Search results for: current vector

Authors: Michael Koblischka, Yassine Slimani, Thomas Karwoth, Anjela Koblischka-Veneva, Essia Hannachi

Abstract:

On bulk FeSe superconductors containing different additions of Ag, a thorough investigation of the microstructures was performed using optical microscopy, SEM and TEM. The electrical resistivity was measured using four-point measurements in the temperature range 2 K ≤ T ≤ 150 K. The data obtained are analyzed in the framework of the excess conductivity approach using the Aslamazov-Larkin (AL) model. The investigated samples comprised of five distinct fluctuation regimes, namely short-wave (SWF), onedimensional (1D), two-dimensional (2D), three-dimensional (3D), and critical (CR) fluctuation regimes. The coherence length along the c-axis at zero-temperature (ξc(0)), the lower and upper critical magnetic fields (Bc1 and Bc2), the critical current density (Jc) and numerous other superconducting parameters were estimated with respect to the Ag content in the samples. The data reveal a reduction of the resistivity and a strong decrease of ξc(0) when doping the 11-samples with silver. The optimum content of the Ag-addition is found at 4 wt.-% Ag, yielding the highest critical current density.

Keywords: iron-based superconductors, FeSe, Ag-addition, excess conductivity, microstructure

Procedia PDF Downloads 145

Authors: K. Khlifi, A. Haddouk, M. Hlaili, H. Mechergui

Abstract:

The present paper provides a detailed analysis of prior methods and approaches for non-linear load identification in residential buildings. The main goal of this analysis is to decipher the distorted signals and to estimate the harmonics influence on power systems. We have performed an analytical study of non-linear loads behavior in the residential environment. Simulations have been performed in order to evaluate the distorted rate of the current and follow his behavior. To complete this work, an instrumental platform has been realized to carry out practical tests on single-phase non-linear loads which illustrate the current consumption of some domestic appliances supplied with single-phase sinusoidal voltage. These non-linear loads have been processed and tracked in order to limit their influence on the power grid and to reduce the Joule effect losses. As a result, the study has allowed to identify responsible circuits of harmonic pollution.

Keywords: distortion rate, harmonic analysis, harmonic pollution, non-linear load, power factor

Procedia PDF Downloads 143

Authors: Nimisha Elsa Koshy, Varun P. Gopi, V. I. Thajudin Ahamed

Abstract:

The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However, analysis of massive images of WCE detection is tedious and time consuming to physicians. Hence, researchers have developed software methods to detect these diseases automatically. Thus, the effectiveness of WCE can be improved. In this paper, a novel textural feature extraction method is proposed based on Contourlet transform and Log Gabor filter to distinguish ulcer regions from normal regions. The results show that the proposed method performs well with a high accuracy rate of 94.16% using Support Vector Machine (SVM) classifier in HSV colour space.

Keywords: contourlet transform, log gabor filter, ulcer, wireless capsule endoscopy

Procedia PDF Downloads 540

Authors: Vera Jancurova

Abstract:

The financial theory is dominated by the believe that weekday effect has disappeared from current markets. The purpose of this article is to study anomalies, especially weekday effect, at real markets that disrupt the efficiency of financial markets. The research is based on the analyses of historical daily exchange rates of significant world indices to determine the presence of weekday effects on financial markets. The methodology used for the study is based on the analyzes of daily averages of particular indexes for different time periods. Average daily gains were analyzed for their whole time interval and then for particular five and ten years periods with the aim to detect the presence on current financial markets. The results confirm the presence of weekday effect at the most significant indices - for example: Nasdaq, S & P 500, FTSE 100 and the Hang Seng. It was confirmed that in the last ten years, the weekend effect disappeared from financial markets. However in last year’s the indicators show that weekday effect is coming back. The study shows that weekday effect has to be taken into consideration on financial markets, especially in the past years.

