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ABSTRACT This study utilizes linear elastic fracture mechanics to assess the fatigue criticality of volumetric defects in notched specimens with varying geometries. Contrasting to the existing literature, this study assesses the fatigue criticality of defects, prior to fracture, via a non‐destructive inspection technique, that is, X‐ray computed tomography (XCT). Treating volumetric defects as cracks, based on Murakami's definition, the approach calculates their Mode‐I stress intensity factor (SIF) with their local stresses obtained via linear elastic finite element analysis and utilizes the SIF to represent their criticality. For validation, cylindrical and flat specimens with notch root radii of 5 and 50 mm of AlSi10Mg and 17‐4 precipitation hardened stainless steel were fabricated, XCT scanned, and tested under fatigue loading. All crack initiating defects, observed from fractography, fell within the 99.3 percentile of the defects with the highest stress intensity factor in the respective specimens. 

Abstract BackgroundChildren are less susceptible to SARS-CoV-2 infection and typically have milder illness courses than adults, but the factors underlying these age-associated differences are not well understood. The upper respiratory microbiome undergoes substantial shifts during childhood and is increasingly recognized to influence host defense against respiratory pathogens. Thus, we sought to identify upper respiratory microbiome features associated with SARS-CoV-2 infection susceptibility and illness severity. MethodsWe collected clinical data and nasopharyngeal swabs from 285 children, adolescents, and young adults (<21 years) with documented SARS-CoV-2 exposure. We used 16S ribosomal RNA gene sequencing to characterize the nasopharyngeal microbiome and evaluated for age-adjusted associations between microbiome characteristics and SARS-CoV-2 infection status and respiratory symptoms. ResultsNasopharyngeal microbiome composition varied with age (PERMANOVA, P < .001; R2 = 0.06) and between SARS-CoV-2–infected individuals with and without respiratory symptoms (PERMANOVA, P  = .002; R2 = 0.009). SARS-CoV-2–infected participants with Corynebacterium/Dolosigranulum-dominant microbiome profiles were less likely to have respiratory symptoms than infected participants with other nasopharyngeal microbiome profiles (OR: .38; 95% CI: .18–.81). Using generalized joint attributed modeling, we identified 9 bacterial taxa associated with SARS-CoV-2 infection and 6 taxa differentially abundant among SARS-CoV-2–infected participants with respiratory symptoms; the magnitude of these associations was strongly influenced by age. ConclusionsWe identified interactive relationships between age and specific nasopharyngeal microbiome features that are associated with SARS-CoV-2 infection susceptibility and symptoms in children, adolescents, and young adults. Our data suggest that the upper respiratory microbiome may be a mechanism by which age influences SARS-CoV-2 susceptibility and illness severity. 

Computational models of distributional semantics can analyze a corpus to derive representations of word meanings in terms of each word’s relationship to all other words in the corpus. While these models are sensitive to topic (e.g., tiger and stripes) and synonymy (e.g., soar and fly), the models have limited sensitivity to part of speech (e.g., book and shirt are both nouns). By augmenting a holographic model of semantic memory with additional levels of representations, we present evidence that sensitivity to syntax is supported by exploiting associations between words at varying degrees of separation. We find that sensitivity to associations at three degrees of separation reinforces the relationships between words that share part-of-speech and improves the ability of the model to construct grammatical sentences. Our model provides evidence that semantics and syntax exist on a continuum and emerge from a unitary cognitive system. 

We explore replacing the declarative memory system of the ACT-R cognitive architecture with a distributional semantics model. ACT-R is a widely used cognitive architecture, but scales poorly to big data applications and lacks a robust model for learning association strengths between stimuli. Distribu- tional semantics models can process millions of data points to infer semantic similarities from language data or to in- fer product recommendations from patterns of user prefer- ences. We demonstrate that a distributional semantics model can account for the primacy and recency effects in free recall, the fan effect in recognition, and human performance on it- erated decisions with initially unknown payoffs. The model we propose provides a flexible, scalable alternative to ACT- R’s declarative memory at a level of description that bridges symbolic, quantum, and neural models of cognition. 

We present analysis of existing memory models, examining how models represent knowledge, structure memory, learn, make decisions, and predict reaction times. On the basis of this analysis, we propose a theoretical framework that characterizes memory modelling in terms of six key decisions: (1) choice of knowledge representation scheme, (2) choice of data structure, (3) choice of associative architecture, (4) choice of learning rule, (5) choice of time variant process, and (6) choice of response decision criteria. This framework is both descriptive and proscriptive: we intend to both describe the state of the literature and outline what we believe is the most fruitful space of possibilities for the development of future memory models.