Search results for: solid coordination network (SCN)

Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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Authors: Seungyeong Choi, Namkyu Lee, Dong Il Shim, Young Mun Lee, Yong-Ki Park, Hyung Hee Cho

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Effect of the cross-sectional geometry on heat transfer and particle motion of circulating fluidized bed riser for CO₂ capture was investigated. Numerical simulation using Eulerian-eulerian method with kinetic theory of granular flow was adopted to analyze gas-solid flow consisting in circulating fluidized bed riser. Circular, square, and rectangular cross-sectional geometry cases of the same area were carried out. Rectangular cross-sectional geometries were analyzed having aspect ratios of 1: 2, 1: 4, 1: 8, and 1:16. The cross-sectional geometry significantly influenced the particle motion and heat transfer. The downward flow pattern of solid particles near the wall was changed. The gas-solid mixing degree of the riser with the rectangular cross section of the high aspect ratio was the lowest. There were differences in bed-to-wall heat transfer coefficient according to rectangular geometry with different aspect ratios.

Keywords: bed geometry, computational fluid dynamics, circulating fluidized bed riser, heat transfer

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Authors: Siddhant Srivastav, Soumyaranjan Mishra, Sumanta Kumar Meher

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Researchers are attempting to develop extremely efficient electrochemical energy storage technologies as a result of the phenomenal advancement of portable electronic devices. Because of their improved electrical conductivity and narrower band gap, transition metal selenide-based nanostructures have piqued the interest of many researchers in this field. Based on this concept, we present a simple anion exchange hydrothermal synthesis method for synthesizing manganese and nickel based selenide (Mn/NiSe2) nanostructure for use in all-solid-state asymmetric supercapacitors. According to the comprehensive physicochemical characterizations, the material has lowly crystalline properties, a distinct porous microstructure, and a significant bonding contact between the metal and the selenium. The electrochemical investigations of the Mn/NiSe2 electrode material revealed supercapacitive charge discharge properties, excellent electro-kinetic reversibility, and minimal charge transfer resistance (Rct). Furthermore, the all-solid-state asymmetric supercapacitor device assembled using Mn/NiSe2 as positive electrode, nitrogen doped reduced graphene oxide (N-rGO) as negative electrode, and PVA-KOH gel as electrolyte/separator exhibit good redox behaviour, excellent charge-discharge properties with negligible voltage (IR) drop, and lower impedance characteristics. The solid state asymmetric supercapacitor device (Mn/NiSe2||N-rGO) demonstrated the power density of ultra-capacitors and the energy density of rechargeable batteries. Conclusively, the Mn/NiSe2 has been proposed as a potential outstanding electrode material for the next generation of all-solid-state asymmetric supercapacitors.

Keywords: anion exchange, asymmetric supercapacitor, supercapacitive charge-discharge, voltage drop

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Authors: Adel Bougamouza, Boualam Remini, Abd El Hadi Ammari, Feteh Sakhraoui

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The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords: artificial neural network, Oued Abiod watershed, feedforward network, generalized regression network, radial basis network, sediment concentration

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Authors: Nazli Eslamirad, Peter C. Junk, Jun Wang, Glen B. Deacon

