Search results for: dynamic network flow

Authors: José M. Carmona, Diana Puigserver, Jofre Herrero

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Chlorinated solvents belong to the group of nonaqueous phase liquids (DNAPL) and have been involved in many contamination episodes. They are carcinogenic and recalcitrant pollutants that may be found in granular aquifers as: i) pools accumulated on low hydraulic conductivity layers; ii) immobile residual phase retained at the pore-scale by capillary forces; iii) dissolved phase in groundwater; iv) sorbed by particulate organic matter; and v) stored into the matrix of low hydraulic conductivity layers where they penetrated by molecular diffusion. The transition zone between granular aquifers and basal aquitards constitute the lowermost part of the aquifer and presents numerous fine-grained interbedded layers that give rise to significant textural contrasts. These layers condition the transport and fate of contaminants and lead to differences from the rest of the aquifer, given that: i) hydraulic conductivity of these layers is lower; ii) DNAPL tends to accumulate on them; iii) groundwater flow is slower in the transition zone and consequently pool dissolution is much slower; iv) sorbed concentrations are higher in the fine-grained layers because of their higher content in organic matter; v) a significant mass of pollutant penetrates into the matrix of these layers; and vi) this contaminant mass back-diffuses after remediation and the aquifer becomes contaminated again. Thus, contamination sources of chlorinated solvents are extremely more recalcitrant in transition zones, which has far-reaching implications for the environment. The aim of this study is to analyze the spatial and temporal differences in the evolution of biogeochemical processes in the transition zone and in the rest of the aquifer. For this, an unconfined aquifer with a transition zone in the lower part was selected at Vilafant (NE Spain). This aquifer was contaminated by perchloroethylene (PCE) in the 80’s. Distribution of PCE and other chloroethenes in groundwater and porewater was analyzed in: a) conventional piezometers along the plume and in two multilevel wells at the source of contamination; and b) porewater of fine grained materials from cores recovered when drilled the two multilevel wells. Currently, the highest concentrations continue to be recorded in the source area in the transition zone. By contrast, the lowest concentrations in this area correspond to the central part of the aquifer, where flow velocities are higher and a greater washing of the residual phase initially retained has occurred. The major findings of the study were: i) PCE metabolites were detected in the transition zone, where conditions were more reducing than in the rest of the aquifer; ii) however, reductive dechlorination was partial since only the formation of cis-dicholoroethylene (DCE) was reached; iii) In the central part of the aquifer, where conditions were predominantly oxidizing, the presence of nitrate significantly hindered the reductive declination of PCE. The remediation strategies to be implemented should be directed to enhance dissolution of the source, especially in the transition zone, where it is more recalcitrant. For example, by combining chemical and bioremediation methods, already tested at the laboratory scale with groundwater and sediments of this site.

Keywords: chlorinated solvents, chloroethenes, DNAPL, partial reductive dechlorination, PCE, transition zone to basal aquitard

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Lidia Sanchis-Marco, Antonio Rubia

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In this paper we analyze the state-dependent risk-spillover in different economic areas. To this end, we apply the quantile regression-based methodology developed in Adams, Füss and Gropp approach to examine the spillover in conditional tails of daily returns of indices of the banking industry in the US, BRICs, Peripheral EMU, Core EMU, Scandinavia, the UK and Emerging Markets. This methodology allow us to characterize size, direction and strength of financial contagion in a network of bilateral exposures to address cross-border vulnerabilities under different states of the economy. The general evidence shows as the spillover effects are higher and more significant in volatile periods than in tranquil ones. There is evidence of tail spillovers of which much is attributable to a spillover from the US on the rest of the analyzed regions, specially on European countries. In sharp contrast, the US banking system show more financial resilience against foreign shocks.

