Search results for: Fourier neural operator

Authors: Karima Bourenane, Aissa Keffous

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In this paper, we present an experimental method on the etching reaction of p-type 6H-SiC, etching that was carried out in HF/K₂Cr₂O₇ solutions. The morphology of the etched surface was examined with varying K₂Cr₂O₇ concentrations, etching time and temperature solution. The surfaces of the etched samples were analyzed using Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR) and Photoluminescence. The surface morphology of samples etched in HF/K₂Cr₂O₇ is shown to depend on the solution composition and bath temperature. The investigation of the HF/K₂Cr₂O₇ solutions on 6H-SiC surface shows that as K₂Cr₂O₇ concentration increases, the etch rate increases to reach a maximum value at about 0.75 M and then decreases. Similar behavior has been observed when the temperature of the solution is increased. The maximum etch rate is found for 80 °C. Taking into account the result, a polishing etching solution of 6H-SiC has been developed. In addition, the result is very interesting when, to date, no chemical polishing solution has been developed on silicon carbide (SiC). Finally, we have proposed a dissolution mechanism of the silicon carbide in HF/K₂Cr₂O₇ solutions.

Keywords: silicon carbide, dissolution, Chemical etching, mechanism

Procedia PDF Downloads 52

Authors: M. Israil Hossain, T. P. Tiwari, Ashrafuzzaman Gulandaz, Nusrat Jahan

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Two-wheel tractor (power tiller) is a common tillage tool in Bangladesh agriculture for easy access in fragmented land with affordable price of small farmers. Traditional maize sheller needs to be carried from place to place by hooking with two-wheel tractor (2WT) and set up again for shelling operation which takes longer time for preparation of maize shelling. The mobile maize sheller eliminates the transportation problem and can start shelling operation instantly any place as it is attached together with 2WT. It is counterclockwise rotating cylinder, axial flow type sheller, and grain separated with a frictional force between spike tooth and concave. The maize sheller is attached with nuts and bolts in front of the engine base of 2WT. The operating power of the sheller comes from the fly wheel of the engine of the tractor through ‘V” belt pulley arrangement. The average shelling capacity of the mobile sheller is 2.0 t/hr, broken kernel 2.2%, and shelling efficiency 97%. The average maize shelling cost is Tk. 0.22/kg and traditional custom hire rate is Tk.1.0/kg, respectively (1 US$=Tk.78.0). The service provider of the 2WT can transport the mobile maize sheller long distance in operator’s seating position. The manufacturers started the fabrication of mobile maize sheller. This mobile maize sheller is also compatible for the other countries where 2WT is available for farming operation.

Keywords: cost effective, mobile maize sheller, maize shelling capacity, small farmers, two wheel tractor

Procedia PDF Downloads 184

Authors: Mohsen Padervand

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Functionalized magnetic core-zeolitic shell nanostructures were prepared by the hydrothermal and coprecipitation methods. The products were characterized by Vibrating Sample Magnetometer (VSM), X-ray powder diffraction (XRD), Fourier Transform Infrared spectra (FTIR), nitrogen adsorption-desorption isotherms (BET) and Transmission Electron Microscopy (TEM). The growth of mordenite nanoparticles on the surface of silica coated nickel ferrite nanoparticles at the presence of organic templates was well approved. The antibacterial activity of prepared samples was investigated by the inactivation of E.coli as a gram negative bacterium. A new mechanism was proposed to inactivate the bacterium over the prepared samples. Minimum Inhibitory Concentration (MIC) and reuse ability were studied too. TEM images of the destroyed microorganism after the treatment time were applied to illustrate the inactivation mechanism. The interaction of the noble metals with organic components on the surface of nanostructures studied theoretically and the results were used to interpret the experimental results.

Keywords: nickel ferrite nanoparticles, magnetic core-zeolitic shell, antibacterial activity, E. coli

Procedia PDF Downloads 331

Authors: Mohammad Ali Karimi, Hossein Tavallali, Abdolhamid Hatefi-Mehrjardi

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Pollen extract of in vitro plants raised of Citrus aurantium as reducer and stabilizer was assessed for the green synthesis of silver nanoparticles (AgNPs). The synthesis of AgNPs was performed at room temperature assisting in solutions by reduction takes place rapidly for 10 min. Surface plasmon resonance (SPR) peaks in UV–Vis spectra indicated the formation of polydispersive AgNPs. Silver ions concentration, pH, temperature and reaction time were optimized in the synthesis of AgNPs. The nanoparticles obtained were characterized by UV-Vis spectrophotometer, transmission electron microscopy (TEM). X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy techniques. The synthesized AgNPs were mostly spherical in shape with an average size of 15 nm. XRD study shows that the AgNPs are crystalline in nature with face-centered cubic (fcc) geometry. It shows the significant antibacterial efficacy against Gram-positive (Staphylococcus aureus) and Gram-negative bacteria (Escherichia coli) by disk diffusion method using Mueller-Hinton Agar.

