Search results for: time delayed SVIRS epidemic model

Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

Abstract:

Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

Procedia PDF Downloads 403

Authors: Alexis Aldo Mendoza Villarroel, Ademir Clemente Villena Zevallos, Cristian Jose Lopez Del Alamo

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With the advancement of technology it is now possible to scan entire objects and obtain their digital representation by using point clouds or polygon meshes. However, some objects may be broken or have missing parts; thus, several methods focused on this problem have been proposed based on Geometric Deep Learning, such as GCNN, ACNN, PointNet, among others. In this article an approach from a different paradigm is proposed, using metric data structures to index global descriptors in the spectral domain and allow the recovery of a set of similar models in polynomial time; to later use the Iterative Close Point algorithm and recover the parts of the incomplete model using the geometry and topology of the model with less Hausdorff distance.

Keywords: 3D reconstruction method, point cloud completion, shape completion, similarity search

Procedia PDF Downloads 117

Authors: Neerav Kumar, Naumaan Nayyar, Sharath Kashyap

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Most companies see an increase in the adoption of machine learning (ML) applications across internal and external-facing use cases. ML applications vend output either in batch or real-time patterns. A complete batch ML pipeline architecture comprises data sourcing, feature engineering, model training, model deployment, model output vending into a data store for downstream application. Due to unclear role expectations, we have observed that scientists specializing in building and optimizing models are investing significant efforts into building the other components of the architecture, which we do not believe is the best use of scientists’ bandwidth. We propose a system architecture created using AWS services that bring industry best practices to managing the workflow and simplifies the process of model deployment and end-to-end data integration for an ML application. This narrows down the scope of scientists’ work to model building and refinement while specialized data engineers take over the deployment, pipeline orchestration, data quality, data permission system, etc. The pipeline infrastructure is built and deployed as code (using terraform, cdk, cloudformation, etc.) which makes it easy to replicate and/or extend the architecture to other models that are used in an organization.

Keywords: data pipeline, machine learning, AWS, architecture, batch machine learning

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Authors: Salah A. Al-Garyani, Yousef Swesi

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Gas hydrates can be hazardous to upstream operations. On the other hand, the high gas storage capacity of hydrate may be utilized for natural gas storage and transport. Research on the promotion of hydrate formation, as related to natural gas storage and transport, has received relatively little attention. The primary objective of this study is to gain a better understanding of the effects of ionic surfactants, particularly their molecular structures and concentration, on the formation of tetrahydrofuran (THF) hydrate, which is often used as a model hydrate former for screening hydrate promoters or inhibitors. The surfactants studied were sodium n-dodecyl sulfate (SDS), sodium n-hexadecyl sulfate (SHS). Our results show that, at concentrations below the solubility limit, the induction time decreases with increasing surfactant concentration. At concentrations near or above the solubility, however, the surfactant concentration no longer has any effect on the induction time. These observations suggest that the effect of surfactant on THF hydrate formation is associated with surfactant monomers, not the formation of micelle as previously reported. The lowest induction time (141.25 ± 21 s, n = 4) was observed in a solution containing 7.5 mM SDS. The induction time decreases by a factor of three at concentrations near or above the solubility, compared to that without surfactant.

Keywords: tetrahydrofuran, hydrate, surfactant, induction time, monomers, micelle

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Authors: Trapti Sharma, Devesh Kumar Srivastava

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This paper is basically a analysis study of above MapReduce implementation and also to verify and validate the MapReduce solution model for Parallel K-Mediod algorithm on Hadoop Cluster. MapReduce is a programming model which authorize the managing of huge amounts of data in parallel, on a large number of devices. It is specially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce has slowly become the framework of choice for “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e. makespan) of a set of MapReduce duty. In this paper, we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Mediod clustering algorithm. We have found that as the amount of nodes increases the completion time decreases.

Keywords: hadoop, mapreduce, k-mediod, validation, verification

Procedia PDF Downloads 363

Authors: Tomoko Usui

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In the field of linguistics, it has been said that concepts associated with space and motion systematically contribute structure to the temporal concept. This is the conceptual metaphor theory. conceptual metaphors typically employ a more abstract concept (time) as their target and a more concrete or physical concept as their source (space). This paper will examine two major temporal conceptual metaphors: Ego-centered Moving Time Metaphor and Time-RP Metaphor. Moving time generally receives a front-back orientation, however, Japanese shows a different orientation given to time. By means of Ego perspective, this paper will illustrate the paradox of a metaphorical orientation in time.

