Search results for: computational neural networks

Authors: S. Mehrab Amiri, Nasser Talebbeydokhti

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Modeling sediment transport processes by means of numerical approach often poses severe challenges. In this way, a number of techniques have been suggested to solve flow and sediment equations in decoupled, semi-coupled or fully coupled forms. Furthermore, in order to capture flow discontinuities, a number of techniques, like artificial viscosity and shock fitting, have been proposed for solving these equations which are mostly required careful calibration processes. In this research, a numerical scheme for solving shallow water and Exner equations in fully coupled form is presented. First-Order Centered scheme is applied for producing required numerical fluxes and the reconstruction process is carried out toward using Monotonic Upstream Scheme for Conservation Laws to achieve a high order scheme.  In order to satisfy C-property of the scheme in presence of bed topography, Surface Gradient Method is proposed. Combining the presented scheme with fourth order Runge-Kutta algorithm for time integration yields a competent numerical scheme. In addition, to handle non-prismatic channels problems, Cartesian Cut Cell Method is employed. A trained Multi-Layer Perceptron Artificial Neural Network which is of Feed Forward Back Propagation (FFBP) type estimates sediment flow discharge in the model rather than usual empirical formulas. Hydrodynamic part of the model is tested for showing its capability in simulation of flow discontinuities, transcritical flows, wetting/drying conditions and non-prismatic channel flows. In this end, dam-break flow onto a locally non-prismatic converging-diverging channel with initially dry bed conditions is modeled. The morphodynamic part of the model is verified simulating dam break on a dry movable bed and bed level variations in an alluvial junction. The results show that the model is capable in capturing the flow discontinuities, solving wetting/drying problems even in non-prismatic channels and presenting proper results for movable bed situations. It can also be deducted that applying Artificial Neural Network, instead of common empirical formulas for estimating sediment flow discharge, leads to more accurate results.

Keywords: artificial neural network, morphodynamic model, sediment continuity equation, shallow water equations

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Authors: Francesco Rizzuto, Matthew Stickland, Stephan Hannot

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The simulation of a positive displacement pump system with commercial software for Computer Fluid Dynamics (CFD), will result in an enormous computational effort due to the complexity of the pump system. This drawback restricts the use of it to a specific part of the pump in one simulation. This research focuses on developing an algorithm that provides a suitable result in agreement with experiment data, without that computational effort. The compressible equations are solved with an explicit algorithm. A comparison is presented between the FV method with Monotonic Upwind scheme for Conservative Laws (MUSCL) with slope limiter and experimental results. The source term for cavitation and friction is introduced into the algorithm with a slipping strategy and solved with a 4th order Runge-Kutta scheme (RK4). Different pumps are modeled and analyzed to evaluate the flexibility of the code. The simulation required minimal computation time and resources without compromising the accuracy of the simulation results. Therefore, this algorithm highlights the feasibility of pressure pulsation simulation as a design tool for an industrial purpose.

Keywords: cavitation, diaphragm, DVCM, finite volume, MUSCL, positive displacement pump

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Authors: Nureni Asafe Yekini, Olawale Nasiru Lawal, Bola Dada, Gabriel Adeyemi Okunlola

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This study is focused on the strengths and challenges associated with using the online social network as a rapidly evolving medium in marketing tourism services and businesses among the youths in Nigeria. The paper examines the Nigerian tourists’ attitude, mainly towards three aspects: application of Internet for travel and tourism; usage of online social networks in sharing travel and tourism experiences; and trust in electronic-media for marketing tourism businesses and services. The aim of this research is to determine the level of application of internet tools in marketing tourism businesses and services in Nigeria. This study reports an empirical analysis based on data obtained from a survey among 1004 Nigerian tourists. The outcome confirms the research hypothesis and points to crucial importance of introducing online social network site for marketing tourism businesses and services in Nigeria, and increasing the awareness for Nigeria as a tourist destination. Moreover, the paper strongly recommends the use of online social network as a tool for marketing tourism businesses and services, and the need for identifying effective framework for application of ICT tools in marketing tourism businesses and services in Nigeria at large.

