Search results for: drift flow model

Authors: Duncan Oteki, Andebut Yeneneh, Daba Gedafa, Nabil Suleiman

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Most agencies lack the equipment required to measure the dynamic modulus (|E*|) of asphalt mixtures, necessitating the need to use predictive models. This study compared measured |E*| values for nine North Dakota asphalt mixes using the original Witczak, modified Witczak, and Hirsch models. The influence of temperature on the |E*| models was investigated, and Pavement ME simulations were conducted using measured |E*| and predictions from the most accurate |E*| model. The results revealed that the original Witczak model yielded the lowest Se/Sy and highest R² values, indicating the lowest bias and highest accuracy, while the poorest overall performance was exhibited by the Hirsch model. Using predicted |E*| as inputs in the Pavement ME generated conservative distress predictions compared to using measured |E*|. The original Witczak model was recommended for predicting |E*| for low-reliability pavements in North Dakota.

Keywords: asphalt mixture, binder, dynamic modulus, MEPDG, pavement ME, performance, prediction

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Authors: Azizul Baten, Kamrul Hossain, Abdullah-Al-Zabir

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The objective of this study is to develop an appropriate stochastic frontier secondary schools efficiency model by ICT Intervention and to examine the impact of ICT challenges on secondary schools efficiency in the Sylhet division in Bangladesh using stochastic frontier analysis. The Translog stochastic frontier model was found an appropriate than the Cobb-Douglas model in secondary schools efficiency by ICT Intervention. Based on the results of the Cobb-Douglas model, it is found that the coefficient of the number of teachers, the number of students, and teaching ability had a positive effect on increasing the level of efficiency. It indicated that these are related to technical efficiency. In the case of inefficiency effects for both Cobb-Douglas and Translog models, the coefficient of the ICT lab decreased secondary school inefficiency, but the online class in school was found to increase the level of inefficiency. The coefficients of teacher’s preference for ICT tools like multimedia projectors played a contributor role in decreasing the secondary school inefficiency in the Sylhet division of Bangladesh. The interaction effects of the number of teachers and the classrooms, and the number of students and the number of classrooms, the number of students and teaching ability, and the classrooms and teaching ability of the teachers were recorded with the positive values and these have a positive impact on increasing the secondary school efficiency. The overall mean efficiency of urban secondary schools was found at 84.66% for the Translog model, while it was 83.63% for the Cobb-Douglas model. The overall mean efficiency of rural secondary schools was found at 80.98% for the Translog model, while it was 81.24% for the Cobb-Douglas model. So, the urban secondary schools performed better than the rural secondary schools in the Sylhet division. It is observed from the results of the Tobit model that the teacher-student ratio had a positive influence on secondary school efficiency. The teaching experiences of those who have 1 to 5 years and 10 years above, MPO type school, conventional teaching method have had a negative and significant influence on secondary school efficiency. The estimated value of σ-square (0.0625) was different from Zero, indicating a good fit. The value of γ (0.9872) was recorded as positive and it can be interpreted as follows: 98.72 percent of random variation around in secondary school outcomes due to inefficiency.

Keywords: efficiency, secondary schools, ICT, stochastic frontier analysis

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Authors: Manik Sharma, Ganapathy Krishnamurthi

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The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

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Authors: Ozgu Hafizoglu

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Analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with attributional, deep structural, casual relations that are essential to learning, to innovation in artificial worlds, and to discovery in science. Cognitive Model of Analogy (CMA) leads and creates information pattern transfer within and between domains and disciplines in science. This paper demonstrates the Cognitive Model of Analogy (CMA) as an evolutionary approach to scientific research. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions. In this paper, the model of analogical reasoning is created based on brain cells, their fractal, and operational forms within the system itself. Visualization techniques are used to show correspondences. Distinct phases of the problem-solving processes are divided thusly: encoding, mapping, inference, and response. The system is revealed relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain cells: glial cells, axons, axon terminals, and neurons, relative to matching conditions of analogical reasoning and relational information. It’s found that encoding, mapping, inference, and response processes in four-term analogical reasoning are corresponding with the fractal and operational forms of brain cells: glial, axons, and neurons.