Keywords: indices, anomalies, behavioral finance, weekday effect

Procedia PDF Downloads 339

Authors: M. Mamor

Abstract:

GaN-based compounds have attracted much interest in the fabrication of high-power, high speed and high-frequency electronic devices. Other examples of GaN-based applications are blue and ultraviolet (UV) light-emitting diodes (LEDs). All these devices require high-quality ohmic and Schottky contacts. Gaining an understanding of the electrical characteristics of metal/GaN contacts is of fundamental and technological importance for developing GaN-based devices. In this work, the barrier characteristics of Pt and Pd Schottky contacts on n-type GaN were studied using temperature-dependent forward current-voltage (I-V) measurements over a wide temperature range 80–400 K. Our results show that the barrier height and ideality factor, extracted from the forward I-V characteristics based on thermionic emission (TE) model, exhibit an abnormal dependence with temperature; i.e., by increasing temperature, the barrier height increases whereas the ideality factor decreases. This abnormal behavior has been explained based on the TE model by considering the presence of double Gaussian distribution (GD) of nonhomogeneous barrier height at the metal/GaN interface. However, in the high-temperature range (160-400 K), the extracted value for the effective Richardson constant A* based on the barrier inhomogeneity (BHi) model is found in fair agreement with the theoretically predicted value of about 26.9 A.cm-2 K-2 for n-type GaN. This result indicates that in this temperature range, the conduction current transport is dominated by the thermionic emission mode. On the other hand, in the lower temperature range (80-160 K), the corresponding effective Richardson constant value according to the BHi model is lower than the theoretical value, suggesting the presence of other current transport, such as tunneling-assisted mode at lower temperatures.

Keywords: Schottky diodes, inhomogeneous barrier height, GaN semiconductors, Schottky barrier heights

Procedia PDF Downloads 55

Authors: Yen-Po Wang, Ming-Chih Huang, Ming-Lian Chang

Abstract:

A scheme integrated with deterministic–stochastic subspace system identification and the method of damage localization vector is proposed in this study for damage detection of structures based on seismic response data. A series of shaking table tests using a five-storey steel frame has been conducted in National Center for Research on Earthquake Engineering (NCREE), Taiwan. Damage condition is simulated by reducing the cross-sectional area of some of the columns at the bottom. Both single and combinations of multiple damage conditions at various locations have been considered. In the system identification analysis, either full or partial observation conditions have been taken into account. It has been shown that the damaged stories can be identified from global responses of the structure to earthquakes if sufficiently observed. In addition to detecting damage(s) with respect to the intact structure, identification of new or extended damages of the as-damaged (ill-conditioned) counterpart has also been studied. The proposed scheme proves to be effective.

Keywords: damage locating vectors, deterministic-stochastic subspace system, shaking table tests, system identification

Procedia PDF Downloads 327

Authors: Jasvinder Singh, Manjinder Singh

Abstract:

Welding of dissimilar materials is a complicated process due to the difference in melting point of two materials. Thermal conductivity and coefficient of thermal expansion of dissimilar materials also different; therefore, residual stresses produced in the weldment and base metal are the most critical problem associated with the joining of dissimilar materials. Tensile strength and impact toughness also reduced due to the residual stresses. In the present research work, an attempt has been made to weld SS304 and MS1018 dissimilar materials by submerged arc welding (SAW). By conducting trail, runs most effective parameters welding current, Arc voltage, welding speed and nozzle to plate distance were selected to weld these materials. The fractional factorial technique was used to optimize the welding parameters. Effect on tensile strength (TS), fracture toughness (FT) and microhardness of weldment were studied. It was concluded that by optimizing welding current, voltage and welding speed the properties of weldment can be enhanced.

