Search results for: mathematical programming model

Authors: Henrique C. C. Andrade, Ana Beatriz C. G. Silva, Fernando Luiz B. Ribeiro, Samir Maghous, Jose Claudio F. Telles, Eduardo M. R. Fairbairn

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The present paper aims at developing a parallel computational model for numerical simulation of poromechanics analyses of heterogeneous reservoirs. In the context of macroscopic poroelastoplasticity, the hydromechanical coupling between the skeleton deformation and the fluid pressure is addressed by means of two constitutive equations. The first state equation relates the stress to skeleton strain and pore pressure, while the second state equation relates the Lagrangian porosity change to skeleton volume strain and pore pressure. A specific algorithm for local plastic integration using a tangent operator is devised. A modified Cam-clay type yield surface with associated plastic flow rule is adopted to account for both contractive and dilative behavior.

Keywords: finite element method, poromechanics, poroplasticity, reservoir analysis

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Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Kerryn Krige, Kerrin Myers

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Social entrepreneurship offers the opportunity to simultaneously address both social and economic inequality in South Africa. Its appeal across racial groups, its attractiveness to young people, its applicability in rural and peri-urban markets, and its acceleration in middle income, large-business economies suits the South African context. However, the potential to deliver much-needed developmental benefits has not been realised because the social entrepreneurship debate lacks evidence as to who social entrepreneurs are, their goals and operations and the socio-economic results they achieve. As a result, policy development has been stunted, and legislative barriers and red tape remain. Social entrepreneurs are isolated from the mainstream economy, and struggle to access funding because of limitations in legislative and organisational structures. The objective of the study is to strengthen the ecosystem for social entrepreneurship in South Africa by producing robust, policy-rich information from and about social enterprises currently in operation across the country. The study employs a quantitative survey methodology, using online and telephonic data collection methods. A purposive sample of 1000 social enterprises was included in the first large-scale study of social entrepreneurship in South Africa. The results offer deep insight into the characteristics of social enterprises; the activities they undertake and the markets they serve; their modes of operation and funding sources as well as key challenges and support systems. The results contribute towards developing a model of social enterprise in the African context.

Keywords: social enterprise, key characteristics, challenges and enablers, towards an African model

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Mai M. Farid, Ximeng Yang, Tomoharu Kuboyama, Chihiro Tohda

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Trigonelline is a major alkaloid component derived from Trigonella foenum-graecum L. (fenugreek) and has been reported before as a potential neuroprotective agent, especially in Alzheimer’s disease (AD). However, the previous data were unclear and used model mice were not well established. In the present study, the effect of trigonelline on memory function was investigated in Alzheimer’s disease transgenic model mouse, 5XFAD which overexpresses the mutated APP and PS1 genes. Oral administration of trigonelline for 14 days significantly enhanced object recognition and object location memories. Plasma and cerebral cortex were isolated at 30 min, 1h, 3h, and 6 h after oral administration of trigonelline. LC-MS/MS analysis indicated that trigonelline was detected in both plasma and cortex from 30 min after, suggesting good penetration of trigonelline into the brain. In addition, trigonelline significantly ameliorated axonal and dendrite atrophy in Amyloid β-treated cortical neurons. These results suggest that trigonelline could be a promising therapeutic candidate for AD.

Keywords: alzheimer’s disease, cortical neurons, LC-MS/MS analysis, trigonelline

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Authors: Ahmed Alaoui

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This paper contributes to the ongoing debate as to the relevance of translation studies to professional practitioners. It exposes the various misconceptions permeating the links between theory and practice in the translation landscape in the Arab World. It is a thesis of this paper that specialization in translation should be redefined; taking account of the fact, that specialized knowledge alone is neither crucial nor sufficient in technical translation. It should be tested against the readability of the translated text, the appropriateness of its style and the usability of its content by end-users to carry out their intended tasks. The paper also proposes a preliminary model to establish a working link between theory and practice from the perspective of professional trainers and practitioners, calling for the latter to participate in the production of knowledge in a systematic fashion. While this proposal is driven by a rather intuitive conviction, a research line is needed to specify the methodological moves to establish the mediation strategies that would relate the components in the model of knowledge transfer proposed in this paper.

