Search results for: mixed effects models

Authors: M. Hosseinnezhad, K. Gharanjig

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Natural dyes, extracted from black carrot and bramble, were utilized as photosensitizers to prepare dye-sensitized solar cells (DSSCs). Spectrophotometric studies of the natural dyes in solution and on a titanium dioxide substrate were carried out in order to assess changes in the status of the dyes. The results show that the bathochromic shift is seen on the photo-electrode substrate. The chemical binding of the natural dyes at the surface photo-electrode were increased by the chelating effect of the Ti(IV) ions. The cyclic voltammetry results showed that all extracts are suitable to be performed in DSSCs. Finally, photochemical performance and stability of DSSCs based on natural dyes were studied. The DSSCs sensitized by black carrot extract have been reported to achieve up to Jsc=1.17 mAcm^-2, Voc= 0.55 V, FF= 0.52, η=0.34%, whereas Bramble extract can obtain up to Jsc=2.24 mAcm^-2, Voc= 0.54 V, FF= 0.57, η=0.71%. The power conversion efficiency was obtained from the mixed dyes in DSSCs. The power conversion efficiency of dye-sensitized solar cells using mixed Black carrot and Bramble dye is the average of the their efficiency in single DSSCs.

Keywords: anthocyanin, dye-sensitized solar cells, green energy, optical materials

Procedia PDF Downloads 227

Authors: Zh. Saffari, A. Naeimi, M. S. Ekrami-Kakhki, Kh. Khandan-Barani

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The textile industries are becoming a major source of environmental contamination because an alarming amount of dye pollutants are generated during the dyeing processes. Organic dyes are one of the largest pollutants released into wastewater from textile and other industrial processes, which have shown severe impacts on human physiology. Nano-structure compounds have gained importance in this category due their anticipated high surface area and improved reactive sites. In recent years several novel adsorbents have been reported to possess great adsorption potential due to their enhanced adsorptive capacity. Nano-MnO2 has great potential applications in environment protection field and has gained importance in this category because it has a wide variety of structure with large surface area. The diverse structures, chemical properties of manganese oxides are taken advantage of in potential applications such as adsorbents, sensor catalysis and it is also used for wide catalytic applications, such as degradation of dyes. In this study, adsorption of Methyl Violet (MV) dye from aqueous solutions onto MnO2 nanoparticles (MNP) has been investigated. The surface characterization of these nano particles was examined by Particle size analysis, Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) spectroscopy and X-Ray Diffraction (XRD). The effects of process parameters such as initial concentration, pH, temperature and contact duration on the adsorption capacities have been evaluated, in which pH has been found to be most effective parameter among all. The data were analyzed using the Langmuir and Freundlich for explaining the equilibrium characteristics of adsorption. And kinetic models like pseudo first- order, second-order model and Elovich equation were utilized to describe the kinetic data. The experimental data were well fitted with Langmuir adsorption isotherm model and pseudo second order kinetic model. The thermodynamic parameters, such as Free energy of adsorption (ΔG°), enthalpy change (ΔH°) and entropy change (ΔS°) were also determined and evaluated.

Keywords: MnO2 nanoparticles, adsorption, methyl violet, isotherm models, kinetic models, surface chemistry

Procedia PDF Downloads 246

Authors: Bret Brooks

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As the international community has focused their attention in recent times on international and transnational terrorism, many nations have ignored their own domestic terrorist groups. Domestic terrorism has significantly evolved over the last 15 years and as such nation states must adequately understand their own individual issues as well as the broader worldwide perspective. Contemporary models show that obtaining peace with domestic groups is not only the end goal, but also very obtainable. By evaluating modern examples and incorporating successful strategies, countries around the world have the ability to bring about a diplomatic resolution to domestic extremism and domestic terrorism.

