Radial Basis Functions (RBFs) have shown the potential to be a universal mesh-free method for solving interpolation and differential equations with highly accurate results. However, the trade-off principle states that while deciding the shape parameter’s value for an RBF such as Multiquadric (MQ) or Gaussian (GA), a compromise must be made between achieving accuracy and stability because of the resultant ill-conditioned matrix. This study focus on the behaviors between the maximum and residual errors for the RBF interpolation. Based on the error behaviors, we propose a new approach, Residual-Error Cross Validation (RECV), to quickly select a suitable c value for an interpolant using an RBF containing a shape parameter. The numerical results showed that an RBF interpolant could yield high accuracy with the RECV c and a sufficiently small fill distance. Combining the RECV method and LOOCV method, we can easily avoid the local optimum issue when applying an optimization algorithm.
This theme-based book review considers four recent titles related to the intersection of business and government: Outsourcing in the UK: Politics, Practices and Outcomes, by Janice Morphet; Public Financial Management in the European Union: Public Finance and Global Crises, by Marta Postula; Handbook of Business and Public Policy, edited by Aynsley Kellow, Tony Porter, and Karsten Ronit; and European Public Procurement: Commentary on Directive 2014/24/EU, edited by Roberto Caranta and Albert Sanchez-Graells.
Understanding dispersal in large marine fauna is necessary for conservation, but movement patterns often vary widely by sex and life stage. In sharks, genetic studies have shown evidence of widespread male-biased dispersal, though tagging and tracking studies on the same populations show both sexes using site fidelity, including philopatry, and moving similar distances. We used a suite of microsatellite loci and DNA samples from 362 previously-tagged tiger sharks (Galeocerdo cuvier) in the northwestern Atlantic, including a large number of residential juveniles, to evaluate reproductive dispersal in light of demographic and published tracking data. We found that lumping size classes together resulted in genetic panmixia across sites, but systematic removal of large individuals showed significant population-level differentiation and three separate population clusters among juveniles less than 260 cm total length. Tests for relatedness found that 8.9% of our sample set was composed of first-order related pairs (N = 16), including several full siblings from different litters, a sign of multi-cycle genetic monogamy which carries implications for effective population size. By mapping genetic assignments of juveniles, we identified a signature of fine-scale genetic structure suggesting broad biparental site fidelity to reproductive habitat in the northeast Gulf of Mexico, which is concordant with both genetic and tracking data. Taken together, these findings demonstrate how lumping individuals from different life stages in genetic studies may obscure fine-scale genetic structure, confounding future conservation efforts.
PurposeTo implement a clinically applicable, predictive model for the lumbar Cobb angle below a selective thoracic fusion in adolescent idiopathic scoliosis.MethodsA series of 146 adolescents with Lenke 1 or 2 idiopathic scoliosis, surgically treated with posterior selective fusion, and minimum follow-up of 5 years (average 7) was analyzed. The cohort was divided in 2 groups: if lumbar Cobb angle at last follow-up was, respectively, ≥ or < 10°. A logistic regression-based prediction model (PredictMed) was implemented to identify variables associated with the group ≥ 10°. The guidelines of the TRIPOD statement were followed.ResultsMean Cobb angle of thoracic main curve was 56° preoperatively and 25° at last follow-up. Mean lumbar Cobb angle was 33° (20; 59) preoperatively and 11° (0; 35) at last follow-up. 53 patients were in group ≥ 10°. The 2 groups had similar demographics, flexibility of both main and lumbar curves, and magnitude of the preoperative main curve, p > 0.1. From univariate analysis, mean magnitude of preoperative lumbar curves (35° vs. 30°), mean correction of main curve (65% vs. 58%), mean ratio of main curve/distal curve (1.9 vs. 1.6) and distribution of lumbar modifiers were statistically different between groups (p < 0.05).PredictMed identified the following variables significantly associated with the group ≥ 10°: main curve % correction at last follow-up (p = 0.01) and distal curve angle (p = 0.04) with a prediction accuracy of 71%.Conclusion The main modifiable factor influencing uninstrumented lumbar curve was the correction of main curve. The clinical model PredictMed showed an accuracy of 71% in prediction of lumbar Cobb angle ≥ 10° at last follow-up. Level of evidence IVLongitudinal comparative study.
