110023 research outputs found
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Bioactive Peptides and Exercise Modulate the AMPK/SIRT1/ PGC-1α/FOXO3 Pathway as a Therapeutic Approach for Hypertensive Rats
No full text[[abstract]]Peptides are fragments of fundamental protein sequences that may have health benefits in addition to basic dietary benefits. Recently, we have reported on the pharmacological benefits of alcalase potato protein hydrolysate (APPH) and bioactive peptides isolated from APPH. The aim was to evaluate the synergistic effect of exercise along with DIKTNKPVIF (DF) peptides in ameliorating hypertension in spontaneously hypertensive rat (SHR) rats. We examined ECG parameters, lipid profiles, cardiac markers, and histology, and quantified the proteins associated with fibrosis, hypertrophy, apoptosis, mitochondrial biogenesis, and longevity pathways. DF peptide administration, along with exercise, reduced the blood pressure and cardiac marker levels in serum. Furthermore, it also suppressed the expression of fibrosis markers COL1A1, CTGF, and uPA and downregulated cardiac-hypertrophy-associated markers such as calcineurin, NFATC3, GATA4, pGATA4 and BNP. Exercise synergistically increases the expression of IFG1, PI3K, and AKT cell-survival pathway proteins, along with DF administration. Moreover, AMPK/SIRT1/PGC-1α/FOXO3 pathway protein expression was increased with the combinatorial administration of DF and exercise. Our data suggest that exercise, along with DF peptides, act synergistically in alleviating hypertension by activating the mitochondrial biogenesis pathway
An Inner Dependence Analysis Dynamic Decision-Making Framework
No full text[[abstract]]During the last decade, with the rapid development of information technology, the immense volume of data poses a challenge to decision-makers. We use a combined dynamic decision-making approach based on the analytic hierarchy process (AHP) to select the best supplier. In this paper, we discuss the interaction between criteria that can lead to expanding our proposed dynamic framework to consider the inner dependencies among criteria. The main contributions are: (1) identifying the most important criteria of supplier selection in a steel bar manufacturer in Taiwan; (2) proposing a simple and rapid analysis of the appropriate supplier selection evaluation framework; and (3) using the AHP and transformation matrix to present the inner dependence among the criteria
Applying Homogeneity Index Modification to High-capacity High-dynamic-range Image Authentication with Distortion Tolerance
No full text[[abstract]]Distortion-free data hiding algorithms for high-dynamic-range (HDR) images use homogeneity index modification for data embedding. No distortion is encountered between a tone-mapped cover and stego images. However, only approximately one-eighth of pixels have two or more homogeneous representations for data embedding, affecting their performance and applications. This study investigates the feasibility of modifying the homogeneity index for a new application known as HDR image authentication. First, a revision threshold is introduced to ensure that each processing pixel has the maximum number of homogeneous representations possible. In the proposed algorithm, each pixel can perform data embedding. Following that, an authentication code is embedded using a multiple-base notational system. The experimental results indicate that the proposed algorithm significantly increases the embedding capacity and supports HDR image authentication. The data-embedded images with embedded messages are of good quality, and tampered pixels can be effectively detected
Research on the Evaluation System of Professional Competence Technology for Interior Design Talent Based on Delphi Technique, IOP Conference Series: Earth and Environmental Science.
No full textDevelopment of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States
No full text[[abstract]]Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70–30% and 80–20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation
Effective mitigation of single?component and mixed textile dyes from aqueous media using recyclable graphene?based nanocomposite
No full text[[abstract]]The present study reported the synthesis and utilization of a graphene-based hybrid nanocomposite (MnFe2O4/G) to mitigate several synthetic dyes, including methylene blue, malachite green, crystal violet, and Rhodamine B. This adsorbent was structurally analyzed by several physicochemical techniques such as X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, Raman spectroscopy, N2 adsorption-desorption isotherm measurement, point of zero charge, and Boehm titrations. BET surface area of MnFe2O4/G was measured at 382.98 m2/g, which was substantially higher than that of MnFe2O4. MnFe2O4/G possessed diverse surface chemistry properties with the presence of many functional groups such as carboxylic acid, phenolic, lactone, and basic groups. MnFe2O4/G was used to remove synthetic dyes in the aqueous media. The effect of many factors, e.g., concentration (5-50 mg/L), pH (4-10), dose (5-20 mg), and temperature (25-45 °C) on adsorption performance of MnFe2O4/G was conducted. Kinetic, isotherm, intraparticle, and thermodynamic models were adopted for investigating adsorption phenomenon of dyes on MnFe2O4/G. The maximum adsorption capacity of dyes over MnFe2O4/G was found as Rhodamine B (67.8 mg/g) < crystal violet (81.3 mg/g) < methylene blue (137.7 mg/g) < malachite green (394.5 mg/g). Some tests were performed to remove mixed dyes, and mixed dyes in the presence of antibiotics with total efficiencies of 65.8-87.9% after 120 min. Moreover, the major role of π-π stacking interaction was clarified to gain insight into the adsorption mechanism. MnFe2O4/G could recycle up to 4 cycles, which may be beneficial for further practical water treatment