Keywords: tourism business, internet, online social networks, tourism services, ICT

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Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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Authors: Said Benaissa, David Plets, Emmeric Tanghe, Jens Trogh, Luc Martens, Leen Vandaele, Annelies Van Nuffel, Frank A. M. Tuyttens, Bart Sonck, Wout Joseph

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The herd monitoring and managing - in particular the detection of ‘attention animals’ that require care, treatment or assistance is crucial for effective reproduction status, health, and overall well-being of dairy cows. In large sized farms, traditional methods based on direct observation or analysis of video recordings become labour-intensive and time-consuming. Thus, automatic monitoring systems using sensors have become increasingly important to continuously and accurately track the health status of dairy cows. Wireless sensor networks (WSNs) and internet-of-things (IoT) can be effectively used in health tracking of dairy cows to facilitate herd management and enhance the cow welfare. Since on-cow measuring devices are energy-constrained, a proper characterization of the off-body wireless channel between the on-cow sensor nodes and the back-end base station is required for a power-optimized deployment of these networks in barns. The aim of this study was to characterize the off-body wireless channel in indoor (barns) environment at 868 MHz using LoRa nodes. LoRa is an emerging wireless technology mainly targeted at WSNs and IoT networks. Both large scale fading (i.e., path loss) and temporal fading were investigated. The obtained path loss values as a function of the transmitter-receiver separation were well fitted by a lognormal path loss model. The path loss showed an additional increase of 4 dB when the wireless node was actually worn by the cow. The temporal fading due to movement of other cows was well described by Rician distributions with a K-factor of 8.5 dB. Based on this characterization, network planning and energy consumption optimization of the on-body wireless nodes could be performed, which enables the deployment of reliable dairy cow monitoring systems.

Keywords: channel, channel modelling, cow monitoring, dairy cows, health monitoring, IoT, LoRa, off-body propagation, PLF, propagation

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Authors: Thiyam Tamphasana Devi, Bimlesh Kumar

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A Computational Fluid Dynamics (CFD) technique has been applied to simulate the gas-liquid phase in double stirred tank of Rushton impeller. Eulerian-Eulerian model was adopted to simulate the multiphase with standard correlation of Schiller and Naumann for drag co-efficient. The turbulence was modeled by using standard k-ε turbulence model. The present CFD model predicts flow pattern, local gas hold-up, and local specific area. It also predicts local kLa (mass transfer rate) for single impeller. The predicted results were compared with experimental and CFD results of published literature. The predicted results are slightly over predicted with the experimental results; however, it is in reasonable agreement with other simulated results of published literature.

Keywords: Eulerian-Eulerian, gas-hold up, gas-liquid phase, local mass transfer rate, local specific area, Rushton Impeller

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Authors: Javed Mohammed

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Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

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Authors: Farshad Ghodoosi

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This article analyzes the effect of supervening events in contracts. Contracts serve an important function: allocation of risks. In spite of its importance, the case law and the doctrine are messy and inconsistent. This article provides a fresh look at excuse doctrines (i.e., force majeure, impracticability, impossibility, and frustration) with a focus on force majeure clauses. The article makes the following contributions: First, it furnishes a new conceptual and theoretical framework of excuse doctrines. By distilling the decisions, it shows that excuse doctrines rests on the triangle of control, foreseeability, and contract language. Second, it analyzes force majeure clauses used by S&P 500 companies to understand the stickiness and similarity of such clauses and the events they cover. Third, using computational and statistical tools, it analyzes US cases since 1810 in order to assess the weight given to the triangle of control, foreseeability, and contract language. It shows that the control factor plays an important role in force majeure analysis, while the contractual interpretation is the least important factor. The Article concludes that it is the standard for control -whether the supervening event is beyond the control of the party- that determines the outcome of cases in the force majeure context and not necessarily the contractual language. This article has important implications on COVID-19-related contractual cases. Unlike the prevailing narrative that it is the language of the force majeure clause that’s determinative, this article shows that the primarily focus of the inquiry will be on whether the effects of COVID-19 have been beyond the control of the promisee. Normatively, the Article suggests that the trifactor of control, foreseeability, and contractual language are not effective for allocation of legal risks in times of crises. It puts forward a novel approach to force majeure clauses whereby that the courts should instead focus on the degree to which parties have relied on (expected) performance, in particular during the time of crisis.

