Search results for: behavioural models

Authors: James Alberto Ortega Morales, Nelson Andrés Martínez Marín

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The bulding industry is one of the most important sectors all around the world in terms of contribution to index like GDP and labor. On the other hand, it is a major contributor to Greenhouse Gases (GHG) and waste generation contributing to global warming. In this sense, it is necessary to establish sustainable practices both from the strategic point of view to the operations point of view as well in all business and industries. Business models don’t scape to this reality attending it´s mediator role between strategy and operations. Business models can turn from the traditional practices searching economic benefits to sustainable bussines models that generate both economic value and value for society and the environment. Recent advances in the analysis of sustainable business models find different classifications that allow finding potential triple bottom line (economic, social and environmental) solutions applicable in every business sector. Into the metioned Advances have been identified, 11 groups and 45 patterns of sustainable business models have been identified; such patterns can be found either in the business models as a whole or found concurrently in their components. This article presents the analysis of a case study, seeking to identify the components and elements that are part of it, using the ECO CANVAS conceptual model. The case study allows showing the concurrent existence of different patterns of business models for sustainability empirically, serving as an example and inspiration for other Latin American companies interested in integrating sustainability into their new and existing business models.

Keywords: sustainable business models, business sustainability, business model patterns, case study, construction industry

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Authors: Nop Sopipan

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In this paper, we forecast the volatility of Baht/USDs using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting Baht/USD volatility. The MRS-GARCH is the best performance model for Baht/USD volatility in short term but the GARCH model is best perform for long term.

Keywords: volatility, Markov Regime Switching, forecasting, Baht/USD

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Authors: Muhammmad Fawad

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The bridge construction industry has been revolutionized by the applications of Virtual Reality (VR) and Augmented Reality (AR). In this article, the author has focused on the field applications of digital technologies in structural, especially in bridge engineering. This research analyzed the use of VR/AR for the assessment of bridge concepts. For this purpose, the author has used Cyber-Physical Devices, i.e., Oculus Quest (OQ) for the implementation of VR, Trimble Microsoft HoloLens (THL), and Trimble Site Vision (TSV) for the implementation of AR/MR by visualizing the models of bridge planned to be constructed in Poland. The visualization of the models in Extended Reality (XR) is based on the development of BIM models of the bridge, which are further uploaded to the platforms required to implement these models in XR. This research helped to implement the models in MR so a bridge with a 1:1 scale at the exact location was placed, and authorities were presented with the possibility to visualize the exact scale and location of the bridge before its construction.

Keywords: augmented reality, virtual reality, HoloLens, BIM, bridges

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Authors: Bernur Acikgoz

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The purpose of this paper is to investigate the effects of public spending on economic growth and examine the sources of economic growth in developed countries since the 1990s. This paper analyses whether public spending effect on economic growth based on Cobb-Douglas Production Function with the two econometric models with Autoregressive Distributed Lag (ARDL) and Dynamic Fixed Effect (DFE) for 21 developed countries (high-income OECD countries), over the period 1990-2013. Our models results are parallel to each other and the models support that public spending has an important role for economic growth. This result is accurate with theories and previous empirical studies.

Keywords: public spending, economic growth, panel data, ARDL models

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Authors: Takeshi Takemoto, Naoya Suzuki, Naohisa Takagaki, Satoru Komori, Masako Terui, George Truscott

