Inform Model Selection Research Articles

Spatiotemporal of Particulate Matter (PM2.5) and Ozone (O3) in Eastern Northeast Brazil

ABSTRACT This research examines particulate matter with a diameter of 2.5 micrometers (PM2.5) and ozone (O3) variation across the climatic mesoregions of Alagoas. Mean PM2.5 and O3 concentrations across three mesoregions were as follows: East (5.91; 44.22 μg.m−3), Hinterland (6.90; 44.18 μg.m−3), and Arid (7.03; 44.23 μg.m−3). Spatial and temporal variations were observed, with PM2.5 concentrations highest in the northwest (NW) and O3 concentrations in the east (E), influenced by local sources, weather, and transportation. Differences between rainy and dry years were noted, attributed to biomass burning and dust particle transport. This study establishes a baseline for understanding air quality, lacking monitoring stations, aiding policymaking. It provides insights into PM2.5 and O3 dynamics, with PM2.5 concentrations highest in the NW and lower O3 concentrations in the E. Temporal variations emphasize the need for dynamic monitoring. Positive correlations between PM2.5 and O3 highlight complex relationships. Practical implications include proactive policymaking and the necessity for monitoring stations. Rigorous statistical methodologies inform model selection, enhancing environmental data understanding. Despite changes in land use, studies on extreme probability distributions are limited, emphasizing the need for robust methodologies for environmental data analysis to address air pollution challenges effectively in Alagoas.

Recreating chronic respiratory infections in vitro using physiologically relevant models.

Despite the need for effective treatments against chronic respiratory infections (often caused by pathogenic biofilms), only a few new antimicrobials have been introduced to the market in recent decades. Although different factors impede the successful advancement of antimicrobial candidates from the bench to the clinic, a major driver is the use of poorly predictive model systems in preclinical research. To bridge this translational gap, significant efforts have been made to develop physiologically relevant models capable of recapitulating the key aspects of the airway microenvironment that are known to influence infection dynamics and antimicrobial activity in vivo In this review, we provide an overview of state-of-the-art cell culture platforms and ex vivo models that have been used to model chronic (biofilm-associated) airway infections, including air-liquid interfaces, three-dimensional cultures obtained with rotating-wall vessel bioreactors, lung-on-a-chips and ex vivo pig lungs. Our focus is on highlighting the advantages of these infection models over standard (abiotic) biofilm methods by describing studies that have benefited from these platforms to investigate chronic bacterial infections and explore novel antibiofilm strategies. Furthermore, we discuss the challenges that still need to be overcome to ensure the widespread application of in vivo-like infection models in antimicrobial drug development, suggesting possible directions for future research. Bearing in mind that no single model is able to faithfully capture the full complexity of the (infected) airways, we emphasise the importance of informed model selection in order to generate clinically relevant experimental data.

Synthesis and drug release kinetics of ciprofloxacin from polyacrylamide/dextran/carbon quantum dots (PAM/Dex/CQD) hydrogels

Sustainable release of drug by utilizing β-cyclodextrin (β-CD) based inclusion complex (IC) is the prime objective of the present work. Herein, polyacrylamide/dextran containing carbon quantum dots (PAM/Dex/CQD) nanocomposite hydrogels are prepared by in situ polymerization of acrylamide. The incorporation of CQD triggers the change in orientation of the PAM/Dex polymeric chains to result the formation of stacked surface morphology of the hydrogel. The average particle size of CQD is found to be 4.13 nm from HRTEM analysis. As-synthesized nanocomposite hydrogel exhibits an optimum swelling ratio of 863 % in aqueous medium. The cytotoxicity study is conducted on HeLa cells by taking up to 2 μM concentration of the prepared nanocomposite hydrogel demonstrate 78 % cell viability. In present study, ciprofloxacin (Cipro) is taken as model drug that achieves release of 64.15 % in 32 h from β-Cipro@PAM/Dex/CQD hydrogels in acidic medium. From theoretical study, release rate constants, R2, Akaike information criterion (AIC) and model selection criterion (MSC) are computed to determine the best fitted kinetics model. Peppas-Sahlin model is the best fitted kinetics model for β-Cipro@PAM/Dex/CQD and concluded that the release of Cipro follows Fickian drug diffusion mechanism in acidic medium.