Keywords: contractual risks, force majeure clauses, foreseeability, control, contractual language, computational analysis

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Authors: Emilio Galdeano-Gómez, Juan C. Pérez-Mesa, Laura Piedra-Muñoz, María C. García-Barranco, Jesús Hernández-Rubio

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The situation in which firms engage in simultaneous cooperation and competition with each other is a phenomenon known as co-opetition. This scenario has received increasing attention in business economics and management analyses. In the domain of supply chain networks and for small and medium-sized enterprises, SMEs, these strategies are of greater relevance given the complex environment of globalization and competition in open markets. These firms face greater challenges regarding technology and access to specific resources due to their limited capabilities and limited market presence. Consequently, alliances and collaborations with both buyers and suppliers prove to be key elements in overcoming these constraints. However, rivalry and competition are also regarded as major factors in successful internationalization processes, as they are drivers for firms to attain a greater degree of specialization and to improve efficiency, for example enabling them to allocate scarce resources optimally and providing incentives for innovation and entrepreneurship. The present work aims to contribute to the literature on SMEs’ internationalization strategies. The sample is constituted by a panel data of marketing firms from the Andalusian food sector and a multivariate regression analysis is developed, measuring variables of co-opetition and international activity. The hierarchical regression equations method has been followed, thus resulting in three estimated models: the first one excluding the variables indicative of channel type, while the latter two include the international retailer chain and wholesaler variable. The findings show that the combination of several factors leads to a complex scenario of inter-organizational relationships of cooperation and competition. In supply chain management analyses, these relationships tend to be classified as either buyer-supplier (vertical level) or supplier-supplier relationships (horizontal level). Several buyers and suppliers tend to participate in supply chain networks, and in which the form of governance (hierarchical and non-hierarchical) influences cooperation and competition strategies. For instance, due to their market power and/or their closeness to the end consumer, some buyers (e.g. large retailers in food markets) can exert an influence on the selection and interaction of several of their intermediate suppliers, thus endowing certain networks in the supply chain with greater stability. This hierarchical influence may in turn allow these suppliers to develop their capabilities (e.g. specialization) to a greater extent. On the other hand, for those suppliers that are outside these networks, this environment of hierarchy, characterized by a “hub firm” or “channel master”, may provide an incentive for developing their co-opetition relationships. These results prove that the analyzed firms have experienced considerable growth in sales to new foreign markets, mainly in Europe, dealing with large retail chains and wholesalers as main buyers. This supply industry is predominantly made up of numerous SMEs, which has implied a certain disadvantage when dealing with the buyers, as negotiations have traditionally been held on an individual basis and in the face of high competition among suppliers. Over recent years, however, cooperation among these marketing firms has become more common, for example regarding R&D, promotion, scheduling of production and sales.

Keywords: co-petition networks, international supply chain, maketing agrifood firms, SMEs strategies

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Authors: Mazen Al-Bulaihed, Nissar Wani, Abdulrahman Al-Arainy, Yasin Khan

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Partial Discharge (PD) measurements are widely used for diagnostic purposes in electrical equipment used in power systems. The main cause of these measurements is to prevent large power failures as cables are prone to aging, which usually results in embrittlement, cracking and eventual failure of the insulating and sheathing materials, exposing the conductor and risking a potential short circuit, a likely cause of the electrical fire. Many distribution networks rely heavily on medium voltage (MV) power cables. The presence of joints in these networks is a vital part of serving the consumer demand for electricity continuously. Such measurements become even more important when the extent of dependence increases. Moreover, it is known that the partial discharge in joints and termination are difficult to track and are the most crucial point of failures in large power systems. This paper discusses the diagnostic techniques of four samples of XLPE insulated cable joints, each included with a different type of defect. Experiments were carried out by measuring PD and tanδ at very low frequency applied high voltage. The results show the importance of combining PD and tanδ for effective cable assessment.

Keywords: partial discharge, tan delta, very low frequency, XLPE cable

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Authors: Wedad Albalawi

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The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is defined as a closed subset contains real numbers. Then the inequalities of time scales version have received a lot of attention and has had a major field in both pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on double integrals to obtain new time-scale inequalities of Copson driven by Steklov operator. They will be applied in the solution of the Cauchy problem for the wave equation. The proof can be done by introducing restriction on the operator in several cases. In addition, the obtained inequalities done by using some concepts in time scale version such as time scales calculus, theorem of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of Hardy, inequality of Coposon, Steklov operator

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Authors: Nicolò Vaiana, Filip C. Filippou, Giorgio Serino