Keywords: SAW, Tensile Strength (TS), fracture toughness, micro hardness

Procedia PDF Downloads 538

Authors: Chelsea Peterson-Salahuddin

Abstract:

Over the past decade, news outlets have increasingly used social media as a means to create and distribute news content to audiences. Ephemerality, the transitory nature of media, and mobility, media viewing on mobile technologies, are two increasingly salient attributes of social media content; yet little is known about how these features influence news selection practices of news outlets when distributing news via social media. To account for this gap, this study examines the influences of ephemerality and mobility on social media news content on the social media application Snapchat, in order to understand how these qualities of digital media influence and shape news content. Findings from this study suggest that understandings of ephemerality and mobility play a key role in influencing social media news. This paper suggests that as these factors become increasingly salient in our dominant news viewing environments, being able to understand how they manifest themselves in online news reporting practices is critical for both scholars and practitioners of news as they aim to understand what 'newsworthiness' means in the current, digital age. Findings from this study also enhance our current understandings of how the technological affordances of online and digital media platforms play a key role in shaping the kinds being produced and what information is being prioritized and highlighted in our contemporary news media environment. This is especially important in our current era where new mediums and technologies for news dissemination are continuously arising, and reorienting our understandings of what is considered ‘news'. As a key site of mass communication, discourse, and stories highlighted in the news do critical work in defining culture and ideology. Thus, better understanding the contours of news in our contemporary moment is critical in understanding cultural norms and meaning-making.

Keywords: content analysis, ephemerality, mobile communication, social media news

Procedia PDF Downloads 136

Authors: Fatna Benmessaoud, Mohammed Cheikh, Vencent Velay, Vanessa Vedal, Farhad Rezai-Aria, Christine Boher

Abstract:

The global mechanical behavior of materials is strongly linked to their microstructure, especially their crystallographic texture and their grains morphology. These material aspects determine the mechanical fields character (heterogeneous or homogeneous), thus, they give to the global behavior a degree of anisotropy according the initial microstructure. For these reasons, the prediction of global behavior of materials in relationship with the microstructure must be performed with a multi-scale approach. Therefore, multi-scale modeling in the context of crystal plasticity is widely used. In this present contribution, a phenomenological elasto-viscoplastic model developed in the crystal plasticity context and finite element method are used to investigate the effects of crystallographic texture and grains sizes on global behavior of a polycrystalline equiaxed Ti-6Al-4V alloy. The constitutive equations of this model are written on local scale for each slip system within each grain while the strain and stress mechanical fields are investigated at the global scale via finite element scale transition. The beta phase of Ti-6Al-4V alloy modeled is negligible; its percent is less than 10%. Three families of slip systems of alpha phase are considered: basal and prismatic families with a burgers vector and pyramidal family with a burgers vector. The twinning mechanism of plastic strain is not observed in Ti-6Al-4V, therefore, it is not considered in the present modeling. Nine representative elementary volumes (REV) are generated with Voronoi tessellations. For each individual equiaxed grain, the own crystallographic orientation vis-à-vis the loading is taken into account. The meshing strategy is optimized in a way to eliminate the meshing effects and at the same time to allow calculating the individual grain size. The stress and strain fields are determined in each Gauss point of the mesh element. A post-treatment is used to calculate the local behavior (in each grain) and then by appropriate homogenization, the macroscopic behavior is calculated. The developed model is validated by comparing the numerical simulation results with an experimental data reported in the literature. It is observed that the present model is able to predict the global mechanical behavior of Ti-6Al-4V alloy and investigate the microstructural parameters' effects. According to the simulations performed on the generated volumes (REV), the macroscopic mechanical behavior of Ti-6Al-4V is strongly linked to the active slip systems family (prismatic, basal or pyramidal). The crystallographic texture determines which family of slip systems can be activated; therefore it gives to the plastic strain a heterogeneous character thus an anisotropic macroscopic mechanical behavior. The average grains size influences also the Ti-6Al-4V mechanical proprieties, especially the yield stress; by decreasing of the average grains size, the yield strength increases according to Hall-Petch relationship. The grains sizes' distribution gives to the strain fields considerable heterogeneity. By increasing grain sizes, the scattering in the localization of plastic strain is observed, thus, in certain areas the stress concentrations are stronger than other regions.

