Adaptive noise-reducing anisotropic diffusion filter

In image processing and computer vision, the denoising process is an important step before several processing tasks. This paper presents a new adaptive noise-reducing anisotropic diffusion (ANRAD) method to improve the image quality, which can be considered as a modified version of a speckle-reducing anisotropic diffusion (SRAD) filter. The SRAD works very well for monochrome images with speckle noise. However, in the case of images corrupted with other types of noise, it cannot provide optimal image quality due to the inaccurate noise model. The ANRAD method introduces an automatic RGB noise model estimator in a partial differential equation system similar to the SRAD diffusion, which estimates at each iteration an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of pre-segment image variances. Compared to the conventional SRAD filter, the proposed filter has the advantage of being adapted to the color noise produced by today’s CCD digital camera. The simulation results show that the ANRAD filter can reduce the noise while preserving image edges and fine details very well. Also, it is favorably compared to the fast non-local means filter, showing an improvement in the quality of the restored image. A quantitative comparison measure is given by the parameters like the mean structural similarity index and the peak signal-to-noise ratio.     

Decision tree classifiers for automated medical diagnosis

Decision support systems help physicians and also play an important role in medical decision-making. They are based on different models, and the best of them are providing an explanation together with an accurate, reliable and quick response. This paper presents a decision support tool for the detection of breast cancer based on three types of decision tree classifiers. They are single decision tree (SDT), boosted decision tree (BDT) and decision tree forest (DTF). Decision tree classification provides a rapid and effective method of categorizing data sets. Decision-making is performed in two stages: training the classifiers with features from Wisconsin breast cancer data set, and then testing. The performance of the proposed structure is evaluated in terms of accuracy, sensitivity, specificity, confusion matrix and receiver operating characteristic (ROC) curves. The results showed that the overall accuracies of SDT and BDT in the training phase achieved 97.07 % with 429 correct classifications and 98.83 % with 437 correct classifications, respectively. BDT performed better than SDT for all performance indices than SDT. Value of ROC and Matthews correlation coefficient (MCC) for BDT in the training phase achieved 0.99971 and 0.9746, respectively, which was superior to SDT classifier. During validation phase, DTF achieved 97.51 %, which was superior to SDT (95.75 %) and BDT (97.07 %) classifiers. Value of ROC and MCC for DTF achieved 0.99382 and 0.9462, respectively. BDT showed the best performance in terms of sensitivity, and SDT was the best only considering speed.     

Bioenergy plantations or long-term carbon sinks? – A model based analysis

In order to mitigate climate change bio-productive land may be used mainly in two ways: afforestation with long-rotation forests with the primary aim to act as carbon sinks, and short-rotation forests that are used for energy purposes and thereby replace fossil fuels. Under an ambitious climate target, land that may be used for both bioenergy plantations and long-rotation forests, are likely to be scarce, and thereby competition between long-rotation forests and bioenergy plantations can be expected. The goal of the study is to analyze the cost-effectiveness of bioenergy plantations versus long-rotations forests aimed at capturing and storing carbon. The study is performed by solving and analyzing a linear optimization model that links the energy system, an afforestation sector and the pulp and timber market. Many earlier studies tend to suggest that long-rotation forests offer lower costs per ton of CO₂ avoided. Our study, however, shows that long-rotation forests for the purpose of carbon sequestration will not be cost-effective in the long run under a stringent climate policy. Thus, economic efficiency considerations tend to support short-rotation plantations for high carbon prices. The reason for this is that scarcity of land increases the opportunity cost of land, a feature which is generally not captured in static near-term analysis, but it is captured in a dynamic model like ours. For less stringent carbon targets long-rotation forests, that are harvested and sold as timber are cost-effective during a transient phase.     

The Metabolic Efficacy of a Cannabidiolic Acid (CBDA) Derivative in Treating Diet- and Genetic-Induced Obesity

Obesity is a global medical problem; its common form is known as diet-induced obesity (DIO); however, there are several rare genetic disorders, such as Prader–Willi syndrome (PWS), that are also associated with obesity (genetic-induced obesity, GIO). The currently available therapeutics for treating DIO and GIO are very limited, and they result in only a partial improvement. Cannabidiolic acid (CBDA), a constituent of Cannabis sativa, gradually decarboxylates to cannabidiol (CBD). Whereas the anti-obesity properties of CBD have been reasonably identified, our knowledge of the pharmacology of CBDA is more limited due to its instability. To stabilize CBDA, a new derivative, CBDA-O-methyl ester (HU-580, EPM301), was synthesized. The therapeutic potential of EPM301 in appetite reduction, weight loss, and metabolic improvements in DIO and GIO was tested in vivo. EPM301 (40 mg/kg/d, i.p.) successfully resulted in weight loss, increased ambulation, as well as improved glycemic and lipid profiles in DIO mice. Additionally, EPM301 ameliorated DIO-induced hepatic dysfunction and steatosis. Importantly, EPM301 (20 and 40 mg/kg/d, i.p.) effectively reduced body weight and hyperphagia in a high-fat diet-fed Magel2^null mouse model for PWS. In addition, when given to standard-diet-fed Magel2^null mice as a preventive treatment, EPM301 completely inhibited weight gain and adiposity. Lastly, EPM301 increased the oxidation of different nutrients in each strain. All together, EPM301 ameliorated obesity and its metabolic abnormalities in both DIO and GIO. These results support the idea to further promote this synthetic CBDA derivative toward clinical evaluation in humans.     

ANFIS modeling of CO2 separation from natural gas using hollow fiber polymeric membrane

The present study is proposed to develop the Adaptive Neuro-Fuzzy Inference System optimized by genetic algorithm to estimate CO₂ value in permeate stream using a hollow fiber polymeric membrane for separation of binary gas containing CO₂ and CH₄ in natural gas. To that end, a number of 65 samples was gathered from the literature. Results indicated that the proposed ANFIS model has great potential with high correlation (R² = 0.9993) and less error (RMSE = 0.0064) for estimation of aforementioned parameter.     