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Drag coefficient is an important parameter in order to correctly estimate the air-sea momentum flux. However, The parameterization of the drag coefficient hasn’t been established due to the variation in the field data. Instead, a number of drag coefficient model formulae have been proposed, even though almost all these models haven’t discussed the extreme wind speed range. With regards to such models, it is unclear how the drag coefficient changes in the extreme wind speed range as the wind speed increased. In this study, we investigated the effect of the drag coefficient models concerning the air-sea momentum flux in the extreme wind range on a global scale, comparing two different drag coefficient models. Interestingly, one model didn’t discuss the extreme wind speed range while the other model considered it. We found that the difference of the models in the annual global air-sea momentum flux was small because the occurrence frequency of strong wind was approximately 1% with a wind speed of 20m/s or more. However, we also discovered that the difference of the models was shown in the middle latitude where the annual mean air-sea momentum flux was large and the occurrence frequency of strong wind was high. In addition, the estimated data showed that the difference of the models in the drag coefficient was large in the extreme wind speed range and that the largest difference became 23% with a wind speed of 35m/s or more. These results clearly show that the difference of the two models concerning the drag coefficient has a significant impact on the estimation of a regional air-sea momentum flux in an extreme wind speed range such as that seen in a tropical cyclone environment. Furthermore, we estimated each air-sea momentum flux using several kinds of drag coefficient models. We will also provide data from an observation tower and result from CFD (Computational Fluid Dynamics) concerning the influence of wind flow at and around the place.

Keywords: air-sea interaction, drag coefficient, air-sea momentum flux, CFD (Computational Fluid Dynamics)

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Authors: Josip Visković, Josip Arnerić, Ante Rozga

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Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modelling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: central and east European countries, financial crisis, Markov switching GARCH model, transition probabilities

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Authors: O. A. Adebiyi, A. O. Oni, A. O. K. Adeshehinwa, I. O. Adejumo

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In forty nine (49) days, twenty four (24) growing pigs (Landrace x Large white) with an average weight of 17 ±2.1kg were allocated to four experimental treatments T1 (dry mash without probiotics), T2 (wet feed without probiotics), T3 (dry mash + Saccaromyces cereviseae probiotics) and T4 (wet feed + Saccaromyces cereviseae probiotics) which were replicated three times with two pigs per replicate in a completely randomised design. The basal feed (dry feed) was formulated to meet the nutritional requirement of the animal with crude protein of 18.00% and metabolisable energy of 2784.00kcal/kgME. Growth pattern, faecal microbial load and behavioural activities (eating, drinking, physical pen interaction and frequency of visiting the drinking troughs) were accessed. Pigs fed dry mash without probiotics (T1) had the highest daily feed intake among the experimental animals (1.10kg) while pigs on supplemented diets (T3 and T4) had an average daily feed intake of 0.95kg. However, the feed conversion ratio was significantly (p < 0.05) affected with pigs on T3 having least value of 6.26 compared those on T4 (wet feed + Saccaromyces cereviseae) with means of 7.41. Total organism counts varied significantly (p < 0.05) with pigs on T1, T2, T3 and T4 with mean values of 179.50 x106cfu; 132.00 x 106cfu; 32.00 x 106cfu and 64.50 x 106cfu respectively. Coliform count was also significantly (p < 0.05) different among the treatments with corresponding values of 117.50 x 106cfu; 49.00 x 106cfu, 8.00 x 106cfu for pigs in T1, T2 and T4 respectively. The faecal Saccaromyces cereviseae was significantly lower in pigs fed supplemented diets compared to their counterparts on unsupplemented diets. This could be due to the inability of yeast organisms to be voided easily through feaces. The pigs in T1 spent the most time eating (7.88%) while their counterparts on T3 spent the least time eating. The corresponding physical pen interaction times expressed in percentage of a day for pigs in T1, T2, T3 and T4 are 6.22%, 5.92%, 4.04% and 4.80% respectively. These behavioural responses exhibited by these pigs (T3) showed that little amount of dry feed supplemented with probiotics is needed for better performance. The water intake increases as a result of the dryness of the feed with consequent decrease in pen interaction and more time was spent resting than engaging in other possible vice-habit like fighting or tail biting. Pigs fed dry feed (T3) which was supplemented with Saccaromyces cereviseae probiotics had a better overall performance, least faecal microbial load than wet fed pigs either supplemented with Saccaromyces cereviseae or non-supplemented.

Keywords: behaviour, feed forms, feed utilization, growth, microbial

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Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

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Authors: Ali S. Gargoum

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Graphical modeling plays an important role in providing efficient probability calculations in high dimensional problems (computational efficiency). In this paper, we address one of such problems where we discuss fragmenting puff models and some distributional assumptions concerning models for the instantaneous, emission readings and for the fragmenting process. A graphical representation in terms of a junction tree of the conditional probability breakdown of puffs and puff fragments is proposed.