Purpose: The present study investigates the spatiotemporal variations in suicide mortality and tests associations between several covariates and suicides for the years 20002019 in the contiguous USA. The epidemiological disease surveillance software was used to identify spatiotemporal variations in suicide mortality rates and to test for significant spatial and space-time clusters with elevated relative suicide risk. Methods: The analysis was done with age-adjusted suicide mortality counts data from the Centers for Disease Control (CDC) with International Classification of Diseases (ICD)-10 codes. Specifically, data with codes ICD-10 codes X60-X84.9 and Y87.0, plus ICD-10 113 codes from the CDC, was used. Fourteen significant spatial clusters and five significant space-time clusters of suicide in the contiguous USA were found, including nine significant bivariate spatial clusters of suicide deaths and opioid deaths. Results: Based on these data, there exist significant and non-random suicide mortality clusters after adjusting for multiple covariates or risk factors. The covariates studied provide evidence to develop a better understanding of possible associations in geographical areas where the suicide mortality rates are higher than expected. In addition, there is a significant association between several of the studied risk factors and suicide mortality. While most suicide clusters are also opioid clusters, there exist some clusters with high opioid deaths that are not suicide clusters. Conclusions: These results have the potential to provide a scientific framework that is based on surveillance, allowing health agencies to intervene and reduce elevated rates of suicides in selected counties in the U.S. The study is limited due to the resolution of the data at the county level, and some covariate data was unavailable for the entire period of the study.
Background Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated. Objectives To develop a prediction model for identifying risk factors of OA in subjects aged 20–50 years and compare the performance of different machine learning models. Methods We included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20–50 years ( n = 19,133), with or without OA. The supervised machine learning model ‘Deep PredictMed’ based on logistic regression, deep neural network (DNN), and support vector machine was used for identifying demographic and personal characteristics that are associated with OA. Finally, we compared the performance of the different models. Results Being a female ( p < 0.001), older age ( p < 0.001), a smoker ( p < 0.001), higher body mass index ( p < 0.001), high blood pressure ( p < 0.001), race/ethnicity (lowest risk among Mexican Americans, p = 0.01), and physical and mental limitations ( p < 0.001) were associated with having OA. Best predictive performance yielded a 75% area under the receiver operating characteristic curve. Conclusion Sex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20–50 years. The best predictive performance was achieved using DNN algorithms.
The novelty of this work lies in examining how 5G, blockchain-based public key infrastructure (PKI), near field communication (NFC), and zero trust architecture securely provide not only a trusted digital identity for telework but also a trusted digital identity for secure online voting. The paper goes on to discuss how blockchain-based PKI, NFC, and the cloud provide a roadmap for how industry and governments can update existing frameworks to obtain a trusted digital identity in cyberspace that would provide secure telework and online voting capabilities.
We conceptually investigate opportunities for social entrepreneurs and non-profit organizations (NPOs) through an Austrian economics lens. To do so we provide an overview of (1) Austrian economics and its role in entrepreneurship, (2) certain institutional shortcomings, and (3) institutional signals and opportunities for social entrepreneurs as leaders of non-governmental organizations (NGOs). We posit that NGOs have the ability to address institutional shortcomings and address societal needs. As such, recognizing these opportunities allows inspired social entrepreneurs and their NGOs to fill certain societal needs.
Insensitive munitions compounds (IMCs) are emerging nitroaromatic contaminants developed by the military as safer-to-handle alternatives to conventional explosives. Biotransformation of nitroaromatics via microbial respiration has only been reported for a limited number of substrates. Important soil microorganisms can respire natural organic matter (NOM) by reducing its quinone moieties to hydroquinones. Thus, we investigated the NOM respiration combined with the abiotic reduction of nitroaromatics by the hydroquinones formed. First, we established nitroaromatic concentration ranges that were nontoxic to the quinone respiration. Then, an enrichment culture dominated by Geobacter anodireducens could indirectly reduce a broad array of nitroaromatics by first respiring NOM components or the NOM surrogate anthraquinone-2,6-disulfonate (AQDS). Without quinones, no nitroaromatic tested was reduced except for the IMC 3-nitro-1,2,4-triazol-5-one (NTO). Thus, the quinone respiration expanded the spectrum of nitroaromatics susceptible to transformation. The system functioned with very low quinone concentrations because NOM was recycled by the nitroaromatic reduction. A metatranscriptomic analysis demonstrated that the microorganisms obtained energy from quinone or NTO reduction since respiratory genes were upregulated when AQDS or NTO was the electron acceptor. The results indicated microbial NOM respiration sustained by the nitroaromatic-dependent cycling of quinones. This process can be applied as a nitroaromatic remediation strategy, provided that a quinone pool is available for microorganisms.