Keywords: spillover effects, Bank Contagion, SDSVaR, expected shortfall, VaR, expectiles

Procedia PDF Downloads 478

Authors: Imen Dhaou

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This study examines conditional Value at Risk by applying the GJR-EVT-Copula model, and finds the optimal portfolio for eight Dow Jones Islamic-conventional pairs. Our methodology consists of modeling the data by a bivariate GJR-GARCH model in which we extract the filtered residuals and then apply the Peak over threshold model (POT) to fit the residual tails in order to model marginal distributions. After that, we use pair-copula to find the optimal portfolio risk dependence structure. Finally, with Monte Carlo simulations, we estimate the Value at Risk (VaR) and the conditional Value at Risk (CVaR). The empirical results show the VaR and CVaR values for an equally weighted portfolio of Dow Jones Islamic-conventional pairs. In sum, we found that the optimal investment focuses on Islamic-conventional US Market index pairs because of high investment proportion; however, all other index pairs have low investment proportion. These results deliver some real repercussions for portfolio managers and policymakers concerning to optimal asset allocations, portfolio risk management and the diversification advantages of these markets.

Keywords: CVaR, Dow Jones Islamic index, GJR-GARCH-EVT-pair copula, portfolio optimization

Procedia PDF Downloads 243

Authors: K. Santacruz-Gomez, E. Silva-Campa, S. Álvarez-García, V. Mata-Haro, D. Soto-Puebla, M. Pedroza-Montero

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Blood gamma irradiation is the only available method to prevent transfusion-associated graft versus host disease (TA-GVHD). However, when blood is irradiated, determine blood shelf time is crucial. Non-irradiated blood has a self-time from 21 to 35 days when is preserved with an anticoagulated solution and stored at 4°C. During their storage, red blood cells (RBC) undergo a series of biochemical, biomechanical and molecular changes involving what is known as storage lesion (SL). SL include loss of structural integrity of RBC, a decrease of 2,3-diphosphatidylglyceric acid levels, and an increase of both ion potassium concentration and hemoglobin (Hb). On the other hand, Atomic force Microscopy (AFM) represents a versatile tool for a nano-scale high-resolution topographic analysis in biological systems. In order to evaluate SL in irradiated and non-irradiated blood, RBC topography and morphometric parameters were obtained from an AFM XE-BIO system. Cell viability was followed using flow cytometry. Our results showed that early markers as nanoscale roughness, allow us to evaluate blood quality since another perspective.

Keywords: AFM, blood γ-irradiation, roughness, storage lesion

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Authors: S. Bouchami, M.N. Lakhoua

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The automotive market is a dynamic market that continues to grow. That’s why several companies belonging to this sector adopt a quality improvement approach. Wanting to be competitive and successful in the environment in which they operate, these companies are dedicated to establishing a system of quality management to ensure the achievement of the objective quality, improving the products and process as well as the satisfaction of the customers. In this paper, the management of the quality and the improvement of a production line in an industrial company is presented. In fact, the project is divided into two essential parts: the creation of the technical line documentation and the quality assurance documentation and the resolution of defects at the line, as well as those claimed by the customer. The creation of the documents has required a deep understanding of the manufacturing process. The analysis and problem solving were done through the implementation of PDCA (Plan Do Check Act) and FTA (Fault Tree Analysis). As perspective, in order to better optimize production and improve the efficiency of the production line, a study on the problems associated with the supply of raw materials should be made to solve the problems of stock-outs which cause delays penalizing for the industrial company.

Keywords: quality management, documentary system, Plan Do Check Act (PDCA), fault tree analysis (FTA) method

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Authors: Hamed A. El-Serehy, Khaled A. Al-Rasheid, Fahad A Al-Misned

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The meiofaunal population fluctuation, microbial dynamic and the composition of the sedimentary organic matter were investigated seasonally in the Egyptian shores along the northern part of Red Sea. Total meiofaunal population densities were extremely low with an annual average of 109 ±26 ind./10 cm2 and largely dominated by nematodes (on annual average from 52% to 94% of total meiofaunal density). The benthic microbial population densities ranged from 0.26±0.02 x 108 to 102.67±18.62 x 108/g dry sediment. Total sedimentary organic matter concentrations varied between 5.8 and 11.6 mg/g and the organic carbon, which was measured as summation of the carbohydrates, proteins and lipids, accounted for only a small fraction of being 32 % of the total organic matter. Chlorophyll a attained very low values and fluctuated between 2 and 11 µg/g. The very low chlorophyll a concentration in the Egyptian coasts along the Red Sea can suggest that the sedimentary organic matter along the Egyptian coasts is dominated by organic detrital and heterotrophic bacteria on one hand, and do not promote carbon transfer towards the higher trophic level on the other hand. However, the present study indicates that the existing of well diversified meiofaunal group, with a total of ten meiofaunal taxa, can serve as food for higher trophic levels in the Red Sea marine ecosystem.