Keywords: graphical models, influence diagrams, junction trees, Bayesian nets

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Authors: Valdir Moura, FranciléIa De Oliveira E. Silva, Erivelto Mercante, Ranieli Dos Anjos De Souza, Jerry Adriani Johann

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Several colonization projects were implemented in the Brazilian Legal Amazon in the 1970s and 1980s. Among all of these colonization projects, the most prominent were those with the Fishbone and Topographic models. Within this scope, the projects of settlements known as Anari and Machadinho were created, which stood out because they are contiguous areas with different models and structure of occupation and colonization. The main objective of this work was to evaluate the dynamics of Land-Use and Land-Cover (LULC) in two different colonization models, implanted in the State of Rondonia in the 1980s. The Fishbone and Topographic models were implanted in the Anari and Machadinho settlements respectively. The understanding of these two forms of occupation will help in future colonization programs of the Brazilian Legal Amazon. These settlements are contiguous areas with different occupancy structures. A 32-year Landsat time series (1984-2016) was used to evaluate the rates and trends in the LULC process in the different colonization models. In the different occupation models analyzed, the results showed a rapid loss of primary and secondary forests (deforestation), mainly due to the dynamics of use, established by the Agriculture/Pasture (A/P) relation and, with heavy dependence due to road construction.

Keywords: land-cover, deforestation, rate fragments, remote sensing, secondary succession

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Authors: Mahsa Hadipour Jahromy, Sara Rezaii

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Parkinson’s disease (PD) results primarily from the death of dopaminergic neurons in the substantia nigra. Soon after the discovery of levodopa and its beneficial effects in chronic administration, debilitating involuntary movements observed, termed levodopa-induced dyskinesia (LID) with poorly understood pathogenesis. Polyphenol-rich compounds, like pomegranate, provided neuroprotection in several animal models of brain diseases. In the present work, we investigated whether pomegranate has preventive effects following 4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic degenerations and the potential to diminish LID in mice. Mice model of PD was induced by MPTP (30 mg/kg daily for five consecutive days). To induce a mice model of LID, valid PD mice were treated with levodopa (50 mg/kg, i.p) for 15 days. Then the effects of chronic co-administration of pomegranate juice (20 ml/kg) with levodopa and continuing for 10 days, evaluated. Behavioural tests were performed in all groups, every other day including: Abnormal involuntary movements (AIMS), forelimb adjusting steps, cylinder, and catatonia tests. Finally, brain tissue sections were prepared to study substantia nigra changes and dopamine neuron density after treatments. With this MPTP regimen, significant movement disorders revealed in AIMS tests and there was a reduction in dopamine striatal density. Levodopa attenuates their loss caused by MPTP, however, in chronic administration, dyskinesia observed in forelimb adjusting step and cylinder tests. Besides, catatonia observed in some cases. Chronic pomegranate co-administration significantly improved LID in both tests and reduced dopaminergic loss in substantia nigra. These data indicate that pomegranate might be a good adjunct for preserving dopaminergic neurons in the substantia nigra and reducing LID in mice.

Keywords: levodopa-induced dyskinesia, MPTP, Parkinson’s disease, pomegranate

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Authors: Raquel Zydeck, Karina Azzolin, Luis Kosteski, Alisson Milani

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This work presents numerical models in plane deformations (2D), using the Discrete Element Method formedbybars (LDEM) andtheFiniteElementMethod (FEM), in structuralmasonrywallswith horizontal chasesof 20%, 30%, and 50% deep, located in the central part and 1/3 oftheupperpartofthewall, withcenteredandeccentricloading. Differentcombinationsofboundaryconditionsandinteractionsbetweenthemethodswerestudied.

