Principal Component Neural Networks for Modeling, Prediction, and Optimization of Hot Mix Asphalt Dynamics Modulus

The dynamic modulus of hot mix asphalt (HMA) is a fundamental material property that defines the stress-strain relationship based on viscoelastic principles and is a function of HMA properties, loading rate, and temperature. Because of the large number of efficacious predictors (factors) and their nonlinear interrelationships, developing predictive models for dynamic modulus can be a challenging task. In this research, results obtained from a series of laboratory tests including mixture dynamic modulus, aggregate gradation, dynamic shear rheometer (on asphalt binder), and mixture volumetric are used to create a database. The created database is used to develop a model for estimating the dynamic modulus. First, the highly correlated predictor variables are detected, then Principal Component Analysis (PCA) is used to first reduce the problem dimensionality, then to produce a set of orthogonal pseudo-inputs from which two separate predictive models were developed using linear regression analysis and Artificial Neural Networks (ANN). These models are compared to existing predictive models using both statistical analysis and Receiver Operating Characteristic (ROC) Analysis. Empirically-based predictive models can behave differently outside of the convex hull of their input variables space, and it is very risky to use them outside of their input space, so this is not common practice of design engineers. To prevent extrapolation, an input hyper-space is added as a constraint to the model. To demonstrate an application of the proposed framework, it was used to solve design-based optimization problems, in two of which optimal and inverse design are presented and solved using a mean-variance mapping optimization algorithm. The design parameters satisfy the current design specifications of asphalt pavement and can be used as a first step in solving real-life design problems

Developing a Robust Modeling Approach for Pavement Performance Prediction and Optimization

In pavement technology, performance models are mathematical expressions that relate pavement condition, surface distresses and structural properties as response variables to a set of predictors including material properties, traffic loading, environmental factors, etc. In the existence of numerous important predictors and their interrelationships, developing a predictive model for pavement performance is not a trivial task. In this study, a machine learning-based framework is developed for predicting pavement performance. The framework starts with a preparation step of data pre-processing and data wrangling. After removing outliers, the framework will conduct principal component analysis (PCA) to firstly reduce the dimensionality of the problem and secondly eliminate pairwise correlation between the inputs by producing orthogonal pseudo-inputs. These pseudo-inputs are used to develop two predictive models using multivariate regression analysis and artificial neural networks (ANN). In empirical predictive models, mapping input space to response space can be threatened by extrapolation. However, it is often disregarded by design engineers. In this study to confront extrapolation, a method is implemented to determine a hyperspace based on the inputs. The hyperspace determines where the predictive model is valid up to given thresholds and is then added as a constraint to the modeling problem. Two of the performance-related characteristics of asphalt mixtures, including rut resistance and dynamic modulus, are considered to examine the robustness of the proposed approach. The developed predictive models are then compared to conventional models for each case and indicated superior performance ( of 0.97 and 0.99 for rutting and dynamic modulus, respectively). A global variable importance analysis is also conducted to obtain the most effective variable in each case. Percent air voids and binder shear properties appeared to be the most effective variables in predicting rutting and dynamic modulus, respectively. To indicate an application of the developed framework in asphalt pavement design, for each of these two cases a design-related optimization problem is defined and solved using a mean-variance mapping optimization (MVMO) algorithm. The obtained optimal design parameters are within the acceptable range of current asphalt pavement design specifications and thus can be used as an appropriate starting point in a design procedure.This proceeding is published as Ghasemi, P.; Aslani, M.; Rollins, D.K.; and Williams, R.C. “Developing a Robust Modeling Approach for Pavement Performance Prediction and Optimization”, Asphalt Paving Technology 2019: Journal of the Association of Asphalt Paving Technologist, volume 88, pp 571-611, 2019. Copyright 2019 by the Association of Asphalt Paving Technologists. Posted with permission

Hybridisation rates, population structure, and dispersal of sambar deer (Cervus unicolor) and rusa deer (Cervus timorensis) in south-eastern Australia

Context. Introduced populations of sambar deer (Cervus unicolor) and rusa deer (Cervus timorensis) are present across south-eastern Australia and are subject to local population control to alleviate their negative impacts. For management to be effective, identification of dispersal capability and management units is necessary. These species also readily hybridise, so additional investigation of hybridisation rates across their distributions is necessary to understand the interactions between the two species. Aims. Measure the hybridisation rate of sambar and rusa deer, assess broad-scale population structure present within both species and identify distinct management units for future population control, and measure the likely dispersal capability of both species. Methods. In total, 198 sambar deer, 189 rusa deer, and three suspected hybrid samples were collected across Victoria and New South Wales (NSW). After sequencing and filtering, 14 099 polymorphic single-nucleotide polymorphism (SNP) markers were retained for analysis. Hybridisation rates were assessed before the data were split by species to identify population structure, diversity indices, and dispersal distances. Key results. Across the entire dataset, 17 hybrids were detected. Broad-scale population structure was evident in sambar deer, but not among the sites where rusa deer were sampled. Analysis of dispersal ability showed that a majority of deer movement occurred within 20 km in both species, suggesting limited dispersal. Conclusions. Distinct management units of sambar deer can be identified from the dataset, allowing independent population control. Although broad-scale population structure was not evident in the rusa deer populations, dispersal limits identified suggest that rusa deer sites sampled in this study could be managed separately. Sambar × rusa deer hybrids are present in both Victoria and NSW and can be difficult to detect on the basis of morphology alone. Implications. Genetic analysis can identify broad-scale management units necessary for population control, and estimates of dispersal capability can assist in delineating management units where broad-scale population structure may not be apparent. The negative impacts associated with hybridisation require further investigation to determine whether removal of hybrids should be considered a priority management aim. © 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing

The Ubiquitin Ligase Siah2 is a Female-Specific Regulator of Circadian Rhythms and Metabolism

Circadian clocks enable organisms to predict and align their behaviors and physiologies to constant daily day-night environmental cycle. Because the ubiquitin ligase Siah2 has been identified as a potential regulator of circadian clock function in cultured cells, we have used SIAH2-deficient mice to examine its function in vivo. Our experiments demonstrate a striking and unexpected sexually dimorphic effect of SIAH2-deficiency on the regulation of rhythmically expressed genes in the liver. The absence of SIAH2 in females, but not in males, altered the expression of core circadian clock genes and drastically remodeled the rhythmic transcriptome in the liver by increasing the number of day-time expressed genes, and flipping the rhythmic expression from nighttime expressed genes to the daytime. These effects are not readily explained by effects on known sexually dimorphic pathways in females. Moreover, loss of SIAH2 in females, not males, preferentially altered the expression of transcription factors and genes involved in regulating lipid and lipoprotein metabolism. Consequently, SIAH2-deficient females, but not males, displayed disrupted daily lipid and lipoprotein patterns, increased adiposity and impaired metabolic homeostasis. Overall, these data suggest that SIAH2 may be a key component of a female-specific circadian transcriptional output circuit that directs the circadian timing of gene expression to regulate physiological rhythms, at least in the liver. In turn, our findings imply that sex-specific transcriptional mechanisms may closely interact with the circadian clock to tailor overt rhythms for sex-specific needs

Aftershocks driven by afterslip and fluid pressure sweeping through a fault‐fracture mesh

A variety of physical mechanisms are thought to be responsible for the triggering and spatiotemporal evolution of aftershocks. Here we analyze a vigorous aftershock sequence and postseismic geodetic strain that occurred in the Yuha Desert following the 2010 Mw 7.2 El Mayor‐Cucapah earthquake. About 155,000 detected aftershocks occurred in a network of orthogonal faults and exhibit features of two distinct mechanisms for aftershock triggering. The earliest aftershocks were likely driven by afterslip that spread away from the main shock with the logarithm of time. A later pulse of aftershocks swept again across the Yuha Desert with square root time dependence and swarm‐like behavior; together with local geological evidence for hydrothermalism, these features suggest that the events were driven by fluid diffusion. The observations illustrate how multiple driving mechanisms and the underlying fault structure jointly control the evolution of an aftershock sequence

A Synopsis of Short-Term Response to Alternative Restoration Treatments in Sagebrush-Steppe: The SageSTEP Project

The Sagebrush Steppe Treatment Evaluation Project (SageSTEP) is an integrated long-term study that evaluates ecological effects of alternative treatments designed to reduce woody fuels and to stimulate the herbaceous understory of sagebrush steppe communities of the Intermountain West. This synopsis summarizes results through 3 yr posttreatment. Woody vegetation reduction by prescribed fire, mechanical treatments, or herbicides initiated a cascade of effects, beginning with increased availability of nitrogen and soil water, followed by increased growth of herbaceous vegetation. Response of butterflies and magnitudes of runoff and erosion closely followed herbaceous vegetation recovery. Effects on shrubs, biological soil crust, tree cover, surface woody fuel loads, and sagebrush-obligate bird communities will take longer to be fully expressed. In the short term, cool wet sites were more resilient than warm dry sites, and resistance was mostly dependent on pretreatment herbaceous cover. At least 10 yr of posttreatment time will likely be necessary to determine outcomes for most sites. Mechanical treatments did not serve as surrogates for prescribed fire in how each influenced the fuel bed, the soil, erosion, and sage-obligate bird communities. Woody vegetation reduction by any means resulted in increased availability of soil water, higher herbaceous cover, and greater butterfly numbers. We identified several trade-offs (desirable outcomes for some variables, undesirable for others), involving most components of the study system. Trade-offs are inevitable when managing complex natural systems, and they underline the importance of asking questions about the whole system when developing management objectives. Substantial spatial and temporal heterogeneity in sagebrush steppe ecosystems emphasizes the point that there will rarely be a “recipe” for choosing management actions on any specific area. Use of a consistent evaluation process linked to monitoring may be the best chance managers have for arresting woodland expansion and cheatgrass invasion that may accelerate in a future warming climate

Setting Research Priorities To Reduce Global Mortality from Childhood Diarrhoea by 2015

Olivier Fontaine and colleagues applied a priority-setting methodology to identify research priorities aimed at reducing global diarrhea mortality by 2015