An experimental approach to manufacturability assessment of microfluidic devices produced by stereolithography

2020 ◽  
Vol 234 (24) ◽  
pp. 4905-4916
Author(s):  
Mattia Mele ◽  
Giampaolo Campana
Keyword(s):  
Regression Models ◽  
Low Cost ◽  
Dimensional Accuracy ◽  
Binary Logistic Regression ◽  
Polymeric Materials ◽  
Microfluidic Devices ◽  
Combined Effect ◽  
Manufacturing Cost ◽  
Linear Regression Models ◽  
Logistic Regression Models

Lab-on-a-Chips integrate a variety of laboratory functions and embed microchannels for small fluid volume handling. These devices are used in medicine, chemistry, and biotechnology applications but a large diffusion is limited due to the manufacturing cost of traditional processes. Additive Manufacturing offers affordable alternatives for the production of microfluidic devices, because the fabrication of embedded micrometric channels is enabled. Stereolithography gained particular attention due to the low cost of both available machines and suitable polymeric materials to be processed. The main restriction to the adoption of this technique comes from the obtainable dimensional accuracy that depends not only on design, but also on process set-up. Firstly, the paper analyses theoretically the physics of stereolithographic processes and focuses on main phenomena related to microchannel manufacturing. Then, specific experimental activities are designed to investigate the combined effect of design and process parameters on the achievable dimensional accuracy of embedded microchannels manufactured through a commercial desktop stereolithography apparatus. In particular, the combined effect of channel nominal dimensions, build orientations and the layer thickness on the obtainable accuracy is examined by referring to a benchmark geometry. The collated experimental data showed that a number of combinations are successful. Besides, the experimental activity revealed that appropriate combinations of design, build orientation and manufacturing parameters can overcome the dimensional limitations reported in previous studies. Both binary logistic regression models to predict the manufacturability of microchannels and linear regression models to estimate the achievable accuracy for those geometries that can be produced successfully are developed.

Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

2021 ◽  
pp. 095679762097165
Author(s):  
Matthew T. McBee ◽  
Rebecca J. Brand ◽  
Wallace E. Dixon
Keyword(s):  
Early Childhood ◽  
Regression Models ◽  
Propensity Score Analysis ◽  
Harmful Effect ◽  
Attention Problems ◽  
Linear Regression Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
Score Analysis ◽  
Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

scholarly journals Can Cut-Off-Values for Tumor Size or Patient Age in Breast Ultrasound Reduce Unnecessary Biopsies or is it all About Bi-rads?– A Retrospective Analysis of 763 Biopsied T1-Sized Lesions

2017 ◽  
Vol 03 (03) ◽  
pp. E94-E98 ◽  
Author(s):  
Laura Holzer-Fruehwald ◽  
Matthias Meissnitzer ◽  
Michael Weber ◽  
Stephan Holzer ◽  
Klaus Hergan ◽  
...  
Keyword(s):  
Regression Models ◽  
Breast Lesion ◽  
Binary Logistic Regression ◽  
Lesion Size ◽  
Breast Lesions ◽  
Predictive Values ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Oval Shape ◽  
Histological Result

Abstract Aims and Objectives To assess whether it is possible to establish a size cut-off-value for sonographically visible breast lesions in a screening situation, under which it is justifiable to obviate a biopsy and to evaluate the grayscale characteristics of the identified lesions. Materials and Methods Images of sonographically visible and biopsied breast lesions of 684 patients were retrospectively reviewed and assessed for the following parameters: size, shape, margin, lesion boundary, vascularity, patient’s age, side of breast, histological result, and initial BI-RADS category. Statistical analyses (t-test for independent variables, ROC analyses, binary logistic regression models, cross-tabulations, positive/negative predictive values) were performed using IBM SPSS (Version 21.0). Results Of all 763 biopsied lesions, 223 (29.2%) showed a malignant histologic result, while 540 (70.8%) were benign. Although we did find a statistically significant correlation of malignancy and lesion size (p=0.031), it was not possible to define a cut-off value, under which it would be justifiable to obviate a biopsy in terms of sensitivity and specificity (AUC: 0.558) at any age. Lesions showing the characteristics of a round or oval shape, a sharp delineation and no echogenic rim (n=112) were benign with an NPV of 99.1%. Conclusion It is not possible to define a cut-off value for size or age, under which a biopsy of a sonographically visible breast lesion can be obviated in the screening situation. The combination of the 3 grayscale characteristics, shape (round or oval), margin (circumscribed) and no echogenic-rim sign, showed an NPV of 99.1%. Therefore, it seems appropriate to classify such lesions as BI-RADS 2.