Keywords: chases in structural masonry walls, discrete element method formed by bars, finite element method, numerical models, boundary condition

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Authors: Nurudeen O. Lasisi, Sirajo Abdulrahman, Abdulkareem A. Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with the virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of the modeling of the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. The comparison of Vaccination, linear incident rate and novel quarantine-adjusted incident rate in the models are discussed. The dynamics of the models yield disease-free and endemic equilibrium states.The effective reproduction numbers of the models are computed in order to measure the relative impact of an individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models and we found that the stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, Endemic state, Mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

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Authors: Saad M. Algharib

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The major concern of planners when placing fire stations is finding their optimal locations such that the fire companies can reach fire locations within reasonable response time or distance. Planners are also concerned with the numbers of fire stations that are needed to cover all service areas and the fires, as demands, with standard response time or distance. One of the tools for such analysis is location-allocation models. Location-allocation models enable planners to determine the optimal locations of facilities in an area in order to serve regional demands in the most efficient way. The purpose of this study is to examine the geographic distribution of the existing fire stations in Kuwait City. This study utilized location-allocation models within the Geographic Information System (GIS) environment and a number of statistical functions to assess the current locations of fire stations in Kuwait City. Further, this study investigated how well all service areas are covered and how many and where additional fire stations are needed. Four different location-allocation models were compared to find which models cover more demands than the others, given the same number of fire stations. This study tests many ways to combine variables instead of using one variable at a time when applying these models in order to create a new measurement that influences the optimal locations for locating fire stations. This study also tests how location-allocation models are sensitive to different levels of spatial dependency. The results indicate that there are some districts in Kuwait City that are not covered by the existing fire stations. These uncovered districts are clustered together. This study also identifies where to locate the new fire stations. This study provides users of these models a new variable that can assist them to select the best locations for fire stations. The results include information about how the location-allocation models behave in response to different levels of spatial dependency of demands. The results show that these models perform better with clustered demands. From the additional analysis carried out in this study, it can be concluded that these models applied differently at different spatial patterns.

Keywords: geographic information science, GIS, location-allocation models, geography

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

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Authors: Mohd Adib Abd Muin, Shuhairimi Abdullah, Azizan Bahari

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Problem: The research problem is lacking of model on social entrepreneurship that focus on Islamic perspective. Objective: The objective of this paper is to analyse the existing model on social entrepreneurship and to identify the research gap on Islamic perspective from existing models. Research Methodology: The research method used in this study is literature review and comparative analysis from 6 existing models of social entrepreneurship. Finding: The research finding shows that 6 existing models on social entrepreneurship has been analysed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.

Keywords: social entrepreneurship, Islamic perspective, research gap, business management

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: G. Calleja-Rodriguez, N. Jimenez-Redondo, J. J. Peralta Escalante

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This research work aims at developing a solution to take advantage of the potential energy saving related to occupants behaviour estimated in between 5-30 % according to existing studies. For that purpose, the following methodology has been followed: 1) literature review and gap analysis, 2) define concept and functional requirements, 3) evaluation and feedback by experts. As result, the concept for a tool-box that implements continuous behavior change interventions named as engagement methods and based on increasing energy literacy, increasing energy visibility, using bonus system, etc. has been defined. These engagement methods are deployed through a set of ICT tools: Building Automation and Control System (BACS) add-ons services installed in buildings and Users Apps installed in smartphones, smart-TVs or dashboards. The tool-box called eTEACHER identifies energy conservation measures (ECM) based on energy behavioral change through a what-if analysis that collects information about the building and its users (comfort feedback, behavior, etc.) and carry out cost-effective calculations to provide outputs such us efficient control settings of building systems. This information is processed and showed in an attractive way as tailored advice to the energy end-users. Therefore, eTEACHER goal is to change the behavior of building´s energy users towards energy efficiency, comfort and better health conditions by deploying customized ICT-based interventions taking into account building typology (schools, residential, offices, health care centres, etc.), users profile (occupants, owners, facility managers, employers, etc.) as well as cultural and demographic factors. One of the main findings of this work is the common failure when technological interventions on behavioural change are done to not consult, train and support users regarding technological changes leading to poor performance in practices. As conclusion, a strong need to carry out social studies to identify relevant behavioural issues and to identify effective pro-evironmental behavioral change strategies has been identified.

