A Strain-Driven Morphotropic Phase Boundary in BiFeO3 Zeches, R. J.; Rossell, M. D.; Zhang, J. X. ...
Science (American Association for the Advancement of Science),
11/2009, Letnik:
326, Številka:
5955
Journal Article
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Piezoelectric materials, which convert mechanical to electrical energy and vice versa, are typically characterized by the intimate coexistence of two phases across a morphotropic phase boundary. ...Electrically switching one to the other yields large electromechanical coupling coefficients. Driven by global environmental concerns, there is currently a strong push to discover practical lead-free piezoelectrics for device engineering. Using a combination of epitaxial growth techniques in conjunction with theoretical approaches, we show the formation of a morphotropic phase boundary through epitaxial constraint in lead-free piezoelectric bismuth ferrite (BiFeO3) films. Electric field–dependent studies show that a tetragonal-like phase can be reversibly converted into a rhombohedral-like phase, accompanied by measurable displacements of the surface, making this new lead-free system of interest for probe-based data storage and actuator applications.
Design, synthesis, and bioactivity evaluation of novel mannich bases (2a‐2j) and triazole‐chalcone derivatives (7a‐7k) of Eugenol 1 were reported. Among all the derivatives tested for ...antiproliferative activity, di‐amine manich derivative 2b (32.92 μM), and 4‐methoxy chalcone triazole derivative 7d (33.05 μM) significantly inhibited HepG2 cell lines when compared to the standard doxorubicin (37.29 μM). Whereas most of the compounds such as diethylamine 2a (17.75 μM), (aminomethyl) methane diamine 2b (17.02 μM), and bis (chloromethyl) amine 2c (20.12 μM) showed moderate to better inhibition towards MCF‐7 cell lines. The synthesized analogues were also tested for antidiabetic and antiobesity potentials. Compounds 2f (55.50%), 2c (54.34%), 7g (55.5%), and 2a (55.5%) have shown moderate inhibitory potentials toward intestinal α‐glucosidase enzyme when compared to the standard Acarbose (72.86%). Likewise, compounds 7d (82.95%), 7f (76.19%), 7g (74.81%), 7e (74.81%), and 2g (72.50%) have shown significant to moderate inhibitory potentials toward Pancreatic lipase enzyme when compared to the standard orlistat (91.10%). ROS induces life‐threatening diseases like diabetes, cancer, etc., and antioxidants play a major role in controlling their production. Compounds 2c (99.81%), 2i (99.80%), 2d (99.26%), 2g (98.79%), and 2f (98.42%) have shown significant antioxidant profiles in ABTS assay when compared to the standard Trolox (99.07%). Further, In silico Molecular docking and pharmacokinetic screening of the eugenol derivatives complemented the in vitro results indicating the drug likeness of the obtained active compounds.
Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians.
To help ...address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages.
The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance.
The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method.
This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The effective method for cardiovascular disease (CVD) risk prediction is done by training the deep neural networks on the well-defined training dataset. The irregular subset from the real dataset ...with a greater data variance is considered for prediction. The proposed system uses the trained datasets to separate common and greatly biased subsets for accurately implementing the prediction models when many previous models are learning from the random samples of training datasets. The feature selection is done with a Binary Krill Herd meta-heuristic optimizer (B-KHA), and the extracted features are fed to the CapNet model for prediction purposes. In addition, the isolated training groups learn the network classifiers. This proposed model used the Cleveland dataset gathered from online resources. The experiment proves that the proposed model improves the network performance by appropriate prediction. The suggested model shows that the experimental outcomes perform better than the traditional machine learning algorithms, with 95% accuracy, 94% specificity, 94% precision, 97% sensitivity, 95% F1-score, and 90% Mathews’ Correlation Coefficient (MCC).
Polymer nanocomposites have attracted great attention world wide from both academic and industrial points of view. The material properties of polymers can be enhanced dramatically by incorporating ...layered silicates at fairly low concentrations. The durability of any material depends upon several factors e.g. light, heat, microwaves, mechanical abrasion etc. The study and the effect of these factors on the performance are essentially required to extend the application limits. The durability of polymer nanocomposites has been evaluated under different environments. The present review describes the durability of different polymer nanocomposites mainly under thermal- and photoageing. Biodegradable nanocomposites of different polymers are also discussed briefly.
Prebiotics alter bacterial content in the colon, and therefore could be useful for obesity management. We investigated the changes following addition of inulin oligofructose (IO) in the food of rats ...fed either a corn starch (C) diet or a high-carbohydrate, high-fat (H) diet as a model of diet-induced metabolic syndrome. IO did not affect food intake, but reduced body weight gain by 5·3 and 12·3 % in corn starch+inulin oligofructose (CIO) and high-carbohydrate, high-fat with inulin oligofructose (HIO) rats, respectively. IO reduced plasma concentrations of free fatty acids by 26·2 % and TAG by 75·8 % in HIO rats. IO increased faecal output by 93·2 %, faecal lipid excretion by 37·9 % and weight of caecum by 23·4 % and colon by 41·5 % in HIO rats. IO improved ileal morphology by reducing inflammation and improving the density of crypt cells in HIO rats. IO attenuated H diet-induced increases in abdominal fat pads (C 275 (sem 19), CIO 264 (sem 40), H 688 (sem 55), HIO 419 (sem 32) mg/mm tibial length), fasting blood glucose concentrations (C 4·5 (sem 0·1), CIO 4·2 (sem 0·1), H 5·2 (sem 0·1), HIO 4·3 (sem 0·1) mmol/l), systolic blood pressure (C 124 (sem 2), CIO 118 (sem 2), H 152 (sem 2), HIO 123 (sem 3) mmHg), left ventricular diastolic stiffness (C 22·9 (sem 0·6), CIO 22·9 (sem 0·5), H 27·8 (sem 0·5), HIO 22·6 (sem 1·2)) and plasma alanine transaminase (C 29·6 (sem 2·8), CIO 32·1 (sem 3·0), H 43·9 (sem 2·6), HIO 33·6 (sem 2·0) U/l). IO attenuated H-induced increases in inflammatory cell infiltration in the heart and liver, lipid droplets in the liver and plasma lipids as well as impaired glucose and insulin tolerance. These results suggest that increasing soluble fibre intake with IO improves signs of the metabolic syndrome by decreasing gastrointestinal carbohydrate and lipid uptake.
