Search results for: reduced form valuation model

Authors: Pedro A. Marques, Francisco Monteiro, Alessandra Zumbo, Alessia Simonini, Miguel A. Mendez

Abstract:

Cryogenic fuels such as Liquid Hydrogen (LH₂) must be transported and stored at extremely low temperatures. Without expensive active cooling solutions, preventing fuel boil-off over time is impossible. Hence, one must resort to venting systems at the cost of significant energy and fuel mass loss. These losses increase significantly in propellant tanks installed on vehicles, as the presence of external accelerations induces sloshing. Sloshing increases heat and mass transfer rates and leads to significant pressure oscillations, which might further trigger propellant venting. To make LH₂ economically viable, it is essential to minimize these factors by using advanced control techniques. However, these require accurate modelling and a full understanding of the tank's thermodynamics. The present research aims to implement a simple thermodynamic model capable of predicting the state of a cryogenic fuel tank under different operating conditions (i.e., filling, pressurization, fuel extraction, long-term storage, and sloshing). Since this model relies on a set of closure parameters to drive the system's transient response, it must be calibrated using experimental or numerical data. This work focuses on the former approach, wherein the model is calibrated through an experimental campaign carried out on a reduced-scale model of a cryogenic tank. The thermodynamic model of the system is composed of three control volumes: the ullage, the liquid, and the insulating walls. Under this lumped formulation, the governing equations are derived from energy and mass balances in each region, with mass-averaged properties assigned to each of them. The gas-liquid interface is treated as an infinitesimally thin region across which both phases can exchange mass and heat. This results in a coupled system of ordinary differential equations, which must be closed with heat and mass transfer coefficients between each control volume. These parameters are linked to the system evolution via empirical relations derived from different operating regimes of the tank. The derivation of these relations is carried out using an inverse method to find the optimal relations that allow the model to reproduce the available data. This approach extends classic system identification methods beyond linear dynamical systems via a nonlinear optimization step. Thanks to the data-driven assimilation of the closure problem, the resulting model accurately predicts the evolution of the tank's thermodynamics at a negligible computational cost. The lumped model can thus be easily integrated with other submodels to perform complete system simulations in real time. Moreover, by setting the model in a dimensionless form, a scaling analysis allowed us to relate the tested configurations to a representative full-size tank for naval applications. It was thus possible to compare the relative importance of different transport phenomena between the laboratory model and the full-size prototype among the different operating regimes.

Keywords: destratification, hydrogen, modeling, pressure-drop, pressurization, sloshing, thermodynamics

Procedia PDF Downloads 79

Authors: Zhou Jianhong

Abstract:

Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

Procedia PDF Downloads 504

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 107

Authors: V. Diaz, B. Pardo , D. Villegas

Abstract:

In premature births most babies have complications at birth, these complications can be reduced, if an atmosphere of relaxation is provided and is also similar to intrauterine life, for this, there are programs where their mothers lull and sway them; however, the conditions in which they do so and the way in they do it may not be the indicated. Here we describe an investigation based on the biomimics of the kinematics of human fetal movement, which consists of determining the movements that the fetus experiences and the deformations of the components that surround the fetus during a gentle walk at week 32 of the gestation stage. This research is based on a 3D model that has the anatomical structure of the pelvis, fetus, muscles, uterus and its most important supporting elements (ligaments). Normal load conditions are applied to this model according to the stage of gestation and the kinematics of a gentle walk of a pregnant mother, which focuses on the pelvic bone, this allows to receive a response from the other elements of the model. To accomplish this modeling and subsequent simulation Solidworks software was used. From this analysis, the curves that describe the movement of the fetus at three different points were obtained. Additionally, we could found the deformation of the uterus and the ligaments that support it, showing the characteristics that these tissues can have in the face of the support of the fetus. These data can be used for the construction of artifacts that help the normal development of premature infants.

