Search results for: predictive modeling

Authors: Yana Snegireva

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Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

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Authors: Chang Qin, Daham Mustafa, Abderrahmane Khiat, Pierre Bienert, Paulo Zanini

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Nowadays, companies are facing lots of challenges in the process of digital transformation, which can be a complex and costly undertaking. Digital transformation involves the collection and analysis of large amounts of data, which can create challenges around data management and governance. Furthermore, it is also challenged to integrate data from multiple systems and technologies. Although with these pains, companies are still pursuing digitalization because by embracing advanced technologies, companies can improve efficiency, quality, decision-making, and customer experience while also creating different business models and revenue streams. In this paper, the issue that data is stored in data silos with different schema and structures is focused. The conventional approaches to addressing this issue involve utilizing data warehousing, data integration tools, data standardization, and business intelligence tools. However, these approaches primarily focus on the grammar and structure of the data and neglect the importance of semantic modeling and semantic standardization, which are essential for achieving data interoperability. In this session, the challenge of data silos in Industry 4.0 is addressed by developing a semantic modeling approach compliant with Asset Administration Shell (AAS) models as an efficient standard for communication in Industry 4.0. The paper highlights how our approach can facilitate the data mapping process and semantic lifting according to existing industry standards such as ECLASS and other industrial dictionaries. It also incorporates the Asset Administration Shell technology to model and map the company’s data and utilize a knowledge graph for data storage and exploration.

Keywords: data interoperability in industry 4.0, digital integration, industrial dictionary, semantic modeling

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Authors: Lotfi O. Gargab, Ruichong R. Zhang

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A wave-based framework is presented for modeling seismic motion in multistory buildings and using measured response for system identification which can be utilized to extract important information regarding structure integrity. With one pair of building response at two locations, a generalized model response is formulated based on wave propagation features and expressed as frequency and time response functions denoted, respectively, as GFRF and GIRF. In particular, GIRF is fundamental in tracking arrival times of impulsive wave motion initiated at response level which is dependent on local model properties. Matching model and measured-structure responses can help in identifying model parameters and infer building properties. To show the effectiveness of this approach, the Millikan Library in Pasadena, California is identified with recordings of the Yorba Linda earthquake of September 3, 2002.

Keywords: system identification, continuous-discrete mass modeling, damage detection, post-earthquake

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Authors: Elhameh Narimani, Mikhail Sorin, Philippe Micheau, Hakim Nesreddine

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Numerical models of the heat exchangers in ejector refrigeration system (ERS) were developed and validated with the experimental data. The models were based on the switched heat exchangers model using the moving boundary method, which were capable of estimating the zones’ lengths, the outlet temperatures of both sides and the heat loads at various experimental points. The developed models were utilized to investigate the influence of the primary flow pressure on the performance of an R245fa ERS based on its coefficient of performance (COP) and exergy efficiency. It was illustrated numerically and proved experimentally that increasing the primary flow pressure slightly reduces the COP while the exergy efficiency goes through a maximum before decreasing.

Keywords: Coefficient of Performance, COP, Ejector Refrigeration System, ERS, exergy efficiency (ηII), heat exchangers modeling, moving boundary method

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Authors: A. Assif

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This paper presents the modeling of a prototype distribution static compensator (D-STATCOM) for voltage sag and swell mitigation in an unbalanced distribution system. Here the concept that an inverter can be used as generalized impedance converter to realize either inductive or capacitive reactance has been used to mitigate power quality issues of distribution networks. The D-STATCOM is here supposed to replace the widely used StaticVar Compensator (SVC). The scheme is based on the Voltage Source Converter (VSC) principle. In this model PWM based control scheme has been implemented to control the electronic valves of VSC. Phase shift control Algorithm method is used for converter control. The D-STATCOM injects a current into the system to mitigate the voltage sags. In this paper the modeling of D¬STATCOM has been designed using MATLAB SIMULINIC. Accordingly, simulations are first carried out to illustrate the use of D-STATCOM in mitigating voltage sag in a distribution system. Simulation results prove that the D-STATCOM is capable of mitigating voltage sag as well as improving power quality of a system.

Keywords: D-STATCOM, voltage sag, voltage source converter (VSC), phase shift control

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Authors: Shaoyi Lu

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Background: Traditional Chinese medicine has been used to treat stroke, which is a major cause of morbidity and mortality. There is, however, no clear agreement about the optimal timing, population, efficacy, and predictive prognosis factors of traditional Chinese medicine supplemental therapy. Method: In this study, we used a retrospective analysis with data collection from stroke patients in Stroke Registry In Chang Gung Healthcare System (SRICHS). Stroke patients who received traditional Chinese medicine consultation in neurology ward of Keelung Chang Gung Memorial Hospital from Jan 2010 to Dec 2014 were enrolled. Clinical profiles including the neurologic deficit, activities of daily living and other basic characteristics were analyzed. Through propensity score matching, we compared the NIHSS and Barthel index before and after the hospitalization, and applied with subgroup analysis, and adjusted by multivariate regression method. Results: Totally 115 stroke patients were enrolled with experiment group in 23 and control group in 92. The most important factor for prognosis prediction were the scores of National Institutes of Health Stroke Scale and Barthel index right before the hospitalization. Traditional Chinese medicine intervention had no statistically significant influence on the neurological deficit of acute stroke patients, and mild negative influence on daily activity performance of acute hemorrhagic stroke patient. Conclusion: Efficacy of traditional Chinese medicine as a supplemental therapy for acute stroke patients was controversial. The reason for this phenomenon might be complex and require more research to comprehend. Key words: traditional Chinese medicine, acupuncture, Stroke, NIH stroke scale, Barthel index, predictive factor. Method: In this study, we used a retrospective analysis with data collection from stroke patients in Stroke Registry In Chang Gung Healthcare System (SRICHS). Stroke patients who received traditional Chinese medicine consultation in neurology ward of Keelung Chang Gung Memorial Hospital from Jan 2010 to Dec 2014 were enrolled. Clinical profiles including the neurologic deficit, activities of daily living and other basic characteristics were analyzed. Through propensity score matching, we compared the NIHSS and Barthel index before and after the hospitalization, and applied with subgroup analysis, and adjusted by multivariate regression method. Results: Totally 115 stroke patients were enrolled with experiment group in 23 and control group in 92. The most important factor for prognosis prediction were the scores of National Institutes of Health Stroke Scale and Barthel index right before the hospitalization. Traditional Chinese medicine intervention had no statistically significant influence on the neurological deficit of acute stroke patients, and mild negative influence on daily activity performance of acute hemorrhagic stroke patient. Conclusion: Efficacy of traditional Chinese medicine as a supplemental therapy for acute stroke patients was controversial. The reason for this phenomenon might be complex and require more research to comprehend.

Keywords: traditional Chinese medicine, complementary and alternative medicine, stroke, acupuncture

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Authors: Tolga Barisik, Ali Fuat Guneri, K. Dastan

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Major industrial facilities have a high potential for fire risk. In particular, the indices used for the detection of hidden fire are used very effectively in order to prevent the fire from becoming dangerous in the initial stage. These indices provide the opportunity to prevent or intervene early by determining the stage of the fire, the potential for hazard, and the type of the combustion agent with the percentage values of the ambient air components. In this system, artificial neural network will be modeled with the input data determined using the Levenberg-Marquardt algorithm, which is a multi-layer sensor (CAA) (teacher-learning) type, before modeling the modeling methods in the literature. The actual values produced by the indices will be compared with the outputs produced by the network. Using the neural network and the curves to be created from the resulting values, the feasibility of performance determination will be investigated.

Keywords: artifical neural networks, fire, Graham Index, levenberg-marquardt algoritm, oxygen decrease percentage index, risk assessment, Trickett Index

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Authors: Haowen Xu, Xiaobing Liu, Jin Dong, Jianming Lian

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Emerging smart buildings often employ cyberinfrastructure, cyber-physical systems, and Internet of Things (IoT) technologies to increase the automation and responsiveness of building operations for better energy efficiency and lower carbon emission. These operations include the control of Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are often considered a major source of energy consumption in both commercial and residential buildings. Developing energy-saving control models for optimizing HVAC operations usually requires the collection of high-quality instrumental data from iterations of in-situ building experiments, which can be time-consuming and labor-intensive. This abstract describes a digital twin approach to automate building energy experiments for optimizing HVAC operations through the design and development of an adaptive web-based platform. The platform is created to enable (a) automated data acquisition from a variety of IoT-connected HVAC instruments, (b) real-time situational awareness through domain-based visualizations, (c) adaption of HVAC optimization algorithms based on experimental data, (d) sharing of experimental data and model predictive controls through web services, and (e) cyber-physical control of individual instruments in the HVAC system using outputs from different optimization algorithms. Through the digital twin approach, we aim to replicate a real-world building and its HVAC systems in an online computing environment to automate the development of building-specific model predictive controls and collaborative experiments in buildings located in different climate zones in the United States. We present two case studies to demonstrate our platform’s capability for real-time situational awareness and cyber-physical control of the HVAC in the flexible research platforms within the Oak Ridge National Laboratory (ORNL) main campus. Our platform is developed using adaptive and flexible architecture design, rendering the platform generalizable and extendable to support HVAC optimization experiments in different types of buildings across the nation.

Keywords: energy-saving buildings, digital twins, HVAC, cyber-physical system, BIM

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Authors: Azar Khodabakhshi, Elham Bolandnazar

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Agriculture as the most important food manufacturing sector is not only the energy consumer, but also is known as energy supplier. Using energy is considered as a helpful parameter for analyzing and evaluating the agricultural sustainability. In this study, the pattern of energy consumption of strawberry greenhouses of Jiroft in Kerman province of Iran was surveyed. The total input energy required in the strawberries production was calculated as 113314.71 MJ /ha. Electricity with 38.34% contribution of the total energy was considered as the most energy consumer in strawberry production. In this study, Neuro Fuzzy networks was used for function modeling in the production of strawberries. Results showed that the best model for predicting the strawberries function had a correlation coefficient, root mean square error (RMSE) and mean absolute percentage error (MAPE) equal to 0.9849, 0.0154 kg/ha and 0.11% respectively. Regards to these results, it can be said that Neuro Fuzzy method can be well predicted and modeled the strawberry crop function.

Keywords: crop yield, energy, neuro-fuzzy method, strawberry

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Authors: Misuk Kim, Seokho Kang, Jehyuk Lee, Hyunchang Cho, Sungzoon Cho

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In semiconductor manufacturing, virtual metrology (VM) refers to methods to predict properties of a wafer based on machine parameters and sensor data of the production equipment, without performing the (costly) physical measurement of the wafer properties (Wikipedia). Additional benefits include avoidance of human bias and identification of important factors affecting the quality of the process which allow improving the process quality in the future. It is however rare to find VM applied to other areas of manufacturing. In this work, we propose to use VM to copper clad laminate (CCL) manufacturing. CCL is a core element of a printed circuit board (PCB) which is used in smartphones, tablets, digital cameras, and laptop computers. The manufacturing of CCL consists of three processes: Treating, lay-up, and pressing. Treating, the most important process among the three, puts resin on glass cloth, heat up in a drying oven, then produces prepreg for lay-up process. In this process, three important quality factors are inspected: Treated weight (T/W), Minimum Viscosity (M/V), and Gel Time (G/T). They are manually inspected, incurring heavy cost in terms of time and money, which makes it a good candidate for VM application. We developed prediction models of the three quality factors T/W, M/V, and G/T, respectively, with process variables, raw material, and environment variables. The actual process data was obtained from a CCL manufacturer. A variety of variable selection methods and learning algorithms were employed to find the best prediction model. We obtained prediction models of M/V and G/T with a high enough accuracy. They also provided us with information on “important” predictor variables, some of which the process engineers had been already aware and the rest of which they had not. They were quite excited to find new insights that the model revealed and set out to do further analysis on them to gain process control implications. T/W did not turn out to be possible to predict with a reasonable accuracy with given factors. The very fact indicates that the factors currently monitored may not affect T/W, thus an effort has to be made to find other factors which are not currently monitored in order to understand the process better and improve the quality of it. In conclusion, VM application to CCL’s treating process was quite successful. The newly built quality prediction model allowed one to reduce the cost associated with actual metrology as well as reveal some insights on the factors affecting the important quality factors and on the level of our less than perfect understanding of the treating process.

Keywords: copper clad laminate, predictive modeling, quality control, virtual metrology

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Authors: Vytautas Galvanauskas, Vykantas Grincas, Rimvydas Simutis

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This paper investigates a possible practical solution for reasonable oxygen supply during the pluripotent stem cells expansion processes, where the stem cells propagate as aggregates in stirred-suspension bioreactors. Low glucose and low oxygen concentrations are preferred for efficient proliferation of pluripotent stem cells. However, strong oxygen limitation, especially inside of cell aggregates, can lead to cell starvation and death. In this research, the oxygen concentration profile inside of stem cell aggregates in a stem cell expansion process was predicted using a modified oxygen diffusion model. This profile can be realized during the stem cells cultivation process by manipulating the oxygen concentration in inlet gas or inlet gas flow. The proposed approach is relatively simple and may be attractive for installation in a real pluripotent stem cell expansion processes.

Keywords: aggregated stem cells, dissolved oxygen profiles, modeling, stirred-tank, 3D expansion

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Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

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Authors: Dimitra Marini, Konstantinos Bakogiannis, Spyros Polychronopoulos, Georgios Kouroupetroglou

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Archaemusicology cannot entirely depend on the study of the excavated ancient musical instruments as most of the time their condition is not ideal (i.e., missing/eroded parts) and moreover, because of the concern damaging the originals during the experiments. Researchers, in order to overcome the above obstacles, build replicas. This technique is still the most popular one, although it is rather expensive and time-consuming. Throughout the last decades, the development of physical modeling techniques has provided tools that enable the study of musical instruments through their digitally simulated models. This is not only a more cost and time-efficient technique but also provides additional flexibility as the user can easily modify parameters such as their geometrical features and materials. This paper thoroughly describes the steps to create a physical model of a woodwind ancient Greek instrument, Plagiaulos. This instrument could be considered as the ancestor of the modern flute due to the common geometry and air-jet excitation mechanism. Plagiaulos is comprised of a single resonator with an open end and a number of tone holes. The combination of closed and open tone holes produces the pitch variations. In this work, the effects of all the instrument’s components are described by means of physics and then simulated based on digital waveguides. The synthesized sound of the proposed model complies with the theory, highlighting its validity. Further, the synthesized sound of the model simulating the Plagiaulos of Koile (2nd century BCE) was compared with its replica build in our laboratory by following the scientific methodologies of archeomusicology. The aforementioned results verify that robust dynamic digital tools can be introduced in the field of computational, experimental archaemusicology.

Keywords: archaeomusicology, digital waveguides, musical acoustics, physical modeling

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Lunliang Zhong, Bin Duan

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The undergraduate graduation project is a vital part of the higher education curriculum, crucial for engineering accreditation. Current evaluations often summarize data without identifying underlying issues. This study applies the Peter-Clark algorithm to analyze causal relationships within the graduation project data of an Electronics and Information Engineering program, creating a causal model. Structural equation modeling confirmed the model's validity. The analysis reveals key teaching stages affecting project success, uncovering problems in the process. Introducing causal discovery and inference into project evaluation helps identify issues and propose targeted improvement measures. The effectiveness of these measures is validated by comparing the learning outcomes of two student cohorts, stratified by confounding factors, leading to improved teaching quality.

Keywords: causal discovery, causal inference, continuous improvement, Peter-Clark algorithm, structural equation modeling

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Authors: Leonard Simoni, Ilir Alimehmeti, Ervina Shirka, Endri Hasimi, Ndricim Kallashi, Verona Beka, Suerta Kabili, Artan Goda

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Background: Diabetes Mellitus type 2, small vessel calibre, stented length of vessel, complex lesion morphology, and prior bypass surgery have resulted risk factors for In-Stent Restenosis (ISR). However, there are some contradictory results about body mass index (BMI) as a risk factor for ISR. Purpose: We want to identify clinical, lesional and procedural factors that can predict clinical ISR in our patients. Methods: Were enrolled 759 patients who underwent first-time elective PCI with Bare Metal Stents (BMS) from September 2011 to December 2013 in our Department of Cardiology and followed them for at least 1.5 years with a median of 862 days (2 years and 4 months). Only the patients re-admitted with ischemic heart disease underwent control coronary angiography but no routine angiographic control was performed. Patients were categorized in ISR and non-ISR groups and compared between them. Multivariate analysis - Binary Logistic Regression: Forward Conditional Method was used to identify independent predictive risk factors. P was considered statistically significant when <0.05. Results: ISR compared to non-ISR individuals had a significantly lower BMI (25.7±3.3 vs. 26.9±3.7, p=0.004), higher risk anatomy (LM + 3-vessel CAD) (23% vs. 14%, p=0.03), higher number of stents/person used (2.1±1.1 vs. 1.75±0.96, p=0.004), greater length of stents/person used (39.3±21.6 vs. 33.3±18.5, p=0.01), and a lower use of clopidogrel and ASA (together) (95% vs. 99%, p=0.012). They also had a higher, although not statistically significant, prevalence of Diabetes Mellitus (42% vs. 32%, p=0.072) and a greater number of treated vessels (1.36±0.5 vs. 1.26±0.5, p=0.08). In the multivariate analysis, Diabetes Mellitus type 2 and multiple stents used were independent predictors risk factors for In-Stent Restenosis, OR 1.66 [1.03-2.68], p=0.039, and OR 1.44 [1.16-1.78,] p=0.001, respectively. On the other side higher BMI and use of clopidogrel and ASA together resulted protective factors OR 0.88 [0.81-0.95], p=0.001 and OR 0.2 [0.06-0.72] p=0.013, respectively. Conclusion: Diabetes Mellitus and multiple stents are strong predictive risk factors, whereas the use of clopidogrel and ASA together are protective factors for clinical In-Stent Restenosis. Paradoxically High BMI is a protective factor for In-stent Restenosis, probably related to a larger diameter of vessels and consequently a larger diameter of stents implanted in these patients. Further studies are needed to clarify this finding.

Keywords: body mass index, diabetes mellitus, in-stent restenosis, percutaneous coronary intervention

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Authors: Wala A. Bagunaid, Maram Meccawy, Arwa Allinjawi, Zilal Meccawy

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Continuous self-assessment becomes crucial in self-paced online learning environments. Students often depend on themselves to assess their progress; which is considered an essential requirement for any successful learning process. Today’s education institutions face major problems around student motivation and engagement. Thus, personalized e-learning systems aim to help and guide the students. Gamification provides an opportunity to help students for self-assessment and social comparison with other students through attempting to harness the motivational power of games and apply it to the learning environment. Furthermore, Open Social Student Modeling (OSSM) as considered as the latest user modeling technologies is believed to improve students’ self-assessment and to allow them to social comparison with other students. This research integrates OSSM approach and gamification concepts in order to provide self-assessment for English language learners at King Abdulaziz University (KAU). This is achieved through an interactive visual representation of their learning progress.

Keywords: e-learning system, gamification, motivation, social comparison, visualization

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Authors: Charbel Aoun, Loic Lagadec

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A Sensor Network (SN) is considered as an operation of two phases: (1) the observation/measuring, which means the accumulation of the gathered data at each sensor node; (2) transferring the collected data to some processing center (e.g., Fusion Servers) within the SN. Therefore, an underwater sensor network can be defined as a sensor network deployed underwater that monitors underwater activity. The deployed sensors, such as Hydrophones, are responsible for registering underwater activity and transferring it to more advanced components. The process of data exchange between the aforementioned components perfectly defines the Marine Observatory (MO) concept which provides information on ocean state, phenomena and processes. The first step towards the implementation of this concept is defining the environmental constraints and the required tools and components (Marine Cables, Smart Sensors, Data Fusion Server, etc). The logical and physical components that are used in these observatories perform some critical functions such as the localization of underwater moving objects. These functions can be orchestrated with other services (e.g. military or civilian reaction). In this paper, we present an extension to our MO meta-model that is used to generate a design tool (ArchiMO). We propose new constraints to be taken into consideration at design time. We illustrate our proposal with an example from the MO domain. Additionally, we generate the corresponding simulation code using our self-developed domain-specific model compiler. On the one hand, this illustrates our approach in relying on Enterprise Architecture (EA) framework that respects: multiple views, perspectives of stakeholders, and domain specificity. On the other hand, it helps reducing both complexity and time spent in design activity, while preventing from design modeling errors during porting this activity in the MO domain. As conclusion, this work aims to demonstrate that we can improve the design activity of complex system based on the use of MDE technologies and a domain-specific modeling language with the associated tooling. The major improvement is to provide an early validation step via models and simulation approach to consolidate the system design.

Keywords: smart sensors, data fusion, distributed fusion architecture, sensor networks, domain specific modeling language, enterprise architecture, underwater moving object, localization, marine observatory, NS-3, IMS

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Authors: Zainab D. Abd Ali, Thamir H. Khalaf

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In this paper, the characteristics of electric discharge in gap between two (parallel-plate) dielectric plates are studies, the gap filled with Argon gas in atm pressure at ambient temperature, the thickness of gap typically less than 1 mm and dielectric may be up 10 cm in diameter. One of dielectric plates a sinusoidal voltage is applied with Rf frequency, the other plates is electrically grounded. The simulation in this work depending on Boltzmann equation solver in first few moments, fluid model and plasma chemistry, in one dimensional modeling. This modeling have insight into characteristics of Dielectric Barrier Discharge through studying properties of breakdown of gas, electric field, electric potential, and calculating electron density, mean electron energy, electron current density ,ion current density, total plasma current density. The investigation also include: 1. The influence of change in thickness of gap between two plates if we doubled or reduced gap to half. 2. The effect of thickness of dielectric plates. 3. The influence of change in type and properties of dielectric material (gass, silicon, Teflon).

Keywords: computational diagnostics, Boltzmann equation, electric discharge, electron density

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Authors: Su Chul Han, Seungwoo Park

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As the increasing incidence of cancer, the technology and modality of radiotherapy have advanced and the importance of preclinical model is increasing in the cancer research. Furthermore, the small animal dosimetry is an essential part of the evaluation of the relationship between the absorbed dose in preclinical small animal and biological effect in preclinical study. In this study, we carried out realistic modeling of the preclinical small animal phantom possible to verify irradiated dose using commercial software. The small animal phantom was modeling from 4D Digital Mouse whole body phantom. To manipulate Moby phantom in commercial software (Mimics, Materialise, Leuven, Belgium), we converted Moby phantom to DICOM image file of CT by Matlab and two- dimensional of CT images were converted to the three-dimensional image and it is possible to segment and crop CT image in Sagittal, Coronal and axial view). The CT images of small animals were modeling following process. Based on the profile line value, the thresholding was carried out to make a mask that was connection of all the regions of the equal threshold range. Using thresholding method, we segmented into three part (bone, body (tissue). lung), to separate neighboring pixels between lung and body (tissue), we used region growing function of Mimics software. We acquired 3D object by 3D calculation in the segmented images. The generated 3D object was smoothing by remeshing operation and smoothing operation factor was 0.4, iteration value was 5. The edge mode was selected to perform triangle reduction. The parameters were that tolerance (0.1mm), edge angle (15 degrees) and the number of iteration (5). The image processing 3D object file was converted to an STL file to output with 3D printer. We modified 3D small animal file using 3- Matic research (Materialise, Leuven, Belgium) to make space for radiation dosimetry chips. We acquired 3D object of realistic small animal phantom. The width of small animal phantom was 2.631 cm, thickness was 2.361 cm, and length was 10.817. Mimics software supported efficiency about 3D object generation and usability of conversion to STL file for user. The development of small preclinical animal phantom would increase reliability of verification of absorbed dose in small animal for preclinical study.

Keywords: mimics, preclinical small animal, segmentation, 3D printer

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Authors: Samuel Fernandes, Dylan Kato, Emin Burak Onat, Patrick Keyantuo, Raja Sengupta, Amine Bouzaghrane

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The COVID-19 pandemic has spread across the United States, forcing cities to impose stay-at-home and shelter-in-place orders. Building operations had to adjust as non-essential personnel worked from home. But as buildings prepare for personnel to return, they need to plan for safe operations amid new COVID-19 guidelines. In this paper we propose a methodology for capacity and flow modeling of personnel within buildings to safely operate under COVID-19 guidelines. We model personnel flow within buildings by network flows with queuing constraints. We study maximum flow, minimum cost, and minimax objectives. We compare our network flow approach with a simulation model through a case study and present the results. Our results showcase various scenarios of how buildings could be operated under new COVID-19 guidelines and provide a framework for building operators to plan and operate buildings in this new paradigm.

Keywords: network analysis, building simulation, COVID-19

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Authors: Adan Valdez, Shawn Gallaher, James Morison, Jordan Aragon

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Global climate change has impacted atmospheric temperatures contributing to rising sea levels, decreasing sea ice, and increased freshening of high latitude oceans. This freshening has contributed to increased stratification inhibiting local mixing and nutrient transport and modifying regional circulations in polar oceans. In recent years, the Western Arctic has seen an increase in freshwater volume at an average rate of 397+-116 km3/year. The majority of the freshwater volume resides in the Beaufort Gyre surface lens driven by anticyclonic wind forcing, sea ice melt, and Arctic river runoff. The total climatological freshwater content is typically defined as water fresher than 34.8. The near-isothermal nature of Arctic seawater and non-linearities in the equation of state for near-freezing waters result in a salinity driven pycnocline as opposed to the temperature driven density structure seen in the lower latitudes. In this study, we investigate the relationship between freshwater content and remotely sensed dynamic ocean topography (DOT). In-situ measurements of freshwater content are useful in providing information on the freshening rate of the Beaufort Gyre; however, their collection is costly and time consuming. NASA’s Advanced Topographic Laser Altimeter System (ATLAS) derived dynamic ocean topography (DOT), and Air Expendable CTD (AXCTD) derived Freshwater Content are used to develop a linear regression model. In-situ data for the regression model is collected across the 150° West meridian, which typically defines the centerline of the Beaufort Gyre. Two freshwater content models are determined by integrating the freshwater volume between the surface and an isopycnal corresponding to reference salinities of 28.7 and 34.8. These salinities correspond to those of the winter pycnocline and total climatological freshwater content, respectively. Using each model, we determine the strength of the linear relationship between freshwater content and satellite derived DOT. The result of this modeling study could provide a future predictive capability of freshwater volume changes in the Beaufort-Chukchi Sea using non in-situ methods. Successful employment of the ICESat-2’s DOT approximation of freshwater content could potentially reduce reliance on field deployment platforms to characterize physical ocean properties.

Keywords: ICESat-2, dynamic ocean topography, freshwater content, beaufort gyre

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Authors: Martín Pratto Burgos

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The Engineering School of the University of the Republic in Uruguay offers an Introductory Mathematical Course from the second semester of 2019. This course has been designed to assist students in preparing themselves for math courses that are essential for Engineering Degrees, namely Math1, Math2, and Math3 in this research. The research proposes to build a model that can accurately predict the student's activity and academic progress based on their performance in the three essential Mathematical courses. Additionally, there is a need for a model that can forecast the incidence of the Introductory Mathematical Course in the three essential courses approval during the first academic year. The techniques used are Principal Component Analysis and predictive modelling using the Generalised Linear Model. The dataset includes information from 5135 engineering students and 12 different characteristics based on activity and course performance. Two models are created for a type of data that follows a binomial distribution using the R programming language. Model 1 is based on a variable's p-value being less than 0.05, and Model 2 uses the stepAIC function to remove variables and get the lowest AIC score. After using Principal Component Analysis, the main components represented in the y-axis are the approval of the Introductory Mathematical Course, and the x-axis is the approval of Math1 and Math2 courses as well as student activity three years after taking the Introductory Mathematical Course. Model 2, which considered student’s activity, performed the best with an AUC of 0.81 and an accuracy of 84%. According to Model 2, the student's engagement in school activities will continue for three years after the approval of the Introductory Mathematical Course. This is because they have successfully completed the Math1 and Math2 courses. Passing the Math3 course does not have any effect on the student’s activity. Concerning academic progress, the best fit is Model 1. It has an AUC of 0.56 and an accuracy rate of 91%. The model says that if the student passes the three first-year courses, they will progress according to the timeline set by the curriculum. Both models show that the Introductory Mathematical Course does not directly affect the student’s activity and academic progress. The best model to explain the impact of the Introductory Mathematical Course on the three first-year courses was Model 1. It has an AUC of 0.76 and 98% accuracy. The model shows that if students pass the Introductory Mathematical Course, it will help them to pass Math1 and Math2 courses without affecting their performance on the Math3 course. Matching the three predictive models, if students pass Math1 and Math2 courses, they will stay active for three years after taking the Introductory Mathematical Course, and also, they will continue following the recommended engineering curriculum. Additionally, the Introductory Mathematical Course helps students to pass Math1 and Math2 when they start Engineering School. Models obtained in the research don't consider the time students took to pass the three Math courses, but they can successfully assess courses in the university curriculum.

Keywords: machine-learning, engineering, university, education, computational models

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Snezhana G. Gocheva-Ilieva, Maya P. Stoimenova

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Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.

Keywords: cross-validation, decision tree, lagged variables, short-term forecasting

Procedia PDF Downloads 193

Authors: Siamack A. Shirazi, Farzin Darihaki

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Solid particles could be found in many multiphase flows, including transport pipelines and pipe fittings. Such particles interact with the pipe material and cause erosion which threats the integrity of the system. Therefore, predicting the erosion rate is an important factor in the design and the monitor of such systems. Mechanistic models can provide reliable predictions for many conditions while demanding only relatively low computational cost. Mechanistic models utilize a representative particle trajectory to predict the impact characteristics of the majority of the particle impacts that cause maximum erosion rate in the domain. The erosion caused by particle impacts is not only due to the direct impacts but also random impingements. In the present study, an alternative model has been introduced to describe the erosion due to random impingement of particles. The present model provides a realistic trend for erosion with changes in the particle size and particle Stokes number. The present model is examined against the experimental data and CFD simulation results and indicates better agreement with the data incomparison to the available models in the literature.

Keywords: erosion, mechanistic modeling, particles, multiphase flow, gas-liquid-solid

Procedia PDF Downloads 167

Authors: Sandra Cecilia Bautista-Rodríguez, Vladimir Melgarejo

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Climate change, loss of ecosystems, increasing poverty, increasing marginalization of rural communities and declining food security are global issues that require urgent attention. In this regard, a great deal of research has focused on how agroecosystems respond to these challenges as they provide ecosystem services (ES) that lead to higher levels of resilience, adaptation, productivity and self-sufficiency. Hence, the valuing of ecosystem services plays an important role in the decision-making process for the design and management of agroecosystems. This paper aims to define the link between ecosystem service valuation methods and ES value dimensions in agroecosystems from ecological economics and agroecology. The method used to identify valuation methodologies was a literature review in the fields of Agroecology and Ecological Economics, based on a strategy of information search and classification. The conceptual framework of the work is based on the multidimensionality of value, considering the social, ecological, political, technological and economic dimensions. Likewise, the valuation process requires consideration of the ecosystem function associated with ES, such as regulation, habitat, production and information functions. In this way, valuation methods for ES in agroecosystems can integrate more than one value dimension and at least one ecosystem function. The results allow correlating the ecosystem functions with the ecosystem services valued, and the specific tools or models used, the dimensions and valuation methods. The main methodologies identified are multi-criteria valuation (1), deliberative - consultative valuation (2), valuation based on system dynamics modeling (3), valuation through energy or biophysical balances (4), valuation through fuzzy logic modeling (5), valuation based on agent-based modeling (6). Amongst the main conclusions, it is highlighted that the system dynamics modeling approach has a high potential for development in valuation processes, due to its ability to integrate other methods, especially multi-criteria valuation and energy and biophysical balances, to describe through causal cycles the interrelationships between ecosystem services, the dimensions of value in agroecosystems, thus showing the relationships between the value of ecosystem services and the welfare of communities. As for methodological challenges, it is relevant to achieve the integration of tools and models provided by different methods, to incorporate the characteristics of a complex system such as the agroecosystem, which allows reducing the limitations in the processes of valuation of ES.

