Search results for: erosion prediction

Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

Procedia PDF Downloads 127

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

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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

Procedia PDF Downloads 147

Authors: Camila Amin-Puello, Lina Vasco-Diaz, Juan Ramirez-Arias, Claudia Munoz, Carlos Gonzalez-Calderon

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Operational flexibility is a fundamental aspect in the field of airlines because although demand is constantly changing, it is the duty of companies to provide a service to users that satisfies their needs in an efficient manner without sacrificing factors such as comfort, safety and other perception variables. The objective of this research is to understand the factors that describe and explain operational flexibility by implementing advanced analytical methods such as exploratory factor analysis and structural equation modeling, examining multiple levels of operational flexibility and understanding how these variable influences users' decision-making when choosing an airline and in turn how it affects the airlines themselves. The use of a hybrid model and latent variables improves the efficiency and accuracy of airline performance prediction in the unpredictable Colombian market. This pioneering study delves into traveler motivations and their impact on domestic flight demand, offering valuable insights to optimize resources and improve the overall traveler experience. Applying the methods, it was identified that low-cost airlines are not useful for flexibility, while users, especially women, found airlines with greater flexibility in terms of ticket costs and flight schedules to be more useful. All of this allows airlines to anticipate and adapt to their customers' needs efficiently: to plan flight capacity appropriately, adjust pricing strategies and improve the overall passenger experience.

Keywords: hybrid choice model, airline, business travelers, domestic flights

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Authors: Obed Asamoah

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Forests are very vital for human survival and our well-being. During the past years, the world has taken an increasingly significant role in the modification of the global environment. The high rate of deforestation in Ghana is of primary national concern as the forests provide many ecosystem services and functions that support the country’s predominantly agrarian economy and foreign earnings. Ghana forest is currently major source of carbon sink that helps to mitigate climate change. Ghana forests, both the reserves and off-reserves, are under pressure of deforestation. The causes of deforestation are varied but can broadly be categorized into anthropogenic and natural factors. For the anthropogenic factors, increased wood fuel collection, clearing of forests for agriculture, illegal and poorly regulated timber extraction, social and environmental conflicts, increasing urbanization and industrialization are the primary known causes for the loss of forests and woodlands. Mineral exploitation in the forest areas is considered as one of the major causes of deforestation in Ghana. Mining activities especially mining of gold by both the licensed mining companies and illegal mining groups who are locally known as "gallantly mining" also cause damage to the nation's forest reserves. Several works have been conducted regarding the causes of the high rate of deforestation in Ghana, major attention has been placed on illegal logging and using forest lands for illegal farming and mining activities. Less emphasis has been placed on the timber production companies on their harvesting methods in the forests in Ghana and other activities that are carried out in the forest. The main objective of the work is to find out the harvesting methods and the activities of the timber production companies and their effects on the forests in Ghana. Both qualitative and quantitative research methods were engaged in the research work. The study population comprised of 20 Timber industries (Sawmills) forest areas of Ghana. These companies were selected randomly. The cluster sampling technique was engaged in selecting the respondents. Both primary and secondary data were employed. In the study, it was observed that most of the timber production companies do not know the age, the weight, the distance covered from the harvesting to the loading site in the forest. It was also observed that old and heavy machines are used by timber production companies in their operations in the forest, which makes the soil compact prevents regeneration and enhances soil erosion. It was observed that timber production companies do not abide by the rules and regulations governing their operations in the forest. The high rate of corruption on the side of the officials of the Ghana forestry commission makes the officials relax and do not embark on proper monitoring on the operations of the timber production companies which makes the timber companies to cause more harm to the forest. In other to curb this situation the Ghana forestry commission with the ministry of lands and natural resources should monitor the activities of the timber production companies and sanction all the companies that make foul play in their activities in the forest. The commission should also pay more attention to the policy “fell one plant 10” to enhance regeneration in both reserves and off-reserves forest.

