Search results for: predictive modeling

Authors: Abul Bashar

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The recent surge in the adoption of cloud computing systems by various organizations has brought forth the challenge of evaluating their performance. One of the major issues faced by the cloud service providers and customers is to assess the ability of cloud computing systems to provide the desired services in accordance to the QoS and SLA constraints. To this end, an opportunity exists to develop means to ensure that the desired performance levels of such systems are met under simulated environments. This will eventually minimize the service disruptions and performance degradation issues during the commissioning and operational phase of cloud computing infrastructure. However, it is observed that several simulators and modelers are available for simulating the cloud computing systems. Therefore, this paper presents a critical evaluation of the state-of-the-art modeling and simulation frameworks applicable to cloud computing systems. It compares the prominent simulation frameworks in terms of the API features, programming flexibility, operating system requirements, supported services, licensing needs and popularity. Subsequently, it provides recommendations regarding the choice of the most appropriate framework for researchers, administrators and managers of cloud computing systems.

Keywords: cloud computing, modeling framework, performance evaluation, simulation tools

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: P. Byrnes, F. A. DiazDelaO

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The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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Authors: Rhoda N. Kayongo

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As behavior analysts in education continue to debate on how higher institutions can continue to benefit from their social and academic related programs, higher education is facing challenges in the area of character development. This is manifested in the percentages of college completion rates, teen pregnancies, drug abuse, sexual abuse, suicide, plagiarism, lack of academic integrity, and violence among their students. Attending college is a perceived opportunity to positively influence the actions and behaviors of the next generation of society; thus colleges and universities have to provide opportunities to develop students’ values and behaviors. Prior studies were mainly conducted in private institutions and more so in developed countries. However, with the complexity of the nature of student body currently due to the changing world, a multidimensional approach combining multiple factors that enhance character development outcomes is needed to suit the changing trends. The main purpose of this study was to identify opportunities in colleges and develop a model for predicting character development outcomes. A survey questionnaire composed of 7 scales including in-classroom interaction, out-of-classroom interaction, school climate, personal lifestyle, home environment, and peer influence as independent variables and character development outcomes as the dependent variable was administered to a total of five hundred and one students of 3rd and 4th year level in selected public colleges and universities in the Philippines and Rwanda. Using structural equation modelling, a predictive model explained 57% of the variance in character development outcomes. Findings from the results of the analysis showed that in-classroom interactions have a substantial direct influence on character development outcomes of the students (r = .75, p < .05). In addition, out-of-classroom interaction, school climate, and home environment contributed to students’ character development outcomes but in an indirect way. The study concluded that in the classroom are many opportunities for teachers to teach, model and integrate character development among their students. Thus, suggestions are made to public colleges and universities to deliberately boost and implement experiences that cultivate character within the classroom. These may contribute tremendously to the students' character development outcomes and hence render effective models of behaviour analysis in higher education.

Keywords: character development, tertiary institutions, predictive model, behavior analysis

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Authors: Damian Islas

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Debates on realism in science concern two different questions: (I) whether the unobservable entities posited by theories can be known; and (II) whether any knowledge we have of them is objective or not. Question (I) arises from the doubt that since observation is the basis of all our factual knowledge, unobservable entities cannot be known. Question (II) arises from the doubt that since scientific representations are inextricably laden with the subjective, idiosyncratic, and a priori features of human cognition and scientific practice, they cannot convey any reliable information on how their objects are in themselves. A way of understanding scientific realism (SR) is through three lines of inquiry: ontological, semantic, and epistemological. Ontologically, scientific realism asserts the existence of a world independent of human mind. Semantically, scientific realism assumes that theoretical claims about reality show truth values and, thus, should be construed literally. Epistemologically, scientific realism believes that theoretical claims offer us knowledge of the world. Nowadays, the literature on scientific realism has proceeded rather far beyond the realism versus antirealism debate. This stance represents a middle-ground position between the two according to which science can attain justified true beliefs concerning relational facts about the unobservable realm but cannot attain justified true beliefs concerning the intrinsic nature of any objects occupying that realm. That is, the structural content of scientific theories about the unobservable can be known, but facts about the intrinsic nature of the entities that figure as place-holders in those structures cannot be known. There are two possible versions of SR: Epistemological Structural Realism (ESR) and Ontic Structural Realism (OSR). On ESR, an agnostic stance is preserved with respect to the natures of unobservable entities, but the possibility of knowing the relations obtaining between those entities is affirmed. OSR includes the rather striking claim that when it comes to the unobservables theorized about within fundamental physics, relations exist, but objects do not. Focusing on ESR, questions arise concerning its ability to explain the empirical success of a theory. Empirical success certainly involves predictive success, and predictive success implies a theory’s power to make accurate predictions. But a theory’s power to make any predictions at all seems to derive precisely from its core axioms or laws concerning unobservable entities and mechanisms, and not simply the sort of structural relations often expressed in equations. The specific challenge to ESR concerns its ability to explain the explanatory and predictive power of successful theories without appealing to their substantive ontologies, which are often not preserved by their successors. The response to this challenge will depend on the various and subtle different versions of ESR and OSR stances, which show a sort of progression through eliminativist OSR to moderate OSR of gradual increase in the ontological status accorded to objects. Knowing the relations between unobserved entities is methodologically identical to assert that these relations between unobserved entities exist.