Keywords: analogy, analogical reasoning, cognitive model, brain and glials

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Authors: Yoonsuh Jung, Steven N. MacEachern

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Check loss function is used to define quantile regression. In the prospect of cross validation, it is also employed as a validation function when underlying truth is unknown. However, our empirical study indicates that the validation with check loss often leads to choosing an over estimated fits. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. It has a large effect of guarding against over fitted model in some extent. Through various simulation settings of linear and non-linear regressions, the improvement of check loss by L2 adjustment is empirically examined. This adjustment is devised to shrink to zero as sample size grows.

Keywords: cross-validation, model selection, quantile regression, tuning parameter selection

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Authors: Mueller Jann, Hoffmann Christian Hugo

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Conventional quantitative risk management in banking is a risk factor of its own, because it rests on assumptions such as independence and availability of data which do not hold when rare events of extreme consequences are involved. There is a growing recognition of the need for alternative risk measures that do not make these assumptions. We propose a novel method for modeling the risk associated with investment products, in particular derivatives, by using a formal language for specifying financial contracts. Expressions in this language are interpreted in the category of values annotated with (a formal representation of) uncertainty. The choice of uncertainty formalism thus becomes a parameter of the model, so it can be adapted to the particular application and it is not constrained to classical probabilities. We demonstrate our approach using a simple logic-based uncertainty model and a case study in which we assess the risk of counter party default in a portfolio of collateralized loans.

Keywords: risk model, uncertainty monad, derivatives, contract algebra

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Authors: R. Sudhesh Solomon, P. Y. K. L. Srinivas, Abhay Narayan, Amitava Das

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Users in social networks either unicast or broadcast their messages. At mention is the popular way of unicasting for Twitter whereas general tweeting could be considered as broadcasting method. Understanding the information flow and dynamics within a Social Network and modeling the same is a promising and an open research area called Information Diffusion. This paper seeks an answer to a fundamental question - understanding if the at-mention network or the unicasting pattern in social media is purely random in nature or is there any user specific selectional preference? To answer the question we present an empirical analysis to understand the sociological aspects of Twitter mentions network within a social network community. To understand the sociological behavior we analyze the values (Schwartz model: Achievement, Benevolence, Conformity, Hedonism, Power, Security, Self-Direction, Stimulation, Traditional and Universalism) of all the users. Empirical results suggest that values traits are indeed salient cue to understand how the mention-based communication network functions. For example, we notice that individuals possessing similar values unicast among themselves more often than with other value type people. We also observe that traditional and self-directed people do not maintain very close relationship in the network with the people of different values traits.

Keywords: information diffusion, personality and values, social network analysis, twitter mentions network

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Authors: Sushil Kumar Gupta, Atharva Joshi, Ayush Sonawale, Sachin Naik, Rajshree Khande

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The usage of artificially created videos and audio by deep learning is a major problem of the current media landscape, as it pursues the goal of misinformation and distrust. In conclusion, the objective of this work targets generating a reliable deepfake detection model using deep learning that will help detect forged videos accurately. In this work, CelebDF v1, one of the largest deepfake benchmark datasets in the literature, is adopted to train and test the proposed models. The data includes authentic and synthetic videos of high quality, therefore allowing an assessment of the model’s performance against realistic distortions.

Keywords: deepfake detection, CelebDF v1, convolutional neural network (CNN), xception model, data augmentation, media manipulation

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Authors: Hamed Zolfaghari, Mojtaba Kord

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Project planners and controllers are frequently faced with the challenge of inadequate software for the preparation of automatic project progress reports based on actual project information updates. They usually make dashboards in Microsoft Excel, which is local and not applicable online. Another shortcoming is that it is not linked to planning software such as Microsoft Project, which lacks the database required for data storage. This study aimed to propose a model for the preparation of reports on automatic online project progress based on actual project information updates by the integration of Primavera P6, SQL database, and Power BI for a construction project. The designed model could be applicable to project planners and controller agents by enabling them to prepare project reports automatically and immediately after updating the project schedule using actual information. To develop the model, the data were entered into P6, and the information was stored on the SQL database. The proposed model could prepare a wide range of reports, such as earned value management, HR reports, and financial, physical, and risk reports automatically on the Power BI application. Furthermore, the reports could be published and shared online.