Keywords: domestic, evolution, peace, terrorism

Procedia PDF Downloads 491

Authors: Valizadeh Gever Bita, Joel Caren, Louisa Huand, Yu-Cheng Zhu, Esmaeil Amiri

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Within a honey bee colony, queen is the sole reproducer of fertilized eggs, while queens are safeguarded by worker bees, trophallactic behavior and food sharing activities could expose them to agrochemicals. Here, we assessed the effects of three widely used pesticides—Acephate, Bifenthrin, and Chlorantraniliprole— on worker bees, to investigate indirect effects on the physiology and reproductive traits of queens as well as the eggs they produce. Using RT-qPCR we measured the expression of several detoxification and immune genes in adult worker bees, queens, and freshly laid eggs after pesticide exposure. These analyses aimed to elucidate the physiological changes in queens and potential transgenerational effects. While no significant changes in reproductive traits were observed following Chlorantraniliprole and Bifenthrin exposure, Acephate caused adverse effects on egg size, egg-laying activity, and queen weight. The expression of detoxification, immune and antioxidant-related genes in workers, queens and freshly laid eggs changed over time in response to these pesticides. The results of this investigation revealed that pesticides can cause negative impact on queen physiology and reproduction indirectly through their effects on exposed worker bees. These effects can potentially extend to the next generation of honey bees.

Keywords: apis mellifera, egg laying, detoxification enzymes, gene expression, honey bee queen

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Authors: Yuchao Yao

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As two of the main characteristics of Chinese demographic trends, increasing divorce rates and decreasing fertility rates both shaped the population structure in the recent decade. Figuring out to what extent can be having a child make a difference in the divorce rate of a couple will not only draw a picture of Chinese families but also bring about a new perspective to evaluate the Chinese child-breeding policies. Based on China Family Panel Studies (CFPS) Data 2010-2018, this paper provides a systematic evaluation of how children influence a couple’s marital stability through a series of empirical models. Using survival analysis and propensity score matching (PSM) model, this paper finds that the number and age of children that a couple has mattered in consolidating marital relationship, and these effects vary little over time; during the last decade, newly having children can in fact decrease the possibility of divorce for Chinese couples; the such decreasing effect is largely due to the birth of a second child. As this is an inclusive attempt to study and compare not only the effects but also the causality of children on divorce risk in the last decade, the results of this research will do a good summary of the status quo of divorce in China. Furthermore, this paper provides implications for further reforming the current marriage and child-breeding policies.

Keywords: divorce risk, fertility, China, survival analysis, propensity score matching

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Authors: Shay Kee Tan, Kok Haur Ng, Jennifer So-Kuen Chan

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This paper extends the conditional autoregressive range (CARR) model to multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain in-sample estimates and forecasts of volatilities for these individual and pairwise sum series. Then covariances are calculated to construct the fitted variance-covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets’ returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions with the scaled realised open-to-close volatility measure as the proxy for the unobserved true volatility. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba, Engle, Kraft, and Kroner type of generalized autoregressive conditional heteroskedasticity (BEKK-GARCH) models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using Kupiec test to confirm the accuracy of the VaR forecasts.

Keywords: range-based volatility, correlation, multivariate CARR-return model, value-at-risk, conditional value-at-risk

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Authors: Proud Arunrangsiwed

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The part of “future direction” in the findings of meta-analysis could provide the great direction to conduct the future studies. This study, “The Documentary Analysis of Meta-Analysis Research in Violence of Media” would conclude “future directions” out of 10 meta-analysis papers. The purposes of this research are to find an appropriate research design or an appropriate methodology for the future research related to the topic, “violence of media”. Further research needs to explore by longitudinal and experimental design, and also needs to have a careful consideration about age effects, time spent effects, enjoyment effects, and ordinary lifestyle of each media consumer.

Keywords: aggressive, future direction, meta-analysis, media, violence

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Authors: Mouhamadou Diop, Mohamed I. Hassan

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Molten metallic flow in metallurgical plant is highly turbulent and presents a complex coupling with heat transfer, phase transfer, chemical reaction, momentum transport, etc. Molten silicon flow has significant effect in directional solidification of multicrystalline silicon by affecting the temperature field and the emerging crystallization interface as well as the transport of species and impurities during casting process. Owing to the complexity and limits of reliable measuring techniques, computational models of fluid flow are useful tools to study and quantify these problems. The overall objective of this study is to investigate the potential of a traveling magnetic field for an efficient operating control of the molten metal flow. A multidimensional numerical model will be developed for the calculations of Lorentz force, molten metal flow, and the related phenomenon. The numerical model is implemented in a laboratory-scale silicon crystallization furnace. This study presents the potential of traveling magnetic field approach for an efficient operating control of the molten flow. A numerical model will be used to study the effects of magnetic force applied on the molten flow, and their interdependencies. In this paper, coupled and decoupled, steady and unsteady models of molten flow and crystallization interface will be compared. This study will allow us to retrieve the optimal traveling magnetic field parameter range for crystallization furnaces and the optimal numerical simulations strategy for industrial application.

