Characterising the high temperature tensile behaviour of laser powder bed fused duplex stainless steel 2205 using the small punch test

Duplex stainless steels (DSS) are a family of stainless steel alloys that benefit from the presence of two relatively equally proportioned phases, ferrite and austenite. The alloys are designed to have an enhanced resistance to corrosion and superior strength properties in comparison to more common stainless steel alloys such as 316 L. Design engineers are now exploring the introduction of additively manufactured (AM) DSS into industrial components, to benefit from these enhanced capabilities provided by the alloy and the greater flexibility in design offered by AM. This research focuses on the mechanical and microstructural characterisation of DSS 2205, manufactured by the AM process laser powder bed fusion (LPBF). Results have been generated through both uniaxial tensile testing and small punch (SP) testing on as built and heat-treated conditions, across a range of temperatures up to 750 °C. Microstructural assessments have been conducted using advanced microscopy to determine relevant phase distributions and texture morphologies present in the materials, to understand how this influences mechanical performance

How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory

There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Learning words in space and time: Contrasting models of the suspicious coincidence effect

In their 2007b Psychological Review paper, Xu and Tenenbaum found that early word learning follows the classic logic of the “suspicious coincidence effect:” when presented with a novel name (‘fep’) and three identical exemplars (three Labradors), word learners generalized novel names more narrowly than when presented with a single exemplar (one Labrador). Xu and Tenenbaum predicted the suspicious coincidence effect based on a Bayesian model of word learning and demonstrated that no other theory captured this effect. Recent empirical studies have revealed, however, that the effect is influenced by factors seemingly outside the purview of the Bayesian account. A process-based perspective correctly predicted that when exemplars are shown sequentially, the effect is eliminated or reversed (Spencer, Perone, Smith, & Samuelson, 2011). Here, we present a new, formal account of the suspicious coincidence effect using a generalization of a Dynamic Neural Field (DNF) model of word learning. The DNF model captures both the original finding and its reversal with sequential presentation. We compare the DNF model's performance with that of a more flexible version of the Bayesian model that allows both strong and weak sampling assumptions. Model comparison results show that the dynamic field account provides a better fit to the empirical data. We discuss the implications of the DNF model with respect to broader contrasts between Bayesian and process-level models

Gender Differences in Compensation, Job Satisfaction and Other Practice Patterns in Urology

The proportion of women in urology has increased from <0.5% in 1981 to 10% today. Furthermore, 33% of students matching in urology are now female. This analysis sought to characterize the female workforce in urology in comparison to men with regard to income, workload, and job satisfaction