Model-building Approach Research Articles

Modeling production facility as a dynamic integrated interacting objects system

Model of enterprise manufacturing environment as an integrated dynamic interacting parallel and/or serial manufacturing objects system is discussed. In this paper we consider what the approach of dynamic model building gives for the identification, organization and management of the enterprise in the case of a serial arrangement of production areas. The results were used to construct new methodology of engineering manufacturing control algorithms for integrated manufacturing executing systems.

An Effective Preprocessing Algorithm for Model Building in Collaborative Filtering based Recommender System

Recommender systems suggest interesting items for online users based on the ratings expressed by them for the other items maintained globally as the rating matrix. The rating matrix is often sparse and very huge due to large number of users expressing their ratings only for a few items among the large number of alternatives. Sparsity and scalability are the challenging issues to achieve accurate predictions in recommender systems. This paper focuses on model building approach to collaborative filtering-based recommender systems using low rank matrix approximation algorithms for achieving scalability and accuracy while dealing with sparse rating matrices. A novel preprocessing methodology is proposed to counter data sparsity problem by transforming the sparse rating matrix denser before extracting latent factors to appropriately characterise the users and items in low dimensional space. The quality of predictions made either directly or indirectly through user clustering were investigated and found to be competitive with the existing collaborative filtering methods in terms of reduced MAE and increased NDCG values on bench mark datasets.

On the short-term predictability of stock returns: A quantile boosting approach

We study the predictability of stock returns using an iterative model-building approach known as quantile boosting. Examining alternative return quantiles that represent normal, bull and bear markets via recursive quantile regressions, we trace the predictive value of extensively studied predictors including the recently suggested short interest and sentiment variables. We find that short-term returns are predictable to some extent for extreme lower quantiles of the conditional distribution of returns. Interestingly, however, short-interest and sentiment variables do not add significant predictive power, challenging the recent findings on the predictive ability of short sellers for future cash flows and associated market returns.

Critical linkages between quality management practices and performance from Indian IT enabled service SMEs

The purpose of this paper is to critically investigate the relationship among quality management (QM) practices with their direct and indirect effects on performance outcome by means of conceptual extension and generalisation. The paper uses factor analysis and structural equation modelling which has been tested using cross-sectional data collected from IT enabled small and medium service firms in India. The analysis shows the interdependency of QM practices and the importance of a systematic approach for managing QM practices. The findings from this study indicate that the nature of the interrelationships between these core QM practices is unique to the Indian context. The unique contribution of this study has been in terms of its model-building approach towards designing a linkage model showing interdependencies among critical QM practices with its direct and indirect effects on performance outcome. Such a holistic framework with plausible interaction effects has not been studied earlier in the Indian context with sector-specific relevance to IT enabled service SMEs. Further the paper recommends suggestions for improvement along with managerial implications and future scope of study.

Vocal Music Education Model Building Approaches under the Background of Music Curriculum Reform and System Renewal

Vocal Music Education Model Building Approaches under the Background of Music Curriculum Reform and System Renewal

On the Short-Term Predictability of Stock Returns: A Quantile Boosting Approach

We study the predictability of stock returns using an iterative model-building approach known as quantile boosting. Examining alternative return quantiles that represent normal, bull and bear markets via recursive quantile regressions, we trace the predictive value of extensively studied predictors including the recently suggested short interest and sentiment variables. We find that short-term returns are predictable to some extent for extreme lower quantiles of the conditional distribution of returns. Interestingly, however, short-interest and sentiment variables do not add significant predictive power, challenging the recent findings on the predictive ability of short sellers for future cash flows and associated market returns.