Keywords: Ego-centered Moving Time Metaphor, Japanese saki, temporal metaphors, Time RP Metaphor

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Authors: Rafal Sochaczewski, Ksenia Siadkowska, Tytus Tulwin

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The paper describes the modeling of a fuel injector for common rail systems. A one-dimensional model of a solenoid-valve-controlled injector with Valve Closes Orifice (VCO) spray was modelled in the AVL Hydsim. This model shows the dynamic phenomena that occur in the injector. The accuracy of the calibration, based on a regulation of the parameters of the control valve and the nozzle needle lift, was verified by comparing the numerical results of injector flow rate. Our model is capable of a precise simulation of injector operating parameters in relation to injection time and fuel pressure in a fuel rail. As a result, there were made characteristics of the injector flow rate and backflow.

Keywords: common rail, diesel engine, fuel injector, modeling

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Authors: Anna Avramenko, Heikki Haario

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This paper reports the development and application of a 2D depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. K-E and 2D LES turbulence models were consider in this article. 2D CFD simulations for one hill was done to check the depth-averaged model in practise.

Keywords: depth-averaged equations, numerical modeling, CFD, wind park model

Procedia PDF Downloads 599

Authors: Ivana Čavlina Tomašević, Višnjica Vučetić, Maja Telišman Prtenjak, Barbara Malečić

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The Split wildfire on the mid-Adriatic Coast in July 2017 is one of the most severe wildfires in Croatian history, given the size and unexpected fire behavior, and it is used in this research as a case study to run the Weather Research and Forecasting Spread Fire (WRF SFIRE) model. This coupled fire-atmosphere model was successfully run for the first time ever for one Croatian wildfire case. Verification of coupled simulations was possible by using the detailed reconstruction of the Split wildfire. Specifically, precise information on ignition time and location, together with mapped fire progressions and spotting within the first 30 hours of the wildfire, was used for both – to initialize simulations and to evaluate the model’s ability to simulate fire’s propagation and final fire scar. The preliminary simulations were obtained using high-resolution vegetation and topography data for the fire area, additionally interpolated to fire grid spacing at 33.3 m. The results demonstrated that the WRF SFIRE model has the ability to work with real data from Croatia and produce adequate results for forecasting fire spread. As the model in its setup has the ability to include and exclude the energy fluxes between the fire and the atmosphere, this was used to investigate possible fire-atmosphere interactions during the Split wildfire. Finally, successfully coupled simulations provided the first numerical evidence that a wildfire from the Adriatic coast region can modify the dynamical structure of the surrounding atmosphere, which agrees with observations from fire grounds. This study has demonstrated that the WRF SFIRE model has the potential for operational application in Croatia with more accurate fire predictions in the future, which could be accomplished by inserting the higher-resolution input data into the model without interpolation. Possible uses for fire management in Croatia include prediction of fire spread and intensity that may vary under changing weather conditions, available fuels and topography, planning effective and safe deployment of ground and aerial firefighting forces, preventing wildland-urban interface fires, effective planning of evacuation routes etc. In addition, the WRF SFIRE model results from this research demonstrated that the model is important for fire weather research and education purposes in order to better understand this hazardous phenomenon that occurs in Croatia.

Keywords: meteorology, agrometeorology, fire weather, wildfires, couple fire-atmosphere model

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Authors: Vinay P., Srinivas K. S.

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Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown.

Keywords: low light image enhancement, real-time video, computer vision, machine learning

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Authors: Salako Rotimi, Oshungade Stephen, Ojewoye Opeyemi

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The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI).