Keywords: microstructural parameters, multi-scale modeling, crystal plasticity, Ti-6Al-4V alloy

Procedia PDF Downloads 126

Authors: Saidu I. Rambo, Emmanuel O. Ibam, Sunday O. Adewale

Abstract:

The configuration of routers for effective communication in the Nigerian Navy (NN) enables the navy to improve on the current communication systems. The current system is faced with challenges that make the systems partially effective. The major implementation of the system is to configure routers using hierarchical model and obtaining a VSAT option on C-band platform. These routers will act as a link between Naval Headquarters and the Commands under it. The routers main responsibilities are to forward packets from source location to destination using a Link State Routing Protocol (LSRP). Also using the Point to Point Protocol (PPP), creates a strong encrypted password using Challenge Handshake Authentication Protocol (CHAP) which uses one-way hash function of Message Digest 5 (MD5) to provide complete protection against hackers/intruders. Routers can be configured using a Linux operating system or internet work operating system in the Microsoft platform. With this, system packets can be forwarded to various locations more effectively than the present system being used.

Keywords: C-band, communication, router, VSAT

Procedia PDF Downloads 366

Authors: Abdulrahman Sumayli, Saad M. AlShahrani

Abstract:

For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

Procedia PDF Downloads 69

Authors: Jayson Vicente

Abstract:

The young men and women of Philippine Military Academy Cadet Corps Armed Forces of the Philippines (PMA CCAFP) are bred to be front liners and last line of defense during war and times of peace; as such, they must be equipped with the most practical and most effective combat-ready Martial Arts and Combative skills to effectively fulfill their duty, as well as to protect and safeguard themselves to continue serving the people and their country. This study shall assess the current Martial Arts and Combative Program of the PMA CCAFP using descriptive methodology by interviews and floating questionnaires. The current Martial Arts and Combative Program of the PMA CCAFP with all of the subjects involved are more sports inclined rather than combat-equipped. Picking the best from each subject used in the program, this study seeks to recommend improvements or create a better Martial Arts and Combative Program that will satisfy the objective of producing Martial Arts combatant graduates. A good Martial Arts and Combative Program for PMA is essential to prepare them for what lies ahead, which is unforgiving and no rules to pacify threat.

Keywords: combative, martial arts, military, program

Procedia PDF Downloads 149

Authors: Kamran Rashid, Tashfeen M. Azhar, Asad-ur-Rahman Wahla

Abstract:

Current research aims to understand the role of successful buyer-supplier relationship in achieving supply chain competitiveness in a developing country perspective. Five hypotheses are developed to test structural model. Survey data is collected from the manufacturing sector of Pakistan. Analysis is conducted using Partial Least Squares (PLS) Structural Equation Modeling (SEM) through Smart PLS version 2.0 M3. Results demonstrate positive impact of effective supplier selection, buyer-supplier engagement, and information sharing capability on success of buyer supplier relationship. This successful buyer supplier relationship drives the supply chain firm financial and market performance. Additional analyses with large sample sizes are required in other developing countries to cross validate the results. Current study provides empirical evidence of the role of successful buyer supplier relationship in achieving supply chain competitiveness.

Keywords: supply chain management, successful buyer-supplier relationship, supply chain competitiveness, developing country

Procedia PDF Downloads 660

Authors: Priya Arora, Ashutosh Mishra

Abstract:

Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method.

