Search results for: facility data model

Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

Abstract:

Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

Procedia PDF Downloads 396

Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

Abstract:

In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

Procedia PDF Downloads 366

Authors: Avram Irina, Coiciu Eugenia-Mihaela, Popovici Mara-Georgiana, Mitu Iani Octavian

Abstract:

ELI-NP stands as a cutting-edge facility globally, hosting unique radiological setups. It conducts experiments leveraging high-power lasers capable of producing extremely brief 10 PW pulses on two fronts. Moreover, it houses an exceptional gamma beam system with distinctive spectral characteristics. The facility hosts various experiments across designated experimental areas, encompassing ultra-short high-power laser tests, high-intensity gamma beam trials, and combined experiments utilizing both setups. The facility hosts a dosimetry laboratory, which recently obtained accreditation, where the radiation safety group employs a host of different types of detectors for monitoring the personnel, environment, and public exposure to ionizing radiation generated in experiments performed. ELI-NP's design was shaped by radiological protection assessments conducted through Monte Carlo simulations. The poster exemplifies an assessment conducted using the FLUKA code in an experimental area where a high-power laser system is implemented, and the future diagnostic system for variable energy gamma beams will soon be operational.

Keywords: radiation protection, Monte Carlo simulation, FLUKA, dosimetry

Procedia PDF Downloads 74

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 520

Authors: Rene Hellmuth

Abstract:

Factory planning has the task of designing products, plants, processes, organization, areas, and the construction of a factory. The requirements for factory planning and the building of a factory have changed in recent years. Regular restructuring of the factory building is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity & Ambiguity) lead to more frequent restructuring measures within a factory. A building information model (BIM) is the planning basis for rebuilding measures and becomes an indispensable data repository to be able to react quickly to changes. Use as a planning basis for restructuring measures in factories only succeeds if the BIM model has adequate data quality. Under this aspect and the industrial requirement, three data quality factors are particularly important for this paper regarding the BIM model: up-to-dateness, completeness, and correctness. The research question is: how can a BIM model be kept up to date with required data quality and which visualization techniques can be applied in a short period of time on the construction site during conversion measures? An as-is analysis is made of how BIM models and digital factory models (including laser scans) are currently being kept up to date. Industrial companies are interviewed, and expert interviews are conducted. Subsequently, the results are evaluated, and a procedure conceived how cost-effective and timesaving updating processes can be carried out. The availability of low-cost hardware and the simplicity of the process are of importance to enable service personnel from facility mnagement to keep digital factory models (BIM models and laser scans) up to date. The approach includes the detection of changes to the building, the recording of the changing area, and the insertion into the overall digital twin. Finally, an overview of the possibilities for visualizations suitable for construction sites is compiled. An augmented reality application is created based on an updated BIM model of a factory and installed on a tablet. Conversion scenarios with costs and time expenditure are displayed. A user interface is designed in such a way that all relevant conversion information is available at a glance for the respective conversion scenario. A total of three essential research results are achieved: As-is analysis of current update processes for BIM models and laser scans, development of a time-saving and cost-effective update process and the conception and implementation of an augmented reality solution for BIM models suitable for construction sites.

Keywords: building information modeling, digital factory model, factory planning, restructuring

Procedia PDF Downloads 115

Authors: Belén Vinaixa Kinnear, Arturo Hidalgo López, Bernardo Elembo Wilasi, Pablo Fernández Pérez, Cecilia Hernández Fuentealba

Abstract:

The expanded use of risk assessment in legislative and corporate decision-making has increased the role of expert judgement in giving data for security-related decision-making. Expert judgements are required in most steps of risk assessment: danger recognizable proof, hazard estimation, risk evaluation, and examination of choices. This paper presents a fault tree analysis (FTA), which implies a probabilistic failure analysis applied to leakage of oil in a subsea production system. In standard FTA, the failure probabilities of items of a framework are treated as exact values while evaluating the failure probability of the top event. There is continuously insufficiency of data for calculating the failure estimation of components within the drilling industry. Therefore, fuzzy hypothesis can be used as a solution to solve the issue. The aim of this paper is to examine the leaks from the Zafiro West subsea oil facility by using fuzzy fault tree analysis (FFTA). As a result, the research has given theoretical and practical contributions to maritime safety and environmental protection. It has been also an effective strategy used traditionally in identifying hazards in nuclear installations and power industries.

Keywords: expert judgment, probability assessment, fault tree analysis, risk analysis, oil pipelines, subsea production system, drilling, quantitative risk analysis, leakage failure, top event, off-shore industry

Procedia PDF Downloads 191

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

Abstract:

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

Procedia PDF Downloads 100

Authors: Guru Prakash, Revanth Dugalam

Abstract:

A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

Procedia PDF Downloads 86

Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

Abstract:

Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

Procedia PDF Downloads 196

Authors: Idrissa Kayijuka

Abstract:

The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

Procedia PDF Downloads 457

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

Abstract:

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

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

Procedia PDF Downloads 175

Authors: M. Illias Kanchan

Abstract:

The death of a woman during pregnancy and childbirth is not only a health issue, but also a matter of social injustice. This study makes an attempt to explore the association between maternal death and associated factors in West Bengal using the approaches of facility-based and community-based maternal death review. Bivariate and binary logistic regression analysis have been performed to understand the causes and circumstances of maternal deaths in West Bengal. Delay in seeking care was the major contributor in maternal deaths, near about one-third women died due to this factor. The most common cause of maternal death is found to be hypertensive disorders of pregnancy or eclampsia. We believe that these deaths can be averted by reducing hypertensive disorders of pregnancy or eclampsia.

Keywords: maternal death, facility-based, community-based, review, west Bengal, eclampsia

Procedia PDF Downloads 433

Authors: Min Kyung An

Abstract:

In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks

Procedia PDF Downloads 223

Authors: Zhang Shuqi, Liu Dan

Abstract:

For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

Procedia PDF Downloads 105

Authors: Tran Duong Nguyen, Marwan Shagar, Qinghao Zeng, Aras Maqsoodi, Pardis Pishdad, Eunhwa Yang

Abstract:

Construction supply chain management (CSCM) involves the collaboration of many disciplines and actors, which generates vast amounts of data. However, inefficient, fragmented, and non-standardized data storage often hinders this data exchange. The industry has adopted building information modeling (BIM) -a digital representation of a facility's physical and functional characteristics to improve collaboration, enhance transmission security, and provide a common data exchange platform. Still, the volume and complexity of data require tailored information categorization, aligning with stakeholders' preferences and demands. To address this, artificial intelligence (AI) can be integrated to handle this data’s magnitude and complexities. This research aims to develop an integrated and efficient approach for data exchange in CSCM by utilizing AI. The paper covers five main objectives: (1) Investigate existing framework and BIM adoption; (2) Identify challenges in data exchange; (3) Propose an integrated framework; (4) Enhance data transmission security; and (5) Develop data exchange in CSCM. The proposed framework demonstrates how integrating BIM and other technologies, such as cloud computing, blockchain, and AI applications, can significantly improve the efficiency and accuracy of data exchange in CSCM.

Keywords: construction supply chain management, BIM, data exchange, artificial intelligence

Procedia PDF Downloads 29

Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

Abstract:

Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

Procedia PDF Downloads 194

Authors: M. Kosacka, I. Kudelska

Abstract:

Vehicle is one of the most influential and complex product worldwide, which affects people’s life, state of the environment and condition of the economy (all aspects of sustainable development concept) during each stage of lifecycle. With the increase of vehicles’ number, there is growing potential for management of End of Life Vehicle (ELV), which is hazardous waste. From one point of view, the ELV should be managed to ensure risk elimination, but from another point, it should be treated as a source of valuable materials and spare parts. In order to obtain materials and spare parts, there are established recycling networks, which are an example of sustainable policy realization at the national level. The basic object in the polish recycling network is dismantling facility. The output material streams in dismantling stations include waste, which very often generate costs and spare parts, that have the biggest potential for revenues creation. Both outputs are stored into warehouses, according to the law. In accordance to the revenue creation and sustainability potential, it has been placed a strong emphasis on storage process. We present the concept of storage method, which takes into account the specific of the dismantling facility in order to support decision-making process with regard to the principles of sustainable development. The method was developed on the basis of case study of one of the greatest dismantling facility in Poland.

Keywords: dismantling, end of life vehicles, sustainability, storage

Procedia PDF Downloads 271

Authors: Ava Dodd, Jonathan Adams

Abstract:

The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

Procedia PDF Downloads 225

Authors: Chae Hoon Park, Jong Kang Park, Jong Tae Kim

Abstract:

Electrical characteristics of capacitive touchscreen need to be accurately analyzed to result in better performance for multi-channel capacitance sensing. In this paper, we extracted the panel resistances and capacitances of the touchscreen by comparing measurement data and model data. By employing a lumped RC model for driver-to-receiver paths in touchscreen, we estimated resistance and capacitance values according to the physical lengths of channel paths which are proportional to the RC model. As a result, we obtained the model having 95.54% accuracy of the measurement data.

Keywords: electrical characteristics of capacitive touchscreen, iterative extraction, lumped RC model, physical lengths of channel paths

Procedia PDF Downloads 335

Authors: Lakrim Abderrazak, Tahri Driss

Abstract:

This paper presents a new behavioural electrothermal model of power Silicon Carbide (SiC) MOSFET under SPICE. This model is based on the MOS model level 1 of SPICE, in which phenomena such as Drain Leakage Current IDSS, On-State Resistance RDSon, gate Threshold voltage VGSth, the transconductance (gfs), I-V Characteristics Body diode, temperature-dependent and self-heating are included and represented using behavioural blocks ABM (Analog Behavioural Models) of Spice library. This ultimately makes this model flexible and easily can be integrated into the various Spice -based simulation softwares. The internal junction temperature of the component is calculated on the basis of the thermal model through the electric power dissipated inside and its thermal impedance in the form of the localized Foster canonical network. The model parameters are extracted from manufacturers' data (curves data sheets) using polynomial interpolation with the method of simulated annealing (S A) and weighted least squares (WLS). This model takes into account the various important phenomena within transistor. The effectiveness of the presented model has been verified by Spice simulation results and as well as by data measurement for SiC MOS transistor C2M0025120D CREE (1200V, 90A).

Keywords: SiC power MOSFET, DC electro-thermal model, ABM Spice library, SPICE modelling, behavioural model, C2M0025120D CREE.

Procedia PDF Downloads 581

Authors: Dana Al-Qattan, Faiza Goodarzi, Heba Al-Resheedan, Kawther Shehab, Shoug Al-Ansari

Abstract:

This project involves working with different types of forecasting methods and facility planning tools to help the company we have chosen to improve and reduce its inventory, increase its sales, and decrease its wastes and losses. The methods that have been used by the company have shown no improvement in decreasing the annual losses. The research made in the company has shown that no interest has been made in exploring different techniques to help the company. In this report, we introduce several methods and techniques that will help the company make more accurate forecasts and use of the available space efficiently. We expect our approach to reduce costs without affecting the quality of the product, and hence making production more viable.

Keywords: production planning, inventory management, inventory control, simulation, facility planning and design, engineering economy and costs

Procedia PDF Downloads 570

Authors: Deike Janssen

Abstract:

In the scenery of the Tyrolean mountains, at an altitude of 1300 meters, stands a building. Residents and activists call it a prison. However, it is not a prison -according to authorities, it is a 'Return Counseling Facility' where migrants and refugees should be "motivated" to return "voluntary" to their countries of origin. This paper argues that the geographical location of the camp functions as a site of exclusion, isolation, and coercion where no one can decide “voluntary” to return, but where people are brought to despair to leave Austria. Through a qualitative case study, this paper documents the heavy impact of offshore detention on the mental, physical and social state of the residents and a variety of human rights problems in the centre. Different developments at the Return Counselling Facility and the law that back up the centre uncover a worrying dynamic that deliberately accepts human rights problems in order to enforce borders, a policy that disregards humanitarian, legal, and ethical stands in order to deport people at all hazards. It, therefore, can be seen as a creative and ultimate exercise of state power, which uses isolated locations to control migration. While the analysis revises the micro and macro implications of the facility and, therefore, the legal and political facets, it also sheds light on the role of the civil society, which tries to increase through constant and collective efforts the human rights efforts of the government.

Keywords: deportation, human rights, migration, refugee detention, voluntary return

Procedia PDF Downloads 137

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

Abstract:

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 365

Authors: Chansiri Singhtaun

Abstract:

This paper proposes a mathematical model and examines the performance of an exact algorithm for a location–transportation problems in humanitarian relief. The model determines the number and location of distribution centers in a relief network, the amount of relief supplies to be stocked at each distribution center and the vehicles to take the supplies to meet the needs of disaster victims under capacity restriction, transportation and budgetary constraints. The computational experiments are conducted on the various sizes of problems that are generated. Branch and bound algorithm is applied for these problems. The results show that this algorithm can solve problem sizes of up to three candidate locations with five demand points and one candidate location with up to twenty demand points without premature termination.

Keywords: disaster response, facility location, humanitarian relief, transportation

Procedia PDF Downloads 451

Authors: Ya-Chuan Hsu, Wen-Chen Ouyang, Wei-Siang Huang

Abstract:

Problem/Background: With constantly increasing aged populations, quality of life (QOL) in persons with dementia has become a significant research concern. The Alzheimer’s Related Quality of Life (ADRQL) is a high-validated, theory-derived, and multidimensional instrument. It has widely utilized in many countries, except in Taiwan. However, diverse results of quality of life from different countries by using the same measurement can provide the potential to help understand the impact of cultural contributor on QOL. Objective: To investigate the extent to which quality of life on older adults with dementia in Taiwan. Methods: Cross-sectional, descriptive study conducted in an assisting living facility affiliated with a daycare center in southern Taiwan. A purposeful sample of 34 participants was recruited. Inclusion criteria included those who were at least 65 years old, able to communicate, and diagnosed with mild to moderate dementia. The QOL was measured by Chinese version ADRQL. This observational instrument consists of 30 items that is divided into five subscales with the full range of each subscale scores from 0 to 100.0. Higher scores indicate better QOL. Results: The means for subscale of the Social Interaction, Awareness of Self, Feelings and Mood, Enjoyment of Activities, and Response to Surroundings were 87.9, 74.7, 91.3, 64.5, and 90.3, respectively. The overall mean for the ADQOL was 0.83. Conclusion: Findings suggest that the level of Enjoyment of Activities is the lowest and may convey information about a need of evaluation on arrangement of facility’s activities.

Keywords: dementia, quality of life, elders, Alzheimer’s related quality of life

Procedia PDF Downloads 301

Authors: Keerthi S. Shetty, Annappa Basava

Abstract:

In recent years, modelling of biological systems represented by biochemical reactions has become increasingly important in Systems Biology. Biological systems represented by biochemical reactions are highly stochastic in nature. Probabilistic model is often used to describe such systems. One of the main challenges in Systems biology is to combine absolute experimental data into probabilistic model. This challenge arises because (1) some molecules may be present in relatively small quantities, (2) there is a switching between individual elements present in the system, and (3) the process is inherently stochastic on the level at which observations are made. In this paper, we describe a novel idea of combining absolute experimental data into probabilistic model using tool R2. Through a case study of the Transcription Process in Prokaryotes we explain how biological systems can be written as probabilistic program to combine experimental data into the model. The model developed is then analysed in terms of intrinsic noise and exact sampling of switching times between individual elements in the system. We have mainly concentrated on inferring number of genes in ON and OFF states from experimental data.

Keywords: systems biology, probabilistic model, inference, biology, model

Procedia PDF Downloads 349

Authors: Abdela Zeinu Yasin

Abstract:

The current research aimed to assess the status of IMC in providing treatment for malnourished children and programs in water, sanitation, and hygiene (WASH), food and livelihood security, and comprehensive healthcare through Mobile health and nutrition programs during the last 5 years period. We have conducted 60 in-depth interviews with women during the period from conception to a child’s birthday, health facility staff, and female community health volunteers (FCHVs), as well as 12 focus group discussions with health facility staff and other household decision-makers. We employed thematic analysis using framework matrices and analytical memorandums. The study revealed that 78% of the respondents, of whom 97% were women, have benefited from the selected vulnerable areas. The use of the clear water and sanitization program has reached the 81% of selected households. The use of a modern baby delivery system among the respondent has been 68% of the women and health facilities among the decision-makers/focal person. More than 8 in 10 participants (84%) could read and understand the health facility instructions, and the majority (82%) of women, health facility staff, and male decision-makers can also read and write bulletins and instructions. We found that decision-maker women preferred participative education, whereas health facilities and the IMC desired educational and motivational bulletins. A Mobile Health and Nutrition program intervention by the IMC is acceptable in the conditions of the Ethiopian community and has the potential to improve community health and nutrition service utilization, particularly by providing clean water and sanitization; women’s birth control, and health improvement in the vulnerable regions of the country. The current research findings shall contribute to text IMC Mobile Health and Nutritional intervention design in under-resourced settings.

Keywords: clean water, health and nutrition services, hygiene, IMC, mobile health, sanitation

Procedia PDF Downloads 95

Authors: Dmitry V. Fomichev, Vladimir I. Solonin

Abstract:

In this paper, experimental and numerical study of hydrodynamic characteristics of the air coolant flow in the test wire-wrapped assembly is presented. The test assembly has 37 rods, which are similar to the real fuel pins of the BREST-OD-300 fuel assemblies geometrically. Air open loop test facility installed at the “Nuclear Power Plants and Installations” department of BMSTU was used to obtain the experimental data. The obtaining altitudinal distribution of static pressure in the near-wall test assembly as well as velocity and temperature distribution of coolant flow in the test sections can give us some new knowledge about the mechanism of formation of the turbulence flow structure in the wire wrapped fuel assemblies. Numerical simulations of the turbulence flow has been accomplished using ANSYS Fluent 14.5. Different non-local turbulence models have been considered, such as standard and RNG k-e models and k-w SST model. Results of numerical simulations of the flow based on the considered turbulence models give the best agreement with the experimental data and help us to carry out strong analysis of flow characteristics.

Keywords: wire-spaces fuel assembly, turbulent flow structure, computation fluid dynamics

Procedia PDF Downloads 460

Authors: Jae Hyun Lee

Abstract:

Manufacturing companies use life-cycle inventory databases to analyze sustainability of their manufacturing processes. Life cycle inventory data provides reference data which may not be accurate for a specific company. Collecting accurate data of manufacturing processes for a specific company requires enormous time and efforts. An information model of typical manufacturing processes can reduce time and efforts to get appropriate reference data for a specific company. This paper shows an attempt to build an abstract information model which can be used to develop information models for specific manufacturing processes.

Keywords: process information model, sustainability, OWL, manufacturing

Procedia PDF Downloads 430

Authors: Amitha Puranik, V. S. Binu, Seena Biju

Abstract:

Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

Procedia PDF Downloads 382