Keywords: simulation, biomimic, uterine model, fetal movement study

Procedia PDF Downloads 154

Authors: Memnun Seven, Mine Bahar, Aygül Akyüz, Hatice Erdoğan

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Background: The workplace has been deemed a suitable location for educating many women at once about cancer screening. Objective: To determine how group education about early diagnostic methods for breast and cervical cancer affects women’s behavior and readiness to receive mammography and Pap smears. Methods: This semi-interventional study was conducted at a textile factory in Istanbul, Turkey. Female workers (n = 125) were included in the study. A participant identification form and knowledge evaluation form developed for this study, along with the trans-theoretical model, were used to collect data. A 45-min interactive group education was given to the participants. Results: Upon contacting participants 3 months after group education, 15.4% (n = 11) stated that they had since received a mammogram and 9.8% (n = 7) a Pap smear. As suggested by the trans-theoretical model, group education increased participants’ readiness to receive cancer screening, along with their knowledge of breast and cervical cancer. Conclusions: Group education positively impacted women’s knowledge of cancer and their readiness to receive mammography and Pap smears. Group education can therefore potentially create awareness of cancer screening tests among women and improve their readiness to receive such tests.

Keywords: cancer screening, educational intervention, participation, women

Procedia PDF Downloads 318

Authors: B. N. Asare, O. A. Ajigini

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Information systems (IS) are highly important in the operation of private and public organisations in developing and developed countries. Developing countries are saddled with many project failures during the implementation of information systems. However, successful information systems are greatly needed in developing countries in order to enhance their economies. This paper is highly important in view of the high failure rate of information systems in developing countries which needs to be reduced to minimum acceptable levels by means of recommended interventions. This paper centres on a review of IS development in developing countries. The paper presents evidences of the IS successes and failures in developing countries and posits a model to address the IS failures. The proposed model can then be utilised by developing countries to reduce their IS project implementation failure rate. A comparison is drawn between IS development in developing countries and developed countries. The paper provides valuable information to assist in reducing IS failure, and developing IS models and theories on IS development for developing countries.

Keywords: developing countries, information systems, IS development, information systems failure, information systems success, information systems success model

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Authors: Pranitha Janapatla, Venkata Suman Gontla

Abstract:

The present paper investigates the effects of MHD, double dispersion and variable properties on mixed convection flow from a vertical surface in a power-law fluid saturated non-Darcy porous medium. The governing non-linear partial differential equations are reduced to a system of ordinary differential equations by using a special form of Lie group transformations viz. scaling group of transformations. These ordinary differential equations are solved numerically by using Shooting technique. The influence of relevant parameters on the non-dimensional velocity, temperature, concentration for pseudo-plastic fluid, Newtonian and dilatant fluid are discussed and displayed graphically. The behavior of heat and mass transfer coefficients are shown in tabular form. Comparisons with the published works are performed and are found to be in very good agreement. From this analysis, it is observed that an increase in variable viscosity causes to decrease in velocity profile and increase the temperature and concentration distributions. It is also concluded that increase in the solutal dispersion decreases the velocity and concentration but raises the temperature profile.

Keywords: power-law fluid, thermal conductivity, thermal dispersion, solutal dispersion, variable viscosity

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Authors: Peter Collett, Mike Pynn, Haseeb Ur Rahman

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For the last few years across the UK there has been a push towards implementing treatment escalation plans (TEP) for every patient admitted to hospital. This is a paper form which is completed by a junior doctor then countersigned by the consultant responsible for the patient's care. It is designed to address what level of care is appropriate for the patient in question at point of entry to hospital. It helps decide whether the patient would benefit for ward based, high dependency or intensive care. They are completed to ensure the patient's best interests are maintained and aim to facilitate difficult decisions which may be required at a later date. For example, a frail patient with significant co-morbidities, unlikely to survive a pathology requiring an intensive care admission is admitted to hospital the decision can be made early to state the patient would not benefit from an ICU admission. This decision can be reversed depending on the clinical course of the patient's admission. It promotes discussions with the patient regarding their wishes to receive certain levels of healthcare. This poster describes the steps taken in the Aneurin Bevan University Health Board (ABUHB) when implementing the TEP form. The team implementing the TEP form campaigned for it's use to the board of directors. The directors were eager to hear of experiences of other health boards who had implemented the TEP form. The team presented the data produced in a number of health boards and demonstrated the proposed form. Concern was raised regarding the legalities of the form and that it could upset patients and relatives if the form was not explained properly. This delayed the effectuation of the TEP form and further research and discussion would be required. When COVID 19 reached the UK the National Institute for Health and Clinical Excellence issued guidance stating every patient admitted to hospital should be issued a TEP form. The TEP form was accelerated through the vetting process and was approved with immediate effect. The TEP form in ABUHB has now been in circulation for a month. An audit investigating it's uptake and a survey gathering opinions have been conducted.

