Conventional Regression Analysis Research Articles

Association of Aspirin and NSAID Use With Risk of Colorectal Cancer According to Genetic Variants

Conclusions: Regular aspirin and/or NSAID use is associated with a lower risk of colorectal cancer and is tied to specific genetic variants. Summary: There is data that suggests that the use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with a lower risk of colorectal neoplasms. (Chan AT, JAMA 2005; 294:914-923 and Friis S et al, Cancer Causes Control 2009; 20:731-740 and Rothwell PM et al, Lancet 2010; 376:1741-1750) Since virtually all vascular surgical patients are treated with aspirin or other antiplatelet agents with potential anti-inflammatory actions, the association of decreased colorectal cancer risks in such patients is of potential interest to those treating vascular surgical patients. The mechanisms, however, behind the association are not well understood. Chemo prevention of cancer is not currently recommended because of the uncertainty of risk benefit profile. The authors sought to identify genetic markers that characterize individuals who may obtain differential benefit from aspirin and NSAIDs. They conducted a discover-based, genome-wide analysis of gene X environment interactions between use of aspirin, NSAIDs or both and single nucleotide polymorphisms (SNPs) in relation to risk of colorectal cancer. This was a case controlled study using data from 5 case-control and 5 cohort studies initiated between 1976 and 2003 across the United States, Canada, Australia and Germany including colorectal cancer cases (N1⁄4 8634) and matched controls (N1⁄4 8553), ascertained between 1976 and 2011. All participants were of European descent. Overall regular use of aspirin and/or NSAIDs was associated with a lower risk of colorectal cancer (prevalence, 28% vs 38%; odd ratio [OR], 0.69 [95% CI, 0.64-0.74]; P 1⁄4 6.2 x 10 ) compared with nonregular use. In the conventional logistic regression analysis, the SNP rs2965667 at chromosome 12p12.3 near the MGST1 gene showed a genome-wide significant interaction with aspirin and/or NSAID use (P 1⁄4 4.6 x 10 9 for interaction). Aspirin and/or NSAID use was associated with a lower risk of colorectal cancer among individuals with re2965667-TT genotype (prevalence, 28% vs 38% OR, 0.66 [95% CI, 0.61-0.70]; P 1⁄4 7.7 x 10 ) but with a higher rate risk among those with rare TA or AA genotypes (prevalence, 35% vs 29%; OR, 1.89 [95% CI, 1.27-2.81]; P 1⁄4 .002). In case-only interaction analysis, the SNP re 16973225 at chromosome 15q25.2 near the IL16 gene showed a genome-wide significant interaction with use of aspirin and/or NSAIDs (P1⁄4 8.2 x 10 9 for interaction). Regular use was associated with a lower risk of colorectal cancer among individuals with rs16973225AA genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.62-0.71]; P1⁄41.9 x 10 ) but was not associated with risk of colorectal cancer among those with less common (9%) AC or CC genotypes (prevalence, 36% vs 39%; OR, 0.97 [95% CI, 0.78-1.20]; P 1⁄4 .76). Comment: The study is intriguing for a couple of reasons. First of all, most vascular surgical patients are treated with aspirin, by doing so we may lower the risk colorectal cancer in many of our patients. In addition, the offtarget effect of aspirin in terms of beneficial effects on colorectal cancer can be attributed to genetic variation at two highly correlated SNPs at specific chromosomes. Perhaps off target and even on target effects of other drugs such as statins will eventually be able to be correlated with specific genetic makers. Ultimately everything tends to boil down to biochemistry and genes. Identification of specific and active approaches to utilization of medications may eventually result in a more efficient use of current medications and development of more targeted and specific future medications. In the meantime our patients have another reason to take their aspirin every day.

Evaluation of the Possible Sources and Controlling Factors of Toxic Metals/Metalloids in the Florida Everglades and Their Potential Risk of Exposure.