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In order to reduce numerical computations in the nonlinear dynamic analysis of seismically base-isolated structures, a Mixed Explicit-Implicit time integration Method (MEIM) has been proposed. Adopting the explicit conditionally stable central difference method to compute the nonlinear response of the base isolation system, and the implicit unconditionally stable Newmark’s constant average acceleration method to determine the superstructure linear response, the proposed MEIM, which is conditionally stable due to the use of the central difference method, allows to avoid the iterative procedure generally required by conventional monolithic solution approaches within each time step of the analysis. The main aim of this paper is to investigate the stability and computational efficiency of the MEIM when employed to perform the nonlinear time history analysis of base-isolated structures with sliding bearings. Indeed, in this case, the critical time step could become smaller than the one used to define accurately the earthquake excitation due to the very high initial stiffness values of such devices. The numerical results obtained from nonlinear dynamic analyses of a base-isolated structure with a friction pendulum bearing system, performed by using the proposed MEIM, are compared to those obtained adopting a conventional monolithic solution approach, i.e. the implicit unconditionally stable Newmark’s constant acceleration method employed in conjunction with the iterative pseudo-force procedure. According to the numerical results, in the presented numerical application, the MEIM does not have stability problems being the critical time step larger than the ground acceleration one despite of the high initial stiffness of the friction pendulum bearings. In addition, compared to the conventional monolithic solution approach, the proposed algorithm preserves its computational efficiency even when it is adopted to perform the nonlinear dynamic analysis using a smaller time step.

Keywords: base isolation, computational efficiency, mixed explicit-implicit method, partitioned solution approach, stability

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Authors: Chothmal

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In this paper, we present a route discovery process which uses the signal strength on a link as a parameter of its inclusion in the route discovery method. The proposed signal-to-interference and noise ratio (SINR) based multipath reactive routing protocol is named as SINR-MP protocol. The proposed SINR-MP routing protocols has two following two features: a) SINR-MP protocol selects routes based on the SINR of the links during the route discovery process therefore it select the routes which has long lifetime and low frame error rate for data transmission, and b) SINR-MP protocols route discovery process is multipath which discovers more than one SINR based route between a given source destination pair. The multiple routes selected by our SINR-MP protocol are node-disjoint in nature which increases their robustness against link failures, as failure of one route will not affect the other route. The secondary route is very useful in situations where the primary route is broken because we can now use the secondary route without causing a new route discovery process. Due to this, the network overhead caused by a route discovery process is avoided. This increases the network performance greatly. The proposed SINR-MP routing protocol is implemented in the trail version of network simulator called Qualnet.

Keywords: ad hoc networks, quality of service, video streaming, H.264/SVC, multiple routes, video traces

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Authors: H. Kazemi Esfeh, V. Akbari, M. Ehdaei, T. N. G. Borhani, A. Shamiri, M. Najafi

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In an industrial fluidized bed polymerization reactor, particle size distribution (PSD) plays a significant role in the reactor efficiency evaluation. The computational fluid dynamic (CFD) models coupled with population balance equation (CFD-PBM) have been extensively employed to investigate the flow behavior in the poly-disperse multiphase fluidized bed reactors (FBRs) utilizing ANSYS Fluent code. In this study, an existing CFD-PBM/ DQMOM coupled modeling framework has been used to highlight its potential to analyze the industrial-scale gas phase polymerization reactor. The predicted results reveal an acceptable agreement with the observed industrial data in terms of pressure drop and bed height. The simulated results also indicate that the higher particle growth rate can be achieved for bigger particles. Hence, the 2D CFD-PBM/DQMOM coupled model can be used as a reliable tool for analyzing and improving the design and operation of the gas phase polymerization FBRs.

Keywords: computational fluid dynamics, population balance equation, fluidized bed polymerization reactor, direct quadrature method of moments

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Authors: Sedat Yayla, Azhin Abdullah

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In the process of constructing hydroelectric power plants, the Pelton turbine, which is characterized by its simple manufacturing and construction, is performed in high head and low water flow. Parameters of the turbine have to be comprised in the designing process for obtaining hydraulic turbine with the highest efficiency during different operating conditions. The present investigation applied three-dimensional computational fluid dynamics (CFD). In addition, the bucket of Pelton turbine models with different splitter angle and inlet velocity values were examined for determining the force and visualizing the flow pattern on the bucket. The study utilized two diverse bucket models at various inlet velocities (20, 25, 30,35and 40m/s) and four different splitter angles (55, 75,90and 115 degree) for finding out the impacts of every single parameter on the effective force on the bucket. The acquired outcomes revealed that there is a linear relationship between force and inlet velocity on the bucket. Furthermore, the results also uncovered that the relationship between splitter angle and force on the bucket is linear until 90 degree.