Keywords: acute medicine, clinical governance, intensive care, patient centered decision making

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Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

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Authors: Katerina Kormusheva

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Pricing plays a pivotal role in the marketing discipline as it directly influences consumer perceptions, purchase decisions, and overall market positioning of a product or service. This paper seeks to expand current knowledge in the area of discriminatory and differential pricing, a main area of marketing research. The methodology includes developing a framework and a model for determining how many price points to implement in differential pricing. We focus on choosing the levels of differentiation, derive a function form of the model framework proposed, and lastly, test it empirically with data from a large-scale marketing pricing experiment of services in telecommunications.

Keywords: marketing, differential pricing, price points, optimization

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Authors: Melody Yin

Abstract:

Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: Alakija Temitope Olufunmilayo, Akinyemi, Yagba Joy

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Hepatitis is the inflammation of the liver tissue that can cause whiteness of the eyes (Jaundice), lack of appetite, vomiting, tiredness, abdominal pain, diarrhea. Hepatitis is acute if it resolves within 6 months and chronic if it last longer than 6 months. Acute hepatitis can resolve on its own, lead to chronic hepatitis or rarely result in acute liver failure. Chronic hepatitis may lead to scarring of the liver (Cirrhosis), liver failure and liver cancer. Modelling Hepatitis B may become necessary in order to reduce its spread. So, dynamic SIR model can be used. This model consists of a system of three coupled non-linear ordinary differential equation which does not have an explicit formula solution. It is an epidemiological model used to predict the dynamics of infectious disease by categorizing the population into three possible compartments. In this study, a five-compartment dynamic model of Hepatitis B disease was proposed and developed by adding control measure of sensitizing the public called awareness. All the mathematical and statistical formulation of the model, especially the general equilibrium of the model, was derived, including the nonlinear least square estimators. The initial parameters of the model were derived using nonlinear least square embedded in R code. The result study shows that the proportion of Hepatitis B patient in the study population is 1.4 per 1,000,000 populations. The estimated Hepatitis B induced death rate is 0.0108, meaning that 1.08% of the infected individuals die of the disease. The reproduction number of Hepatitis B diseases in Nigeria is 6.0, meaning that one individual can infect more than 6.0 people. The effect of sensitizing the public on the basic reproduction number is significant as the reproduction number is reduced. The study therefore recommends that programme should be designed by government and non-governmental organization to sensitize the entire Nigeria population in order to reduce cases of Hepatitis B disease among the citizens.

Keywords: hepatitis B, modelling, non-linear ordinary differential equation, sihar model, sensitization

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Authors: S. Aneeka, S.M. Phyoe, R. Guo, Z.W. Zhong

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Arrival flights tend to spend long waiting times at holding stacks if the arrival airport is congested. However, the waiting time spent in the air in the vicinity of the arrival airport may be reduced if the delays are distributed to the cruising phase of the arrival flights by means of speed control. Here, a case study was conducted for the flights arriving at Changi Airport. The flights that were assigned holdings were simulated to fly at a reduced speed during the cruising phase. As the study involves a single airport and is limited to imposing speed constraints to arrivals within 200 NM from its location, the simulation setup in this study could be considered as an application of the Extended Arrival Management (E-AMAN) technique, which is proven to result in considerable fuel savings and more efficient management of delays. The objective of this experiment was to quantify the benefits of imposing cruise speed constraints to arrivals at Changi Airport and to assess the effects on controllers’ workload. The simulation results indicated considerable fuel savings, reduced aircraft emissions and reduced controller workload.