The Florida Everglades is an environmentally sensitive wetland ecosystem with a number of threatened and endangered fauna species susceptible to the deterioration of water quality. Several potential toxic metal sources exist in the Everglades, including farming, atmospheric deposition, and human activities in urban areas, causing concerns of potential metal exposure risks. However, little is known about the pollution status of toxic metals/metalloids of potential concern, except for Hg. In this study, eight toxic metals/metalloids (Cd, Cr, Pb, Ni, Cu, Zn, As, and Hg) in Everglades soils were investigated in both dry and wet seasons. Pb, Cr, As, Cu, Cd, and Ni were identified to be above Florida SQGs (sediment quality guidelines) at a number of sampling sites, particularly Pb, which had a level of potential risk to organisms similar to that of Hg. In addition, a method was developed for quantitative source identification and controlling factor elucidation of toxic metals/metalloids by introducing an index, enrichment factor (EF), in the conventional multiple regression analysis. EFs represent the effects of anthropogenic sources on metals/metalloids in soils. Multiple regression analysis showed that Cr and Ni were mainly controlled by anthropogenic loading, whereas soil characteristics, in particular natural organic matter (NOM), played a more important role for Hg, As, Cd, and Zn. NOM may control the distribution of these toxic metals/metalloids by affecting their mobility in soils. For Cu and Pb, the effects of EFs and environmental factors are comparable, suggesting combined effects of loading and soil characteristics. This study is the first comprehensive research with a vast amount of sampling sites on the distribution and potential risks of toxic metals/metalloids in the Everglades. The finding suggests that in addition to Hg other metals/metalloids could also potentially be an environmental problem in this wetland ecosystem.

The evolution of tail length in snakes associated with different gravitational environments

SummaryGravity imposes potentially important constraints on blood circulation in tall or elongate animals during upright posture or climbing. Upright postures create vertical gradients of gravitational (=hydrostatic) pressures within circulatory vessels. In terrestrial animals, this pressure potentially induces blood pooling and oedema in dependent (inferior) tissues and, secondarily, decreases blood volume reaching the head and vital organs. Arboreal snakes exhibit a suite of adaptations for countering the effects of gravity on blood circulation, including relatively non‐compliant tissue compartments in the tail. However, patterns of tail length related to arboreal habitats and gravity have not been previously studied. Here, we test the hypothesis that arboreal snakes have relatively longer tails than non‐climbing species, and we interpret the results within the context of adaptation to gravity stress.Length data were obtained for 226 snake species, 139 genera and 15 snake families assigned to three broad gravitational habitat categories (G‐habitats: stenotopically arboreal, eurytopically arboreal/terrestrial and non‐scansorial). We constructed a composite phylogenetic tree and quantified the relationships between snake morphology and G‐habitat using conventional regression analysis of species data and regressions of phylogenetically independent contrasts.Mean relative tail length (RTL) increased and mean relative snout‐vent length decreased with increasing arboreality among the three G‐habitats. However, mean total body length (TL) was not different between the two arboreal G‐habitats.Snakes with longerRTLs have a higher percentage of elongate blood vessels contained within the relatively non‐compliant integument of the tail, which mitigates postural blood pooling experienced during climbing.TLin adult female arboreal snakes is hypothesized to be an evolutionary trade‐off between demands for a longer tail to enhance cardiovascular performance and the length of body cavity to provide space for young.Our results complement previous studies correlatingRTLwith arboreal habitat use, in addition to physiological experiments quantifying the effects of gravity on blood flow in snakes. These results provide evidence that longRTLs of arboreal snakes function, at least in part, as an adaptive response to cardiovascular stresses on blood circulation imposed by gravity. Selection on tail length related to other factors seems less likely, but is in need of further investigation.

Individual differences in predicting occupational success: The effect of population heterogeneity

Using a sample of 339 university graduates from the University of Alicante (Spain) three years after completion of their studies, we studied the relationships between general intelligence (GI), personality traits, emotional intelligence (EI), academic performance, and occupational attainment and compared the results of conventional regression analysis with the results obtained from applying regression mixture models. The results reveal the influence of unobserved population heterogeneity (latent class) on the relationship between predictors and criteria and the improvement in the prediction obtained from applying regression mixture models compared to applying a conventional regression model.

