Search results for: uranium processing

Authors: Noura Al-Ajmi, Mohammed A. Almulla

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With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit.

Keywords: headache diagnosis system, prescription recommender system, expert system, backward rule-based system

Procedia PDF Downloads 215

Authors: Bourama Mane, Ibrahima Fall, Mamadou Samba Camara, Alassane Bah

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At the level of the National Statistical Institutes, there is a large volume of data which is generally in a format which conditions the method of publication of the information they contain. Each household or business data collection project includes a dissemination platform for its implementation. Thus, these dissemination methods previously used, do not promote rapid access to information and especially does not offer the option of being able to link data for in-depth processing. In this paper, we present an approach to modeling these data to publish them in a format intended for the Semantic Web. Our objective is to be able to publish all this data in a single platform and offer the option to link with other external data sources. An application of the approach will be made on data from major national surveys such as the one on employment, poverty, child labor and the general census of the population of Senegal.

Keywords: Semantic Web, linked open data, database, statistic

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Authors: Kunj Parikh, S. Bhattacharya, V. Natarajan

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TCAD numerical simulation is one of the most tried and tested powerful tools for designing devices in semiconductor foundries worldwide. It has also been used to explain conduction in organic thin films where the processing temperature is often enough to make homogeneous samples (often imperfect, but homogeneously imperfect). In this report, we have presented the results of TCAD simulation in multi-grain thin films. The work has addressed the inhomogeneity in one dimension, but can easily be extended to two and three dimensions. The effect of grain boundaries has mainly been approximated as barriers located at the junction between two adjacent grains. The effect of the value of grain boundary barrier, the bulk traps, and the measurement temperature have been investigated.

Keywords: polycrystalline thin films, space charge limited conduction, Technology Computer-Aided Design (TCAD) simulation, traps

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Authors: D. Sun, L. Saw, A. Onyianta, D. O’Rourke, Z. Lu, C. See, C. Wilson, C. Popescu, M. Dorris

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With the rapid development of wearable technology (e.g., smartwatch, activity trackers and health monitor devices), flexible dielectric materials with environmental-friendly, low-cost and high-energy efficiency characteristics are in increasing demand. In this work, a flexible dielectric nanocomposite was processed by incorporating two components: cellulose nanofibrils and alum sludge in a polymer matrix. The two components were used in the reinforcement phase as well as for enhancing the dielectric properties; they were processed using waste materials that would otherwise be disposed to landfills. Alum sludge is a by-product of the water treatment process in which aluminum sulfate is prevalently used as the primary coagulant. According to the data from a project partner-Scottish Water: there are approximately 10k tons of alum sludge generated as a waste from the water treatment work to be landfilled every year in Scotland. The industry has been facing escalating financial and environmental pressure to develop more sustainable strategies to deal with alum sludge wastes. In the available literature, some work on reusing alum sludge has been reported (e.g., aluminum recovery or agriculture and land reclamation). However, little work can be found in applying it to processing energy materials (e.g., dielectrics) for enhanced energy density and efficiency. The alum sludge was collected directly from a water treatment plant of Scottish Water and heat-treated and refined before being used in preparing composites. Cellulose nanofibrils were derived from water hyacinth, an invasive aquatic weed that causes significant ecological issues in tropical regions. The harvested water hyacinth was dried and processed using a cost-effective method, including a chemical extraction followed by a homogenization process in order to extract cellulose nanofibrils. Biodegradable elastomer polydimethylsiloxane (PDMS) was used as the polymer matrix and the nanocomposites were processed by casting raw materials in Petri dishes. The processed composites were characterized using various methods, including scanning electron microscopy (SEM), rheological analysis, thermogravimetric and X-ray diffraction analysis. The SEM result showed that cellulose nanofibrils of approximately 20nm in diameter and 100nm in length were obtained and the alum sludge particles were of approximately 200um in diameters. The TGA/DSC analysis result showed that a weight loss of up to 48% can be seen in the raw material of alum sludge and its crystallization process has been started at approximately 800°C. This observation coincides with the XRD result. Other experiments also showed that the composites exhibit comprehensive mechanical and dielectric performances. This work depicts that it is a sustainable practice of reusing such waste materials in preparing flexible, lightweight and miniature dielectric materials for wearable technology applications.