Keywords: green synthesis, Citrus aurantium, silver nanoparticles, antibacterial activity

Procedia PDF Downloads 287

Authors: Chompoonut Chaiyaraksa, Chanida Singbubpha, Kliaothong Angkabkingkaew, Thitikorn Boonyasawin

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Heavy metal contamination in an environment is an important problem in Thailand that needs to be addressed urgently, particularly contaminated with water. It can spread to other environments faster. This research aims to study the adsorption of cadmium ion by unmodified biochar and sodium dodecyl sulfate modified magnetic biochar derived from Eichhornia Crassipes. The determination of the adsorbent characteristics was by Scanning Electron Microscope, Fourier Transform Infrared Spectrometer, X-ray Diffractometer, and the pH drift method. This study also included the comparison of adsorption efficiency of both types of biochar, adsorption isotherms, and kinetics. The pH value at the point of zero charges of the unmodified biochar and modified magnetic biochar was 7.40 and 3.00, respectively. The maximum value of adsorption reached when using pH 8. The equilibrium adsorption time was 5 hours and 1 hour for unmodified biochar and modified magnetic biochar, respectively. The cadmium adsorption by both adsorbents followed Freundlich, Temkin, and Dubinin – Radushkevich isotherm model and the pseudo-second-order kinetic. The adsorption process was spontaneous at high temperatures and non-spontaneous at low temperatures. It was an endothermic process, physisorption in nature, and can occur naturally.

Keywords: Eichhornia crassipes, magnetic biochar, sodium dodecyl sulfate, water treatment

Procedia PDF Downloads 172

Authors: Ehsan Yadegari

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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.

Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation

Procedia PDF Downloads 52

Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

Procedia PDF Downloads 181

Authors: Abdelghani Boucheham, Djoudi Bouhafs, Nabil Khelifati, Baya Palahouane

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The main aim of this study is to investigate the low temperature internal gettering of manganese and chromium transition metals content in p-type multicrystalline silicon grown by Heat Exchanger Method (HEM). The minority carrier lifetime variation, the transition metal elements behavior, the sheet resistivity and the interstitial oxygen concentration after different temperatures annealing under N2 ambient were investigated using quasi-steady state photoconductance technique (QSSPC), secondary ion mass spectroscopy (SIMS), four-probe measurement and Fourier transform infrared spectrometer (FTIR), respectively. The obtained results indicate in the temperature range of 300°C to 700°C that the effective lifetime increases and reaches its maximum values of 28 μs at 500 °C and decreasing to 6 μs at 700 °C. This amelioration is due probably to metallic impurities internal gettering in the extended defects and in the oxygen precipitates as observed on SIMS profiles and the FTIR spectra. From 300 °C to 500 °C the sheet resistivity values rest unchanged at 30 Ohm/sq and rises significantly to reach 45 Ohm/sq for T> 500 °C.

Keywords: mc-Si, low temperature annealing, internal gettering, minority carrier lifetime, interstitial oxygen, resistivity

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Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

Procedia PDF Downloads 77

Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Amit Keshav

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Waste bone, produced in large quantity (8-10 kg./day) from a slaughterhouse, could be a cheap (cost $0.20 per kg) substitute for commercial catalysts. In the present work, catalyst for esterification reaction was prepared from waste bone and characterized by various techniques. Bone was deoiled and then sulfonated. Fourier-transform infrared spectroscopy (FTIR) spectra of prepared catalyst predicted –OH vibration at 3416 and 1630 cm⁻¹, S-O stretching at 1124 cm⁻¹ and intense bands of hydroxypatite in a region between 500 and 700 cm⁻¹. X-ray diffraction (XRD) predicts peaks of hydroxyapatite, CaO, and tricalcium phosphate. Scanning electron microscope (SEM) was employed to reveal the presence of non-uniformity deposited fine particles on the catalyst surface that represents active acidic sites. The prepared catalyst was employed to study its performance on esterification reaction between acrylic acid and ethanol in a molar ratio of 1:1 at a set temperature of 60 °C. Results show an equilibrium conversion of 49% which is matched to the commercial catalysts employed in literature. Thus waste bone could be a good catalyst for acrylic acid removal from waste industrial streams via the process of esterification.Keywords— Heterogeneous catalyst, characterization, esterification, equilibrium conversion