Estimation of Soil Saturated Hydraulic Conductivity in Part of Central Zagroos Using Regression and ANNs method

With the advent of advanced geographical informational systems (GIS) and remote sensing technologies in recent years, topographic (elevation, slope, and aspect) and vegetation attributes are routinely available from digital elevation models (DEMs) and normalized difference vegetation index (NDVI) at different spatial (watershed, regional) scales. This study explores the use of topographic and vegetation attributes in addition to soil attributes to develop pedotransfer functions (PTFs) for estimating soil saturated hydraulic conductivity in the rangeland of central Zagros. We investigated the use of artificial neural networks (ANNs) in estimating soil saturated hydraulic conductivity from measured particle size distribution, bulk density, topographic attributes, normalized difference vegetation index (NDVI), soil organic carbon (SOC), and CaCo3 in topsoil and subsoil horizon. Three neural networks structures were used and compared with conventional multiple linear regression analysis. The performances of the models were evaluated using spearman’s correlation coefficient (r) based on the observed and the estimated values and normalized mean square error (NMSE). Topographic and vegetation attributes were found to be the most sensitive variables to estimate soil saturated hydraulic conductivity in the rangeland of central Zagros. Improvements were achieved with neural network (r=0.87) models compared with the conventional multiple linear regression (MLR) model (r=0.69).

Association of aspirin and NSAID use with risk of colorectal cancer according to genetic variants.

Use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with lower risk of colorectal cancer. To identify common genetic markers that may confer differential benefit from aspirin or NSAID chemoprevention, we tested gene × environment interactions between regular use of aspirin and/or NSAIDs and single-nucleotide polymorphisms (SNPs) in relation to risk of colorectal cancer. Case-control study using data from 5 case-control and 5 cohort studies initiated between 1976 and 2003 across the United States, Canada, Australia, and Germany and including colorectal cancer cases (n=8634) and matched controls (n=8553) ascertained between 1976 and 2011. Participants were all of European descent. Genome-wide SNP data and information on regular use of aspirin and/or NSAIDs and other risk factors. Colorectal cancer. Regular use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer (prevalence, 28% vs 38%; odds ratio [OR], 0.69 [95% CI, 0.64-0.74]; P = 6.2 × 10(-28)) compared with nonregular use. In the conventional logistic regression analysis, the SNP rs2965667 at chromosome 12p12.3 near the MGST1 gene showed a genome-wide significant interaction with aspirin and/or NSAID use (P = 4.6 × 10(-9) for interaction). Aspirin and/or NSAID use was associated with a lower risk of colorectal cancer among individuals with rs2965667-TT genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.61-0.70]; P = 7.7 × 10(-33)) but with a higher risk among those with rare (4%) TA or AA genotypes (prevalence, 35% vs 29%; OR, 1.89 [95% CI, 1.27-2.81]; P = .002). In case-only interaction analysis, the SNP rs16973225 at chromosome 15q25.2 near the IL16 gene showed a genome-wide significant interaction with use of aspirin and/or NSAIDs (P = 8.2 × 10(-9) for interaction). Regular use was associated with a lower risk of colorectal cancer among individuals with rs16973225-AA genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.62-0.71]; P = 1.9 × 10(-30)) but was not associated with risk of colorectal cancer among those with less common (9%) AC or CC genotypes (prevalence, 36% vs 39%; OR, 0.97 [95% CI, 0.78-1.20]; P = .76). In this genome-wide investigation of gene × environment interactions, use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer, and this association differed according to genetic variation at 2 SNPs at chromosomes 12 and 15. Validation of these findings in additional populations may facilitate targeted colorectal cancer prevention strategies.

Improving Forecast of Binary Rare Events Data: A GAM‐Based Approach

This paper develops a method for modelling binary response data in a regression model with highly unbalanced class sizes. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional regression analysis, i.e. logistic regression models, could underestimate the probability of the rare event. To overcome this drawback, we introduce a flexible skewed link function based on the quantile function of the generalized extreme value (GEV) distribution in a generalized additive model (GAM). The proposed model is known as generalized extreme value additive (GEVA) regression model, and a modified version of the local scoring algorithm is suggested to estimate it. We apply the proposed model to a dataset on Italian small and medium enterprises (SMEs) to estimate the default probability of SMEs. Our proposal performs better than the logistic (linear or additive) model in terms of predictive accuracy. Copyright © 2015 John Wiley & Sons, Ltd.