Introduction: Researchers are limited when using traditional recruitment methods to access hidden and vulnerable populations, including transgender persons. Social media platforms such as Facebook can provide access to the transgender population and facilitate recruitment of a representative sample. There is little regulatory guidance for using social media as a recruitment strategy. Methodology: This article presents recruitment recommendations based on a study that generated a diverse sample of transgender-identified persons using Facebook as the sole recruitment method. Results: Despite taking precautions, computer bots penetrated the initial survey. A second survey distribution collected data from a diverse sample of transgender-identified individuals. Discussion: Researchers should design social media recruitment methods with attention to privacy and transparency. Thus, using social media platforms such as Facebook to recruit transgender participants that otherwise would be challenging to reach is a viable and ethically sound alternative to traditional recruitment methods.
This paper outlines the methods, results, and statistical analysis of a model we developed to demonstrate the feasibility of applying remote sensor meteorological data to navigation by using meteorological contour matching (METCOM). Terrain contour matching (TERCOM), a contemporary navigation system, possesses inherent performance flaws that may be resolved and improved by METCOM for subsonic and hypersonic missile or aircraft navigation. Remote sensor imagery data for this model was accessed from the Geostationary Operational Environmental Satellites-R Series operated by the National Oceanic and Atmospheric Administration by using Amazon Web Services through a script we developed in Python. Data processed for the model included imagery data and corresponding geospatial data from the legacy atmospheric profile products: legacy vertical temperature and legacy vertical moisture. Our analysis of the model included an error assessment to determine model accuracy, geostatistical analysis through semivariograms, meteorological signal of model data, and a combinatorial analysis to evaluate navigation performance. We conducted a model assessment which indicated an accuracy of 66.2% in the data used as a combined result of instrument error and interference of cloud formations. Results of the remaining analysis offered methods to evaluate METCOM performance and compare different meteorological data products. These results allowed us to statistically compare METCOM and TERCOM, yielding several indications of improved performance including an increase by a factor of at least 13.5 in data variability and contourability. The analysis we conducted served as a proof of concept to justify further research into the feasibility and application of METCOM.
Purpose: To describe the socio-demographics and clinical characteristics of children in a pulmonology clinic or admitted to a children's hospital with well-controlled and poorly controlled asthma, and to assess caregiver knowledge of asthma pathogenesis, treatment, and self-management. Patients and methods: A cohort of 132 children aged 2-18 years and their caregivers seen in a pediatric pulmonology clinic with a diagnosis of asthma (n=112) or admitted to the hospital with a diagnosis of asthma exacerbation (n=20) were invited to participate in a cross-sectional study. Caregivers completed a survey, which healthcare providers then used to tailor asthma education to the patient and caregiver. Two-tail t-tests and Chi-square tests were used to compare demographics and clinical characteristics of children with well-controlled vs poorly controlled asthma. Results: Of 132 children, 111 children in this cohort had poorly controlled asthma (84%). Medicaid insurance was associated with poorly controlled asthma versus well-controlled asthma (63% vs 35% p=0.01). Asthma action plans (AAP) had previously been given to 113 caregivers (86%), but caregivers of children with both well-controlled and poorly controlled asthma still reported misconceptions about asthma pathology and management, such as stopping daily medications when asthma is controlled. Conclusion: This study contributes to the existing evidence that socio-demographics have a significant impact on asthma prevalence and proper management. Our study suggests that caregivers of children with asthma need comprehensive asthma education beyond the AAP focusing on asthma-related misconceptions.
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