Keywords: companies, deforestation, forest, Ghana, timber

Procedia PDF Downloads 189

Authors: Rahul Paliwal, Gulshan Maheshwari, Anant S. Jhaveri, Channamallikarjun S. Mathpati

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Large pumping facility is the necessary requirement of the cooling water systems for power plants, process and manufacturing facilities, flood control and water or waste water treatment plant. With a large capacity of few hundred to 50,000 m3/hr, cares must be taken to ensure the uniform flow to the pump to limit vibration, flow induced cavitation and performance problems due to formation of air entrained vortex and swirl flow. Successful prediction of these phenomena requires numerical method and turbulence model to characterize the dynamics of these flows. In the past years, single phase shear stress transport (SST) Reynolds averaged Navier Stokes Models (like k-ε, k-ω and RSM) were used to predict the behavior of flow. Literature study showed that two phase model will be more accurate over single phase model. In this paper, a 3D geometries simulated using detached eddy simulation (LES) is used to predict the behavior of the fluid and the results are compared with experimental results. Effect of different grid structure and boundary condition is also studied. It is observed that two phase flow model can more accurately predict the mean flow and turbulence statistics compared to the steady SST model. These validate model will be used for further analysis of vortex structure in lab scale model to generate their frequency-plot and intensity at different location in the set-up. This study will help in minimizing the ill effect of vortex on pump performance.

Keywords: grid structure, pump intake, simulation, vibration, vortex

Procedia PDF Downloads 173

Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

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Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: bridge management systems (BMS), cost optimization condition assessment, fund allocation, Markov chain

Procedia PDF Downloads 287

Authors: Paschal A. Ochang, Emmanuel C. Oji

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The Duffing’s Equation is a second order system that is very important because they are fundamental to the behaviour of higher order systems and they have applications in almost all fields of science and engineering. In the biological area, it is useful in plant stem dependence and natural frequency and model of the Brain Crash Analysis (BCA). In Engineering, it is useful in the study of Damping indoor construction and Traffic lights and to the meteorologist it is used in the prediction of weather conditions. However, most Problems in real life that occur are non-linear in nature and may not have analytical solutions except approximations or simulations, so trying to find an exact explicit solution may in general be complicated and sometimes impossible. Therefore we aim to find out if it is possible to obtain one analytical fixed point to the non-linear ordinary equation using fixed point analytical method. We started by exposing the scope of the Duffing’s equation and other related works on it. With a major focus on the fixed point and fixed point iterative scheme, we tried different iterative schemes on the Duffing’s Equation. We were able to identify that one can only see the fixed points to a Damped Duffing’s Equation and not to the Undamped Duffing’s Equation. This is because the cubic nonlinearity term is the determining factor to the Duffing’s Equation. We finally came to the results where we identified the stability of an equation that is damped, forced and second order in nature. Generally, in this research, we approximate the solution of Duffing’s Equation by converting it to a system of First and Second Order Ordinary Differential Equation and using Fixed Point Iterative approach. This approach shows that for different versions of Duffing’s Equations (damped), we find fixed points, therefore the order of computations and running time of applied software in all fields using the Duffing’s equation will be reduced.

Keywords: damping, Duffing's equation, fixed point analysis, second order differential, stability analysis

Procedia PDF Downloads 281

Authors: Abdullah Abdu Qaseem Shamsan

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Background: Gliomas represent the most frequent, heterogeneous group of tumors arising from glial cells, characterized by difficult monitoring, poor prognosis, and fatality. Tissue biopsy is an established procedure for tumor cell sampling that aids diagnosis, tumor grading, and prediction of prognosis. We studied and compared the levels of liquid biopsy markers in patients with different grades of glioma. Also, it tried to establish the potential association between glioma and specific blood groups antigen. Result: 78 patients were identified, among whom maximum percentage with glioblastoma possessed blood group O+ (53.8%). The second highest frequency had blood group A+ (20.4%), followed by B+ (9.0%) and A- (5.1%), and least with O-. Liquid biopsy biomarkers comprised of ALT, LDH, lymphocytes, Urea, Alkaline phosphatase, AST Neutrophils, and CRP. The levels of all the components increased significantly with the severity of glioma, with maximum levels seen in glioblastoma (grade IV), followed by grade III and grade II respectively. Conclusion: Gliomas possess significant clinical challenges due to their progression with heterogeneous nature and aggressive behavior. Liquid biopsy is a non-invasive approach which aids to establish the status of the patient and determine the tumor grade, therefore may show diagnostic and prognostic utility. Additionally, our study provides evidence to demonstrate the role of ABO blood group antigens in the development of glioma. However, future clinical research on liquid biopsy will improve the sensitivity and specificity of these tests and validate their clinical usefulness to guide treatment approaches.