Transition metal dichalcogenides (TMDs) materials are from the two-dimensional (2D) materials family and have many benefits, comprising high carrier mobility and conductivity, high optical ...transparency, outstanding mechanical flexibility, and chemical stability, and are also favorable gas sensing materials because of their high surface-area-to-volume ratio. Nevertheless, their low gas-sensing performance in terms of low response, partial recovery, and poor selectivity obstruct the apprehension as high-performance 2D TMDs gas sensing materials. At this time, we explain the enhancement in gas-sensing performance of molybdenum disulfide (MoS2) nanoflakes (NF) by decorating with Lanthanum (La) at room temperature (25 °C). Our experiments show that the dynamic sensing response of the La decorated few-layered MoS2 (La@MoS2) sensor increases by ∼6 times than the pristine few-layered MoS2, which positions it first-ever reported values for NO2 gas detection. The sensitivity of the MoS2 and La@MoS2 found 0.627 and 3.346 ppm−1, respectively, towards NO2 gas. It is noteworthy that La has introduced to MoS2, and its selectivity towards the volatile organic compounds (VOCs) and other toxic gases improved drastically. Our outcomes show that the suggested method represents a successful approach for improving the gas sensing response of 2D TMDs sensors.
The financial time series data is a highly nonlinear signal and hence difficult to predict precisely. The prediction accuracy can be improved by linearizing the signal. In this paper, the nonlinear ...data sample is linearized by decomposing it into several Intrinsic Mode Functions (IMFs). A hybrid multi-layer decomposition technique is developed. The decomposition method proposed in this paper is composed of both Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) methods individually. As a new contribution to the previous literature in this study, the VMD is used to decompose further the higher frequency signals obtained from the EMD-based decomposed signal. The result analysis shows that the double decomposition technique improves prediction accuracy. This is a new introduction to the field of stock market prediction. The prediction accuracy of the proposed model is verified by applying it to three different stock market data. Historical data (closing price) is implemented to obtain one day ahead predicted closing price. Comparative analysis of other previously implemented methods like Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), along with the proposed method, is performed. Firefly algorithm is implemented for optimizing the kernel factors. It is observed that the proposed hybrid model outperformed other methods discussed in this study.
We report the use of a laser-based fabrication process in the creation of paper-based flow-through filters that when combined with a traditional lateral flow immunoassay provide an alternative ...pathway for the detection of a pre-determined analyte over a wide concentration range. The laser-patterned approach was used to create polymeric structures that alter the porosity of the paper to produce porous flow-through filters, with controllable levels of porosity. When located on the top of the front end of a lateral flow immunoassay the flow-through filters were shown to block particles (of known sizes of 200 nm, 500 nm, 1000 nm and 3000 nm) that exceed the effective pore size of the filter while allowing smaller particles to flow through onto a lateral flow immunoassay. The analyte detection is based on the use of a size-exclusive filter that retains a complex (∼3 μm in size) formed by the binding of the target analyte with two antibodies each of which is tagged with different-sized labels (40 nm Au-nanoparticles and 3 μm latex beads), and which is larger than the effective pore size of the filter. This method was tested for the detection of C-reactive protein in a broad concentration range from 10 ng/ml to 100,000 ng/ml with a limit-of-detection found at 13 ng/ml and unlike other reported methods used for analyte detection, with this technique we are able to counter the Hook effect which is a limiting factor in many lateral flow immunoassays.
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A widespread forest fire episode occurred over Uttarakhand during April 24–May 2, 2016. This event released large amount of carbon monoxide (CO), nitrogen dioxide (NO
2
) and aerosols in the pristine ...environment of Uttarakhand. AIRS observations showed 60–125 ppbv higher CO during fire-impacted period with respect to background CO at 925, 850 and 700 hPa. Spatial distribution of CO and fire hotspots over Uttarakhand showed high level of CO over the region of intense biomass burning specifically. Over Dehradun, rate of increase in daily average surface CO was found to be 45 ppbv/day during fire period. Average background and fire-impacted tropospheric column NO
2
were found to be 1.7 × 10
15
± 5.0 × 10
14
mol/cm
2
and 3.0 × 10
15
± 8.5 × 10
14
mol/cm
2
, respectively. Similarly, average background and fire-impacted aerosol optical depth (AOD) were found to be 0.47 ± 0.25 and 0.90 ± 0.35, respectively, for Terra and 0.44 ± 0.17 and 0.86 ± 0.47, respectively, for Aqua observations. Size- and shape-segregated AOD distributions showed enhancement of medium-to-coarse (radius > 0.35 µm) non-spherical particles due to fire episode. CALIPSO height-resolved aerosol subtyping showed dominance of smoke near Uttarakhand up to 10 km altitude. Interspecies correlations indicated the common sources of near-surface CO, CO aloft, AOD and NO
2
. A relatively poor correlation between near-surface CO and tropospheric column NO
2
might be due to chemical transformation of reactive NO
2
within the fire plume. Forward trajectories calculated over fire-affected region at 500 m AGL indicated that fire emissions might have influenced the air quality of southern Nepal and northern Uttar Pradesh/Bihar within lower 3 km.
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