Understanding latent structures of clinical information logistics: A bottom-up approach for model building and validating the workflow composite score

Background and purposeClinical information logistics is a construct that aims to describe and explain various phenomena of information provision to drive clinical processes. It can be measured by the workflow composite score, an aggregated indicator of the degree of IT support in clinical processes. This study primarily aimed to investigate the yet unknown empirical patterns constituting this construct. The second goal was to derive a data-driven weighting scheme for the constituents of the workflow composite score and to contrast this scheme with a literature based, top-down procedure. This approach should finally test the validity and robustness of the workflow composite score. MethodsBased on secondary data from 183 German hospitals, a tiered factor analytic approach (confirmatory and subsequent exploratory factor analysis) was pursued. A weighting scheme, which was based on factor loadings obtained in the analyses, was put into practice. ResultsWe were able to identify five statistically significant factors of clinical information logistics that accounted for 63% of the overall variance. These factors were “flow of data and information”, “mobility”, “clinical decision support and patient safety”, “electronic patient record” and “integration and distribution”. The system of weights derived from the factor loadings resulted in values for the workflow composite score that differed only slightly from the score values that had been previously published based on a top-down approach. ConclusionOur findings give insight into the internal composition of clinical information logistics both in terms of factors and weights. They also allowed us to propose a coherent model of clinical information logistics from a technical perspective that joins empirical findings with theoretical knowledge. Despite the new scheme of weights applied to the calculation of the workflow composite score, the score behaved robustly, which is yet another hint of its validity and therefore its usefulness.

Concept mapping as an approach for expert-guided model building: The example of health literacy

Concept mapping served as the starting point for the aim of capturing the comprehensive structure of the construct of ‘health literacy.’ Ideas about health literacy were generated by 99 experts and resulted in 105 statements that were subsequently organized by 27 experts in an unstructured card sorting. Multidimensional scaling was applied to the sorting data and a two and three-dimensional solution was computed. The three dimensional solution was used in subsequent cluster analysis and resulted in a concept map of nine “clusters”: (1) self-regulation, (2) self-perception, (3) proactive approach to health, (4) basic literacy and numeracy skills, (5) information appraisal, (6) information search, (7) health care system knowledge and acting, (8) communication and cooperation, and (9) beneficial personality traits. Subsequently, this concept map served as a starting point for developing a “qualitative” structural model of health literacy and a questionnaire for the measurement of health literacy. On the basis of questionnaire data, a “quantitative” structural model was created by first applying exploratory factor analyses (EFA) and then cross-validating the model with confirmatory factor analyses (CFA). Concept mapping proved to be a highly valuable tool for the process of model building up to translational research in the “real world”.

Predictors of 30-Day Hospital Readmission among Maintenance Hemodialysis Patients: A Hospital's Perspective.

Over 35% of patients on maintenance dialysis are readmitted to the hospital within 30 days of hospital discharge. Outpatient dialysis facilities often assume responsibility for readmission prevention. Hospital care and discharge practices may increase readmission risk. We undertook this study to elucidate risk factors identifiable from hospital-derived data for 30-day readmission among patients on hemodialysis. Data were taken from patients on maintenance hemodialysis discharged from University of North Carolina Hospitals between May of 2008 and June of 2013 who received in-patient hemodialysis during their index hospitalizations. Multivariable logistic regression models with 30-day readmission as the dependent outcome were used to identify readmission risk factors. Models considered variables available at hospital admission and discharge separately. Among 349 patients, 112 (32.1%) had a 30-day hospital readmission. The discharge (versus admission) model was more predictive of 30-day readmission. In the discharge model, malignancy comorbid condition (odds ratio [OR], 2.08; 95% confidence interval [95% CI], 1.04 to 3.11), three or more hospitalizations in the prior year (OR, 1.97; 95% CI, 1.06 to 3.64), ≥10 outpatient medications at hospital admission (OR, 1.69; 95% CI, 1.00 to 2.88), catheter vascular access (OR, 1.82; 95% CI, 1.01 to 3.65), outpatient dialysis at a nonuniversity-affiliated dialysis facility (OR, 3.59; 95% CI, 2.03 to 6.36), intradialytic hypotension (OR, 3.10; 95% CI, 1.45 to 6.61), weekend discharge day (OR, 1.82; 95% CI, 1.01 to 3.31), and serum albumin <3.3 g/dl (OR, 4.28; 95% CI, 2.37 to 7.73) were associated with higher readmission odds. A decrease in prescribed medications from admission to discharge (OR, 0.20; 95% CI, 0.08 to 0.51) was associated with lower readmission odds. Findings were robust across different model-building approaches. Models containing discharge day data had greater predictive capacity of 30-day readmission than admission models. Identified modifiable readmission risk factors suggest that improved medication education and improved transitions from hospital to community may potentially reduce readmissions. Studies evaluating targeted transition programs among patients on dialysis are needed.