Keywords: knowledge transfer, misconceptions, specialized texts, translation theory, translation practice

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Authors: Jean Pierre Ribeiro Daquila

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This paper aims to analyze the use of the Theory of Multiple Intelligences as a tool to facilitate students’ learning. This theory, proposed by the American psychologist and educator Howard Gardner, was first established in 1983 and advocates that human beings possess eight intelligence and not only one, as defended by psychologists prior to his theory. These intelligence are bodily-kinesthetic intelligence, musical, linguistic, logical-mathematical, spatial, interpersonal, intrapersonal, and naturalist. This paper will focus on bodily-kinesthetic intelligence. Spatial and bodily-kinesthetic intelligences are sensed by athletes, dancers, and others who use their bodies in ways that exceed normal abilities. These are intelligences that are closely related. A quarterback or a ballet dancer needs to have both an awareness of body motions and abilities as well as a sense of the space involved in the action. Nevertheless, there are many reasons which make classical ballet dance more integrated with other intelligences. Ballet dancers make it look effortless as they move across the stage, from the lifts to the toe points; therefore, there is acting both in the performance of the repertoire and in hiding the pain or physical stress. The ballet dancer has to have great mathematical intelligence to perform a fast allegro; for instance, each movement has to be executed in a specific millisecond. Flamenco dancers need to rely as well on their mathematic abilities, as the footwork requires the ability to make half, two, three, four or even six movements in just one beat. However, the precision of the arm movements is freer than in ballet dance; for this reason, ballet dancers need to be more holistically aware of their movements; therefore, our experiment will test whether this greater attention required by ballet dancers makes them acquire better results in the training sessions when compared to flamenco dancers. An experiment will be carried out in this study by training ballet dancers through dance (four years of experience dancing minimum – experimental group 1); a group of flamenco dancers (four years of experience dancing minimum – experimental group 2). Both experimental groups will be trained in two different domains – phonetics and chemistry – to examine whether there is a significant improvement in these areas compared to the control group (a group of regular students who will receive the same training through a traditional method). However, this paper will focus on phonetic training. Experimental group 1 will be trained with the aid of classical music plus bodily work. Experimental group 2 will be trained with flamenco rhythm and kinesthetic work. We would like to highlight that this study takes dance as an example of a possible area of strength; nonetheless, other types of arts can and should be used to support students, such as drama, creative writing, music and others. The main aim of this work is to suggest that other intelligences, in the case of this study, bodily-kinesthetic, can be used to help improve pronunciation.

Keywords: multiple intelligences, pronunciation, effective pronunciation trainings, short drills, musical intelligence, bodily-kinesthetic intelligence

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Authors: Jixiang Zhang, Yanhua Wang

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A dynamic of Bertrand duopoly game is analyzed, where players use different production methods and choose their prices with bounded rationality. The equilibriums of the corresponding discrete dynamical systems are investigated. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability of Nash equilibrium, as some parameters of the model are varied, gives rise to complex dynamics such as cycles of higher order and chaos. On this basis, we discover that an increase of adjustment speed of bounded rational player can make Bertrand market sink into the chaotic state. Finally, the complex dynamics, bifurcations and chaos are displayed by numerical simulation.

Keywords: Bertrand duopoly model, discrete dynamical system, heterogeneous expectations, nash equilibrium

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Ahmet Diken, Tahsin Karabulut

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The factor which developing countries are in need is capital. Such countries make an effort to increase their income in order to meet their expenses for employment, infrastructure, superstructure investments, education, health and defense. The sole income of the countries is taxes collected from businesses. The businesses should drive profit and return in order to be able to toll. In a world where competition exists, different strategies may be followed by business in developing countries and they must specify their target markets. İn order to minimize cost and maximize profit, SMEs have to concentrate on target markets and select cost oriented strategy. In this study, a theoretical model is suggested that SME firms have to act as cluster between each other, and also must be optimal provider for large scale firms. SMEs’ policy must be supported by public. This relationship can benefit large scale firms to have brand over the world, and this organization increases value added for developing countries.

