Microstructure of charged AdS black hole via P−VP-V criticality

We suggest a new thermodynamic curvature, constructed via adiabatic compressibility, for examining the internal microstructure of charged black holes in an anti-de Sitter (AdS) background. We analyze the microscopic properties of small-large phase transition of black holes with pressure and volume as the fluctuation variables. We observe that strong repulsive interactions dominate among the micro-structures of near extremal small black holes, and the thermodynamic curvature diverges to positive infinity for the extremal black holes. At the critical point, however, thermodynamic curvature diverges to negative infinity.Comment: 7 pages,4 figure

Efficient QRS complex detection algorithm implementation on SOC-based embedded system

This paper studies two different Electrocardiography ( ECG ) preprocessing algorithms , namely Pan and Tompkins (PT) and Derivative Based (DB) algorithm, which is crucial of QRS complex detection in cardiovascular disease detection . Both algorithms are compared in terms of QRS detection accuracy and computation timing performance , with implementation on System - on - C hip (SoC) based embedded system that prototype on Altera DE2 - 115 Field Programmable Gate Array (FPGA) platform as embedded software . Both algorithm s are tested with 30 minutes ECG data from each of 48 different patient records obtain from MIT - BIH arrhythmia database. Results show that PT algorithm achieve 98.15% accuracy with 56. 33 seconds computation while DB algorithm achieve 96.74% with only 22. 14 seconds processing time. Based on the study, an optimized PT algorithm with improvement on Moving Windows Integrator (MWI) has been proposed to accelerate its computation. Result show s that the proposed optimized Moving Windows Integrator algorithm achieve s 9.5 times speed up than original MWI while retaining its QRS detection accuracy

Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data

With the development of information and communications technology, user-generated content and crowdsourced data are playing a large role in studies of transport and public health. Recently, Strava, a popular website and mobile app dedicated to tracking athletic activity (cycling and running), began offering a data service called Strava Metro, designed to help transportation researchers and urban planners to improve infrastructure for cyclists and pedestrians. Strava Metro data has the potential to promote studies of cycling and health by indicating where commuting and non-commuting cycling activities are at a large spatial scale (street level and intersection level). The assessment of spatially varying effects of air pollution during active travel (cycling or walking) might benefit from Strava Metro data, as a variation in air pollution levels within a city would be expected. In this paper, to explore the potential of Strava Metro data in research of active travel and health, we investigate spatial patterns of non-commuting cycling activities and associations between cycling purpose (commuting and non-commuting) and air pollution exposure at a large scale. Additionally, we attempt to estimate the number of non-commuting cycling trips according to environmental characteristics that may help identify cycling behavior. Researchers who are undertaking studies relating to cycling purpose could benefit from this approach in their use of cycling trip data sets that lack trip purpose. We use the Strava Metro Nodes data from Glasgow, United Kingdom in an empirical study. Empirical results reveal some findings that (1) when compared with commuting cycling activities, non-commuting cycling activities are more likely to be located in outskirts of the city; (2) spatially speaking, cyclists riding for recreation and other purposes are more likely to be exposed to relatively low levels of air pollution than cyclists riding for commuting; and (3) the method for estimating of the number of non-commuting cycling activities works well in this study. The results highlight: (1) a need for policymakers to consider how to improve cycling infrastructure and road safety in outskirts of cities; and (2) a possible way of estimating the number of non-commuting cycling activities when the trip purpose of cycling data is unknown

Bioactive substance contents and antioxidant capacity of raw and blanched vegetables.