A General Model for Aggregating Annotations AcrossSimple, Complex, and Multi-object Annotation Tasks (Abstract Reprint)

Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A common strategy to improve label quality is to ask multiple annotators to label the same item and then aggregate their labels. To date, many aggregation models have been proposed for simple categorical or numerical annotation tasks, but far less work has considered more complex annotation tasks, such as those involving open-ended, multivariate, or structured responses. Similarly, while a variety of bespoke models have been proposed for specific tasks, our work is the first we are aware of to introduce aggregation methods that generalize across many, diverse complex tasks, including sequence labeling, translation, syntactic parsing, ranking, bounding boxes, and keypoints. This generality is achieved by applying readily available task-specific distance functions, then devising a task-agnostic method to model these distances between labels, rather than the labels themselves. This article presents a unified treatment of our prior work on complex annotation modeling and extends that work with investigation of three new research questions. First, how do complex annotation task and dataset properties impact aggregation accuracy? Second, how should a task owner navigate the many modeling choices in order to maximize aggregation accuracy? Finally, what tests and diagnoses can verify that aggregation models are specified correctly for the given data? To understand how various factors impact accuracy and to inform model selection, we conduct large-scale simulation studies and broad experiments on real, complex datasets. Regarding testing, we introduce the concept of unit tests for aggregation models and present a suite of such tests to ensure that a given model is not mis-specified and exhibits expected behavior. Beyond investigating these research questions above, we discuss the foundational concept and nature of annotation complexity, present a new aggregation model as a conceptual bridge between traditional models and our own, and contribute a new general semisupervised learning method for complex label aggregation that outperforms prior work.

Model manifold of transmission loss from a Pekeris waveguide using VGS parameters

In geoacoustic inversion, selecting an appropriate seabed parametrization, especially with an unknown number of sediment layers, is a challenge that is compounded by potential bias when establishing bounds in the parameter search space. One approach to addressing these issues is rooted in the techniques of Information Geometry. Information Geometry informs model selection and parameterization by quantifying which model parameters are informed by observational data. This paper provides an information geometric analysis of the Pekeris waveguide, where the acoustic properties of the lower half-space are derived from the viscous grain-shearing (VGS) model. Specifically, we consider single frequency transmission loss (TL) across a wide range of VGS parameters. By exploring the limits and boundaries of the geometric manifolds, particularly as parameters approach both low and high extremes, this approach provides indications of parameter hierarchies and correlations. Results will include slices of the model manifold and an evaluation of the boundary structure, providing insight into the relative impact of VGS parameters and the delineation of limiting regions. In doing so, this paper seeks to inform model selection and parameterization in geoacoustic inversion studies. [Work supported by the Office of Naval Research. Grant N00014-21-S-B001.]

Estimation and Prediction of the Polymers' Physical Characteristics Using the Machine Learning Models.

This article investigates the utility of machine learning (ML) methods for predicting and analyzing the diverse physical characteristics of polymers. Leveraging a rich dataset of polymers' characteristics, the study encompasses an extensive range of polymer properties, spanning compressive and tensile strength to thermal and electrical behaviors. Using various regression methods like Ensemble, Tree-based, Regularization, and Distance-based, the research undergoes thorough evaluation using the most common quality metrics. As a result of a series of experimental studies on the selection of effective model parameters, those that provide a high-quality solution to the stated problem were found. The best results were achieved by Random Forest with the highest R2 scores of 0.71, 0.73, and 0.88 for glass transition, thermal decomposition, and melting temperatures, respectively. The outcomes are intricately compared, providing valuable insights into the efficiency of distinct ML approaches in predicting polymer properties. Unknown values for each characteristic were predicted, and a method validation was performed by training on the predicted values, comparing the results with the specified variance values of each characteristic. The research not only advances our comprehension of polymer physics but also contributes to informed model selection and optimization for materials science applications.