Keywords: heterogeneous catalyst, characterization, esterification, equilibrium conversion

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Authors: Khaing Su Su Than, Hibino Yo

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Seismic evaluation is needed upon the buildings in Myanmar. Microtremor measurement was conducted in the main cities, Mandalay and Yangon. In order to evaluate the seismic properties of buildings currently under construction, seismic information was gathered for six buildings in Yangon city and four buildings in Mandalay city. The investigated buildings vary from 12m-80 m in height, and mostly public residence structures. The predominant period obtained from frequency results of the investigated buildings were given by horizontal to vertical spectral ratio (HVSR) for each building. The fundamental period results have been calculated in the form of Fourier amplitude spectra of translation along with the main structure. Based on that, the height (H)-period(T) relationship was observed as T=0.012H-0.017H in the buildings of Yangon and, observed the relationship as T=0.014H-0.019H in the buildings of Mandalay. The results showed that the relationship between height and natural period was slightly under the relationship T=0.02H that is used for Japanese reinforced concrete buildings, which indicated that the results depend on the properties and characteristics of materials used.

Keywords: HVSR, height-period relationship, microtremor, Myanmar earthquake, reinforced concrete structures

Procedia PDF Downloads 157

Authors: M. Ananthakrishnan, Sunil K Patil, Koti Naveen, Inuganti Hemanth Kumar

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State estimation output of EMS forms the base case for all other advanced applications used in real time by a power system operator. Ensuring tuning of state estimator is a repeated process and cannot be left once a good solution is obtained. This paper attempts to demonstrate methods to improve state estimator solution by identifying incorrect modelling and telemetry inputs to the application. In this work, identification of database topology modelling error by plotting static network using node-to-node connection details is demonstrated with examples. Analytical methods to identify wrong transmission parameters, incorrect limits and mistakes in pseudo load and generator modelling are explained with various cases observed. Further, methods used for active and reactive power tuning using bus summation display, reactive power absorption summary, and transformer tap correction are also described. In a large power system, verifying all network static data and modelling parameter on regular basis is difficult .The proposed tuning methods can be easily used by operators to quickly identify errors to obtain the best possible state estimation performance. This, in turn, can lead to improved decision-support capabilities, ultimately enhancing the safety and reliability of the power grid.

Keywords: active power tuning, database modelling, reactive power, state estimator

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Authors: Amira Shakila Razali, Faridah Lisa Supian, Muhammad Mat Salleh, Suriani Abu Bakar

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Water contamination by toxic compound is one of the serious environmental problems today. These toxic compounds mostly originated from industrial effluents, agriculture, natural sources and human waste. These study are focused on modification of multiwalled carbon nanotube (MWCNTs) with nanoparticle of calixarene and explore the possibility of using this nanocomposites for the remediation of cadmium in water. The nanocomposites were prepared by dissolving calixarene in chloroform solution as solvent, followed by additional multiwalled carbon nanotube (MWCNTs) then sonication process for 3 hour and fabricated the nanocomposites on substrate by spin coating method. Finally, the nanocomposites were tested on cadmium ion (10 mg/ml). The morphology of nanocomposites was investigated by FESEM showing the formation of calixarene on the outer walls of carbon nanotube and cadmium ion also clearly seen from the micrograph. This formation was supported by using energy dispersive x-ray (EDX). The presence of cadmium ions in the films, leads to some changes in the surface potential and Fourier Transform Infrared spectroscopy (FTIR).This nanocomposites have potential for development of sensor for pollutant monitoring and nanoelectronics devices applications

Keywords: calixarene, multiwalled carbon nanotubes, cadmium, surface potential

Procedia PDF Downloads 492

Authors: Lal Hussain, Wajid Aziz, Sajjad Ahmed Nadeem, Saeed Arif Shah, Abdul Majid

