the stock amplitudes and volumes as the other factors to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast some other influential indexes as well.

Multivariate geostatistical simulation with sum and fraction constraints

ABSTRACTGeostatistical modelling of grades in mineral deposits often requires the simulation of multiple related variables that have sum and fraction constraints. Sum constraints occur when the sum of some variables may not exceed or must equal a given constant. Fraction constraints occur when a variable may not exceed another variable. In this case, the geostatistical simulations should reproduce the histograms, variograms, multivariate relationships and honour the constraints. We present a methodology for the geostatistical simulation of multiple related variables that considers sum and fraction constraints. The methodology is illustrated in a case study. The data were obtained from a bauxite deposit. First, the original variables were re-expressed as ratios. Second, the variables were transformed using the Projection Pursuit Multivariate Transform (PPMT). Then the PPMT transformed variables were simulated independently using sequential Gaussian simulation and back-transformed. The simulations reproduced the histograms, variograms and bivariate relationships and honoured the sum and fraction constraints.

IMPROVING THE COMPLEXITY OF CHAOTIC SEQUENCE BASED ON THE PCA ALGORITHM

The principal component analysis (PCA) is an effective statistical analysis method in statistical data analysis, feature extraction and data compression. The method simplifies multiple related variables into a linear combination of several irrelevant variables, through the less-comprehensive index as far as possible to replace many of the original data, and can reflect the information provided by the original data. This paper studies the signal feature extraction algorithm based on PCA, and extracts sequences’ feature which generated by Logistic mapping. Then we measured the complexity of the reconstructed chaotic sequences by the permutation entropy algorithm. The testing results show that the complexity of the reconstruction sequences is significantly higher than the original sequences.

Prediction of β-glucosidase and β-glucosaminidase activities, soil organic C, and amino sugar N in a diverse population of soils using near infrared reflectance spectroscopy

There is a need for methods that can rapidly measure multiple biological properties simultaneously. A near infrared spectroscopy (NIRS) method was used to predict β-glucosidase (EC 3.2.1.21) and β-glucosaminidase (NAGase, EC 3.2.1.52) activities, and soil organic C and amino sugar N concentrations in 184 diverse soils of Ohio. The laboratory-measured values of the variables were calibrated against NIR spectral data with partial least squares regression analysis. Statistical analysis of the spectral data was done using the multivariate analysis software Unscrambler 8.0 (CAMO Inc). The first differential transformation of the spectral data in the NIR region (1100–2498nm) generally yielded best results for developing multivariate calibration models. The multivariate models developed were validated using the full cross validation method and the test set method with a test set size of approximately 45 samples. The R2 values, testing variation between concentrations as measured by the NIR method and chemical methods, were 0.91 for organic carbon (OC), 0.92 for amino sugar N, and 0.82 for both soil β-glucosidase and β-glucosaminidase enzyme activities. Our study showed that the NIRS method has the potential to simultaneously, rapidly and accurately predict values of multiple related variables. The equipment needed for the NIRS method is not expensive and can be used where very large numbers of samples need to be rapidly analyzed. Indeed, the prediction equations can be constantly improved as more data points are entered into the correlations between laboratory-measured values and NIRS values.

Strategies for aggregating gene expression data: the collapseRows R function.

BackgroundGenomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied.ResultsWe introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data ("expression deconvolution"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected "hub" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways.ConclusionsThe R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.

Making Biology Teaching More “Graphic”

In an age of enhanced visual technology, the use of visual supplements in the classroom has become increasingly effective in conveying information about complex biological concepts. What is more, many students feel the need to “see” concepts depicted in one form or another. Over many years of teaching biology at a university, I discovered the usefulness of incorporating simple, generic graphs into lectures and assignments to show relationships between assorted variables discussed in class. Here, I show how to illustrate a relationship between two variables on a single graph and integrate relationships for multiple related variables on a series of “stacked” graphs. I also demonstrate how graphs can help make a distinction between two commonly confused concepts: negative correlation and negative feedback.

