Search results for: highly accurate

Authors: Areej A. Alzarqaa, Shahira S. Roushdy, Ali A. Alderfasi, Fahad A. AL-Yahya, Ahmed A. Dawaba

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The resistance of mungbean (Vigna radiata L. Wilczeck) and its physiological responses to drought stress was studied in a greenhouse pot experiment. A randomized complete block Design (RCBD) with factorial arrangement having three replications of each treatment was used. Treatments included three water deficit samples (80%, 40% and 20% of field capacity), two mungbean genotypes (Kawmay-1 and VC2010) and two root-knot nematode (Meloidogyne javanica) infection levels (infected and non-infected). Results showed that water deficit stress significantly hampered most of the studied parameters, except for the shoot water content, whereas genotypes showed highly significant differences for stomatal conductance, shoot dry weight and leaf area. Shoot water content was found to be non-significant in relation to chlorophyll b, shoot dry weight and leaf area, whereas highly significant but negatively correlated with chlorophyll a and stomatal conductance. However, all other possible correlations among studied parameters were found to be highly and positively significant. Results also showed that VC 2010 surpassed Kawmay-1 in most of studied characteristics. In the present study, genotypic variation was observed for these parameters and can be used as a basis for selection of the most promising variety under drought conditions.

Keywords: drought stress, Meloidogyne javanica, mungbean, stomatal conductivity, leaf area, root-knot nematode, shoot water content

Procedia PDF Downloads 368

Authors: Aicha Majda, Abdelhamid El Hassani

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Lung CT image segmentation is a prerequisite in lung CT image analysis. Most of the conventional methods need a post-processing to deal with the abnormal lung CT scans such as lung nodules or other lesions. The simplest similarity measure in the standard Graph Cuts Algorithm consists of directly comparing the pixel values of the two neighboring regions, which is not accurate because this kind of metrics is extremely sensitive to minor transformations such as noise or other artifacts problems. In this work, we propose an improved version of the standard graph cuts algorithm based on the Patch-Based similarity metric. The boundary penalty term in the graph cut algorithm is defined Based on Patch-Based similarity measurement instead of the simple intensity measurement in the standard method. The weights between each pixel and its neighboring pixels are Based on the obtained new term. The graph is then created using theses weights between its nodes. Finally, the segmentation is completed with the minimum cut/Max-Flow algorithm. Experimental results show that the proposed method is very accurate and efficient, and can directly provide explicit lung regions without any post-processing operations compared to the standard method.

Keywords: graph cuts, lung CT scan, lung parenchyma segmentation, patch-based similarity metric

Procedia PDF Downloads 165

Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

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Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

Procedia PDF Downloads 43

Authors: Mohammadamin Esmaeilzadehazimi, Morteza Shayan Arani, Mohammad Toorani, Aouni Lakis

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This study uses a hybrid finite element method to predict the dynamic behavior of both fully and partially-filled truncated conical shells stiffened with ring stiffeners. The method combines classical shell theory and the finite element method, and employs displacement functions derived from exact solutions of Sanders' shell equilibrium equations for conical shells. The shell-fluid interface is analyzed by utilizing the velocity potential, Bernoulli's equation, and impermeability conditions to determine an explicit expression for fluid pressure. The equations of motion presented in this study apply to both conical and cylindrical shells. This study presents the first comparison of the method applied to ring-stiffened shells with other numerical and experimental findings. Vibration frequencies for conical shells with various boundary conditions and geometries in a vacuum and filled with water are compared with experimental and numerical investigations, achieving good agreement. The study thoroughly investigates the influence of geometric parameters, stiffener quantity, semi-vertex cone angle, level of water filled in the cone, and applied boundary conditions on the natural frequency of fluid-loaded ring-stiffened conical shells, and draws some useful conclusions. The primary advantage of the current method is its use of a minimal number of finite elements while achieving highly accurate results.