Keywords: cellulose, biodegradable, sustainable, alum sludge, nanocomposite, wearable technology, dielectric

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Authors: Senem Suna

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Extraction of bioactive compounds from several food/food products comes as an important topic and new trend related with health promoting effects. As a result of the increasing interest in natural foods, different methods are used for the acquisition of these components especially polyphenols. However, special attention has to be paid to the selection of proper techniques or several processing technologies (supercritical fluid extraction, microwave-assisted extraction, ultrasound-assisted extraction, powdered extracts production) for each kind of food to get maximum benefit as well as the obtainment of phenolic compounds. In order to meet consumer’s demand for healthy food and the management of quality and safety requirements, advanced research and development are needed. In this review, advantages, and disadvantages of different extraction methods, their opportunities to be used in food industry and the effects of polyphenols are mentioned in details. Consequently, with the evaluation of the results of several studies, the selection of the most suitable food specific method was aimed.

Keywords: bioactives, extraction, powdered extracts, supercritical fluid extraction

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Authors: Benjamin Castillo, Luis Pastenes, Fernando Cordova

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The pathogen growth in animal source foods is a common problem in the food industry, causing monetary losses due to the spoiling of products or food intoxication outbreaks in the community. In this sense, the quality of the product is reflected by the population of deteriorating agents present in it, which are mainly bacteria. The factors which are likely associated with freshness in animal source foods are temperature and processing, storage, and transport times. However, the level of deterioration of products depends, in turn, on the characteristics of the bacterial population, causing the decomposition or spoiling, such as pH level and toxins. Knowing the growth dynamics of the agents that are involved in product contamination allows the monitoring for more efficient processing. This means better quality and reasonable costs, along with a better estimation of necessary time and temperature intervals for transport and storage in order to preserve product quality. The objective of this project is to design a secondary model that allows measuring the impact on temperature bacterial growth and the competition for pH adequacy and release of bacteriocins in order to describe such phenomenon and, thus, estimate food product half-life with the least possible risk of deterioration or spoiling. In order to achieve this objective, the authors propose an analysis of a three-dimensional ordinary differential which includes; logistic bacterial growth extended by the inhibitory action of bacteriocins including the effect of the medium pH; change in the medium pH levels through an adaptation of the Luedeking-Piret kinetic model; Bacteriocin concentration modeled similarly to pH levels. These three dimensions are being influenced by the temperature at all times. Then, this differential system is expanded, taking into consideration the variable temperature and the concentration of pulsed bacteriocins, which represent characteristics inherent of the modeling, such as transport and storage, as well as the incorporation of substances that inhibit bacterial growth. The main results lead to the fact that temperature changes in an early stage of transport increased the bacterial population significantly more than if it had increased during the final stage. On the other hand, the incorporation of bacteriocins, as in other investigations, proved to be efficient in the short and medium-term since, although the population of bacteria decreased, once the bacteriocins were depleted or degraded over time, the bacteria eventually returned to their regular growth rate. The efficacy of the bacteriocins at low temperatures decreased slightly, which equates with the fact that their natural degradation rate also decreased. In summary, the implementation of the mathematical model allowed the simulation of a set of possible bacteria present in animal based products, along with their properties, in various transport and storage situations, which led us to state that for inhibiting bacterial growth, the optimum is complementary low constant temperatures and the initial use of bacteriocins.