Keywords: ecological economics, agroecosystems, ecosystem services, valuation of ecosystem services

Procedia PDF Downloads 122

Authors: Michael Zhang, Nicholas Marriage, Valerie Astle, Marie-Louise Ratican, Jonathan Ash, Haddijatou Hughes

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Objective: Young infants are often brought to the Emergency Department (ED) with a variety of complaints, some of them are non-specific and present as a diagnostic challenge to the attending clinician. Whilst invasive investigations such as blood tests and lumbar puncture are necessary in some cases to exclude serious infections, some basic clinical tools in additional to thorough clinical history can be useful to assess the risks of serious conditions in these young infants. This study aimed to examine the utilization of one of clinical tools in this regard. Methods: This retrospective observational study examined the medical records of infants under 6 months presenting to a mixed urban ED between January 2013 and December 2014. The infants deemed to have non-specific complaints or diagnoses by the emergency clinicians were selected for analysis. The ones with clear systemic diagnoses were excluded. Among all relevant clinical information and investigation results, utilization of Standard Paediatric Observation Chart (SPOC) was particularly scrutinized in these medical records. This specific chart was developed by the expert clinicians in local health department. It categorizes important clinical signs into some color-coded zones as a visual cue for serious implication of some abnormalities. An infant is regarded as SPOC positive when fulfills 1 red zone or 2 yellow zones criteria, and the attending clinician would be prompted to investigate and treat for potential serious conditions accordingly. Results: Eight hundred and thirty-five infants met the inclusion criteria for this project. The ones admitted to the hospital for further management were more likely to have SPOC positive criteria than the discharged infants (Odds ratio: 12.26, 95% CI: 8.04 – 18.69). Similarly, Sepsis alert criteria on SPOC were positive in a higher percentage of patients with serious infections (56.52%) in comparison to those with mild conditions (15.89%) (p < 0.001). The SPOC sepsis criteria had a sensitivity of 56.5% (95% CI: 47.0% - 65.7%) and a moderate specificity of 84.1% (95% CI: 80.8% - 87.0%) to identify serious infections. Applying to this infant population, with a 17.4% prevalence of serious infection, the positive predictive value was only 42.8% (95% CI: 36.9% - 49.0%). However, the negative predictive value was high at 90.2% (95% CI: 88.1% - 91.9%). Conclusions: Standard Paediatric Observation Chart has been applied as a useful clinical tool in the clinical practice to help identify and manage young sick infants in ED effectively.

Keywords: clinical tool, infants, non-specific complaints, Standard Paediatric Observation Chart

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Authors: Miroslav Popelka, Jakub Nozicka

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The whole work is based on possibility to use Lego Mindstorms robotics systems to reduce costs. Lego Mindstorms consists of a wide variety of hardware components necessary to simulate, programme and test of robotics systems in practice. To programme algorithm, which simulates space using the ultrasonic sensor, was used development environment supplied with kit. Software Matlab was used to render values afterwards they were measured by ultrasonic sensor. The algorithm created for this paper uses theoretical knowledge from area of signal processing. Data being processed by algorithm are collected by ultrasonic sensor that scans 2D space in front of it. Ultrasonic sensor is placed on moving arm of robot which provides horizontal moving of sensor. Vertical movement of sensor is provided by wheel drive. The robot follows map in order to get correct positioning of measured data. Based on discovered facts it is possible to consider Lego Mindstorm for low-cost and capable kit for real-time modelling.

Keywords: LEGO Mindstorms, ultrasonic sensor, real-time modeling, 2D object, low-cost robotics systems, sensors, Matlab, EV3 Home Edition Software

Procedia PDF Downloads 471

Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

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The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.

Keywords: neural network computing, continuous functions generating the input-output mapping, decreasing the training time, machines with big memories

Procedia PDF Downloads 282