Keywords: aircraft emissions, air traffic flow management, controller workload, fuel consumption

Procedia PDF Downloads 134

Authors: Aminu Hamza

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This study examines the factors that play vital role in the adoption of mobile payment system among consumers in Nigeria. Technology Acceptance Model (TAM) was used with two additional variables to form the conceptual model. The study was conducted in three Universities in Kano state, Nigeria. Convenience sampling method was used with a total valid 202 respondents which involved the students of Bayero University Kano (BUK), Northwest University, and Kano University of Science and Technology (KUST) Wudil, Kano, Nigeria. Results of the regression analysis revealed that Perceived ease of use (PEOU) and Perceived usefulness (PU) have significant and positive correlation with the behavioral intention to adopt mobile payment system. The findings of this study would be useful to the policy makers Central Bank of Nigeria (CBN), mobile network operators and providers of the services.

Keywords: mobile payment system, Nigeria, technology adoption, technology acceptance model

Procedia PDF Downloads 290

Authors: Kareem Saber

Abstract:

Many products’ design instructors search for how to help students to develop their designs simply by reducing design stages and extrapolating simple design process forms to achieve design creativity. So, the researcher extrapolated a new design process form called “hierarchical design” which reduced design process into three stages and he had tried that methodology on about two hundred students. That trial had led to great results as students could develop their designs which characterized by creativity and innovation. That proved the success and effectiveness of the proposed methodology.

Keywords: mind mapping, morphological analysis, product design, design process

Procedia PDF Downloads 159

Authors: Ekaterina M. Myasnikova, Andrey A. Makashov, Alexander V. Spirov

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First manifestation of a segmentation pattern in the early Drosophila development is the formation of expression domains (along with the main embryo axis) of genes belonging to the trunk gene class. Highly variable expression of genes from gap family in early Drosophila embryo is strongly reduced by the start of gastrulation due to the gene cross-regulation. The dynamics of gene expression is described by a gene circuit model for a system of four gap genes. It is shown that for the formation of a steep and stationary border by the model it is necessary that there existed a nucleus (modeling point) in which the gene expression level is constant in time and hence is described by a stationary equation. All the rest genes expressed in this nucleus are in a dynamic equilibrium. The mechanism of border formation associated with the existence of a stationary nucleus is also confirmed by the experiment. An important advantage of this approach is that properties of the system in a stationary nucleus are described by algebraic equations and can be easily handled analytically. Thus we explicitly characterize the cross-regulation properties necessary for the robustness and formulate the conditions providing this effect through the properties of the initial input data. It is shown that our formally derived conditions are satisfied for the previously published model solutions.

Keywords: drosophila, gap genes, reaction-diffusion model, robustness

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Authors: Gurfateh Singh, Mu Khan, Razia Khanam, Govind Mohan

Abstract:

Objective: The present study has been designed to investigate the beneficial role of Fenofibrate & Clofibrate in attenuated the cardioprotective effect of ischemic preconditioning (IPC) in hyperlipidemic rat hearts. Materials & Methods: Experimental hyperlipidemia was produced by feeding high fat diet to rats for a period of 28 days. Isolated langendorff’s perfused normal and hyperlipidemic rat hearts were subjected to global ischemia for 30 min followed by reperfusion for 120 min. The myocardial infarct size was assessed macroscopically using triphenyltetrazolium chloride staining. Coronary effluent was analyzed for lactate dehydrogenase (LDH) and creatine kinase-MB release to assess the extent of cardiac injury. Moreover, the oxidative stress in heart was assessed by measuring thiobarbituric acid reactive substance, superoxide anion generation and reduced form of glutathione. Results: The ischemia-reperfusion (I/R) has been noted to induce oxidative stress by increasing TBARS, superoxide anion generation and decreasing reduced form of glutathione in normal and hyperlipidemic rat hearts. Moreover, I/R produced myocardial injury, which was assessed in terms of increase in myocardial infarct size, LDH and CK-MB release in coronary effluent and decrease in coronary flow rate in normal and hyperlipidemic rat hearts. In addition, the hyperlipidemic rat hearts showed enhanced I/R-induced myocardial injury with high degree of oxidative stress as compared with normal rat hearts subjected to I/R. Four episodes of IPC (5 min each) afforded cardioprotection against I/R-induced myocardial injury in normal rat hearts as assessed in terms of improvement in coronary flow rate and reduction in myocardial infarct size, LDH, CK-MB and oxidative stress. On the other hand, IPC mediated myocardial protection against I/R-injury was abolished in hyperlipidemic rat hearts. However, Treatment with Fenofibrate (100 mg/kg/day, i.p.), Clofibrate (300mg/kg/day, i.p.) as a agonists of PPAR-α have not affected the cardioprotective effect of IPC in normal rat hearts, but its treatment markedly restored the cardioprotective potentials of IPC in hyperlipidemic rat hearts. Conclusion: It is noted that the high degree of oxidative stress produced in hyperlipidemic rat heart during reperfusion and consequent down regulation of PPAR-α may be responsible to abolish the cardioprotective potentials of IPC.

Keywords: Hyperlipidemia, ischemia-reperfusion injury, ischemic preconditioning, PPAR-α

Procedia PDF Downloads 277

Authors: Pratch Kittipongpattana, Thongchai Fongsamootr

Abstract:

This research aimed to study the influences of a soot blowing operation and geometrical variables to the stress characteristic of water wall tubes located in soot blowing areas which caused the boilers of Mae Moh power plant to lose their generation hour. The research method is divided into 2 parts (a) measuring the strain on water wall tubes by using 3-element rosette strain gages orientation during a full capacity plant operation and in periods of soot blowing operations (b) creating a finite element model in order to calculate stresses on tubes and validating the model by using experimental data in a steady state plant operation. Then, the geometrical variables in the model were changed to study stresses on the tubes. The results revealed that the stress was not affected by the soot blowing process and the finite element model gave the results 1.24% errors from the experiment. The geometrical variables influenced the stress, with the most optimum tubes design in this research reduced the average stress from the present design 31.28%.

Keywords: boiler water wall tube, finite element, stress analysis, strain gage rosette

Procedia PDF Downloads 379

Authors: Tarek Ninouh, Ahmed Rouili

Abstract:

The Roman paths or roads offer a very interesting archaeological material, because they allow us to understand the history of human settlement and are also factors that increase territorial identity. Roman roads are one of the hallmarks of the Roman empire, which extends to North Africa. The objective of this investigation is to attract the attention of researchers to the importance of Roman roads and paths, which are found in Algeria, according to the quality of the materials and techniques used in this period of our history, and to encourage other decision makers to protect and enhance these routes because the current urbanization, intensive agricultural practices, or simply forgotten, decreases the sustainability of this important historical heritage.

Keywords: Roman paths, quality of materials, property, valuation

Procedia PDF Downloads 418

Authors: L. Madouni, M. Ould Ouali, N. E. Hannachi

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Two constitutive models for concrete are available in ABAQUS/Explicit, the Brittle Cracking Model and the Concrete Damaged Plasticity Model, and their suitability and limitations are well known. The aim of the present paper is to implement a damage-friction concrete constitutive model and to evaluate the performance of this model by comparing the predicted response with experimental data. The constitutive formulation of this material model is reviewed. In order to have consistent results, the parameter identification and calibration for the model have been performed. Several numerical simulations are presented in this paper, whose results allow for validating the capability of the proposed model for reproducing the typical nonlinear performances of concrete structures under different monotonic and cyclic load conditions. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated model.