Keywords: multidimensional, numerical simulation, solidification, multicrystalline, traveling magnetic field

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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Authors: Slim Namouchi, Bruno Vallet, Imed Riadh Farah

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3D urban models provide powerful tools for decision making, urban planning, and smart city services. The accuracy of this 3D based systems is directly related to the quality of these models. Since manual large-scale modeling, such as cities or countries is highly time intensive and very expensive process, a fully automatic 3D building generation is needed. However, 3D modeling process result depends on the input data, the proprieties of the captured objects, and the required characteristics of the reconstructed 3D model. Nowadays, producing 3D real-world model is no longer a problem. Remotely sensed data had experienced a remarkable increase in the recent years, especially data acquired using unmanned aerial vehicles (UAV). While the scanning techniques are developing, the captured data amount and the resolution are getting bigger and more precise. This paper presents a literature review, which aims to identify different methods of automatic 3D buildings extractions either from LiDAR or the combination of LiDAR and satellite or aerial images. Then, we present open source technologies, and data models (e.g., CityGML, PostGIS, Cesiumjs) used to integrate these models in geospatial base layers for smart city services.

Keywords: CityGML, LiDAR, remote sensing, SIG, Smart City, 3D urban modeling

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Authors: Qian Jin

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Museums have been using interactive technology to spark visitor interest and improve understanding. These technologies can play a crucial role in helping visitors understand more about an exhibition site by using multimedia to provide information. Google Arts and Culture and Smartify are two very successful digital heritage products. They used mobile augmented reality to visualise the museum's 3D models and heritage images but did not include 3D models of the collection and audio information. In this research, service-oriented mobile augmented reality application was developed for users to access collections from multiple museums(including V and A, the British Museum, and British Library). The third-party API (Application Programming Interface) is requested to collect metadata (including images, 3D models, videos, and text) of three museums' collections. The acquired content is then visualized in AR environments. This product will help users who cannot visit the museum offline due to various reasons (inconvenience of transportation, physical disability, time schedule).

Keywords: digital heritage, argument reality, museum, flutter, ARcore

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Doaa Abbas Zaher

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For the past two decades demand for knowledge has been increasing. Management of society’s knowledge has far reaching effects to economic growth through increased capacity to complete viable activities. Organizations use information technology to effect organizational change. This is a challenge for the less developed nations whose capacity to integrate knowledge in core functions is limited. This creates organizational problems as there is global competition amongst organizations. Cross-cultural perceptions influence difference knowledge Management. This study provides a cross-cultural analysis on the influence in knowledge culture in Japan and Saudi Arabia to effect change in organizations. Since different countries adopt different knowledge management strategies, this dictates the state of organizational development through enriched organizational culture. The research uses a mixed approach design to collect data from primary and secondary sources. Primary source will use the questionnaires while secondary sources uses case analysis from books, articles, reports, and journals. The study will take a period of three years to come up with a complete paper.

Keywords: knowledge management, organizational culture, information, society knowledge

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Authors: Julaine Allan, Jacqui Cameron, Helen Simpson, Kenny Kor

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The pleasurable and intoxicating effects of psychoactive substances result in widespread use. However, deaths and injuries from psychoactive substance use, particularly among young people, are a global public health problem. Understanding the benefits and problems associated with different drugs is an important part of creating contextually and physiologically relevant harm reduction strategies. Nitrous oxide use is increasing. A systematic review sought information for harm reduction strategies. The aim of this study was to systematically collate and synthesize the disparate body of research on recreational nitrous oxide use to inform harm reduction approaches tailored for young people. A mixed-methods systematic review combined quantitative data such as prevalence and incidence statistics as well as interpretive data on the experience of N₂O use. Thirty-four studies were included in the final analysis. There was minimal information available to inform policy, health care, or individuals using N₂O. The cultural, contextual, and personal reasons for N₂O use are largely unexplored.