Keywords: bacterial growth, bacteriocins, mathematical modelling, temperature

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Authors: Rogelio Luck, Yucheng Liu

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This paper proposes the continuous-time singular value decomposition (SVD) for the impulse response function, a special kind of Green’s functions e⁻⁽ᵗ⁻ ᵀ⁾, in order to find a set of singular functions and singular values so that the convolutions of such function with the set of singular functions on a specified domain are the solutions to the inhomogeneous differential equations for those singular functions. A numerical example was illustrated to verify the proposed method. Besides the continuous-time SVD, a discrete-time SVD is also presented for the impulse response function, which is modeled using a Toeplitz matrix in the discrete system. The proposed method has broad applications in signal processing, dynamic system analysis, acoustic analysis, thermal analysis, as well as macroeconomic modeling.

Keywords: singular value decomposition, impulse response function, Green’s function , Toeplitz matrix , Hankel matrix

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Authors: Daniel S. Brox

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Starting from earthquake fault dynamic equations, a correspondence between earthquake occurrence statistics in a seismic region before a major earthquake and eigenvalue statistics of a differential operator whose bound state eigenfunctions characterize the distribution of stress in the seismic region is derived. Modeling these eigenvalue statistics with a 2D Coulomb gas statistical physics model, previously reported deviation of seismic activation earthquake occurrence statistics from Gutenberg-Richter statistics in time intervals preceding the major earthquake is derived. It also explains how statistical physics modeling predicts a finite-dimensional nonlinear dynamic system that describes real-time velocity model evolution in the region undergoing seismic activation and how this prediction can be tested experimentally.

Keywords: seismic activation, statistical physics, geodynamics, signal processing

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Authors: Amit Kamra, V. K. Jain, Pragya

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Mammography is widely used technique for breast cancer screening. There are various other techniques for breast cancer screening but mammography is the most reliable and effective technique. The images obtained through mammography are of low contrast which causes problem for the radiologists to interpret. Hence, a high quality image is mandatory for the processing of the image for extracting any kind of information from it. Many contrast enhancement algorithms have been developed over the years. In the present work, an efficient morphology based technique is proposed for contrast enhancement of masses in mammographic images. The proposed method is based on Multiscale Morphology and it takes into consideration the scale of the structuring element. The proposed method is compared with other state-of-the-art techniques. The experimental results show that the proposed method is better both qualitatively and quantitatively than the other standard contrast enhancement techniques.

Keywords: enhancement, mammography, multi-scale, mathematical morphology

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Authors: Faiza Hussain, Usman Qamar

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Synonym-based searching is recognized to be a complicated problem as text mining from unstructured data of web is challenging. Finding useful information which matches user need from bulk of web pages is a cumbersome task. In this paper, a novel and practical synonym retrieval technique is proposed for addressing this problem. For replacement of semantics, user intent is taken into consideration to realize the technique. Parts-of-Speech tagging is applied for pattern generation of the query and a thesaurus for this experiment was formed and used. Comparison with Non-Context Based Searching, Context Based searching proved to be a more efficient approach while dealing with linguistic semantics. This approach is very beneficial in doing intent based searching. Finally, results and future dimensions are presented.

Keywords: natural language processing, text mining, information retrieval, parts-of-speech tagging, grammar, semantics

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Authors: Ridha Fethi Mechlouch, Ahlem Ayadi, Ammar Ben Brahim

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Given the importance of the preservation of polyphenols and carotenoids during thermal processing, we attempted in this study to investigate the variation of these two parameters in faba beans during microwave treatment using different power densities (1; 2; and 3W/g), then to perform a mathematical modeling by using non-linear regression analysis to evaluate the models constants. The variation of the carotenoids and polyphenols ratio of faba beans and the models are tested to validate the experimental results. Exponential models were found to be suitable to describe the variation of caratenoid ratio (R²= 0.945, 0.927 and 0.946) for power densities (1; 2; and 3W/g) respectively, and polyphenol ratio (R²= 0.931, 0.989 and 0.982) for power densities (1; 2; and 3W/g) respectively. The effect of microwave power density Pd(W/g) on the coefficient k of models were also investigated. The coefficient is highly correlated (R² = 1) and can be expressed as a polynomial function.