Keywords: finite element method, fluid–structure interaction, conical shell, natural frequency, ring-stiffener

Procedia PDF Downloads 74

Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

Procedia PDF Downloads 48

Authors: Heba Kamal, Ghada Saudi

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Due to The rapid expansion of energy and growing number of wind turbines construction in earthquake areas, a design method for simple and accurate evaluation of seismic load to ensure structural integrity is required. In Egypt, there are some appropriate places to build wind turbine towers lie in active seismically regions, so accurate analysis is necessary for prediction of seismic loads with consideration of intensity of the earthquake, soil and structural characteristics. In this research, seismic behavior of wind turbine towers Gamesa Type G52 in Zafarana Wind Farm Egypt is investigated using experimental work by ambient vibration test, and fully dynamic analysis based on time history from El Aqaba Earthquake 1995 using 3D by PLAXIS 3D software, including the soil structure interaction effect. The results obtained from dynamic analyses are discussed. From this study, it is concluded that, the fully dynamic seismic analysis based on used PLAXIS 3D with the aid of the full scale ambient vibration test gives almost good simulation for the seismic loads that can be applied to wind turbine tower design in Egypt.

Keywords: Wind turbine towers, Zafarana Wind Farm, Gamesa Type G52, ambient vibration test

Procedia PDF Downloads 205

Authors: Indri Mahadiraka Rumamby, R. R. Dwinanti Rika Marthanty, Jessica Sjah

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Smoothed particle hydrodynamics (SPH) was originally created to simulate nonaxisymmetric phenomena in astrophysics. However, this method still has several shortcomings, namely the high computational cost required to model values with high resolution and problems with boundary conditions. The difficulty of modeling boundary conditions occurs because the SPH method is influenced by particle deficiency due to the integral of the kernel function being truncated by boundary conditions. This research aims to answer if SPH modeling with a focus on boundary layer interactions and continuous flow can produce quantifiably accurate values with low computational cost. This research will combine algorithms and coding in the main program of meandering river, continuous flow algorithm, and solid-fluid algorithm with the aim of obtaining quantitatively accurate results on solid-fluid interactions with the continuous flow on a meandering channel using the SPH method. This study uses the Fortran programming language for modeling the SPH (Smoothed Particle Hydrodynamics) numerical method; the model is conducted in the form of a U-shaped meandering open channel in 3D, where the channel walls are soil particles and uses a continuous flow with a limited number of particles.

Keywords: smoothed particle hydrodynamics, computational fluid dynamics, numerical simulation, fluid mechanics

Procedia PDF Downloads 123

Authors: Muhammad Naeem, Abdus Salam, Sumera Yasmin, Abir Ishtiaq

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In the present study, eighty-three wild Oreochromis aureus of different body size ranging 5.3-14.6 cm in total length were collected from the River Chenab, District Muzzafer Garh, Pakistan to investigate the parameters of length –weight, length-length relationships and condition factor in relation to size. Each fish was measured and weighed on arrival at laboratory. Log transformed regressions were used to test the allometric growth. Length-weight relationship was found highly significant (r = 0.964; P < 0.01). The values of exponent “ b” in Length–weight regression (W=aL^b), deviated from 3, showing isometric growth (b = 2.75). Results for LLRs indicated that these are highly correlated (P < 0.001). Condition factor (K) found constant with increasing body weight, however, showed negative influence with increasing total length.

Keywords: lenght-weight, Oreochromis aureus, morphometric study

Procedia PDF Downloads 437

Authors: Thein Thein, Kalyar Myo San

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Lip reading system is one of the different supportive technologies for hearing impaired, or elderly people or non-native speakers. For normal hearing persons in noisy environments or in conditions where the audio signal is not available, lip reading techniques can be used to increase their understanding of spoken language. Hearing impaired persons have used lip reading techniques as important tools to find out what was said by other people without hearing voice. Thus, visual speech information is important and become active research area. Using visual information from lip movements can improve the accuracy and robustness of a speech recognition system and the need for lip reading system is ever increasing for every language. However, the recognition of lip movement is a difficult task because of the region of interest (ROI) is nonlinear and noisy. Therefore, this paper proposes method to detect the accurate lips shape and to localize lip movement towards automatic lip tracking by using the combination of Otsu global thresholding technique and Moore Neighborhood Tracing Algorithm. Proposed method shows how accurate lip localization and tracking which is useful for speech recognition. In this work of study and experiments will be carried out the automatic lip localizing the lip shape for Myanmar consonants using the only visual information from lip movements which is useful for visual speech of Myanmar languages.

