Search results for: due dates prediction

Authors: Abd El Rahman Mohamed

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Canopic jars made by the ancient Egyptians from different materials were used to preserve the viscera during the mummification process. The canopic jar studied here dates back to the Late Period (712-332 BC). It is found in the Grand Egyptian Museum (GEM), Giza, Egypt. This jar was carved from limestone and covered with a monkey head lid with painted eyes and ears with red pigment and surrounded with black pigment. The jar contains bandages of textile containing mummy viscera with resin and black resin blocks. The canopic jars were made using the sculpting tools that were used by the ancient Egyptians, such as metal chisels (made of copper) and hammers and emptying the mass of the jar from the inside using a tool invented by the ancient Egyptians, which called the emptying drill. This study also aims to use analytical techniques to identify the components of the jar, its contents, pigments, and previous restoration materials and to understand its deterioration aspects. Visual assessment, isolation and identification of fungi, optical microscopy (OM), scanning electron microscopy (SEM), X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) were used in our study. The jar showed different signs of deterioration, such as dust, dirt, stains, scratches, classifications, missing parts, and breaks; previous conservation materials include using iron wire, completion mortar and an adhesive for assembly. The results revealed that the jar was carved from Dolomite Limestone, red Hematite pigment, Mastic resin, and Linen textile bandages. The previous adhesive was Animal Glue and used Gypsum for the previous completion. The most dominant Microbial infection on the jar was found in the fungi of (Penicillium waksmanii), (Nigrospora sphaerica), (Actinomycetes sp) and (Spore-Forming Gram-Positive Bacilli). Conservation procedures have been applied with high accuracy to conserve the jar, including mechanical and chemical cleaning, re-assembling, completion and consolidation.

Keywords: Canopic jar, Consolidation, Mummification, Resin, Viscera.

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Autcha Araveeporn

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This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method.

Keywords: nonparametric regression model, penalized spline regression method, smoothing spline method, Stock Exchange of Thailand (SET)

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Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

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Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Renzo Munoz-Najar, Cristina Wong, Daniel De La Torre Ugarte

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The literature on price transmission usually deals with asymmetries related to different commodities and/or the short and long term. The role of domestic regional differences and the relationship with asymmetries within a country are usually left out. This paper looks at the asymmetry in the transmission of rice prices from the international price to the farm gate prices in four northern regions of Peru for the last period 2001-2016. These regions are San Martín, Piura, Lambayeque and La Libertad. The relevance of the study lies in its ability to assess the need for policies aimed at improving the competitiveness of the market and ensuring the benefit of producers. There are differences in planting and harvesting dates, as well as in geographic location that justify the hypothesis of the existence of differences in the price transition asymmetries between these regions. Those differences are due to at least three factors geography, infrastructure development, and distribution systems. For this, the Threshold Vector Error Correction Model and the Autoregressive Vector Model with Threshold are used. Both models, collect asymmetric effects in the price adjustments. In this way, it is sought to verify that farm prices react more to falls than increases in international prices due to the high bargaining power of intermediaries. The results of the investigation suggest that the transmission of prices is significant only for Lambayeque and La Libertad. Likewise, the asymmetry in the transmission of prices for these regions is checked. However, these results are not met for San Martin and Piura, the main rice producers nationwide. A significant price transmission is verified only in the Lambayeque and La Libertad regions. San Martin and Piura, in spite of being the main rice producing regions of Peru, do not present a significant transmission of international prices; a high degree of self-sufficient supply might be at the center of the logic for this result. An additional finding is the short-term adjustment with respect to international prices, it is higher in La Libertad compared to Lambayeque, which could be explained by the greater bargaining power of intermediaries in the last-mentioned region due to the greater technological development in the mills.

Keywords: asymmetric price transmission, rice prices, price transmission, regional economics

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Authors: Seung Cho Yang, Jae Sung Lee, Se Hee Park

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This study suggests the analysis method to predict the damages of tunnel concrete caused by fires. The result obtained from the analyses of concrete temperatures at a fire in a tunnel using ABAQUS was compared with the test result. After the reliability of the analysis method was verified, the temperatures of a tunnel at a real fire and those of concrete during the fire were estimated to predict fire damages. The temperatures inside the tunnel were estimated by FDS, a CFD model. It was deduced that the fire performance of tunnel lining and the fire damages of the structure at an actual fire could be estimated by the analysis method.