A Strategy to Characterize Nanofabrication Processes With Large RPM (Experimental Run, Physics, and Measurement) Uncertainties

The bottom-up fabrication of nanostructures can simultaneously face large uncertainties from experimental runs (R), physical understanding (P), and measurement (M). No systematic strategy has been reported to manage these three types of uncertainties, abbreviated as RPM, concurrently to achieve better understanding of nano fabrication processes. Previously, we developed cross-domain model building and validation (CDMV) approach to handle large physical and measurement (PM) uncertainties in nano fabrication process modeling. In this paper, we propose to prioritize RPM uncertainties and to incorporate the analysis of run variations into process modeling. Under a Bayesian hierarchical framework, this new strategy will first handle PM uncertainties at the basic level to identify a model structure using CDMV approach. The rationale is that the uncertainty due to experimental runs should not fundamentally change the process physics or the model structure, but impacts on the model parameters. At a lower hierarchy, process model parameters varying or invariant to runs are treated as random effects or fixed effects to be identified respectively. Demonstrated in a nanowire growth process example, the new strategy not only assists to establish an improved process model, but also to uncover the variation sources contributing to large run variations. The obtained physical insights can guide further process investigation. Note to practitioners: experimental investigation of nanofabrication processes often encounters large uncertainties due to a lack of conclusive understanding of process physics, measurement noise, and variability among experimental runs. Trial-and-error strategy is commonly adopted under this scenario to explore the process physics with little guidance, resulting in increased cost of experimentation or fabrication. This paper presents an alternative strategy to make more efficient use of data to manage large RPM uncertainties and achieve better process understandings for process improvement.

A predictive model for the development of chronic obstructive pulmonary disease.

The screening of a person at risk for chronic obstructive pulmonary disease (COPD) and timely treatment may provide opportunities to delay the progressive destruction of lung function. Therefore, a model to predict the disease is required. We hypothesized that demographic and clinical information in combination with genetic markers would aid in the prediction of COPD development, prior to its onset. The aim of the present study was to create a predictive model for COPD development. Demographic, clinical presentation and genetic polymorphisms were recorded in COPD patients and control subjects. Nighty-six single-nucleotide polymorphisms of 46 genes were selected for genotyping in the case-control study. A predictive model was produced using logistic regression with a stepwise model-building approach and was validated. A total of 331 patients and 351 control subjects were included. The logistic regression identified the following predictors: Gender, respiratory infection in early life, low birth weight, smoking history and genotype polymorphisms (rs2070600, rs10947233, rs1800629, rs2241712 and rs1205). The model was established using the following formula: COPD = 1/[1 + exp (-2.4933-1.2197 gender + 1.1842 respiratory infection in early life + 2.4350 low birth weight + 1.8524 smoking - 1.1978 rs2070600 + 2.0270 rs10947233 + 1.1913 rs10947233 + 0.6468 rs1800629 + 0.5272 rs2241712 + 0.4024 rs1205)] (when the value is >0.5). The Hosmer-Lemeshow test showed no significant deviations between the observed and predicted events. Validation of the model in 50 patients showed a modest sensitivity and specificity. Therefore, a predictive model based on demographic, clinical and genetic information may identify COPD prior to its onset.