Keywords: eliminativist ontic structural realism, epistemological structuralism, moderate ontic structural realism, ontic structuralism

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Authors: Gia Surguladze, Nino Topuria, Lily Petriashvili, Giorgi Surguladze

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Modeling of building processes of a multimodal freight transportation support information system is discussed based on modern CASE technologies. Functional efficiencies of ports in the eastern part of the Black Sea are analyzed taking into account their ecological, seasonal, resource usage parameters. By resources, we mean capacities of berths, cranes, automotive transport, as well as work crews and neighbouring airports. For the purpose of designing database of computer support system for Managerial (Logistics) function, using Object-Role Modeling (ORM) tool (NORMA – Natural ORM Architecture) is proposed, after which Entity Relationship Model (ERM) is generated in automated process. The software is developed based on Process-Oriented and Service-Oriented architecture, in Visual Studio.NET environment.

Keywords: seaport resources, business-processes, multimodal transportation, CASE technology, object-role model, entity relationship model, SOA

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Authors: W. Sriwattanapongse, S. Prasitwattanaseree, S. Wongtrangan

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The transportation accidents mortality is a major problem that leads to loss of human lives, and economic. The objective was to identify patterns of statistical modeling for estimating mortality rates due to transportation accidents in Thailand by using data from 2000 to 2009. The data was taken from the death certificate, vital registration database. The number of deaths and mortality rates were computed classifying by gender, age, year and region. There were 114,790 cases of transportation accidents deaths. The highest average age-specific transport accident mortality rate is 3.11 per 100,000 per year in males, Southern region and the lowest average age-specific transport accident mortality rate is 1.79 per 100,000 per year in females, North-East region. Linear, poisson and negative binomial models were chosen for fitting statistical model. Among the models fitted, the best was chosen based on the analysis of deviance and AIC. The negative binomial model was clearly appropriate fitted.

Keywords: transportation accidents, mortality, modeling, analysis of deviance

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Authors: S. Zolghadri, M. Mousavi-Daramoroudi, H. Yousefnia, F. Abbasi-Davani

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In this study, the absorbed dose of human organs after injection of ¹⁷⁷Lu-DOTATOC was studied based on the biodistribution of the complex in adenocarcinoma breast cancer bearing mice. For this purpose, the biodistribution of the radiolabelled complex was studied and compartmental modeling was applied to calculate the absorbed dose with high precision. As expected, ¹⁷⁷Lu-DOTATOC illustrated a notable specific uptake in tumor and pancreas, organs with high level of somatostatin receptor on their surface and the effectiveness of the radio-conjugate for targeting of the breast adenocarcinoma tumors was indicated. The elicited results of modeling were the exponential equations, and those are utilized for obtaining the cumulated activity data by taking their integral. The results also exemplified that non-target absorbed-doses such as the liver, spleen and pancreas were approximately 0.008, 0.004, and 0.039, respectively. While these values were so much lower than target (tumor) absorbed-dose, it seems due to this low toxicity, this complex is a good agent for therapy.

Keywords: ¹⁷⁷Lu, breast cancer, compartmental modeling, dosimetry

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Authors: Phumelele Kubheka, Pius Owolawi, Gbolahan Aiyetoro

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Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model.