Keywords: substance misuse, nitrous oxide, harms, harm reduction, systematic review

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Authors: Jaan-Willem Simon, Bertram Stier, Brett Bednarcyk, Evan Pineda, Stefanie Reese

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Textile composites, in which the reinforcing fibers are woven or braided, have become very popular in numerous applications in aerospace, automotive, and maritime industry. These textile composites are advantageous due to their ease of manufacture, damage tolerance, and relatively low cost. However, physics-based modeling of the mechanical behavior of textile composites is challenging. Compared to their unidirectional counterparts, textile composites introduce additional geometric complexities, which cause significant local stress and strain concentrations. Since these internal concentrations are primary drivers of nonlinearity, damage, and failure within textile composites, they must be taken into account in order for the models to be predictive. The macro-scale approach to modeling textile-reinforced composites treats the whole composite as an effective, homogenized material. This approach is very computationally efficient, but it cannot be considered predictive beyond the elastic regime because the complex microstructural geometry is not considered. Further, this approach can, at best, offer a phenomenological treatment of nonlinear deformation and failure. In contrast, the mesoscale approach to modeling textile composites explicitly considers the internal geometry of the reinforcing tows, and thus, their interaction, and the effects of their curved paths can be modeled. The tows are treated as effective (homogenized) materials, requiring the use of anisotropic material models to capture their behavior. Finally, the micro-scale approach goes one level lower, modeling the individual filaments that constitute the tows. This paper will compare meso- and micro-scale approaches to modeling the deformation, damage, and failure of textile-reinforced polymer matrix composites. For the mesoscale approach, the woven composite architecture will be modeled using the finite element method, and an anisotropic damage model for the tows will be employed to capture the local nonlinear behavior. For the micro-scale, two different models will be used, the one being based on the finite element method, whereas the other one makes use of an embedded semi-analytical approach. The goal will be the comparison and evaluation of these approaches to modeling textile-reinforced composites in terms of accuracy, efficiency, and utility.

Keywords: multiscale modeling, continuum damage model, damage interaction, textile composites

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Authors: Kailash C. Madan

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We study four models of a three server queueing system with Bernoulli schedule optional server vacations. Customers arriving at the system one by one in a Poisson process are provided identical exponential service by three parallel servers according to a first-come, first served queue discipline. In model A, all three servers may be allowed a vacation at one time, in Model B at the most two of the three servers may be allowed a vacation at one time, in model C at the most one server is allowed a vacation, and in model D no server is allowed a vacation. We study steady the state behavior of the four models and obtain steady state probability generating functions for the queue size at a random point of time for all states of the system. In model D, a known result for a three server queueing system without server vacations is derived.

Keywords: a three server queue, Bernoulli schedule server vacations, queue size distribution at a random epoch, steady state

Procedia PDF Downloads 287

Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Hamid Fazlollahi, Behzad Rafezy, Hassan Afshin

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There masonry infill increases the stiffness of reinforced concrete frames, thus increasing the force of the earthquake also the interaction between the frame and infill, which can have devastating effects on structures. In contrast presence of infill to increase the structural strength and stability. What is seen in the construction and design of structures has largely ignored the effects of infill and regardless infill structure and its positive and negative effects analyzes and designs, that it is not economically justified and the positive effects of positive infill to be increased and almost all of the useful capacity of moment frames used for infill. In this paper, by using ABAQUS software, reinforced concrete frame with masonry infill will be modeled, then add a mechanical rubber element to modify the interaction between the frame and infill and thus reduce the losses caused by the presence of infill explains. Finally, by comparing the analytical curves, benefits of this approach we will study and to present the results of the interaction between the frame and infill masonry needs modification and methods it will provide.

Keywords: masonry infill, mechanical rubber, reinforced concrete frame, interaction, ductility

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Authors: Deepa Kapoor, Rajshree Singh

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This paper appreciates the emergence and growing importance as a new production factor makes the development of technologies, methodologies and strategies for measurement, creation, and diffusion into one of the main priorities of the organizations in the knowledge society. There are many models for creation and management of knowledge and diverse and varied perspectives for study, analysis, and understanding. In this article, we will conduct a theoretical approach to the type of models for the creation and management of knowledge; we will discuss some of them and see some of the difficulties and the key factors that determine the success of the processes for the creation and management of knowledge.