Effects of binary hybrid nanofluid on heat transfer and fluid flow in a triangular-corrugated channel: An experimental and numerical study
No full text[[abstract]]The flow and heat transfer over a modulated surface in the form of triangular protrusions using MWCNTs-TiO2 nanohybrids were investigated, and its results are detailed in this paper. The nanomaterials were functionalized and prepared at weight concentrations of 0.025%-wt., 0.05%-wt., 0.075%-wt., and 0.1%-wt., then had its morphology elucidated using the high-resolution transmission electron microscopy (HR-TEM), field emission transmission electron microscopy (FE-SEM), and Energy Dispersive X-Ray Analysis (EDX). The mixture's thermophysical properties at 303 K were determined and utilized in the investigation's numerical aspect. A steady-state 2D analysis of flow over a modulated surface was solved in ANSYS-FLUENT with turbulent flow at 5000 ? Re ? 15,000. The model was validated using the Dittus-Boelter Eq., and its average error was 9.840%. The heat transfer was enhanced by 13.181%, 18.056%, 22.033% and 26.107% for nanoparticles' weight concentrations of 0.025%-wt., 0.05%-wt., 0.075%-wt., and 0.1%-wt., respectively. The water flow and MWCNTs-TiO2 nanohybrids' turbulent kinetic energy, pressure, velocity, turbulence intensity contours, and velocity vectors were determined, and correlations were formed from the results to predict the average Nusselt number for the DW and MWCNTs-TiO2 as a function of the nanoparticles' weight concentrations
Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects
No full text[[abstract]]Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and Tavg oC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction
On the prediction of methane fluxes from pristine tropical peatland in Sarawak: application of a denitrification–decomposition (DNDC) model
No full text[[abstract]]Tropical peatlands have high potential function as a major source of atmospheric methane (CH4) and can contribute to global warming due to their large soil carbon stock, high groundwater level (GWL), high humidity and high temperature. In this study, a process-based denitrification–decomposition (DNDC) model was used to simulate CH4 fluxes in a pristine tropical peatland in Sarawak. To test the accuracy of the model, eddy covariance tower datasets were compared. The model was validated for the year 2014, which showed the good performance of the model for simulating CH4 emissions. The monthly predictive ability of the model was better than the daily predictive ability, with a determination coefficient (R2) of 0.67, model error (ME) of 2.47, root mean square error (RMSE) of 3.33, mean absolute error (MAE) of 2.92 and mean square error (MSE) of 11.08. The simulated years of 2015 and 2016 showed the good performance of the DNDC model, although under- and overestimations were found during the drier and rainy months. Similarly, the monthly simulations for the year were better than the daily simulations for the year, showing good correlations at R2 at 0.84 (2015) and 0.87 (2016). Better statistical performance in terms of monthly ME, RMSE, MAE and MSE at???0.11, 3.38, 3.05 and 11.45 for 2015 and???1.14, 5.28, 4.93 and 27.83 for 2016, respectively, was also observed. Although the statistical performance of the model simulation for daily average CH4 fluxes was lower than that of the monthly average, we found that the results for total fluxes agreed well between the observed and the simulated values (E?=?6.79% and difference?=?3.3%). Principal component analysis (PCA) showed that CH4, GWL and rainfall were correlated with each other and explained 41.7% of the total variation. GWL was found to be relatively important in determining the CH4 fluxes in the naturally inundated pristine tropical peatland. These results suggest that GWL is an essential input variable for the DNDC model for predicting CH4 fluxes from the pristine tropical peatland in Sarawak on a monthly basis
Piezoelectric catalysis for efficient reduction of CO2 using lead-free ferroelectric particulates
No full text[[abstract]]The increase in global energy demand, together with a rise in carbon dioxide (CO2) levels have encouraged research into the reduction of CO2 into useful chemicals and fuels. In this paper, we demonstrate the piezo-catalytic reduction of CO2 using lead-free lithium-doped potassium sodium niobate (KNN) ferroelectric ceramic particulates. The application of acoustic waves generated by ultrasound to a suspension of the ceramics particles creates pressure waves result in a large change in the spontaneous polarisation of the KNN particles via the piezoelectric effect, which in turn creates surfaces charges for CO2 reduction. The effect of CO2 gas concentration, the presence of dissolved species, and catalyst loading on piezo-catalytic performance are explored. By optimization of the piezo-catalytic effect, a promising piezo-catalytic CO2 reduction rate of 438 μmol g?1 h?1 is achieved, which is much larger than the those obtained from pyro-catalytic effects. This efficient and polarisation tuneable piezo-catalytic route has potential to promote the development of CO2 reduction via the utilisation of vibrational energy for environmental benefit