Search results for: fast Fourier algorithms

Authors: Ingrid R. Avanzi, Marcela dos P. G. Baltazar, Louise H. Gracioso, Luciana J. Gimenes, Bruno Karolski, Elen A. Perpetuo, Claudio Auguto Oller do Nascimento

Abstract:

In this study was developed a natural process using a biological system for the uptake of Copper and possible removal of copper from wastewater by dead biomass of the strain Burkholderia cenocepacia. Dead and live biomass of Burkholderia cenocepacia was used to analyze the equilibrium and kinetics of copper biosorption by this strain in function of the pH. Living biomass exhibited the highest biosorption capacity of copper, 50 mg g−1, which was achieved within 5 hours of contact, at pH 7.0, temperature of 30°C, and agitation speed of 150 rpm. The dead biomass of Burkholderia cenocepacia may be considered an efficiently bioprocess, being fast and low-cost to production of copper and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process. In this study was developed a natural process using a biological system for the uptake of Copper and possible removal of copper from wastewater by dead biomass of the strain Burkholderia cenocepacia. Dead and live biomass of Burkholderia cenocepacia was used to analyze the equilibrium and kinetics of copper biosorption by this strain in function of the pH. Living biomass exhibited the highest biosorption capacity of copper, 50 mg g−1, which was achieved within 5 hours of contact, at pH 7.0, temperature of 30°C, and agitation speed of 150 rpm. The dead biomass of Burkholderia cenocepacia may be considered an efficiently bioprocess, being fast and low-cost to production of copper and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process.

Keywords: biosorption, dead biomass, biotechnology, copper recovery

Procedia PDF Downloads 337

Authors: H. Aghayan, F. A. Hashemi, R. Yavari, S. Zolghadri

Abstract:

In this work, a new composite adsorbent based on a mesoporous silica SBA-15 with platelet morphology and tin salt of tungstomolybdophosphoric (TWMP) acid was synthesized and applied for uranium adsorption from aqueous solution. The sample was characterized by X-ray diffraction, Fourier transfer infra-red, and N₂ adsorption-desorption analysis, and then, effect of various parameters such as concentration of metal ions and contact time on adsorption behavior was examined. The experimental result showed that the adsorption process was explained by the Langmuir isotherm model very well, and predominant reaction mechanism is physisorption. Kinetic data of adsorption suggest that the adsorption process can be described by the pseudo second-order reaction rate model.

Keywords: platelet SBA-15, tungstomolybdophosphoric acid, adsorption, uranium ion

Procedia PDF Downloads 188

Authors: Elcin Timur Cakmak, Ayse Oguzlar

Abstract:

This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

Procedia PDF Downloads 75

Authors: Dionisio Andujar, Juan lópez-Correa, Hugo Moreno, Angela Ri

Abstract:

The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step.

Keywords: deep learning, object detection, cnn, tomato, weeds

Procedia PDF Downloads 106

Authors: Vinayak Bassi, Rajpreet Singh

Abstract:

Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

Procedia PDF Downloads 165

Authors: Irshad U. Khan, Tanmay Paul, Murali Mohan Seepana

Abstract:

This paper presents a study on synthesizing and characterizing a Copper oxide doped Carbon (CuO-C) electrocatalyst for the negative half-cell reactions of Vanadium Redox Flow Battery (VRFB). The CuO was synthesized using a microreactor. The electrocatalyst was characterized using X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Field Emission Scanning Electron Microscopy (SEM). The electrochemical performance was assessed by linear sweep voltammetry (LSV). The findings suggest that the synthesized CuO exhibited favorable crystallinity, morphology, and surface area, which reflects improved cell performance.