Keywords: Abaqus, concrete, constitutive model, numerical simulation

Procedia PDF Downloads 350

Authors: Ismail Muratoglu, Ahmet Meyveci, Abdurrahman Tuncer, Erkan Demirci

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This study is dedicated to the analysis of the problems of occupational safety in Turkey, Italy and Poland. The need analysis was applied to three different countries which are Turkey; 4, Poland; 1, Italy; 1 state. The number of the subjects is 891 in Turkey. The number of the subjects is 26 in Italy and the number of the subjects is 19 in Poland. The total number of samples of study is 936. Four different forms (Job Security Experts Form, Student Form, Teacher Form and Company Form) were applied. Results of experts of job security forms are rate of 7.1%. Then, the students’ forms are rate of 34.3%, teacher or instructor forms are rate of 9.9%. The last corporation forms are rate of 48.7%.

Keywords: Europe, need analysis, occupational safety, Turkey, vocational education

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Authors: Snehal R. Pathak, Sachin S. Munnoli

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Evaluation of dynamic earth pressure on retaining wall is a topic of primary importance. In present paper, dynamic active earth pressure and displacement of flexible cantilever retaining wall has been evaluated analytically using 2-DOF mass-spring-dashpot model by incorporating both wall and backfill properties. The effect of wall flexibility on dynamic active earth pressure and wall displacement are studied and presented in graphical form. The obtained results are then compared with the various conventional methods, experimental analysis and also with PLAXIS analysis. It is observed that the dynamic active earth pressure decreases with increase in the wall flexibility while wall displacement increases linearly with flexibility of the wall. The results obtained by proposed 2-DOF analytical model are found to be more realistic and economical.

Keywords: earth pressure, earthquake, 2-DOF model, Plaxis, retaining walls, wall movement

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Authors: Ahteshamul Haq, Srikant Gupta, Murshid Kamal, Irfan Ali

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This case study designs and builds a multi-objective production planning model for a hardware firm with certain & uncertain data. During the time of interaction with the manager of the firm, they indicate some of the parameters may be vague. This vagueness in the formulated model is handled by the concept of fuzzy set theory. Triangular & Trapezoidal fuzzy numbers are used to represent the uncertainty in the collected data. The fuzzy nature is de-fuzzified into the crisp form using well-known defuzzification method via graded mean integration representation method. The proposed model attempts to maximize the production of the firm, profit related to the manufactured items & minimize the carrying inventory costs in both certain & uncertain environment. The recommended optimal plan is determined via fuzzy programming approach, and the formulated models are solved by using optimizing software LINGO 16.0 for getting the optimal production plan. The proposed model yields an efficient compromise solution with the overall satisfaction of decision maker.

Keywords: production planning problem, multi-objective optimization, fuzzy programming, fuzzy sets

Procedia PDF Downloads 196

Authors: Jun Yang, Ching-Liang Hsieh, Yi-Wen Lin

Abstract:

Chronic inflammatory pain results from peripheral tissue injury or local inflammation to increase the release of protons, histamines, adenosine triphosphate, and several proinflammatory cytokines. Transient receptor potential vanilloid 1 (TRPV1) is involved in fibromyalgia, neuropathic, and inflammatory pain; however, its exact mechanisms in chronic inflammatory pain are still unclear. We investigate the analgesic effect of EA by injecting complete Freund’s adjuvant (CFA) in the hind paw of mice to induce chronic inflammatory pain ( > 14 d). Our results showed that EA significantly reduced chronic mechanical and thermal hyperalgesia in the chronic inflammatory pain model. Chronic mechanical and thermal hyperalgesia was also abolished in TRPV1−/− mice. TRPV1 increased in the dorsal root ganglion (DRG) and spinal cord (SC) at 2 weeks after CFA injection. The expression levels of downstream molecules such as pPKA, pPI3K, and pPKC increased, as did those of pERK, pp38, and pJNK. Transcription factors (pCREB and pNFκB) and nociceptive ion channels (Nav1.7 and Nav1.8) were involved in this process. Inflammatory mediators such as GFAP (Glial fibrillary acidic protein), S100B, and RAGE (Receptor for advanced glycation endproducts) were also involved. The expression levels of these molecules were reduced in EA (electroacupuncture) and TRPV1−/−mice but not in the sham EA group. The present study demonstrated that EA or TRPV1 gene deletion reduced chronic inflammatory pain through TRPV1 and related molecules. In addition, our data provided evidence to support the clinical use of EA for treating chronic inflammatory pain.