Keywords: economic, inflation, model, series

Procedia PDF Downloads 238

Authors: Aloke Bapli, Debabrata Seth

Abstract:

Graphene oxide (GO) is the two-dimensional (2D) nanoscale allotrope of carbon having several physiochemical properties such as high mechanical strength, high surface area, strong thermal and electrical conductivity makes it an important candidate in various modern applications such as drug delivery, supercapacitors, sensors etc. GO has been used in the photothermal treatment of cancers and Alzheimer’s disease etc. The main idea to choose GO in our work is that it is a surface active molecule, it has a large number of hydrophilic functional groups such as carboxylic acid, hydroxyl, epoxide on its surface and in basal plane. So it can easily interact with organic fluorophores through hydrogen bonding or any other kind of interaction and easily modulate the photophysics of the probe molecules. We have used different spectroscopic techniques for our work. The Ground-state absorption spectra and steady-state fluorescence emission spectra were measured by using UV-Vis spectrophotometer from Shimadzu (model-UV-2550) and spectrofluorometer from Horiba Jobin Yvon (model-Fluoromax 4P) respectively. All the fluorescence lifetime and anisotropy decays were collected by using time-correlated single photon counting (TCSPC) setup from Edinburgh instrument (model: LifeSpec-II, U.K.). Herein, we described the photophysics of a hydrophilic molecule 7-(n,n׀-diethylamino) coumarin-3-carboxylic acid (7-DCCA) in the reverse micelles containing GO. It was observed that photophysics of dye is modulated in the presence of GO compared to photophysics of dye in the absence of GO inside the reverse micelles. Here we have reported the solvent relaxation and rotational relaxation time in GO containing reverse micelle and compare our work with normal reverse micelle system by using 7-DCCA molecule. Normal reverse micelle means reverse micelle in the absence of GO. The absorption maxima of 7-DCCA were blue shifted and emission maxima were red shifted in GO containing reverse micelle compared to normal reverse micelle. The rotational relaxation time in GO containing reverse micelle is always faster compare to normal reverse micelle. Solvent relaxation time, at lower w₀ values, is always slower in GO containing reverse micelle compare to normal reverse micelle and at higher w₀ solvent relaxation time of GO containing reverse micelle becomes almost equal to normal reverse micelle. Here emission maximum of 7-DCCA exhibit bathochromic shift in GO containing reverse micelles compared to that in normal reverse micelles because in presence of GO the polarity of the system increases, as polarity increases the emission maxima was red shifted an average decay time of GO containing reverse micelle is less than that of the normal reverse micelle. In GO containing reverse micelle quantum yield, decay time, rotational relaxation time, solvent relaxation time at λₑₓ=375 nm is always higher than λₑₓ=405 nm, shows the excitation wavelength dependent photophysics of 7-DCCA in GO containing reverse micelles.

Keywords: photophysics, reverse micelle, rotational relaxation, solvent relaxation

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Authors: Rui Li, Min Wen, Kim Bang Salling

Abstract:

For the modern railways, maintenance is critical for ensuring safety, train punctuality and overall capacity utilization. The cost of railway maintenance in Europe is high, on average between 30,000 – 100,000 Euros per kilometer per year. In order to reduce such maintenance expenditures, this paper presents a mixed 0-1 linear mathematical model designed to optimize the predictive railway tamping activities for ballast track in the planning horizon of three to four years. The objective function is to minimize the tamping machine actual costs. The approach of the research is using the simple dynamic model for modelling condition-based tamping process and the solution method for finding optimal condition-based tamping schedule. Seven technical and practical aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality recovery on the track quality after tamping operation; (5) Tamping machine operation practices (6) tamping budgets and (7) differentiating the open track from the station sections. A Danish railway track between Odense and Fredericia with 42.6 km of length is applied for a time period of three and four years in the proposed maintenance model. The generated tamping schedule is reasonable and robust. Based on the result from the Danish railway corridor, the total costs can be reduced significantly (50%) than the previous model which is based on optimizing the number of tamping. The different maintenance strategies have been discussed in the paper. The analysis from the results obtained from the model also shows a longer period of predictive tamping planning has more optimal scheduling of maintenance actions than continuous short term preventive maintenance, namely yearly condition-based planning.