Keywords: GBM: glioblastoma multiforme, CT: computed tomography, MRI: magnetic resonance imaging, ctRNA: circulating tumor RNA

Procedia PDF Downloads 41

Authors: Nkemjika Chinenye-Kanu, Mamdud Hossain, Ghazi Droubi

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Prediction of multiphase flow induced forces, void fraction and pressure is crucial at both design and operating stages of practical energy and process pipe systems. In this study, transient numerical simulations of upward slug-churn flow through a vertical 90-degree elbow have been conducted. The volume of fluid (VOF) method was used to model the two-phase flows while the K-epsilon Reynolds-Averaged Navier-Stokes (RANS) equations were used to model turbulence in the flows. The simulation results were validated using experimental results. Void fraction signal, peak frequency and maximum magnitude of void fraction fluctuation of the slug-churn flow validation case studies compared well with experimental results. The x and y direction force fluctuation signals at the elbow control volume were obtained by carrying out force balance calculations using the directly extracted time domain signals of flow properties through the control volume in the numerical simulation. The computed force signal compared well with experiment for the slug and churn flow validation case studies. Hence, the present numerical simulation technique was able to predict the behaviours of the one-way flow induced forces and void fraction fluctuations.

Keywords: computational fluid dynamics, flow induced vibration, slug-churn flow, void fraction and force fluctuation

Procedia PDF Downloads 153

Authors: Marine Kobalava, Eter Narimanishvili, Nino Grigolaia

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The paper analyzes the contemporary problems of public relations in Georgia. The approaches to public attitudes towards the relationship with the population of the country are studied on a global scale, the importance of forming the concept of public relations in Georgia in terms of globalization is justified. The basic components of public relations are characterized by the RACE system, namely analyzing research, action, communication, evaluation. The main challenges of public relations are identified in the research process; taking into consideration the scope of globalization, the influence of social, economic, and political changes in Georgia on PR development are identified. The article discusses the public relations as the strategic management function that facilitates communication with the society, recognition of public interests, and their prediction. In addition, the feminization of the sector is considered to be the most important achievement of public relations in the modern world. The conclusion is that the feminization indicator of the field is an unconditional increase in the employment rates of women. In the paper, the problems of globalization and public relations in the industrial countries are studied, the directions of improvement of public relations with the background of peculiarities of different countries and globalization process are proposed. Public relations under globalization are assessed in accordance with the theory of benefits and requirements, and the requirements are classified according to informational, self-identification, integration, social interaction, and other types of signs. In the article, conclusions on the current challenges of public relations in Georgia are made, and the recommendations for their solution, taking into consideration globalization processes in the world, are proposed.

Keywords: public relations, globalization, RACE system, public relationship concept, feminization

Procedia PDF Downloads 163

Authors: Christodoulou Christos, Politis Anastasios

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The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

Procedia PDF Downloads 45

Authors: C. R. Danielle, S. Erik, T. Patrick, M. Hugh

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In the last decade there has been an increase in demand for fine grinding due to the depletion of coarse-grained orebodies and an increase of processing fine disseminated minerals and complex orebodies. These ores have provided new challenges in concentrator design because fine and ultra-fine grinding is required to achieve acceptable recovery rates. Therefore, the correct design of a grinding circuit is important for minimizing unit costs and increasing product quality. The use of ball mills for grinding in fine size ranges is inefficient and, therefore, vertical stirred grinding mills are becoming increasingly popular in the mineral processing industry due to its already known high energy efficiency. This work presents a hypothesis of a methodology to predict the product size distribution of a vertical stirred mill using a Bond ball mill. The Population Balance Model (PBM) was used to empirically analyze the performance of a vertical mill and a Bond ball mill. The breakage parameters obtained for both grinding mills are compared to determine the possibility of predicting the product size distribution of a vertical mill based on the results obtained from the Bond ball mill. The biggest advantage of this methodology is that most of the minerals processing laboratories already have a Bond ball mill to perform the tests suggested in this study. Preliminary results show the possibility of predicting the performance of a laboratory vertical stirred mill using a Bond ball mill.

Keywords: bond ball mill, population balance model, product size distribution, vertical stirred mill

Procedia PDF Downloads 286

Authors: Ghada E. Esheba, Ghadeer F. Alharthi, Duaa A. Alhejaili, Rawan E. Hudairy, Wafaa A. Altaezi, Raghad M. Alhejaili