Transformative Progress in Document Digitization: An In-Depth Exploration of Machine and Deep Learning Models for Character Recognition

Introduction: this paper explores the effectiveness of character recognition models for document digitization, leveraging diverse machine learning and deep learning techniques. The study, driven by the increasing relevance of image classification in various applications, focuses on evaluating Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning. The research employs a challenging French alphabet dataset, comprising 82 classes, to assess the models' capacity to discern intricate patterns and generalize across diverse characters. Objective: This study investigates the effectiveness of character recognition models for document digitization using diverse machine learning and deep learning techniques. Methods: the methodology initiates with data preparation, involving the creation of a merged dataset from distinct sections, encompassing digits, French special characters, symbols, and the French alphabet. The dataset is subsequently partitioned into training, test, and evaluation sets. Each model undergoes meticulous training and evaluation over a specific number of epochs. The recording of fundamental metrics includes accuracy, precision, recall, and F1-score for CNN, RNN, and VGG16, while SVM and KNN are evaluated based on accuracy, macro avg, and weighted avg. Results: the outcomes highlight distinct strengths and areas for improvement across the evaluated models. SVM demonstrates remarkable accuracy of 98.63%, emphasizing its efficacy in character recognition. KNN exhibits high reliability with an overall accuracy of 97%, while the RNN model faces challenges in training and generalization. The CNN model excels with an accuracy of 97.268%, and VGG16 with transfer learning achieves notable enhancements, reaching accuracy rates of 94.83% on test images and 94.55% on evaluation images. Conclusion: our study evaluates the performance of five models—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning—on character recognition tasks. SVM and KNN demonstrate high accuracy, while RNN faces challenges in training. CNN excels in image classification, and VGG16, with transfer learning, enhances accuracy significantly. This comparative analysis aids in informed model selection for character recognition applications.

A General Model for Aggregating Annotations Across Simple, Complex, and Multi-Object Annotation Tasks

Human annotations are vital to supervised learning, yet annotators often disagree on the correct label, especially as annotation tasks increase in complexity. A common strategy to improve label quality is to ask multiple annotators to label the same item and then aggregate their labels. To date, many aggregation models have been proposed for simple categorical or numerical annotation tasks, but far less work has considered more complex annotation tasks, such as those involving open-ended, multivariate, or structured responses. Similarly, while a variety of bespoke models have been proposed for specific tasks, our work is the first we are aware of to introduce aggregation methods that generalize across many, diverse complex tasks, including sequence labeling, translation, syntactic parsing, ranking, bounding boxes, and keypoints. This generality is achieved by applying readily available task-specific distance functions, then devising a task-agnostic method to model these distances between labels, rather than the labels themselves. This article presents a unified treatment of our prior work on complex annotation modeling and extends that work with investigation of three new research questions. First, how do complex annotation task and dataset properties impact aggregation accuracy? Second, how should a task owner navigate the many modeling choices in order to maximize aggregation accuracy? Finally, what tests and diagnoses can verify that aggregation models are specified correctly for the given data? To understand how various factors impact accuracy and to inform model selection, we conduct large-scale simulation studies and broad experiments on real, complex datasets. Regarding testing, we introduce the concept of unit tests for aggregation models and present a suite of such tests to ensure that a given model is not mis-specified and exhibits expected behavior. Beyond investigating these research questions above, we discuss the foundational concept and nature of annotation complexity, present a new aggregation model as a conceptual bridge between traditional models and our own, and contribute a new general semisupervised learning method for complex label aggregation that outperforms prior work.