Keywords: fire resistance, heat transfer, numerical analysis, tunnel fire

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Toby Li, Julian Zhu

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In this paper, a model based on a simplified geometry is introduced to give a very conservative collision probability prediction for the Starlink satellite in its most densely clustered region. Under the model in this paper, the probability of collision for Starlink satellite where it clustered most densely is found to be 8.484 ∗ 10^−4. It is found that the predicted collision probability increased nonlinearly with the increased safety distance set. This simple model provides evidence that the continuous development of maneuver avoidance systems is necessary for the future of the orbital safety of satellites under the harsher Lower Earth Orbit environment.

Keywords: Starlink, collision probability, debris, geometry model

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Authors: F. Sabri, J. Jamali

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In this article, the wrinkling failure of orthotropic composite membranes due to in-plane shear deformation is investigated using nonlinear finite element analyses. A nonlinear post-buckling analysis is performed to show the evolution of shear-induced wrinkles. The method of investigation is based on the post-buckling finite element analysis adopted from commercial FEM code; ANSYS. The resulting wrinkling patterns, their amplitude and their wavelengths under the prescribed loads and boundary conditions were confirmed by experimental results. Our study reveals that wrinkles develop when both the magnitudes and coverage of the minimum principal stresses in the laminated composite laminates are sufficiently large to trigger wrinkling.

Keywords: composite, FEM, membrane, wrinkling

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Authors: V. Jagadish, Mathiyalagan V.

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Prediction of Door Touch temperature of a Cooking Oven using CFD Simulation. Self-Clean cycle is carried out in Cooking ovens to convert food spilling into ashes which makes cleaning easy. During this cycle cavity of oven is exposed to high temperature around 460 C. At this operating point the user may prone to touch the Door surfaces, Side Shield, Control Panel. To prevent heat experienced by user, cooling system is built in oven. The most effective cooling system is developed with existing design constraints through CFD Simulations. Cross Flow fan is used for Cooling system due to its cost effectiveness and it can give more air flow with low pressure drop.

Keywords: CFD, MRF, RBM, RANS, new product development, simulation, thermal analysis

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Authors: Saba Ebrahimi, Hedieh Ashrafi

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Predicting the cover type of forests is a challenge for natural resource managers. In this project, we aim to perform a comprehensive comparative study of two well-known classification methods, support vector machine (SVM) and decision tree (DT). The comparison is first performed among different types of each classifier, and then the best of each classifier will be compared by considering different evaluation metrics. The effect of boosting and bagging for decision trees is also explored. Furthermore, the effect of principal component analysis (PCA) and feature selection is also investigated. During the project, the forest cover-type dataset from the remote sensing and GIS program is used in all computations.

Keywords: classification methods, support vector machine, decision tree, forest cover-type dataset

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Authors: Reza Hedayati, Meysam Jahanbakhshi

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Fracture Mechanics is used to predict debonding propagation in adhesive joint between aluminum and composite plates. Three types of loadings and two types of glass-epoxy composite sequences: [0/90]2s and [0/45/-45/90]s are considered for the composite plate and their results are compared. It was seen that generally the cases with stacking sequence of [0/45/-45/90]s have much shorter lives than cases with [0/90]2s. It was also seen that in cases with λ=0 the ends of the debonding front propagates forward more than its middle, while in cases with λ=0.5 or λ=1 it is vice versa. Moreover, regardless of value of λ, the difference between the debonding propagations of the ends and the middle of the debonding front is very close in cases λ=0.5 and λ=1. Another main conclusion was the non-dimensionalized debonding front profile is almost independent of sequence type or the applied load value.

Keywords: fatigue, debonding, Paris law, APDL, adhesive

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: Adel Hanna, Tahar Ayadat, Mohammad Etezad, Cyrille Cros

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A number of theoretical methods have been developed over the years to calculate the amount settlement of the soil reinforced with group of stone columns. The results deduced from these methods sometimes show large disagreement with the experimental observations. The reason of this divergence might be due to the fact that many of the previous methods assumed the deform shape of the columns which is different with the actual case. A new method to calculate settlement of the ground reinforced with group of stone columns is presented in this paper which overcomes the restrictions made by previous theories. This method is based on results deduced from numerical modeling. Results obtained from the model are validated.