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Brain electrical activity as reflected in Electroencephalography (EEG) have been analyzed and diagnosed using various techniques. Among them, complexity measure, nonlinearity, disorder, and unpredictability play vital role due to the nonlinear interconnection between functional and anatomical subsystem emerged in brain in healthy state and during various diseases. There are many social and economical issues of alcoholic abuse as memory weakness, decision making, impairments, and concentrations etc. Alcoholism not only defect the brains but also associated with emotional, behavior, and cognitive impairments damaging the white and gray brain matters. A recently developed signal analysis method i.e. Multiscale Permutation Entropy (MPE) is proposed to estimate the complexity of long-range temporal correlation time series EEG of Alcoholic and Control subjects acquired from University of California Machine Learning repository and results are compared with MSE. Using MPE, coarsed grained series is first generated and the PE is computed for each coarsed grained time series against the electrodes O1, O2, C3, C4, F2, F3, F4, F7, F8, Fp1, Fp2, P3, P4, T7, and T8. The results computed against each electrode using MPE gives higher significant values as compared to MSE as well as mean rank differences accordingly. Likewise, ROC and Area under the ROC also gives higher separation against each electrode using MPE in comparison to MSE.

Keywords: electroencephalogram (EEG), multiscale permutation entropy (MPE), multiscale sample entropy (MSE), permutation entropy (PE), mann whitney test (MMT), receiver operator curve (ROC), complexity measure

Procedia PDF Downloads 495

Authors: Obsi Terfasa, Bhanupriya Das, Shiao-Shing Chen

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Converting coal fly ash (CFA) waste into ceramic membranes presents a promising alternative to traditional disposal methods, offering potential economic and environmental advantages that warrant further investigation. This research focuses on the creation of ceramic membranes exclusively from CFA using a uniaxial compaction technique. The membranes' properties were examined through various analytical methods: Scanning Electron Microscopy (SEM) revealed a porous and flawless membrane surface, X-Ray Diffraction (XRD) identified mullite and quartz crystalline structures, and Fourier-Transform Infrared Spectroscopy (FTIR) characterized the membrane's functional groups. Thermogravimetric analysis (TGA) determined the ideal sintering temperature to be 800°C. To evaluate its separation capabilities, the synthesized membrane was tested on wastewater from denim jeans production at 0.2 bar pressure. The results were impressive, with 97.42% removal of Chemical Oxygen Demand (COD), 95% color elimination, and a pure water flux of 4.5 Lm⁻²h⁻¹bar⁻¹. These findings suggest that CFA, a byproduct of thermal power plants, can be effectively repurposed to produce ultrafiltration membranes suitable for various industrial purification and separations.

Keywords: wastewater treatment, separator, coal fly ash, ceramic membrane, ultrafiltration

Procedia PDF Downloads 37

Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

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An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

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Authors: Alexander Vandesompele, Joni Dambre

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The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.

Keywords: dendritic computation, spiking neural networks, point neuron model

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Authors: J. Suwanprateeb, F. Thammarakcharoen

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Calcium phosphate coating (CaP) has been employed for protein delivery, but the typical direct protein adsorption on the coating led to low incorporation content and fast release of the protein from the coating. By using bovine serum albumin (BSA) as a model protein, rapid biomimetic co-precipitation between calcium phosphate and BSA was employed to control the distribution of BSA within calcium phosphate coating during biomimetic formation on titanium surface for only 6 h at 50 oC in an accelerated calcium phosphate solution. As a result, the amount of BSA incorporation and release duration could be increased by using a rapid biomimetic co-precipitation technique. Up to 43 fold increases in the BSA incorporation content and the increase from 6 h to more than 360 h in release duration compared to typical direct adsorption technique were observed depending on the initial BSA concentration used during co-precipitation (1, 10, and 100 microgram/ml). From X-ray diffraction and Fourier transform infrared spectroscopy studies, the coating composition was not altered with the incorporation of BSA by this rapid biomimetic co-precipitation and mainly comprised octacalcium phosphate and hydroxyapatite. However, the microstructure of calcium phosphate crystals changed from straight, plate-like units to curved, plate-like units with increasing BSA content.