Comparison of Coverage-Prediction Models for Modern Mobile Radio Networks

The accurate prediction of a signal’s attenuation is essential for the development of reliable, modern, mobile radio-communication networks. In this study, the accuracies of four propagation models in diverse terrains and environments were analyzed using field measurements along a comprehensive test route. We evaluated the ability of the models—Egli, Okumura, Hata–Davidson, and Longley–Rice—to predict signal propagation in the Very-High-Frequency (VHF) and Ultra-High-Frequency (UHF) bands. Based on a meticulous comparison, we present valuable insights into the strengths and limitations of these models, enhancing coverage-prediction methodologies for evolving mobile radio networks. The Egli model, despite its simplicity, introduces significant inaccuracies due to its assumptions and a lack of consideration for the terrain. The Okumura model, which is widely used in urban areas, requires careful correction selection, while the Hata–Davidson model improves upon the former’s weaknesses. The Longley–Rice model excels in flexibility and accuracy, especially in the VHF bands, using topographical data, though it can overestimate the attenuation in shadowed areas. The study concluded that no single model was universally accurate, as each model has its strengths and limitations. It highlights the need for informed model selection based on the terrain’s characteristics and specific requirements. The results will be useful to network planners, helping them to design efficient, mobile communication networks with reliable coverage and optimal spectrum utilization. The Longley–Rice model emerged as particularly powerful, offering detailed predictions across diverse environments.

SARS-CoV-2 viral dynamic modeling to inform model selection and timing and efficacy of antiviral therapy.

Mathematical models of viral dynamics have been reported to describe adequately the dynamic changes of severe acute respiratory syndrome-coronavirus 2 viral load within an individual host. In this study, eight published viral dynamic models were assessed, and model selection was performed. Viral load data were collected from a community surveillance study, including 2155 measurements from 162 patients (124 household and 38 non-household contacts). An extended version of the target-cell limited model that includes an eclipse phase and an immune response component that enhances viral clearance described best the data. In general, the parameter estimates showed good precision (relative standard error <10), apart from the death rate of infected cells. The parameter estimates were used to simulate the outcomes of a clinical trial of the antiviral tixagevimab-cilgavimab, a monoclonal antibody combination which blocks infection of the target cells by neutralizing the virus. The simulated outcome of the effectiveness of the antiviral therapy in controlling viral replication was in a good agreement with the clinical trial data. Early treatment with high antiviral efficacy is important for desired therapeutic outcome.

Accurate Broadband Gradient Estimates Enable Local Sensitivity Analysis of Ocean Acoustic Models

Sensitivity analysis is a powerful tool for analyzing multi-parameter models. For example, the Fisher information matrix (FIM) and the Cramér–Rao bound (CRB) involve derivatives of a forward model with respect to parameters. However, these derivatives are difficult to estimate in ocean acoustic models. This work presents a frequency-agnostic methodology for accurately estimating numerical derivatives using physics-based parameter preconditioning and Richardson extrapolation. The methodology is validated on a case study of transmission loss in the 50–400[Formula: see text]Hz band from a range-independent normal mode model for parameters of the sediment. Results demonstrate the utility of this methodology for obtaining Cramér–Rao bound (CRB) related to both model sensitivities and parameter uncertainties, which reveal parameter correlation in the model. This methodology is a general tool that can inform model selection and experimental design for inverse problems in different applications.

FLIPI24: An Improved International Prognostic Model Developed on Early Events in Follicular Lymphoma

