Carbon fiber is increasingly being utilized as a reinforcing material due to its high strength and high modulus, which is imparted into the properties of the final composite. A comprehensive review ...of the carbon fiber production process, from polymerization to fiber spinning and stabilization and carbonization, is provided. Relationships between carbon fiber microstructure and material performance is undertaken in order to assess the current status of the mechanical, thermal, and electrical properties of commercially available PAN and pitch based carbon fibers, as well as recently developed experimental carbon fibers. A discussion of next generation carbon fibers is also provided, with a discussion on carbon fiber derived from alternative precursor materials, as well as hierarchical carbon fiber composites.
•Selecting the appropriate machine learning method depends on the digital soil mappers’ purpose.•Artificial neural network is strong with large sample sizes, but is black box.•Cubist produces ...interpretable results; however, Random Forests’ results are semi interpretable.•R2 is more sensitive to outliers than RMSE.•Independent validation is necessary to evaluate the predictive power of the model.
Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to identify relationships between soil properties and multiple covariates that can be detected across landscapes. Selecting the appropriate algorithm for model building is critical for optimizing results in the context of the available data. Over the past decade, many studies have tested different machine learning (ML) approaches on a variety of soil data sets. Here, we review the application of some of the most popular ML algorithms for digital soil mapping. Specifically, we compare the strengths and weaknesses of multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), Cubist, random forest (RF), and artificial neural networks (ANN) for DSM. These algorithms were compared on the basis of five factors: (1) quantity of hyperparameters, (2) sample size, (3) covariate selection, (4) learning time, and (5) interpretability of the resulting model. If training time is a limitation, then algorithms that have fewer model parameters and hyperparameters should be considered, e.g., MLR, KNN, SVR, and Cubist. If the data set is large (thousands of samples) and computation time is not an issue, ANN would likely produce the best results. If the data set is small (<100), then Cubist, KNN, RF, and SVR are likely to perform better than ANN and MLR. The uncertainty in predictions produced by Cubist, KNN, RF, and SVR may not decrease with large datasets. When interpretability of the resulting model is important to the user, Cubist, MLR, and RF are more appropriate algorithms as they do not function as “black boxes.” There is no one correct approach to produce models for predicting the spatial distribution of soil properties. Nonetheless, some algorithms are more appropriate than others considering the nature of the data and purpose of mapping activity.
Karoli Lwanga Hospital and Global Emergency Care, a 501(c)(3) nongovernmental organization, operate an Emergency Department (ED) in Uganda's rural Rukungiri District. Despite available emergency care ...(EC), preventable death and disability persist due to delayed patient presentations. This study seeks to understand the emergency care seeking behavior of community members utilizing the established ED. We purposefully sampled and interviewed patients and caregivers presenting to the ED more than 12 hours after onset of chief complaint in January-March 2017 to include various ages, genders, and complaints. Semistructured interviews addressing actions taken before seeking EC and delays to presentation once the need for EC was recognized were conducted until a diverse sample and theoretical saturation were obtained. An interdisciplinary and multicultural research team conducted thematic analysis based on descriptive phenomenology. The 50 ED patients for whom care was sought (mean age 33) had approximately even distribution of gender, as well as occupation (none, subsistence farmers and small business owner). Interviews were conducted with 13 ED patients and 37 caregivers, on the behalf of patients when unavailable. The median duration of patients' chief complaint on ED presentation was 5.5 days. On average, participants identified severe symptoms necessitating EC 1 day before presentation. Four themes of treatment delay before and after severity were recognized were identified: 1) Cultural factors and limited knowledge of emergency signs and initial actions to take; 2) Use of local health facilities despite perception of inadequate services; 3) Lack of resources to cover the anticipated cost of obtaining EC; 4) Inadequate transportation options.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The purpose of this report was to provide overall arteriovenous malformation (AVM) hemorrhage rates and, with enhanced statistical power, to elucidate significant risk factors for hemorrhage.
The ...authors performed a meta-analysis via the PubMed database through January 2012 using the terms "AVM," "arteriovenous malformation," "natural history," "bleed," and "hemorrhage." Additional studies were identified through reference searches in each reviewed article. English language studies providing annual hemorrhage rates for AVMs were included. Data extraction, performed independently by the authors, included demographic data, hemorrhage rates, and hazard ratios for hemorrhage risk factors. The analysis was performed using a random effects model.
Nine natural history studies with 3923 patients and 18,423 patient-years of follow-up were identified for analysis. The overall annual hemorrhage rate was 3.0% (95% CI 2.7%-3.4%). The rate of hemorrhage was 2.2% (95% CI 1.7%-2.7%) for unruptured AVMs and 4.5% (95% CI 3.7%-5.5%) for ruptured AVMs. Prior hemorrhage (HR 3.2, 95% CI 2.1-4.3), deep AVM location (HR 2.4, 95% CI 1.4-3.4), exclusively deep venous drainage (HR 2.4, 95% CI 1.1-3.8), and associated aneurysms (HR 1.8, 95% CI 1.6-2.0) were statistically significant risk factors for hemorrhage. Any deep venous drainage (HR 1.3, 95% CI 0.9-1.75) and female sex (HR 1.4, 95% CI 0.6-2.1) demonstrated a trend toward an increased risk of hemorrhage that was not statistically significant. Small AVM size and older patient age were not significant risk factors for hemorrhage.