Keywords: biomimetic, Calcium Phosphate Coating, protein, titanium

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Authors: Rashad Al-Gaashani, Muataz A. Atieh

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In this study, the effect of preparation temperature, namely room temperature (RT), 40, 60, and 85°C, on producing of high-quality graphene oxide (GO) has been investigated. GO samples have been prepared by chemical oxidation of graphite via a safe improved chemical technique using a blend of two deferent acids: sulphuric acid (H₂SO₄) and phosphoric acid (H₃PO₄) with volume ratio 4:1, respectively. potassium permanganate (KMnO₄) and hydrogen peroxide (H₂O₂) were applied as oxidizing agents. In this work, sodium nitrate (NaNO₃) was excluded, so the emission of hazardous explosive gases such as NO₂ and N₂O₂ was shunned. Ice and oil baths were used to carefully control the temperature. Several characterization instruments including X-Ray diffraction, transmission electron microscopy, scanning electron microscopy, electron dispersive spectroscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and UV-vis spectroscopy were used to study and compare the synthesized samples. The results indicated that GO can be prepared at RT with graphite oxide, and the purity of GO increased with rising of the solvent temperature. Optical properties of GO samples were studied using UV-vis absorption spectra.

Keywords: chemical method, graphite, graphene oxide, optical properties

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Authors: Farzaneh Ziaee, Mohammad Ziaee

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N-acetyl-L-cysteine (NAC) is a well-known mucolytic agent, and recently its efficacy has been examined for the prevention and remediation of several diseases such as lung infections caused by Coronavirus. Also, it is administrated as the main antidote in paracetamol overdose and is effective for the treatment of idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD). This medicine is used as an antioxidant to prevent diabetic kidney disease (nephropathy). In this study, a method for the acylation of amino acids is employed to manufacture this drug in a height yield. Regarding this patented path, NAC can be made in a single batch step at ambient pressure and temperature. Moreover, this study offers a technique to make peptide bonds which is of interest for pharmaceutical and medicinal industries. The separation process was undertaken using appropriate solvents to achieve an excellent purification level. The synthesized drug was characterized via proton nuclear magnetic resonance (1H NMR), high-performance liquid chromatography (HPLC), Fourier transform infrared spectroscopy (FT-IR), elemental analysis, and melting point.

Keywords: N-acetylcysteine, synthesis, mucolytic medication, lung anti-inflammatory, COVID-19, antioxidant, pharmaceutical supplement, characterization

Procedia PDF Downloads 193

Authors: Dumont Maxime, Correia Carlos, Sauvage Jean-François, Schwartz Noah, Gray Morgan

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Context: The concept of the segmented telescope is unavoidable to build extremely large telescopes (ELT) in the quest for spatial resolution, but it also allows one to fit a large telescope within a reduced volume of space (JWST) or into an even smaller volume (Standard Cubesat). Cubesats have tight constraints on the computational burden available and the small payload volume allowed. At the same time, they undergo thermal gradients leading to large and evolving optical aberrations. The pupil segmentation comes nevertheless with an obvious difficulty: to co-phase the different segments. The CubeSat constraints prevent the use of a dedicated wavefront sensor (WFS), making the focal-plane images acquired by the science detector the most practical alternative. Yet, one of the challenges for the wavefront sensing is the non-linearity between the image intensity and the phase aberrations. Plus, for Earth observation, the object is unknown and unrepeatable. Recently, several studies have suggested Neural Networks (NN) for wavefront sensing; especially convolutional NN, which are well known for being non-linear and image-friendly problem solvers. Aims: We study in this paper the prospect of using NN to measure the phasing aberrations of a segmented pupil from the focal-plane image directly without a dedicated wavefront sensing. Methods: In our application, we take the case of a deployable telescope fitting in a CubeSat for Earth observations which triples the aperture size (compared to the 10cm CubeSat standard) and therefore triples the angular resolution capacity. In order to reach the diffraction-limited regime in the visible wavelength, typically, a wavefront error below lambda/50 is required. The telescope focal-plane detector, used for imaging, will be used as a wavefront-sensor. In this work, we study a point source, i.e. the Point Spread Function [PSF] of the optical system as an input of a VGG-net neural network, an architecture designed for image regression/classification. Results: This approach shows some promising results (about 2nm RMS, which is sub lambda/50 of residual WFE with 40-100nm RMS of input WFE) using a relatively fast computational time less than 30 ms which translates a small computation burder. These results allow one further study for higher aberrations and noise.