Keywords: bacteria, meiofauna, intertidal sediments, Red Sea

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Authors: A. Chegenizadeh, M. Keramatikerman, H. Nikraz

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Bitumen is one of the most applicable materials in pavement engineering. It is a binding material with unique viscoelastic properties, especially when it mixes with polymer. In this study, to figure out the viscoelastic behaviour of the polymer modified with bitumen (PMB), a series of dynamic shearing rheological (DSR) tests were conducted. Four percentages of lime (i.e. 1%, 2%, 4% and 5%) were mixed with PMB and tested under four different temperatures including 64ºC, 70ºC, 76ºC and 82ºC. The results indicated that complex shearing modulus (G*) increased by increasing the frequency due to raised resistance against deformation. The phase angle (δ) showed a decreasing trend by incrementing the frequency. The addition of lime percentages increased the complex modulus value and declined phase angle parameter. Increasing the temperature decreased the complex modulus and increased the phase angle until 70ºC. The decreasing trend of rutting factor with increasing temperature revealed that rutting factor improved by the addition of the lime to the PMB.

Keywords: rheological properties, DSR test, polymer mixed with bitumen (PMB), complex modulus, lime

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Authors: Ali Raza

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The dynamic landscape of communication has brought about significant advancements that intersect with the realms of public health and journalism. This abstract explores the evolving synergy between these fields, highlighting how their intersection has contributed to informed societies and improved public health outcomes. In the digital age, communication plays a pivotal role in shaping public perception, policy formulation, and collective action. Public health, concerned with safeguarding and improving community well-being, relies on effective communication to disseminate information, encourage healthy behaviors, and mitigate health risks. Simultaneously, journalism, with its commitment to accurate and timely reporting, serves as the conduit through which health information reaches the masses. Advancements in communication technologies have revolutionized the ways in which public health information is both generated and shared. The advent of social media platforms, mobile applications, and online forums has democratized the dissemination of health-related news and insights. This democratization, however, brings challenges, such as the rapid spread of misinformation and the need for nuanced strategies to engage diverse audiences. Effective collaboration between public health professionals and journalists is pivotal in countering these challenges, ensuring that accurate information prevails. The synergy between public health and journalism is most evident during public health crises. The COVID-19 pandemic underscored the pivotal role of journalism in providing accurate and up-to-date information to the public. However, it also highlighted the importance of responsible reporting, as sensationalism and misinformation could exacerbate the crisis. Collaborative efforts between public health experts and journalists led to the amplification of preventive measures, the debunking of myths, and the promotion of evidence-based interventions. Moreover, the accessibility of information in the digital era necessitates a strategic approach to health communication. Behavioral economics and data analytics offer insights into human decision-making and allow tailored health messages to resonate more effectively with specific audiences. This approach, when integrated into journalism, enables the crafting of narratives that not only inform but also influence positive health behaviors. Ethical considerations emerge prominently in this alliance. The responsibility to balance the public's right to know with the potential consequences of sensational reporting underscores the significance of ethical journalism. Health journalists must meticulously source information from reputable experts and institutions to maintain credibility, thus fortifying the bridge between public health and the public. As both public health and journalism undergo transformative shifts, fostering collaboration between these domains becomes essential. Training programs that familiarize journalists with public health concepts and practices can enhance their capacity to report accurately and comprehensively on health issues. Likewise, public health professionals can gain insights into effective communication strategies from seasoned journalists, ensuring that health information reaches a wider audience. In conclusion, the convergence of public health and journalism, facilitated by communication advancements, is a cornerstone of informed societies. Effective communication strategies, driven by collaboration, ensure the accurate dissemination of health information and foster positive behavior change. As the world navigates complex health challenges, the continued evolution of this synergy holds the promise of healthier communities and a more engaged and educated public.

