Thermal and mechanical properties of epoxy resin reinforced with modified iron oxide nanoparticles

Epoxy polymers, having good mechanical properties and thermal stability, are often used for engineering applications. Their properties can be further enhanced by the addition of iron oxide (Fe₃O₄) nanoparticles (NPs) as fillers to the resin. In this study, pristine Fe₃O₄ NPs were functionalized with polydopamine (PDA), (3-glycidoxypropyl)trimethoxysilane (GPTMS), and (3-aminopropyl)trimethoxysilane (APTES). X-ray diffraction and scanning electron microscopy (SEM) were used to study any changes in the crystal structure and size of the NPs while Fourier-Transform Infrared Spectroscopy (FTIR) and Thermogravimetric Analysis (TGA) were used to ensure the presence of functional groups on the surface. The mechanical properties of the Fe₃O₄-based nanocomposites generally improved except when reinforced with Fe₃O₄/PDA. The maximum improvement in tensile strength (∼34%) and fracture toughness (∼13%) were observed for pristine Fe₃O₄-based nanocomposites. Dynamic mechanical analysis (DMA) showed that the use of any of the treated NPs improved the material's initial storage modulus and had a substantial impact on its dissipation potential. Also, it was observed that the glass transition temperature measurements by DMA and differential scanning calorimetry were below that of pure epoxy. SEM of the cracked surfaces shows that the incorporation of any NPs leads to an enhancement in its thermal and mechanical properties.     

Model-based graph-cut method for automatic flower segmentation with spatial constraints

In this paper, we present an accelerated system for segmenting flower images based on graph-cut technique which formulates the segmentation problem as an energy function minimization. The contribution of this paper consists to propose an improvement of the classical used energy function, which is composed of a data-consistent term and a boundary term. For this, we integrate an additional data-consistent term based on the spatial prior and we add gradient information in the boundary term. Then, we propose an automated coarse-to-fine segmentation method composed mainly of two levels: coarse segmentation and fine segmentation. First, the coarse segmentation level is based on minimizing the proposed energy function. Then, the fine segmentation is done by optimizing the energy function through the standard graph-cut technique. Experiments were performed on a subset of Oxford flower database and the obtained results are compared to the reimplemented method of Nilsback et al. [1]. The evaluation shows that our method consumes less CPU time and it has a satisfactory accuracy compared with the mentioned method above [1].     

Relationship between profile length and roughness variables for natural surfaces

Surface roughness variables such as the rms height and the correlation length are commonly used as inputs for electromagnetic models to predict the backscattering coefficient. The objective of this study is to investigate the influence of the roughness profile length on the estimation of roughness variables. Surface profiles of 25 m long obtained by a laser profiler have been examined. A clear dependence of exponential type was observed between the roughness variables and the profile length.     

Microstructural evolution,dislocation density and tensile properties of Al-6.5Si-2.1Cu-0.35Mg alloy produced by different casting processes

Al-Si-Cu-Mg foundry alloys are used in casting process technologies.However,their strength proper-ties remain low due to their microstructural characteristics and porosity.In this work,the microstruc-tural characteristics,dislocation densities,and mechanical properties of Al-Si-Cu-Mg cast alloys prepared through different casting methods were studied experimentally.Four casting processes,namely,gravity casting (GC),rheocasting (RC),thixoforming (Thixo),and Thixo with heat treatment,were used.The GC and RC samples had mainly dendritic α-Al phase microstructures and exhibited coarse Si particles and intermetallic compounds in their interdendritic regions.By contrast,the Thixo and heat-treated Thixo(HT-Thixo) samples exhibited microstructural refinement with uniformly distributed α-Al globules,fine fibrous Si particles,and fragmented intermetallic compounds among α-Al globules.The accumulation of dislocation densities increased in the Thixo sample as the strain was increased due to plastic deforma-tion.Furthermore,the ultimate tensile strength and yield strength of the HT-Thixo sample increased by 87％ and 63％,respectively,relative to those of the GC sample.The cleavage fracture displayed by the GC and RC samples led to brittle failure.Meanwhile,the Thixo and HT-Thixo samples presented dimple-based ductile fracture.     

The Effect of Applied Voltage and Operating Pressure on Emitted X-Ray from Nitrogen (N<Subscript>2</Subscript>) Gas in APF Plasma Focus Device

In the present work, the effect of applied voltage and operating pressure on behaviour of X-rays emitted from nitrogen gas (N₂) used in APF plasma focus facility is investigated. It was found that the optimum conditions for high emissions of SXR and HXR from the plasma focus (PF) are different. At four applied voltages of 10, 11, 12, and 13 kV, the optimum pressures for SXR and HXR emissions of nitrogen gas (N₂) were obtained. At lower voltages, 10, and 11 kV optimum pressure for SXR emission was 3.5 torr while for HXR emission was 2.5 torr. At higher voltages, 12, and 13 kV, the optimum pressures shift to higher values at 4 and 3 torr for SXR and HXR emissions, respectively. Among the applied voltages, the least intensity of both SXR and HXR was at voltage 10 kV and the most intensity was for 13 kV which confirm with increasing voltage, the intensity of X-ray emission increases. Also the results obtained by images of pin-hole camera were in compatible with the results of detected signals by different filtered Pin-diodes and Scintillation detector. Our results illustrate that the voltage and the pressure are effective parameters in X-ray emission from the PF.     

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process. Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions. Since the medical diagnostic outcomes need to be prompt and accurate, the recently developed artificial intelligence (AI) and deep learning (DL) models have received considerable attention among research communities. This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification (MDL-BADDC) model. The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing, feature selection, classification, and parameter tuning. Besides, the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer (QOBMO) based feature selection technique. Moreover, the deep stacked autoencoder (DSAE) based classification model is designed for the detection and classification of atherosclerosis disease. Furthermore, the krill herd algorithm (KHA) based parameter tuning technique is applied to properly adjust the parameter values. In order to showcase the enhanced classification performance of the MDL-BADDC technique, a wide range of simulations take place on three benchmarks biomedical datasets. The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.     

Applying LSSVM algorithm as a novel and accurate method for estimation of interfacial tension of brine and hydrocarbons

One of the critical parameters in petroleum and chemical engineering is the interfacial tension between brine and hydrocarbon which has major effects on trapping and residual oil in reservoir pore throat so it becomes one of the interesting topics in enhancement of oil recovery in this work Least squares support vector machine (LSSVM) algorithm was applied as a novel predicting machine for prediction of interfacial tension of brine and hydrocarbons in terms of hydrocarbon carbon number, temperature, pressure and ionic strength of brine. A total number of 175 interfacial tensions were collected from literature in the purpose of training and testing of the model. The root mean squared error (RMSE), average absolute relative deviation (AARD) and the coefficient of determination (R²) were calculated overall datasets as 0.23964, 0.27444 and 0.98509 respectively. The results of study showed that predicting LSSVM machine can be applicable for estimation of interfacial tension and EOR processes.     

Environmental impact of alternative fuel mix in electricity generation in Malaysia

The Fuel Diversification Strategy was incorporated into the Malaysian National Energy Policy in order to achieve a more balanced consumption of fuel, namely gas, hydro, coal and petroleum. The objective of this paper is to evaluate changes in CO_2, SO₂ and NO_x emission due to changes in the fuel mix specified in the Fuel Diversification Strategy. Using the environmental extended Leontief's input–output framework it was found that the fuel mix as envisioned by the Fuel Diversification Strategy generates higher CO_2, SO₂ and NO_x emissions. As such, to ensure a sustainable energy policy, the proposed fuel mix must be accompanied by efficiency gain so that the negative impact on the environment could be mitigated.     

Comprehensive Study on Soft X-Ray Emission from Admixtures of Nitrogen and Neon Gases in the APF Plasma Focus Device

The present work is an investigation on the effect of working gas composition as well as applied voltage and operating pressure on the behavior of SXR emitted from the APF device. Three volumetric ratios(90:10), (75:25), and (50:50) of nitrogen:neon (N₂:Ne) admixture were used with operating conditions at applied voltages of 11, 12, and 13 kV and operating pressures of 1.5, 2, 2.5, 3, 3.5, 4, 4.5, and 5 torr. Using (N₂:Ne) gas mixture ratios of (90:10) and (75:25) and at applied voltage of 11 kV, the optimum pressure for maximum intensity of SXR was 3.5 torr. However, for the percentage of (50:50), it shifts to higher pressure of 4 torr. At higher applied voltages of 12 and 13 kV, the optimum pressures shift to higher values, 4 torr for both volumetric ratios (90:10) and (75:25), and 4.5 torr for the ratio of (50:50). It was found that the intensity of SXR increases with the increase of neon (Ne) percentage in the admixture of (N₂:Ne) and applied voltage. The highest intensity was for the volumetric ratio of (50:50) operating at the voltage of 13 kV. Our results illustrated that mixing neon (Ne) with nitrogen (N₂) as the working gas in the PF is a power source of SXR emission.     

Effect of Quartz and Pyrex Insulators Length on Hard-X ray Signals in APF Plasma Focus Device

This paper, presents the variation of hard X-ray (HXR) intensity in the APF plasma focus device for different insulator sleeves. For Pyrex and Quartz insulators, the lengths of 40 and 50 mm seems optimal to yield maximum HXR intensity, respectively. Also using the Pyrex insulator leads to production of higher HXR intensity than the Quartz insulator. The results illustrate that the length and the material of the insulator has a considerable effect on the HXR intensity.