Synthesis and characterization of polymeric linseed oil grafted methyl methacrylate or styrene

Syntheses of wholly natural polymeric linseed oil (PLO) containing peroxide groups have been reported. Peroxidation, epoxidation and/or perepoxidation reactions of linseed oil, either under air or under oxygen flow at room temperature, resulted in polymeric peroxides, PLO-air and PLO-ofl, containing 1.3 and 3.5 wt.% of peroxide, with molecular weights of 2100 and 3780 Da, respectively. PLO-air contained cross-linked film up to 46.1 wt.% after a reaction time of 60 d, associated with a waxy, soluble part (PLO-air-s) that was isolated with chloroform extraction. PLO-ofl was obtained as a waxy, viscous liquid without any cross-linked part at the end of 24 d under visible irradation and oxygen flow. Polymeric peroxides, PLO-air-s and PLO-ofl initiated the free radical polymerization of both methyl methacrylate (MMA) and styreine (S) to give PMMA-graft-PLO and PS-graft-PLO graft copolymers in high yields with M-w varying from 37 to 470 kDa. The polymers obtained were characterized by FT-IR. H-1 NMR, TGA, DSC and GPC techniques. Cross-linked polymers were also studied by means of swelling measurements. PMNA-graft-PLO graft copolymer film samples were also used in cell-culture studies. Fibroblast cells were well adhered and proliferated on the copolymer film surfaces, which is important in tissue engineering

The Changes in Electrical and Interfacial Properties of Polyimide Exposed to Dielectric Barrier Discharge in SF 6

The formation mechanism of space charges in polyimide (PI) which was exposed to dielectric barrier discharge (DBD) in SF6 medium and the effects of the space charges on interfacial and electrical properties of PI were investigated. The variation of normalized surface charge density on PI sample was calculated and illustrated for different DBD exposure times. The surface potential was measured to determine the effect of the space charges on the sample. Then, the contact angle values were measured to obtain the relation between the surface energy and the surface charge density. The expressions for the total charge and the concentration of trapped electrons were derived by using Poisson and continuity equations at stationary state. The space charges were determined experimentally by using thermally stimulated depolarization current (TSDC) method. Also, SEM image and FTIR spectrum of virgin and treated samples were presented to observe the structural variations. It was seen that the approach for the formation mechanism of the space charges agreed with the experimental data. However, it was concluded particularly for the short-time DBD treatments that the space charges accumulated in the sample should be considered besides the effects of surface functionalization in the determination of the surface energy

The Infinite Mixture of Infinite Gaussian Mixtures

Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering and density estimation problems. However, many real-world data exhibit cluster distributions that cannot be captured by a single Gaussian. Modeling such data sets by DPMG creates several extraneous clusters even when clusters are relatively well-defined. Herein, we present the infinite mixture of infinite Gaussian mixtures (I2GMM) for more flexible modeling of data sets with skewed and multi-modal cluster distributions. Instead of using a single Gaussian for each cluster as in the standard DPMG model, the generative model of I2GMM uses a single DPMG for each cluster. The individual DPMGs are linked together through centering of their base distributions at the atoms of a higher level DP prior. Inference is performed by a collapsed Gibbs sampler that also enables partial parallelization. Experimental results on several artificial and real-world data sets suggest the proposed I2GMM model can predict clusters more accurately than existing variational Bayes and Gibbs sampler versions of DPMG

Reasoning about Uncertain Information and Conflict Resolution through Trust Revision

Publisher PD

A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects

BACKGROUND: Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way. RESULTS: We demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively. CONCLUSIONS: These results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples

Experiments on vibration absorption using energy sinks

This paper presents experiments that demonstrate the concept of energy sinks, which when attached to a vibrating structure can absorb most of its energy. Energy sinks consist of a set of undamped linear oscillators and, in principle, do not require presence of damping in the classical sense. The set of undamped oscillators that make up an energy sink collectively absorb the vibratory energy and retain it in their phase space. Earlier optimization studies by the authors have shown the feasibility of vibration absorption and energy retention by energy sinks if the set of oscillators have a particular frequency distribution. Experimental results presented in this paper support the concept of energy sinks. Different physical realizations of energy sinks demonstrate the significance of frequency distributions and the ability of energy sinks to reduce vibration amplitude of a primary structure to which they are attache

Using AI Uncertainty Quantification to Improve Human Decision-Making

AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone by providing additional useful probabilistic information to users. The majority of past research on AI and human decision-making has concentrated on model explainability and interpretability. We implemented instance-based UQ for three real datasets. To achieve this, we trained different AI models for classification for each dataset, and used random samples generated around the neighborhood of the given instance to create confidence intervals for UQ. The computed UQ was calibrated using a strictly proper scoring rule as a form of quality assurance for UQ. We then conducted two preregistered online behavioral experiments that compared objective human decision-making performance under different AI information conditions, including UQ. In Experiment 1, we compared decision-making for no AI (control), AI prediction alone, and AI prediction with a visualization of UQ. We found UQ significantly improved decision-making beyond the other two conditions. In Experiment 2, we focused on comparing different representations of UQ information: Point vs. distribution of uncertainty and visualization type (needle vs. dotplot). We did not find meaningful differences in decision-making performance among these different representations of UQ. Overall, our results indicate that human decision-making can be improved by providing UQ information along with AI predictions, and that this benefit generalizes across a variety of representations of UQ.Comment: 10 pages and 7 figure

Evaluation of electromagnetics radiation for stroke patients and non-stroke participants according to body segmentation

This research evaluates the electromagnetic radiation (EMR) for the stroke patients and non-stroke patients according to body segmentation. The human body is divided into three segments: top, middle and bottom. The frequency in hertz is collected at 23 points around the human body namely left side, right side and chakra points from 199 subjects undergoing post-stroke treatment and 100 non-stroke participants. The EMR is captured using frequency detector equipped with a dipole antenna. The data is collected by taking the reading of the frequency 5 times at each point at the same location; hence, the average value is calculated. The statistical analysis of the EMR are examined using SPSS software and Microsoft excel is used to calculate the average frequency of the data. In conclusion, the findings significantly shows that stroke patients has lower frequency value of EMR for both right side and left side but has higher frequency for chakra system. This is true for all the three segments of the body. Furthermore, it is also shown that there is no correlation between the left and the right side frequency for the stroke patients whereas the left-right correlation values are significantly high for the non-stroke participants. This observation justify that EMR from human body can contribute to early detection for stroke