Keywords: public awareness, journalism ethics, health promotion, media influence, health literacy

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

Procedia PDF Downloads 131

Authors: Rachel F. Ajayi, Unathi Sidwaba, Usisipho Feleni, Samantha F. Douman, Ezo Nxusani, Lindsay Wilson, Candice Rassie, Oluwakemi Tovide, Priscilla G.L. Baker, Sibulelo L. Vilakazi, Robert Tshikhudo, Emmanuel I. Iwuoha

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Pyrazinamide (PZA) is among the first-line pro-drugs in the tuberculosis (TB) combination chemotherapy used to treat Mycobacterium tuberculosis. Numerous reports have suggested that hepatotoxicity due to pyrazinamide in patients is due to inappropriate dosing. It is therefore necessary to develop sensitive and reliable techniques for determining the PZA metabolic profile of diagnosed patients promptly and at point-of-care. This study reports the determination of PZA based on nanobiosensor systems developed from disuccinimidyl octanedioate modified Cytochrome P450-2E1 (CYP2E1) electrodeposited on gold substrates derivatised with (poly(8-anilino-1-napthalene sulphonic acid) PANSA/PVP-AgNPs nanocomposites. The rapid and sensitive amperometric PZA detection gave a dynamic linear range of 2 µM to 16 µM revealing a limit of detection of 0.044 µM and a sensitivity of 1.38 µA/µM. The Michaelis-Menten parameters; KM, KMapp and IMAX were also calculated and found to be 6.0 µM, 1.41 µM and 1.51 µA respectively indicating a nanobiosensor suitable for use in serum.

Keywords: tuberculosis, cytochrome P450-2E1, disuccinimidyl octanedioate, pyrazinamide

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Authors: Van Tran Thi Thuy, Cheol Won Lim, Dukjoon Kim

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A series of biodegradable copolymers based on polyaspartamide (PASPAM) were synthesized by grafting hydrophilic O-(2-aminoethyl)-O'-methylpoly(ethylene glycol) (MPEG), hydrophobic cholic acid (CA), and pH-sensitive hydrazine (Hyd) segments on a PASPAM backbone. The hydrazine group was effectively cleaved to release doxorubicin (DOX) conjugated on PASPAM in an acidic environment. The chemical structure of the polymer and the degree of substitution of each graft segment were analyzed using FT-IR and 1H-NMR spectroscopy. The size of the MPEG/Hyd/CA-g-PASPAM copolymer self-aggregates was examined by dynamic light scattering (DLS) and transmission electron microscope (TEM). The mean diameter of the self - aggregates increased from 125 to 200 nm at pH 7.4, as the degree of substitution of CA increased from 10 to 20 %. The release kinetics of DOX was strongly affected by the pH of the releasing medium. While less than 30% of the DOX-loaded was released in about 30 h at pH 7.4, more than 60% was released at pH 5.0 within the same time. The viability tests of human breast cancer cells (MCF-7) and human embryonic kidney cells (293T) show the potential application of MPEG/Hyd/CA-g-PASPAM copolymer self-aggregates in the controlled intracellular delivery for cancer treatments.

Keywords: pH-sensitive, drug delivery, polyaspartamide, self-assembly, nano-aggregates

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Authors: Evgenia Dimova-Aleksandrova, Elitsa Deianova

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Dynamic changes in modern life and habitation due to demographic, urban, technology, and ecological factors affect the size of modern homes leading to a trend of decreasing their area. The current paper aims to investigate the differences between “micro” homes and “small” homes. In Bulgaria, these two types are not included in legal regulations, and therefore, a precise definition and special requirements are needed and sought in order to include their characteristic features in contemporary individual habitation. The purpose of the current study is to determine limits in built-up volume for the two types, to create a definition of the terms “micro” and “small” home, and to find methods to distinguish them. A comparative analysis will differentiate these types of habitation units, thus determining the boundaries for the built-up area for both concepts. The analysis is based on a case study from European practices and is focused on defining minimal requirements for “micro” and “small” home in the context of contemporary demands for high quality habitation in limited areas.

Keywords: Bulgaria, differentiation, micro home, requirements, small home

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Authors: G. L. Rithika, Arvind B. S., Akash R., Ananda Vinayak, Hema M. S.