Keywords: ECSA, electrocatalyst, energy storage, Tafel

Procedia PDF Downloads 91

Authors: Tebogo Sethibe, Jabulile Mabuza

Abstract:

The COVID-19 pandemic has been commonly referred to as a ‘black swan event’; it has changed the world, from how people live, learn, work and socialise. It is believed that the pandemic has fast-tracked the adoption of technology in many organisations to ensure business continuity and business sustainability; broadly said, the pandemic has fast-tracked digital transformation (DT) in different organisations. This paper aims to study the impact of the COVID-19 pandemic on DT in organisations in South Africa by focusing on the changes in IT capabilities in the DT framework. The research design is qualitative. The data collection was through semi-structured interviews with information communication technology (ICT) leaders representing different organisations in South Africa. The data were analysed using the thematic analysis process. The results from the study show that, in terms of ICT in the organisation, the pandemic had a direct and positive impact on ICT strategy and ICT operations. In terms of IT capability transformation, the pandemic resulted in the optimisation and expansion of existing IT capabilities in the organisation and the building of new IT capabilities to meet emerging business needs. In terms of the focus of activities during the pandemic, there seems to be a split in organisations between the primary focus being on ‘digital IT’ or ‘traditional IT’. Overall, the findings of the study show that the pandemic had a positive and significant impact on DT in organisations. However, a definitive conclusion on this would require expanding the scope of the research to all the components of a comprehensive DT framework. This study is significant because it is one of the first studies to investigate the impact of the COVID-19 pandemic on organisations, on ICT in the organisation, on IT capability transformation and, to a greater extent, DT. The findings from the study show that in response to the pandemic, there is a need for: (i) agility in organisations; (ii) organisations to execute on their existing strategy; (iii) the future-proofing of IT capabilities; (iv) the adoption of a hybrid working model; and for (v) organisations to take risks and embrace new ideas.

Keywords: digital transformation, COVID-19, bimodal-IT, digital transformation framework

Procedia PDF Downloads 179

Authors: Debdut Roy, Vidyasagar Guggilla

Abstract:

In this research, we highlight our exploratory work on modified zeolite based catalysts for catalytic cracking of hydrocarbons for production of light olefin i.e. ethylene and propylene. The work is focused on understanding the catalyst structure and activity correlation. Catalysts are characterized by surface area and pore size distribution analysis, inductively coupled plasma optical emission spectrometry (ICP-OES), Temperature Programmed Desorption (TPD) of ammonia, pyridine Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermo-gravimetric Analysis (TGA) and correlated with the catalytic activity. It is observed that the yield of lighter olefins increases with increase of Bronsted acid strength.

Keywords: catalytic cracking, zeolite, propylene, structure-activity correlation

Procedia PDF Downloads 220

Authors: Ewelina Musielak, Agnieszka Feliczak-Guzik, Izabela Nowak

Abstract:

Based on the latest data, it is expected that the substances of therapeutic interest used will be as natural as possible. Therefore, active substances with the highest possible efficacy and low toxicity are sought. Among natural substances with therapeutic effects, those of plant origin stand out. Curcumin isolated from the Curcuma longa plant has proven to be particularly important from a medical point of view. Due to its ability to regulate many important transcription factors, cytokines, and protein kinases, curcumin has found use as an anti-inflammatory, antioxidant, antiproliferative, antiangiogenic, and anticancer agent. The unfavorable properties of curcumin, such as low solubility, poor bioavailability, and rapid degradation under neutral or alkaline pH conditions, limit its clinical application. These problems can be solved by combining curcumin with suitable carriers such as hierarchical zeolites. This is a new class of materials that exhibit several advantages. Hierarchical zeolites used as drug carriers enable delayed release of the active ingredient and promote drug transport to the desired tissues and organs. In addition, hierarchical zeolites play an important role in regulating micronutrient levels in the body and have been used successfully in cancer diagnosis and therapy. To apply curcumin to hierarchical zeolites synthesized from commercial FAU zeolite, solutions containing curcumin, carrier and acetone were prepared. The prepared mixtures were then stirred on a magnetic stirrer for 24 h at room temperature. The curcumin-filled hierarchical zeolites were drained into a glass funnel, where they were washed three times with acetone and distilled water, after which the obtained material was air-dried until completely dry. In addition, the effect of piperine addition to zeolite carrier containing a sufficient amount of curcumin was studied. The resulting products were weighed and the percentage of pure curcumin in the hierarchical zeolite was calculated. All the synthesized materials were characterized by several techniques: elemental analysis, transmission electron microscopy (TEM), Fourier transform infrared spectroscopy, Fourier transform infrared (FT-IR), N2 adsorption, and X-ray diffraction (XRD) and thermogravimetric analysis (TGA). The aim of the presented study was to improve the biological activity of curcumin by applying it to hierarchical zeolites based on FAU zeolite. The results showed that the loading efficiency of curcumin into hierarchical zeolites based on commercial FAU-type zeolite is enhanced by modifying the zeolite carrier itself. The hierarchical zeolites proved to be very good and efficient carriers of plant-derived active ingredients such as curcumin.