Keywords: stone columns, group, soft soil, settlement, prediction

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Authors: Etienne K. Ngoy

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This analysis suggests that the comprehensive degradation caused by any environmental factor on fiber reinforced plastics under mechanical stress can be measured as a change in viscoelastic properties of the material. The change in viscoelastic characteristics is experimentally determined as a time-dependent function expressing the amplification of the stress relaxation. The variation of this experimental function provides a measure of the environmental degradation rate. Where real service environment conditions can be reliably simulated in the laboratory, it is possible to generate master curves that include environmental degradation effect and hence predict the durability of the fiber reinforced plastics under environmental degradation.

Keywords: environmental effects, fiber reinforced plastics durability, prediction, stress effect

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Authors: Mehran Dizbadi, Daniyal Safarzadeh, Behrooz Arastoo, Ansgar Brunn

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Rapid and uncontrolled expansion of cities has led to unplanned aerial development. In this way, modeling and predicting the urban growth of a city helps decision-makers. In this study, the aspect of sustainable urban development has been studied for the city of Mashhad. In general, the prediction of urban aerial development is one of the most important topics of modern town management. In this research, using the Cellular Automaton (CA) model developed for geo data of Geographic Information Systems (GIS) and presenting a simple and powerful model, a simulation of complex urban processes has been done.

Keywords: urban modeling, sustainable development, fuzzy cellular automaton, geo-information system

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

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Authors: Lorato Itumeleng Kenosi

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Background: Negative attitudes towards the mentally ill are widespread and a course for concern as they have a detrimental impact on individuals affected by mental illness. A possible avenue for changing attitudes towards mental illness is through mental health literacy. In a college or university setting, an abnormal psychology course may be introduced in an attempt to change student’s attitudes towards the mentally ill. Objective: To determine if and how students’ attitudes towards the mentally ill change as a result of taking a course in abnormal psychology. Methods: Twenty nine (29) students were recruited from an abnormal psychology class at the University of Botswana. Attitude Scale for Mental Illness (ASMI) questionnaire was administered to participants at the beginning and end of the semester. SPSS was employed to analyze data. Pooled means were used to determine whether the student’s attitudes towards mental illness were negative or positive. A mean of 2.5 translated to negative attitude for both total attitude and attitudes in different domains of the scale. Paired sample t-test was then used to assess whether any changes noted in attitudes were statistically significant or not. Statistical significance was assumed at p < 0.05. Results: Students’ general attitude towards mental illness remained positive although the pooled mean value increased from 2.08 to 2.24. The change was not statistically significant. In relation to different sub scales, the values of the pooled means for all the sub scales showed an increase although the changes were not statistically significant except for the Stereotyping sub scale (p = 0.031). The stereotyping domain reflected a statistically significant change in student’s attitude from positive attitude to negative (X² = 2.06 to X² = 2.55). For the pessimistic prediction domain, students consistently showed a negative attitude (X² = 3.34 to X² = 3.55). The other 4 domains indicated that students had positive attitude toward mentally ill throughout. Discussion: Abnormal psychology students have a positive attitude towards the mentally ill generally. This could be attributed to the fact that all students in the abnormal psychology course are majoring in psychology and research has shown that interest in psychology can affect one’s attitude towards mental illness. The students continuously held the view that people with mental illness are unlikely to improve as evidenced by a high score for Pessimistic prediction domain for both pre and post-test. Students initially had no stereotyping attitude towards the mentally ill, but at the end of the course, they were of the opinion that people with mental illness can be defined in a certain behavioural pattern and mental ability. This results could be an indication that students have learnt well how to differentiate abnormal from normal behaviour not necessarily that students had developed a negative attitude. Conclusion: A course in abnormal psychology does have an impact on the students’ attitudes towards the mentally ill. The impact does not solely depend on knowledge of mental illness but also on several other factors such as contact with the mentally ill, interest in psychology, and teaching methods. However, it should be noted that sometimes improved knowledge in mental illness can be misunderstood for a negative attitude. For example, stereotyping attitudes may be a reflection of the ability to differentiate between abnormal and normal behaviour.