Keywords: integer programming, railway tamping, predictive maintenance model, preventive condition-based maintenance

Procedia PDF Downloads 439

Authors: Poonam Goswami

Abstract:

Background: The diagnosis of a serious illness like cancer can have an impact on a patient’s emotional well-being and may result in psychological symptoms, anxiety, depression, and loss of control. Advance care planning discussions ensure patients’ values and goals of care, including patients’ freedom to choose their place of death, are respected. Unfortunately, these discussions are often delayed and are not initiated early in patients’ cancer trajectory. As a result, patients’ wishes often remains unknown until the last phase of their life. Evidence suggests that many patients inappropriately receive aggressive treatment near the end of life, which does lead to higher resource utilization, decreased quality of life, and increased cost. Additionally, the novel coronavirus disease 2019 (COVID-19) pandemic challenged the health care systems worldwide and raised important ethical issues, especially regarding the potential need for rationing health care in the context of scarce resources and crisis capacity. The importance of goal concordant care is now even substantially important and is heightened in the context of this pandemic. Problem: Although there is growing evidence on the effects of the ACP on the completion of advanced directives, improved patient and family concordance for preferences for medical care, and receipt of care, there is still a lack of standardized ACP conversation strategies for patients with cancer. Methods: The Key concepts of ACP include (1) assessing patient and family readiness, (2) identifying a surrogate decision maker ( medical power of attorney), (3) exploring patient and family understanding of the disease and treatment options,(4) discussing the values and goals of care, and options for end-of-life care, (5) documenting patient preferences in the medical record, and (6) revisiting the discussions at every change in the treatment plan and /or change in clinical status, including at every hospitalization. Conclusion/Implication for practice: Advance Care Planning (ACP) and end-of-life (EOL) discussions are important for patients, families, and health care providers. Adopting the verbal and nonverbal communication strategies can help overcome the barriers to effective communication on these difficult discussions. ACP with goals of care discussions should not be delayed until the patient is hospitalized.

Keywords: advance care planning, end of life, cancer, global, pandemic

Procedia PDF Downloads 124

Authors: Jui-Ching Chou

Abstract:

Numerical simulation is a popular method used to evaluate the effects of soil liquefaction on a structure or the effectiveness of a mitigation plan. Many constitutive models (UBCSAND model, PM4 model, SANISAND model, etc.) were presented to model the liquefaction phenomenon. In general, inputs of a constitutive model need to be calibrated against the soil cyclic resistance before being applied to the numerical simulation model. Then, simulation results can be compared with results from simplified liquefaction potential assessing methods. In this article, inputs of the UBCSAND model, a simple elastic-plastic stress-strain model, are calibrated against several popular generic liquefaction triggering curves of simplified liquefaction potential assessing methods via FLAC program. Calibrated inputs can provide engineers to perform a preliminary evaluation of an existing structure or a new design project.

Keywords: calibration, liquefaction, numerical simulation, UBCSAND Model

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Authors: Yudhajit Datta

Abstract:

Will is a phenomenon that has puzzled humanity for a long time. It is a belief that Will Power of an individual affects the success achieved by an individual in life. It is thought that a person endowed with great will power can overcome even the most crippling setbacks of life while a person with a weak will cannot make the most of life even the greatest assets. Behavioral aspects of the human experience such as will are rarely subjected to quantitative study owing to the numerous uncontrollable parameters involved. This work is an attempt to subject the phenomena of will to the test of an artificial neural network. The claim being tested is that will power of an individual largely determines success achieved in life. In the study, an attempt is made to incorporate the behavioral phenomenon of will into a computational model using data pertaining to the success of individuals obtained from an experiment. A neural network is to be trained using data based upon part of the model, and subsequently used to make predictions regarding will corresponding to data points of success. If the prediction is in agreement with the model values, the model is to be retained as a candidate. Ultimately, the best-fit model from among the many different candidates is to be selected, and used for studying the correlation between success and will.

Keywords: will power, will, success, apathy factor, random factor, characteristic function, life story

Procedia PDF Downloads 376

Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: H.Karnezi, K. Tierney

Abstract:

Childhood fears that persist over time and interfere with the children’s normal functioning may have detrimental effects on their social and emotional development. Cognitive behavior therapy is considered highly effective in treating fears and anxieties. However, given that many childhood fears are based on fantasy, the applicability of CBT may be hindered by cognitive immaturity. Furthermore, a lack of motivation to engage in therapy is another commonly encountered obstacle. The purpose of this study was to introduce and evaluate a more developmentally appropriate intervention model, specifically designed to provide phobic children with the motivation to overcome their fears. To this end, principles and techniques from cognitive and behavior therapies are incorporated into the ‘Drama in Education’ model. The Cognitive Behaviour Drama (CBD) method involves using the phobic children’s creativity to involve them in the therapeutic process. The children are invited to engage in exciting fictional scenarios tailored around their strengths and special interests. Once their commitment to the drama is established, a problem that they will feel motivated to solve is introduced. To resolve it, the children will have to overcome a number of obstacles culminating in an in vivo confrontation with the fear stimulus. The study examined the application of the CBD model in three single cases. Results in all three cases shown complete elimination of all fear-related symptoms. Preliminary results justify further evaluation of the Cognitive Behaviour Drama model. It is time and cost-effective, ensuring the clients' immediate engagement in the therapeutic process.

