Mathematical models to describe the growth curves of Vietnamese Ri chicken

Ri chicken is the most popular backyard chicken breed in Vietnam, but little is known about the growth curve of this breed. This study compared the performances of models with three parameters (Gompertz, Brody, and Logistic) and models containing four parameters (Richards, Bridges, and Janoschek) for describing the growth of Ri chicken. The bodyweight of Ri chicken was recorded weekly from week 1 to week 19. Growth models were fitted using minpack.lm package in R software and Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and root mean square error (RMSE) were used for model comparison. Based on these criteria, the models having four parameters showed better performance than the ones with three parameters, and the Richards model was the best one for males and females. The lowest and highest value of asymmetric weights (α) were obtained by Bridges and Brody models for each of sexes, respectively. Age and weight estimated by the Richard model were 8.46 and 7.51 weeks and 696.88 and 487.58 g for males and for females, respectively. Differences in the growth curves were observed between males and female chicken. Overall, the results suggested using the Richards model for describing the growth curve of Ri chickens. Further studies on the genetics and genomics of the obtained growth parameters are required before using them for the genetic improvement of Ri chickens.

The Warm Winter Paradox in the Pliocene High Latitudes

Reconciling palaeodata with model simulations of the Pliocene climate is essential for understanding a world with atmospheric CO2 concentration near 400 parts per million by volume. Both models and data indicate an amplified warming of the high latitudes during the Pliocene, however terrestrial data suggests Pliocene high latitude temperatures were much higher than can be simulated by models. Here we show that understanding the Pliocene high latitude terrestrial temperatures is particularly difficult for the coldest months, where the temperatures obtained from models and different proxies can vary by more than 20 °C. We refer to this mismatch as the ‘warm winter paradox’. Analysis suggests the warm winter paradox could be due to a number of factors including: model structural uncertainty, proxy data not being strongly constrained by winter temperatures, uncertainties on data reconstruction methods and also that the Pliocene high latitude climate does not have a modern analogue. Refinements to model boundary conditions or proxy dating are unlikely to contribute significantly to the resolution of the warm winter paradox. For the Pliocene, high latitude, terrestrial, summer temperatures, models and different proxies are in good agreement. Those factors which cause uncertainty on winter temperatures are shown to be much less important for the summer. Until some of the uncertainties on winter, high latitude, Pliocene temperatures can be reduced, we suggest a data-model comparison should focus on the summer. This is expected to give more meaningful and accurate results than a data-model comparison which focuses on the annual mean.

An Approach for Material Model Identification of a Composite Coating Using Micro-Indentation and Multi-Scale Simulations

While deposited thin film coatings can help enhance surface characteristics such as hardness and friction, their effective incorporation in product design is restricted by the limited understanding of their mechanical behavior. To address this, an approach combining micro-indentation and meso/micro-scale simulations was proposed. In this approach, micro-indentation testing was conducted on both the coating and the substrate. A meso-scale uniaxial compression finite element model was developed to obtain a material model of the coating. This material model was incorporated within an axisymmetric micro-scale model of the coating to simulate the indentation. The proposed approach was applied to a Ti/SiC metal matrix nanocomposite (MMNC) coating, with a 5% weight of SiC nanoparticles deposited over a Ti-6Al-4V substrate using selective laser melting (SLM). Micro-indentation testing was conducted on both the Ti/SiC MMNC coating and the Ti-6Al-4V substrate. The results of the meso-scale finite element indicated that the MMNC coating can be represented using a bi-linear elastic-plastic material model, which was incorporated within an axisymmetric micro-scale model. Comparison of the experimental and micro-scale model results indicated that the proposed approach was effective in capturing the post-indentation behavior of the Ti/SiC MMNC coating. This methodology can also be used for studying the response of composite coatings with different percentages of reinforcements.

You can’t “count” how many items people remember in working memory: The importance of signal detection-based measures for understanding change detection performance

Change detection tasks are commonly used to measure and understand the nature of visual working memory capacity. Across two experiments, we examine whether the nature of the latent memory signals used to perform change detection are continuous or all-or-none, and consider the implications for proper measurement of performance. In Experiment 1, we find evidence from confidence reports that visual working memory is continuous in strength, with strong support for equal variance signal detection models. We then tested a critical implication of this result without relying on model comparison or confidence reports in Experiment 2 by asking whether a simple instruction change would improve performance when measured with K, an all-or-none-measure, compared to d’, a measure based on continuous strength signals. We found strong evidence that K values increased by roughly 30% despite no change in the underlying memory signals. By contrast, we found that d’ is fixed across these same instructions, demonstrating that it correctly separates response criterion from memory performance. Overall, our data call into question a large body of work using threshold measures, like K, to analyze change detection data since this metric confounds response bias with memory performance in standard change detection tasks.

No evidence for trait- and state-level urgency moderating the daily association between negative affect and subsequent alcohol use in two college samples

It remains unclear whether the negative reinforcement pathway to problematic drinking exists, and if so, for whom. One idea that has received some support recently is that people who tend to act impulsively in response to negative emotions (i.e., people high in negative urgency) may specifically respond to negative affect with increased alcohol consumption. We tested this idea in a preregistered secondary data analysis of two ecological momentary assessment studies using college samples. Participants (N = 226) reported on their current affective state multiple times per day and the following morning reported alcohol use the previous night. We assessed urgency both at baseline and during the momentary affect assessments. Results from our Bayesian model comparison procedure, which penalizes increasing model complexity, indicate that no combination of the variables of interest (negative affect, urgency, and the respective interactions) outperformed a baseline model that included two known demographic predictors of alcohol use. A non- preregistered exploratory analysis provided some evidence for the effect of daily positive affect, positive urgency, as well as their interaction on subsequent alcohol use. Taken together, our results suggest that college students’ drinking may be better described by a positive rather than negative reinforcement cycle.