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Cryptocurrencies were first launched in the year 2009 and have been a great asset to own. Cryptocurrencies are a representation of a completely distinct decentralization model for money. They also contribute to the elimination of currency monopolies and the liberation of money from control. The fact that no government agency can determine a coin's value or flow is what cryptocurrency advocates believe makes them safe and secure. The aim of this paper is to analyze the economic implications of cryptocurrency and how it would disrupt traditional financial systems. This paper analyses the growth of Cryptocurrency over the years and the potential threats of cryptocurrency to financial systems. Our analysis shows that although the DeFi design, like the traditional financial system, may have the ability to lower transaction costs, there are multiple layers where rents might build up because of endogenous competition limitations. The permissionless and anonymous design of DeFi poses issues for ensuring tax compliance, anti-money laundering laws and regulations, and preventing financial misconduct.

Keywords: cryptocurrencies, bitcoin, blockchain technology, traditional financial systems, decentralisation, regulatory framework

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Authors: Sergio Luna, Raquel Rodríguez-Carvajal

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This research examines the effectiveness of a mindfulness-based intervention in optimizing psychological well-being, with a particular focus on self-esteem, due to the rapid growth and consolidation of social network use and the increased frequency and intensity of upward comparisons of the self. The study aims to assess the potential of a mindfulness-based intervention to improve self-esteem and, in particular, to contribute to its greater stability by reducing levels of contingent self-esteem. Results show that an 8-week mindfulness-based stress reduction program was effective in increasing participants' (n=206) trait mindfulness, explicit self-esteem, and well-being, while decreasing contingent self-esteem. Furthermore, the study found that improvements in both explicit and contingent self-esteem were significantly correlated with increases in psychological well-being, but that contingent self-esteem had a stronger effect on well-being than explicit self-esteem. These findings highlight the importance of considering additional dimensions of self-esteem beyond levels, and suggest that mindfulness-based interventions may be a valuable tool for promoting a healthier form of self-esteem that contributes to personal well-being.

Keywords: MBSR, contingent self-esteem, explicit self-esteem, well-being

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Authors: Xuewen Yu, Danhui Dan

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The identification of impact load and some hard-to-obtain system parameters is crucial for the activities of analysis, validation, and evaluation in the engineering field. This paper proposes a method that utilizes neural networks based on 1D-CNN to identify the impact load and partial system parameters from measured responses. To this end, forward computations are conducted to provide datasets consisting of the triples (parameter θ, input u, output y). Then neural networks are trained to learn the mapping from input to output, fu|{θ} : y → u, as well as from input and output to parameter, fθ : (u, y) → θ. Afterward, feeding the trained neural networks the measured output response, the input impact load and system parameter can be calculated, respectively. The method is tested on two simulated examples and shows sound accuracy in estimating the impact load (waveform and location) and system parameters.

Keywords: convolutional neural network, impact load identification, system parameter identification, inverse problem

Procedia PDF Downloads 88

Authors: L. Q. Yuan, J. Yang, A. Siddiqui

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A new regression uncertainty analysis methodology was applied to determine the uncertainties of the critical heat flux (CHF) power correlation for an advanced 43-element bundle design, which was developed by Canadian Nuclear Laboratories (CNL) to achieve improved economics, resource utilization and energy sustainability. The new methodology is considered more appropriate than the traditional methodology in the assessment of the experimental uncertainty associated with regressions. The methodology was first assessed using both the Monte Carlo Method (MCM) and the Taylor Series Method (TSM) for a simple linear regression model, and then extended successfully to a non-linear CHF power regression model (CHF power as a function of inlet temperature, outlet pressure and mass flow rate). The regression uncertainty assessed by MCM agrees well with that by TSM. An equation to evaluate the CHF power regression uncertainty was developed and expressed as a function of independent variables that determine the CHF power.