Keywords: microwave treatment, power density, carotenoid, polyphenol, modeling

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Authors: Arnold S. Freitas Neto, Rodrigo E. Coelho, Erick S. Mendonça

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This study aimed to obtain an alloy of Iron and Aluminum in the proportion of 50% of atomicity for each constituent. Alloys were obtained by processing recycled aluminum and chips of 1200 series carbon steel in a high-energy mill. For the experiment, raw materials were processed thorough high energy milling before mixing the substances. Subsequently, the mixture of 1200 series carbon steel and Aluminum powder was carried out a milling process. Thereafter, hot compression was performed in a closed die in order to obtain the samples. The pieces underwent heat treatments, sintering and aging. Lastly, the composition and the mechanical properties of their hardness were analyzed. In this paper, results are compared with previous studies, which used iron powder of high purity instead of Carbon steel in the composition.

Keywords: Fe-Al alloys, high energy milling, metallography characterization, powder metallurgy

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Authors: Osama Mohamed Elrajubi, Idris El-Feghi, Mohamed Abu Baker Saghayer

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This paper describes an identification of specific shapes within binary images using the morphological Hit-or-Miss Transform (HMT). Hit-or-Miss transform is a general binary morphological operation that can be used in searching of particular patterns of foreground and background pixels in an image. It is actually a basic operation of binary morphology since almost all other binary morphological operators are derived from it. The input of this method is a binary image and a structuring element (a template which will be searched in a binary image) while the output is another binary image. In this paper a modification of Hit-or-Miss transform has been proposed. The accuracy of algorithm is adjusted according to the similarity of the template and the sought template. The implementation of this method has been done by C language. The algorithm has been tested on several images and the results have shown that this new method can be used for similar shape detection.

Keywords: hit-or-miss operator transform, HMT, binary morphological operation, shape detection, binary images processing

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: Long Kim Vu

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The central processing unit in Electro-Optical devices is a Field-programmable gate array (FPGA) chip package allowing flexible, reconfigurable computing but energy consumption. Because chip package is placed in isolated devices based on IP67 waterproof standard, there is no air circulation and the heat dissipation is a challenge. In this paper, the author successfully modeled a chip package which various interposer materials such as silicon, glass and organics. Computational fluid dynamics (CFD) was utilized to analyze the thermal performance of chip package in the case of considering comprehensive heat transfer modes: conduction, convection and radiation, which proposes equivalent heat dissipation. The logic chip temperature varying with time is compared between the simulation and experiment results showing the excellent correlation, proving the reasonable chip modeling and simulation method.

Keywords: CFD, FPGA, heat transfer, thermal analysis

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Authors: Ashish Kumar, Tejendra Dixit, I. A. Palani, Vipul Singh

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Zinc oxide, with its interesting properties such as large band gap (3.37eV), high exciton binding energy (60 meV) and intense UV absorption has been studied in literature for various applications viz. optoelectronics, biosensors, UV-photodetectors etc. The performance of ZnO devices is highly influenced by morphologies, size, crystallinity of the ZnO active layer and processing conditions. Recently, our group has shown the influence of the in situ addition of KMnO4 in the precursor solution during the hydrothermal growth of ZnO nanorods (NRs) on their near band edge (NBE) emission. In this paper, we have investigated the effect of post-growth annealing on the variations in NBE and deep level (DL) emissions of as grown ZnO nanorods. These observed results have been explained on the basis of X-ray Diffraction (XRD) and Raman spectroscopic analysis, which clearly show that improved crystalinity and quantum confinement in ZnO nanorods.