Fast and accurate lithography simulation using cluster analysis in resist model building

As technology nodes continue to shrink, optical proximity correction (OPC) has become an integral part of mask design to improve the subwavelength printability. The success of lithography simulation to perform OPC on an entire chip relies heavily on the performance of lithography process models. Any small enhancement in the performance of process models can result in a valuable improvement in the yield. We propose a robust approach for lithography process model building. The proposed scheme uses the clustering algorithm for model building and thereby improves the accuracy and computational efficiency of lithography simulation. The effectiveness of the proposed method is verified by simulating some critical layers in 14- and 22-nm complementary metal oxide semiconductor technology nodes. Experimental results show that compared with a conventional approach, the proposed method reduces the simulation time by 50× with ∼5% improvement in accuracy.

De novo protein structure determination from near-atomic-resolution cryo-EM maps.

We present a de novo model building approach that combines predicted backbone conformations with side-chain density-fit to accurately assign sequence into density maps. We show this method yields accurate models for six experimental maps at 3.3–4.8 Å resolution, and produces a nearly complete model for an unsolved map containing a 660-residue hetero-dimeric protein. This method should enable rapid and reliable protein structure determination from near-atomic resolution cryo-EM maps.

Heart failure symptom clusters and functional status - a cross-sectional study.

To identify symptom clusters in individuals with heart failure and evaluate the relationship of the identified clusters to functional status. Heart Failure is a global health problem affecting approximately 1-2% of the adult population in developed countries worldwide. Individuals with heart failure may experience as many as nine symptoms and may limit activities that worsen their symptoms or adjust the way they engage in activities. Cross-sectional. A convenience sample of individuals (n=117) with a confirmed diagnosis of heart failure was recruited from an academic medical centre during 2011-2012. Prevalent heart failure symptoms and functional status outcomes (functional limitations and mobility) were evaluated. Factor analysis using the principal components method was used to extract symptom clusters. Regression analysis using a backwards stepwise model-building approach was used to examine the effects of the symptom clusters, age and co-morbidity on functional limitations and mobility. Three symptom clusters, sickness behaviour, discomforts of illness and gastrointestinal distress were extracted. When sickness behaviours and discomforts of illness were both present, functional limitations were more sensitive to sickness behaviours. Sickness behaviour and co-morbidity were related to limited mobility. Individuals with heart failure may be helped to improve their functional status by managing sickness behaviour and discomforts of illness symptoms. Identification of symptom clusters may lead to the development of interventions focusing on a cluster of heart failure symptoms.

An experimental design approach for modeling As(V) adsorption from aqueous solution by activated carbon.

The present paper discusses response surface methodology as an efficient approach for predictive model building and optimization of As(V) adsorption on activated carbon derived from a food industry waste: peach stones. The objectives of the study are application of a three-factor 2³ full factorial and central composite design technique for maximizing As(V) removal by produced activated carbon, and examination of the interactive effects of three independent variables (i.e., solution pH, temperature, and initial concentration) on As(V) adsorption capacity. Adsorption equilibrium was investigated by using Langmuir, Freundlich, and Dubinin-Radushkevich isotherm models. First-order and second-order kinetic equations were used for modeling of adsorption kinetics. Thermodynamic parameters (ΔG °, ΔH °, and ΔS °) were calculated and used to explain the As(V) adsorption mechanism. The negative value of ΔH (-7.778 kJ mol⁻¹) supported the exothermic nature of the sorption process and the Gibbs free energy values (ΔG°) were found to be negative, which indicates that the As(V) adsorption is feasible and spontaneous.

Minimal modifications to the Tri-Bimaximal neutrino mixing

Abstract In light of the observation of a relatively large θ 13, the ever popular Tri-Bimaximal (TBM) neutrino mixing which predicts a vanishing θ 13 needs modifications. In this paper, we shall discuss the possibility of modifying it in a minimal way to fulfil this task. In the first part, a neutrino mass matrix with three independent parameters, which leads to the TM2 mixing, is obtained by analogy with that for the TBM mixing. In the second part, a model that can realize the TM2 mixing is constructed with flavor symmetries A 4 × U(1) × Z 2 × Z 2 × Z 2. It is the variant of a model that gives the TBM mixing, with only one more flavon field included. Furthermore, the imaginary vacuum expectation value (VEV) of this flavon breaks the imposed CP symmetry and results in θ 23 = 45° and the maximal CP violation. Besides, this model building approach can be generalized to the TM1 mixing in a straightforward way.