Keywords: disease gene identification, Parkinson’s disease, physicochemical properties of amino acid, protein sequences

Procedia PDF Downloads 140

Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

Procedia PDF Downloads 23

Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

Procedia PDF Downloads 94

Authors: Sheelagh Heugh, Michael Upton, Kriya Kalidas, Stephen Breen

Abstract:

This article aims to create awareness of the opportunities and issues the artificial intelligence (AI) tool GPT-3 (Generative Pre-trained Transformer-3) brings to higher education. Technological disruptors have featured in higher education (HE) since Konrad Klaus developed the first functional programmable automatic digital computer. The flurry of technological advances, such as personal computers, smartphones, the world wide web, search engines, and artificial intelligence (AI), have regularly caused disruption and discourse across the educational landscape around harnessing the change for the good. Accepting AI influences are inevitable; we took mixed methods through participatory action research and evaluation approach. Joining HE communities, reviewing the literature, and conducting our own research around Chat GPT-3, we reviewed our institutional approach to changing our current practices and developing policy linked to assessments and the use of Chat GPT-3. We review the impact of GPT-3, a high-powered natural language processing (NLP) system first seen in 2020 on HE. Historically HE has flexed and adapted with each technological advancement, and the latest debates for educationalists are focusing on the issues around this version of AI which creates natural human language text from prompts and other forms that can generate code and images. This paper explores how Chat GPT-3 affects the current educational landscape: we debate current views around plagiarism, research misconduct, and the credibility of assessment and determine the tool's value in developing skills for the workplace and enhancing critical analysis skills. These questions led us to review our institutional policy and explore the effects on our current assessments and the development of new assessments. Conclusions: After exploring the pros and cons of Chat GTP-3, it is evident that this form of AI cannot be un-invented. Technology needs to be harnessed for positive outcomes in higher education. We have observed that materials developed through AI and potential effects on our development of future assessments and teaching methods. Materials developed through Chat GPT-3 can still aid student learning but lead to redeveloping our institutional policy around plagiarism and academic integrity.

Keywords: artificial intelligence, Chat GPT-3, intellectual property, plagiarism, research misconduct

Procedia PDF Downloads 89

Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

Abstract:

Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

Procedia PDF Downloads 399

Authors: A-Q. Nguyen. A. Amrhar, J. Zambrano, G. Brown, O.A. Yeste-Ojeda, R. Jr. Landry

Abstract:

Since the presence of Next Generation Air Transportation System (NextGen), Automatic Dependent Surveillance-Broadcast (ADS-B) has raised specific concerns related to the privacy and security, due to its vulnerable, low-level of security and limited payload. In this paper, the authors introduce and analyze the combination of Pulse Amplitude Modulation (PAM) and Phase Shift Keying (PSK) Modulation in conventional ADS-B, forming Secure ADS-B (SADS-B) avionics. In order to demonstrate the potential of this combination, Hardware-in-the-loop (HIL) simulation was used. The tests' results show that, on the one hand, SADS-B can offer five times the payload as its predecessor. This additional payload of SADS-B can be used in various applications, therefore enhancing the ability and efficiency of the current ADS-B. On the other hand, by using the extra phase modulated bits as a digital signature to authenticate ADS-B messages, SADS-B can increase the security of ADS-B, thus ensure a more secure aviation as well. More importantly, SADS-B is compatible with the current ADS-B In and Out. Hence, no significant modifications will be needed to implement this idea. As a result, SADS-B can be considered the most promising approach to enhance the capability and security of ADS-B.

Keywords: ADS-B authentication, ADS-B security, NextGen ADS-B, PSK signature, secure ADS-B

Procedia PDF Downloads 319

Authors: Vincent Otieno, Alfred Agwanda, Anne Khasakhala

Abstract:

Among the neo-Malthusian adherents, it is believed that rapid population growth strain countries’ capacity and performance. Fertility have however decelerated in most of the countries in the recent past. Scholars have concentrated on wide range of factors associated with fertility majorly at the national scale with some opining that analysis of trends and differentials in the various fertility parameters have been discussed extensively. However, others believe that considerably less attention has been paid to the fertility preference- a pathway through which various variables act on fertility. The Sub-Saharan African countries’ disparities amid almost similarities in policies is a cause of concern to demographers. One would point at the meager synergies that have been focused on the fertility preference as well, especially at the macro scale. Using Bongaarts reformulation of Easterlin and Crimmins (1985) conceptual scheme, the understanding of the current transition based on the fertility preference in general would help to provide explanations to the observed latest dynamics. This study therefore is an attempt to explain the current fertility transition through women’s fertility preference. Results reveal that indeed fertility transition is on course in most of the sub-Saharan countries with huge disparities in fertility preferences and its implementation indices.

Keywords: fertility preference, the degree of implementation index, sub-Saharan Africa, transition

Procedia PDF Downloads 241

Authors: Mahtab Baghaei, Seyed Mahmoud Tabatabaei

Abstract:

Aim & Background: Electrical stimulation of transcranial direct current is considered one of the treatment methods for mental disorders. The aim of this study was to evaluate the effectiveness of transcranial electrical stimulation on the delta, theta, alpha, beta and systolic and diastolic blood pressure in patients with generalized anxiety disorder. Materials and Methods: The present study was a double-blind intervention with a pre-test and post-test design on people with generalized anxiety disorder in Tabriz in 1400. In this study, 30 patients with generalized anxiety disorder were selected by purposive sampling method based on the criteria specified in DSM-5 and randomly divided into an experimental group (n = 15) and a control group (n = 15). The experimental group received two sessions of 30 minutes of electrical stimulation of transcranial direct current with an intensity of 2 mA in the area of the lateral dorsal prefrontal cortex, and the control group also received artificial stimulation. Results: The results showed that transcranial electrical stimulation reduces delta and theta waves and increases beta and alpha brain waves in the experimental group. On the other hand, this method also showed a significant decrease in systolic and diastolic blood pressure in these patients (p <0.01). Conclusion: The results show that transcranial electrical stimulation has a statistically significant effect on brain waves and blood pressure, and this non-invasive method can be used as one of the treatment methods in people with generalized anxiety disorder.

Keywords: transcranial direct current electrical stimulation, brain waves, systolic blood pressure, diastolic blood pressure

Procedia PDF Downloads 102

Authors: Helan Satish, M. Ramasubba Reddy

Abstract:

The simulation of an endocardial cell for gene mutation in the cardiac sodium ion channel NaV1.5, encoded by SCN5A gene, is discussed. The characterization of Brugada Syndrome by loss of function effect on SCN5A mutation due to L812Q mutant present in the DII-S4 transmembrane region of the NaV1.5 channel protein and its effect in an endocardial cell is studied. Ten Tusscher model of human ventricular action potential is modified to incorporate the changes contributed by L812Q mutant in the endocardial cells. Results show that BrS-associated SCN5A mutation causes reduction in the inward sodium current by modifications in the channel gating dynamics such as delayed activation, enhanced inactivation, and slowed recovery from inactivation in the endocardial cell. A decrease in the inward sodium current was also observed, which affects depolarization phase (Phase 0) that leads to reduction in the spike amplitude of the cardiac action potential.

Keywords: SCN5A gene mutation, sodium channel, Brugada syndrome, cardiac arrhythmia, action potential

Procedia PDF Downloads 126

Authors: Waleed Alsaidy, Mourad Mbarki

Abstract:

In this work, it have investigated the effect of electron irradiation on the output characteristics of n+/p GaAs solar cell. The studied solar cell is exposed to an electron beam with kinetic energy of 1 MeV under AM0 illumination. In this work, it have used our own software to calculate the damage caused by these energetic particles. Indeed, these particles produce severe degradation on the performances of the solar cells. The aim of this work is to investigate the effect of electronic irradiation on the J(V) characteristics upon the fluence of particles φ (electron/cm2). Thereafter, we have evaluated the degradation of its performances such as the short circuit current J_sc, the open circuit voltage V_oc the efficiency η with respect to the fluence φ of electrons. it have shown that the variation of these parameters decrease linearly with the logarithm of the fluence φ, and their degradation begins from a threshold value φ_m. To validate our calculation, we have compared our results with other theoretical and experimental results available in the literature and we have found a good agreement between them.