Keywords: auricular electric-stimulation, epileptic seizures, anti-inflammation, electroacupuncture

Procedia PDF Downloads 160

Authors: R. Benelmir, M. El Kadri, A. Donnot, D. Descieux

Abstract:

HVAC represents a significant part of energy needs in buildings. Integrating renewable energy in cooling processes contributes to reducing primary energy consumption. Sorption refrigeration allows cold production through the use of solar/biomass/geothermal energy or even valuation of waste heat. This work presents an analysis of an experimental bench incorporating an adsorption chiller driven by hybrid energy resources associating solar thermal collectors with a cogeneration gas engine and a geothermal heat pump.

Keywords: solar cooling, cogeneration, geothermal heat pump, hybrid energy resources

Procedia PDF Downloads 342

Authors: Daniel Y. Abebe, Jaehyouk Choi

Abstract:

Link-to-column connection in eccentrically braced frames (EBF) has been a critical problem since the link flange connected to the column fractured prior to the required link rotation. Even though the problem in link-to-column connection still exist, the use of an eccentrically braced frame (EBF) is increasing day by day as EBF have high elastic stiffness, stable inelastic response under repeated lateral loading, and excellent ductility and energy dissipation capacity. In order to address this problem, a reduced web and flange link section is proposed and evaluated in this study. Reducing the web with holes makes the link to control the failure at the edge of holes introduced. Reducing the flange allows the link to control the location at which the plastic hinge is formed. Thus, the failure supposed to occur in the link flange connected at the connection move to the web and to the reduced link flange. Nonlinear FE analysis and experimental investigations have been done on the developed links, and the result shows that the link satisfies the plastic rotation limit recommended in AICS-360-10. Design equations that define the behavior of the proposed link have been recommended, and the equations were verified through the experimental and FE analysis results.

Keywords: EBFs, earthquake disaster, link-to-column connection, reduced link section

Procedia PDF Downloads 365

Authors: Ladda Her, Juntip Kanjanasilp, Ratree Sawangjit, Nathorn Chaiyakunapruk

Abstract:

Cough is the common symptom of the respiratory tract infections or non-infections; the duration of cough indicates a classification and severity of disease. Herbal medicines can be used as the alternative to drugs for relief of cough symptoms from acute and chronic disease. Eucalyptus was used for reducing cough with evidences suggesting it has an active role in reduction of airway inflammation. The present study aims to evaluate efficacy and safety of eucalyptus for relief of cough symptom in respiratory disease. Method: The Cochrane Library, MEDLINE (PubMed), Scopus, CINAHL, Springer, Science direct, ProQuest, and THAILIS databases. From its inception until 01/02/2019 for randomized control trials. We follow for the efficacy and safety of eucalyptus for reducing cough. Methodological quality was evaluated by using the Cochrane risk of bias tool; two reviewers in our team screened eligibility and extracted data. Result: Six studies were included for the review and five studies were included in the meta-analysis, there were 1.911 persons including children (n: 1) and adult (n: 5) studies; for study in children and adult were between 1 and 80 years old, respectively. Eucalyptus was used as mono herb (n: 2) and in combination with other herbs form (n: 4). All of the studies with eucalyptus were compared for efficacy and safety with placebo or standard treatment, Eucalyptus dosage form in studies included capsules, spray, and syrup. Heterogeneity was 32.44 used random effect model (I² = 1.2%, χ² = 1.01; P-value = 0.314). The efficacy of eucalyptus was showed a reduced cough symptom statistically significant (n = 402, RR: 1.40, 95%CI [1.19, 1.65], P-value < 0.0001) when compared with placebo. Adverse events (AEs) were reported mild to moderate intensity with mostly gastrointestinal symptom. The methodological quality of the included trials was overall poor. Conclusion: Eucalyptus appears to be beneficial and safe for relieving in respiratory diseases focus on cough frequency. The evidence was inconclusive due to limited quality trial. Well-designed trials for evaluating the effectiveness in humans, the effectiveness for reducing cough symptom in human is needed. Eucalyptus had safety as monotherapy or in combination with other herbs.