Keywords: ZnO, nanorods, hydrothermal, KMnO4

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Authors: A. S. Ibrahim Mamane, S. Giljean, M.-J. Pac, G. L’Hostis

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Fiber reinforced composite laminates are particularly subject to residual stresses due to their heterogeneity and the complex chemical, mechanical and thermal mechanisms that occur during their processing. Residual stresses are now well known to cause damage accumulation, shape instability, and behavior disturbance in composite parts. Many works exist in the literature on techniques for minimizing residual stresses in thermosetting and thermoplastic composites mainly. To study in-depth the influence of processing mechanisms on the formation of residual stresses and to minimize them by establishing a reliable correlation, it is essential to be able to measure very precisely the profile of residual stresses in the composite. Residual stresses are important data to consider when sizing composite parts and predicting their behavior. The incremental hole drilling is very effective in measuring the gradient of residual stresses in composite laminates. This method is semi-destructive and consists of drilling incrementally a hole through the thickness of the material and measuring relaxation strains around the hole for each increment using three strain gauges. These strains are then converted into residual stresses using a matrix of coefficients. These coefficients, called calibration coefficients, depending on the diameter of the hole and the dimensions of the gauges used. The reliability of the incremental hole drilling depends on the accuracy with which the calibration coefficients are determined. These coefficients are calculated using a finite element model. The samples’ features and the experimental conditions must be considered in the simulation. Any mismatch can lead to inadequate calibration coefficients, thus introducing errors on residual stresses. Several calibration coefficient correction methods exist for isotropic material, but there is a lack of information on this subject concerning composite laminates. In this work, a Python program was developed to automatically generate the adequate finite element model. This model allowed us to perform a parametric study to assess the influence of experimental errors on the calibration coefficients. The results highlighted the sensitivity of the calibration coefficients to the considered errors and gave an order of magnitude of the precisions required on the experimental device to have reliable measurements. On the basis of these results, improvements were proposed on the experimental device. Furthermore, a numerical method was proposed to correct the calibration coefficients for different types of materials, including thick composite parts for which the analytical approach is too complex. This method consists of taking into account the experimental errors in the simulation. Accurate measurement of the experimental errors (such as eccentricity of the hole, angular deviation of the gauges from their theoretical position, or errors on increment depth) is therefore necessary. The aim is to determine more precisely the residual stresses and to expand the validity domain of the incremental hole drilling technique.

Keywords: fiber reinforced composites, finite element simulation, incremental hole drilling method, numerical correction of the calibration coefficients, residual stresses

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Authors: Abdolvahab Ehsani Rad, Mohd Shafry Mohd Rahim, Alireza Norouzi

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Medical image analysis is one of the great effects of computer image processing. There are several processes to analysis the medical images which the segmentation process is one of the challenging and most important step. In this paper the segmentation method proposed in order to segment the dental radiograph images. Thresholding method has been applied to simplify the images and to morphologically open binary image technique performed to eliminate the unnecessary regions on images. Furthermore, horizontal and vertical integral projection techniques used to extract the each individual tooth from radiograph images. Segmentation process has been done by applying the level set method on each extracted images. Nevertheless, the experiments results by 90% accuracy demonstrate that proposed method achieves high accuracy and promising result.

Keywords: integral production, level set method, morphological operation, segmentation

Procedia PDF Downloads 317

Authors: A. Boateng, La Gil-Alana, M. Lesaoana; Hj. Siweya, A. Belete

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This study examines long memory or long-range dependence in the CPI inflation rates of Ghana and South Africa using Whittle methods and autoregressive fractionally integrated moving average (ARFIMA) models. Standard I(0)/I(1) methods such as Augmented Dickey-Fuller (ADF), Philips-Perron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests were also employed. Our findings indicate that long memory exists in the CPI inflation rates of both countries. After processing fractional differencing and determining the short memory components, the models were specified as ARFIMA (4,0.35,2) and ARFIMA (3,0.49,3) respectively for Ghana and South Africa. Consequently, the CPI inflation rates of both countries are fractionally integrated and mean reverting. The implication of this result will assist in policy formulation and identification of inflationary pressures in an economy.