Keywords: big data, latent Dirichlet allocation, latent semantic indexing, telco, topic modeling, twitter

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Authors: Catherine Felce, Lior Pachter

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Current methods for building phylogenetic trees from gene expression data consider mean expression levels. With single-cell technologies, we can leverage more information about cell dynamics by considering the entire distribution of gene expression across cells. Using biophysical modeling, we propose a method for constructing phylogenetic trees from scRNA-seq data, building on Felsenstein's method of continuous characters. This method can highlight genes whose level of expression may be unchanged between species, but whose rates of transcription/decay may have evolved over time.

Keywords: phylogenetics, single-cell, biophysical modeling, transcription

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Authors: Omer Burak Iskender, Keck Voon Ling, Vincent Dubanchet, Luca Simonini

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This paper presents Guidance and Control (G&C) strategy to approach and synchronize with potentially rotating targets. The proposed strategy generates and tracks a safe trajectory for space servicing missions, including tasks like approaching, inspecting, and capturing. The main objective of this paper is to validate the G&C laws using a Hardware-In-the-Loop (HIL) setup with realistic rendezvous and docking equipment. Throughout this work, the assumption of full relative state feedback is relaxed by onboard sensors that bring realistic errors and delays and, while the proposed closed loop approach demonstrates the robustness to the above mentioned challenge. Moreover, G&C blocks are unified via the Model Predictive Control (MPC) paradigm, and the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description. In this work, G&C is formulated as a convex optimization problem where constraints such as thruster limits and the output constraints are explicitly handled. Furthermore, the Monte-Carlo method is used to evaluate the robustness of the proposed method to the initial condition errors, the uncertainty of the target's motion and attitude, and actuator errors. A capture scenario is tested with the robotic test bench that has onboard sensors which estimate the position and orientation of a drifting satellite through camera imagery. Finally, the approach is compared with currently used robust H-infinity controllers and guidance profile provided by the industrial partner. The HIL experiments demonstrate that the proposed strategy is a potential candidate for future space servicing missions because 1) the algorithm is real-time implementable as convex programming offers deterministic convergence properties and guarantee finite time solution, 2) critical physical and output constraints are respected, 3) robustness to sensor errors and uncertainties in the system is proven, 4) couples translational motion with rotational motion.

Keywords: dual quaternion, model predictive control, real-time experimental test, rendezvous and docking, spacecraft autonomy, space servicing

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Authors: Mehrnaz Farzingohar, Mehran Yasemi, Ahmad Savari

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The oil products are imported and exported via Rajaee’s tanker terminal. Within loading and discharging in several cases the oil is released into the berths and made oil spills. The spills are distributed within short time and seriously affected Rajaee port’s environment and even extended areas. The trajectory and fate of oil spills investigated by modeling and parted by three risk levels base on the modeling results. First GNOME (General NOAA Operational Modeling Environment) applied to trajectory the liquid oil. Second, ALOHA (Areal Location Of Hazardous Atmosphere) air quality model, is integrated to predict the oil evaporation path within the air. Base on the identified zones the high risk areas are signed by colored dots which their densities calculated and clarified on a map which displayed the harm places. Wind and water circulation moved the pollution to the East of Rajaee Port that accumulated about 12 km of coastline. Approximately 20 km of north east of Qeshm Island shore is covered by the three levels of risky areas. Since the main wind direction is SSW the pollution pushed to the east and the highest risk zones formed on the crests edges hence the low risk appeared on the concavities. This assessment help the management and emergency systems to monitor the exposure places base on the priority factors and find the best approaches to protect the environment.

Keywords: oil spill, modeling, pollution, risk assessment

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Authors: Rim Bouhaouel, Naoufel Kraïem, Zuhoor Al Khanjari

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Unified Modeling Language (UML) is considered as one of the widespread modeling language standardized by the Object Management Group (OMG). Therefore, the model driving engineering (MDE) community attempts to provide reuse of UML diagrams, and do not construct it from scratch. The UML model appears according to a specific software development process. The existing method generation models focused on the different techniques of transformation without considering the development process. Our work aims to construct an UML component from fragments of UML diagram basing on an agile method. We define UML fragment as a portion of a UML diagram, which express a business target. To guide the generation of fragments of UML models using an agile process, we need a flexible approach, which adapts to the agile changes and covers all its activities. We use the software product line (SPL) to derive a fragment of process agile method. This paper explains our approach, named RECUP, to generate UML fragments following an agile process, and overviews the different aspects. In this paper, we present the approach and we define the different phases and artifacts.