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Since coordination behavior of pyrazoles and pyrazolate ions are widely versatile towards a great range of metals such as d-block, f-block as well as main group elements; they attract interest as ligands for preparing compounds. A variety of rare-earth pyrazolate complexes have been synthesized by redox transmetalation/protolysis (RTP) previously, therefore, a variety of rare-earth pyrazolate complexes using two pyrazoles, 3,5-dimethylpyrazole (Me₂pzH) and 3,5-di-tert -butylpyrazolate (t-Bu₂pzH), in which the structures span the whole La-Lu array beside Sc and Y has been synthesized by RTP reaction. There have been further developments in this study: Synthesizing structure of [Tb(Me₂pz)₃(thf)]₂ which is isomorphous with those of the previously reported [Dy(Me₂pz)₃(thf)]₂ and [Lu(Me₂pz)₃(thf)]₂ analogous that has two µ-1(N):2(Nʹ)-Me2pz ligands (the most common pyrazolate ligation for non-rare-earth complexes). Previously most of the reported compounds using t-Bu2pzH were monomeric compounds however the lanthanum derivative [La(Me₂pz)₃thf₂] ,which has been reported previously without crystal structure, has now been structurally characterized, along with cerium and lutetium analogue. Also a polymeric structure with samarium has now been synthesized which the neodymium analogue has been reported previously and comparing these polymeric structures can support the idea that the geometry of Sm(tBu₂pz)₃ affect the coordination of the solvent. Also, by using 1,2-dimethoxyethane (DME) instead of tetrahydrofuran (THF) new [Er(tBu₂pz)₃ (dme)₂] has now been reported.

Keywords: lanthanoid complexes, pyrazolate, redox transmetalation/protolysis, x-ray crystal structures

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Authors: Alhaji Musa Abdullahi, Usman Abdullahi, Adamu Lawan, Aminu Maidala

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Abstract 16 healthy lactating cows will be randomly selected for the trial and will be randomly divided in to 4 groups with 4 cows in each. They will be kept under similar management condition (conventional management system). Animals of relatively same weight and age will be used. After 11days for adaptation, feed intake and performance of the experimental animals will be determine. Milk sample will be collected at each milking in the morning and afternoon to determine; Milk yield, Milk fat percentage, Solid not fat percentage, Total solid percentage of milk. Cows dung will be observe to determine; Score 1 very loose watery stool, Score 2 semi solid with undigested raw material, Score 3 semi solid with less undigested raw material, Score 4 solid with very less undigested raw material, Score 5 good dung no undigested raw material. At the end of the experiment, blood samples will be analyzed for full blood counts and differentials {White Blood Cells (WBC), Red Blood Cells (RBC), Hemoglobin (Hb), Packed Cell Volume (PCV), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), Platelets (PLT), Lymphocytes (LYM), Basophils, Eosinophils and Monocytes Proportion (MXD) and Neutrophils (NEUT)} using automated hematology analyzer. Serum samples will be analyzed for heat shock transcription factors, heat shock proteins and hormones (Serum glucocorticoid, prolactin and cortisol). Moreover, biochemical analysis will also be conducted to check for Total protein (TP), Albumen (ALB), Globulin (GBL), Total cholesterol (TCH), glucose (G), sodium (Na+), potassium (K+), chloride (Cl-) and pH. Keywords: Lactating cows, milk composition, dung score and blood parameters.

Keywords: Lactating cows , Milk yield , Dung score , Blood parameters

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Authors: Saeed Moarrefi, Shou-Han Zhou, Liyuan Fan

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Coupling with solid oxide fuel cells, methane dry reforming is a promising pathway for energy production while mitigating carbon emissions. However, the influence of carbon dioxide and electrochemical reactions on the internal dry reforming reaction within the fuel cells remains debatable, requiring accurate kinetic models to describe the internal reforming behaviors. We employed the Power-Law and Langmuir Hinshelwood–Hougen Watson models in an electrolyte-supported solid oxide fuel cell with a NiO-GDC-YSZ anode. The current density used in this study ranges from 0 to 1000 A/m2 at 973 K to 1173 K to estimate various kinetic parameters. The influence of the electrochemical reactions on the adsorption terms, the equilibrium of the reactions, the activation energy, the pre-exponential factor of the rate constant, and the adsorption equilibrium constant were studied. This study provides essential parameters for future simulations and highlights the need for a more detailed examination of reforming kinetic models.