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Background: Inflammatory bowel disease (IBD) consists of two specific gastrointestinal disorders: ulcerative colitis (UC) and Crohn's disease (CD). Despite their distinct natures, these two diseases share many similar etiologic, clinical and pathological features, as a result, their accurate differential diagnosis may sometimes be difficult. Correct diagnosis is important because surgical treatment and long-term prognosis differ from UC and CD. Aim: This study aims to study the characteristic clinicopathological features which help in the differential diagnosis between UC and CD, and assess the disease activity in ulcerative colitis. Materials and methods: This study was carried out on 50 selected cases. The cases included 27 cases of UC and 23 cases of CD. All the cases were examined using H& E and immunohistochemically for bcl-2 expression. Results: Characteristic features of UC include: decrease in mucous content, irregular or villous surface, crypt distortion, and cryptitis, whereas the main cardinal histopathological features seen in CD were: epitheloid granuloma, transmural chronic inflammation, absence of mucin depletion, irregular surface, or crypt distortion. 3 cases of UC were found to be associated with dysplasia. UC mucosa contains fewer Bcl-2+ cells compared with CD mucosa. Conclusion: This study using multiple parameters such clinicopathological features and Bcl-2 expression as studied by immunohistochemical stain, helped to gain an accurate differentiation between UC and CD. Furthermore, this work spotted the light on the activity and different grades of UC which could be important for the prediction of relapse.

Keywords: Crohn's disease, dysplasia, inflammatory bowel disease, ulcerative colitis

Procedia PDF Downloads 184

Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

Procedia PDF Downloads 34

Authors: Seyed M. Khatami, H. Naderpour, R. Vahdani, R. C. Barros

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Many previous studies have been carried out to calculate the impact force and the dissipated energy between two neighboring buildings during seismic excitation, when they collide with each other. Numerical studies are an important part of impact, which several researchers have tried to simulate the impact by using different formulas. Estimation of the impact force and the dissipated energy depends significantly on some parameters of impact. Mass of bodies, stiffness of spring, coefficient of restitution, damping ratio of dashpot and impact velocity are some known and unknown parameters to simulate the impact and measure dissipated energy during collision. Collision is usually shown by force-displacement hysteresis curve. The enclosed area of the hysteresis loop explains the dissipated energy during impact. In this paper, the effect of using different types of impact models is investigated in order to calculate the impact force. To increase the accuracy of impact model and to optimize the results of simulations, a new damping equation is assumed and is validated to get the best results of impact force and dissipated energy, which can show the accuracy of suggested equation of motion in comparison with other formulas. This relation is called "n-m". Based on mathematical relation, an initial value is selected for the mentioned coefficients and kinetic energy loss is calculated. After each simulation, kinetic energy loss and energy dissipation are compared with each other. If they are equal, selected parameters are true and, if not, the constant of parameters are modified and a new analysis is performed. Finally, two unknown parameters are suggested to estimate the impact force and calculate the dissipated energy.

Keywords: impact force, dissipated energy, kinetic energy loss, damping relation

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Authors: Seyed Salahedin Naghshbandi, Zahra Bozorgzadeh, Leila Zakizadeh, Siavash Hamidzadeh

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From the sport economy experts point of view, the existence of competitive balance among sport leagues and its numerous effects on league is an important and undeniable issue. In general, sport events fans are so eager to unpredictable results of competition in order to reach the top of excitement and necessary motivation for following competitions. The purpose of this research is to consider and compare the competitive balance among five provisional European football leagues (Spain, England, Italy, France and Germany) during 1966 - 2015 seasons. Research data are secondary and obtained from Premier League final tables of selected countries in 1966 - 2015 seasons. For analyzing data, C5 and C5ICB indicators used. whatever these indicators be less, more balance establishes in the league and vice-versa. The result showed that Le champion of France reached from 1,259 to 1,395; Italy Serie-A league from 1,316 to 1,432; England premier league from 1, 342 to 1,455; Germany Bundesliga from 1,238 to 1,465 and Spain La liga from 1,295 to 1,495. So by comparing C5ICB charts during 1966 - 2015 seasons, La liga of Spain moved more toward imbalance and enjoyed less balance with other European Leagues. Also, La champion of France during the mentioned season, enjoyed less imbalance and preserved its relative balance with monotonous process. It seems that football in France has been followed as stable during 1966 to 2015, and prediction of results was more difficult and competitions were so attractive for spectators, but in Italy, England, Germany, and Spain there were less balance, respectively.