Keywords: phobias, autism, intervention, drama

Procedia PDF Downloads 124

Authors: Herlangga Pramaditya, Agus Sulistyono, Risa Etika, Budiono Budiono, Alvin Saputra

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Preterm birth and anemia of prematurity are the most common cause of morbidity and mortality in neonates, and anemia of the preterm neonates has become a major issue. The timing of umbilical cord clamping after a baby is born determines the amount of blood transferred from the placenta to fetus, Delayed Cord Clamping (DCC) has proven to prevent anemia in the neonates but it is constrained concern regarding the delayed in neonatal resuscitation. Umbilical Cord Milking (UCM) could be an alternative method for clamping the umbilical cord due to the active blood transfer from the placenta to the fetus. The aim of this study was to analyze the difference between maternal and neonatal outcome in preterm abdominal delivery who underwent UCM compared to ECC. This was an experimental study with randomized post-test only control design. Analyzed maternal and neonatal outcomes, significant P values (P <0.05). Statistical comparison was carried out using Paired Samples t-test (α two tailed 0,05). The result was the mean of preoperative mother’s hemoglobin in UCM group compared to ECC (10,9 + 0,9 g/dL vs 10,4 + 0,9 g/dL) and postoperative (11,1 + 1,1 g/dL vs 10,5 + 0,7 g/dL), the delta was (0,2 + 0,7 vs 0,1 + 0,6.). It showed no significant difference (P=0,395 vs 0,627). The mean of 3rd phase labor duration in UCM group vs ECC was (20,5 + 3,5 second vs 21,1 + 3,3 second), showed insignificant difference (P=0,634). The amount of bleeding after delivery in UCM group compared to ECC has the median of 190 cc (100-280cc) vs 210 cc (150-330 cc) showed insignificant difference (P=0,083) so the incidence of post-partum bleeding was not found. The mean of the neonates hemoglobin, hematocrit and erythrocytes of UCM group compared to ECC was (19,3 + 0,7 vs 15,9 + 0,8 g/dl), (57,1 + 3,6 % vs 47,2 + 2,8 %), and (5,4 + 0,4 g/dl vs 4,5 + 0,3 g/dl) showed significant difference (P<0,0001). There was no baby in UCM group received blood transfusion and one baby in the control ECC group received blood transfusion was found. Umbilical Cord Milking has shown to increase the baby’s blood component such as hemoglobin, hematocrit, and erythrocytes 6 hours after birth as well as lowering the incidence of blood transfusions. Maternal and neonatal morbidity were not found. Umbilical Cord Milking was the act of clamping the umbilical cord that was more beneficial to the baby and no adverse or negative effects on the mother.

Keywords: umbilical cord milking, early cord clamping, maternal and neonatal outcome, preterm, abdominal delivery

Procedia PDF Downloads 237

Authors: Aziz Sezgin, Yuksel Hacioglu, Nurkan Yagiz

Abstract:

Sliding mode controller for a vehicle active suspension system is designed in this study. The widely used quarter car model is preferred and it is aimed to improve the ride comfort of the passengers. The effect of the actuator time delay, which may arise due to the information processing, sensors or actuator dynamics, is also taken into account during the design of the controller. A sliding mode controller was designed that has taken into account the actuator time delay by using Smith predictor. The successful performance of the designed controller is confirmed via numerical results.