Keywords: Consumer Price Index (CPI) inflation rates, Whittle method, long memory, ARFIMA model

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Authors: Masaya Yoshikawa, Toshiya Asai, Ryoma Matsuhisa, Yusuke Nozaki, Kensaku Asahi

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Recently, side-channel attacks, which estimate secret keys using side-channel information such as power consumption and compromising emanations of cryptography circuits embedded in hardware, have become a serious problem. In particular, electromagnetic analysis attacks against cryptographic circuits between information processing and electromagnetic fields, which are related to secret keys in cryptography circuits, are the most threatening side-channel attacks. Therefore, it is important to evaluate tamper resistance against electromagnetic analysis attacks for cryptography circuits. The present study performs basic examination of the tamper resistance of cryptography circuits using electromagnetic analysis attacks with noise resources.

Keywords: tamper resistance, cryptographic circuit, hardware security evaluation, noise resources

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: E. M. S. Azzam, S. A. Ahmed, H. H. Mohamed, M. A. Adly, E. A. M. Gad

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In this work we prepared 3-(p-methyl) phenyl-5-thionyl-1,2,4-triazoline (C1). The nanostructure of the prepared C1 compound was fabricated by assembling on silver nanoparticles. The UV and TEM analyses confirm the assembling of C1 compound on silver nanoparticles. The effect of C1 compound on the removal of Iron (II) from Iron contaminated samples and industrial wastewater samples (produced water from oil processing facility) were studied before and after their assembling on silver nanoparticles. The removal of Iron was studied at different concentrations of FeSO4 solution (5, 14 and 39 mg/l) and field sample concentration (661 mg/l). In addition, the removal of Iron (II) was investigated at different times. The Prepared compound and its nanostructure with AgNPs show highly efficient in removing the Iron ions. Quantum chemical descriptors using DFT was discussed. The output of the study pronounces that the C1 molecule can act as chelating agent for Iron (II).

Keywords: triazole derivatives, silver nanoparticles, iron (II), oil field

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Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

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Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

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Authors: Marcel Schulze, Behrem Aslan, Silke Lux, Alexandra Philipsen

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Objective: Patients with Attention Deficit/Hyperactivity Disorder (ADHD) often report that they are being flooded by sensory impressions. Studies investigating sensory processing show hypersensitivity for sensory inputs across the senses in children and adults with ADHD. Especially the auditory modality is affected by deficient acoustical inhibition and modulation of signals. While studying unimodal signal-processing is relevant and well-suited in a controlled laboratory environment, everyday life situations occur multimodal. A complex interplay of the senses is necessary to form a unified percept. In order to achieve this, the unimodal sensory modalities are bound together in a process called multisensory integration (MI). In the current study we investigate MI in an adult ADHD sample using the McGurk-effect – a well-known illusion where incongruent speech like phonemes lead in case of successful integration to a new perceived phoneme via late top-down attentional allocation . In ADHD neuronal dysregulation at rest e.g., aberrant within or between network functional connectivity may also account for difficulties in integrating across the senses. Therefore, the current study includes resting-state functional connectivity to investigate a possible relation of deficient network connectivity and the ability of stimulus integration. Method: Twenty-five ADHD patients (6 females, age: 30.08 (SD:9,3) years) and twenty-four healthy controls (9 females; age: 26.88 (SD: 6.3) years) were recruited. MI was examined using the McGurk effect, where - in case of successful MI - incongruent speech-like phonemes between visual and auditory modality are leading to a perception of a new phoneme. Mann-Whitney-U test was applied to assess statistical differences between groups. Echo-planar imaging-resting-state functional MRI was acquired on a 3.0 Tesla Siemens Magnetom MR scanner. A seed-to-voxel analysis was realized using the CONN toolbox. Results: Susceptibility to McGurk was significantly lowered for ADHD patients (ADHDMdn:5.83%, ControlsMdn:44.2%, U= 160.5, p=0.022, r=-0.34). When ADHD patients integrated phonemes, reaction times were significantly longer (ADHDMdn:1260ms, ControlsMdn:582ms, U=41.0, p<.000, r= -0.56). In functional connectivity medio temporal gyrus (seed) was negatively associated with primary auditory cortex, inferior frontal gyrus, precentral gyrus, and fusiform gyrus. Conclusion: MI seems to be deficient for ADHD patients for stimuli that need top-down attentional allocation. This finding is supported by stronger functional connectivity from unimodal sensory areas to polymodal, MI convergence zones for complex stimuli in ADHD patients.