Keywords: lip reading, lip localization, lip tracking, Moore neighborhood tracing algorithm

Procedia PDF Downloads 349

Authors: Milca F. Coelho, K. Bousson, Kawser Ahmed

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To accurately track an aerospace vehicle in a time-critical situation and in a highly nonlinear environment, is one of the strongest interests within the aerospace community. The tracking is achieved by estimating accurately the state of a moving target, which is composed of a set of variables that can provide a complete status of the system at a given time. One of the main ingredients for a good estimation performance is the use of efficient estimation algorithms. A well-known framework is the Kalman filtering methods, designed for prediction and estimation problems. The success of the Kalman Filter (KF) in engineering applications is mostly due to the Extended Kalman Filter (EKF), which is based on local linearization. Besides its popularity, the EKF presents several limitations. To address these limitations and as a possible solution to tracking problems, this paper proposes the use of the Ensemble Kalman Filter (EnKF). Although the EnKF is being extensively used in the context of weather forecasting and it is being recognized for producing accurate and computationally effective estimation on systems with a very high dimension, it is almost unknown by the tracking community. The EnKF was initially proposed as an attempt to improve the error covariance calculation, which on the classic Kalman Filter is difficult to implement. Also, in the EnKF method the prediction and analysis error covariances have ensemble representations. These ensembles have sizes which limit the number of degrees of freedom, in a way that the filter error covariance calculations are a lot more practical for modest ensemble sizes. In this paper, a realistic simulation of a radar tracking was performed, where the EnKF was applied and compared with the Extended Kalman Filter. The results suggested that the EnKF is a promising tool for tracking applications, offering more advantages in terms of performance.

Keywords: Kalman filter, nonlinear state estimation, optimal tracking, stochastic environment

Procedia PDF Downloads 139

Authors: Mohsen Torabi, Nader Karimi, Kaili Zhang

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This work aims to provide a comprehensive study on the heat transfer and entropy generation rates of a horizontal channel partially filled with a porous medium which experiences internal heat generation or consumption due to exothermic or endothermic chemical reaction. The focus has been given to the local thermal non-equilibrium (LTNE) model. The LTNE approach helps us to deliver more accurate data regarding temperature distribution within the system and accordingly to provide more accurate Nusselt number and entropy generation rates. Darcy-Brinkman model is used for the momentum equations, and constant heat flux is assumed for boundary conditions for both upper and lower surfaces. Analytical solutions have been provided for both velocity and temperature fields. By incorporating the investigated velocity and temperature formulas into the provided fundamental equations for the entropy generation, both local and total entropy generation rates are plotted for a number of cases. Bifurcation phenomena regarding temperature distribution and interface heat flux ratio are observed. It has been found that the exothermicity or endothermicity characteristic of the channel does have a considerable impact on the temperature fields and entropy generation rates.