Keywords: primavera P6, SQL, Power BI, EVM, integration management

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Authors: Hao Yan, Xiaobing Zhang

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The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

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Authors: Kazi Rizvan, Yamin Rekhu

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Due to high competitiveness in industries throughout the world, every industry is trying hard to utilize all their resources to keep their productivity as high as possible. Many tools have been being used to ensure smoother flow of an operation, to balance tasks, to maintain proper schedules for tasks, to maintain proper sequence for tasks, to reduce unproductive time. All of these tools are used to augment productivity within an industry. Kanban board is one of them and of the many important tools of lean production system. Kanban Board is a visual depiction of the status of tasks. Kanban board shows the actual status of the tasks. It conveys the progress and issues of tasks as well. Using Kanban Board, tasks can be distributed among workers and operation targets can be visually represented to them. In this paper, an example of Kanban board from the aspects of Brothers Furniture Limited was taken and how the Kanban board system was implemented, how the board was designed and how it was made easily perceivable for the less literate or illiterate workers. The Kanban board was designed for the packing section of Brothers Furniture Limited. It was implemented for the purpose of representing the tasks flow to the workers and to mitigate the time that was wasted while the workers remained wondering about what task they should start after they finish one. Kanban board subsumed seven columns and there was a column for comments where if any problem occurred during working on the tasks. Kanban board was helpful for the workers as the board showed the urgency of the tasks. It was also helpful for the store section as they could understand which products and how much of them could be delivered to store at any certain time. Kanban board had all the information centralized which is why the work-flow got paced up and idle time was minimized. Regardless of many workers being illiterate or less literate, Kanban board was still explicable for the workers as the Kanban cards were colored. Since the significance of colors can be conveniently interpretable to them, colored cards helped a great deal in that matter. Hence, the illiterate or less literate workers didn’t have to spend time wondering about the significance of the cards. Even when the workers weren’t told the significance of the colored cards, they could grow a feeling about their meaning as colors can trigger anyone’s mind to perceive the situation. As a result, the board elucidated the workers about what board required them to do, when to do and what to do next. Kanban board alleviated excessive time between tasks by setting day-plan for targeted tasks and it also reduced time during tasks as the workers were acknowledged of forthcoming tasks for a day. Being very specific to the tasks, Kanban board helped the workers become more focused on their tasks helped them do their job with more perfection. As a result, The Kanban board helped achieve a 8.75% increase in productivity than the productivity before the Kanban board was implemented.

Keywords: color, Kanban Board, Lean Tool, literacy, packing, productivity

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Authors: D. Hephzibah, P. Kumaran, N. M. Saifuddin

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Anaerobic digestion is a well-known technique for sustainable energy recovery from sewage sludge. However, sewage sludge digestion is restricted due to certain factors. Pre-treatment methods have been established in various publications as a promising technique to improve the digestibility of the sewage sludge and to enhance the biogas generated which can be used for energy recovery. In this study, continuous flow microwave (MW) pre-treatment with different intensities were compared by using 5 L semi-continuous digesters at a hydraulic retention time of 27 days. We focused on the effects of MW at different intensities on the sludge solubilization, sludge digestibility, and biogas production of the untreated and MW pre-treated sludge. The MW pre-treatment demonstrated an increase in the ratio of soluble chemical oxygen demand to total chemical oxygen demand (sCOD/tCOD) and volatile fatty acid (VFA) concentration. Besides that, the total volatile solid (TVS) removal efficiency and tCOD removal efficiency also increased during the digestion of the MW pre-treated sewage sludge compared to the untreated sewage sludge. Furthermore, the biogas yield also subsequently increases due to the pre-treatment effect. A higher MW power level and irradiation time generally enhanced the biogas generation which has potential for sustainable energy recovery from sewage treatment plant. However, the net energy balance tabulation shows that the MW pre-treatment leads to negative net energy production.

Keywords: anaerobic digestion, biogas, microwave pre-treatment, sewage sludge

Procedia PDF Downloads 319

Authors: Roberto M. G. Velásquez, Jonas R. Gazoli, Nelson Ponce Jr, Valério L. Borges, Alessandro Sete, Fernanda M. C. Tomé, Julian D. Hunt, Heitor C. Lira, Cristiano L. de Souza, Fabio T. Bindemann, Wilmar Wounnsoscky