Background: Early events are associated with inferior outcomes in follicular lymphoma (FL) and increased risk of death due to refractory FL. Risk prediction of early events prior to the start of therapy may enhance clinical management and research strategies. We report the FL International Prognostic Index model developed specifically to predict the risk of event within 24 months (EFS24) of starting first-line immunochemotherapy (IC) (FLIPI24). Methods: Modeling was performed using individual patient data from 10 observational cohorts from Europe, North America, and Australia. Eligible patients were diagnosed with grades 1-3A FL and initiated frontline R-CHOP, R-CVP, R-bendamustine (B-R), or like therapies. Event-free survival (EFS) was defined as time from start of IC to progression, retreatment, transformation, or death due to any cause. Model development utilized a truncated Cox model stratified on IC type and cohort with a time-dependent adjustment for maintenance therapy. Multiple imputation was implemented to address missing data. Functional forms of variables were assessed using splines. Model selection was based on model performance, clinical relevance, and parsimony. Gradient boosting machines were evaluated as a complementary approach and used to inform model selection. Model building was performed on an 80% split sample with 20% held for internal testing. External validation occurred in an independent cohort. FLIPI24 risk scores assumed B-R or R-CHOP based chemotherapy and 50% likelihood of maintenance. Results: 4485 patients initiating frontline IC between 2002 and 2018 were utilized in the analysis; 3577 patients were utilized for model building and 908 for internal model testing. Median age at diagnosis in the model build dataset was 61 years (IQR 53-69) and 51% were male. FLIPI was 21%, 32%, and 48% for low, intermediate, and high risk, respectively. IC type was 1874 R-CHOP or like (52%), 730 B-R or like (20%), and 973 R-CVP (27%); 2164 received CD20 antibody maintenance (61%). At median follow-up of 77 months (IQR 48-115), EFS24 estimate was 81% (95% CI: 79-82) and 835 patients (23%) died. 5-year survival after an early (non-death) event was 46% (95% CI: 42-50). The FLIPI24 model utilizes 5 continuous variables: age (linear 60-90 years with inflection at age 75), hemoglobin (linear 8-17 g/dL), white blood cell count (linear 4-11 109/L), normalized lactate dehydrogenase (linear 0.5-5), and beta-2-microglobulin (linear 1-10 mg/L). Linear variables and valid ranges were utilized based on close examination of functional forms across potential models. Continuous FLIPI24 risk scores were grouped for visualization in K-M curves as follows: very low risk (0-0.10), low risk (0.10-0.15), average risk (0.15-0.20), high risk (0.20-0.40), very high risk (>0.40), Figure 1. Internal validation was performed on the 20% holdout set (N=908). The FLIPI24 had superior concordance (c-stat) for EFS24 (c-stat=0.666) compared to FLIPI (c-stat=0.630) and PRIMA-PI (c-stat=0.590) models, Table 1. Importantly, FLIPI24 also improved prognostic ability for 10-year OS following IC (c-stat = 0.672) vs FLIPI (c-stat=0.629) or PRIMA-PI (c-stat=0.595). External validation was performed in a prospective cohort of 1438 newly diagnosed patients with FL (558 treated with IC) enrolled in the multicenter US LEO Cohort between 2015 and 2020. The FLIPI24 had superior c-stats compared to the FLIPI and PRIMA-PI for EFS24 (0.671 vs 0.600 vs 0.602) and OS (0.732 vs. 0.652 vs 0.635) in IC treated patients and for OS (0.785 vs 0.703 vs 0.662) when assessed in all patients. Conclusion: The FLIPI24 model provides an individual risk score at diagnosis for the likelihood of experiencing an event within 24 months from starting IC. The FLIPI24 was rigorously developed, tested, and externally validated using large harmonized and pooled international observational cohorts from the IC era. The FLIPI24 utilizes objective variables easily and reliably measured at diagnosis. A novel feature of the model is the significance of established blood-based measurements in the model, which appears to capture underlying tumor and host biology. Model performance for FLIPI24 was superior to standard clinical models for prediction of both EFS24 and OS in internal and external validation sets. Elevated FLIPI24 risk represents a patient population for further biologic studies and who should be considered for novel frontline therapies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

On the Treatment of Optimization Problems With L1 Penalty Terms via Multiobjective Continuation.

We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization. Sparsity is of great importance in many scientific areas such as image and signal processing, medical imaging, compressed sensing, and machine learning, as it ensures robustness against noisy data and yields models that are easier to interpret due to the small number of relevant terms. It is common practice to enforce sparsity by adding the l1-norm as a penalty term. In order to gain a better understanding and to allow for an informed model selection, we directly solve the corresponding multiobjective optimization problem (MOP) that arises when minimizing the main objective and the l1-norm simultaneously. As this MOP is in general non-convex for nonlinear objectives, the penalty method will fail to provide all optimal compromises. To avoid this issue, we present a continuation method specifically tailored to MOPs with two objective functions one of which is the l1-norm. Our method can be seen as a generalization of homotopy methods for linear regression problems to the nonlinear case. Several numerical examples - including neural network training - demonstrate our theoretical findings and the additional insight gained by this multiobjective approach.