Keywords: attitudes, mental illness, psychopathology, students

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Authors: Nurlaila Effendy

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Companies in Asia face a number of constraints in tight competitiveness in ASEAN Economic Community 2015 and globalization. An economic capitalism system as an integral part of globalization processing brings broad impacts. They need to improve business performance in globalization and ASEAN Economic Community. Organizational development has quite clearly demonstrated that aligning individual’s personal goals with the goals of the organization translates into measurable and sustained performance improvement. Human capital is a key to achieve company performance. Employee Engagement (EE) creates and expresses themselves physically, cognitively and emotionally to achieve company goals and individual goals. One will experience a total involvement when they undertake their jobs and feel a self integration to their job and organization. A leader plays key role in attaining the goals and objectives of a company/organization. Any Manager in a company needs to have leadership competence and global mindset. As one the of positive organizational behavior developments, psychological capital (PsyCap) is assumed to be one of the most important capitals in the global mindset, in addition to intellectual capital and social capital. Textile companies also need to face a number of constraints in tight competitiveness in regional and global. This research involved 42 managers in two textiles and a spinning companies in a group, in Central Java, Indonesia. It is a quantitative research with Partial Least Squares (PLS) studying job resource (Social Support & Organizational Climate) and Personal Resource (4 dimensions of Psychological Capital & Leadership Competence) as prediction of Employee Engagement, also Employee Engagement and leadership competence as prediction of leader’s performance. The performance of a leader is measured by means of achievement on objective strategies in terms of 4 perspectives (financial and non-financial perspectives) in a Balanced Score Card (BSC). It took one year during a business plan of year 2014, from January to December 2014. The result of this research is there is correlation between Job Resource (coefficient value of Social Support is 0.036 & coefficient value of organizational climate is 0.220) and Personal Resource (coefficient value of PsyCap is 0.513 & coefficient value of Leadership Competence is 0.249) with employee engagement. There is correlation between employee engagement (coefficient value is 0.279) and leadership competence (coefficient value is 0.581) with performance.

Keywords: organizational climate, social support, psychological capital leadership competence, employee engagement, performance, integrated textile companies

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Authors: R. Andreola, O. A. A. Santos, L. M. M. Jorge

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An industrial system for the production of white liquor of a paper industry, Klabin Paraná Papé is, formed by ten reactors was modeled, simulated, and analyzed. The developed model considered possible water losses by evaporation and reaction, in addition to variations in volumetric flow of lime mud across the reactors due to composition variations. The model predictions agreed well with the process measurements at the plant and the results showed that the slaking reaction is nearly complete at the third causticizing reactor, while causticizing ends by the seventh reactor. Water loss due to slaking reaction and evaporation occurs more pronouncedly in the slaking reaction than in the final causticizing reactors; nevertheless, the lime mud flow remains nearly constant across the reactors.

Keywords: causticizing, lime, prediction, process

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Busra Mutlu Ipek, Merve Mutlu, Ahmet Mutlu

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Novel coronavirus COVID-19, which has recently influenced the world, poses a great threat to humanity. In order to overcome this challenging situation, scientists are working on developing effective vaccine against coronavirus. Many experts and researchers have also produced articles and done studies on this highly important subject. In this direction, this special topic was chosen for article to make a contribution to this area. The purpose of this article is to perform RNA sequence analysis of selected virus forms in the Coronaviridae family and predict/classify SARS-CoV-2 (COVID-19) from other selected complete genomes in coronaviridae family using proposed Artificial Neural Network(ANN) algorithm.

Keywords: Coronaviridae family, COVID-19, RNA sequencing, ANN, neural network

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Authors: Lívia B. Meirelles, Erika C. A. N. Chrisman, Flávia B. de Andrade, Lilian C. M. de Oliveira

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True Boiling Point distillation (TBP) is one of the most common experimental techniques for the determination of petroleum properties. This curve provides information about the performance of petroleum in terms of its cuts. The experiment is performed in a few days. Techniques are used to determine the properties faster with a software that calculates the distillation curve when a little information about crude oil is known. In order to evaluate the accuracy of distillation curve prediction, eight points of the TBP curve and specific gravity curve (348 K and 523 K) were inserted into the HYSYS Oil Manager, and the extended curve was evaluated up to 748 K. The methods were able to predict the curve with the accuracy of 0.6%-9.2% error (Software X ASTM), 0.2%-5.1% error (Software X Spaltrohr).