Keywords: UML, component, fragment, agile, SPL

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Authors: Arpan Dwivedi, Yogesh Pahariya

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In this paper, optimal design of hybrid standalone power supply system (SAPS) is done for off grid applications in remote areas where transmission of power is difficult. The hybrid SAPS system uses two primary energy sources, wind and solar, and in addition to these diesel generator is also connected to meet the load demand in case of failure of wind and solar system. This paper presents mathematical modeling of 1.4 MW hybrid SAPS system for rural electrification. This paper firstly focuses on mathematical modeling of PV module connected in a string, secondly focuses on modeling of permanent magnet wind turbine generator (PMWTG). The hybrid controller is also designed for selection of power from the source available as per the load demand. The power output of hybrid SAPS system is analyzed for meeting load demands at urban as well as for rural areas.

Keywords: SAPS, DG, PMWTG, rural area, off-grid, PV module

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Authors: Maha Bakkari, Fatiha Lemmeni, Rachid Tadili

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In this work, we present some characteristics of the furnace studied, its operating principle and the experimental measurements of the evolutions of the temperatures inside and outside the walls of the This work deals with the problem of energy consumption of pottery kilns whose energy consumption is relatively too high. In this work, we determined the sources of energy loss by studying the heat transfer of a pottery furnace, we proposed a recovery system to reduce energy consumption, and then we developed a numerical model modeling the transfers through the walls of the furnace and to optimize the insulation (reduce heat losses) by testing multiple insulators. The recovery and reuse of energy recovered by the recovery system will present a significant gain in energy consumption of the oven and cooking time. This research is one of the solutions that helps reduce the greenhouse effect of the planet earth, a problem that worries the world.

Keywords: recovery lost energy, energy efficiency, modeling, heat transfer

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Authors: Anna Avramenko, Heikki Haario

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

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

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Authors: Salima Djehaichia, Rachid Lassoued

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Reinforced concrete structural walls are very efficient elements for protecting buildings against excessive early damage and against collapse under earthquake actions. It is therefore of interest to develop a numerical model which simulates the typical behavior of these units, this paper presents and describes different modeling techniques that have been used by researchers and their advantages and limitations mentioned. The earthquake of Boumerdes in 2003 has demonstrated the fragility of structures and total neglect of sismique design rules in the realization of old buildings. Significant damage and destruction of buildings caused by this earthquake are not due to the choice of type of material, but the design and the study does not congruent with seismic code requirements and bad quality of materials. For idealizing the failure of rules, a parametric study focuses on: low rate of reinforcements, type of reinforcement, resistance moderate of concrete. As an application the modeling strategy based on finite elements combined with a discretization of wall more solicited by successive thin layers. The estimated performance level achieved during a seismic action is obtained from capacity curves under incrementally increasing loads. Using a pushover analysis, a characteristic non linear force-displacement relationship can be determined. The results of numeric model are confronted with those of Algerian Para seismic Rules (RPA) in force have allowed the determination of profits in terms of displacement, shearing action, ductility.

Keywords: modeling, old building, pushover analysis, structural walls

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Authors: Larry Audet

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Creativity is suddenly and fortunately a new educational focus in the United Arab Emirates and around the world. Yet still today many leaders of creativity are not sure how to introduce it to their teachers. It is impossible to simultaneously introduce every aspect of creativity into a work climate and reach any degree of organizational coherence. The number of alternatives to explore is so great; the information teachers need to learn is so vast, that even an approximation to including every concept and theory of creativity into the school organization is hard to conceive. Effective leaders of creativity need evidence-based and practical guidance for introducing and stimulating creativity in others. Machine learning models reveal new findings from KEYS Survey© data about teacher perceptions of stimulants and barriers to their individual and collective creativity. Findings from predictive and causal models provide leaders with a rational for decision-making when introducing creativity into their organization. Leaders should focus on management practices first. Analyses reveal that creative outcomes are more likely to occur when teachers perceive supportive management practices: providing teachers with challenging work that calls for their best efforts; allowing freedom and autonomy in their practice of work; allowing teachers to form creative work-groups; and, recognizing them for their efforts. Once management practices are in place, leaders should focus their efforts on modeling risk-taking, providing optimal amounts of preparation time, and evaluating teachers fairly.