Low-Intensity Agriculture Shapes Amphibian and Reptile Communities: Insights from a 10-Year Monitoring Study

Intensification and expansion of agriculture is one of the most pressing concerns for biodiversity conservation. Amphibian and reptile communities can be particularly sensitive to land-use change; therefore, understanding how land use and agricultural practices influence these communities in areas under increasing agricultural pressure is prudent. Over 10 yr, we sampled amphibian and reptile communities in and around 18 man-made ponds set within an agricultural/forest matrix on a military installation in western Tennessee. Ponds had either cattle access or were cattle restricted and had different surrounding land uses (forest, pasture, row crops, and industrial development). We used Akaike information criterion, corrected (AICc), model selection and nonmetric multidimensional scaling (NMDS) to assess how land-cover and pond characteristics influence amphibian and reptile richness, diversity, and community structure. Our results indicate that, despite reduced water quality, amphibian diversity was higher in ponds with cattle access, though this is likely influenced by surrounding forest, pasture, and row-crop cover. Pond permanence was important in shaping amphibian community structure and is a key factor in determining amphibian richness. We did not find any link between land cover and cattle access on reptile richness or diversity, though reptile community structure was influenced by cattle access, pasture, and row-crop cover.

Normalized Information Criteria and Model Selection in the Presence of Missing Data

Information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC) are commonly used for model selection. However, the current theory does not support unconventional data, so naive use of these criteria is not suitable for data with missing values. Imputation, at the core of most alternative methods, is both distorted as well as computationally demanding. We propose a new approach that enables the use of classic well-known information criteria for model selection when there are missing data. We adapt the current theory of information criteria through normalization, accounting for the different sample sizes used for each candidate model (focusing on AIC and BIC). Interestingly, when the sample sizes are different, our theoretical analysis finds that AICj/nj is the proper correction for AICj that we need to optimize (where nj is the sample size available to the jth model) while −(BICj−BICi)/(nj−ni) is the correction of BIC. Furthermore, we find that the computational complexity of normalized information criteria methods is exponentially better than that of imputation methods. In a series of simulation studies, we find that normalized-AIC and normalized-BIC outperform previous methods (i.e., normalized-AIC is more efficient, and normalized BIC includes only important variables, although it tends to exclude some of them in cases of large correlation). We propose three additional methods aimed at increasing the statistical efficiency of normalized-AIC: post-selection imputation, Akaike sub-model averaging, and minimum-variance averaging. The latter succeeds in increasing efficiency further.

Fisher information and Cramér-Rao bound analysis with stable gradients for ocean acoustic models

The Fisher Information Matrix (FIM) and the Cramér-Rao Bound (CRB) are powerful tools for quantifying uncertainty in multi-parameter models. However, it is difficult to estimate the derivatives needed to calculate the FIM and CRB in ocean acoustic models. In this work, a methodology for estimating derivatives using physics-guided parameter preconditioning and Richardson extrapolation is presented. The method is validated on a case study of transmission loss from a range-independent normal mode model in ocean environments with a single sediment layer over a basalt basement. Five examples of sediment types ranging from mud to gravel are considered across frequencies from 50–400 Hz. Results demonstrate the utility of FIM and CRB analysis in quantifying both model sensitivities and parameter uncertainties, and in revealing parameter coupling in the model. This methodology is a general tool that can inform model selection and experimental design for inverse problem applications. [Work supported by ONR Contract No. N00014-19-C-2001.]

Plant hydraulic transport controls transpiration sensitivity to soil water stress