Keywords: CHF experiment, CHF correlation, regression uncertainty, Monte Carlo Method, Taylor Series Method

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Authors: Ahmed Nour El Islam Ayad, Tahar Rouibah, Wafa Krika, Houari Boudjella, Larab Moulay, Farid Benhamida, Selma Benmoussa

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The purpose of this study is to evaluate the electromagnetic field of an underground cable of very high voltage perforated by nail. The aim of this work shows a numerical simulation of the electromagnetic field of 400 kV line after perforation through a ferrous nail in four positions for the pinch pin at different distances. From results for a longitudinal section, we observe and evaluate the distribution and the variation of the electromagnetic field in the cable and the earth. When the nail approaches the underground power cable, the distribution of the magnetic field changes and takes several forms, the magnetic field increase and become very important when the nail breaks the metal screen and will produce a significant leak of the electric field, characterized by a large electric arc and or electric discharge to earth and then a fault in the electrical network. These electromagnetic analysis results help to detect defects in underground cables.

Keywords: underground, electromagnetic, nail, defect

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Authors: Salman Mohamadi, Seyed Mohammad Ali Tayaranian Hosseini, Hamidreza Amindavar

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In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes.

Keywords: epileptic seizure prediction , ARIMA, ARCH and GARCH modeling, heteroskedasticity, EEG

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Authors: Adetoun Adedotun Amubode

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Nigeria's Textile Industry was the second largest in Africa after Egypt, with above 250 vibrant factories and over 50 percent capacity utilization contributing to foreign exchange earnings and employment generation. Currently, multifaceted challenges such as epileptic power supply, inconsistent government policies, growing digitalization, smuggling of foreign textiles, insecurity and the inability of the local industries to compete with foreign products, especially Chinese textile, has created a hostile environment for the sector. This led to the closure of most of the textile industries. China's textile industry has experienced institutional change and industrial restructuring, having 30% of the world's market share. This paper examined the strategies adopted by China in transforming her textile and clothing industries and designed a model for the integration of these strategies to improve the competitive strength and growth of the Nigerian textile and clothing industries in a dynamic and changing market. The paper concludes that institutional support, regional production, export-oriented policy, value-added and branding cultivation, technological upgrading and enterprise resource planning be integrated into the Nigerian clothing and textile industries.

Keywords: clothing, industry, integration, Nigerian, textile, transformation.

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Authors: Truong Nguyen Luan Vu, Le Hieu Giang, Le Linh

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The FOPI controller is proposed based on the main properties of the decoupling control scheme, as well as the fractional calculus. By using the simplified decoupling technique, the transfer function of decoupled apparent process is firstly separated into a set of n equivalent independent processes in terms of a ratio of the diagonal elements of original open-loop transfer function to those of dynamic relative gain array and the fraction – order PI controller is then developed for each control loops due to the Bode’s ideal transfer function that gives the desired fractional closed-loop response in the frequency domain. The simulation studies were carried out to evaluate the proposed design approach in a fair compared with the other existing methods in accordance with the structured singular value (SSV) theory that used to measure the robust stability of control systems under multiplicative output uncertainty. The simulation results indicate that the proposed method consistently performs well with fast and well-balanced closed-loop time responses.

Keywords: ideal transfer function of bode, fractional calculus, fractional order proportional integral (FOPI) controller, decoupling control system

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Authors: Nemi Bhattarai

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In recent years, there has been a rapid increase in the use of Unmanned Aerial Vehicle (UAVs) in search and rescue (SAR) operations, disaster management, and many more areas where information about the location of human beings are important. This research will primarily focus on the use of optical and thermal camera via UAV platform in real-time detection, localization, and visualization of human beings on GIS. This research will be beneficial in disaster management search of lost humans in wilderness or difficult terrain, detecting abnormal human behaviors in border or security tight areas, studying distribution of people at night, counting people density in crowd, manage people flow during evacuation, planning provisions in areas with high human density and many more.

Keywords: UAV, human detection, real-time, localization, visualization, haar-like, GIS, thermal sensor

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Authors: Paul Ojeaga

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Can the level of innovation use drive un-validated business models across regions? To what extent does industrial sector attractiveness drive firm’s success across regions at the time of start-up? This study examines the role of innovation on start-up success in six regions of the world (namely Sub Saharan Africa, the Middle East and North Africa, Latin America, South East Asia Pacific, the European Union and the United States representing North America) using macroeconomic variables. While there have been studies using firm level data, results from such studies are not suitable for national policy decisions. The need to drive a regional innovation policy also begs for an answer, therefore providing room for this study. Results using dynamic panel estimation show that innovation counts in the early infancy stage of new business life cycle. The results are robust even after controlling for time fixed effects and the study present variance-covariance estimation robust standard errors.