Keywords: entropy generation, exothermicity or endothermicity, forced convection, local thermal non-equilibrium, analytical modelling

Procedia PDF Downloads 410

Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

Procedia PDF Downloads 195

Authors: Gaurav Malik, Satyendra Mourya, Jyoti Jaiswal, Ramesh Chandra

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The attractive and multifunctional properties of ZnO make it a promising material for the fabrication of highly sensitive and selective efficient gas sensors at room temperature. This presented article focuses on the development of highly selective and sensitive H₂ gas sensor based on the Pd-Pt decorated ZnO framework and its sensing mechanisms. The gas sensing performance of sputter made Pd-Pt/ZnO electrode on anodized porous silicon (PSi) substrate toward H₂ gas is studied under low detection limit (2–500 ppm) of H₂ in the air. The chemiresistive sensor demonstrated sublimate selectivity, good sensing response, and fast response/recovery time with excellent stability towards H₂ at low temperature operation under ambient environment. The elaborate selective measurement of Pd-Pt/ZnO/PSi structure was performed towards different oxidizing and reducing gases. This structure exhibited advance and reversible response to H₂ gas, which revealed that the acquired architecture with ZnO framework is a promising candidate for H₂ gas sensor.

Keywords: sputtering, porous silicon, ZnO framework, XPS spectra, gas sensor

Procedia PDF Downloads 388

Authors: Zohreh Derikvand

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Nanoreactors Zr-MCM-41were prepared via the reaction of ZrOCl2, Fumed silica, sodium hydroxide and cethyltrimethyl ammonium bromide under hydrothermal condition. The prepared nanoreactors were characterized by FT-IR spectroscopy, X-ray diffraction (XRD), Scanning electron micrographs (SEM) and nitrogen adsorption-desorption. The XRD pattern of Zr-MCM-41 exhibits a high-intensity (100) and two low-intensity reflections (110 and 200) which are characteristic of hexagonal structure, exhibiting the long-range order and good textural uniformity of mesoporous structure. Based on the green chemistry approach, we report an efficient and environmentally benign protocol to study the catalytic activity of Zr-MCM-41 in the Biginelli type reactions initially. Nanoreactors Zr-MCM-41 were used as highly recoverable and reusable catalyst for synthesis of 3,4-dihydropyrimidin-2(1H)-one, octahydroquinazolinone, benzimidazolo-quinazolineone and 4,6-diarylpyrimidin-2(1H)-one. The methodology offers several advantages such as short reaction time, high yields and simple operation. The catalyst was active up to three cycles.

Keywords: Zr-MCM-41 nanoreactors, Biginelli like reactions, 3, 4-dihydropyrimidin-2(1H)-ones, ctahydroquinazolinones

Procedia PDF Downloads 201

Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

Procedia PDF Downloads 436

Authors: Sophie Mills, Leila L. Touil, Richard Sisson

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Introduction: The TNM classification is the global standard for the staging of head and neck cancers. Accurate clinical-radiological staging of tumours (cTNM) is essential to predict prognosis, facilitate surgical planning and determine the need for other therapeutic modalities. This study aims to determine the accuracy of pre-operative cTNM staging using pathological TNM (pTNM) and consider possible causes of TNM stage migration, noting any variation throughout the COVID-19 pandemic. Materials and Methods: A retrospective cohort study examined records of patients with surgical management of head and neck cancer at a tertiary head and neck centre from November 2019 to November 2020. Data was extracted from Somerset Cancer Registry and histopathology reports. cTNM and pTNM were compared before and during the first wave of COVID-19, as well as with other potential prognostic factors such as tumour site and tumour stage. Results: 119 cases were identified, of which 52.1% (n=62) were male, and 47.9% (n=57) were female with a mean age of 67 years. Clinical and pathological staging differed in 54.6% (n=65) of cases. Of the patients with stage migration, 40.4% (n=23) were up-staged and 59.6% (n=34) were down-staged compared with pTNM. There was no significant difference in the accuracy of cTNM staging compared with age, sex, or tumour site. There was a statistically highly significant (p < 0.001) correlation between cTNM accuracy and tumour stage, with the accuracy of cTNM staging decreasing with the advancement of pTNM staging. No statistically significant variation was noted between patients staged prior to and during COVID-19. Conclusions: Discrepancies in staging can impact management and outcomes for patients. This study found that the higher the pTNM, the more likely stage migration will occur. These findings are concordant with the oncology literature, which highlights the need to improve the accuracy of cTNM staging for more advanced tumours.

