Search results for: spectroscopy data analysis

Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

Abstract:

In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

Procedia PDF Downloads 102

Authors: Bagher Bayat, Wouter Verhoef, Behnaz Arabi, Christiaan Van der Tol

Abstract:

The aim of this study was to follow the canopy reflectance patterns in response to soil water deficit and to detect trends of changes in biophysical and biochemical parameters of grass (Poa pratensis species). We used visual interpretation, imaging spectroscopy and radiative transfer model inversion to monitor the gradual manifestation of water stress effects in a laboratory setting. Plots of 21 cm x 14.5 cm surface area with Poa pratensis plants that formed a closed canopy were subjected to water stress for 50 days. In a regular weekly schedule, canopy reflectance was measured. In addition, Leaf Area Index (LAI), Chlorophyll (a+b) content (Cab) and Leaf Water Content (Cw) were measured at regular time intervals. The 1-D bidirectional canopy reflectance model SAIL, coupled with the leaf optical properties model PROSPECT, was inverted using hyperspectral measurements by means of an iterative optimization method to retrieve vegetation biophysical and biochemical parameters. The relationships between retrieved LAI, Cab, Cw, and Cs (Senescent material) with soil moisture content were established in two separated groups; stress and non-stressed. To differentiate the water stress condition from the non-stressed condition, a threshold was defined that was based on the laboratory produced Soil Water Characteristic (SWC) curve. All parameters retrieved by model inversion using canopy spectral data showed good correlation with soil water content in the water stress condition. These parameters co-varied with soil moisture content under the stress condition (Chl: R2= 0.91, Cw: R2= 0.97, Cs: R2= 0.88 and LAI: R2=0.48) at the canopy level. To validate the results, the relationship between vegetation parameters that were measured in the laboratory and soil moisture content was established. The results were totally in agreement with the modeling outputs and confirmed the results produced by radiative transfer model inversion and spectroscopy. Since water stress changes all parts of the spectrum, we concluded that analysis of the reflectance spectrum in the VIS-NIR-MIR region is a promising tool for monitoring water stress impacts on vegetation.

Keywords: hyperspectral remote sensing, model inversion, vegetation responses, water stress

Procedia PDF Downloads 207

Authors: Sourour Ramzi, Claudia Sarrico

Abstract:

Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data.

Keywords: supply chain, education, data envelopment analysis, network DEA

Procedia PDF Downloads 353

Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

Abstract:

The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

Procedia PDF Downloads 216

Authors: Haifa Ben Saber, Mourad Elloumi

Abstract:

In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

Procedia PDF Downloads 359

Authors: Kheira Hamaida, Mohamed Salah Halati

Abstract:

In this current paper, our aim work is to link as possible the obtained simulation results and the other experimental ones, just focusing on the electronic and optical properties of ZnSe. The predictive spectra of the total and partial densities of states using the Full Potential Linearized/Augmented Plane Wave method with the newly Tran-Blaha (TB) modified Becke-Johnson (mBJ) exchange-correlation potential (EXC). So the upper valence energy (UVE) levels contain the relative contribution of Se-(4p and 3d) states with considerable contribution from the electrons of Zn-2s orbital. The dielectric function of w-ZnSe, with its two parts, appears with a noticeable anisotropy character. The microscopic origins of the electronic states that are responsible for the observed peaks in the spectrum are determined through the decomposition of the spectrum to the individual contributions of the electronic transitions between the pairs of bands, where Vi is an occupied state in the valence band, and Ci is an unoccupied state in the conduction band. X-PES (X Ray-Photo Electron Spectroscopy) is an important technique used to probe the homogeneity, stoichiometry, and purity state of the title compound. In order to check the electron transitions derived from simulations and the others from Reflected Electron Energy Loss Spectroscopy (REELS) technique which was of great sensitivity, is used to determine the interband electronic transitions. In the optical window (Eg), all the electron energy states created were also determined through the specific gaussian deconvolution of the photoluminescence spectrum (PLS) that probed under a room temperature (RT).

Keywords: spectroscopy, WIEN2K, IIB-VIA semiconductors, dielectric function

Procedia PDF Downloads 53

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 64

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 79

Authors: Pratima Kumari, Sukha Ranjan Samadder

Abstract:

This research aims to contribute to the sustainable production of graphene materials by exploring the extraction of graphite from spent primary and secondary batteries. The increasing demand for graphene materials, a versatile and high-performance material, necessitates environmentally friendly methods for its synthesis. The process involves a well-planned methodology, beginning with the gathering and categorization of batteries, followed by the disassembly and careful removal of graphite from anode structures. The use of environmentally friendly solvents and mechanical techniques ensures an efficient and eco-friendly extraction of graphite. Advanced approaches such as the modified Hummers' method and chemical reduction process are utilized for the synthesis of graphene materials, with a focus on optimizing parameters. Various analytical techniques such as Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, and Raman spectroscopy were employed to validate the quality and structure of the produced graphene materials. The major findings of this study reveal the successful implementation of the methodology, leading to the production of high-quality graphene materials suitable for advanced material applications. Thorough characterization using various advanced techniques validates the structural integrity and purity of the graphene. The economic viability of the process is demonstrated through a comprehensive economic analysis, highlighting the potential for large-scale production. This research contributes to the field of sustainable production of graphene materials by offering a systematic methodology that efficiently transforms spent batteries into valuable graphene resources. Furthermore, the findings not only showcase the potential for upcycling electronic waste but also address the pressing need for environmentally conscious processes in advanced material synthesis.

Keywords: spent primary batteries, spent secondary batteries, graphite extraction, advanced material synthesis, circular economy approach

Procedia PDF Downloads 38

Authors: Ubeyd Toçoğlu, Miraç Alaf, Hatem Akbulut

Abstract:

We present a work which was conducted in order to improve the cycle life of silicon based lithium ion battery anodes by utilizing novel composite structure. In this study, carbon coated nano sized (50-100 nm) silicon particles were embedded into Graphene/MWCNT silicon matrix to produce free standing silicon based electrodes. Also, conventional Si powder anodes were produced from Si powder slurry on copper current collectors in order to make comparison of composite and conventional anode structures. Free –standing composite anodes (binder-free) were produced via vacuum filtration from a well dispersion of Graphene, MWCNT and carbon coated silicon powders. Carbon coating process of silicon powders was carried out via microwave reaction system. The certain amount of silicon powder and glucose was mixed under ultrasonication and then coating was conducted at 200 °C for two hours in Teflon lined autoclave reaction chamber. Graphene which was used in this study was synthesized from well-known Hummers method and hydrazine reduction of graphene oxide. X-Ray diffraction analysis and RAMAN spectroscopy techniques were used for phase characterization of anodes. Scanning electron microscopy analyses were conducted for morphological characterization. The electrochemical performance tests were carried out by means of galvanostatic charge/discharge, cyclic voltammetry and electrochemical impedance spectroscopy.

Keywords: graphene, Li-Ion, MWCNT, silicon

Procedia PDF Downloads 235

Authors: Nungki Rositaningsih, Emil Budianto

Abstract:

Starch has been widely used as an encapsulation material for drug delivery system. However, starch hydrogel is very easily degraded during metabolism in human stomach. Modification of this material is needed to improve the encapsulation process in drug delivery system, especially for gastrointestinal drug. In this research, three modified starch-based hydrogels are synthesized i.e. Crosslinked starch hydrogel, Semi- and Full- Interpenetrating Polymer Network (IPN) starch hydrogel using Poly(N-Vinyl-Pyrrolidone). Non-modified starch hydrogel was also synthesized as a control. All of those samples were compared as biomaterials, floating drug delivery, and their ability in loading drug test. Biomaterial characterizations were swelling test, stereomicroscopy observation, Differential Scanning Calorimetry (DSC), and Fourier Transform Infrared Spectroscopy (FTIR). Buoyancy test and stereomicroscopy scanning were done for floating drug delivery characterizations. Lastly, amoxicillin was used as test drug, and characterized with UV-Vis spectroscopy for loading drug observation. Preliminary observation showed that Full-IPN has the most dense and elastic texture, followed by Semi-IPN, Crosslinked, and Non-modified in the last position. Semi-IPN and Crosslinked starch hydrogel have the most ideal properties and will not be degraded easily during metabolism. Therefore, both hydrogels could be considered as promising candidates for encapsulation material. Further analysis and issues will be discussed in the paper.

Keywords: biomaterial, drug delivery system, interpenetrating polymer network, poly(N-vinyl-pyrrolidone), starch hydrogel

Procedia PDF Downloads 233

Authors: John Gaber

Abstract:

Planners regularly use citizen input as empirical data to help them better understand community issues they know very little about. This type of community data is based on the lived experiences of local residents and is known as "emic" data. What is becoming more common practice for planners is their use of data from local experts and stakeholders (known as "etic" data or the outsider perspective) to help them fill in the gaps in their analysis of applied planning research projects. Utilizing international Health Impact Assessment (HIA) data, I look at who planners invite to their citizen input investigations. Research presented in this paper shows that planners access a wide range of emic and etic community perspectives in their search for the “community’s view.” The paper concludes with how planners can chart out a new empirical path in their execution of emic/etic citizen participation strategies in their applied planning research projects.

Keywords: citizen participation, emic data, etic data, Health Impact Assessment (HIA)

Procedia PDF Downloads 472

Authors: Fei Gao, Rodolfo C. Raga Jr.

Abstract:

This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

Procedia PDF Downloads 54

Authors: Raghvendra Singh Yadav, Ivo Kuritka, Jarmila Vilcakova, Pavel Urbanek, Michal Machovsky, David Skoda, Milan Masar

Abstract:

The elastomer nanocomposites were synthesized by solution mixing method with an elastomer as a matrix and in situ thermally reduced graphene oxide (RGO) and spinel ferrite NiFe₂O₄ nanoparticles as filler. Spinel ferrite NiFe₂O₄ nanoparticles were prepared by the starch-assisted sol-gel auto-combustion method. The influence of filler on the microstructure, morphology, dielectric, electrical and magnetic properties of Reduced Graphene Oxide-Nickel Ferrite-Elastomer nanocomposite was characterized by X-ray diffraction, Raman spectroscopy, Fourier transform infrared spectroscopy, field emission scanning electron microscopy, X-ray photoelectron spectroscopy, the Dielectric Impedance analyzer, and vibrating sample magnetometer. Scanning electron microscopy study revealed that the fillers were incorporated in elastomer matrix homogeneously. The dielectric constant and dielectric tangent loss of nanocomposites was decreased with the increase of frequency, whereas, the dielectric constant increases with the addition of filler. Further, AC conductivity was increased with the increase of frequency and addition of fillers. Furthermore, the prepared nanocomposites exhibited ferromagnetic behavior. This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic – Program NPU I (LO1504).

Keywords: polymer-matrix composites, nanoparticles as filler, dielectric property, magnetic property

Procedia PDF Downloads 156

Authors: Arpan Bera, Rini Ganguly, Raja Chakraborty, Amlan J. Pal

Abstract:

To deal with the increasing demands for the high-density non-volatile memory devices, we need nano-sites with efficient and stable charge storage capabilities. We prepared nanocrystals (NCs) of inorganic perovskite, CsPbCl₃ coated with Cs₄PbCl₆, by colloidal synthesis. Due to the type-I band alignment at the junction, this core-shell composite is expected to behave as a charge trapping site. Using Scanning Tunneling Spectroscopy (STS), we investigated voltage-controlled resistive switching in this heterostructure by tracking the change in its current-voltage (I-V) characteristics. By applying voltage pulse of appropriate magnitude on the NCs through this non-invasive method, different resistive states of this system were systematically accessed. For suitable pulse-magnitude, the response jumped to a branch with enhanced current indicating a high-resistance state (HRS) to low-resistance state (LRS) switching in the core-shell NCs. We could reverse this process by using a pulse of opposite polarity. These two distinct resistive states can be considered as two logic states, 0 and 1, which are accessible by varying voltage magnitude and polarity. STS being a local probe in space enabled us to capture this switching at individual NC site. Hence, we claim a bright prospect of these core-shell NCs made of inorganic halide perovskites in future high density memory application.

Keywords: Core-shell perovskite, CsPbCl₃-Cs₄PbCl₆, resistive switching, Scanning Tunneling Spectroscopy

Procedia PDF Downloads 77

Authors: Wonil Nam, Xiang Ren, Inyoung Kim, Masoud Agah, Wei Zhou

Abstract:

Despite its ultrasensitive detection capability, surface-enhanced Raman spectroscopy (SERS) faces challenges as a quantitative biochemical analysis tool due to the significant dependence of local field intensity in hotspots on nanoscale geometric variations of plasmonic nanostructures. Therefore, despite enormous progress in plasmonic nanoengineering of high-performance SERS devices, it is still challenging to quantitatively correlate the measured SERS signals with the actual molecule concentrations at hotspots. A significant effort has been devoted to developing SERS calibration methods by introducing internal standards. It has been achieved by placing Raman tags at plasmonic hotspots. Raman tags undergo similar SERS enhancement at the same hotspots, and ratiometric SERS signals for analytes of interest can be generated with reduced dependence on geometrical variations. However, using Raman tags still faces challenges for real-world applications, including spatial competition between the analyte and tags in hotspots, spectral interference, laser-induced degradation/desorption due to plasmon-enhanced photochemical/photothermal effects. We show that electronic Raman scattering (ERS) signals from metallic nanostructures at hotspots can serve as the internal calibration standard to enable quantitative SERS analysis and improve biostatistical analysis. We perform SERS with Au-SiO₂ multilayered metal-insulator-metal nano laminated plasmonic nanostructures. Since the ERS signal is proportional to the volume density of electron-hole occupation in hotspots, the ERS signals exponentially increase when the wavenumber is approaching the zero value. By a long-pass filter, generally used in backscattered SERS configurations, to chop the ERS background continuum, we can observe an ERS pseudo-peak, IERS. Both ERS and SERS processes experience the |E|⁴ local enhancements during the excitation and inelastic scattering transitions. We calibrated IMRS of 10 μM Rhodamine 6G in solution by IERS. The results show that ERS calibration generates a new analytical value, ISERS/IERS, insensitive to variations from different hotspots and thus can quantitatively reflect the molecular concentration information. Given the calibration capability of ERS signals, we performed label-free SERS analysis of living biological systems using four different breast normal and cancer cell lines cultured on nano-laminated SERS devices. 2D Raman mapping over 100 μm × 100 μm, containing several cells, was conducted. The SERS spectra were subsequently analyzed by multivariate analysis using partial least square discriminant analysis. Remarkably, after ERS calibration, MCF-10A and MCF-7 cells are further separated while the two triple-negative breast cancer cells (MDA-MB-231 and HCC-1806) are more overlapped, in good agreement with the well-known cancer categorization regarding the degree of malignancy. To assess the strength of ERS calibration, we further carried out a drug efficacy study using MDA-MB-231 and different concentrations of anti-cancer drug paclitaxel (PTX). After ERS calibration, we can more clearly segregate the control/low-dosage groups (0 and 1.5 nM), the middle-dosage group (5 nM), and the group treated with half-maximal inhibitory concentration (IC50, 15 nM). Therefore, we envision that ERS calibrated SERS can find crucial opportunities in label-free molecular profiling of complicated biological systems.

Keywords: cancer cell drug efficacy, plasmonics, surface-enhanced Raman spectroscopy (SERS), SERS calibration

Procedia PDF Downloads 123

Authors: S. S. Pati, L. Herojit Singh, A. C. Oliveira, V. K. Garg

Abstract:

Chitosan functionalized Fe3O4-Au core shell nanoparticles have been prepared using a two step wet chemical approach using NaBH4 as reducing agent for formation of Au inethylene glycol. X-ray diffraction studies shows individual phases of Fe3O4 and Au in the as prepared samples with crystallite size of 5.9 and 11.4 nm respectively. The functionalization of the core-shell nanostructure with Chitosan has been confirmed using Fourier transform infrared spectroscopy along with signatures of octahedral and tetrahedral sites of Fe3O4 below 600cm-1. Mössbauer spectroscopy shows decrease in particle-particle interaction in presence of Au shell (72% sextet) than pure oleic coated Fe3O4 nanoparticles (88% sextet) at room temperature. At 80K, oleic acid coated Fe3O4 shows only sextets whereas the Chitosan functionalized Fe3O4 and Chitosan functionalized Fe3O4@Au core shell show presence of 5 and 11% doublet, respectively.

Keywords: core shell, drug delivery, gold nanoparticles, magnetic nanoparticles

Procedia PDF Downloads 358

Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

Abstract:

Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

Procedia PDF Downloads 121

Authors: H. K. G. K. D. K. Hapuhinna, R. D. Gunaratne, H. M. J. C. Pitawala

Abstract:

Hydroxyapatite is a bioceramic which can be used for applications in orthopedics and dentistry due to its structural similarity with the mineral phase of mammalian bones and teeth. In this study, it was synthesized, chemically changing natural Eppawala chloroapatite mineral as a value-added product. Sol-gel approach and solid state sintering were used to synthesize products using diluted nitric acid, ethanol and calcium hydroxide under different conditions. Synthesized Eppawala hydroxyapatite powder was characterized using X-ray Fluorescence (XRF), X-ray Powder Diffraction (XRD), Fourier-transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC) in order to find out its composition, crystallinity, presence of functional groups, bonding type, surface morphology, microstructural features, and thermal dependence and stability, respectively. The XRD results reflected the formation of a hexagonal crystal structure of hydroxyapatite. Elementary composition and microstructural features of products were discussed based on the XRF and SEM results of the synthesized hydroxyapatite powder. TGA and DSC results of synthesized products showed high thermal stability and good material stability in nature. Also, FTIR spectroscopy results confirmed the formation of hydroxyapatite from apatite via the presence of hydroxyl groups. Those results coincided with the FTIR results of mammalian bones including human bones. The study concludes that there is a possibility of producing hydroxyapatite using commercially available Eppawala chloroapatite in Sri Lanka.

Keywords: dentistry, Eppawala chlorapatite, hydroxyapatite, orthopedics

Procedia PDF Downloads 223

Authors: Leila Samiee, Amir Yadegari, Saeedeh Tasharrofi

Abstract:

In the work presented here, nitrogen-doped graphene materials were synthesized and used as metal-free electrocatalysts for oxygen reduction reaction (ORR) under alkaline conditions. Paraphenylenediamine was used as N precursor. The N-doped graphene was synthesized under hydrothermal treatment at 200°C. All the materials have been characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Transmission electron microscopy (TEM) and X-ray photo-electron spectroscopy (XPS). Moreover, for electrochemical evaluation of samples, Rotating Disk electrode (RDE) and Cyclic Voltammetry techniques (CV) were employed. The resulting material exhibits an outstanding catalytic activity for the oxygen reduction reaction (ORR) as well as excellent resistance towards methanol crossover effects, indicating their promising potential as ORR electrocatalysts for alkaline fuel cells.

Keywords: alkaline fuel cell, graphene, metal-free catalyst, paraphenylen diamine

Procedia PDF Downloads 457

Authors: Marie Kudrnova, Jana Petru

Abstract:

This study is part of the international research - Materials for Molten Salt Reactors (MSR) and addresses the part of the project dealing with the corrosion behavior of candidate construction materials. Inconel 625 was characterized by x-ray photoelectron spectroscopy (XPS) before and after high–temperature experiment in molten salt. The experiment was performed in a horizontal tube furnace molten salt reactor, at 450 °C in argon, at atmospheric pressure, for 150 hours. Industrially produced HITEC salt was used (NaNO3, KNO3, NaNO2). The XPS study was carried out using the ESCAProbe P apparatus (Omicron Nanotechnology Ltd.) equipped with a monochromatic Al Kα (1486.6 eV) X-ray source. The surface layer on alloy 625 after exposure contains only Na, C, O, and Ni (as NiOx) and Nb (as NbOx BE 206.8 eV). Ni was detected in the metallic state (Ni0 – Ni 2p BE-852.7 eV, NiOx - Ni 2p BE-854.7 eV) after a short Ar sputtering because the oxide layer on the surface was very thin. Nickel oxides can form a protective layer in the molten salt, but only future long-term exposures can determine the suitability of Inconel 625 for MSR.

Keywords: Inconel 625, molten salt, oxide layer, XPS

Procedia PDF Downloads 127

Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

Abstract:

Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

Procedia PDF Downloads 52

Authors: Marie Bieber, Fabrice Cosson, Olivier Schmitt

Abstract:

Europe's Global Navigation Satellite System, Galileo, provides worldwide positioning and navigation services. The satellites in space are only one part of the Galileo system. An extensive ground infrastructure is essential to oversee the satellites and ensure accurate navigation signals. High reliability and availability of the entire Galileo system are crucial to continuously provide positioning information of high quality to users. Outages are tracked, and operational availability is regularly assessed. A highly flexible and adaptive tool has been developed to automate the Galileo system availability analysis. Not only does it enable a quick availability consolidation, but it also provides first steps towards improving the data quality of maintenance tickets used for the analysis. This includes data import and data preparation, with a focus on processing strings used for classification and identifying faulty data. Furthermore, the tool allows to handle a low amount of data, which is a major constraint when the aim is to provide accurate statistics.

Keywords: availability, data quality, system performance, Galileo, aerospace

Procedia PDF Downloads 145

Authors: H. Li, L. Ji, J. Su

Abstract:

Forward osmosis is an emerging technology for separation and has great potential in the concentration of liquid products such as protein, pharmaceutical, and natural products. In pharmacy industry, one of the very tough talks is to concentrate the product in a gentle way since some of the key components may lose bioactivity when exposed to heating or pressurization. Therefore, forward osmosis (FO), which uses inherently existed osmosis pressure instead of externally applied hydraulic pressure, is attractive for pharmaceutical enrichments in a much efficient and energy-saving way. Recently, coordination complexes have been explored as the new class of draw solutes in FO processes due to their bulky configuration and excellent performance in terms of high water flux and low reverse solute flux. Among these coordination complexes, ferric citrate complex with lots of hydrophilic groups and ionic species which make them good solubility and high osmotic pressure in aqueous solution, as well as its low toxicity, has received much attention. However, the chemistry of ferric complexation by citrate is complicated, and disagreement prevails in the literature, especially for the structure of the ferric citrate. In this study, we investigated the chemical reaction with various molar ratio of iron and citrate. It was observed that the ferric citrate complex (Fe-CA2) with molar ratio of 1:1 for iron and citrate formed at the beginning of the reaction, then Fecit would convert to ferric citrate complex at the molar ratio of 1:2 with the proper excess of citrate in the base solution. The structures of the ferric citrate complexes synthesized were systematically characterized by X-ray diffraction (XRD), UV-vis spectroscopy, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR) and Thermogravimetric analysis (TGA). Fe-CA2 solutions exhibit osmotic pressures more than twice of that for NaCl solutions at the same concentrations. Higher osmotic pressure means higher driving force, and this is preferable for the FO process. Fe-CA2 and NaCl draw solutions were prepared with the same osmotic pressure and used in FO process for BSA protein concentration. Within 180 min, BSA concentration was enriched from 0.2 to 0.27 L using Fe-CA draw solutions. However, it was only increased from 0.20 to 0.22 g/L using NaCl draw solutions. A reverse flux of 11 g/m²h was observed for NaCl draw solutes while it was only 0.1 g/m²h for Fe-CA2 draw solutes. It is safe to conclude that Fe-CA2 is much better than NaCl as draw solute and it is suitable for the enrichment of liquid product.

Keywords: draw solutes, ferric citrate complex, forward osmosis, protein enrichment

Procedia PDF Downloads 139

Authors: Josef Fládr, Jiří Němeček, Veronika Koudelková, Petr Bílý

Abstract:

Mechanical parameters of cementitious composites differ quite significantly based on the composition of cement matrix. They are also influenced by mixing times and procedure. The research presented in this paper was aimed at identification of differences in microstructure of normal strength (NSC) and differently mixed high strength (HSC) cementitious composites. Scanning electron microscopy (SEM) investigation together with energy dispersive X-ray spectroscopy (EDX) phase analysis of NSC and HSC samples was conducted. Evaluation of interfacial transition zone (ITZ) between the aggregate and cement matrix was performed. Volume share, thickness, porosity and composition of ITZ were studied. In case of HSC, samples obtained by several different mixing procedures were compared in order to find the most suitable procedure. In case of NSC, ITZ was identified around 40-50% of aggregate grains and its thickness typically ranged between 10 and 40 µm. Higher porosity and lower share of clinker was observed in this area as a result of increased water-to-cement ratio (w/c) and the lack of fine particles improving the grading curve of the aggregate. Typical ITZ with lower content of Ca was observed only in one HSC sample, where it was developed around less than 15% of aggregate grains. The typical thickness of ITZ in this sample was similar to ITZ in NSC (between 5 and 40 µm). In the remaining four HSC samples, no ITZ was observed. In general, the share of ITZ in HSC samples was found to be significantly smaller than in NSC samples. As ITZ is the weakest part of the material, this result explains to large extent the improved mechanical properties of HSC compared to NSC. Based on the comparison of characteristics of ITZ in HSC samples prepared by different mixing procedures, the most suitable mixing procedure from the point of view of properties of ITZ was identified.

Keywords: electron diffraction spectroscopy, high strength concrete, interfacial transition zone, normal strength concrete, scanning electron microscopy

Procedia PDF Downloads 280

Authors: Rosemary Anibogwu, Kavita Sharma, Karl De Jesus

Abstract:

Sagebrush is an abundant and naturally occurring plant in the Intermountain West region of the United States. The plant contains an array of bioactive compounds such as flavonoids, terpenoids, sterols, and phenolic acids. It is important to identify and characterize these compounds because Native Americans use sagebrush as herbal medicine. These compounds are also utilized for preventing infection in wounds, treating headaches and colds, and possess antitumor properties. This research is an exploratory study on the sesquiterpene present in the leaves of sagebrush. The leaf foliage was extracted with 100 % chloroform and 100 % methanol. The percentage yield for the crude was considerably higher in chloroform. The Thin Layer Chromatography (TLC) analysis of the crude extracted unveiled a brown band at Rf = 0.25 and a dark brown band at Rf = 0.74, along with three unknown faint bands the 254 nm UV lamp. Furthermore, the two distinct brown (Achillin) and dark brown band (Hydroxyachillin) in TLC were further utilized in the isolation of pure compounds with column chromatography. The structures of Achillin and Hydroxyachillin were elucidated based on extensive spectroscopic analysis, including TLC, High-Performance Liquid Chromatography (HPLC), 1D- and 2D-Nuclear Magnetic Resonance (NMR), and Mass Spectroscopy (MS). The antioxidant activities of crude extract and three pure compounds were evaluated in terms of their peroxyl radical scavenging by Ferric Reducing Ability of Plasma (FRAP) and 1,1-Diphenyl-2-picryl-hydrazyl (DPPH) methods. The crude extract showed the antioxidant activity of 18.99 ± 0.51 µmol TEg -1 FW for FRAP and 11.59 ± 0.38 µmol TEg -1 FW for DPPH. The activities of Achillin, Hydroxyachillin, and Quercetagetin trimethyl ether were 13.03, 15.90 and 14.02 µmol TEg -1 FW respectively for the FRAP assay. The three purified compounds have been submitted to the National Cancer Institute 60 cancer cell line for further study.

Keywords: HPLC, nuclear magnetic resonance spectroscopy, sagebrush, sesquiterpene lactones

Procedia PDF Downloads 110

Authors: Peter Thissen

Abstract:

In this work, we report about the influence of the chemical potential of water on the carbonation reaction of wollastonite (CaSiO3) as model surface of cement and concrete. Total energy calculations based on density functional theory (DFT) combined with kinetic barrier predictions based on nudge elastic band (NEB) method show that the exposure of the water-free wollastonite surface to CO2 results in a barrier-less carbonation. CO2 reacts with the surface oxygen and forms carbonate (CO32-) complexes together with a major reconstruction of the surface. The reaction comes to a standstill after one carbonate monolayer has been formed. In case one water monolayer is covering the wollastonite surface, the carbonation is no more barrier-less, yet ending in a localized monolayer. Covered with multilayers of water, the thermodynamic ground state of the wollastonite completely changes due to a metal-proton exchange reaction (MPER, also called early stage hydration) and Ca2+ ions are partially removed from solid phase into the H2O/wollastonite interface. Mobile Ca2+ react again with CO2 and form carbonate complexes, ending in a delocalized layer. By means of high resolution time-of-flight secondary-ion mass-spectroscopy images (ToF-SIMS), we confirm that hydration can lead to a partially delocalization of Ca2+ ions on wollastonite surfaces. Finally, we evaluate the impact of our model surface results by means of Low Energy Ion Scattering (LEIS) spectroscopy combined with careful discussion about the competing reactions of carbonation vs. hydration.

Keywords: Calcium-silicate, carbonation, hydration, metal-proton exchange reaction

Procedia PDF Downloads 346

Authors: Ali Razmi, Saeed Golian

Abstract:

Climate change is known to have the potential to impact adversely hydrologic patterns for variables such as rainfall, maximum and minimum temperature and sea level rise. Long-term average of these climate variables could possibly change over time due to climate change impacts. In this study, trend analysis was performed on rainfall, maximum and minimum temperature and water level data of a coastal area in Manhattan, New York City, Central Park and Battery Park stations to investigate if there is a significant change in the data mean. Partial Man-Kendall test was used for trend analysis. Frequency analysis was then performed on data using common probability distribution functions such as Generalized Extreme Value (GEV), normal, log-normal and log-Pearson. Goodness of fit tests such as Kolmogorov-Smirnov are used to determine the most appropriate distributions. In flood frequency analysis, rainfall and water level data are often separately investigated. However, in determining flood zones, simultaneous consideration of rainfall and water level in frequency analysis could have considerable effect on floodplain delineation (flood extent and depth). The present study aims to perform flood frequency analysis considering joint probability distribution for rainfall and storm surge. First, correlation between the considered variables was investigated. Joint probability distribution of extreme water level and temperature was also investigated to examine how global warming could affect sea level flooding impacts. Copula functions were fitted to data and joint probability of water level with rainfall and temperature for different recurrence intervals of 2, 5, 25, 50, 100, 200, 500, 600 and 1000 was determined and compared with the severity of individual events. Results for trend analysis showed increase in long-term average of data that could be attributed to climate change impacts. GEV distribution was found as the most appropriate function to be fitted to the extreme climate variables. The results for joint probability distribution analysis confirmed the necessity for incorporation of both rainfall and water level data in flood frequency analysis.

Keywords: climate change, climate variables, copula, joint probability

Procedia PDF Downloads 340

Authors: Ji Eun Shin, Dong Hoon Lim

Abstract:

Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

Procedia PDF Downloads 415

Authors: Mehrnaz Mostafavi, Jalaledin Ghanavi

Abstract:

This research investigates the production of nanoring structures through a chemical reduction approach, exploring gradual reduction processes assisted by reductant agents, leading to the formation of these specialized nanorings. The study focuses on the controlled reduction of metal atoms within these agents, crucial for shaping these nanoring structures over time. The paper commences by highlighting the wide-ranging applications of metal nanostructures across fields like Nanomedicine, Nanobiotechnology, and advanced spectroscopy methods such as Surface Enhanced Raman Spectroscopy (SERS) and Surface Enhanced Infrared Absorption Spectroscopy (SEIRA). Particularly, gold nanoparticles, especially in the nanoring configuration, have gained significant attention due to their distinctive properties, offering accessible spaces suitable for sensing and spectroscopic applications. The methodology involves utilizing human serum albumin as a reducing agent to create gold nanoparticles through a chemical reduction process. This process involves the transfer of electrons from albumin's carboxylic groups, converting them into carbonyl, while AuCl4− acquires electrons to form gold nanoparticles. Various characterization techniques like Ultraviolet–visible spectroscopy (UV-Vis), Atomic-force microscopy (AFM), and Transmission electron microscopy (TEM) were employed to examine and validate the creation and properties of the gold nanoparticles and nanorings. The findings suggest that precise and gradual reduction processes, in conjunction with optimal pH conditions, play a pivotal role in generating nanoring structures. Experiments manipulating optical properties revealed distinct responses in the visible and infrared spectrums, demonstrating the tunability of these nanorings. Detailed examinations of the morphology confirmed the formation of gold nanorings, elucidating their size, distribution, and structural characteristics. These nanorings, characterized by an empty volume enclosed by uniform walls, exhibit promising potential in the realms of Nanomedicine and Nanobiotechnology. In summary, this study presents a chemical synthesis approach using organic reducing agents to produce gold nanorings. The results underscore the significance of controlled and gradual reduction processes in crafting nanoring structures with unique optical traits, offering considerable value across diverse nanotechnological applications.

Keywords: nanoring structures, chemical reduction approach, gold nanoparticles, spectroscopy methods, nano medicine applications

Procedia PDF Downloads 94