Keywords: creativity, leadership, KEYS survey, teaching, work climate

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Authors: Juan Portoviejo Brito, Daniel Icaza Alvarez, Christian Castro Samaniego

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In this article, we present the modeling, simulations, and energy conversion analysis of the solar-wind system for the Quingeo Heritage Center in Ecuador. A numerical model was constructed based on the 19 equations, it was coded in MATLAB R2017a, and the results were compared with the experimental data of the site. The model is built with the purpose of using it as a computer development for the optimization of resources and designs of hybrid systems in the Parish of Quingeo and its surroundings. The model obtained a fairly similar pattern compared to the data and curves obtained in the field experimentally and detailed in manuscript. It is important to indicate that this analysis has been carried out so that in the near future one or two of these power generation systems can be exploited in a massive way according to the budget assigned by the Parish GAD of Quingeo or other national or international organizations with the purpose of preserving this unique colonial helmet in Ecuador.

Keywords: hybrid system, wind turbine, modeling, simulation, Smart Grid, Quingeo Azuay Ecuador

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Authors: Seyedtaghi Mirmohammadi, J. Yazdani, S. M. Asadi, M. Rokni, A. Toosi

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There are many indoor air quality studies with an emphasis on indoor particulate matters (PM2.5) monitoring. Whereas, there is a lake of data about indoor PM2.5 concentrations in rural area schools (especially in classrooms), since preliminary children are assumed to be more defenseless to health hazards and spend a large part of their time in classrooms. The objective of this study was indoor PM2.5 concentration quality assessment. Fifteen preliminary schools by time-series sampling were selected to evaluate the indoor air quality in the rural district of Sari city, Iran. Data on indoor air climate parameters (temperature, relative humidity and wind speed) were measured by a hygrometer and thermometer. Particulate matters (PM2.5) were collected and assessed by Real Time Dust Monitor, (MicroDust Pro, Casella, UK). The mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3 in average. The multiple linear regression revealed that a correlation between PM2.5 concentration and relative humidity, distance from city center and classroom size. Classroom size yields reasonable negative relationship, the PM2.5 concentration was ranged from 65 to 540μg/m3 and statistically significant at 0.05 level and the relative humidity was ranged from 70 to 85% and dry bulb temperature ranged from 28 to 29°C were statistically significant at 0.035 and 0.05 level, respectively. A statistical predictive model was obtained from multiple regressions modeling for PM2.5 and indoor psychrometric parameters.

Keywords: particulate matters, classrooms, regression, concentration, humidity

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Authors: Mohammad Arabi

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Business Process Reengineering (BPR) is a systematic approach to fundamentally redesign organizational processes to achieve significant improvements in organizational performance. At Fars Regional Electric Company, implementing BPR is deemed essential to increase productivity, reduce costs, and improve service quality. This article examines how BPR can help enhance the performance of Fars Regional Electric Company. The objective of this research is to evaluate and analyze the advancements in process modeling and reengineering at Fars Regional Electric Company and to provide solutions for improving the productivity and efficiency of organizational processes. This study aims to demonstrate how BPR can be used to improve organizational processes and enhance the overall performance of the company. This research employs both qualitative and quantitative research methods and includes interviews with senior managers and experts at Fars Regional Electric Company. The analytical tools include process modeling software such as Bizagi and ARIS, and statistical analysis software such as SPSS and Minitab. Data analysis was conducted using advanced statistical methods. The results indicate that the use of BPR techniques can lead to a significant reduction in process execution time and overall improvement in quality. Implementing BPR at Fars Regional Electric Company has led to increased productivity, reduced costs, and improved overall performance of the company. This study shows that with proper implementation of BPR and the use of modeling tools, the company can achieve significant improvements in its processes. Recommendations: (1) Continuous Training for Staff: Invest in continuous training of staff to enhance their skills and knowledge in BPR. (2) Use of Advanced Technologies: Utilize modeling and analysis software to improve processes. (3) Implementation of Effective Management Systems: Employ knowledge and information management systems to enhance organizational performance. (4) Continuous Monitoring and Review of Processes: Regularly review and revise processes to ensure ongoing improvements. This article highlights the importance of improving organizational processes at Fars Regional Electric Company and recommends that managers and decision-makers at the company seriously consider reengineering processes and utilizing modeling technologies to achieve developmental goals and continuous improvement.