An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method

Surface roughness is one of the most important requirements in machining process. In order to obtain needed surface roughness, the proper setting of cutting parameters is crucial before the process take place. Therefore, an accurate mathematical model to predict surface roughness is totally needed. This research presents a hybrid method which combine conventional multiple regression analysis and genetic algorithm to improve the accuracy of mathematical model to predict surface roughness. In experiment, three independent variables: spindle speed, feed rate and depth of cut were manipulated in collecting data. Full factorials cut were performed using FANUC CNC Milling α-Τ14ιE. The results show that the proposed hybrid method capable to improve accuracy of model with 23% and 28% of reduction in error.

Evaluating the performance of biotechnology companies by causal recipes

This study empirically investigates six variables and their combinations that may affect revenues of Taiwan's biotechnology industry. Examine variables include annual government investment, annual private investment, number of national biotechnology incubator yearly, number of manufacturers that biotechnology incubators foster yearly, number of patents that listed companies own yearly, and listed companies' annual R&D expenses. Using original and incremental Taiwanese data, this study contrasts conventional multiple regression analysis (MRA) of net effects and fuzzy-set qualitative comparative analysis (fsQCA) of causal complexities. Results indicate that fsQCA outperforms MRA and successfully models both types of data with causal complexities.

Clinical effectiveness and clinical toxicity associated with platinum-based doublets in the first-line setting for advanced non-squamous non-small cell lung cancer in Chinese patients: a retrospective cohort study.

BackgroundReal-world evidence lacks for clinical effectiveness and clinical toxicity associated with platinum-based doublets in the first-line setting for advanced non-squamous non-small cell lung cancer (advNS-NSCLC) in Chinese patients.MethodsPatients receiving first-line chemotherapy for advNS-NSCLC in four Chinese tertiary care hospitals from 2007 to 2012 were retrospectively identified for chart review. Propensity score methods created best matched pairs for platinum/pemetrexed versus other platinum-based doublets for head-to-head comparisons of early treatment discontinuation (completed treatment cycles <4), treatment failure (progressive disease or early treatment discontinuation), and adverse events (AE). Conventional multiple logistic regression analyses were also performed to confirm the impact of the studied platinum-based doublets on early treatment discontinuation, treatment failure, and hematological AE using vinorelbine/platinum as reference.Results1,846 patients were included to create propensity score matched treatment groups for platinum/pemetrexed versus docetaxel (95 pairs), paclitaxel (118 pairs), gemcitabine (199 pairs), and vinorelbine (72 pairs)-contained doublet, respectively. Platinum/pemetrexed was associated with significantly lower risks of early treatment discontinuation (odds ratio (OR) ranged from 0.239, p = 0.001 relative to platinum/docetaxel to 0.389, p = 0.003 relative to platinum/paclitaxel) and treatment failure (OR ranged from 0.257, p < 0.001 relative to platinum/paclitaxel to 0.381, p < 0.001 relative to platinum/gemcitabine) than the other four studied doublets. Platinum/pemetrexed was also associated with several significantly lower hematological AE rates, such as versus platinum/paclitaxel (any hematological AE: OR 0.508, p = 0.032), platinum/gemcitabine (i.e., any hematological AE: OR 0.383, p < 0.001; anemia: OR 0.357, p < 0.001; thrombocytopenia: OR 0.345, p < 0.001) or platinum/vinorelbine (i.e., neutropenia: OR 0.360, p = 0.046; anemia: OR 0.181, p = 0.014) in matched patients. Further conventional logistic regression analyses indicated that pemetrexed/platinum was ranked lowest for the risks of early treatment discontinuation (OR 0.326, p < 0.001), treatment failure (OR 0.460, p < 0.001), and any hematological AE (OR 0.329, p < 0.001).ConclusionsPemetrexed plus platinum had significantly superior clinical effectiveness as compared to the other platinum-based doublets with third-generation cytotoxic agents and was also associated with several lower hematological toxicity rates than gemcitabine or vinorelbine-based doublet in the first-line setting for advNS-NSCLC in Chinese patients.

Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm

The aim of this study is to investigate the applicability of teaching–learning based optimization (TLBO) algorithm for the first time in modeling stream dissolved oxygen (DO) prediction. The input parameters selected from a surface water-quality study including 20 indicators for the models are water pH, temperature, electrical conductivity, and hardness, which were measured semimonthly at six monitoring sites selected in an untreated wastewater impacted urban stream during a year, due to their direct and indirect effect on DO concentration. The accuracy of TLBO method is compared with those of the artificial bee colony algorithm and conventional regression analysis methods. These methods are applied to four different regression forms: quadratic, exponential, linear, and power. There are 144 data for each water-quality indicator, 114 of which are designated for training and the rest for testing patterns in the models. To evaluate the performance of the models, five statistical indices, i.e., sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient, are used. The TLBO method with quadratic form from among all models yielded better prediction, with an improvement of nearly 20 %. It can be concluded that the equations obtained by employing the TLBO algorithms predict the stream DO concentration successfully. Therefore, the employment of the TLBO algorithm by water resources and environment managers is encouraged and recommended for future studies.

Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats

Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing stable and accurate models. A detailed quantification of the differences between methods is provided.

Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats

Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing stable and accurate models. A detailed quantification of the differences between methods is provided.

Reconsidering the role of drugs in Spanish prisons: a preliminary ethnographic reflection

Purpose– The authors thought of the idea for this exploratory paper forDrugs and Alcohol Todayafter visiting a local prison on the outskirts of Madrid from which these field notes are taken. The authors have also had informal conversations with the contacts working in the Spanish Prison service. When the authors looked at some of the literature around the relationships between drugs and prisons in Spain, the authors found lots of statistics, and material which either said there were lots of drugs in prison or literature which presented over-medicalised processes of drug treatment. In short, the authors found few studies which could bring to life the kind of problems drugs bring to the prison and how the dynamics of the prison are not only directly impacted by drug use but also as drug dealing/trafficking. The paper aims to discuss these issues.Design/methodology/approach– What the authors offer is only really to draw your attention to the issue. The authors have no real methodology to reflect on other than one of us is an experienced participant observer and the other is a lawyer and criminologist who has worked with numerous clients processed for drug offences in and around prisons in Madrid. Between us, we have undertaken six visits to Madrileñas prisons.Findings– In this explorative paper the authors want to do three things. First, draw attention to the extent of problem of drugs in prison in Spain. Second, the authors want to suggest that the role drugs needs reconsideration as it plays a pivotal role in the functioning of the prison. Lastly, the authors push for more research into this issue which goes beyond conventional surveys and unnecessary complex regression analyses and instead takes a qualitative approach using observational data and informal conversations to explore these dynamics in more detail.Originality/value– First, that there is an urgent need to go beyond these official statistics and explore in some nuanced detail about the prison experience in Spain. The existing research is limiting in that it talks tiresomely about the numbers incarcerated and fails to admit the significance of drugs not only as a motivating factor for incarceration but also the role drugs play in the prison environment. The authors need to consider as much the changing demography of Spanish prisons – for example more immigrants, different drugs, etc. – as the everyday experience of drugs, debt, disagreements and violence and how they intersect as a lived experience rather than consider them as separate issues of analysis, dormant from the interconnectedness of the micro-interactions of the prison environment and the respective institutional power structures. The key to this debate, and the general messages of this paper, is to realize a study which can explore the nuances of the role and function of drugs play in Spanish prisons.

Association between urinary cadmium levels and prediabetes in the NHANES 2005–2010 population

Evidence suggests an association between exposure to cadmium and dysglycemia. To investigate this matter, we examined the relationship between urinary cadmium and prediabetes in the cross sectional National Health and Nutrition Examination Survey (NHANES). NHANES participants for the years 2005 through 2010 aged ≥ 40 years were included in the analysis. Participants with nephropathy, overt diabetes, or missing required data were excluded. To assess the non-linear relationship between cadmium and Prediabetes, non-parametric logistic regression with B spline expansion of urinary cadmium/creatinine ratio was performed. This analysis revealed a complex non-linear association between higher cadmium levels and prediabetes. This relationship persisted, though with varying magnitudes across smoking groups (never smokers, moderate smokers, heavy smokers). In a conventional logistic regression analysis, this relationship was less evident with significantly increased OR for prediabetes was found in the highest quintile of urine cadmium compared to the lowest quintile in the overall population and in moderate smokers. In an age stratified analysis, a significant linear association was found only in the age groups 60-69 and ≥ 70. We conclude that there is a significant non-linear, complex relationship between urinary Cd levels, age, smoking habits and odds of prediabetes.