Keywords: attention-deficit hyperactivity disorder, audiovisual integration, McGurk-effect, resting-state functional connectivity

Procedia PDF Downloads 127

Authors: Mosab Alrashed, Wei Xu, Stephen Abineri, Yifan Zhao, Jörn Mehnen

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Thermographic inspection is a relatively new technique for Non-Destructive Testing (NDT) which has been gathering increasing interest due to its relatively low cost hardware and extremely fast data acquisition properties. This technique is especially promising in the area of rapid automated damage detection and quantification. In collaboration with a major industry partner from the aerospace sector advanced thermography-based NDT software for impact damaged composites is introduced. The software is based on correlation analysis of time-temperature profiles in combination with an image enhancement process. The prototype software is aiming to a) better visualise the damages in a relatively easy-to-use way and b) automatically and quantitatively measure the properties of the degradation. Knowing that degradation properties play an important role in the identification of degradation types, tests and results on specimens which were artificially damaged have been performed and analyzed.

Keywords: NDT, correlation analysis, image processing, damage, inspection

Procedia PDF Downloads 547

Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: data integration, data warehousing, federated architecture, Online Analytical Processing (OLAP)

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Authors: Aleksey Fedorov

Abstract:

Building a scalable platform for quantum computing remains one of the most challenging tasks in quantum science and technologies. However, the implementation of most important quantum operations with qubits (quantum analogues of classical bits), such as multiqubit Toffoli gate, requires either a polynomial number of operation or a linear number of operations with the use of ancilla qubits. Therefore, the reduction of the number of operations in the presence of scalability is a crucial goal in quantum information processing. One of the most elegant ideas in this direction is to use qudits (multilevel systems) instead of qubits and rely on additional levels of qudits instead of ancillas. Although some of the already obtained results demonstrate a reduction of the number of operation, they suffer from high complexity and/or of the absence of scalability. We show a strong reduction of the number of operations for the realization of the Toffoli gate by using qudits for a scalable multi-qudit processor. This is done on the basis of a general relation between the dimensionality of qudits and their topology of connections, that we derived.

Keywords: quantum computing, qudits, Toffoli gates, gate decomposition

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Authors: Fahad Suleiman

Abstract:

Queuing, in medical system is the process of moving patients in a specific sequence to a specific service according to the patients’ nature of illness. The term scheduling stands for the process of computing a schedule. This may be done by a queuing based scheduler. This paper focuses on the medical consultancy system, the different queuing algorithms that are used in healthcare system to serve the patients, and the average waiting time. The aim of this paper is to build automatic queuing system for organizing the medical queuing system that can analyses the queue status and take decision which patient to serve. The new queuing architecture model can switch between different scheduling algorithms according to the testing results and the factor of the average waiting time. The main innovation of this work concerns the modeling of the average waiting time is taken into processing, in addition with the process of switching to the scheduling algorithm that gives the best average waiting time.

Keywords: queuing systems, queuing system models, scheduling algorithms, patients

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Authors: Jerzy Merkisz, Piotr Lijewski, Pawel Fuc, Maciej Siedlecki, Andrzej Ziolkowski, Sylwester Weymann

Abstract:

The paper discusses the problem of load transport using farm tractors and road tractor units. This type of carriage of goods is often done with farm vehicles. The tests were performed with the PEMS equipment (Portable Emission Measurement System) under actual traffic conditions. The vehicles carried a load of 20000 kg. This research method is one of the most desired because it provides reliable information on the actual vehicle emissions and fuel consumption (carbon balance method). For the tests, a route was selected that simulated a trip from a small town to a food-processing facility located in a city. The analysis of the obtained results gave a clear answer as to what vehicles need to be used for the carriage of this type of cargo in terms of exhaust emissions and fuel consumption.

Keywords: emission, transport, fuel consumption, PEMS

Procedia PDF Downloads 530