Keywords: competitive balance, professional football league, competition, C5ICB

Procedia PDF Downloads 135

Authors: Ali Kadir, O. Anwar Beg

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Thermal barrier coatings are among the most popular methods for providing corrosion protection in high temperature applications including aircraft engine systems, external spacecraft structures, rocket chambers etc. Many different materials are available for such coatings, of which ceramics generally perform the best. Motivated by these applications, the current investigation presents detailed finite element simulations of coating stress analysis for a 3- dimensional, 3-layered model of a test sample representing a typical gas turbine component scenario. Structural steel is selected for the main inner layer, Titanium (Ti) alloy for the middle layer and Silicon Carbide (SiC) for the outermost layer. The model dimensions are 20 mm (width), 10 mm (height) and three 1mm deep layers. ANSYS software is employed to conduct three types of analysis- static structural, thermal stress analysis and also computational fluid dynamic erosion/corrosion analysis (via ANSYS FLUENT). The specified geometry which corresponds to corrosion test samples exactly is discretized using a body-sizing meshing approach, comprising mainly of tetrahedron cells. Refinements were concentrated at the connection points between the layers to shift the focus towards the static effects dissipated between them. A detailed grid independence study is conducted to confirm the accuracy of the selected mesh densities. To recreate gas turbine scenarios; in the stress analysis simulations, static loading and thermal environment conditions of up to 1000 N and 1000 degrees Kelvin are imposed. The default solver was used to set the controls for the simulation with the fixed support being set as one side of the model while subjecting the opposite side to a tabular force of 500 and 1000 Newtons. Equivalent elastic strain, total deformation, equivalent stress and strain energy were computed for all cases. Each analysis was duplicated twice to remove one of the layers each time, to allow testing of the static and thermal effects with each of the coatings. ANSYS FLUENT simulation was conducted to study the effect of corrosion on the model under similar thermal conditions. The momentum and energy equations were solved and the viscous heating option was applied to represent improved thermal physics of heat transfer between the layers of the structures. A Discrete Phase Model (DPM) in ANSYS FLUENT was employed which allows for the injection of continuous uniform air particles onto the model, thereby enabling an option for calculating the corrosion factor caused by hot air injection (particles prescribed 5 m/s velocity and 1273.15 K). Extensive visualization of results is provided. The simulations reveal interesting features associated with coating response to realistic gas turbine loading conditions including significantly different stress concentrations with different coatings.

Keywords: thermal coating, corrosion, ANSYS FEA, CFD

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Authors: Omar Imine, Mohammed Aounallah, Mustapha Belkadi

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Steady three-dimensional and two free surface waves generated by moving bodies are presented, the flow problem to be simulated is rich in complexity and poses many modeling challenges because of the existence of breaking waves around the ship hull, and because of the interaction of the two-phase flow with the turbulent boundary layer. The results of several simulations are reported. The first study was performed for NACA0012 of hydrofoil with different meshes, this section is analyzed at h/c= 1, 0345 for 2D. In the second simulation, a mathematically defined Wigley hull form is used to investigate the application of a commercial CFD code in prediction of the total resistance and its components from tangential and normal forces on the hull wetted surface. The computed resistance and wave profiles are used to estimate the coefficient of the total resistance for Wigley hull advancing in calm water under steady conditions. The commercial CFD software FLUENT version 12 is used for the computations in the present study. The calculated grid is established using the code computer GAMBIT 2.3.26. The shear stress k-ωSST model is used for turbulence modeling and the volume of the fluid technique is employed to simulate the free-surface motion. The second order upwind scheme is used for discretizing the convection terms in the momentum transport equations, the Modified HRICscheme for VOF discretization. The results obtained compare well with the experimental data.

Keywords: free surface flows, breaking waves, boundary layer, Wigley hull, volume of fluid

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Authors: Misganaw Abebe Baye, Ji-Woo Park, Beom-Soo Kang

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The product quality of multi-point dieless forming (MDF) is identified to be dependent on the process parameters. Moreover, a certain variation of friction and material properties may have a substantially worse influence on the final product quality. This study proposed on how to compensate the MDF product defects by minimizing the sensitivity of noise parameter variations. This can be attained by reliability-based robust optimization (RRO) technique to obtain the optimal process setting of the controllable parameters. Initially two MDF Finite Element (FE) simulations of AA3003-H14 saddle shape showed a substantial amount of dimpling, wrinkling, and shape error. FE analyses are consequently applied on ABAQUS commercial software to obtain the correlation between the control process setting and noise variation with regard to the product defects. The best prediction models are chosen from the family of metamodels to swap the computational expensive FE simulation. Genetic algorithm (GA) is applied to determine the optimal process settings of the control parameters. Monte Carlo Analysis (MCA) is executed to determine how the noise parameter variation affects the final product quality. Finally, the RRO FE simulation and the experimental result show that the amendment of the control parameters in the final forming process leads to a considerably better-quality product.