Keywords: business process reengineering, electric company, Fars province, process modeling advancements

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Authors: Hanbey Hazar, Hakan Gul

Abstract:

In this study; piston, exhaust, and suction valves of a diesel engine were coated in 300 mm thickness with Tungsten Carbide (WC) by using the HVOF coating method. Mathematical modeling of a coated and uncoated (standardized) engine was performed by using ANN (Artificial Neural Networks). The purpose was to decrease the number of repetitions of tests and reduce the test cost through mathematical modeling of engines by using ANN. The results obtained from the tests were entered in ANN and therefore engines' values at all speeds were estimated. Results obtained from the tests were compared with those obtained from ANN and they were observed to be compatible. It was also observed that, with thermal barrier coating, hydrocarbon (HC), carbon monoxide (CO), and smoke density values of the diesel engine decreased; but nitrogen oxides (NOx) increased. Furthermore, it was determined that results obtained through mathematical modeling by means of ANN reduced the number of test repetitions. Therefore, it was understood that time, fuel and labor could be saved in this way.

Keywords: Artificial Neural Network, Diesel Engine, Mathematical Modelling, Thermal Barrier Coating

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Authors: Strahinja Kovačević, Sanja Podunavac-Kuzmanović, Lidija Jevrić, Cristina Prandi, Piermichele Kobauri

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The present study is based on molecular modeling of a series of twelve 5-(4-substituent-phenoxy)-3-methylfuran-2(5H)-one derivatives which have potential of strigolactones mimics toward Striga hermonthica. The first step of the analysis included the calculation of molecular descriptors which numerically describe the structures of the analyzed compounds. The descriptors ALOGP (lipophilicity), AClogS (water solubility) and BBB (blood-brain barrier penetration), served as the input variables in multiple linear regression (MLR) modeling of germination activity toward S. hermonthica. Two MLR models were obtained. The first MLR model contains ALOGP and AClogS descriptors, while the second one is based on these two descriptors plus BBB descriptor. Despite the braking Topliss-Costello rule in the second MLR model, it has much better statistical and cross-validation characteristics than the first one. The ALOGP and AClogS descriptors are often very suitable predictors of the biological activity of many compounds. They are very important descriptors of the biological behavior and availability of a compound in any biological system (i.e. the ability to pass through the cell membranes). BBB descriptor defines the ability of a molecule to pass through the blood-brain barrier. Besides the lipophilicity of a compound, this descriptor carries the information of the molecular bulkiness (its value strongly depends on molecular bulkiness). According to the obtained results of MLR modeling, these three descriptors are considered as very good predictors of germination activity of the analyzed compounds toward S. hermonthica seeds. This article is based upon work from COST Action (FA1206), supported by COST (European Cooperation in Science and Technology).

Keywords: chemometrics, germination activity, molecular modeling, QSAR analysis, strigolactones

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Authors: Farzingohar Mehrnaz, Yasemi Mehran, Esmaili Zinat, Baharlouian Maedeh

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Aboozar (Ardeshir) and Bahregansar are the two important Iranian oilfields in Persian Gulf waters. The operation activities cause to create spills which impacted on the marine environment. Assumed spills are molded by ADIOS II (Automated Data Inquiry for Oil Spills) which is NOAA’s weathering oil software. Various atmospheric and marine data with different oil types are used for the modeling. Numerous scenarios for 100 bbls with mean daily air temperature and wind speed are input for 5 days. To find the model sensitivity in each setting, one parameter is changed, but the others stayed constant. In both fields, the evaporated and dispersed output values increased hence the remaining rate is reduced. The results clarified that wind speed first, second air temperature and finally oil type respectively were the most effective factors on the oil weathering process. The obtained results can help the emergency systems to predict the floating (dispersed and remained) volume spill in order to find the suitable cleanup tools and methods.