Keywords: dry reforming kinetics, Langmuir Hinshelwood–Hougen Watson, power-law, SOFC

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez, Mehdi Mrad

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We investigate the large scale of networks in the context of network survivability under attack. We use appropriate techniques to evaluate and the attacker-based- and the defender-based-network survivability. The attacker is unaware of the operated links by the defender. Each attacked link has some pre-specified probability to be disconnected. The defender choice is so that to maximize the chance of successfully sending the flow to the destination node. The attacker however will select the cut-set with the highest chance to be disabled in order to partition the network. Moreover, we extend the problem to the case of selecting the best p paths to operate by the defender and the best k cut-sets to target by the attacker, for arbitrary integers p,k > 1. We investigate some variations of the problem and suggest polynomial-time solutions.

Keywords: defense/attack strategies, large scale, networks, partitioning a network

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Authors: Vishal H. Shukla, Sanjay B. Deshmukh

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Traffic engineering, at its center, is the ability of moving traffic approximately so that traffic from a congested link is moved onto the unused capacity on another link. Traffic Engineering ensures the best possible use of the resources. Now to support traffic engineering in the today’s network, Multiprotocol Label Switching (MPLS) is being used which is very helpful for reliable packets delivery in an ongoing internet services. Here a topology is been implemented on GNS3 to focus on the analysis of the communication take place from one site to other through the ISP. The comparison is made between the IP network & MPLS network based on Bandwidth & Jitter which are one of the performance parameters using JPERF simulator.

Keywords: GNS3, JPERF, MPLS, traffic engineering, VMware

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Authors: Fatemeh Nejati, Farah Habib, Sayeh Goudarzi

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Architecture and structure have always been closely interrelated so that they should be integrated into a unified, coherent and beautiful universe, while in the contemporary era, both structures and architecture proceed separately. The purpose of architecture is the art of creating form and space and order for human service, and the goal of the structural engineer is the transfer of loads to the structure, too. This research seeks to achieve the goal by looking at the relationship between the form of architecture and structure from its inception to the present day to the Global Identification and Management Plan. Finally, by identifying the main components of the design of the structure in interaction with the architectural form, an effective step is conducted in the Professional training direction and solutions to professionals. Therefore, after reviewing the evolution of structural and architectural coordination in various historical periods as well as how to reach the form of the structure in different times and places, components are required to test the components and present the final theory that one hundred to be tested in this regard. Finally, this research indicates the fact that the form of architecture and structure has an aesthetic link, which is influenced by a number of components that could be edited and has a regular order throughout history that could be regular. The research methodology is analytic, and it is comparative using analytical and matrix diagrams and diagrams and tools for conducting library research and interviewing.

Keywords: architecture, structural form, structural and architectural coordination, effective components, aesthetics

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Authors: Katharigatta N. Venugopala, Fernando Albericio, Bander E. Al-Dhubiab, T. Govender

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Recent years have witnessed a renaissance in the field of peptides that are obtained from various natural sources such as many bacteria, fungi, plants, seaweeds, vertebrates, invertebrates and have been reported for various pharmacological properties such as anti-TB, anticancer, antimalarial, anti-inflammatory, anti-HIV, antibacterial, antifungal, and antidiabetic, activities. In view of the pharmacological significance of natural peptides, serious research efforts of many scientific groups and pharmaceutical companies have consequently focused on them to explore the possibility of developing their potential analogues as therapeutic agents. Solid phase and solution phase peptide synthesis are the two methodologies currently available for the synthesis of natural or synthetic linear or cyclic depsi-peptides. From a synthetic point of view, there is no doubt that the solid-phase methodology gained added advantages over solution phase methodology in terms of simplicity, purity of the compound and the speed with which peptides can be synthesised. In the present study total synthesis, purification and structural elucidation of analogues of natural anti-TB cyclic depsi-peptides such as depsidomycin, massetolides and viscosin has been attempted by solid phase method using standard Fmoc protocols and finally off resin cyclization in solution phase method. In case of depsidomycin, synthesis of linear peptide on solid phase could not be achieved because of two turn inducing amino acids in the peptide sequence, but total synthesis was achieved by convergent solid phase peptide synthesis followed by cyclization in solution phase method. The title compounds obtained were in good yields and characterized by NMR and HRMS. Anti-TB results revealed that the potential title compound exhibited promising activity at 4 µg/mL against H37Rv and 16 µg/mL against MDR strains of tuberculosis.