Keywords: carriers of active substances, curcumin, hierarchical zeolites, incorporation

Procedia PDF Downloads 98

Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

Abstract:

Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

Procedia PDF Downloads 53

Authors: Homayon Ahmad Panahi, Seyed Mehdi Seyed Nejad, Elham Moniri

Abstract:

A novel bio-adsorbent is fabricated by attaching a cibacron blue to yeast cells. The modified bio-sorbent has been characterized by some techniques like Fourier transform infrared spectroscopy (FT-IR) and elemental analysis (CHN) and applied for the preconcentration and determination of samarium from aqueous water samples. The best pH value for adsorption of the brilliant crecyle blue by yeast cells- cibacron blue was 7. The sorption capacity of modified biosorbent was 18.5 mg. g⁻¹. A recovery of 95.3% was obtained for Sm(III) when eluted with 0.5 M nitric acid. The method was applied for Sm(III) preconcentration and determination in river water sample.

Keywords: samarium, solid phase extraction, yeast cells, water sample, removal

Procedia PDF Downloads 259

Authors: Hilal Kivrak, Aykut ÇağLar, Nahit Aktaş, Ali Osman Solak

Abstract:

At present, Cd/TiO₂ and CdTe/TiO₂ catalysts are prepared via sodium borohydride (NaBH4) reduction method. These catalysts are characterized by fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). These Cd/TiO₂ and CdTe/TiO₂ are employed as catalysts for the photocatalytic oxidation of glucose. Cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS) measurements are used to investigate their glucose electrooxidation activities of catalysts at long and under UV illumination (ʎ=354 nm). CdTe/TiO₂ catalyst is showed the best photocatalytic glucose electrooxidation activity compared to Cd/TiO₂ catalyst.

Keywords: cadmium, NaBH4 reduction method, photocatalytic glucose electrooxidation, Tellerium, TiO2

Procedia PDF Downloads 277

Authors: Salah Abdul Hameed Saleh, Ghada Hasan

Abstract:

The aim of this work is to produce an empirical model for the determination of particulate matter (PM10) concentration in the atmosphere using visible bands of Landsat 8 OLI satellite image over Kirkuk city- IRAQ. The suggested algorithm is established on the aerosol optical reflectance model. The reflectance model is a function of the optical properties of the atmosphere, which can be related to its concentrations. The concentration of PM10 measurements was collected using Particle Mass Profiler and Counter in a Single Handheld Unit (Aerocet 531) meter simultaneously by the Landsat 8 OLI satellite image date. The PM10 measurement locations were defined by a handheld global positioning system (GPS). The obtained reflectance values for visible bands (Coastal aerosol, Blue, Green and blue bands) of landsat 8 OLI image were correlated with in-suite measured PM10. The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and root-mean-square error (RMSE) compared with the PM10 ground measurement data. A choice of our proposed multispectral model was founded on the highest value correlation coefficient (R) and lowest value of the root mean square error (RMSE) with PM10 ground data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM10 concentration with an acceptable level of accuracy.

Keywords: air pollution, PM10 concentration, Lansat8 OLI image, reflectance, multispectral algorithms, Kirkuk area

Procedia PDF Downloads 442

Authors: Merve Küçük, M. Lütfi Öveçoğlu

Abstract:

Zinc oxide particles were synthesized using the sol-gel method and dip coated on polyester fabric. X-ray diffraction (XRD) analysis revealed a single crystal phase of ZnO particles. Chemical characteristics of the polyester fabric surface were investigated using attenuated total reflection-Fourier transform infrared (ATR-FTIR) measurements. Morphology of ZnO coated fabric was analyzed using field emission scanning electron microscopy (FESEM). After particle analysis, the aqueous ZnO solution resulted in a narrow size distribution at submicron levels. The deposit of ZnO on polyester fabrics yielded a homogeneous spread of spherical particles. Energy dispersive X-ray spectroscopy (EDX) results also affirmed the presence of ZnO particles on the polyester fabrics.

Keywords: dip coating, polyester fabrics, sol gel, zinc oxide

Procedia PDF Downloads 435

Authors: S. Shankar Bharathi

Abstract:

Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

Procedia PDF Downloads 429

Authors: Shivani A. Singh, Pravin S. More

Abstract:

In the present inspection, we indicate the falsification of Fe-doped graphene nanoparticles by modified Hummers method. Structural and physiochemical properties of the resulting pallets were explored with the help of ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD) and scanning electron microscopy (SEM), Photoluminescence spectroscopy (PL) for graphene sample exhibits absorption peaks ~248nm. Pure graphene shows PL peak at 348 nm. After doping of Fe with graphene the PL peak shifted from 348 nm to 332 nm. The oxidation degree, i.e. the relative amount of oxygen functional groups was estimated from the relative intensities of the oxygen related bands (ORB) in the FTIR measurements. These analyses show that this modified material can be useful for gas sensing applications and to be used in diverse areas.

Keywords: chemical doping, graphene, gas sensing, sensing

Procedia PDF Downloads 218

Authors: Ekaterina A. Savchenko, Elena N. Velichko, Evgenii T. Aksenov

Abstract:

The development of pathological processes, such as cardiovascular and oncological diseases, are accompanied by changes in molecular parameters in cells, tissues, and serum. The study of the behavior of protein molecules in solutions is of primarily importance for diagnosis of such diseases. Various physical and chemical methods are used to study molecular systems. With the advent of the laser and advances in electronics, optical methods, such as scanning electron microscopy, sedimentation analysis, nephelometry, static and dynamic light scattering, have become the most universal, informative and accurate tools for estimating the parameters of nanoscale objects. The electrophoretic light scattering is the most effective technique. It has a high potential in the study of biological solutions and their properties. This technique allows one to investigate the processes of aggregation and dissociation of different macromolecules and obtain information on their shapes, sizes and molecular weights. Electrophoretic light scattering is an analytical method for registration of the motion of microscopic particles under the influence of an electric field by means of quasi-elastic light scattering in a homogeneous solution with a subsequent registration of the spectral or correlation characteristics of the light scattered from a moving object. We modified the technique by using the regime of total internal reflection with the aim of increasing its sensitivity and reducing the volume of the sample to be investigated, which opens the prospects of automating simultaneous multiparameter measurements. In addition, the method of total internal reflection allows one to study biological fluids on the level of single molecules, which also makes it possible to increase the sensitivity and the informativeness of the results because the data obtained from an individual molecule is not averaged over an ensemble, which is important in the study of bimolecular fluids. To our best knowledge the study of electrophoretic light scattering in the regime of total internal reflection is proposed for the first time, latex microspheres 1 μm in size were used as test objects. In this study, the total internal reflection regime was realized on a quartz prism where the free electrophoresis regime was set. A semiconductor laser with a wavelength of 655 nm was used as a radiation source, and the light scattering signal was registered by a pin-diode. Then the signal from a photodetector was transmitted to a digital oscilloscope and to a computer. The autocorrelation functions and the fast Fourier transform in the regime of Brownian motion and under the action of the field were calculated to obtain the parameters of the object investigated. The main result of the study was the dependence of the autocorrelation function on the concentration of microspheres and the applied field magnitude. The effect of heating became more pronounced with increasing sample concentrations and electric field. The results obtained in our study demonstrated the applicability of the method for the examination of liquid solutions, including biological fluids.

Keywords: light scattering, electrophoretic light scattering, electrophoresis, total internal reflection

Procedia PDF Downloads 216

Authors: Beier Zhu, Rui Zhang, Qi Song

Abstract:

Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

Procedia PDF Downloads 198

Authors: Anika Zafiah M. Rus, Nur Munirah Abdullah, M. F. L. Abdullah

Abstract:

The fabrication and characterization of composite films of graphite- bioepoxy is described. Free-standing thin films of ~0.1 mm thick are prepared using a simple solution mixing with mass proportion of 7/3 (bioepoxy/graphite) and drop casting at room temperature. Fourier transform infra-red spectroscopy (FTIR) and Ultraviolet-visible (UV-vis) spectrophotometer are performed to evaluate the changes in chemical structure and adsorption spectra arising with the increasing of graphite weight loading (wt.%) into the biopolymer matrix. The morphologic study shows a homogeneously dispersed and strong particle bonding between the graphite and the bioepoxy, with conductivity of the film 103 S/m, confirming the efficiency of the processes.

Keywords: absorbance peak, biopolymer, graphite- bioepoxy composites, particle bonding

Procedia PDF Downloads 517

Authors: Sunil Dehipawala, Gayathrie Amarasuriya, N. Gadura, G. Tremberger Jr, D.Lieberman, Harry Gafney, Todd Holden, T. Cheung

Abstract:

The iron environment in Fe-doped Vycor Anode was investigated with EXAFS using Brookhaven Synchrotron Light Source. The iron-reducing Shewanella oneidensis culture was grown in a microbial fuel cell under anaerobic respiration. The Fe bond length was found to decrease and correlate with the amount of biofilm growth on the Fe-doped Vycor Anode. The data suggests that Fe-doped Vycor Anode would be a good substrate to study the Shewanella oneidensis nanowire structure using EXAFS.

Keywords: EXAFS, fourier transform, Shewanella oneidensis, microbial fuel cell

Procedia PDF Downloads 402

Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

Procedia PDF Downloads 133

Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

Procedia PDF Downloads 131

Authors: O. Sunantha, S. Zhenfeng, S. Phattraporn, A. Zeeshan

Abstract:

The decline of soil organic carbon (SOC) in global agriculture is a critical issue requiring rapid and accurate estimation for informed policymaking. While it is recognized that SOC predictors vary significantly when derived from remote sensing data and environmental variables, identifying the specific parameters most suitable for accurately estimating SOC in diverse agricultural areas remains a challenge. This study utilizes remote sensing data to precisely estimate SOC and identify influential factors in diverse agricultural areas, such as paddy, corn, sugarcane, cassava, and perennial crops. Extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR) models are employed to analyze these factors' impact on SOC estimation. The results show key factors influencing SOC estimation include slope, vegetation indices (EVI), spectral reflectance indices (red index, red edge2), temperature, land use, and surface soil moisture, as indicated by their averaged importance scores across XGBoost, RF, and SVR models. Therefore, using different machine learning algorithms for SOC estimation reveals varying influential factors from remote sensing data and environmental variables. This approach emphasizes feature selection, as different machine learning algorithms identify various key factors from remote sensing data and environmental variables for accurate SOC estimation.

Keywords: factors influencing SOC estimation, remote sensing data, environmental variables, machine learning

Procedia PDF Downloads 37

Authors: Marcin Stasiulewicz, Sebastian Filipkowski, Aneta Panuszko

Abstract:

Background: The hallmark of neurodegenerative diseases, including Alzheimer's and Parkinson's diseases, is an aggregation of the abnormal forms of peptides and proteins. Water is essential to functioning biomolecules, and it is one of the key factors influencing protein folding and misfolding. However, the hydration studies of proteins are complicated due to the complexity of protein systems. The use of model compounds can facilitate the interpretation of results involving larger systems. Objectives: The goal of the research was to characterize the properties of the hydration water surrounding the two three-residue K peptide fragments INS (Isoleucine - Asparagine - Serine) and NSR (Asparagine - Serine - Arginine). Methods: Fourier-transform infrared spectra of aqueous solutions of the tripeptides were recorded on Nicolet 8700 spectrometer (Thermo Electron Co.) Measurements were carried out at 25°C for varying molality of solute. To remove oscillation couplings from water spectra and, consequently, obtain narrow O-D semi-heavy water bands (HDO), the isotopic dilution method of HDO in H₂O was used. The difference spectra method allowed us to isolate the tripeptide-affected HDO spectrum. Results: The structural and energetic properties of water affected by the tripeptides were compared to the properties of pure water. The shift of the values of the gravity center of bands (related to the mean energy of water hydrogen bonds) towards lower values with respect to the ones corresponding to pure water suggests that the energy of hydrogen bonds between water molecules surrounding tripeptides is higher than in pure water. A comparison of the values of the mean oxygen-oxygen distances in water affected by tripeptides and pure water indicates that water-water hydrogen bonds are shorter in the presence of these tripeptides. The analysis of differences in oxygen-oxygen distance distributions between the tripeptide-affected water and pure water indicates that around the tripeptides, the contribution of water molecules with the mean energy of hydrogen bonds decreases, and simultaneously the contribution of strong hydrogen bonds increases. Conclusions: It was found that hydrogen bonds between water molecules in the hydration sphere of tripeptides are shorter and stronger than in pure water. It means that in the presence of the tested tripeptides, the structure of water is strengthened compared to pure water. Moreover, it has been shown that in the vicinity of the Asparagine - Serine - Arginine, water forms stronger and shorter hydrogen bonds. Acknowledgments: This work was funded by the National Science Centre, Poland (grant 2017/26/D/NZ1/00497).

Keywords: amyloids, K-peptide, hydration, FTIR spectroscopy

Procedia PDF Downloads 178

Authors: ATM Emdadul Haque

Abstract:

Phosphorus is an essential nutrient which is regularly consumed with food and exists in the body as phosphate. Phosphate is an important component of cellular structures and needed for bone mineralization. Excessive accumulation of phosphate is an important driving factor of mortality in chronic renal failure patients; of relevance, these patients are usually provided health care by doctors, nurses, and pharmacists. Hence, this study was planned to determine the level of awareness of the future healthcare providers about the phosphate-containing foods and beverages and to access their knowledge on the harmful effects of excess phosphate consumption. A questionnaire was developed and distributed among the year-1 medical, nursing and pharmacy students. 432 medical, nursing and pharmacy students responded with age ranging from 18-24 years. About 70% of the respondents were female with a majority (90.7%) from Malay ethnicity. Among the respondents, 29.9% were medical, 35.4% were the pharmacy and 34.7% were nursing students. 79.2% students knew that phosphate was an important component of the body, but only 61.8% knew that consuming too much phosphate could be harmful to the body. Despite 97% of the students knew that carbonated soda contained high sugar, surprisingly 77% of them did not know the presence of high phosphate in the same soda drinks; in the similar line of observation, 67% did not know the presence of it in the fast food. However, it was encouraging that 94% of the students wanted to know more about the effects of phosphate consumption, 74.3% were willing to give up drinking soda and eating fast food, and 52% considered taking green coconut water instead of soda drinks. It is, therefore, central to take an educational initiative to increase the awareness of the future healthcare providers about phosphate-containing food and its harmful effects in excessive consumptions.

Keywords: high phosphate containing foods and beverages, excessive consumption, future health care providers, phosphorus

Procedia PDF Downloads 370

Authors: Homayon Ahmad Panahi, Nika Shakerin, Farahnaz Zolriasatain, Elham Moniri

Abstract:

In this research, poly-ethylene terephthalate granule was prepared from Tak Corporation. The granule was characterized by fourier transform infra-red spectroscopy. Then the effects of various parameters on brilliant green sorption such as pH, contact time were studied. The optimum pH value for sorption of brilliant green was 6. The sorption capacity of the granule for brilliant green was 4.6 mg g−1. The profile of brilliant green uptake on this sorbent reflects a good accessibility of the chelating sites in the poly-ethylene terephthalate granule. The developed method was utilized for the determination of brilliant green in environmental water samples by UV/Vis spectrophotometry with satisfactory results.

Keywords: poly-ethylene terephthalate granule, brilliant green, environmental sample, removal

Procedia PDF Downloads 433

Authors: Homayon Ahmad Panahi, Niusha Mohseni Darabi, Elham Moniri

Abstract:

A new biosorbent is prepared by coupling a cibacron blue to yeast cells. The modified yeast cells with cibacron blue has been characterized by Fourier transform infrared spectroscopy (FT-IR) and elemental analysis and applied for the preconcentration and solid phase extraction of trace cadmium ion from water samples. The optimum pH value for sorption of the cadmium ions by yeast cells- cibacron blue was 5.5. The sorption capacity of modified biosorbent was 45 mg. g−1. A recovery of 98.2% was obtained for Cd(II) when eluted with 0.5 M nitric acid. The method was applied for Cd(II) preconcentration and determination in sea water sample.

Keywords: solid phase extraction, yeast cells, Nickl, isotherm study

Procedia PDF Downloads 264

Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

Abstract:

With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

Procedia PDF Downloads 352

Authors: Zoubida Mahi

Abstract:

A successful satellite mission in space requires a reliable attitude and orbit control system to command, control and position the satellite in appropriate orbits. Several sensors are used for attitude control, such as magnetic sensors, earth sensors, horizon sensors, gyroscopes, and solar sensors. The star tracker is the most accurate sensor compared to other sensors, and it is able to offer high-accuracy attitude control without the need for prior attitude information. There are mainly three approaches in star sensor research: digital simulation, hardware in the loop simulation, and field test of star observation. In the digital simulation approach, all of the processes are done in software, including star image simulation. Hence, it is necessary to develop star image simulation software that could simulate real space environments and various star sensor configurations. In this paper, we present a new stellar image simulation tool that is used to test and validate the stellar sensor algorithms; the developed tool allows to simulate of stellar images with several types of noise, such as background noise, gaussian noise, Poisson noise, multiplicative noise, and several scenarios that exist in space such as the presence of the moon, the presence of optical system problem, illumination and false objects. On the other hand, we present in this paper a new star extraction algorithm based on a new centroid calculation method. We compared our algorithm with other star extraction algorithms from the literature, and the results obtained show the star extraction capability of the proposed algorithm.

Keywords: star tracker, star simulation, star detection, centroid, noise, scenario

Procedia PDF Downloads 97

Authors: Amira Yousef Mahrous Yousef

Abstract:

This paper includes fast fashion verses sustainable fashion or slow fashion Indian based consumers. The expression ‘Fast fashion’ is generally referred to low-cost clothing collections that considered first hand copy of luxury brands, sometime interchangeably used with ‘mass fashion’. Whereas slow fashion or limited fashion which are consider to be more organic or eco-friendly. "Sustainable fashion is ethical fashion and here the consumer is just not design conscious but also social-environment conscious". Paper will deal with desire of young Indian consumer towards such luxury brands present in India, and their understanding of sustainable fashion, how to maintain the equilibrium between never newer fashion, style, and fashion sustainability. The green fashion market is growing rapidly as eco-friendly consumers are willing to expand their organic lifestyle to include clothing. With an increasing share of fashion consumers globally, Indian consumers are observed to consider the social and environmental ethics while making purchasing decisions. While some research clearly identifies the efforts of responsible consumers towards green fashion, some argue that fashion-orientated consumers who are sensitive towards environment do not actively participate towards supporting green fashion. This study aims to analyze the current perception of green fashion among Indian consumers. A small-scale exploratory study is conducted where consumers’ perception of green fashion is examined followed by an analysis of translation of this perception into purchase decision making. This research paper gives insight into consumer awareness on green fashion and provides scope towards the expansion of ethical fashion consumption .

Keywords: inclusions, temperature gradient, HPHT synthetic fibers, polyamide fibers, fiber volume, compressive strength. gold nano clusters, copper ions, wool keratin, fluorescence

Procedia PDF Downloads 50