Keywords: knowledge creation, knowledge management, organizational development, organization learning

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Authors: A. M. Ahmed, Mona A. Darwish

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Removal of Ni++ ions from aqueous solution by sorption ontoNano-bentonite was investigated. Experiments were carried out as a function amount of Nano-bentonite, pH, concentration of metal, constant time, agitation speed and temperature. The adsorption parameter of metal ions followed the Langmuir Freundlich adsorption isotherm were applied to analyze adsorption data. The adsorption process has fit pseudo-second order kinetic models. Thermodynamics parameters e.g.ΔG*, ΔS °and ΔH ° of adsorption process have also been calculated and the sorption process was found to be endothermic. The adsorption process has fit pseudo-second order kinetic models. Langmuir and Freundich adsorption isotherm models were applied to analyze adsorption data and both were found to be applicable to the adsorption process. Thermodynamic parameters, e.g., ∆G °, ∆S ° and ∆H ° of the on-going adsorption process have also been calculated and the sorption process was found to be endothermic. Finally, it can be seen that Bentonite was found to be more effective for the removal of Ni (II) same with some experimental conditions.

Keywords: waste water, nickel, bentonite, adsorption

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Authors: Anna Dabrowska

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Taking Daniel Goleman’s (1994) belief that success in life depends on IQ in 20% and in 80% on emotional intelligence, and that it is worth considering emotional intelligence as an important factor in human performance and development potential, the aim of the paper is to explore the range of emotions experienced by university students who represent Society 5.0. This quantitative and qualitative study is meant to explore not only the list of the most and least experienced emotions by the students, but also the main reasons behind these feelings. The database of the study consists of 115 respondents out of 129 students of the 1st and 5th year of Applied Linguistics at Maria Curie-Skłodowska University, which constitutes 89% of those being surveyed. The data is extracted from the anonymous questionnaire, which comprises young people’s answers and discourse concerning the causes of their most experienced emotions. Following Robert Plutchik’s theory of eight primary emotions, i.e. anger, fear, sadness, disgust, surprise, anticipation, trust, and joy, we adopt his argument for the primacy of these emotions by showing each to be the trigger of behaviour with high survival value. In fact, all other emotions are mixed or derivative states; that is, they occur as combinations, mixtures, or compounds of the primary emotions. Accordingly, the eight primary emotions, and their mixed states, are checked in the study on the students.

Keywords: emotions, intelligence, students, discourse study, emotional intelligence

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Authors: Karolína Vozková, Matěj Kuc

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This paper deals with the impact of decrease in interest rates on the performance of commercial and cooperative banks in the Eurozone measured by net interest margin. The analysis was performed on balanced dataset of 268 commercial and 726 cooperative banks spanning the 2008-2015 period. We employed Fixed Effects estimation panel method. As expected, we found a negative relationship between market rates and net interest margin. Our results suggest that the impact of negative interest income differs across individual banking business models. More precisely, those cooperative banks were much more hit by the decrease of market interest rates which might be due to their ownership structure and more restrictive business regulation.

Keywords: cooperative banks, performance, negative interest rates, risk management

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Authors: Carlos Theran, Yohn Parra Bautista, Victor Adankai, Richard Alo, Jimwi Liu, Clement G. Yedjou

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Alzheimer's disease (AD) is a neurodegenerative disorder primarily characterized by deteriorating cognitive functions. AD has gained relevant attention in the last decade. An estimated 24 million people worldwide suffered from this disease by 2011. In 2016 an estimated 40 million were diagnosed with AD, and for 2050 is expected to reach 131 million people affected by AD. Therefore, detecting and confirming AD at its different stages is a priority for medical practices to provide adequate and accurate treatments. Recently, Machine Learning (ML) models have been used to study AD's stages handling missing values in multiclass, focusing on the delineation of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and normal cognitive (CN). But, to our best knowledge, robust performance information of these models and the missing data analysis has not been presented in the literature. In this paper, we propose studying the performance of five different machine learning models for AD's stages multiclass prediction in terms of accuracy, precision, and F1-score. Also, the analysis of three imputation methods to handle the missing value problem is presented. A framework that integrates ML model for AD's stages multiclass prediction is proposed, performing an average accuracy of 84%.