A new formulation of non-relativistic diffeomorphism invariance

We provide a new formulation of non-relativistic diffeomorphism invariance. It is generated by localising the usual global Galilean symmetry. The correspondence with the type of diffeomorphism invariant models currently in vogue in the theory of fractional quantum Hall effect has been discussed. Our construction is shown to open up a general approach of model building in theoretical condensed matter physics. Also, this formulation has the capacity of obtaining Newton–Cartan geometry from the gauge procedure.

Lifespan and aggregate size variables in specifications of mortality or survivorship.

A specification of mortality or survivorship provides respective explicit details about mortality's or survivorship's relationships with one or more other variables (e.g., age, sex, etc.). Previous studies have discovered and analyzed diverse specifications of mortality or survivorship; these discoveries and analyses suggest that additional specifications of mortality or survivorship have yet to be discovered and analyzed. In consistency with previous research, multivariable limited powered polynomials regression analyses of mortality and survivorship of selected humans (Swedes, 1760–2008) and selected insects (caged medflies) show age-specific, historical-time-specific, environmental-context-specific, and sex-specific mortality and survivorship. These analyses also present discoveries of hitherto unknown lifespan-specific, contemporary-aggregate-size-specific, and lifespan-aggregate-size-specific mortality and survivorship. The results of this investigation and results of previous research help identify variables for inclusion in regression models of mortality or survivorship. Moreover, these results and results of previous research strengthen the suggestion that additional specifications of mortality or survivorship have yet to be discovered and analyzed, and they also suggest that specifications of mortality and survivorship indicate corresponding specifications of frailty and vitality. Furthermore, the present analyses reveal the usefulness of a multivariable limited powered polynomials regression model-building approach. This article shows that much has yet to be learned about specifications of mortality or survivorship of diverse kinds of individuals in diverse times and places.

Determination of the Thermal Insulation for the Model Building Approach and the Global Effects in Turkey

One of the most important considerations to be considered in the design of energy efficient buildings is the thickness of the insulation to be applied to the building. In this study the existing building stock in Turkey has been investigated depending on parameters such as the height and the area. A model building has been created covering all of these buildings. Fuel emission reduction of combustion system was calculated in the case of insulation applied to this model building. Heat loss of the existing building stock and exhaust emissions and the contribution to the country's economy with the model building methodology are also determined. The results show that the optimum insulation thicknesses vary between 3.21 and 7.12 cm, the energy savings vary between 9.23 US$/m2 and43.95 US$/m2, and the payback periods vary between 1 and 8.8 years depending on the regions. As a result of the study when the optimum insulation thickness is applied in the model building, the total energy savings for the country are calculated to be 41.7 billion US$. And also total CO2 emissions for the country are calculated to be 57.2 billion kg CO2 per year after insulation.

Advances in velocity modelling and imaging techniques in the Taranaki Basin

SUMMARY This paper demonstrates recent advances in grid-based reflection tomography model building in conjunction with anisotropic pre-stack depth migration. These advances significantly benefit the imaging and resolution of 3D seismic data in a number of structurally complex basins. Using 3D seismic imaging examples from Petroleum Exploration Permit PEP 51558 in New Zealand’s Taranaki Basin, this paper demonstrate how reflection tomography techniques can utilize implicit geological constraints to resolve complex velocity variations and, thereby, reduce structural and imaging uncertainty. One challenge associated with velocity modelling in the Taranaki Basin is to adequately resolve the large lateral velocity contrasts across major faults. This work shows how the use of steering filters during the reflection tomography process significantly improves the resolution and delineation of these fault-constrained velocity contrasts. This approach is compared with the conventional gridded tomography approach of velocity model building. Using a detailed velocity model from the Taranaki Basin, comparisons were made between time and depth migration, as well as alternate depth migration techniques.