Keywords: total synthesis, cyclic depsi-peptides, anti-TB activity, tuberculosis

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Authors: Afaneen Anwer, Samara M. Kamil

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Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load.

Keywords: load forecasting, neural network, back-propagation algorithm, Iraqi power system

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Authors: Hyun-Jong Choi, Minjun Kwak, Doo-Won Seo, Sang-Kuk Woo, Sun-Dong Kim

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Solid Oxide Cell(SOC) systems can contribute to the transition to the hydrogen society by utilized as a power and hydrogen generator by the electrochemical reaction with high efficiency at high operation temperature (>750 ℃). La1-xSrxCo1-yFeyO3, which is an air electrode, is occurred stability degradations due to reaction and delamination with yittria stabilized zirconia(YSZ) electrolyte in a water electrolysis mode. To complement this phenomenon SOCs need gadolinium doped ceria(GDC) buffer layer between electrolyte and air electrode. However, GDC buffer layer requires a high sintering temperature and it causes a reaction with YSZ electrolyte. This study carried out low temperature sintering of GDC layer by applying Cu-oxide as a sintering aid. The effect of a copper additive as a sintering aid to lower the sintering temperature for the construction of solid oxide fuel cells (SOFCs) was investigated. GDC buffer layer with 0.25-10 mol% CuO sintering aid was prepared by reacting GDC power and copper nitrate solution followed by heating at 600 ℃. The sintering of CuO-added GDC powder was optimized by investigating linear shrinkage, microstructure, grain size, ionic conductivity, and activation energy of CuO-GDC electrolytes at temperatures ranging from 1100 to 1400 ℃. The sintering temperature of the CuO-GDC electrolyte decreases from 1400 ℃ to 1100 ℃ by adding the CuO sintering aid. The ionic conductivity of the CuO-GDC electrolyte shows a maximum value at 0.5 mol% of CuO. However, the addition of CuO has no significant effects on the activation energy of GDC electrolyte. GDC-LSCF layers were co-sintering at 1050 and 1100 ℃ and button cell tests were carried out at 750 ℃.

Keywords: Co-Sintering, GDC-LSCF, Sintering Aid, solid Oxide Cells

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Authors: Boukari Nassim

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This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Bernd Sauer, Michel Werner, Stefan Emrich, Michael Kopnarski, Oliver Koch

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Friction reduction is an important aspect in the context of sustainability and energy transition. Rolling bearings are therefore used in many applications in which components move relative to each other. Conventionally lubricated rolling bearings are used in a wide range of applications, but are not suitable under certain conditions. Conventional lubricants such as grease or oil cannot be used at very high or very low temperatures. In addition, these lubricants evaporate at very low ambient pressure, e.g. in a high vacuum environment, making the use of solid lubricated bearings unavoidable. With the use of solid-lubricated bearings, predicting the service life becomes more complex. While the end of the service life of bearings with conventional lubrication is mainly caused by the failure of the bearing components due to material fatigue, solid-lubricated bearings fail at the moment when the lubrication layer is worn and the rolling elements come into direct contact with the raceway during operation. In order to extend the service life of these bearings beyond the service life of the initial coating, the use of transfer lubrication is recommended, in which pockets or sacrificial cages are used in which the balls run and can thus absorb the lubricant, which is then available for lubrication in tribological contact. This contribution presents the results of wear and service life tests on solid-lubricated rolling bearings with sacrificial cage pockets. The cage of the bearing consists of a polyimide (PI) matrix with 15% molybdenum disulfide (MoS2) and serves as a lubrication depot alongside the silver-coated balls. The bearings are tested under high vacuum (pE < 10-2 Pa) at a temperature of 300 °C on a four-bearing test rig. First, investigations of the bearing system within the bearing service life are presented and the torque curve, the wear mass and surface analyses are discussed. With regard to wear, it can be seen that the bearing rings tend to increase in mass over the service life of the bearing, while the balls and the cage tend to lose mass. With regard to the elementary surface properties, the surfaces of the bearing rings and balls are examined in terms of the mass of the elements on them. Furthermore, service life investigations with different material pairings are presented, whereby the focus here is on the service life achieved in addition to the torque curve, wear development and surface analysis. It was shown that MoS2 in the cage leads to a longer service life, while a silver (Ag) coating on the balls has no positive influence on the service life and even appears to reduce it in combination with MoS2.