Quality of life and cost-effectiveness of anti-HCV therapy in HIV-infected patients

Quality of life studies in chronic hepatitis C using specific questionnaires have been performed in mono-infected patients; however, these studies have just begun in HIV-HCV co-infected patients. The questionnaires used in mono-infected patients are not well adapted for co-infected patients and need to be redesigned. Typically, co-infected patients have multiple symptoms that may be attributable to HIV, to HCV, to a combination of both diseases or even to side effects related to drug therapy. These patients usually need substitutive therapy to manage side effects related to HCV therapy, particularly anaemia, leukopenia and depression. There are no cost-effectiveness studies published on the current HCV standard therapy with pegylated interferon and ribavirin. However, there is some research on the old standard interferon and ribavirin, which shows that HCV therapy is cost-effective. Cost-effectiveness studies in co-infected patients will have to take into account variables that do not affect mono-infected patients, such as the different levels of CD4, the increase in the fibrosis progression rate and the use of other expensive drugs for the management of side effects. Currently, the literature does not provide adequate information on the effect of HCV infection on the quality of life of HIV-HCV co-infected patients or the most cost-effective HCV therapy.

Control loop noise rejection using fuzzy logic

This paper describes an application of fuzzy logic to noise rejection in a control loop. This new use of fuzzy logic solves the problem of sluggish control loop response when using a set-point range to stop constant valve chattering due to noise in the output signal being sent to a control valve. Multiple related variables and a general understanding of their inter-relationship must be available for this method to be successfully applied. An overview of the specific fuzzy logic method used for this application is presented along with guidelines for the practical application. In addition, this paper includes results from the successful implementation of fuzzy logic to a control loop on a pilot plant distillation column.

Epidemiology of musculoskeletal disorders among computer users: lesson learned from the role of posture and keyboard use

Reports in the scientific literature and lay press have suggested that computer users are at increased risk of upper extremity musculoskeletal disorders (MSDs). Early studies often found elevated rates of MSD outcomes among keyboard users when compared to non-users. Attention soon focused on specific aspects of keyboard work that might be responsible for the observed rate increase. In this review, the epidemiological evidence examining associations between MSD outcomes and computer user posture and keyboard use intensity (hours of computer use per day or per week) are examined. Results of epidemiological studies of posture and MSD outcomes have not been entirely consistent. Reasons for the inconsistency in results include cross-sectional study design (with possible failure to assure that measured exposure preceded health effect), imprecision of posture measures used, and difficulties involved in analyzing multiple related variables. Despite the inconsistencies, it appears from the literature that posture is an independent risk factor of modest magnitude for MSDs among computer users. It appears that lowering the height of the keyboard to or below the height of the elbow and resting the arms on the desk surface or chair armrests is associated with reduced risk of neck and shoulder MSDs. Results of epidemiological studies examining computer use (hours keying per day or per week) are more consistent than those examining posture, although some inconsistency is observed. Reasons for the inconsistency include possible selective survival bias resulting from cross-sectional study design, differences in exposure categorization, and possible interaction with other exposure variables. Overall, the literature shows that daily or weekly hours of computer use is more consistently associated with hand and arm MSDs than with neck and shoulder MSDs.

Stepwise Conditional Transformation for Simulation of Multiple Variables

Most geostatistical studies consider multiple-related variables. These relationships often show complex features such as nonlinearity, heteroscedasticity, and mineralogical or other constraints. These features are not handled by the well-established Gaussian simulation techniques. Earth science variables are rarely Gaussian. Transformation or anamorphosis techniques make each variable univariate Gaussian, but do not enforce bivariate or higher order Gaussianity. The stepwise conditional transformation technique is proposed to transform multiple variables to be univariate Gaussian and multivariate Gaussian with no cross correlation. This makes it remarkably easy to simulate multiple variables with arbitrarily complex relationships: (1) transform the multiple variables, (2) perform independent Gaussian simulation on the transformed variables, and (3) back transform to the original variables. The back transformation enforces reproduction of the original complex features. The methodology and underlying assumptions are explained. Several petroleum and mining examples are used to show features of the transformation and implementation details.

Documenting Patterns of Nursing Interventions Using Cluster Analysis

Use of inferential statistics in research applications of the Nursing Intervention Classification has been rare, yet use of these statistical techniques is needed to answer questions related to intervention patterns. Using data from a descriptive study of 3,733 visits documented by 19 adult nurse practitioner students, hierarchical agglomerative cluster analysis was used to determine whether meaningful nursing intervention patterns could be depicted. Eight intervention clusters were derived, replicated, and validated. Clusters of intervention classes differed in the type of nursing and medical diagnosis, amount of time the nurse spent during the visit, cost of visit, and the age of the patient. Clustering supported the utility of standardized nursing diagnosis and intervention typologies to identify actual practice patterns of adult nurse practitioner students. Cluster analysis is a valuable data analysis strategy when analyzing multiple related variables.