Keywords: cough, eucalyptus, cineole, herbal medicine, systematic review, meta-analysis

Procedia PDF Downloads 141

Authors: Jianming Cai

Abstract:

Exposure to ionizing radiation causes severe damage to human body and an safe and effective radioprotector is urgently required for alleviating radiation damage. In 2008, flagellin, an agonist of TLR5, was found to exert radioprotective effects on radiation injury through activating NF-kB signaling pathway. From then, the radioprotective effects of TLR ligands has shed new lights on radiation protection. CpG-ODN is an unmethylated oligonucleotide which activates TLR9 signaling pathway. In this study, we demonstrated that CpG-ODN has therapeutic effects on radiation injuries induced by γ ray and 12C6+ heavy ion particles. Our data showed that CpG-ODN increased the survival rate of mice after whole body irradiation and increased the number of leukocytes as well as the bone marrow cells. CpG-ODN also alleviated radiation damage on intestinal crypt through regulating apoptosis signaling pathway including bcl2, bax, and caspase 3 etc. By using a radiation-induced pulmonary fibrosis model, we found that CpG-ODN could alleviate structural damage, within 20 week after whole–thorax 15Gy irradiation. In this model, Th1/Th2 imbalance induced by irradiation was also reversed by CpG-ODN. We also found that TGFβ-Smad signaling pathway was regulated by CpG-ODN, which accounts for the therapeutic effects of CpG-ODN in radiation-induced pulmonary injury. On another hand, for high LET radiation protection, we investigated protective effects of CpG-ODN against 12C6+ heavy ion irradiation and found that after CpG-ODN treatment, the apoptosis and cell cycle arrest induced by 12C6+ irradiation was reduced. CpG-ODN also reduced the expression of Bax and caspase 3, while increased the level of bcl2. Then we detected the effect of CpG-ODN on heavy ion induced immune dysfunction. Our data showed that CpG-ODN increased the survival rate of mice and also the leukocytes after 12C6+ irradiation. Besides, the structural damage of immune organ such as thymus and spleen was also alleviated by CpG-ODN treatment. In conclusion, we found that TLR9 ligand, CpG-ODN reduced radiation injuries in response to γ ray and 12C6+ heavy ion irradiation. On one hand, CpG-ODN inhibited the activation of apoptosis induced by radiation through regulating bcl2, bax and caspase 3. On another hand, through activating TLR9, CpG-ODN recruit MyD88-IRAK-TRAF6 complex, activating TAK1, IRF5 and NF-kB pathway, and thus alleviates radiation damage. This study provides novel insights into protection and therapy of radiation damages.

Keywords: TLR9, CpG-ODN, radiation injury, high LET radiation

Procedia PDF Downloads 468

Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

Abstract:

Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud Ccomputing, data security, SAAS, PAAS, IAAS, Blowfish

Procedia PDF Downloads 465

Authors: Purva Joshi, Rohit Thanki, Omar Hanif

Abstract:

Unmanned aerial vehicles (UAVs) are becoming increasingly important in various industrial applications and sectors. Nowadays, a multi UAV network is used for specific types of communication (e.g., military) and monitoring purposes. Therefore, it is critical to reducing propagation delay during communication between UAVs, which is essential in a multi UAV network. This paper presents how the propagation delay between the base station (BS) and the UAVs is reduced using a searching algorithm. Furthermore, the iterative-based K-nearest neighbor (k-NN) algorithm and Travelling Salesmen Problem (TSP) algorthm were utilized to optimize the distance between BS and individual UAV to overcome the problem of propagation delay in multi UAV networks. The simulation results show that this proposed method reduced complexity, improved reliability, and reduced propagation delay in multi UAV networks.

Keywords: multi UAV network, optimal distance, propagation delay, K - nearest neighbor, traveling salesmen problem

Procedia PDF Downloads 181