Influence of Geometric Features Associativity of CAD Class Models on the Process of Their Updating – Comparative Analysis

This article deals with the topic of one of the most important features of modern CAx class systems – associativity. The term refers to the ability to form relations (links) between two or more objects (in terms of their selected features), and with the consequence creating an associative (linked) three-dimensional model. The author pays special attention to the very process of creating relations between objects, as it has a key impact on the structural stability of CAD class models, and thus on their susceptibility to possible modifications. To show that not all associativity brings a positive effect, the author presents two examples of its implementation. In order to emphasize the influence of the method of linking individual elements, both examples are based on the same 3D model – a thin-walled part with a positioning pin. That means the geometric form of the default part is the same, whereas only relations of the individual objects of the 3D model change. In the first scenario, correctly defined relations between objects make that the positioning pin offset does not affect the initial design conditions. The second scenario shows an incorrect implementation of associativity, as a result of which the same operation of positioning pin offset gives non-compliance with the initial design conditions and with the consequence an undesirable change in its geometry. The article is an attempt to draw attention to the fact that the associative structure of 3D models is not always equal to the optimal solution. Only the well-thought-out nature of associativity allows to use all its advantages.

Key population size estimation in Nigeria: applying novel Bayesian methods for analysis of three-source capture-recapture data (Preprint)

BACKGROUND Nigeria has the fourth largest burden of HIV globally. Key populations (KP) including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID) often have poor social visibility and are more vulnerable to HIV than the general population due to stigma, discrimination, and criminalization of KP-defining behaviors. Reliable, empirical population size estimates (PSE) are needed to guide focused and appropriately scaled HIV epidemic response efforts for KP. We used novel approaches to sampling and analysis to calculate PSE in Nigeria. OBJECTIVE We sampled the population using three-source capture-recapture (3S-CRC) and analyzed results using Bayesian nonparametric latent-class models to generate median PSE with 80% highest density intervals. METHODS During October–December 2018, we used three-source capture-recapture (3S-CRC) to estimate the size of KP in seven United States President’s Emergency Plan for AIDS Relief (PEPFAR) priority states in Nigeria. Hotspots were mapped before 3S-CRC started. We sampled FSW, MSM, and PWID during three independent captures approximately one week apart. During encounters in KP hotspots, distributors offered inexpensive and memorable objects to KP, unique to each capture round and KP type. In subsequent rounds, participants were offered an object and asked to produce or identify objects received during previous rounds (if any); affirmative responses were tallied upon producing or identifying the correct object. Distributors recorded responses on tablets and uploaded to a secure server after each encounter. Data were aggregated by KP and state for analysis. Median PSE were derived using Bayesian nonparametric latent-class models with 80% highest density intervals for precision. RESULTS We sampled approximately 310,000 persons at 9,015 hotspots during three independent captures in all seven states. Overall, FSW PSE ranged from 14,500-64,300; MSM PSE, 3,200-41,400; and PWID PSE, 3,400-30,400. CONCLUSIONS This study represents the first implementation of these 3S-CRC sampling and novel analysis methods for large-scale population size estimation in Nigeria. Overall, our estimates were larger than previously documented for each KP in all states. The current Bayesian models account for factors (i.e., social visibility and stigma) that influence heterogeneous capture probabilities resulting in more reliable PSE. The larger estimates suggest a need for programmatic scale-up to reach these populations at highest risk for HIV.

Latent Class Analysis of Obesogenic Behaviors among Korean Adolescents: Associations with Weight-Related Outcomes

This study aimed to explore sex-specific latent class models of adolescent obesogenic behaviors (OBs), predictors of latent class membership (LCM), and associations between LCM and weight-related outcomes (i.e., weight status and unhealthy weight control behaviors). We analyzed nationally representative data from the 2019 Korea Youth Risk Behavior Survey. To identify latent classes for boys (n = 29,841) and girls (n = 27,462), we conducted a multiple-group latent class analysis using eight OBs (e.g., breakfast skipping, physical activity, and tobacco product use). Moreover, we performed a multinomial logistic regression analysis and a three-step method to examine associations of LCM with predictors and weight-related outcomes. Among both sexes, the 3-class models best fit the data: (a) mostly healthy behavior class, (b) poor dietary habits and high Internet use class, and (c) poor dietary habits and substance use class. School year, residential area, academic performance, and psychological status predicted the LCM for both sexes. In addition, perceived economic status predicted the LCM for girls. The distribution of weight-related outcomes differed across sex-specific classes. Our findings highlight the importance of developing obesity prevention and treatment interventions tailored to each homogeneous pattern of adolescent OBs, considering differences in their associations with predictors and weight-related outcomes.

Prediction of Inclusion Types From BSE Images: RF vs. CNN

The analysis of non-metallic inclusions is crucial for the assessment of steel properties. Scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS) is one of the most prominent methods for inclusion analysis. This study utilizes the output generated from SEM/EDS analysis to predict inclusion types from BSE images. Prediction models were generated using two different algorithms, Random Forest (RF) and convolutional neural networks (CNN), for comparison. For each method, three separate models were developed. Starting with a simple binary model to differentiate between inclusions and non-inclusions, then developing to more complex four and five class models. For the 4-class model, inclusions were split into oxides, sulfides, and oxy-sulfides, in addition to the non-inclusion class. The 5-class model included specific types of inclusions only, namely alumina, calcium aluminates, calcium sulfides, complex calcium-manganese sulfides, and oxy-sulfide inclusions. CNN achieved better accuracy for the binary (92%) and 4-class (78%) models, compared to RF (binary 87%, 4-class 75%). For the 5-class model, the results were similar, 60% accuracy for RF and 59% for CNN.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists. Methods This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models. Result We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September. Discussion Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.