Effect of Heating Rate on Phase and Microstructural Evolution During Pressureless Sintering of a Nanostructured Transition Alumina

Deagglomeration of a nanocrystalline transition alumina performed using different techniques was first demonstrated to be active in the achievement of a better powder compaction ability under uniaxial pressing and consequently in the development of a highly dense and homogeneous microstructure during pressureless sintering. A major effect, however, was associated to the heating rate chosen during the densification cycle. In fact, the influence of different heating rates (10°C/min or 1°C/min) on phase and microstructural evolution during sintering was investigated in depth on the above best green bodies. A low-rate thermal cycle leads to a significant reduction of the α-Al₂O₃ crystallization temperature and promotes a more effective particle rearrangement during phase transformation. As a consequence, in the low-rate treated material, it was possible to avoid the development of a vermicular structure as usually expected during the densification of a transition alumina and to yield a more homogenously fired microstructure.     

A new method for scaling of earthquake records using input energy of structures

Nonlinear response history analysis is conducted as the most trusted and accurate method in the seismic analysis and design of buildings. Since the selection of appropriate records is inherent in this procedure, numerous attempts have been made to use earthquake records that represent a good prediction of future earthquakes. Scaling existing earthquake records is considered as one of the most important tools to fulfill this goal. This paper introduces a new method for scaling ground motion time histories in which elastic input energy is considered as intensity measure. In the new scaling procedure, the scale factor is obtained by minimizing the residual between the elastic input energy of structures for the desired records and the maximum probable input energy of the target spectrum. The efficiency and the accuracy of the proposed method are evaluated and compared with those resulting from the method of ASCE/SEI 7. The results demonstrate that the records scaled with the proposed method lead to more accurate and efficient demand parameters.     

Rosiglitazone,but Not Epigallocatechin‐3‐Gallate,Attenuates the Decrease in PGC‐1α Protein Levels in Palmitate‐Induced Insulin‐Resistant C2C12 Cells

Alteration of lipid metabolism is an important mechanism for the treatment of insulin resistance. PGC‐1α, a key regulator of mitochondrial biogenesis and function, plays an important role in the improvement of insulin sensitivity by increasing fatty acids β‐oxidation. In the present study, the effects of epigallocatechin‐3‐gallate (EGCG), an anti‐obesity agent and enhancer of lipid catabolism, on PGC‐1α protein expression was examined and compared with anti‐diabetic drug rosiglitazone (RGZ). After differentiation of C2C12 myoblasts to myotubes, insulin resistance was induced by palmitate treatment. Then the expression of the PGC‐1a gene and glucose uptake were evaluated before and after treatment with RGZ and EGCG. Palmitate treatment significantly decreased PGC‐1α protein expression in C2C12 cells (P < 0.05). RGZ could restore the expression of PGC‐1α in palmitate treated cells (P > 0.05), while EGCG had no significant effect on the expression of this gene (P < 0.05). RGZ and EGCG significantly improved glucose uptake (by 2‐ and 1.54‐fold, respectively) in myotubes treated with palmitate. These data suggest that RGZ and EGCG both exert their anti‐diabetic activity by increasing insulin sensitivity, but with different molecular mechanisms. This effect of RGZ, unlike EGCG, is mediated, at least partly, by increasing PGC‐1α protein expression.     

An Experimental Study of Nozzle Temperature and Heat Treatment (Annealing) Effects on Mechanical Properties of High-Temperature Polylactic Acid in Fused Deposition Modeling

Fused deposition modeling (FDM) is the trendiest three-dimensional (3D) printing method among additive manufacturing technologies. In this process, the final parts are constructed through layer-by-layer adhesion of thermoplastic polymers. Amorphous thermoplastic polymers have better printability compared to semicrystalline ones; so, they are most popular with FDM users. Generally, the overall mechanical properties of FDM 3D printed parts are weaker in comparison to the traditional methods (such as injection molding) due to the weak bonds between the deposited rasters and layers. Therefore, the introduction of new materials with higher mechanical properties and easy printing process of the semicrystalline polymers has always been challenging to progress the mechanical properties of the products. In this study by the FDM process, the effect of nozzle temperature and heat treatment (annealing) on the mechanical properties of high-temperature polylactic acids is investigated. The increase in the nozzle temperature develops the rasters and layers bonding, and the heat treatment of the parts after printing rises the crystallinity percentage, which is crucial for the improvement of mechanical properties. Experimental results show that an increase in the nozzle temperature raises the tensile strength and modulus to 65.7 MPa and 4.97 GPa, respectively. Furthermore, the heat treatment process increases the tensile strength and modulus up to 67.4 MPa and 5.65 GPa. The final tensile modulus values are the highest ones reported for pure materials printed by the FDM process. POLYM. ENG. SCI., 60:979–987, 2020. © 2020 Society of Plastics Engineers     

Influence of pH on the photocatalytic activity of ZnO nanoparticles

In this study, ZnO nanoparticles were fabricated by co-precipitation method. The synthesized nanoparticles possessed monodispersity with the average size 20–30 nm. Since the industrial effluents may not be at neutral pH, the effect of pH on the rate of degradation is important and need to be considered. In order to investigate the effect of pH on ZnO nanoparticles photocatalytic activity, the photocatalytic degradation of Rose Bengal, Methylene blue, and Bromocresol green dyes, was studied with different pH values. It was observed that the adsorption of the dyes onto ZnO nanoparticles surface is strongly dependent on the pH of the solution which plays an important role in photocatalytic degradation.