Keywords: solar cells, GaAs, short circuit current, open circuit voltage, fluence, degradation

Procedia PDF Downloads 21

Authors: Jihène Khalifa

Abstract:

This study analyzed the relationships between Tunisia’s economic growth, food security, agricultural productivity, and climate change using the ARDL model for the period from 1990 to 2022. The ARDL model reveals a positive correlation between economic growth and lagged agricultural productivity. Additionally, the vector autoregressive (VAR) model highlights the beneficial impact of lagged agricultural productivity on economic growth and the negative effect of rainfall on economic growth. Granger causality analysis identifies unidirectional relationships from economic growth to agricultural productivity, crop production, food security, and temperature variations, as well as from temperature variations to crop production. Furthermore, a bidirectional causality is established between crop production and food security. The study underscores the impact of climate change on crop production and suggests the need for adaptive strategies to mitigate these climate effects.

Keywords: economic growth, agriculture, food security, climate change, ARDl, VAR

Procedia PDF Downloads 31

Authors: Salem Alajmi, Bader Altaweel

Abstract:

Kuwait, like many parts of the world, has started facing the dangerous growth of electrical and electronic wastes. This growth has been noted last two decades, coming along with the development of mobile phones, computers, TVs, as well as other electronic devices and electrical equipment. Kuwait is already among the highest global producers of electronic waste (E-waste) in kg per capita. Furthermore, Kuwait is among the global countries that set high-level future targets in renewable energy projects. Accumulation of this electronic waste, as well as accelerated renewable energy projects, will lead to the increase of future threats to the country. In this research, factors that lead to the increase the e-waste in Kuwait are presented. Also, the current situations of dealing with e-waste in the country as well as current challenges are examined. The impact of renewable energy projects on future E-wastes accumulation is considered. Moreover, this research proposes the best strategies and practices toward successfully dealing with the waste of electronic devices and renewable energy technologies.

Keywords: Kuwait, e-waste, extended producer responsibility, environment, recycle, recovery

Procedia PDF Downloads 182

Authors: Amal Alshahrani, Allan Ramsay

Abstract:

Paraphrasing is one of the important tasks in natural language processing; i.e. alternative ways to express the same concept by using different words or phrases. Paraphrases can be used in many natural language applications, such as Information Retrieval, Machine Translation, Question Answering, Text Summarization, or Information Extraction. To obtain pairs of sentences that are paraphrases we create a system that automatically extracts paraphrases from a corpus, which is built from different sources of news article since these are likely to contain paraphrases when they report the same event on the same day. There are existing simple standard approaches (e.g. TF-IDF vector space, cosine similarity) and alignment technique (e.g. Dynamic Time Warping (DTW)) for extracting paraphrase which have been applied to the English. However, the performance of these approaches could be affected when they are applied to another language, for instance Arabic language, due to the presence of phenomena which are not present in English, such as Free Word Order, Zero copula, and Pro-dropping. These phenomena will affect the performance of these algorithms. Thus, if we can analysis how the existing algorithms for English fail for Arabic then we can find a solution for Arabic. The results are promising.

Keywords: natural language processing, TF-IDF, cosine similarity, dynamic time warping (DTW)

Procedia PDF Downloads 386

Authors: Vesile Evrim, Aliyu Awwal

Abstract:

By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

Procedia PDF Downloads 382

Authors: Abdelsabour G. A. Khaled, Ahmed W. A. Abdalla And Sabry Y. M. Mahmoud

Abstract:

Banana plants showing typical mosaic and yellow stripes on leaves as symptoms were collected from Assiut Governorate in Egypt. The causal agent was identified as Cucumber mosaic virus (CMV) on the basis of symptoms, transmission, serology, transmission electron microscopy and reverse transcription polymerase chain reaction (RT-PCR). Coat protein (CP) gene was amplified using gene specific primers for coat protein (CP), followed by cloning into desired cloning vector for sequencing. In this study the CMV was transmitted into propagation host either by aphid or mechanically. The transmission was confirmed through Direct Antigen Coating Enzyme Linked Immuno Sorbent Assay (DAC-ELISA). Analysis of the 120 deduced amino acid sequence of the coat protein gene revealed that the EG-A strain of CMV shared from 97.50 to 98.33% with those strains belonging to subgroup IA. The cluster analysis grouped the Egyptian isolate with strains Fny and Ri8 belonging sub-group IA. It appears that there occurs a high incidence of CMV infecting banana belonging to IA subgroup in most parts of Egypt.