Particle swarm optimization approach for modelling a turning process

This paper proposes the modelling of a turning process using particle swarm optimization (PSO). The independent input machining parameters for the modelling were cutting speed, feed rate, and cutting depth. The input parameters affected three dependent output parameters that were the main cutting force, surface roughness, and tool life. The values of the independent and dependent parameters were acquired by experimental work and served as knowledge base for the PSO process. By utilizing the knowledge base and the PSO approach, various models could be acquired for describing the cutting process. In our case, three different polynomial models were obtained: models a) for the main cutting force, b) for surface roughness, and c) for tool life. All the models had exactly the same basic polynomial form which was chosen similarly to that in the conventional regression analysis method. The PSO approach was used for optimization of the polynomials' coefficients. Several different randomly-selected data sets were used for the learning and testing phases. The accuracies of the developed models were analysed. It was discovered that the accuracies of the models for different learning and testing data sets were very good, having almost the same deviations. The least deviation was noted for the cutting force, whilst the most deviation, as expected was for tool life. The obtained models could then be used for later optimization of the turning process.

An extended Drude model for the in-situ spectroscopic ellipsometry analysis of ZnO thin layers and surface modifications

The accurate determination of the dielectric function and thickness of thin films by means of spectroscopic ellipsometry becomes increasingly difficult or even impossible if the layer thickness approaches the nano- or sub-nanometre range. In the case of in-situ ellipsometric process monitoring, where rough and instable experimental conditions may pose a severe problem, errors introduced by windows and less accurate sample alignment make the data evaluation even more complicated. If the experimental setup cannot be improved and therefore errors are difficult to correct, we find the almost forgotten analytic model introduced by Drude [P. Drude, Ann. Phys. 279, 126 (1891)] to be useful to detect errors and artefacts in the commonly used regression analysis. Here we present the extension of Drude's analytical three phase model for the case of slightly absorbing materials. We show its applicability using the example of the surface modification of a ZnO bulk single crystal under vacuum conditions. As only a few spectral features of the ellipsometric spectrum are used in the analysis, the obtained results are subject to more noise compared to conventional regression analysis. We show that it is useful to apply both complementary techniques, the regression analysis and the extended Drude model, for the analysis of thin layers, especially in the case of in-situ ellipsometry monitoring during growth or modification of thin layers respective surfaces.

Connections between Land Use and Driving Distance: Causal Investigation Using Directed Acyclic Graphs

AbstractThis paper examines the causal connections between land use and household driving distance in the Houston-Galveston metropolitan area by introducing the directed acyclic graphs. It is found that population density and roadway design measure have direct causal relationships with total household driving distance. In terms of elasticity estimates of driving distance with respect to land-use measures, the impacts of land use on household driving distance are found to be overrated in the conventional regression analyses compared with the results based on the directed acyclic graphs. Urban development and design strategies can play a significant role in reducing household driving distance, which then helps moderate air pollution and climate change.

Grey system theory and fuzzy time series forecasting for the growth of green electronic materials

The development of green materials is an important part of corporate social responsibility. Companies need to use resources legitimately and have environmental protection responsibility. Forecasting the growth trend of green copper clad laminate (CCL) material is crucial for manufacturers of printed circuit boards and green CCLs. Early and accurate understanding of such trends in this industry can lead to the early acquisition of opportunities for related markets and technological development. Because historical data samples associated with green CCL are small and typically lack a normal distribution, employing conventional regression analysis or time series models for forecasting is not suitable for testing-related presumptions. To address this issue, this study adopts grey system theory (GST) and a fuzzy time series (FTS) model as forecasting methods. The results show that effective forecasting can be achieved by applying either a GM (1,1) α model or heuristic FTS model to the data of a small and non-normally distributed sample.

Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.

Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine-learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine-learning algorithms. We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine-learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared with the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis, and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95% confidence interval (CI) 0.56-0.67), whereas the machine-learning algorithm had a c-statistic of 0.64 (95% CI 0.60-0.69) in the validation cohort. The HALT-C model had a c-statistic of 0.60 (95% CI 0.50-0.70) in the validation cohort and was outperformed by the machine-learning algorithm. The machine-learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (P<0.001) and integrated discrimination improvement (P=0.04). Machine-learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC.