Artificial neural networks: a novel tool for detecting GMO

Introduction of artificial neural network (ANN) into the field of GMO detection is the aim of this investigation. The usefulness of ANN to predict transgenic maize (Bt-176) based on chemical composition of the extracted crude oil was evaluated. The training set, comprised of a composition of major and minor lipid components as inputs and outputs. Crude oil extracted from the genetically modified maize (Bt-176) and non-transgenic maize was characterized in terms of its fatty acids, phytosterols and tocopherols distribution as well as of its lipid classes and unsaponifiables amounts. The results obtained from lipid distribution analysis showed that the grains of Bt-176 maize were comparable in their composition to that of the control maize. The analytical data have been elaborated by supervised pattern recognition technique ANN in order to classify genetically modified maize (Bt-176) and conventional maize as well as to authenticate the origin of the samples.     

Meaningful Learning for Deep Facial Emotional Features

Neural Processing Letters - Facial expression is an important aspect to recognize emotions between humans. However, this task remains difficult for machines. Several approaches have been developed...     

Synthesis and Characterization of Hetero-metallic Oxides-Reduced Graphene Oxide Nanocomposites for Photocatalytic Applications

A new AgO.CuO.WO₃/rGO nanocomposite was designed for the investigation of the degradation ability of the hybrid material under visible light irradiation. The AgO, CuO, WO₃ NPs, and AgO.CuO.WO₃ hetero-metallic oxides were fabricated via the chemical co-precipitation method. The crystallite sizes and phase analyses were investigated by recording X-ray diffraction patterns. The crystallite sizes of three metal oxides in the AgO.CuO.WO₃ hetero metal oxide were 16.7, 15.9, and 16.9 nm, respectively. The FESEM images at various magnifications were probed to study the morphology of synthesized materials. The micrographs of hetero-metallic oxides AgO.CuO.WO₃ exposed that three metal oxides merged like small particles and gives a large bulbous appearance. EDX analyses confirmed the formation of required materials with high purity. FTIR data was in agreement with the literature which facilitated to ensure the purity of synthesized samples. The optical bandgap energy was calculated via the Tauc plot indicating that the blend of three metal oxides generated a new energy level in the electronic structure is suitable for photocatalysis in the presence of visible light. The bandgap energy of hetero metallic oxides was 1.25 eV which is less than individual metal oxides signifying the tuning of the bandgap. The incorporation of rGO in AgO.CuO.WO₃ hetero-metallic oxides gives a new photocatalyst for optimum photodegradation of methylene blue in minimum time. The percentage degradation via AgO.CuO.WO₃ was 87.20% in 70 min while the percentage degradation via AgO.CuO.WO₃/rGO recorded by photocatalytic experiment was 95% in 40 min. The photocatalysis data revealed that AgO.CuO.WO₃ hetero-metallic oxides-rGO nanocomposite ensured a strong potential to uptake organic dyes from water by promoting redox reactions during photocatalysis in the minimum time limit.     

Hybrid marine predators algorithm for image segmentation: analysis and validations

Naturally, to analyze an image accurately, all the similar objects within it should be separated to pay attention to the most important object for reaching more details and hence achieving better accuracy. Therefore, multilevel thresholding is an indispensable image processing technique in the field of image segmentation and is employed widely to separate those similar objects. However, with increasing thresholds, the existing image segmentation techniques might suffer from exponentially-grown computational cost and low accuracy due to local optima shortage. Therefore, in this paper, a new image segmentation algorithm based on the improved marine predators algorithm (MPA) is proposed. MPA is improved using a strategy to find a number of the worst solutions within the population then tries to search for other better ones for those solutions by moving them gradually towards the best solutions to avoid accelerating to local optima and randomly within the search space based on a certain probability. In addition, this number of the worst solutions is increased with the iteration. This strategy is known as the linearly increased worst solutions improvement strategy (LIS). Also, we suggested that apply the ranking strategy based on a novel updating scheme, namely ranking-based updating strategy (RUS), on the solutions that could find better solutions in the last number iterations, perIter, in the hope of finding better solutions near it. RUS updates the particles/solutions which could not find better solutions than the best-local one in a number of consecutive iterations, with those that are generated based on a novel updating strategy. LIS is integrated with MPA to produce a new segmentation meta-heuristic algorithm abbreviated as MPALS. Also, MPALS and RUS are combined to tackle ISP in a strong variant abbreviated as HMPA for overcoming the image segmentation problem. The two proposed algorithms are validated on 14 test images and compared with seven state-of-the-arts meta-heuristic algorithms. The experimental results show the effectiveness of HMPA with increasing the threshold levels compared to the seven state-of-the-arts algorithms when segmenting an image, while their performance is roughly the same for the image with a small threshold level.

     

Production of a halotolerant lipase from Halomonas sp. strain C2SS100: Optimization by response-surface methodology and application in detergent formulations

The aim of this work was to optimize the production of a new lipase by a halotolerant bacterial strain Halomonas sp. C2SS100, by means of the response-surface methodology (RSM). The process parameters having the most significant effect on lipase production were identified using the Plackett–Burman screening design-of-experiments. Then, Box–Behnken design was applied to optimize lipase activity and the quadratic regression model of the lipase production was built. Indeed, the lipase yield was increased, and the value obtained experimentally (39 ± 2 U/ml) was very close to the rate predicted by the model (40.3 U/ml). Likewise, optimization of parameters by RSM resulted in 2.78-fold increase in lipase activity. These findings provide the first report on lipase production and optimization by a halotolerant bacterial strain belonging to Halomonas genus. Afterward, the biochemical properties of the produced lipase were studied for apply in oil stains removal. The crude lipase showed a maximum activity at 60°C and at pH ranging from 7 to 10. It displayed an important stability at high temperature, pH, and NaCl. Interestingly, this bacterial lipase exhibited a prominent stability toward some commercial solid and liquid detergents after 30 min of incubation at 50°C. The capability of the crude lipase to eliminate stain was ascertained on polycotton fabric pieces stained with lubricating oil. Whether with the addition of hot water alone or of a commercially available detergent, lipase is able to considerably boost the elimination of oil stains. The actual findings highlight the capacity of Halomonas sp. lipase for energy-efficient biocatalytic application.     

A formal approach to AADL model-based software engineering

International Journal on Software Tools for Technology Transfer - Formal methods have become a recommended practice in safety-critical software engineering. To be formally verified, a system should...     

Diagnosability Analysis of Intermittent Faults in Discrete Event Systems Using a Twin-plant Structure

Most research in fault diagnosis of discrete event systems has been focused on permanent failures. However, experience with monitoring of dynamic systems shows that intermittent faults are predominant, and that their diagnosis constitutes one of the most challenging tasks for surveillance activities. Among the main existing approaches to deal with permanent faults, two were widely investigated while considering different settings: the Diagnoser based approach, and the Twin-plant based approach. The latter was developed to cope with some complexity limitations of the former. In the present paper, we propose a twin-plant based approach to deal with diagnosability of intermittent faults. Firstly, we discuss various notions of diagnosability, while considering the occurrence of faults, their recovery, and the identification of the system status. Then, we establish the necessary and sufficient conditions for each notion, and develop on-the-fly algorithms to check these properties. The discussed approach is implemented in a prototype tool that is used to conduct experiments on a railway control benchmark.

     

Crystallization of P Type Amorphous Silicon (a-Si: H) by AIC Method: Effect of Aluminum Thickness

In this work, we will study the crystallization of P type hydrogenated amorphous silicon (a-Si:H) by Aluminum Induced Crystallization technique (CIA) by varying the thickness of the aluminum films. We have deposited a 100 nm thickness of p-type a-Si:H layer on Corning glass substrates using PECVD technique. An aluminum layer with thickness ranging from 10 to 400 nm was thermally evaporated on the a-Si:H surface. The thermal annealing was performed in a conventional furnace at temperature of 550 °C for 4 h in flowing N2 ambient. The study of the crystallization of the Al/a-Si:H/Glass structure according the aluminum thickness was carried out by using Raman spectroscopy, X-rays diffraction and Hall Effect measurements. Raman results reveal the presence of the peaks between 510 and 520 cm−1, which are close to the peak of crystallized Si (about 521 cm−1) proving the crystallization of all samples. The XRD measurements show the presence of the characteristic peaks of the crystalline silicon, thus the a-Si: H (p) layer was effectively crystallized by the AIC method in a short time. Through Hall measurements we found an improvement in electrical properties and an increase in dopant concentration (+ 5.3 1014 to + 2.9 1017 cm2).     

Polystyrene latex particles bearing primary amine groups via soap‐free emulsion polymerization

Polystyrene latexes were prepared in the presence of an amino‐containing functional comonomer, N‐(3‐aminopropyl)methacrylamide hydrochloride (APMH), via soap‐free batch emulsion polymerization initiated by the cationic initiator 2,2′‐azobis(2‐amidinopropane) dihydrochloride. These latexes were characterized by studying the influence of the ionic comonomers on the polymerization kinetics, particle size, surface charge density and colloidal properties. The synthesized latexes were monodisperse with a final size between 100 and 600 nm depending on the APMH concentration. The initial polymerization rate and the particle number increased in accordance with the Smith–Ewart theory for soap‐free styrene emulsion polymerization with a hydrophilic functional comonomer. The final functionalization rate of the particles has been particularly studied with the intention of fitting the prepared latexes to be used in the immobilization of biological molecules for biological sample preparation and diagnostic applications. © 2020 Society of Chemical Industry