Keywords: dimpling, multi-point dieless forming, reliability-based robust optimization, shape error, variation, wrinkling

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Authors: S. Rajeswari

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Medicinal Plant extracts and their bioactive compounds have been used for antimicrobial activities and have significant remedial properties. In the recent years, a wide range of investigations have been carried out throughout the world to confirm antimicrobial properties of different medicinally important plants. A number of plants showed efficient antimicrobial activities, which were comparable to that of synthetic standard drugs or antimicrobial agents. The large family Euphorbiaceae contains nearly about 300 genera and 7,500 speciesand one among is Ricinus communis or castor plant which has high traditional and medicinal value for disease free healthy life. Traditionally the plant is used as laxative, purgative, fertilizer and fungicide etc. whereas the plant possess beneficial effects such as anti-oxidant, antihistamine, antinociceptive, antiasthmatic, antiulcer, immunomodulatory anti diabetic, hepatoprotective, anti inflammatory, antimicrobial, and many other medicinal properties. This activity of the plant possess due to the important phytochemical constituents like flavonoids, saponins, glycosides, alkaloids and steroids. The presents study includes the phytochemical properties of Ricinus communis and to prediction of the anti-microbial activity of Ricinus communis using DNA gyrase of Staphylococcus aureus as molecular target. Docking results of varies chemicals compounds of Ricinus communis against DNA gyrase of Staphylococcus aureus by maestro 9.8 of Schrodinger show that the phytochemicals are effective against the target protein DNA gyrase. our studies suggest that the phytochemical from Ricinus communis such has INDICAN (G.Score 4.98) and SUPLOPIN-2(G.Score 5.74) can be used as lead molecule against Staphylococcus infections.

Keywords: euphorbiaceae, antimicrobial activity, Ricinus communis, Staphylococcus aureus

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Authors: Jailan Mohamed El Demerdash

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Millennials are a distinctive and dominant consumer group whose behavior, preferences and purchase decisions are broadly explored but not fully understood yet. Making up the largest market segment in the world, and in Egypt, they have the power to reinvent the hospitality industry and contribute to forming prospective demand for green hotels by showing willingness to adopting their environmental-friendly practices. The current study aims to enhance better understanding of Millennials' perception about sustainable initiatives and to increase the prediction power of their intentions regarding green hotel practices in Egypt. In doing so, the study is exploring the relation among different factors; Millennials' environmental awareness, their acceptance of green practices and their willingness to pay more for them. Millennials' profile, their preferences and environmental decision-making process are brought under light to stimulate actions of hospitality decision-makers and hoteliers. Bearing in mind that responsible consumerism is depending on understanding the different influences on consumption. The study questionnaire was composed of four sections and it was distributed to random Egyptian travelers' blogs and Facebook groups, with approximately 8000 members. Analysis of variance test (ANOVA) was used to examine the study variables. The findings indicated that Millennials' environmental awareness will not be a significant factor in their acceptance of hotel green practices, as well as, their willingness to pay more for them. However, Millennials' acceptance of the level of hotel green practices will have an impact on their willingness to pay more. Millennials were found to have a noticeable level of environmental awareness but lack commitment to tolerating hotel green practices and their associated high prices.

Keywords: millennials, environment, awareness, paying more

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Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

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Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

Procedia PDF Downloads 338

Authors: Dhruv Mehta, Alexander van Zuijlen, Hester Bijl

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Large Eddy Simulation (LES) numerically resolves the large energy-containing eddies of a turbulent flow, while modelling the small dissipative eddies. On a wind farm, these large scales carry the energy wind turbines extracts and are also responsible for transporting the turbines’ wakes, which may interact with downstream turbines and certainly with the atmospheric boundary layer (ABL). In this situation, it is important to conserve the energy that these wake’s carry and which could be altered artificially through numerical dissipation brought about by the schemes used for the spatial discretisation and temporal integration. Numerical dissipation has been reported to cause the premature recovery of turbine wakes, leading to an over prediction in the power produced by wind farms.An energy-conserving scheme is free from numerical dissipation and ensures that the energy of the wakes is increased or decreased only by the action of molecular viscosity or the action of wind turbines (body forces). The aim is to create an LES package with energy-conserving schemes to simulate wind turbine wakes correctly to gain insight into power-production, wake meandering etc. Such knowledge will be useful in designing more efficient wind farms with minimal wake interaction, which if unchecked could lead to major losses in energy production per unit area of the wind farm. For their research, the authors intend to use the Energy-Conserving Navier-Stokes code developed by the Energy Research Centre of the Netherlands.

Keywords: energy-conserving schemes, modelling turbulence, Large Eddy Simulation, atmospheric boundary layer

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Authors: Manikandan S. P., Sharmila N.