Arteriovenous malformations with prior hemorrhage, deep location, exclusively deep venous drainage, and associated aneurysms have greater annual hemorrhage rates than their counterparts, influencing surgical decision making and the selection of radiosurgery for these lesions.
The definition of pulmonary hypertension (PH) has changed recently based, in part, on contemporary outcome data and to focus on early disease detection. Now, PH includes patients with mean pulmonary ...artery pressure >20 mm Hg measured by right heart catheterization. In contrast to the classical era, pulmonary vascular resistance >2.0 Wood units is also used for diagnosis and prognostication. These lowered thresholds aim to identify patients early in the disease course, which is important because delay to diagnosis of PH is common and linked to elevated morbidity and shortened lifespan. This clinical primer highlights key changes in diagnosis and approach to PH management, focusing on concepts that are likely to be encountered frequently in general practice. Specifically, this includes hemodynamic assessment of at-risk patients, pharmacotherapeutic management of pulmonary arterial hypertension, approach to PH in patients with heart failure with preserved ejection fraction, and newly established indications for early referral to PH centers to prompt comanagement of patients with pulmonary vascular disease experts.
Concerns about genetic privacy affect individuals' willingness to accept genetic testing in clinical care and to participate in genomics research. To learn what is already known about these views, we ...conducted a systematic review, which ultimately analyzed 53 studies involving the perspectives of 47,974 participants on real or hypothetical privacy issues related to human genetic data. Bibliographic databases included MEDLINE, Web of Knowledge, and Sociological Abstracts. Three investigators independently screened studies against predetermined criteria and assessed risk of bias. The picture of genetic privacy that emerges from this systematic literature review is complex and riddled with gaps. When asked specifically "are you worried about genetic privacy," the general public, patients, and professionals frequently said yes. In many cases, however, that question was posed poorly or only in the most general terms. While many participants expressed concern that genomic and medical information would be revealed to others, respondents frequently seemed to conflate privacy, confidentiality, control, and security. People varied widely in how much control they wanted over the use of data. They were more concerned about use by employers, insurers, and the government than they were about researchers and commercial entities. In addition, people are often willing to give up some privacy to obtain other goods. Importantly, little attention was paid to understanding the factors-sociocultural, relational, and media-that influence people's opinions and decisions. Future investigations should explore in greater depth which concerns about genetic privacy are most salient to people and the social forces and contexts that influence those perceptions. It is also critical to identify the social practices that will make the collection and use of these data more trustworthy for participants as well as to identify the circumstances that lead people to set aside worries and decide to participate in research.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over ...seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme climate conducive to eight ‘billion-dollar’ historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecast than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales.
OBJECTIVE The aim of this paper is to define an overall cavernous malformation (CM) hemorrhage rate and risk factors for hemorrhage. METHODS The authors performed a systematic, pooled analysis via ...the PubMed database through October 2015 using the terms "cavernoma," "cavernous malformation," "natural history," "bleeding," and "hemorrhage." English-language studies providing annual rates and/or risk factors for CM hemorrhage were included. Data extraction, performed independently by the authors, included demographic data, hemorrhage rates, and hemorrhage risk factors. RESULTS Across 12 natural history studies with 1610 patients, the mean age at presentation was 42.7 years old and 52% of patients (95% CI 49%-55%) were female. Presentation modality was seizure in 30% (95% CI 25%-35%), hemorrhage in 26% (95% CI 17%-37%), incidental in 17% (95% CI 9%-31%), and focal deficits only in 16% of cases (95% CI 11%-23%). CM location was lobar in 66% (95% CI 61%-70%), brainstem in 18% (95% CI 13%-24%), deep supratentorial in 8% (95% CI 6%-10%), and cerebellar in 8% (95% CI 5%-11%). Pooling 7 studies that did not assume CM presence since birth, the annual hemorrhage rate was 2.5% per patient-year over 5081.2 patient-years of follow-up (95% CI 1.3%-5.1%). Pooling hazard ratios across 5 studies that evaluated hemorrhage risk factors, prior CM hemorrhage was a significant risk factor for hemorrhage (HR 3.73, 95% CI 1.26-11.1; p = 0.02) while younger age, female sex, deep location, size, multiplicity, and associated developmental venous anomalies (DVAs) were not. CONCLUSIONS Although limited by the heterogeneity of incorporated reports and selection bias, this study found prior hemorrhage to be a significant risk factor for CM bleeding, while age, sex, CM location, size, multiplicity, and associated DVAs were not. Future natural history studies should compound annual hemorrhage rate with prospective seizure and nonhemorrhagic neurological deficit rates.
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