Keywords: energy saving, behavioral bhange, building users, engagement methods, energy conservation measures

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Authors: Sumaira Ashraf, Elisabete G.S. Félix, Zélia Serrasqueiro

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Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets.

Keywords: financial distress, emerging market, prediction models, Z-Score, logit analysis, probit model

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: Nurudeen Oluwasola Lasisi, Abdulkareem Afolabi Ibrahim

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Newcastle disease is an infection of domestic poultry and other bird species with virulent Newcastle disease virus (NDV). In this paper, we study the dynamics of modeling the Newcastle disease virus (NDV) using a novel quarantine-adjusted incidence. We do a comparison of Vaccination, linear incident rate, and novel quarantine adjusted incident rate in the models. The dynamics of the models yield disease free and endemic equilibrium states. The effective reproduction numbers of the models are computed in order to measure the relative impact for the individual bird or combined intervention for effective disease control. We showed the local and global stability of endemic equilibrium states of the models, and we found that stability of endemic equilibrium states of models are globally asymptotically stable if the effective reproduction numbers of the models equations are greater than a unit.

Keywords: effective reproduction number, endemic state, mathematical model, Newcastle disease virus, novel quarantine-adjusted incidence, stability analysis

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Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

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Authors: Meng-Fei Cheng, Wei-Lun Chen, Han-Chang Ma, Chi-Che Tsai

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Recent K–12 Taiwan Science Education Curriculum Guideline emphasize the essential role of modeling curriculum in science learning; however, few modeling curricula have been designed and adopted in current science teaching. Therefore, this study aims to develop modeling curriculum on electric circuits to investigate any learning difficulties students have with modeling curriculum and further enhance modeling teaching. This study was conducted with 44 10th-grade students in Central Taiwan. Data collection included a students’ understanding of models in science (SUMS) survey that explored the students' epistemology of scientific models and modeling and a complex circuit problem to investigate the students’ modeling abilities. Data analysis included the following: (1) Paired sample t-tests were used to examine the improvement of students’ modeling abilities and conceptual understanding before and after the curriculum was taught. (2) Paired sample t-tests were also utilized to determine the students’ modeling abilities before and after the modeling activities, and a Pearson correlation was used to understand the relationship between students’ modeling abilities during the activities and on the posttest. (3) ANOVA analysis was used during different stages of the modeling curriculum to investigate the differences between the students’ who developed microscopic models and macroscopic models after the modeling curriculum was taught. (4) Independent sample t-tests were employed to determine whether the students who changed their models had significantly different understandings of scientific models than the students who did not change their models. The results revealed the following: (1) After the modeling curriculum was taught, the students had made significant progress in both their understanding of the science concept and their modeling abilities. In terms of science concepts, this modeling curriculum helped the students overcome the misconception that electric currents reduce after flowing through light bulbs. In terms of modeling abilities, this modeling curriculum helped students employ macroscopic or microscopic models to explain their observed phenomena. (2) Encouraging the students to explain scientific phenomena in different context prompts during the modeling process allowed them to convert their models to microscopic models, but it did not help them continuously employ microscopic models throughout the whole curriculum. The students finally consistently employed microscopic models when they had help visualizing the microscopic models. (3) During the modeling process, the students who revised their own models better understood that models can be changed than the students who did not revise their own models. Also, the students who revised their models to explain different scientific phenomena tended to regard models as explanatory tools. In short, this study explored different strategies to facilitate students’ modeling processes as well as their difficulties with the modeling process. The findings can be used to design and teach modeling curricula and help students enhance their modeling abilities.