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Nowadays, society is working toward reducing energy losses and greenhouse gas emissions, as well as seeking clean energy sources, as a result of the constant increase in energy demand and emissions. Energy loss occurs in the gas pressure reduction stations at the delivery points in natural gas distribution systems (city gates). Installing pressure reduction turbines (PRT) parallel to the static reduction valves at the city gates enhances the energy efficiency of the system by recovering the enthalpy of the pressurized natural gas, obtaining in the pressure-lowering process shaft work and generating electrical power. Currently, the Brazilian natural gas transportation network has 9,409 km in extension, while the system has 16 national and 3 international natural gas processing plants, including more than 143 delivery points to final consumers. Thus, the potential of installing PRT in Brazil is 66 MW of power, which could yearly avoid the emission of 235,800 tons of CO2 and generate 333 GWh/year of electricity. On the other hand, an economic viability analysis of these energy efficiency projects is commonly carried out based on estimates of the project's cash flow obtained from several variables forecast. Usually, the cash flow analysis is performed using representative values of these variables, obtaining a deterministic set of financial indicators associated with the project. However, in most cases, these variables cannot be predicted with sufficient accuracy, resulting in the need to consider, to a greater or lesser degree, the risk associated with the calculated financial return. This paper presents an approach applied to the technical-economic viability analysis of PRTs projects that explicitly considers the uncertainties associated with the input parameters for the financial model, such as gas pressure at the delivery point, amount of energy generated by TRP, the future price of energy, among others, using sensitivity analysis techniques, scenario analysis, and Monte Carlo methods. In the latter case, estimates of several financial risk indicators, as well as their empirical probability distributions, can be obtained. This is a methodology for the financial risk analysis of PRT projects. The results of this paper allow a more accurate assessment of the potential PRT project's financial feasibility in Brazil. This methodology will be tested at the Cuiabá thermoelectric plant, located in the state of Mato Grosso, Brazil, and can be applied to study the potential in other countries.

Keywords: pressure reduction turbine, natural gas pressure drop station, energy efficiency, electricity generation, monte carlo methods

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Authors: Aarthi Sridhar, Henri Gavin, Karah Kelly

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Rolling Isolation Systems (RISs) are simple and effective means to mitigate earthquake hazards to equipment in critical and precious facilities, such as hospitals, network collocation facilities, supercomputer centers, and museums. The RIS works by isolating components acceleration the inertial forces felt by the subsystem. The RIS consists of two platforms with counter-facing concave surfaces (dishes) in each corner. Steel balls lie inside the dishes and allow the relative motion between the top and bottom platform. Formerly, a mathematical model for the dynamics of RISs was developed using Lagrange’s equations (LE) and experimentally validated. A new mathematical model was developed using Gauss’s Principle of Least Constraint (GPLC) and verified by comparing impulse response trajectories of the GPLC model and the LE model in terms of the peak displacements and accelerations of the top platform. Mathematical models for the RIS are tedious to derive because of the non-holonomic rolling constraints imposed on the system. However, using Gauss’s Principle of Least constraint to find the equations of motion removes some of the obscurity and yields a system that can be easily extended. Though the GPLC model requires more state variables, the equations of motion are far simpler. The non-holonomic constraint is enforced in terms of accelerations and therefore requires additional constraint stabilization methods in order to avoid the possibility that numerical integration methods can cause the system to go unstable. The GPLC model allows the incorporation of more physical aspects related to the RIS, such as contribution of the vertical velocity of the platform to the kinetic energy and the mass of the balls. This mathematical model for the RIS is a tool to predict the motion of the isolation platform. The ability to statistically quantify the expected responses of the RIS is critical in the implementation of earthquake hazard mitigation.

Keywords: earthquake hazard mitigation, earthquake isolation, Gauss’s Principle of Least Constraint, nonlinear dynamics, rolling isolation system

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

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Authors: Ogbe E. E., Ossia. C. V., Saturday. E. G., Ezekwe M. C.