Keywords: ADIOS, modeling, oil spill, sensitivity analysis

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Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Adil A. Mohammed, Seif-Eddeen K. Fateen, Tamer S. Ahmed, Tarek M. Moustafa

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Falling film were widely used for gas-liquid absorption and reaction process. Modeling of falling film for oligomerization of ethylene reaction to linear alpha olefins is developed. Although there are many researchers discuss modeling of falling film in many processes, there has been no publish study the simulation of falling film for the oligomerization of ethylene reaction to produce linear alpha olefins. The Comsol multiphysics software was used to simulate the mass transfer with chemical reaction in falling film absorption process. The effect of concentration profile absorption of the products through falling thickness is discussed. The effect of catalyst concentration, catalyst/co-catalyst ratio, and temperature is also studied. For the effect of the temperature, as it increase the concentration of C4 increase. For catalyst concentration and catalyst/co-catalyst ratio as they increases the concentration of C4 increases, till it reached almost constant value.

Keywords: falling film, oligomerization, comsol mutiphysics, linear alpha olefins

Procedia PDF Downloads 463

Authors: Joseph Salim

Abstract:

This paper proposes the development of a supervised machine learning system to predict the consequences of untreated bruxism (teeth grinding) on gingival (gum) recession and jaw pain (most often bilateral jaw pain with possible headaches and limited ability to open the mouth). As a general dentist in a multi-specialty practice, the author has encountered many patients suffering from these issues due to uncontrolled bruxism (teeth grinding) at night. The most effective treatment for managing this problem involves wearing a nightguard during sleep and receiving therapeutic Botox injections to relax the muscles (the masseter muscle) responsible for grinding. However, some patients choose to postpone these treatments, leading to potentially irreversible and costlier consequences in the future. The proposed machine learning model aims to track patients who forgo the recommended treatments and assess the percentage of individuals who will experience worsening jaw pain, gingival (gum) recession, or both within a 3-to-5-year timeframe. By accurately predicting these outcomes, the model seeks to motivate patients to address the root cause proactively, ultimately saving time and pain while improving quality of life and avoiding much costlier treatments such as full-mouth rehabilitation to help recover the loss of vertical dimension of occlusion due to shortened clinical crowns because of bruxism, gingival grafts, etc.

Keywords: artificial intelligence, machine learning, predictive insights, bruxism, teeth grinding, therapeutic botox, nightguard, gingival recession, gum recession, jaw pain

Procedia PDF Downloads 84

Authors: Biruk Wolde, Atalay Ayele, Yonatan Garkabo, Trufat Hailmariam, Zemenu Germewu

Abstract:

A comprehensive three-dimensional (3D) geological modeling and engineering geological characterization of shallow subsurface soils and rocks are essential for a wide range of geotechnical and seismological engineering applications, particularly in urban environments. The spatial distribution and geological variation of the shallow subsurface of Addis Ababa city have not been studied so far in terms of geological and geotechnical modeling. This study aims at the construction of a 3D geological model, as well as provides awareness into the engineering geological characteristics of shallow subsurface soil and rock of Addis Ababa city. The 3D geological model was constructed by using more than 1500 geotechnical boreholes, well-drilling data, and geological maps. A well-known geostatistical kriging 3D interpolation algorithm was applied to visualize the spatial distribution and geological variation of the shallow subsurface. Due to the complex nature of geological formations, vertical and lateral variation of the geological profiles horizons-solid command has been selected via the Groundwater Modelling System (GMS) graphical user interface software. For the engineering geological characterization of typical soils and rocks, both index and engineering laboratory tests have been used. The geotechnical properties of soil and rocks vary from place to place due to the uneven nature of subsurface formations observed in the study areas. The constructed model ascertains the thickness, extent, and 3D distribution of the important geological units of the city. This study is the first comprehensive research work on 3D geological modeling and subsurface characterization of soils and rocks in Addis Ababa city, and the outcomes will be important for further future research on subsurface conditions in the city. Furthermore, these findings provide a reference for developing a geo-database for the city.

Keywords: 3d geological modeling, addis ababa, engineering geology, geostatistics, horizons-solid

Procedia PDF Downloads 86

Authors: Erin Lynne Plettenberg, Jeremy Vickery

Abstract:

In an access-control situation, the role of a user determines whether a data request is appropriate. This paper combines vetted web mining and logic modeling to build a lightweight system for determining the role of a health care provider based only on their prior authorized requests. The model identifies provider roles with 100% recall from very little data. This shows the value of vetted web mining in AI systems, and suggests the impact of the ICD classification on medical practice.

Keywords: electronic medical records, information extraction, logic modeling, ontology, vetted web mining

Procedia PDF Downloads 168

Authors: Sara ElElimy, Samir Moustafa

Abstract:

Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

Procedia PDF Downloads 136