Keywords: competitiveness, countries, SMEs developing, sustainability

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Authors: Cleyton S. Stampa

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This paper addresses the perspectives of using low melting point metals (LMPMs) as phase change materials (PCMs) in latent thermal energy storage (LTES) units, through a numerical approach. This is a new class of PCMs that has been one of the most prospective alternatives to be considered in LTES, due to these materials present high thermal conductivity and elevated heat of fusion, per unit volume. The chosen type of LTES consists of several horizontal parallel slabs filled with PCM. The heat transfer fluid (HTF) circulates through the channel formed between each two consecutive slabs on a laminar regime through forced convection. The study deals with the LTES charging process (heat-storing) by using pure gallium as PCM, and it considers heat conduction in the solid phase during melting driven by natural convection in the melt. The transient heat transfer problem is analyzed in one arbitrary slab under the influence of the HTF. The mathematical model to simulate the isothermal phase change is based on a volume-averaged enthalpy method, which is successfully verified by comparing its predictions with experimental data from works available in the pertinent literature. Regarding the convective heat transfer problem in the HTF, it is assumed that the flow is thermally developing, whereas the velocity profile is already fully developed. The study aims to learn about the effect of the solid subcooling in the melting rate through comparisons with the melting process of the solid in which it starts to melt from its fusion temperature. In order to best understand this effect in a metallic compound, as it is the case of pure gallium, the study also evaluates under the same conditions established for the gallium, the melting process of commercial paraffin wax (organic compound) and of the calcium chloride hexahydrate (CaCl₂ 6H₂O-inorganic compound). In the present work, it is adopted the best options that have been established by several researchers in their parametric studies with respect to this type of LTES, which lead to high values of thermal efficiency. To do so, concerning with the geometric aspects, one considers a gap of the channel formed by two consecutive slabs, thickness and length of the slab. About the HTF, one considers the type of fluid, the mass flow rate, and inlet temperature.

Keywords: flat slab, heat storing, pure metal, solid subcooling

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Authors: Jikhil Joseph, S. R. Satish Kumar

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Steel Structures are commonly used for rapid erections and multistory constructions due to its inherent advantages. However, the high accuracy required in detailing and heavier sections, make it difficult to erect in place and transport. Cold Formed steel which are specially made by reducing carbon and other alloys are used nowadays to make thin-walled structures. Various types of connections are being reported as well as practiced for the thin-walled members such as bolting, riveting, welding and other mechanical connections. Commonly self-drilling screw connections are used for cold-formed purlin sheeting connection. In this paper an attempt is made to develop a moment resting frame which can be rapidly and remotely constructed with thin walled sections and self-drilling screws. Semi-rigid Moment connections are developed with Rectangular thin-walled tubes and the screws. The Finite Element Analysis programme ABAQUS is used for modelling the screwed connections. The various modelling procedures for simulating the connection behavior such as tie-constraint model, oriented spring model and solid interaction modelling are compared and are critically reviewed. From the experimental validations the solid-interaction modelling identified to be the most accurate one and are used for predicting the connection behaviors. From the finite element analysis, hysteresis curves and the modes of failure were identified. Parametric studies were done on the connection model to optimize the connection configurations to get desired connection characteristics.

Keywords: buckling, cold formed steel, finite element analysis, screwed connections

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Authors: Kais Amir Kadhim

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Context: The study focuses on the rhetorical structure of chapters in engineering final year projects, specifically the Introduction chapter, written by UMT (University of Marine Technology) engineering students. Existing research has explored the use of genre-based approaches to analyze the writing of final year projects in various disciplines. Research Aim: The aim of this study is to investigate the rhetorical structure of Introduction chapters in engineering final year projects by UMT students. The study aims to identify the frequency of communicative moves and their constituent steps within the Introduction chapters, as well as understand how students justify their research projects. Methodology: The research design will utilize a mixed method approach, combining both quantitative and qualitative methods. Forty Introduction chapters from two different fields in UMT engineering undergraduate programs will be selected for analysis. Findings: The study intends to identify the types of moves present in the Introduction chapters of engineering final year projects by UMT students. Additionally, it aims to determine if these moves and steps are obligatory, conventional, or optional. Theoretical Importance: The study draws upon Bunton's modified CARS (Creating a Research Space) model, which is a conceptual framework used for analyzing the introduction sections of theses. By applying this model, the study contributes to the understanding of the rhetorical structure of Introduction chapters in engineering final year projects. Data Collection: The study will collect data from forty Introduction chapters of engineering final year projects written by UMT engineering students. These chapters will be selected from two different fields within UMT's engineering undergraduate programs. Analysis Procedures: The analysis will involve identifying and categorizing the communicative moves and their constituent steps within the Introduction chapters. The study will utilize both quantitative and qualitative analysis methods to examine the frequency and nature of these moves. Question Addressed: The study aims to address the question of how UMT engineering students structure and justify their research projects within the Introduction chapters of their final year projects. Conclusion: The study aims to contribute to the knowledge of rhetorical structure in engineering final year projects by investigating the Introduction chapters written by UMT engineering students. By using a mixed method research design and applying the modified CARS model, the study intends to identify the types of moves and steps employed by students and explore their justifications for their research projects. The findings have the potential to enhance the understanding of effective academic writing in engineering disciplines.