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The disparity in power output between a simple and a modified gas turbine plant is noticeable when the gas turbine functions under local environmental conditions that deviate from the standard ISO specifications. Extensive research and literature have demonstrated a well-known direct correlation between ambient temperature and the power output of a gas turbine plant. In this study, the Omotosho gas turbine plant was modified into three different configurations. The reason for the modification is to improve its performance and reduce the fuel consumption and emission rate. Aspen Hysys software was used to simulate both the simple (Omotosho) and the three modified gas turbine plants. The input parameters considered include ambient temperature, air mass flow rate, fuel mass flow rate, water mass flow rate, turbine inlet temperature, compressor efficiency, and turbine efficiency, while the output parameters considered are thermal efficiency, specific fuel consumption, heat rate, emission rate, compressor power, turbine power and power output. The three modified gas turbine power plants incorporate an inlet air cooling system and a heat recovery steam generator. The variations between the modifications are due to additional components or enhancements alongside the inlet air cooling system and heat recovery steam generator incorporated; the first modification has an additional turbine, the second modification has an additional combustion chamber, and the third modification has an additional turbine and combustion chamber. This paper clearly shows ambient temperature effects on both the simple and three modified gas turbine plants. for every 10-degree kelvin increase in ambient temperature, there is an approximate reduction of 3977 kW, 4795 kW, 4681 kW, and 4793 kW of the power output for the simple gas turbine, first, second, and third modifications, respectively. Also, for every 10-degree kelvin increase in temperature, there is a thermal efficiency decrease of 1.22%, 1.45%, 1.43%, and 1.44% for the simple gas turbine, first, second, and third modifications respectively. Low ambient temperature will help save fuel; looking at the high price of fuel presently in Nigeria for every 10 degrees kelvin increase in temperature, there is a specific fuel consumption increase of 0.0074 kg/kWh, 0.0051 kg/kWh, 0.0061 kg/kWh, and 0.0057 kg/kWh for the simple gas turbine, first, second, and third modifications respectively. These findings will aid in accurately evaluating local power generating plants, particularly in hotter regions, for installing gas turbine inlet air cooling (GTIAC) systems.

Keywords: Aspen HYSYS software, Brayton Cycle, modified gas turbine, power plant, simple gas turbine, thermal efficiency.

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Authors: Anil Nis, Nilufer Ozyurt Zihnioglu

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The object of the study is to investigate fresh state properties of the steel fiber reinforced self-compacting concrete (SFR-SCC). Three different steel fibers; straight (Vf:0.5%), hooked-end long (Vf:0.5% and 1%) and hybrid fibers (0.5%short+0.5%long) were used in the research aiming to obtain flow properties of non-fibrous self-compacting concrete. Fly ash was used as a supplementary with an optimum dosage of 30% of the total cementitious materials. Polycarboxylic ether based high-performance concrete superplasticizer was used to get high flowability with percentages ranging from 0.81% (non-fibrous SCC) to 1.07% (hybrid SF-SCC) of the cement weight. The flowability properties of SCCs were measured via slump flow and V-funnel tests; passing ability properties of SCCs were measured with J-Ring, L-Box, and U-Box tests. Workability results indicate that small increase on the superplasticizer dosages compensate the adverse effects of steel fibers on flowability properties of SSC. However, higher dosage fiber addition has a negative effect on passing ability properties, causing blocking of the mixes. In addition, compressive strength, tensile strength, and four point bending results were given. Results indicate that SCCs including steel fibers have superior performances on tensile and bending strength of concrete. Crack bridging capability of steel fibers prevents concrete from splitting, yields higher deformation and energy absorption capacities than non-fibrous SCCs.

Keywords: fiber reinforced self-compacting concrete, fly ash, fresh state properties, steel fiber

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Authors: Vatasta Koul, Ayush Gupta, Vaibhav Sharma, Rajesh Yadav

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Today we are unmarking the secrets of the universe by exploring different stars and planets and most of the space exploration is done by unmanned space robots. In addition to our planet Earth, there are pieces of evidence that show other astronomical objects in our solar system such as Venus, Mars, Saturn’s moon Titan and Uranus support the flight of fixed wing air vehicles. In this paper, we take forward the concept of presence of large rounded tubercles along the leading edge of a wing and use it as a passive flow control device that will help in improving its aerodynamic performance and maneuverability. Furthermore, in this research, aerodynamic measurements and performance analysis of wavy leading tubercles on the fixed wings at 5-degree angle of attack are carried out after determination of the flow conditions on the selected planetary bodies. Wavelength and amplitude for the sinusoidal modifications on the leading edge are analyzed and simulations are carried out for three-dimensional NACA 0012 airfoil maintaining unity AR (Aspect Ratio). Tubercles have consistently demonstrated the ability to delay and decrease the severity of stall as per the studies were done in the Earth’s atmosphere. Implementing the same design on the leading edges of Micro-Air Vehicles (MAVs) and UAVs could make these aircrafts more stable over a greater range of angles of attack in different planetary environments of our solar system.