Abstract. Plant transpiration downregulation in the presence of soil water stress is a critical mechanism for predicting global water, carbon, and energy cycles. Currently, many terrestrial biosphere models (TBMs) represent this mechanism with an empirical correction function (β) of soil moisture – a convenient approach that can produce large prediction uncertainties. To reduce this uncertainty, TBMs have increasingly incorporated physically based plant hydraulic models (PHMs). However, PHMs introduce additional parameter uncertainty and computational demands. Therefore, understanding why and when PHM and β predictions diverge would usefully inform model selection within TBMs. Here, we use a minimalist PHM to demonstrate that coupling the effects of soil water stress and atmospheric moisture demand leads to a spectrum of transpiration responses controlled by soil–plant hydraulic transport (conductance). Within this transport-limitation spectrum, β emerges as an end-member scenario of PHMs with infinite conductance, completely decoupling the effects of soil water stress and atmospheric moisture demand on transpiration. As a result, PHM and β transpiration predictions diverge most for soil–plant systems with low hydraulic conductance (transport-limited) that experience high variation in atmospheric moisture demand and have moderate soil moisture supply for plants. We test these minimalist model results by using a land surface model at an AmeriFlux site. At this transport-limited site, a PHM downregulation scheme outperforms the β scheme due to its sensitivity to variations in atmospheric moisture demand. Based on this observation, we develop a new “dynamic β” that varies with atmospheric moisture demand – an approach that overcomes existing biases within β schemes and has potential to simplify existing PHM parameterization and implementation.

Optimizing Consistency and Coverage in Configurational Causal Modeling

Consistency and coverage are two core parameters of model fit used by configurational comparative methods (CCMs) of causal inference. Among causal models that perform equally well in other respects (e.g., robustness or compliance with background theories), those with higher consistency and coverage are typically considered preferable. Finding the optimally obtainable consistency and coverage scores for data [Formula: see text], so far, is a matter of repeatedly applying CCMs to [Formula: see text] while varying threshold settings. This article introduces a procedure called ConCovOpt that calculates, prior to actual CCM analyses, the consistency and coverage scores that can optimally be obtained by models inferred from [Formula: see text]. Moreover, we show how models reaching optimal scores can be methodically built in case of crisp-set and multi-value data. ConCovOpt is a tool, not for blindly maximizing model fit, but for rendering transparent the space of viable models at optimal fit scores in order to facilitate informed model selection—which, as we demonstrate by various data examples, may have substantive modeling implications.

Learning Through Shared Care : Allomaternal Care Impacts Cognitive Development in Early Infancy in a Western Population.

This study investigates how allomaternal care (AMC) impacts human development outside of energetics by evaluating relations between important qualitative and quantitative aspects of AMC and developmental outcomes in a Western population. This study seeks to determine whether there are measurable differences in cognitive and language outcomes as predicted by differences in exposure to AMC via formal (e.g., childcare facilities) and informal (e.g., family and friends) networks. Data were collected from 102 mothers and their typically developing infants aged 13-18months. AMC predictor data were collected using questionnaires, structured daily diaries, and longitudinal interviews. Developmental outcomes were assessed using the Cognitive, Receptive Language, and Expressive Language subtests of the Bayley III Screening Test. Additional demographic covariates were also evaluated. Akaike Information Criterion (AIC)-informed model selection was used to identify the best-fitting model for each outcome across three working linear regression models. Although AMC variables had no significant effects on Receptive and Expressive Language subtest scores, highly involved familial AMC had a significant medium effect on Cognitive subtest score (β = 0.23, p < 0.01, semi-partial r = 0.28). Formal childcare had no effect on any outcome. This study provides preliminary evidence that there is a measurable connection between AMC and cognitive development in some populations and provides a methodological base from which to assess these connections cross-culturally through future studies. As these effects are attributable to AMC interactions with networks of mostly related individuals, these findings present an area for further investigation regarding the kin selection hypothesis for AMC.

Aliased informed model selection strategies for six‐factor no‐confounding designs

AbstractNonregular designs are a preferable alternative to regular resolution IV designs because they avoid confounding two‐factor interactions. As a result nonregular designs can estimate and identify a few active two‐factor interactions. However, due to the sometimes complex alias structure of nonregular designs, standard screening strategies can fail to identify all active effects. In this paper, we explore a specific no‐confounding six‐factor 16‐run nonregular design with orthogonal main effects. By utilizing our knowledge of the alias structure, we can inform the model selection process. Our aliased informed model selection (AIMS) strategy is a design‐specific approach that we compare to three generic model selection methods; stepwise regression, Lasso, and the Dantzig selector. The AIMS approach substantially increases the power to detect active main effects and two‐factor interactions versus the aforementioned generic methodologies.