Keywords: COVID-19, head and neck cancer, stage migration, TNM staging

Procedia PDF Downloads 103

Authors: Ahmed Elaksher, Islam Omar

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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.

Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition

Procedia PDF Downloads 63

Authors: Muhammad Naeem, Amina Zubari, Abdus Salam, Summera Yasmeen, Syed Ali Ayub Bukhari, Abir Ishtiaq

Abstract:

In the present study, eighty three wild Oreochromis aureus of different body size ranging 5.3-14.6 cm in total length were collected from the River Chenab, District Muzzafer Garh, Pakistan to investigate the parameters of length –weight, length-length relationships and condition factor in relation to size. Each fish was measured and weighed on arrival at laboratory. Log transformed regressions were used to test the allometric growth. Length-weight relationship was found highly significant (r = 0.964; P < 0.01). The values of exponent “ b” in Length–weight regression (W=aLb), deviated from 3, showing isometric growth (b = 2.75). Results for LLRs indicated that these are highly correlated (P< 0.001). Condition factor (K) found constant with increasing body weight, however, showed negative influence with increasing total length.

Keywords: Oreochromis aureus, weight-length relationship, condition factor, predictive equations

Procedia PDF Downloads 829

Authors: Chia-Min Lai, Wan-Ching Lin, Hahn-Ming Lee, Ching-Hao Mao

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As world wild internet has non-stop developments, making profit by lending registered domain names emerges as a new business in recent years. Unfortunately, the larger the market scale of domain lending service becomes, the riskier that there exist malicious behaviors or malwares hiding behind parked domains will be. Also, previous work for differentiating parked domain suffers two main defects: 1) too much data-collecting effort and CPU latency needed for features engineering and 2) ineffectiveness when detecting parked domains containing external links that are usually abused by hackers, e.g., drive-by download attack. Aiming for alleviating above defects without sacrificing practical usability, this paper proposes ParkedGuard as an efficient and accurate parked domain detector. Several scripting behavioral features were analyzed, while those with special statistical significance are adopted in ParkedGuard to make feature engineering much more cost-efficient. On the other hand, finding memberships between external links and parked domains was modeled as a graph mining problem, and a coarse-to-fine strategy was elaborately designed by leverage the graphical locality such that ParkedGuard outperforms the state-of-the-art in terms of both recall and precision rates.

Keywords: coarse-to-fine strategy, domain parking service, graphical locality analysis, parked domain

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Authors: Igor Izosimov

Abstract:

This work is devoted to applications of the Time-resolved laser-induced luminescence (TRLIF) spectroscopy and time-resolved laser-induced chemiluminescence spectroscopy for detection of lanthanides and actinides. Results of the experiments on Eu, Sm, U, and Pu detection in solutions are presented. The limit of uranyl detection (LOD) in urine in our TRLIF experiments was up to 5 pg/ml. In blood plasma LOD was 0.1 ng/ml and after mineralization was up to 8pg/ml – 10pg/ml. In pure solution, the limit of detection of europium was 0.005ng/ml and samarium, 0.07ng/ml. After addition urine, the limit of detection of europium was 0.015 ng/ml and samarium, 0.2 ng/ml. Pu, Np, and some U compounds do not produce direct luminescence in solutions, but when excited by laser radiation, they can induce chemiluminescence of some chemiluminogen (luminol in our experiments). It is shown that multi-photon scheme of chemiluminescence excitation makes chemiluminescence not only a highly sensitive but also a highly selective tool for the detection of lanthanides/actinides in solutions.