Keywords: cohesive markers, learning, meaning, students

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

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Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li

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Transports of solid particles through the drill pipe, drill string-hole annulus and hydraulically generated fractures are important dynamic processes encountered in oil and gas well drilling and completion operations. Different from particle transport in infinite space, the transports of cuttings, proppants and formation sand are hindered by a finite boundary. Therefore, an accurate description of the particle transport behavior under the bounded wall conditions encountered in drilling and hydraulic fracturing operations is needed to improve drilling safety and efficiency. In this study, the particle settling experiments were carried out to investigate the particle settling behavior in the pipe, annulus and between the parallel plates filled with power-law fluids. Experimental conditions simulated the particle Reynolds number ranges of 0.01-123.87, the dimensionless diameter ranges of 0.20-0.80 and the fluid flow behavior index ranges of 0.48-0.69. Firstly, the wall effect of the annulus is revealed by analyzing the settling process of the particles in the annular geometry with variable inner pipe diameter. Then, the geometric continuity among the pipe, annulus and parallel plates was determined by introducing the ratio of inner diameter to an outer diameter of the annulus. Further, a unified dimensionless diameter was defined to confirm the relationship between the three different geometry in terms of the wall effect. In addition, a dimensionless term independent from the settling velocity was introduced to establish a unified explicit settling velocity model applicable to pipes, annulus and fractures with a mean relative error of 8.71%. An example case study was provided to demonstrate the application of the unified model for predicting particle settling velocity. This paper is the first study of annulus wall effects based on the geometric continuity concept and the unified model presented here will provide theoretical guidance for improved hydraulic design of cuttings transport, proppant placement and sand management operations.

Keywords: wall effect, particle settling velocity, cuttings transport, proppant transport in fracture

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Authors: Helia Moayedi, Mahdi Moaidi

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Nowadays, the managing and planning of hospitals is facing many problems. Failure to recognize the main criteria for strategic management to ensure long-term hospital performance can lead to many health problems. To achieve this goal, a qualitative-quantitate method titled Delphi-Fuzzy has been applied. This strategy makes it possible for experts to screen among the most important criteria in strategic management. To conduct this operation, a statistical society consisting of 20 experts in Ahwaz hospitals has been questioned. The final model confirms the key criterions after three stages of Delphi. This model provides the possibility to focus on the basic criteria and can determine the organization’s main orientation.

Keywords: Delphi-fuzzy method, hospital management, long-term planning, qualitative-quantitate method, screening of strategic criteria, strategic planning

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Authors: Lisa Kasmer

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Teachers’ instructional decisions shape the structure and content of mathematics lessons and influence the mathematics that students are given the opportunity to learn. Therefore, it is important to better understand how teachers make instructional decisions and thus find new ways to help practicing and future teachers give their students a more effective and robust learning experience. Understanding the relationship between teachers’ instructional decisions and their goals, resources, and orientations (beliefs) is important given the heightened focus on geometric transformations in the middle school mathematics curriculum. This work is significant as the development and support of current and future teachers need more effective ways to teach geometry to their students. The following research questions frame this study: (1) As middle school mathematics teachers plan and enact instruction related to teaching transformations, what thinking processes do they engage in to make decisions about teaching transformations with or without a coordinate system and (2) How do the goals, resources and orientations of these teachers impact their instructional decisions and reveal about their understanding of teaching transformations? Teachers and students alike struggle with understanding transformations; many teachers skip or hurriedly teach transformations at the end of the school year. However, transformations are an important mathematical topic as this topic supports students’ understanding of geometric and spatial reasoning. Geometric transformations are a foundational concept in mathematics, not only for understanding congruence and similarity but for proofs, algebraic functions, and calculus etc. Geometric transformations also underpin the secondary mathematics curriculum, as features of transformations transfer to other areas of mathematics. Teachers’ instructional decisions in terms of goals, orientations, and resources that support these instructional decisions were analyzed using open-coding. Open-coding is recognized as an initial first step in qualitative analysis, where comparisons are made, and preliminary categories are considered. Initial codes and categories from current research on teachers’ thinking processes that are related to the decisions they make while planning and reflecting on the lessons were also noted. Surfacing ideas and additional themes common across teachers while seeking patterns, were compared and analyzed. Finally, attributes of teachers’ goals, orientations and resources were identified in order to begin to build a picture of the reasoning behind their instructional decisions. These categories became the basis for the organization and conceptualization of the data. Preliminary results suggest that teachers often rely on their own orientations about teaching geometric transformations. These beliefs are underpinned by the teachers’ own mathematical knowledge related to teaching transformations. When a teacher does not have a robust understanding of transformations, they are limited by this lack of knowledge. These shortcomings impact students’ opportunities to learn, and thus disadvantage their own understanding of transformations. Teachers’ goals are also limited by their paucity of knowledge regarding transformations, as these goals do not fully represent the range of comprehension a teacher needs to teach this topic well.