Keywords: amplitude, NACA0012, tubercles, unmanned space robots

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Authors: Paolo Pietro Biancone

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The spread of Islamic financial instruments is an opportunity to offer integration for the immigrant population and to attract, through the specific products, the richness of sovereign funds from the "Arab" countries. However, it is important to consider the possibility of comparing a traditional finance model, which in recent times has given rise to many doubts, with an "alternative" finance model, where the ethical aspect arising from religious principles is very important.

Keywords: banks, Europe, Islamic finance, Italy

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Authors: Damian Gordon, Paul Doyle, Anna Becevel, Júlia Vilafranca Molero, Cinta Gascon, Arianna Vitiello, Tina Baloh

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It has long been recognized that the creation of any teaching content can be enhanced if the development process follows a pre-defined approach, which is often referred to as an instructional design methodology. These methodologies typically define a number of stages, or phases, that an educator should undertake to help ensure the quality of the final teaching content that is developed. In this paper, we present an instructional design methodology that is focused specifically on the introduction of blended resources into a heretofore bricks-and-mortar course. To achieve this, research was undertaken concerning a range of models of instructional design, as well as literature covering some of the key challenges and “pain points” of blending. Following this, our model, the BL-5D model, is presented, which incorporates some key questions at each stage of this five-stage methodology to guide the development process. Finally, a discussion of some of the key themes and issues that have been uncovered in this work is presented, as well as a template for a blended learning case study that emerged from this approach.

Keywords: blended learning, challenges of blended learning, design methodologies, instructional design

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Authors: Chih-I. Tsai, Yu-Chien. Lin

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Through experiences of clinical practices, it is discovered that locations on the body at a level of four fingerbreadth above and below the joints are the points at which muscles connect to tendons, and since the muscles and tendons possess opposite characteristics, muscles are full of blood but lack qi, while tendons are full of qi but lack blood, these points on our body become easily blocked. It is proposed that through doing acupuncture or creating localized pressure to the areas four fingerbreadths above and below our joints, with an elastic bandage, we could help the energy, also known as qi, to flow smoothly in our body and further improve our health. Based on the Four Fingers Theory, we understand that human height is 22 four fingerbreadths. In addition, qi and blood travel through 24 meridians, 50 times each day, and they flow through 6 cun with every human breath. We can also understand the average number of human heartbeats is 75 times per minute. And the function of qi-blood circulation system in Traditional Chinese Medicine is the same as the blood circulation in Western Medical Science. Informed by Four Fingers Theory, this study further examined its applications in acupuncture practices. The research question is how Four Fingers Theory proves what has been mentioned in Nei Jing that there are 66 acupoints under a human’s elbow and knee. In responding to the research question, there are 66 acupoints under a human’s elbow and knee. Four Fingers Theory facilitated the creation of the acupuncture naming and teaching system. It is expected to serve as an approachable and effective way to deliver knowledge of acupuncture to the public worldwide.

Keywords: four fingers theory, meridians circulation, 66 acupoints under human elbow and knee, acupuncture

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Authors: Jia-Jang Wu

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The objective of this research is to develop a general technique so that one may predict the dynamic behaviour of a three-dimensional scale crane model subjected to time-dependent moving point forces by means of conventional finite element computer packages. To this end, the whole scale crane model is divided into two parts: the stationary framework and the moving substructure. In such a case, the dynamic responses of a scale crane model can be predicted from the forced vibration responses of the stationary framework due to actions of the four time-dependent moving point forces induced by the moving substructure. Since the magnitudes and positions of the moving point forces are dependent on the relative positions between the trolley, moving substructure and the stationary framework, it can be found from the numerical results that the time histories for the moving speeds of the moving substructure and the trolley are the key factors affecting the dynamic responses of the scale crane model.