Keywords: alzheimer's disease, missing value, machine learning, performance evaluation

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Authors: Y. Sunita Rani, Y. Hari Krishna, M. V. Ramana Murthy, K. Sudhaker Reddy

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This article studied effects of radiation and chemical reaction on MHD casson fluoid flow past a Permeable Stretching Sheet in a Porous Medium. Suitable transformations are considered to transform the governing partial differential equations as ordinary ones and then solved by the numerical procedures like Runge- Kutta – Fehlberg shooting technique method. The effects of various governing parameters, on the velocity, temperature and concentration are displayed through graphs and discussed numerically.

Keywords: MHD, Casson fluid, porous medium, permeable stretching sheet

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Authors: Ines Gasmi, Salima Smiti, Makram Soui, Khaled Ghedira

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Credit risk is considered as one of the important issues for financial institutions. It provokes great losses for banks. To this objective, numerous methods for credit risk evaluation have been proposed. Many evaluation methods are black box models that cannot adequately reveal information hidden in the data. However, several works have focused on building transparent rules-based models. For credit risk assessment, generated rules must be not only highly accurate, but also highly interpretable. In this paper, we aim to build both, an accurate and transparent credit risk evaluation model which proposes a set of classification rules. In fact, we consider the credit risk evaluation as an optimization problem which uses a genetic programming (GP) algorithm, where the goal is to maximize the accuracy of generated rules. We evaluate our proposed approach on the base of German and Australian credit datasets. We compared our finding with some existing works; the result shows that the proposed GP outperforms the other models.

Keywords: credit risk assessment, rule generation, genetic programming, feature selection

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP.

Keywords: Chinese restaurant process, Dirichlet prior, infinite mixture model, PCR stutter

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Authors: Pierluigi Gallo, Francesco Randazzo, Ignazio Gallo

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In recent years, many new and interesting models of successful online business have been developed. Many of these are based on the competition between users, such as online auctions, where the product price is not fixed and tends to rise. Other models, including group-buying, are based on cooperation between users, characterized by a dynamic price of the product that tends to go down. There is not yet a business model in which both sellers and buyers are grouped in order to negotiate on a specific product or service. The present study investigates a new extension of the group-buying model, called fair, which allows aggregation of demand and supply for price optimization, in a cooperative manner. Additionally, our system also aggregates products and destinations for shipping optimization. We introduced the following new relevant input parameters in order to implement a double-side aggregation: (a) price-quantity curves provided by the seller; (b) waiting time, that is, the longer buyers wait, the greater discount they get; (c) payment time, which determines if the buyer pays before, during or after receiving the product; (d) the distance between the place where products are available and the place of shipment, provided in advance by the buyer or dynamically suggested by the system. To analyze the proposed model we implemented a system prototype and a simulator that allows studying effects of changing some input parameters. We analyzed the dynamic price model in fairs having one single seller and a combination of selected sellers. The results are very encouraging and motivate further investigation on this topic.

Keywords: auction, aggregation, fair, group buying, social buying

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Authors: Tahani Salman Alangari

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Student success can be impacted by internal factors such as their emotional well-being and external factors such as organizational support and instructional support in the classroom. This study is to identify at least one factor that forecasts student engagement. It is a cross-sectional, conducted on 6206 teachers and encompassed three years of data collection and observations of math instruction in approximately 50 schools and 300 classrooms. A multiple linear regression revealed that a model predicting student engagement from emotional support, classroom organization, and instructional support was significant. Four linear regression models were tested using hierarchical regression to examine the effects of independent variables: emotional support was the highest predictor of student engagement while instructional support was the lowest.

Keywords: student engagement, emotional support, organizational support, instructional support, well-being

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Authors: Anita Setianingrum, Oki S. Jaya, Zuherman Rustam

Abstract:

Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy.

Keywords: ANFIS, fuzzy time series, stock forecasting, SVR

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Authors: S. A. Al-Qallaf, S. A. Al-Mawsawi, A. Haider

Abstract:

Among all FACTS devices, the unified power flow controller (UPFC) is considered to be the most versatile device. This is due to its capability to control all the transmission system parameters (impedance, voltage magnitude, and phase angle). With the growing interest in UPFC, the attention to develop a mathematical model has increased. Several models were introduced for UPFC in literature for different type of studies in power systems. In this paper a novel comparison study between two dynamic models of UPFC with their proposed control strategies.

Keywords: FACTS, UPFC, dynamic modeling, PWM, fundamental frequency

Procedia PDF Downloads 335