Keywords: distillation curve, petroleum distillation, simulation, true boiling point curve

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Authors: Layal Mansour

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The aim of this paper is to identify whether oil price volatility or geopolitical risks can predict future financial stress periods or economic recessions in Kuwait. We construct the first Financial Stress Index for Kuwait (FSIK) that includes informative vulnerable indicators of the main financial sectors: the banking sector, the equities market, and the foreign exchange market. The study covers the period from 2000 to 2020, so it includes the two recent most devastating world economic crises with oil price fluctuation: the Covid-19 pandemic crisis and Ukraine-Russia War. All data are taken by the central bank of Kuwait, the World Bank, IMF, DataStream, and from Federal Reserve System St Louis. The variables are computed as the percentage growth rate, then standardized and aggregated into one index using the variance equal weights method, the most frequently used in the literature. The graphical FSIK analysis provides detailed information (by dates) to policymakers on how internal financial stability depends on internal policy and events such as government elections or resignation. It also shows how monetary authorities or internal policymakers’ decisions to relieve personal loans or increase/decrease the public budget trigger internal financial instability. The empirical analysis under vector autoregression (VAR) models shows the dynamic causal relationship between the oil price fluctuation and the Kuwaiti economy, which relies heavily on the oil price. Similarly, using vector autoregression (VAR) models to assess the impact of the global geopolitical risks on Kuwaiti financial stability, results reveal whether Kuwait is confronted with or sheltered from geopolitical risks. The Financial Stress Index serves as a guide for macroprudential regulators in order to understand the weakness of the overall Kuwaiti financial market and economy regardless of the Kuwaiti dinar strength and exchange rate stability. It helps policymakers predict future stress periods and, thus, address alternative cushions to confront future possible financial threats.

Keywords: Kuwait, financial stress index, causality test, VAR, oil price, geopolitical risks

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Authors: Cemile Kütmeç Yilmaz, Güler Duru Asiret, Gulcan Bagcivan

Abstract:

The aim of this study was to evaluate the reliability and validity of the Turkish version of the Carer’s Assessment of Satisfaction Index (CASI-TR). The study was conducted between the dates of June 2016 and September 2017 at the Training and Research Hospital of Aksaray University with the caregiving family members of the inpatients with chronic diseases. For this study, the sample size was calculated as at least 10 individuals for each item (item number (30)X10=300). The study sample included 300 caregiving family members, who provided primer care for at least three months for a patient (who had at least one chronic disease and received inpatient treatment in general internal medicine and palliative care units). Data were collected by using a demographic questionnaire and CASI-TR. Descriptive statistics, and psychometric tests were used for the data analysis. Of those caregivers, 76.7% were female, 86.3% were 65 years old and below, 43.7% were primary school graduates, 87% were married, 86% were not working, 66.3% were housewives, and 60.3% defined their income status as having an income covering one’s expenses. Care recipients often had problems in terms of walking, sleep, balance, feeding and urinary incontinence. The Cronbach Alpha value calculated for the CASI-TR (30 items) was 0,949. Internal consistency coefficients calculated for subscales were: 0.922 for the subscale of ‘caregiver satisfaction related to care recipient’, 0.875 for the subscale of ‘caregiver satisfaction related to themselves’, and 0.723 for the subscale of ‘dynamics of interpersonal relations’. Factor analysis revealed that three factors accounted for 57.67% of the total variance, with an eigenvalue of >1. assessed in terms of significance, we saw that the items came together in a significant manner. The factor load of the items were between 0.311 and 0.874. These results show that the CASI-TR is a valid and reliable scale. The adoption of the translated CASI in Turkey is found reliable and valid to assessing the satisfaction of caregivers. CASI-TR can be used easily in clinics or house visits by nurses and other health professionals for assessing caregiver satisfaction from caregiving.

Keywords: carer’s assessment of satisfaction index, caregiver, validity, reliability

Procedia PDF Downloads 204

Authors: Darrol Stanley, Levan Efremidze, Jannie Rossouw

Abstract:

We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets.

Keywords: currency trading, entropy, market timing, risk factor model

Procedia PDF Downloads 271