A STUDY OF SURFACE MODIFICATION EFFECT OF HEMP FIBERS ON THE BULK PROPERTIES OF HEMP-POLY (LACTIC ACID) COMPOSITES: THERMAL STABILITY, MECHANICAL, THERMO-MECHANICAL AND BIODEGRADABILITY

Biocomposites made with, natural fiber and bio-based polymers, have many advantages over their synthetic counterparts including low cost, low density, high strength and biodegradability. However, some biocomposites can present problems due to high moisture absorption, low thermal stability during processing, and poor adhesion between the fiber and polymer matrix. Recent studies have shown that surface modification of the fiber can improve its adhesion to the polymer matrix and enhance the bulk material properties. Nevertheless, the mechanisms by which such surface modifications exert their effects on bulk material properties have not been systematically studied. Therefore, the main goal of this study is to investigate the impact of surface modifications of hemp on the thermal stability, mechanical, thermo-mechanical, and biodegradability of biocomposites comprised of hemp and poly (lactic acid) (PLA). This pairing was selected because it offers superior mechanical properties. The three surface treatments tested were: alkali (mechanical interlocking), silane (coupling) and acetic anhydride (grafting). The latter was most effective at improving thermal stability, mechanical, and thermo-mechanical properties of hemp-PLA biocomposites, and all treatments improved these properties relative to untreated hemp-PLA controls. The thermal stability of the composites increased with an increase in fiber content up to 30% by fiber volume fraction for both silane and acetic anhydride modified hemp. However, thermal stability decreased with fiber content for alkali and untreated composites due to hydrogen bonding and inferior fiber-matrix adhesion, respectively. The activation energy of thermal degradation was assessed by applying Flynn-Wall-Osawa kinetic modeling to understand the fiber-matrix interface. The model predictions were consistent with experimental results and suggested that the mechanism by which, acetic anhydride treatment yielded superior thermal properties was related to high energy bond formation (C=O) between the fiber and polymer matrix. When tensile and flexural properties of composites were assessed, 30% fiber volume fraction was optimal, and this ratio also improved stiffness and damping properties of the composites during thermo-mechanical study. A biodegradability study of the treated and untreated hemp-PLA biocomposites was undertaken. ASTM standard 5511-11 was modified to stimulate landfill disposal conditions. Degradation of all treatments as well as untreated biocomposites was negligible over 50 d, although visual inspection of SEM images showed greater evidence of cracking in the composite samples than in pure PLA controls. From this study it can be concluded that higher bond energy at the fiber-matrix interface due to surface modification of natural fiber results in higher activation energy of thermal degradation resulting in enhanced bulk material properties of the biocomposites

A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses

<p>Abstract</p> <p>Background</p> <p>Kawasaki disease is an acute vasculitis of infants and young children that is recognized through a constellation of clinical signs that can mimic other benign conditions of childhood. The etiology remains unknown and there is no specific laboratory-based test to identify patients with Kawasaki disease. Treatment to prevent the complication of coronary artery aneurysms is most effective if administered early in the course of the illness. We sought to develop a diagnostic algorithm to help clinicians distinguish Kawasaki disease patients from febrile controls to allow timely initiation of treatment.</p> <p>Methods</p> <p>Urine peptidome profiling and whole blood cell type-specific gene expression analyses were integrated with clinical multivariate analysis to improve differentiation of Kawasaki disease subjects from febrile controls.</p> <p>Results</p> <p>Comparative analyses of multidimensional protein identification using 23 pooled Kawasaki disease and 23 pooled febrile control urine peptide samples revealed 139 candidate markers, of which 13 were confirmed (area under the receiver operating characteristic curve (ROC AUC 0.919)) in an independent cohort of 30 Kawasaki disease and 30 febrile control urine peptidomes. Cell type-specific analysis of microarrays (csSAM) on 26 Kawasaki disease and 13 febrile control whole blood samples revealed a 32-lymphocyte-specific-gene panel (ROC AUC 0.969). The integration of the urine/blood based biomarker panels and a multivariate analysis of 7 clinical parameters (ROC AUC 0.803) effectively stratified 441 Kawasaki disease and 342 febrile control subjects to diagnose Kawasaki disease.</p> <p>Conclusions</p> <p>A hybrid approach using a multi-step diagnostic algorithm integrating both clinical and molecular findings was successful in differentiating children with acute Kawasaki disease from febrile controls.</p

Automated Adaptation Strategies for Stream Learning

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism

Concept drift for big data

The term “concept drift” refers to a change in statistical distribution of the data. In machine learning and predictive analysis, a fundamental assumption exits which reasons that the data is a random variable which is being generated independently from an underlying stationary distribution. In this chapter we present discussions on concept drifts that are inherent in the context big data. We discuss different forms of concept drifts that are evident in streaming data and outline different techniques for handling them. Handling concept drift is important for big data where the data flow occurs continuously causing existing learned models to lose their predictive accuracy. This chapter will serve as a reference to academicians and industry practitioners who are interested in the niche area of handling concept drift for big data applications