Keywords: wavefront sensing, deep learning, deployable telescope, space telescope

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Authors: Pajaree Donkhampa, Fuangfa Unob

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Ceftazidime is an antibiotic drug commonly used to treat several human and veterinary infections. However, the presence of ceftazidime residues in the environment may induce microbial resistance and cause side effects to humans. Therefore, monitoring the level of ceftazidime in environmental resources is important. In this work, a melamine foam platform was proposed for simultaneous extraction and colorimetric detection of ceftazidime based on the azo dye formation on the surface. The melamine foam was chemically modified with polyethyleneimine (PEI) and characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Ceftazidime is a sample that was extracted on the PEI-modified melamine foam and further reacted with nitrite in an acidic medium to form an intermediate diazonium ion. The diazotized molecule underwent an azo coupling reaction with chromotropic acid to generate a red-colored compound. The material color changed from pale yellow to pink depending on the ceftazidime concentration. The photo of the obtained material was taken by a smartphone camera and the color intensity was determined by Image J software. The material fabrication and ceftazidime extraction and detection procedures were optimized. The detection of a sub-ppm level of ceftazidime was achieved without using a complex analytical instrument.

Keywords: colorimetric detection, ceftazidime, melamine foam, extraction, azo dye

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Authors: Armanul Hoque Shaon, Md Baizid Mahmud, Askander Nobi, Md. Raju Ahmed, Md. Jiabul Hoque

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In the Internet of Things (IoT), devices are linked to the internet through a wireless network, allowing them to collect and transmit data without the need for a human operator. Agriculture relies heavily on wireless sensors, which are a vital component of the Internet of Things (IoT). This kind of wireless sensor network monitors physical or environmental variables like temperatures, sound, vibration, pressure, or motion without relying on a central location or sink and collaboratively passes its data across the network to be analyzed. As the primary source of plant nutrients, the soil is critical to the agricultural industry's continued growth. We're excited about the prospect of developing an Internet of Things (IoT) solution. To arrange the network, the sink node collects groundwater levels and sends them to the Gateway, which centralizes the data and forwards it to the sensor nodes. The sink node gathers soil moisture data, transmits the mean to the Gateways, and then forwards it to the website for dissemination. The web server is in charge of storing and presenting the moisture in the soil data to the web application's users. Soil characteristics may be collected using a networked method that we developed to improve rice production. Paddy land is running out as the population of our nation grows. The success of this project will be dependent on the appropriate use of the existing land base.

Keywords: IoT based agriculture monitoring, intelligent irrigation, communicating network, rice production

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Authors: Humayun R. H. Kabir

Abstract:

An analytical solution before used for static and free-vibration response has been extended for thermal buckling response on cylindrical panel with anti-symmetric laminations. The partial differential equations that govern kinematic behavior of shells produce five coupled differential equations. The basic displacement and rotational unknowns are similar to first order shear deformation theory---three displacement in spatial space, and two rotations about in-plane axes. No drilling degree of freedom is considered. Boundary conditions are considered as complete hinge in all edges so that the panel respond on thermal inductions. Two sets of double Fourier series are considered in the analytical solution process. The sets are selected that satisfy mixed type of natural boundary conditions. Numerical results are presented for the first 10 eigenvalues, and first 10 mode shapes for Ux, Uy, and Uz components. The numerical results are compared with a finite element based solution.

Keywords: higher order shear deformation, composite, thermal buckling, angle-ply laminations

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Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer

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This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.

Keywords: non-stationary, BINARMA(1, 1) model, Poisson innovations, conditional maximum likelihood, CML

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Authors: Hesham Moustafa, Ahmed M. Youssef

Abstract:

The demand for green packagingin the food field has been gained increasing attention in recent decades because of its degradability and safely. Thus, this study revealed that the by-product bagasse papers (BPs) derived from sugarcane waste can be decorated with a thin layer of polystyrene (PS) nanocomposites using the spreading approach.Three variable concentrations of TiO2 nanoparticles (i.e. 0.5, 1.0, 1.5 wt.%) were used to fabricate PS nanocomposites. The morphology of coated BP-PS biofilms was examined by X-ray diffraction, Fourier transferred Infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM). Moreover, other measurements such as mechanical, thermal stability, flammability, wettability by the contact angle, water vapor, and gas barrier properties were carried out on the fabricated BP-PS biofilms. Most outcomes showed that the major properties were enhanced when the PS nanocomposites were implemented. The use of 1.5 wt.% TiO2 in PS nanocomposite for coated BP-PS biofilm increased the tensile stress by ~ 217 % compared to uncoated BP film. Furthermore, the rate of burning for BP-PS-1.5% film was reduced to ~ 33 mm/min because of the crystallinity of PS and the barrier effect provided by TiO₂ NPs. These coated sheets provide a promising candidate for use in advanced packaging applications.