Keywords: sliding mode control, active suspension system, actuator, time delay, vehicle

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Authors: Amir Hossein Haji, Nabeel Al-Rawahi, Gholamreza Vakili-Nezhaad

Abstract:

An innovative design has been examined for a gas-oil separator based on pressure reduction over an airfoil surface. The primary motivations are to shorten the release trajectory of the bubbles by minimizing the thickness of the oil layer as well as improving uniform pressure reduction zones. Restricted airflow over an airfoil is investigated for its effect on the pressure drop enhancement and the maximum attainable attack angle prior to the stall condition. Aerodynamic separation is delayed based on numerical simulation of Wortmann FX 63137 Airfoil in a confined domain using FLUENT 6.3.26. The proposed set up results in higher pressure drop compared with the free stream case. With the aim of optimum power consumption we have pursued further restriction to an air jet case over the airfoil. Then, a curved strip model is suggested for the air jet which can be applied as an analysis/design tool for the best performance conditions. Pressure reduction is shown to be inversely proportional to the curvature of the upper airfoil profile. This reduction occurs within the tracking zones where the air jet is effectively attached to the airfoil surface. The zero slope condition is suggested to estimate the onset of these zones after which the minimum curvature should be searched. The corresponding zero slope curvature is applied for estimation of the maximum pressure drop which shows satisfactory agreement with the simulation results.

Keywords: airfoil, air jet, curved fluid flow, gas-oil separator

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Authors: Henry Memczak, Marc Hovestaedt, Bernhard Ay, Sandra Saenger, Thorsten Wolff, Frank F. Bier

Abstract:

The influenza viruses cause flu epidemics every year and serious pandemics in larger time intervals. The only cost-effective protection against influenza is vaccination. Due to rapid mutation continuously new subtypes appear, what requires annual reimmunization. For a correct vaccination recommendation, the circulating influenza strains had to be detected promptly and exactly and characterized due to their antigenic properties. During the flu season 2016/17, a wrong vaccination recommendation has been given because of the great time interval between identification of the relevant influenza vaccine strains and outbreak of the flu epidemic during the following winter. Due to such recurring incidents of vaccine mismatches, there is a great need to speed up the process chain from identifying the right vaccine strains to their administration. The monitoring of subtypes as part of this process chain is carried out by national reference laboratories within the WHO Global Influenza Surveillance and Response System (GISRS). To this end, thousands of viruses from patient samples (e.g., throat smears) are isolated and analyzed each year. Currently, this analysis involves complex and time-intensive (several weeks) animal experiments to produce specific hyperimmune sera in ferrets, which are necessary for the determination of the antigen profiles of circulating virus strains. These tests also bear difficulties in standardization and reproducibility, which restricts the significance of the results. To replace this test a peptide-based assay for influenza virus subtyping from corresponding virus samples was developed. The differentiation of the viruses takes place by a set of specifically designed peptidic recognition molecules which interact differently with the different influenza virus subtypes. The differentiation of influenza subtypes is performed by pattern recognition guided by machine learning algorithms, without any animal experiments. Synthetic peptides are immobilized in multiplex format on various platforms (e.g., 96-well microtiter plate, microarray). Afterwards, the viruses are incubated and analyzed comparing different signaling mechanisms and a variety of assay conditions. Differentiation of a range of influenza subtypes, including H1N1, H3N2, H5N1, as well as fine differentiation of single strains within these subtypes is possible using the peptide-based subtyping platform. Thereby, the platform could be capable of replacing the current antigenic characterization of influenza strains using ferret hyperimmune sera.

Keywords: antigenic characterization, influenza-binding peptides, influenza subtyping, influenza surveillance

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Authors: Samuel Emeka Anarah, Kingsley Nnaemeka Ogbu, Obasi Arinze

Abstract:

Quantifying the hydrological response due to changes in climate change is imperative for proper management of water resources within a watershed. The impact of climate change on the hydrology of the Upper Ebony River (UER) watershed, South East Nigeria, was studied using the Soil and Water Assessment Tool (SWAT) hydrological model. A climatological time series analysis from 1985 - 2014 using non-parametric test showed significant negative trends in precipitation and relative humidity trend while minimum and maximum temperature, solar radiation and wind speed showed significant positive trends. Future hypothetical land-use change scenarios (Scenarios 1, 2, 3 and 4) representing urbanization and conversion of forest to agricultural land were combined with future downscaled climate model (CSIRO-Mk3-6-0) and simulated in SWAT model. Relative to the Baseline scenario (2005 - 2014), the results showed a decrease in streamflow by 10.29%, 26.20%, 11.80% and 26.72% for Scenarios 1, 2, 3, and 4 respectively. Model results suggest development of adaptation strategies to cope with the predicted hydrological conditions under future climate change in the watershed.