Keywords: coordinate plane, geometric transformations, instructional decisions, middle school mathematics

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Authors: K. Krishna Milan Rao, Nelvin Joseph, Praveen Dwarakanath

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Augmented and Virtual Realties is quickly becoming a hotbed of activity with millions of dollars being spent on R & D and companies such as Google and Microsoft rushing to stake their claim. Augmented reality (AR) is however marching ahead due to the spread of the ideal AR device – the smartphone. Despite its potential, there remains a deep digital divide between the Developed and Developing Countries. The Technological Acceptance Model (TAM) and Hofstede cultural dimensions also predict the behaviour intention to uptake AR in India will be large. This paper takes a quantified approach by collecting 340 survey responses to AR scenarios and analyzing them through statistics. The Survey responses show that the Intention to Use, Perceived Usefulness and Perceived Enjoyment dimensions are high among the urban population in India. This along with the exponential smartphone indicates that India is on the cusp of a boom in the AR sector.

Keywords: mobile augmented reality, technology acceptance model, Hofstede, cultural dimensions, India

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Authors: G. Roshan Deen, J. S. Pedersen

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Temperature-responsive poly(N-isopropyl acrylamide) PNIPAM microgels crosslinked with a new hydrophobic chemical crosslinker was prepared by surfactant-mediated precipitation emulsion polymerization. The temperature-responsive property of the microgel and the influence of the crosslinker on the swelling behaviour was studied systematically by light scattering and small-angle X-ray scattering (SAXS). The radius of gyration (Rg) and the hydrodynamic radius (Rh) of the microgels decreased with increase in temperature due to the volume phase transition from a swollen to a collapsed state. The ratio of Rg/Rh below the transition temperature was lower than that of hard-spheres due to the lower crosslinking density of the microgels. The SAXS data was analysed by a model in which the microgels were modelled as core-shell particles with a graded interface. The model at intermediate temperatures included a central core and a more diffuse outer layer describing pending polymer chains with a low crosslinking density. In the fully swollen state, the microgels were modelled with a single component with a broad graded surface. In the collapsed state they were modelled as homogeneous and relatively compact particles. The polymer volume fraction inside the microgel was also derived based on the model and was found to increase with increase in temperature as a result of collapse of the microgel to compact particles. The polymer volume fraction in the core of the microgel in the collapsed state was about 60% which is higher than that of similar microgels crosslinked with hydrophilic and flexible cross-linkers.

Keywords: microgels, SAXS, hydrophobic crosslinker, light scattering

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Authors: Mohammad-Ali Latifi, Harry Bouwman