Keywords: bucket design, computational fluid dynamics (CFD), free surface flow, two-phase flow, volume of fluid (VOF)

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Authors: Ali A. Hammadi

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In this work, a practical study on a commissioned Giga Passive Optical Network (GPON) fiber to the home access network in Kuwait is presented. The work covers the framework of the conceptual design of the deployed Passive Optical Networks (PONs), access network, optical fiber cable network distribution, technologies, and standards. The work also describes methodologies applied by system engineers for design of Optical Network Terminals (ONTs) and Optical Line Terminals (OLTs) transceivers with respect to the distance, operating wavelengths, splitting ratios. The results have demonstrated and justified the limitation of transmission distance of a PON link in Fiber to The Premises (FTTP) to not exceed 20 km. Optical Time Domain Reflector (OTDR) test has been carried for this project to confirm compliance with International Telecommunication Union (ITU) specifications regarding the total length of the deployed optical cable, total loss in dB, and loss per km in dB/km with respect to the operating wavelengths. OTDR test results with traces for segments of implemented fiber network will be provided and discussed.

Keywords: passive optical networks (PONs), fiber to the premises (FTTx), access network, OTDR

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Authors: M. Riazat, H. Abdolvand, M. Baniassadi

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In this present work, 3D reconstruction of cathode of SOFC is developed with various volume fractions and porosity. Three Phase Boundary (TPB) of construction of such derived micro structures is calculated. The neural network is used to optimize the porosity and volume fraction of each phase to reach a structure with maximum TPB.

Keywords: fuel cell, solid oxide, TPB, 3D reconstruction

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Authors: Samuel J. Ahmad, Michael Zhu, Andrew J. Kobets

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Segmentation tools continue to advance, evolving from manual methods to automated contouring technologies utilizing convolutional neural networks. These techniques have evaluated ventricular and hemorrhagic volumes in the past but may be applied in novel ways to assess posterior fossa-associated pathologies such as Chiari malformations. Herein, we summarize literature pertaining to segmentation in the context of this and other posterior fossa-based diseases such as trigeminal neuralgia, hemifacial spasm, and posterior fossa syndrome. A literature search for volumetric analysis of the posterior fossa identified 27 papers where semi-automated, automated, manual segmentation, linear measurement-based formulas, and the Cavalieri estimator were utilized. These studies produced superior data than older methods utilizing formulas for rough volumetric estimations. The most commonly used segmentation technique was semi-automated segmentation (12 studies). Manual segmentation was the second most common technique (7 studies). Automated segmentation techniques (4 studies) and the Cavalieri estimator (3 studies), a point-counting method that uses a grid of points to estimate the volume of a region, were the next most commonly used techniques. The least commonly utilized segmentation technique was linear measurement-based formulas (1 study). Semi-automated segmentation produced accurate, reproducible results. However, it is apparent that there does not exist a single semi-automated software, open source or otherwise, that has been widely applied to the posterior fossa. Fully-automated segmentation via such open source software as FSL and Freesurfer produced highly accurate posterior fossa segmentations. Various forms of segmentation have been used to assess posterior fossa pathologies and each has its advantages and disadvantages. According to our results, semi-automated segmentation is the predominant method. However, atlas-based automated segmentation is an extremely promising method that produces accurate results. Future evolution of segmentation technologies will undoubtedly yield superior results, which may be applied to posterior fossa related pathologies. Medical professionals will save time and effort analyzing large sets of data due to these advances.