Keywords: moving load, moving substructure, dynamic responses, forced vibration responses

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Authors: Wera Supa, Panita Wannapiroon

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This paper is a report on the designing of the social collaborative learning model based on proactive involvement to Promote the global merit principle in cultivating youths’ morality. The research procedures into two phases, the first phase is to design the social collaborative learning model based on proactive involvement to promote the global merit principle in cultivating youths’ morality, and the second is to evaluate the social collaborative learning model based on proactive involvement. The sample group in this study consists of 15 experts who are dominant in proactive participation, moral merit principle and youths’ morality cultivation from executive level, lecturers and the professionals in information and communication technology expertise selected using the purposive sampling method. Data analyzed by arithmetic mean and standard deviation. This study has explored that there are four significant factors in promoting the hands-on collaboration of global merit scheme in order to implant virtues to adolescences which are: 1) information and communication Technology Usage; 2) proactive involvement; 3) morality cultivation policy, and 4) global merit principle. The experts agree that the social collaborative learning model based on proactive involvement is highly appropriate.

Keywords: social collaborative learning, proactive involvement, global merit principle, morality

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Authors: Anvesh Gaddam, Amit Agrawal, Suhas Joshi, Mark Thompson

Abstract:

An increase in surface area to volume ratio with a decrease in characteristic length scale, leads to a rapid increase in pressure drop across the microchannel. Texturing the microchannel surfaces reduce the effective surface area, thereby decreasing the pressured drop. Surface texturing introduces two wetting states: a metastable Cassie-Baxter state and stable Wenzel state. Predicting wetting transition in textured microchannels is essential for identifying optimal parameters leading to maximum drag reduction. Optical methods allow visualization only in confined areas, therefore, obtaining whole-field information on wetting transition is challenging. In this work, we propose a non-invasive method to capture wetting transitions in textured microchannels under flow conditions. To this end, we tracked the behavior of the Poiseuille number Po = f.Re, (with f the friction factor and Re the Reynolds number), for a range of flow rates (5 < Re < 50), and different wetting states were qualitatively demarcated by observing the inflection points in the f.Re curve. Microchannels with both longitudinal and transverse ribs with a fixed gas fraction (δ, a ratio of shear-free area to total area) and at a different confinement ratios (ε, a ratio of rib height to channel height) were fabricated. The measured pressure drop values for all the flow rates across the textured microchannels were converted into Poiseuille number. Transient behavior of the pressure drop across the textured microchannels revealed the collapse of liquid-gas interface into the gas cavities. Three wetting states were observed at ε = 0.65 for both longitudinal and transverse ribs, whereas, an early transition occurred at Re ~ 35 for longitudinal ribs at ε = 0.5, due to spontaneous flooding of the gas cavities as the liquid-gas interface ruptured at the inlet. In addition, the pressure drop in the Wenzel state was found to be less than the Cassie-Baxter state. Three-dimensional numerical simulations confirmed the initiation of the completely wetted Wenzel state in the textured microchannels. Furthermore, laser confocal microscopy was employed to identify the location of the liquid-gas interface in the Cassie-Baxter state. In conclusion, the present method can overcome the limitations posed by existing techniques, to conveniently capture wetting transition in textured microchannels.

Keywords: drag reduction, Poiseuille number, textured surfaces, wetting transition

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Authors: Christian Liebchen

Abstract:

Both from an environmental perspective and with respect to road traffic flow quality, planning so-called green waves along major road axes is a well-established target for traffic engineers. For one-way road axes (e.g. the Avenues in Manhattan), this is a trivial downstream task. For bidirectional arterials, the well-known necessary condition for establishing a green wave in both directions is that the driving times between two subsequent crossings must be an integer multiple of half of the cycle time of the signal programs at the nodes. In this paper, we propose an integer linear optimization model to establish fixed-time green waves in both directions that are as long and as wide as possible, even in the situation where the driving time condition is not fulfilled. In particular, we are considering an arterial along whose nodes separate left-turn signal groups are realized. In our computational results, we show that scheduling left-turn phases before or after the straight phases can reduce waiting times along the arterial. Moreover, we show that there is always a solution with green waves in both directions that are as long and as wide as possible, where absolute priority is put on just one direction. Compared to optimizing both directions together, establishing an ideal green wave into one direction can only provide suboptimal quality when considering prioritized parts of a green band (e.g., first few seconds).

Keywords: traffic light coordination, synchronization, phase sequencing, green waves, integer programming

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Authors: T. Alghamdi, G. Alaghband

Abstract:

In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution Neural Network, Edges, Face Recognition , Support Vector Machine.

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

Abstract:

Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Bry X., Trottier C., Mortier F., Cornu G., Verron T.

Abstract:

We address component-based regularization of a Multivariate Generalized Linear Model (MGLM). A set of random responses Y is assumed to depend, through a GLM, on a set X of explanatory variables, as well as on a set T of additional covariates. X is partitioned into R conceptually homogeneous blocks X1, ... , XR , viewed as explanatory themes. Variables in each Xr are assumed many and redundant. Thus, Generalised Linear Regression (GLR) demands regularization with respect to each Xr. By contrast, variables in T are assumed selected so as to demand no regularization. Regularization is performed searching each Xr for an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in Xr. We propose a very general criterion to measure structural relevance (SR) of a component in a block, and show how to take SR into account within a Fisher-scoring-type algorithm in order to estimate the model. We show how to deal with mixed-type explanatory variables. The method, named THEME-SCGLR, is tested on simulated data.

Keywords: Component-Model, Fisher Scoring Algorithm, GLM, PLS Regression, SCGLR, SEER, THEME

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Authors: Vine Petzer, Mirna Nel

Abstract:

An explanatory mixed method design was used to investigate accounting classroom conditions in the Further Education and Training (FET) Phase in South Africa. A descriptive survey research study with a heterogeneous group of learners and teachers was conducted in the first phase. In the qualitative phase, semi-structured individual interviews with learners and teachers, as well as observations in the accounting classroom, were employed to gain more in depth understanding of the learning conditions in the accounting classroom. The findings of the empirical research informed the development of a model for teachers in accounting, supporting them to use more effective teaching methods and create positive learning conditions for all learners to experience successful learning. A model towards creating positive Accounting classroom conditions that support successful learning was developed and recommended for education policy and decision-makers for use as a classroom intervention capacity building tool. The model identifies and delineates classroom practices that exert significant effect on learner attainment of quality education.

Keywords: accounting classroom conditions, positive education, successful learning, teaching accounting

Procedia PDF Downloads 146

Authors: Diego F. Camacho, Cristian J. Mejia, Carlos Duque-Daza

Abstract:

The recent widespread use of Unmanned Aerial Vehicles (UAVs) has fueled a renewed interest in efficiency and performance of airfoils, particularly for applications at low and moderate Reynolds numbers, typical of this kind of vehicles. Most of previous efforts in the aeronautical industry, regarding aerodynamic efficiency, had been focused on high Reynolds numbers applications, typical of commercial airliners and large size aircrafts. However, in order to increase the levels of efficiency and to boost the performance of these UAV, it is necessary to explore new alternatives in terms of airfoil design and application of drag reduction techniques. The objective of the present work is to carry out the analysis and comparison of performance levels between a standard NACA0012 profile against another one featuring a wall protuberance or surface perturbation. A computational model, based on the finite volume method, is employed to evaluate the effect of the presence of geometrical distortions on the wall. The performance evaluation is achieved in terms of variations of drag and lift coefficients for the given profile. In particular, the aerodynamic performance of the new design, i.e. the airfoil with a surface perturbation, is examined under conditions of incompressible and subsonic flow in transient state. The perturbation considered is a shaped protrusion prescribed as a small surface deformation on the top wall of the aerodynamic profile. The ultimate goal by including such a controlled smooth artificial roughness was to alter the turbulent boundary layer. It is shown in the present work that such a modification has a dramatic impact on the aerodynamic characteristics of the airfoil, and if properly adjusted, in a positive way. The computational model was implemented using the unstructured, FVM-based open source C++ platform OpenFOAM. A number of numerical experiments were carried out at Reynolds number 5x104, based on the length of the chord and the free-stream velocity, and angles of attack 6° and 12°. A Large Eddy Simulation (LES) approach was used, together with the dynamic Smagorinsky approach as subgrid scale (SGS) model, in order to account for the effect of the small turbulent scales. The impact of the surface perturbation on the performance of the airfoil is judged in terms of changes in the drag and lift coefficients, as well as in terms of alterations of the main characteristics of the turbulent boundary layer on the upper wall. A dramatic change in the whole performance can be appreciated, including an arguably large level of lift-to-drag coefficient ratio increase for all angles and a size reduction of laminar separation bubble (LSB) for a twelve-angle-of-attack.

Keywords: CFD, LES, Lift-to-drag ratio, LSB, NACA 0012 airfoil

Procedia PDF Downloads 386