Keywords: actinides/lanthanides detection, laser spectroscopy with time resolution, luminescence/chemiluminescence, solutions

Procedia PDF Downloads 326

Authors: Isao Tomita

Abstract:

Electrical conduction in a quasi-one-dimensional polycrystalline metallic ring with a long electron phase coherence length realized at low temperature is investigated. In this situation, the wave nature of electrons is important in the ring, where the electrical current I can be induced by a vector potential that arises from a static magnetic field applied perpendicularly to the ring’s area. It is shown that if the average grain size of the polycrystalline ring becomes large (or comparable to the Fermi wavelength), the electrical current I increases to ~I0, where I0 is a current in a disorder-free ring. The cause of this increasing effect is examined, and this takes place if the electron localization length in the polycrystalline potential increases with increasing grain size, which gives rise to coherent connection of tails of a localized electron wave function in the ring and thus provides highly coherent electrical conduction.

Keywords: electrical conduction, electron phase coherence, polycrystalline metal, magnetic field

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Authors: Gobinda Gopal Khan, Ashutosh K. Singh, Debasish Sarkar

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Unique one-dimensional (1D) Ni-NiO and Co-Ni/Co3O4-NiO core/shell nano-heterostructures are fabricated by combining the electrochemical deposition and annealing. The high-performance pseudo-capacitor electrode based on the Ni-NiO and Co-Ni/Co3O4-NiO core/shell nano-heterostructures is designed and demonstrated. The Co-Ni/Co3O4-NiO core/shell nano-heterostructures exhibit high specific capacitance (2013 Fg-1 at 2.5 Ag-1), high energy and power density (23 Wh kg-1 and 5.5 kW kg-1, at the discharge current density of 20.8 A g-1.), good capacitance retention, and long cyclicality. The remarkable electrochemical property of the large surface area nano-heterostructures is demonstrated based on the novel nano-architectural design of the electrode with the coexistence of the two highly redox active materials at the surface supported by highly conducting metal alloy channel at the core for faster charge transport.

Keywords: nano-heterostructures, energy storage, supercapacitors, electrochemical deposition

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Authors: Ashwani Kumar

Abstract:

Use of NIR light for imaging the biological tissue and to quantify its optical properties is a good choice over other invasive methods. Optical tomography involves two steps. One is the forward problem and the other is the reconstruction problem. The forward problem consists of finding the measurements of transmitted light through the tissue from source to detector, given the spatial distribution of absorption and scattering properties. The second step is the reconstruction problem. In X-ray tomography, there is standard method for reconstruction called filtered back projection method or the algebraic reconstruction methods. But this method cannot be applied as such, in optical tomography due to highly scattering nature of biological tissue. A hybrid algorithm for reconstruction has been implemented in this work which takes into account the highly scattered path taken by photons while back projecting the forward data obtained during Monte Carlo simulation. The reconstructed image suffers from blurring due to point spread function.

Keywords: NIR light, tissue, blurring, Monte Carlo simulation

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Authors: D. Reich, R. Stark

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To provide reliable and valid findings when evaluating innovative in-car devices in the automotive context highly realistic driving environments are recommended. Nowadays, in-car devices are mostly evaluated due to driving simulator studies followed by real car driving experiments. Driving simulators are characterized by high internal validity, but weak regarding ecological validity. Real car driving experiments are ecologically valid, but difficult to standardize, more time-robbing and costly. One economizing suggestion is to implement more immersive driving environments when applying driving simulator studies. This paper presents research comparing non-immersive standard PC conditions with mobile and highly immersive Oculus Rift conditions while performing the Lane Change Task (LCT). Subjective data with twenty participants show advantages regarding presence and immersion experience when performing the LCT with the Oculus Rift, but affect adversely cognitive workload and simulator sickness, compared to non-immersive PC condition.