Keywords: chiari, posterior fossa, segmentation, volumetric

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Authors: Atul K. Desai, Jigar K. Sevalia, Sandip A. Vasanwala

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When a building is located in an urban area, it is exposed to a wind of different characteristics then wind over an open terrain. This is development of turbulent wake region behind an upstream building. The interaction with upstream building can produce significant changes in the response of the tall building. Here, in this paper, an attempt has been made to study wind induced interference effects on tall building. In order to study wind induced interference effect (IF) on Tall Building, initially a tall building (which is termed as Principal Building now on wards) with square plan shape has been considered with different Height to Width Ratio and total drag force is obtained considering different terrain conditions as well as different incident wind direction. Then total drag force on Principal Building is obtained by considering adjacent building which is termed as Interfering Building now on wards with different terrain conditions and incident wind angle. To execute study, Computational Fluid Dynamics (CFD) Code namely Fluent and Gambit have been used.

Keywords: computational fluid dynamics, tall building, turbulent, wake region, wind

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Authors: Kartik Gupta, Umar Khan, Mayur Parab, Dhiraj Chaudhari, Afzal Ansari

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The report focuses on the study related to the Design and Simulation of a stabilator (an all-movable horizontal stabilizer) for a fixed-wing aircraft. The project involves the development of a computerized direct optimization procedure for designing an aircraft all-movable stabilator. This procedure evaluates various design variables to synthesize an optimal stabilator that meets specific requirements, including performance, control, stability, strength, and flutter velocity constraints. The work signifies the CFD (Computational Fluid Dynamics) analysis of the airfoils used in the stabilator along with the CFD analysis of the Stabilizer and Stabilator of an aircraft named Thorp- T18 in software like XFLR5 and ANSYS-Fluent. A comparative analysis between a Stabilizer and Stabilator of equal surface area and under the same environmental conditions was done, and the percentage of drag reduced by the Stabilator for the same amount of lift generated as the Stabilizer was also calculated lastly, Wind tunnel testing was performed on a scale down model of the Stabilizer and Stabilator and the results of the Wind tunnel testing were compared with the results of CFD.

Keywords: wind tunnel testing, CFD, stabilizer, stabilator

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Authors: Ahmed Alutaibi, Ganti Sudhakar

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Providing better Quality-of-Service (QoS) to end users has been a challenging problem for researchers and service providers. Building applications relying on best effort network protocols hindered the adoption of guaranteed service parameters and, ultimately, Quality of Service. The introduction of Software Defined Networking (SDN) opened the door for a new paradigm shift towards a more controlled programmable configurable behavior. Openflow has been and still is the main implementation of the SDN vision. To facilitate better QoS for applications, the network must calculate and measure certain parameters. One of those parameters is the delay between the two ends of the connection. Using the power of SDN and the knowledge of application and network behavior, SDN networks can adjust to different conditions and specifications. In this paper, we use the capabilities of SDN to implement multiple algorithms to measure delay end-to-end not only inside the SDN network. The results of applying the algorithms on an emulated environment show that we can get measurements close to the emulated delay. The results also show that depending on the algorithm, load on the network and controller can differ. In addition, the transport layer handshake algorithm performs best among the tested algorithms. Out of the results and implementation, we show the limitations of Openflow and develop suggestions to solve them.

Keywords: software defined networking, quality of service, delay measurement, openflow, mininet

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Authors: Ashraf Aly

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The aim of this study is to examine the impact of 5G communication technology radiation on human health and airport safety. The term 5G refers to the fifth generation of wireless mobile technology. The 5G wireless technology will increase the number of high-frequency-powered base stations and other devices and browsing and download speeds, as well as improve the network connectivity and play a big part in improving the performance of integrated applications, such as self-driving cars, medical devices, and robotics. 4G was the latest embedded version of mobile networking technology called 4G, and 5G is the new version of wireless technology. 5G networks have more features than 4G networks, such as lower latency, higher capacity, and increased bandwidth compared to 4G. 5G network improvements over 4G will have big impacts on how people live, business, and work all over the world. But neither 4G nor 5G have been tested for safety and show harmful effects from this wireless radiation. This paper presents biological factors on the effects of 5G radiation on human health. 5G services use C-band radio frequencies; these frequencies are close to those used by radio altimeters, which represent important equipment for airport and aircraft safety. The aviation industry, telecommunications companies, and their regulators have been discussing and weighing these interference concerns for years.