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Pain in the neck, intermediate and anterior, and even low back may occur in one or more locations. Numerous factors can lead to back discomfort, which can manifest into sensations in the other parts of your body. Up to 80% of people will have low back problems at a certain stage of their lives, making spine-related pain a highly prevalent ailment. Roughly twice as commonly as neck pain, low back discomfort also happens about as often as knee pain. According to current studies, using digital devices for extended periods of time and poor sitting posture are the main causes of neck and low back pain. There are numerous monitoring techniques provided to enhance the sitting posture for the aforementioned problems. A sophisticated technique to monitor the extended sitting position is suggested in this research based on this problem. The system is made up of an inertial measurement unit, a T-shirt, an Arduino board, a buzzer, and a mobile app with cloud services. Based on the anatomical position of the spinal cord, the inertial measurement unit was positioned on the inner back side of the T-shirt. The IMU (inertial measurement unit) sensor will evaluate the hip position, imbalanced shoulder, and bending angle. Based on the output provided by the IMU, the data will be analyzed by Arduino, supplied through the cloud, and shared with a mobile app for continuous monitoring. The buzzer will sound if the measured data is mismatched with the human body's natural position. The implementation and data prediction with design to identify balanced and unbalanced posture using a posture monitoring t-shirt will be further discussed in this research article.

Keywords: IMU, posture, IOT, textile

Procedia PDF Downloads 81

Authors: Chen Chan Wei

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The purpose of this study was to investigate the impact of swim 1500m, 10000m run, VO2 max, and body fat on Olympic distance triathlon competition performance. The subjects were thirteen college triathletes with endurance training, with an average age, height and weight of 20.61±1.04 years (mean ± SD), 171.76±8.54 cm and 65.32±8.14 kg respectively. All subjects were required to take the tests of swim 1500m, run 10000m, VO2 max, body fat, and participate in the Olympic distance triathlon competition. First, the swim 1500m test was taken in the standardized 50m pool, with a depth of 2m, and the 10000m run test on the standardized 400m track. After three days, VO2 max was tested with the MetaMax 3B and body fat was measured with the DEXA machine. After two weeks, all 13 subjects joined the Olympic distance triathlon competition at the 2016 New Taipei City Asian Cup. The relationships between swim 1500m, 10000m run, VO2 max, body fat test, and Olympic distance triathlon competition performance were evaluated using Pearson's product-moment correlation. The results show that 10000m run and body fat had a significant positive correlation with Olympic distance triathlon performance (r=.830, .768), but VO2 max has a significant negative correlation with Olympic distance triathlon performance (r=-.735). In conclusion, for improved non-draft Olympic distance triathlon performance, triathletes should focus on running than swimming training and can be measure VO2 max to prediction triathlon performance. Also, managing body fat can improve Olympic distance triathlon performance. In addition, swimming performance was not significantly correlated to Olympic distance triathlon performance, possibly because the 2016 New Taipei City Asian Cup age group was not a drafting competition. The swimming race is the shortest component of Olympic distance triathlons. Therefore, in a non-draft competition, swimming ability is not significantly correlated with overall performance.

Keywords: triathletes, olympic, non-drafting, correlation

Procedia PDF Downloads 247

Authors: Moustafa Osman Mohammed

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This paper will explore formation of HCl aerosol at atmospheric boundary layers and encourages the uptake of environmental modeling systems (EMSs) as a practice evaluation of gaseous emissions (“framework measures”) from small and medium-sized enterprises (SMEs). The conceptual model predicts greenhouse gas emissions to ecological points beyond landfill site operations. It focuses on incorporation traditional knowledge into baseline information for both measurement data and the mathematical results, regarding parameters influence model variable inputs. The paper has simplified parameters of aerosol processes based on the more complex aerosol process computations. The simple model can be implemented to both Gaussian and Eulerian rural dispersion models. Aerosol processes considered in this study were (i) the coagulation of particles, (ii) the condensation and evaporation of organic vapors, and (iii) dry deposition. The chemical transformation of gas-phase compounds is taken into account photochemical formulation with exposure effects according to HCl concentrations as starting point of risk assessment. The discussion set out distinctly aspect of sustainability in reflection inputs, outputs, and modes of impact on the environment. Thereby, models incorporate abiotic and biotic species to broaden the scope of integration for both quantification impact and assessment risks. The later environmental obligations suggest either a recommendation or a decision of what is a legislative should be achieved for mitigation measures of landfill gas (LFG) ultimately.