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Changes in the business environment accelerated dramatically over the last decades as a result of changes in technology, regulation, market, and competitors’ behavior. Firms need to change the way they do business in order to survive or maintain their growth. Innovating business model (BM) can create competitive advantages and enhance firm performance. However, many companies fail to achieve expected outcomes in practice, mostly due to irreversible fundamental changes in key components of the company’s BM. This leads to more ambiguity, uncertainty, and risks associated with business performance. However, the relationship among BM Innovation, moderating factors, and the firm’s overall performance is by and large ignored in the current literature. In this study, we identified twenty moderating factors from our comprehensive literature review. We categorized these factors based on two criteria regarding the extent to which: the moderating factors can be controlled and managed by firms, and they are generic or specific changes to the firms. This leads to four moderation groups. The first group is BM implementation, which includes management support, employees’ commitment, employees’ skills, communication, detailed plan. The second group is called BM practices, which consists of BM tooling, BM experimentation, the scope of change, speed of change, degree of novelty. The third group is Firm characteristics, including firm size, age, and ownership. The last group is called Industry characteristics, which considers the industry sector, competitive intensity, industry life cycle, environmental dynamism, high-tech vs. low-tech industry. Through collecting data from 508 European small and medium-sized enterprises (SMEs) and using the structural equation modeling technique, the developed moderation model was examined. Results revealed that all factors highlighted through these four groups moderate the relation between BMI and firm performance significantly. Particularly, factors related to BM-Implementation and BM-Practices are more manageable and would potentially improve firm overall performance. We believe that this result is more important for researchers and practitioners since the possibility of working on factors in Firm characteristics and Industry characteristics groups are limited, and the firm can hardly control and manage them to improve the performance of BMI efforts.

Keywords: business model innovation, firm performance, implementation, moderation

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Authors: Raymond Chi Fai Si Tou, Jacey Ja Young Choe, Amy Siu Ian So

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Smart hotels can be defined as the hotel which has an intelligent system, through digitalization and networking which achieve hotel management and service information. In addition, smart hotels include high-end designs that integrate information and communication technology with hotel management fulfilling the guests’ needs and improving the quality, efficiency and satisfaction of hotel management. The purpose of this study is to identify appropriate factors that may influence guests’ satisfaction and intention to revisit Smart Hotels based on service quality measurement of lodging quality index and extended UTAUT theory. Unified Theory of Acceptance and Use of Technology (UTAUT) is adopted as a framework to explain technology acceptance and use. Since smart hotels are technology-based infrastructure hotels, UTATU theory could be as the theoretical background to examine the guests’ acceptance and use after staying in smart hotels. The UTAUT identifies four key drivers of the adoption of information systems: performance expectancy, effort expectancy, social influence, and facilitating conditions. The extended UTAUT modifies the definitions of the seven constructs for consideration; the four previously cited constructs of the UTAUT model together with three new additional constructs, which including hedonic motivation, price value and habit. Thus, the seven constructs from the extended UTAUT theory could be adopted to understand their intention to revisit smart hotels. The service quality model will also be adopted and integrated into the framework to understand the guests’ intention of smart hotels. There are rare studies to examine the service quality on guests’ satisfaction and intention to revisit in smart hotels. In this study, Lodging Quality Index (LQI) will be adopted to measure the service quality in smart hotels. Using integrated UTAUT theory and service quality model because technological applications and services require using more than one model to understand the complicated situation for customers’ acceptance of new technology. Moreover, an integrated model could provide more perspective insights to explain the relationships of the constructs that could not be obtained from only one model. For this research, ten in-depth interviews are planned to recruit this study. In order to confirm the applicability of the proposed framework and gain an overview of the guest experience of smart hotels from the hospitality industry, in-depth interviews with the hotel guests and industry practitioners will be accomplished. In terms of the theoretical contribution, it predicts that the integrated models from the UTAUT theory and the service quality will provide new insights to understand factors that influence the guests’ satisfaction and intention to revisit smart hotels. After this study identifies influential factors, smart hotel practitioners could understand which factors may significantly influence smart hotel guests’ satisfaction and intention to revisit. In addition, smart hotel practitioners could also provide outstanding guests experience by improving their service quality based on the identified dimensions from the service quality measurement. Thus, it will be beneficial to the sustainability of the smart hotels business.

Keywords: intention to revisit, guest satisfaction, qualitative interviews, smart hotels