Keywords: bagasse paper, polystyrene nanocomposites, TiO2 nanoparticles, active packaging, mechanical properties, flammability

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Authors: Ahmed Farghali, Heba Amar, Mohamed Khedr

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NiAl-Layered Double Hydroxide (NiAl-LDH), one of anionic functional layered materials, has been prepared by a simple co-precipitation process. X-ray diffraction patterns confirm the formation of the desired compounds of NiAl hydroxide single phase and the crystallite size was found to be about 4.6 nm. The morphology of the prepared samples was investigated using scanning electron microscopy and the layered structure was appeared under the transmission electron microscope. The thermal stability and the function groups of NiAl-LDH were investigated using thermal gravimetric analysis (TGA) and Fourier transform infrared (FTIR) respectively. NiAl-LDH was investigated as a photo-catalyst for the degradation of some toxic dyes such as toluidine blue and bromopyrogallol red. It shows good catalytic efficiency in visible light and even in dark. For the first time NiAl-LDH was used for hydrogen storage application. NiAl-LDH samples were exposed to 20 bar applied hydrogen pressure at room temperature, 100 and -193 oC. NiAl-LDH samples appear to have feasible hydrogen storage capacity. It was capable to adsorb 0.1wt% at room temperature, 0.15 wt% at 100oC and storage capacity reached 0.3 wt% at -193 oC.

Keywords: NiAl-LDH, preparation, characterization, photo-catalysis, hydrogen storage

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Authors: Adel A. Ghobbar, Ahmad Bakkar

Abstract:

Although special care is given to maintenance, aircraft systems fail, and these failures cause delays and cancellations. The occurrence of Delays and Cancellations affects operators and manufacturers negatively. To reduce technical delays and cancellations, one should be able to determine the important systems causing them. The goal of this research is to find a method to define the most expensive delays and cancellations systems for Airline operators. A predictive model was introduced to forecast the failure and their impact after carrying out research that identifies relevant information to tackle the problems faced while answering the questions of this paper. Data were obtained from the manufacturers’ services reliability team database. Subsequently, delays and cancellations evaluation methods were identified. No cost estimation methods were used due to their complexity. The model was developed, and it takes into account the frequency of delays and cancellations and uses weighting factors to give an indication of the severity of their duration. The weighting factors are based on customer experience. The data Analysis approach has shown that delays and cancellations events are not seasonal and do not follow any specific trends. The use of weighting factor does have an influence on the shortlist over short periods (Monthly) but not the analyzed period of three years. Landing gear and the navigation system are among the top 3 factors causing delays and cancellations for all three aircraft types. The results did confirm that the cooperation between certain operators and manufacture reduce the impact of delays and cancellations.

Keywords: reliability, availability, delays & cancellations, aircraft maintenance

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Authors: Raghvendra Singh Yadav, Ivo Kuřitka, Jarmila Vilcakova, Pavel Urbanek, Michal Machovsky, Milan Masař, Martin Holek

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ZnFe2O4 spinel ferrite nanoparticles were synthesized by sol-gel auto-combustion method. The synthesized spinel ferrite nanoparticles were annealed at different higher temperature to achieve different size nanoparticles. The as synthesized and annealed samples were characterized by powder X-ray Diffraction Spectroscopy, Raman Spectroscopy, Fourier Transform Infrared Spectroscopy, UV-Vis absorption Spectroscopy and Scanning Electron Microscopy. The magnetic properties were studied by vibrating sample magnetometer. The variation in magnetic parameters was noticed with variation in grain size. The dielectric constant and dielectric loss with variation of frequency shows normal behaviour of spinel ferrite. The variation in conductivity with variation in grain size is noticed. Modulus and Impedance Spectroscopy shows the role of grain and grain boundary on the electrical resistance and capacitance of different grain sized spinel ferrite nanoparticles. Acknowledgment: This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic – Program NPU I (LO1504).

Keywords: spinel ferrite, nanoparticles, magnetic properties, dielectric properties

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