Keywords: industrial economics, un-validated business models, scalable models, entrepreneurship

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Authors: Ece A. Irmak, Amdulla O. Mekhrabov, M. Vedat Akdeniz

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There is a growing interest in the modeling and simulation of magnetic nanoalloys by various computational methods. Magnetic crystalline/amorphous nanoparticles (NP) are interesting materials from both the applied and fundamental points of view, as their properties differ from those of bulk materials and are essential for advanced applications such as high-performance permanent magnets, high-density magnetic recording media, drug carriers, sensors in biomedical technology, etc. As an important magnetic material, Fe-Ni based nanoalloys have promising applications in the chemical industry (catalysis, battery), aerospace and stealth industry (radar absorbing material, jet engine alloys), magnetic biomedical applications (drug delivery, magnetic resonance imaging, biosensor) and computer hardware industry (data storage). The physical and chemical properties of the nanoalloys depend not only on the particle or crystallite size but also on composition and atomic ordering. Therefore, computer modeling is an essential tool to predict structural, electronic, magnetic and optical behavior at atomistic levels and consequently reduce the time for designing and development of new materials with novel/enhanced properties. Although first-principles quantum mechanical methods provide the most accurate results, they require huge computational effort to solve the Schrodinger equation for only a few tens of atoms. On the other hand, molecular dynamics method with appropriate empirical or semi-empirical inter-atomic potentials can give accurate results for the static and dynamic properties of larger systems in a short span of time. In this study, structural evolutions, magnetic and electronic properties of Fe-Ni based nanoalloys have been studied by using molecular dynamics (MD) method in Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and Density Functional Theory (DFT) in the Vienna Ab initio Simulation Package (VASP). The effects of particle size (in 2-10 nm particle size range) and temperature (300-1500 K) on stability and structural evolutions of amorphous and crystalline Fe-Ni bulk/nanoalloys have been investigated by combining molecular dynamic (MD) simulation method with Embedded Atom Model (EAM). EAM is applicable for the Fe-Ni based bimetallic systems because it considers both the pairwise interatomic interaction potentials and electron densities. Structural evolution of Fe-Ni bulk and nanoparticles (NPs) have been studied by calculation of radial distribution functions (RDF), interatomic distances, coordination number, core-to-surface concentration profiles as well as Voronoi analysis and surface energy dependences on temperature and particle size. Moreover, spin-polarized DFT calculations were performed by using a plane-wave basis set with generalized gradient approximation (GGA) exchange and correlation effects in the VASP-MedeA package to predict magnetic and electronic properties of the Fe-Ni based alloys in bulk and nanostructured phases. The result of theoretical modeling and simulations for the structural evolutions, magnetic and electronic properties of Fe-Ni based nanostructured alloys were compared with experimental and other theoretical results published in the literature.

Keywords: density functional theory, embedded atom model, Fe-Ni systems, molecular dynamics, nanoalloys

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Authors: Ayu S. Muhamad, Michael Gleeson

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Immunomodulators are substances that alter immune system via dynamic regulation of messenger molecules. It can be divided into immunostimulant and immunosuppressant. It can help to increase immunity of people with a low immune system, and also can help to normalize an overactive immune system. Aim of this study is to investigate the effects of in vitro exposure to low and high doses of several immunomodulators which include caffeine, kaloba and quercetin on antigen-stimulated whole blood culture cytokine production. Whole blood samples were taken from 5 healthy males (age: 32 ± 12 years; weight: 75.7 ± 6.1 kg; BMI: 24.3 ± 1.5 kg/m2) following an overnight fast with no vigorous activity during the preceding 24 h. The whole blood was then stimulated with 50 µl of 100 x diluted Pediacel vaccine and low or high dose of immunomodulators in the culture plate. After 20 h incubation (5% CO2, 37°C), it was analysed using the Evidence Investigator to determine the production of cytokines including IL-2, IL-4, IL-10, IFN-γ, and IL-1α. Caffeine and quercetin showed a tendency towards decrease cytokine production as the doses were increased. On the other hand, an upward trend was evident with kaloba, where a high dose of kaloba seemed to increase the cytokine production. In conclusion, we found that caffeine and quercetin have potential as immunosuppressant and kaloba as immunostimulant.