Women’s Autonomy and Support for Wife Beating: Findings From a Population-Based Survey in Jordan

2010 ◽  
Vol 25 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Natalia Linos ◽  
Marwan Khawaja ◽  
Mohannad Al-Nsour
Keyword(s):  
Regression Models ◽  
Binary Logistic Regression ◽  
Population Based ◽  
Married Women ◽  
Wife Beating ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Hypothetical Scenario ◽  
Population Based Survey

The aim of this study is to examine attitudes among married women toward wife beating and to investigate the hypothesis that female individual empowerment is associated with such attitudes within a broader context of societal patriarchy in Jordan. The study uses data from a cross-sectional survey of a representative sample of married women (n = 5,390) conducted in 2002. Associations between acceptance of wife beating and several women’s empowerment variables, including decision-making power, as well as other risk factors were assessed, using odds ratios from binary logistic regression models. The key finding is that the vast majority (87.5%) of Jordanian women believe that wife beating is justified in at least one hypothetical scenario, and justification is negatively associated with empowerment variables and some demographic, geographic, and socioeconomic factors.

scholarly journals Application of Logistic Regression Models for the Marketability of Cucumber Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
José-Antonio Salinas-Andújar ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Weight Loss ◽  
Logistic Regression ◽  
Shelf Life ◽  
Regression Models ◽  
Chilling Injury ◽  
Binary Logistic Regression ◽  
Fruit Weight ◽  
Binary Logistic Regression Analysis ◽  
Cucumis Sativus L ◽  
Logistic Regression Models

The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Vol 23 (9) ◽  
pp. 3765-3786 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Logistic Regression ◽  
Air Temperature ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Snow Accumulation ◽  
Phase Method ◽  
Logistic Regression Models ◽  
Temperature Thresholds ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

scholarly journals Predicting the Criminal Records of Male-on-Female UK Homicide Offenders From Crime Scene Behaviors

2019 ◽  
pp. 088626051988852
Author(s):  
Louise Almond ◽  
Elias Matin ◽  
Michelle McManus
Keyword(s):  
Regression Models ◽  
Statistical Approach ◽  
Binary Logistic Regression ◽  
Crime Scene ◽  
The Other ◽  
Background Characteristics ◽  
Criminal Records ◽  
Logistic Regression Models ◽  
Offender Profiling ◽  
Criminal Histories

Offender profiling follows the idea that if offenders’ crime scene actions can be empirically linked to their background characteristics, it will be possible to predict one from the other. There is a lack of research exploring whether homicide offenders’ crime scene actions are predictive of their criminal histories, despite the potential utility of such information. The current study addresses this gap in the literature. A sample of 213 adult male-on-female homicides with sexual or unknown motive was drawn from a U.K.-wide database. Relationships between 13 preconviction variables and 29 crime scene behaviors were explored using a bivariate statistical approach. Subsequently, binary logistic regression models were used to predict the presence, or absence, of specific preconvictions based on a combination of offense behaviors. Analyses highlighted 16 statistically significant associations between key offense behaviors and previous convictions, these associations were often “less likely” to result in previous conviction. The analysis failed to find any association for various other variables, most notably sexual preconvictions. Results indicate offenders’ criminal histories can be predicted from their offense behaviors, though not all preconvictions may be similarly suited. Implications for practice are discussed.

Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models

2019 ◽  
Vol 19 (4) ◽  
pp. 913-930 ◽  
Author(s):  
E. Keith Smith ◽  
Michael G. Lacy ◽  
Adam Mayer
Keyword(s):  
Logistic Regression ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Experimental Conditions ◽  
Logistic Regression Models ◽  
Nonlinear Regression Models ◽  
Size Number ◽  
Confidence Interval Coverage ◽  
The Difference

Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be investigated. In this article, we evaluate khb‘s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of mediation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.

scholarly journals The impact of demographic, clinical, genetic, and imaging variables on tau PET status

2020 ◽  
Author(s):  
Rik Ossenkoppele ◽  
◽  
Antoine Leuzy ◽  
Hanna Cho ◽  
Carole H. Sudre ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cortical Thickness ◽  
Regression Models ◽  
Neurodegenerative Disorder ◽  
Amyloid Β ◽  
Binary Logistic Regression ◽  
Clinical Genetic ◽  
Logistic Regression Models ◽  
Tau Pet ◽  
The Impact

Abstract Purpose A substantial proportion of amyloid-β (Aβ)+ patients with clinically diagnosed Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI) are tau PET–negative, while some clinically diagnosed non-AD neurodegenerative disorder (non-AD) patients or cognitively unimpaired (CU) subjects are tau PET–positive. We investigated which demographic, clinical, genetic, and imaging variables contributed to tau PET status. Methods We included 2338 participants (430 Aβ+ AD dementia, 381 Aβ+ MCI, 370 non-AD, and 1157 CU) who underwent [18F]flortaucipir (n = 1944) or [18F]RO948 (n = 719) PET. Tau PET positivity was determined in the entorhinal cortex, temporal meta-ROI, and Braak V-VI regions using previously established cutoffs. We performed bivariate binary logistic regression models with tau PET status (positive/negative) as dependent variable and age, sex, APOEε4, Aβ status (only in CU and non-AD analyses), MMSE, global white matter hyperintensities (WMH), and AD-signature cortical thickness as predictors. Additionally, we performed multivariable binary logistic regression models to account for all other predictors in the same model. Results Tau PET positivity in the temporal meta-ROI was 88.6% for AD dementia, 46.5% for MCI, 9.5% for non-AD, and 6.1% for CU. Among Aβ+ participants with AD dementia and MCI, lower age, MMSE score, and AD-signature cortical thickness showed the strongest associations with tau PET positivity. In non-AD and CU participants, presence of Aβ was the strongest predictor of a positive tau PET scan. Conclusion We identified several demographic, clinical, and neurobiological factors that are important to explain the variance in tau PET retention observed across the AD pathological continuum, non-AD neurodegenerative disorders, and cognitively unimpaired persons.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

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