Keywords: ball bearings, molybdenum disulfide, solid lubricated bearings, solid lubrication mechanisms

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Authors: Zeta Dooly, Aidan Duane

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The collaborative European funded research and development landscape provides prime environmental conditions for multi-disciplinary teams to learn and enhance their knowledge beyond the capability of training and learning within their own organisation cocoons. Whilst the emergence of the academic entrepreneur has changed the focus of educational institutions to that of quasi-businesses, the training and professional development of lecturers and academic staff are often not formalised to the same level as industry. This research focuses on industry and academic collaborative research funded by the European Commission. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness, the nature of relationships, links, and nodes within a research network, and the enhancement of the network’s knowledge. The contribution of this paper extends our understanding of establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. This research provides evidence to support the impact collaborative research has on the disparate individuals toward their innovative contributions to their organisations and their own professional development. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, intra-network challenges in relation to open data, competition, friendships, and competency enhancement. The network capability is enhanced by the adoption of the relevant theories; network theory, open innovation, and social exchange, with the understanding that the network structure has an impact on innovation and social exchange in research networks. The research concludes that there is an opportunity to deepen our understanding of the impact of network reuse and network hoping that provides scaffolding for the network members to enhance and build upon their knowledge using a progressive approach.

Keywords: research networks, competency building, network theory, case study

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Authors: Nwanneka Mmonwuba

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Solid waste disposal to the environment was investigated by analyzing the quality characteristics of waste, air quality, and heavy metal concentration in the soil. The characteristics of waste were analyzed by enumerating the number of houses, hostels, hotels, markets, schools, and industries with the type of waste being discharged or deposited into the dump site. The percentage of waste was estimated with organic ranking first for both wet and dry seasons, 54% and 44%, respectively. The ambient air quality was analyzed using the crown gas monitor analyzer. The analysis showed that the mean concentration of NO₂, SO₂, and Co is 0.74, 0.37, and 47.35 ppm for the wet season and 0.47, 0.35, and 37.65 ppm for the dry season, respectively, and do not conform with the USEPA standard. The chemical analysis of the groundwater sample indicates alkalinity ranging from 7.38 to 9.11. the heavy metals concentration in the soil of cadmium, iron, copper, calcium, and potassium with 0.053, 0.722, 0227, 21.3, and 9.019, respectively, obtained from 0.3 m at the subsurface failed to conform to the NRC (2013) standard. Iron consent in the soil can be corrected using ascorbic acid and soda ash. The permanent reduction of effects is to try relocating people who live very close to the dumpsite, or the dumpsite should be sited elsewhere and replaced with a sanitary landfill.

Keywords: solid waste, groundwater, disposal, dumpsite

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Authors: Wanida Suwunniponth

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The purpose of this research was to investigate the relationship between the factors of using online social network for tourism destination in case of Bangkok area in Thailand, by extending the use of technology acceptance model (TAM). This study employed by quantitative research and the target population were entrepreneurs and local people in Bangkok who use social network-Facebook concerning tourist destinations in Bangkok. Questionnaire was used to collect data from 300 purposive samples. The multiple regression analysis and path analysis were used to analyze data. The results revealed that most people who used Facebook for promoting tourism destinations in Bangkok perceived ease of use, perceived usefulness, perceived trust in using Facebook and influenced by social normative as well as having positive attitude towards using this application. Addition, the hypothesis results indicate that acceptance of online social network-Facebook was related to the positive attitude towards using of Facebook and related to their intention to use this application for tourism.