Keywords: electric circuits, modeling curriculum, science learning, scientific model

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Authors: R. Hellmuth

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The method of factory planning has changed a lot, especially when it is about planning the factory building itself. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity and Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Furthermore, digital building models are increasingly being used in factories to support facility management and manufacturing processes. The main research question of this paper is, therefore: What kind of digital factory model is suitable for the different areas of application during the operation of a factory? First, different types of digital factory models are investigated, and their properties and usabilities for use cases are analysed. Within the scope of investigation are point cloud models, building information models, photogrammetry models, and these enriched with sensor data are examined. It is investigated which digital models allow a simple integration of sensor data and where the differences are. Subsequently, possible application areas of digital factory models are determined by means of a survey and the respective digital factory models are assigned to the application areas. Finally, an application case from maintenance is selected and implemented with the help of the appropriate digital factory model. It is shown how a completely digitalized maintenance process can be supported by a digital factory model by providing information. Among other purposes, the digital factory model is used for indoor navigation, information provision, and display of sensor data. In summary, the paper shows a structuring of digital factory models that concentrates on the geometric representation of a factory building and its technical facilities. A practical application case is shown and implemented. Thus, the systematic selection of digital factory models with the corresponding application cases is evaluated.

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

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Authors: Yoko Yamada, Chizumi Yamada

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Narrative psychology is based on the dialogical relationship between self and other. The dialogue can consist of divided, competitive, or opposite communication between self and other. We constructed models of coexistent dialogue in which self and other were positioned side by side and communicated sympathetically. We propose new mediation models for narrative relationships. The mediation models are based on triadic relationships that incorporate a medium or a mediator along with self and other. We constructed three types of mediation model. In the first type, called the “Joint Attention Model”, self and other are positioned side by side and share attention with the medium. In the second type, the “Triangle Model”, an agent mediates between self and other. In the third type, the “Caring Model”, a caregiver stands beside the communication between self and other. We apply the three models to the illness narratives of medical professionals and patients. As these groups have different views and experiences of disease or illness, triadic mediation facilitates the ability to see things from the other person’s perspective and to bridge differences in people’s experiences and feelings. These models would be useful for medical education in various situations, such as in considering the relationships between senior and junior doctors and between old and young patients.

Keywords: illness narrative, mediation, psychology, model, medical education

Procedia PDF Downloads 385

Authors: A. A. A. Aboalnour, Ahmed M. Amasaib, Mohammed-Almujtaba A. Mohammed-Farah, Abdelhakam, A. Noreldien

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This paper presents a design and study of Parabolic Trough Solar Collector (PTC). Mathematical models were used in this work to find the direct and reflected solar radiation from the air layer on the surface of the earth per hour based on the total daily solar radiation on a horizontal surface. Also mathematical models had been used to calculate the radiation of the tilted surfaces. Most of the ingredients used in this project as previews data required on several solar energy applications, thermal simulation, and solar power systems. In addition, mathematical models had been used to study the flow of the fluid inside the tube (receiver), and study the effect of direct and reflected solar radiation on the pressure, temperature, speed, kinetic energy and forces of fluid inside the tube. Finally, the mathematical models had been used to study the (PTC) performances and estimate its thermal efficiency.

Keywords: CFD, experimental, mathematical models, parabolic trough, radiation

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

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Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models on two different realworld electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, machine learning, imputation, laboratory variables, algorithmic bias

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Authors: Julio Macedo

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Several methodologies are now available to conceive the improvements of a process so that it becomes competitive as for example total quality, process reengineering, six sigma, define measure analysis improvement control method. These improvements are of different nature and can be external to the process represented by an optimization model or a discrete simulation model. In addition, the process stakeholders are several and have different desired performances for the process. Hence, the methodologies above do not have a tool to aid in the conception of the required improvements. In order to fill this void we suggest the use of intelligent reference models. A reference model is a set of qualitative differential equations and an objective function that minimizes the gap between the current and the desired performance indexes of the process. The reference models are intelligent so when they receive the current state of the problematic process and the desired performance indexes they generate the required improvements for the problematic process. The reference models are fuzzy cognitive maps added with an objective function and trained using the improvements implemented by the high performance firms. Experiments done in a set of students show the reference models allow them to conceive more improvements than students that do not use these models.

Keywords: continuous improvement, fuzzy cognitive maps, process competitiveness, qualitative simulation, system dynamics

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

Procedia PDF Downloads 21