Five commonly consumed vegetables in Malaysia namely, four-angled bean (Psophocarpus tetragonolobus D.C.), French bean (Phaseolus vulgaris L.), long bean (Vigna sesquipedalis L.), snow pea (Pisum sativum var. macrocarpon L.) and snap pea (Pisum sativum) were blanched in boiling water for 10 min. The contents of total phenolics, ascorbic acid and β-carotene, and the antioxidant capacity as typified by β-carotene and free radical scavenging activity (DPPH) assays were determined for the raw and blanched vegetables. The study revealed that blanching caused a significant (p < 0.05) increase in β-carotene content [fresh (389–539 µg/100 g), blanched (510–818 µg/100 g)], except in snow pea. Conversely, there was a significant (p < 0.05) decrease in ascorbic acid content [fresh (1.2–7.8 mg/100 g), blanched (0.67–3.8 mg/100 g)]. After blanching, the total phenolic content and antioxidant activity either decreased or increased depending on the type of vegetables. The total phenolic content was positively correlated with the antioxidant activity of the studied vegetables to some extent, but not with ascorbic acid or β-carotene

Loss Function Modeling of Efficiency Maps of Electrical Machines

This paper presents a novel approach in the modeling of efficiency maps for electrical machines. It is based on using the sum of terms in the form of kmnTmωn to represent the variation of the stator and rotor copper, iron and magnet losses with torque and speed. The effect of each term on the shape of the efficiency map is explored. Analysis is performed on the calculated efficiency and loss maps of an induction, an interior permanent magnet and a surface permanent magnet machine to demonstrate the validity of the approach

Precision Localization of Lipid-Based Nanoparticles by Dual-Fluorescent Labeling for Accurate and High-Resolution Imaging in Living Cells

In nanomedicine, lipid-based nanoparticles (NPs) such as liposomes (LPs) have established an important position. Precise delineation of NP interaction with cells and detailed characterization of activity are becoming essential, which mainly rely on labeling with lipophilic fluorescent molecules and assuming stable association with NPs. However, because of label separation from NPs in (biological) media, or when processed by cells, fluorescence-based detection of an NP incorporating a single label may not necessarily indicate the actual presence of an NP but may be from the dissociated label, rendering results unreliable. Herein, flow cytometry and confocal microscopy are employed to demonstrate that to verify the localization of LPs in a cell with perfect accuracy, dual-labeling, and contemporaneous detection of both fluorescent signals in one pixel are required. This is combined with size exclusion chromatography (SEC) and mass spectrometry measurements to indicate factors involved in label dissociation, which helps to understand the possible conditions of dissociated label and NP. It is shown that determining label colocalization with, and label dissociation from, dual-labeled NPs are needed to provide accurate spatiotemporal insight into targeting destination (colocalized signals) and disintegration (separated signals) of NPs during intracellular processing and in studying payload delivery with precision in nanomedicine.</p

ARRHYTHMIA DETECTION BASED ON HERMITE POLYNOMIAL EXPANSION AND MULTILAYER PERCEPTRON ON SYSTEM-ON-CHIP IMPLEMENTATION

ABSTRACT As the number of health issues caused by heart problems is on the rise worldwide, the need for an efficient and portable device for detecting heart arrhythmia is needed. This work proposes a Premature Ventricular Contraction detection system, which is one of the most common arrhythmia, based on Hermite Polynomial Expansion and Artificial Neural Network Algorithm. The algorithm is implemented as a System-On-Chip on Altera DE2-115 FPGA board to form a portable, lightweight and cost effective biomedical embedded system to serve for arrhythmia screening and monitoring purposes. The complete Premature Ventricular Contraction classification computation includes pre-processing, segmentation, morphological information extraction based on Hermite Polynomial Expansion and classification based on artificial Neural Network algorithm. The MIT-BIH Database containing 48 patients&apos; ECG records was used for training and testing purposes and Multilayer Perceptron training is performed using back propagation algorithm. Results show that the algorithm can detect the PVC arrhythmia for 48 different patients with 92.1% accuracy

Diabetic Ketoacidosis Following Bariatric Surgery in Patients With Type 2 Diabetes

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New 2-Methoxy Acetylenic Acids and Pyrazole Alkaloids from the Marine Sponge Cinachyrella sp.

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