Keywords: Facebook, online social network, technology acceptance model, tourism destination

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Authors: Behzad Niknam, Kourosh Shahriar, Hassan Madani

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This paper demonstrates the applicability of Hybrid Neural Networks that combine with back propagation networks (BPN) and Genetic Algorithms (GAs) for predicting the flame length of tunnel fire A hybrid neural network model has been developed to predict the flame length of tunnel fire based parameters such as Fire Heat Release rate, air velocity, tunnel width, height and cross section area. The network has been trained with experimental data obtained from experimental work. The hybrid neural network model learned the relationship for predicting the flame length in just 3000 training epochs. After successful learning, the model predicted the flame length.

Keywords: tunnel fire, flame length, ANN, genetic algorithm

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Authors: A. Hakimiyan, H. Namazi

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Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

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Authors: Byung-Ik Kim, Hong-Koo Kang, Tae-Jin Lee, Hae-Ryong Park

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Cyber terrors against specific enterprises or countries have been increasing recently. Such attacks against specific targets are called advanced persistent threat (APT), and they are giving rise to serious social problems. The malicious behaviors of APT attacks mostly affect websites and penetrate enterprise networks to perform malevolent acts. Although many enterprises invest heavily in security to defend against such APT threats, they recognize the APT attacks only after the latter are already in action. This paper discusses the characteristics of APT attacks at each step as well as the strengths and weaknesses of existing malicious code detection technologies to check their suitability for detecting APT attacks. It then proposes a network-based malicious behavior detection algorithm to protect the enterprise or national networks.

Keywords: Advanced Persistent Threat (APT), malware, network security, network packet, exploit kits

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Authors: Jiawen Wang, Qijun Chen

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The performance of most machine-learning methods for image inpainting depends on the quantity and quality of the training samples. However, it is very expensive or even impossible to obtain a great number of training samples in many scenarios. In this paper, an image inpainting model based on a generative adversary network (GAN) is constructed for the cases when the number of training samples is small. Firstly, a feature extraction network (F-net) is incorporated into the GAN network to utilize the available information of the inpainting image. The weighted sum of the extracted feature and the random noise acts as the input to the generative network (G-net). The proposed network can be trained well even when the sample size is very small. Secondly, in the phase of the completion for each damaged image, a genetic algorithm is designed to search an optimized noise input for G-net; based on this optimized input, the parameters of the G-net and F-net are further learned (Once the completion for a certain damaged image ends, the parameters restore to its original values obtained in the training phase) to generate an image patch that not only can fill the missing part of the damaged image smoothly but also has visual semantics.

Keywords: image inpainting, generative adversary nets, genetic algorithm, small-sample size

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Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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Authors: Reem I. Al-Ya' Goub, Nasim Shatarat

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This paper presents the results of analytical and comparative study among software programs' outputs in analysis of some two way solid slabs; flat plate, flat slab with beams and flat slab with drop panels problems that already been analyzed using Classical Equivalent Frame Method (CEFM) by several reinforced concrete book authors. The primary objective of this research is to determine the moment results using various software programs. Then, a summary of the results and differences percentages were obtained to show how analysis procedure effects the outputs of calculations that vary from software program to another when comparing them with the results of CEFM. Moment values were obtained using either the Equivalent Frame Method (EFM) or Finite Element Method (FEM) that's used among many software programs. The results of the analyses demonstrate that software programs vary markedly in terms of the information they provide to the structural designer regarding values of the model insertion, stiffness, effective moment of inertia used and specially the moment values.

Keywords: two-way solid slabs, flat plate, flat slab with beams, flat slab with drop panels, analysis, modeling, EFM, CEFM, FEM

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Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

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The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

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The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.