Keywords: caffeine, cytokine, immunomodulators, kaloba, quercetin

Procedia PDF Downloads 451

Authors: Lien-Fa Lin, Yung-Ming Li, Fu-Shun Hsieh

Abstract:

With the advancement of information technology and development of group commerce, people have obviously changed in their lifestyle. However, group commerce faces some challenging problems. The products or services provided by vendors do not satisfactorily reflect customers’ opinions, so that the sale and revenue of group commerce gradually become lower. On the other hand, the process for a formed customer group to reach group-purchasing consensus is time-consuming and the final decision is not the best choice for each group members. In this paper, we design a social decision support mechanism, by using group discussion message to recommend suitable options for group members and we consider social influence and personal preference to generate option ranking list. The proposed mechanism can enhance the group purchasing decision making efficiently and effectively and venders can provide group products or services according to the group option ranking list.

Keywords: social network, group decision, text mining, group commerce

Procedia PDF Downloads 475

Authors: Gagandeep Kaur

Abstract:

Cloud computing uses various computing resources such as CPU, memory, processor etc. which is used to deliver service over the network and is one of the emerging fields for large scale distributed computing. In cloud computing, execution of large number of tasks with available resources to achieve high performance, minimal total time for completion, minimum response time, effective utilization of resources etc. are the major research areas. In the proposed research, an algorithm has been proposed to achieve high performance in load balancing and resource utilization. The proposed algorithm is used to reduce the makespan, increase the resource utilization and performance cost for independent tasks. Further scheduling metrics based on algorithm in cloud computing has been proposed.

Keywords: resource utilization, response time, load balancing, performance cost

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

Abstract:

The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Jonida Canaj

Abstract:

A simple method of extraction and determination of fifteen priority polycyclic aromatic hydrocarbons (PAHs) from drinking water using high performance liquid chromatography (HPLC) has been validated with limits of detection (LOD) and limits of quantification (LOQ), method recovery and reproducibility, and other factors. HPLC parameters, such as mobile phase composition and flow standardized for determination of PAHs using fluorescent detector (FLD). PAH was carried out by liquid-liquid extraction using dichloromethane. Linearity of calibration curves was good for all PAH (R², 0.9954-1.0000) in the concentration range 0.1-100 ppb. Analysis of standard spiked water samples resulted in good recoveries between 78.5-150%(0.1ppb) and 93.04-137.47% (10ppb). The estimated LOD and LOQ ranged between 0.0018-0.98 ppb. The method described has been used for determination of the fifteen PAHs contents in drinking water samples.

Keywords: high performance liquid chromatography, HPLC, method validation, polycyclic aromatic hydrocarbons, PAHs, water

Procedia PDF Downloads 86

Authors: Asad Naeem, Mohamed Nour Eldin, Jinkoo Kim

Abstract:

In this study, the seismic performance and life cycle cost (LCC) are evaluated of the structure retrofitted with the damped cable system (DCS). The DCS is a seismic retrofit system composed of a high-strength steel cable and pressurized viscous dampers. The analysis model of the system is first derived using various link elements in SAP2000, and fragility curves of the structure retrofitted with the DCS and viscous dampers are obtained using incremental dynamic analyses. The analysis results show that the residual displacements of the structure equipped with the DCS are smaller than those of the structure with retrofitted with only conventional viscous dampers, due to the enhanced stiffness/strength and self-centering capability of the damped cable system. The fragility analysis shows that the structure retrofitted with the DCS has the least probability of reaching the specific limit states compared to the bare structure and the structure with viscous damper. It is also observed that the initial cost of the DCS method required for the seismic retrofit is smaller than that of the structure with viscous dampers and that the LCC of the structure equipped with the DCS is smaller than that of the structure with viscous dampers.

Keywords: damped cable system, fragility curve, life cycle cost, seismic retrofit, self-centering

Procedia PDF Downloads 536