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Authors: Hsing Yuan Liu

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Background: Considerable theoretical and empirical work has identified a relationship between collaboration interactions and creativity in an organizational context. The mechanisms underlying this link, however, are not well understood in healthcare education. Objectives: The aims of this study were to explore the impact of collaboration interactions on team creativity and its underlying mechanism and to verify a moderated mediation model. Design, setting, and participants: This study utilized a cross-sectional, quantitative, descriptive design. The survey data were collected from 177 nursing students who enrolled in 18-week capstone courses of small interdisciplinary groups collaborating to design healthcare products in Taiwan during 2018 and 2019. Methods: Questionnaires assessed the nursing students' perceptions about their teams' swift trust (of cognition- and affect-based), conflicts (of task, process, and relationship), interaction behaviors (constructive controversy, helping behaviors, and spontaneous communication), and creativity. This study used descriptive statistics to compare demographics, swift trust scores, conflict scores, interaction behavior scores, and creativity scores for interdisciplinary teams. Data were analyzed using Pearson’s correlation coefficient and simple and hierarchical multiple regression models. Results: Pearson’s correlation analysis showed the cognition-based team swift trust was positively correlated with team creativity. The mediation model indicated constructive controversy fully mediated the effect of cognition-based team swift trust on student teams’ creativity. The moderated mediation model indicated that task conflict negatively moderates the mediating effect of the constructive controversy on the link between cognition-based team swift trust and team creativity. Conclusion: Our findings suggest nursing student teams’ interaction behaviors and task conflict are crucial mediating and moderated mediation variables on the relationship between collaboration interactions and team creativity, respectively. The empirical data confirms the validity of our proposed moderated mediation models of team creativity. Therefore, this study's validated moderated mediation model could provide guidance for nursing educators to improve collaboration interaction outcomes and creativity on nursing student teams.

Keywords: team swift trust, team conflict, team interaction behavior, moderated mediating effects, interdisciplinary education, nursing students

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Authors: Armel Asongu Nkembi, Ahmad Fawad

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In this paper, we design a smart car parking system using the Node-Red interface, which enables the user to find the nearest parking area from his current location and gives the availability of parking slots in that respective parking area. The closest parking area is determined by sending an HTTP request to an API, and the shortest distance is computed using some mathematical formulations based on the coordinates retrieved. There is also the use of IR sensors to signal the availability or lack of available parking lots within any parking area. The aim is to reduce the time and effort needed to find empty parking lots and also avoid unnecessary traveling through filled parking lots in a parking area. Thus, it reduces fuel consumption, which in turn reduces carbon footprints in the atmosphere and, overall, makes the city much smarter.

Keywords: node-red, smart parking system, API, http request, IR sensors, Internet of Things, smart city, parking lots.

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Authors: M. Yilmaz, I. Yilmaz, M. Uysal

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The Earth system generates different phenomena that are observable at the surface of the Earth such as mass deformations and displacements leading to plate tectonics, earthquakes, and volcanism. The dynamic processes associated with the interior, surface, and atmosphere of the Earth affect the three pillars of geodesy: shape of the Earth, its gravity field, and its rotation. Geodesy establishes a characteristic structure in order to define, monitor, and predict of the whole Earth system. The traditional and new instruments, observables, and techniques in geodesy are related to the gravity field. Therefore, the geodesy monitors the gravity field and its temporal variability in order to transform the geodetic observations made on the physical surface of the Earth into the geometrical surface in which positions are mathematically defined. In this paper, the main components of the gravity field modeling, (Free-air and Bouguer) gravity anomalies are calculated via recent global models (EGM2008, EIGEN6C4, and GECO) over a selected study area. The model-based gravity anomalies are compared with the corresponding terrestrial gravity data in terms of standard deviation (SD) and root mean square error (RMSE) for determining the best fit global model in the study area at a regional scale in Turkey. The least SD (13.63 mGal) and RMSE (15.71 mGal) were obtained by EGM2008 for the Free-air gravity anomaly residuals. For the Bouguer gravity anomaly residuals, EIGEN6C4 provides the least SD (8.05 mGal) and RMSE (8.12 mGal). The results indicated that EIGEN6C4 can be a useful tool for modeling the gravity field of the Earth over the study area.

Keywords: free-air gravity anomaly, Bouguer gravity anomaly, global model, land gravity

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Authors: Sani Isa, Ali Musa

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Burgers’ fluid with a fractional derivatives model in an annulus was analyzed. Combining appropriately the basic equations, with the fractionalized fractional Burger’s fluid model allow us to determine the velocity field, temperature and shear stress. The governing partial differential equation was solved using the combine Laplace transformation method and Riemann sum approximation to give velocity field, temperature and shear stress on the fluid flow. The influence of various parameters like fractional parameters, relaxation time and retardation time, are drawn. The results obtained are simulated using Mathcad software and presented graphically. From the graphical results, we observed that the relaxation time and time helps the flow pattern, on the other hand, other material constants resist the fluid flow while fractional parameters effect on fluid flow is opposite to each other.

Keywords: sani isa, Ali musaburger’s fluid, Laplace transform, fractional derivatives, annulus

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