Keywords: banana, CMV, transmission, CP gene, RT-PCR

Procedia PDF Downloads 341

Authors: Dejene Gizaw Kidane

Abstract:

This study examines the impact of foreign direct investment on economic growth of Ethiopia using yearly time-series data for 1974 through 2013. Economic growth is proxies by real per capita gross domestic product and foreign direct investment proxies by the inflow of foreign direct investment. Other control variables such as gross domestic saving, trade, government consumption and inflation has been incorporated. In order to fully account for feedbacks, a vector autoregressive model is utilized. The results show that there is a stable, long-run relationship between foreign direct investment and economic growth. The variance decomposition results show that the main sources of Ethiopia economic growth variations are due largely own shocks. The pairwise Granger causality results show that there is a unidirectional causality that runs from FDI to economic growth of Ethiopia. Hence, the researcher therefore recommends that, FDI facilitate economic growth, so the government has to exert much effort in order to attract more FDI into the country.

Keywords: real per capita GDP, FDI, co-integration, VECM, Granger causality

Procedia PDF Downloads 436

Authors: Obiajulu Anthony Ugochukwu Nnedum, Bernard Chukwukelue Chine, Jerome Ogochukwu Ezisi

Abstract:

A notable challenge of organizational citizenship behavior in Nigerian organizations is the prevalence of individualistic work cultures among employees, as this mindset can result in employees being less willing to go beyond their formal job requirements to contribute to the organization overall success. However, the dearth and scarce research on the antecedents of organizational citizenship behavior, such as paternalistic leadership and employee loyalty to supervisors in sub-Saharan African cultures such as Nigeria, motivated the current study to take a deep investigation into the moderating role of employee loyalty to supervisor on the relationship between paternalistic leadership and organizational citizenship behavior. The relevance of the current study ensures that when employees are loyal to their paternalistic leaders who show care and support, they are more likely to exhibit organizational citizenship behavior. The current study employed a sample size of four hundred and twenty participants (one hundred and five managers and three hundred and five subordinates) from eleven large organizations randomly selected through lucky dip from twenty-two large organizations from the directory of the Chamber of Commerce and Industry in Anambra state, south-eastern Nigeria. Also, a twelve-item organizational citizenship behavior scale, a thirty-nine-item paternalistic leadership scale, and a six-item loyalty to supervisor scale were employed for the collection of data for the current study. Adopting a one manager/Leader by triad subordinates cross-sectional survey design, Hayes process micro model and statistical package for social sciences (SPSS) version twenty-five, the findings from the result of the analysis of the hypotheses demonstrated that loyalty to supervisor moderated the relationship between paternalistic leadership and organizational citizenship behavior-conscientiousness. Also, the findings from the result revealed that loyalty to the supervisor moderated the relationship between authoritative leadership and organizational citizenship behavior identification. Furthermore, the findings from the result showed that loyalty to the supervisor moderated the relationship between moral leadership and organizational citizenship behavior. Accordingly, the result from the analysis implies that when employees are loyal to their supervisors, they are more likely to exhibit organizational citizenship behavior by going above and beyond their formal job requirements, as this loyalty can be fostered through a paternalistic leadership style that emphasizes a supportive and caring relationship between supervisors and subordinates.

Keywords: authoritative leadership, moral leadership, loyalty to supervisor, organizational citizenship behavior

Procedia PDF Downloads 57