Keywords: air pollution, landfill emission, environmental management, monitoring/methods and impact assessment

Procedia PDF Downloads 311

Authors: Arfan Ali Nagra, Tariq Shahzad, Meshal Alharbi, Khalid Masood Khan, Muhammad Mugees Asif, Taher M. Ghazal, Khmaies Ouahada

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Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify a number of genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Keywords: microarray cancer, improved PSO, ELM, SVM, evolutionary algorithms

Procedia PDF Downloads 77

Authors: Ameer A. Jebur, William Atherton, Rafid M. Alkhaddar, Edward Loffill

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Understanding the behaviour of the piles under the action of the independent lateral loads and the precise prediction of the capacity of piles subjected to different lateral loads are vital topics in foundation design and analysis. Moreover, the laterally loaded behaviour of deep foundations penetrated in cohesive and non-cohesive soils is basically analysed by the Winkler Model (beam on elastic foundation), in which the interaction between the pile embedded depth and contacted soil is simulated by nonlinear p–y curves. The presence of many approaches to interpret the behaviour of soil-pile interaction has resulted in numerous outputs and indicates that no general approach has yet been adopted. The current study presents the result of numerical modelling of the behaviour of steel tubular piles (25.4mm) outside diameter with various embedment depth-to-diameter ratios (L/d) embedded in a sand calibrated chamber of known relative density. The study revealed that the shear strength parameters of the sand specimens and the (L/d) ratios are the most significant factor influencing the response of the pile and its capacity while taking into consideration the complex interaction between the pile and soil. Good agreement has been achieved when comparing the application of this modelling approach with experimental physical modelling carried out by another researcher.

Keywords: deep foundations, slenderness ratio, soil-pile interaction, winkler model (beam on elastic foundation), non-cohesive soil

Procedia PDF Downloads 295

Authors: Y. Baba, A. Archibong-Eso, H. Yeung

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Experimental study of high viscosity oil-gas flows in horizontal pipelines published in literature has indicated that hydrodynamic slug flow is the dominant flow pattern observed. Investigations have shown that hydrodynamic slugging brings about high instabilities in pressure that can damage production facilities thereby making it inherent to study high viscous slug flow regime so as to improve the understanding of its flow dynamics. Most slug flow models used in the petroleum industry for the design of pipelines together with their closure relationships were formulated based on observations of low viscosity liquid-gas flows. New experimental investigations and data are therefore required to validate these models. In cases where these models underperform, improving upon or building new predictive models and correlations will also depend on the new experimental dataset and further understanding of the flow dynamics in high viscous oil-gas flows. In this study conducted at the Flow laboratory, Oil and Gas Engineering Centre of Cranfield University, slug flow variables such as pressure gradient, mean liquid holdup, frequency and slug length for oil viscosity ranging from 1..0 – 5.5 Pa.s are experimentally investigated and analysed. The study was carried out in a 0.076m ID pipe, two fast sampling gamma densitometer and pressure transducers (differential and point) were used to obtain experimental measurements. Comparison of the measured slug flow parameters to the existing slug flow prediction models available in the literature showed disagreement with high viscosity experimental data thus highlighting the importance of building new predictive models and correlations.

Keywords: gamma densitometer, mean liquid holdup, pressure gradient, slug frequency and slug length

Procedia PDF Downloads 325

Authors: Zoe Goldberger, Priscilla S. Briquez, Jeffrey A. Hubbell

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Tumor cells have mutations resulting from genetic instability that the immune system can actively recognize. Immune checkpoint immunotherapy (ICI) is commonly used in the clinic to re-activate immune reactions against mutated proteins, called neoantigens, resulting in tumor remission in cancer patients. However, only around 20% of patients show durable response to ICI. While tumor mutational burden (TMB) has been approved by the Food and Drug Administration (FDA) as a criterion for ICI therapy, the relevance of the subcellular localizations of the mutated proteins within the tumor cell has not been investigated. Here, we hypothesized that localization of mutations impacts the effect of immune responsiveness to ICI. We analyzed publicly available tumor mutation sequencing data of ICI treated patients from 3 independent datasets. We extracted the subcellular localization from the UniProtKB/Swiss-Prot database and quantified the proportion of membrane, cytoplasmic, nuclear, or secreted mutations per patient. We analyzed this information in relation to response to ICI treatment and overall survival of patients showing with 1722 ICI-treated patients that high mutational burden localized at the membrane (mTMB), correlate with ICI responsiveness, and improved overall survival in multiple cancer types. We anticipate that our results will ameliorate predictability of cancer patient response to ICI with potential implications in clinical guidelines to tailor ICI treatment. This would not only increase patient survival for those receiving ICI, but also patients’ quality of life by reducing the number of patients enduring non-effective ICI treatments.

Keywords: cancer, immunotherapy, membrane neoantigens, efficacy prediction, biomarkers

Procedia PDF Downloads 105