Keywords: neural network computing, continuous functions generating the input-output mapping, decreasing the training time, machines with big memories

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Authors: Dorota Galeza

Abstract:

This paper argues that networks, such as the ECN and the American network, are affected by certain small events which are inherent to path dependence and preclude the full evolution towards efficiency. It is advocated that the American network is superior to the ECN in many respects due to its greater flexibility and longer history. This stems in particular from the creation of the American network, which was based on a small number of cases. Such structure encourages further changes and modifications which are not necessarily radical. The ECN, by contrast, was established by legislative action, which explains its rigid structure and resistance to change. It might be the case that the ECN is subject not so much to path dependence but to past dependence. It might have to be replaced, as happened to its predecessor. This paper is an attempt to transpose the superiority of the American network on to the ECN. It looks at concepts such as judicial cooperation, harmonization of procedure, peer review and regulatory impact assessments (RIAs), and dispute resolution procedures. The aim is to adopt these concepts into the EU setting without recourse to legal transplantation. The major difficulty is that many of these concepts have been tested only in the US and it is difficult to tell whether they could be modified to meet EU standards. Concepts such as judicial cooperation might be difficult due to different language traditions in EU member states. It is hoped that greater flexibility, as in the American network, would boost legitimacy and transparency.

Keywords: ECN, networks, regulation, competition

Procedia PDF Downloads 423

Authors: Debajyoti Mukherjee, Arathy B. S., Arpita Sahu, Saranga P. Pogula

Abstract:

A functional model to mimic the functional correctness of a neural network compute accelerator IP is very crucial for design validation. Neural network workloads are based on a Brain Floating Point (BF-16) data type. The major challenge we were facing was the incompatibility of GCC compilers to the BF-16 datatype, which we addressed with a native BF-16 generator integrated into our functional model. Moreover, working with big GEMM (General Matrix Multiplication) or SpMM (Sparse Matrix Multiplication) Work Loads (Dense or Sparse) and debugging the failures related to data integrity is highly painstaking. In this paper, we are addressing the quality challenge of such a complex neural network accelerator design by proposing a functional model-based scoreboard or software model using SystemC. The proposed functional model executes the assembly code based on the ISA of the processor IP, decodes all instructions, and executes as expected to be done by the DUT. The said model would give a lot of visibility and debug capability in the DUT, bringing up micro-steps of execution.

Keywords: ISA, neural network, Brain Float-16, DUT

Procedia PDF Downloads 87

Authors: Edward P. Guillen, A. Karina Martinez Barliza

Abstract:

Powerline Communications (PLC) as last mile solution to provide communication services, has the advantage of transmitting over channels already used for electrical distribution. However these channels have been not designed with this purpose, for that reason telecommunication companies in Colombia want to know how good would be using PLC in costs and network performance in comparison to cable modem or DSL. This paper analyzes PLC throughput for residential complex scenarios using a PLC network scenarios and some statistical results are shown.

Keywords: home network, power line communication, throughput analysis, power factor, cost, last mile solution

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Authors: A. S. Obaid, K. H. T. Hassan, A. M. Asij, B. M. Salih, M. Bououdina

Abstract:

Cadmium Oxide (CdO) thin films have been prepared by vacuum evaporation method on Si (111) substrate at room temperature using CdCl2 as a source of Cd. Detailed structural properties of the films are presented using XRD and SEM. The films was pure polycrystalline CdO phase with high crystallinity. The lattice constant average crystallite size of the nanocrystalline CdO thin films were calculated. SEM image confirms the formation nanostructure. Energy dispersive X-ray analysis spectra of CdO thin films shows the presence of Cd and O peaks only, no additional peaks attributed to impurities or contamination are observed.

Keywords: nanostructured CdO, solid-vapor deposition, quantum size effect, cadmium oxide

Procedia PDF Downloads 662