Keywords: immersion, oculus rift, presence, situation awareness

Procedia PDF Downloads 183

Authors: Barbara Polo Martin

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During the 18th century, the border between Spain and France underwent various changes, primarily due to territorial agreements, wars, and treaties between the two nations and other European powers. For studying these changes, the Cassini maps remain valuable historical documents, offering a glimpse into the landscape and geography of 18th-century France and its neighboring regions, including the border between Spain and France. However, it's essential to recognize that these maps may not reflect modern political boundaries or territorial changes that have occurred since their creation. The project was initiated by King Louis XV in 1744 and continued by his successor, Louis XVI. The primary objective was to produce accurate maps of France, which would serve various purposes, including military, administrative, and scientific. The Cassini maps were groundbreaking for their time, as they were among the earliest attempts to create topographic maps on a national scale. They covered the entirety of France and were based on meticulous surveying and cartographic techniques. The maps featured precise geographic details, including elevation contours, rivers, roads, forests, and settlements. This study aims to analyze this rich and unknown cartography of France, study the rich place names it offers, as well as the accuracy of delimitations created over time between both empires in a historical way but also through a Geographical Information System. This study will offer a deeper knowledge about the cartography that supposes the beginning of topography in Europe.

Keywords: cartography, engineering, borders, Spain, France, Cassini

Procedia PDF Downloads 58

Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

Abstract:

Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

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Authors: Amine Ben Slama, Aymen Mouelhi, Sondes Manoubi, Chiraz Mbarek, Hedi Trabelsi, Mounir Sayadi, Farhat Fnaiech

Abstract:

Vertigo is a sensation of feeling off balance; the cause of this symptom is very difficult to interpret and needs a complementary exam. Generally, vertigo is caused by an ear problem. Some of the most common causes include: benign paroxysmal positional vertigo (BPPV), Meniere's disease and vestibular neuritis (VN). In clinical practice, different tests of videonystagmographic (VNG) technique are used to detect the presence of vestibular neuritis (VN). The topographical diagnosis of this disease presents a large diversity in its characteristics that confirm a mixture of problems for usual etiological analysis methods. In this study, a vestibular neuritis analysis method is proposed with videonystagmography (VNG) applications using an estimation of pupil movements in the case of an uncontrolled motion to obtain an efficient and reliable diagnosis results. First, an estimation of the pupil displacement vectors using with Hough Transform (HT) is performed to approximate the location of pupil region. Then, temporal and frequency features are computed from the rotation angle variation of the pupil motion. Finally, optimized features are selected using Fisher criterion evaluation for discrimination and classification of the VN disease.Experimental results are analyzed using two categories: normal and pathologic. By classifying the reduced features using the Support Vector Machine (SVM), 94% is achieved as classification accuracy. Compared to recent studies, the proposed expert system is extremely helpful and highly effective to resolve the problem of VNG analysis and provide an accurate diagnostic for medical devices.

Keywords: nystagmus, vestibular neuritis, videonystagmographic system, VNG, Fisher criterion, support vector machine, SVM

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Authors: Claudia Camargo Wilson, Enrique Javier de la Vega Bustillos, Jesús Everardo Olguín Tiznado, Juan Andrés López Barreras, Sandra K. Enriquez

Abstract:

This study focuses on the application of sensory thermography, as a non-invasive method to evaluate the musculoskeletal injuries that industry workers performing Highly Repetitive Movements (HRM) may acquire. It was made at a wireless company having the target of analyze temperatures in worker’s wrists, elbows and shoulders in workstations during their activities, this thru sensorial thermography with the goal of detecting maximum temperatures (Tmax) that could indicate possible injuries. The tests were applied during 3 hours for only 2 workers that work in workstations where there’s been the highest index of injuries and accidents. We were made comparisons for each part of the body that were study for both because of the similitude between the activities of the workstations; they were requiring both an immediate evaluation. The Tmax was recorder during the test of the worker 2, in the left wrist, reaching a temperature of 35.088ºC and with a maximum increase of 1.856°C.