Keywords: wireless communication, radiofrequency, Electromagnetic field, environmental issues

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Nigel Q. Kelly, Zaid Siddiqi, Jin W. Lee

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It is known that using Coanda aerosurfaces can drastically augment the lift forces when applied to an Unmanned Aerial Vehicle (UAV) platform. However, Coanda saucer UAVs, which commonly use a dish-like, radially-extending structure, have shown no significant increases in thrust/lift force and therefore have never been commercially successful: the additional thrust/lift generated by the Coanda surface diminishes since the airstreams emerging from the rotor compartment expand radially causing serious loss of momentums and therefore a net loss of total thrust/lift. To overcome this technical weakness, we propose to examine a Coanda surface of straight, cylindrical design and optimize its geometry for highest thrust/lift utilizing computational fluid dynamics software ANSYS Fluent®. The results of this study reveal that a Coanda UAV configured with 4 sides of straight, cylindrical Coanda surface achieve an overall 45% increase in lift compared to conventional Coanda Saucer UAV configurations. This venture integrates with an ongoing research project where a Coanda prototype is being assembled. Additionally, a custom thrust-stand has been constructed for thrust/lift measurement.

Keywords: CFD, Coanda, lift, UAV

Procedia PDF Downloads 139

Authors: Naveed Ghani

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With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.

Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2

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Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Seyed Sadegh Naseralavi, Sohrab Nemati, Ehsan Khojastehfar, Sadegh Balaghi

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In the present research, various formulations of wavelet transform are applied on acceleration time history of earthquake. The mentioned transforms decompose the strong ground motion into low and high frequency parts. Since the high frequency portion of strong ground motion has a minor effect on dynamic response of structures, the structure is excited by low frequency part. Consequently, the seismic response of structure is predicted consuming one half of computational time, comparing with conventional time history analysis. Towards reducing the computational effort needed in seismic optimization of structure, seismic optimization of a shear frame structure is conducted by applying various forms of mentioned transformation through genetic algorithm.

Keywords: time history analysis, wavelet transform, optimization, earthquake

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Authors: Amirhossein Mohajerzadeh, Abolghasem Mohajerzadeh

Abstract:

In this paper, target parameter is estimated with desirable precision in hierarchical wireless sensor networks (WSN) while the proposed algorithm also tries to prolong network lifetime as much as possible, using efficient data collecting algorithm. Target parameter distribution function is considered unknown. Sensor nodes sense the environment and send the data to the base station called fusion center (FC) using hierarchical data collecting algorithm. FC builds underlying phenomena based on collected data. Considering the aggregation level, x, the goal is providing the essential infrastructure to find the best value for aggregation level in order to prolong network lifetime as much as possible, while desirable accuracy is guaranteed (required sample size is fully depended on desirable precision). First, the sample size calculation algorithm is discussed, second, the average queue length based on M/M[x]/1/K queue model is determined and it is used for energy consumption calculation. Nodes can decrease transmission cost by aggregating incoming data. Furthermore, the performance of the new algorithm is evaluated in terms of lifetime and estimation accuracy.

Keywords: aggregation, estimation, queuing, wireless sensor network

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Authors: WenYing Claire Shih, Konstantinos Agrafiotis

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The circular economy has become one of the seven fundamental pillars of Taiwan’s economic development, as this is promulgated by the government. The model of the circular economy, with its fundamental premise of waste elimination, can transform the textile and clothing sectors from major pollutant industries to a much cleaner alternative for a better quality of all citizens’ lives. In a related vein, the notion of the creative economy and more specifically the fashion industry can prompt similar results in terms of jobs and wealth creation. The combining forces of the circular and creative economies and their beneficial output have resulted in the configuration of ethical urban networks which potentially may lead to sources of competitive advantage. All actors involved in the configuration of this urban ethical fashion network from public authorities to private enterprise can bring about positive changes in the urban setting. Preliminary results through action research show that this configuration is an attainable task in terms of circularity by reducing fabric waste produced from local textile mills and through innovative methods of design, production and retail around urban spaces where the network has managed to generate a stream of jobs and financial revenues for all participants. The municipal authorities as the facilitating platform have been of paramount importance in this public-private partnership. In the explorative pilot study conducted about a network of production, consumption in terms of circularity of fashion products, we have experienced a positive disposition. As the network will be fully functional by attracting more participant firms from the textile and clothing sectors, it can be beneficial to Taiwan’s soft power in the region and simultaneously elevate citizens’ awareness on circular methods of fashion production, consumption and disposal which can also lead to the betterment of urban lifestyle and may open export horizons for the firms.

Keywords: the circular economy, the creative economy, ethical urban networks, action research

Procedia PDF Downloads 135