Keywords: climate change, hydrology, runoff, SWAT model

Procedia PDF Downloads 136

Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

Abstract:

Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies.

Keywords: runoff, roughness coefficient, PAR, WRM model

Procedia PDF Downloads 371

Authors: Ignatius Madu

Abstract:

The study aims at assessing the environmental and socioeconomic determinants of climate change resilience in rural Nigeria. This is necessary because researches and development efforts on building climate change resilience of rural areas in developing countries are usually made without the knowledge of the impacts of the inherent rural characteristics that determine resilient capacities of the households. This has, in many cases, led to costly mistakes, delayed responses, inaccurate outcomes, and other difficulties. Consequently, this assessment becomes crucial not only to policymakers and people living in risk-prone environments in rural areas but also to fill the research gap. To achieve the aim, secondary data were obtained from the Annual Abstract of Statistics 2017, LSMS-Integrated Surveys on Agriculture and General Household Survey Panel 2015/2016, and National Agriculture Sample Survey (NASS), 2010/2011.Resilience was calculated by weighting and adding the adaptive, absorptive and anticipatory measures of households variables aggregated at state levels and then regressed against rural environmental and socioeconomic characteristics influencing it. From the regression, the coefficients of the variables were used to compute the impacts of the variables using the Stochastic Regression of Impacts on Population, Affluence and Technology (STIRPAT) Model. The results showed that the northern States are generally low in resilient indices and are impacted less by the development indicators. The major determining factors are percentage of non-poor, environmental protection, road transport development, landholding, agricultural input, population density, dependency ratio (inverse), household asserts, education and maternal care. The paper concludes that any effort to a successful resilient building in rural areas of the country should first address these key factors that enhance rural development and wellbeing since it is better to take action before shocks take place.

Keywords: climate change resilience; spatial impacts; STIRPAT model; Nigeria

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Authors: Jaehwan Jung, Sung-Ah Kim

Abstract:

Building Information Modeling (BIM) provides project stakeholders with various information about property and geometry of entire component as a 3D object-based parametric building model. BIM represents a set of Information and solutions that are expected to improve collaborative work process and quality of the building design. To improve collaboration among project participants, the BIM model should provide the necessary information to remote participants in real time and manage the information in the process. The purpose of this paper is to propose a process model that can apply effective architectural design collaborative work process in architectural design education in BIM-Cloud environment.

Keywords: BIM, cloud computing, collaborative design, digital design education

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

Abstract:

Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Lukasz Baka

Abstract:

Objective. The study aimed to investigate the direct and indirect - mediated through insomnia - effect of coronavirus anxiety on exhaustion from the perspective of Hobfol Conservation of Resources (COR) theory. According to COR theory, critical events (e.g. the coronavirus epidemic) make people fearful of losing their valuable resources. A prolonged state of anxiety may lead to sleep troubles, which over time, results in an increase in exhaustion. Materials and Methods: Data were collected among 440 Polish healthcare providers, including nurses and midwives, doctors, paramedics, medical assistance, and wardens. Three measurements were used: Coronavirus Anxiety Scale (CAS), Copenhagen Psychosocial Questionnaire (COPSOQ, sleep trouble subscale) and Oldenburg Burnout Inventory (OLBI, exhaustion subscale). Hypotheses were tested by the use of Structural Equation Modelling (SEM). Results: The obtained results fully support the hypotheses. Both the direct and indirect relationships between coronavirus anxiety and exhaustion were observed. Specifically, high coronavirus anxiety increased insomnia, which in turn contributed to the development of exhaustion. Conclusion: The results are consistent with the COR theory. Prolonged coronavirus anxiety and sleep problems depleted healthcare providers’ resources and made them feel exhausted. Exhaustion among these workers can have serious consequences not only for themselves but also for the health of their patients, therefore researches into effective ways to deal with coronavirus anxiety are needed.

Keywords: coronavirus anxiety, front-line healt-care workers, insomnia, job burnout

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Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

Abstract:

Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

Abstract:

This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

Procedia PDF Downloads 542