Keywords: thermography, maximum temperaturas (Tmax), highly repetitive movements (HRM), operator

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Authors: Abeer Mohammed, Nevein Mustafa Elashery, Mona Hassan Abdel Aal, Ereny Wilson Nagib

Abstract:

The Aim of the current study is to investigate nutrition role in the management of psychiatric disorders. Research Design: A quasi- experimental research design was utilized for this study. Setting The study was conducted at outpatient clinic at Institute of Psychiatry affiliated to Ain Shams University hospitals, using a convenient sample of 50 psychiatric patients with depression, schizophrenia, bipolar disorders, and obsessive compulsive disorders. Tools: data were collected through; first, an interview questionnaire covering socio-demographic characteristics, second, nutrition assessment tools Third, nutrition risk assessment. Fourth, nutrition management program Results showed that there were highly statistically significant improvements in modified nutritional supplements for patients with depression, schizophrenia, bipolar disorders, and obsessive compulsive disorders' patients after conducting the nutrition management program. Regarding psychiatric patients’ knowledge about healthy food, healthy nutritional habits, and patients’ awareness & readiness for change, there were highly statistically significant improvements. Concerning signs and symptoms of psychiatric disorders, there were highly statistically significant improvements for depression, schizophrenia, bipolar disorders, and obsessive-compulsive patients after conducting the management program. In conclusion, the nutrition management program was effective in improving symptoms associated with, depression, schizophrenia, bipolar disorders, and obsessive compulsive disorders. The study recommended that nurses should have more contribution in counseling psychiatric patients, and their families about healthy diet and healthy habits. Further research should recommend studying the effectiveness of herbs on enhancing mental health for psychiatric patients.

Keywords: nutrition, role, management, psychiatric disorders

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Authors: M. Amo, A. Alvaro, A. Astudillo, R. Mc Culloch, J. C. del Castillo, M. Gómez, J. M. Martín

Abstract:

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) of course comprise a range of highly toxic compounds that may exist as particulates within the air or accumulate within water supplies, soil, or vegetation. They may be created either ubiquitously or naturally within the environment as a product of forest fires or volcanic eruptions. It is only since the industrial revolution, however, that it has become necessary to closely monitor their generation as a byproduct of manufacturing/combustion processes, in an effort to mitigate widespread contamination events. Of course, the environmental concentrations of these toxins are expected to be extremely low, therefore highly sensitive and accurate methods are required for their determination. Since ionization of non-polar compounds through electrospray and APCI is difficult and inefficient, we evaluate the performance of a novel low-flow Atmospheric Pressure Photoionization (APPI) source for the trace detection of various dioxins and furans using rapid Mass Spectrometry workflows. Air, soil and biota (vegetable matter) samples were collected monthly during one year from various locations within the vicinity of an industrial incinerator in Spain. Analytes were extracted and concentrated using soxhlet extraction in toluene and concentrated by rotavapor and nitrogen flow. Various ionization methods as electrospray (ES) and atmospheric pressure chemical ionization (APCI) were evaluated, however, only the low-flow APPI source was capable of providing the necessary performance, in terms of sensitivity, required for detecting all targeted analytes. In total, 10 analytes including 2,3,7,8-tetrachlorodibenzodioxin (TCDD) were detected and characterized using the APPI-MS method. Both PCDDs and PCFDs were detected most efficiently in negative ionization mode. The most abundant ion always corresponded to the loss of a chlorine and addition of an oxygen, yielding [M-Cl+O]- ions. MRM methods were created in order to provide selectivity for each analyte. No chromatographic separation was employed; however, matrix effects were determined to have a negligible impact on analyte signals. Triple Quadrupole Mass Spectrometry was chosen because of its unique potential for high sensitivity and selectivity. The mass spectrometer used was a Sciex´s Qtrap3200 working in negative Multi Reacting Monitoring Mode (MRM). Typically mass detection limits were determined to be near the 1-pg level. The APPI-MS2 technology applied to the detection of PCDD/Fs allows fast and reliable atmospheric analysis, minimizing considerably operational times and costs, with respect other technologies available. In addition, the limit of detection can be easily improved using a more sensitive mass spectrometer since the background in the analysis channel is very low. The APPI developed by SEADM allows polar and non-polar compounds ionization with high efficiency and repeatability.

Keywords: atmospheric pressure photoionization-mass spectrometry (APPI-MS), dioxin, furan, incinerator

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