Search results for: linear and polynomial model

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

Procedia PDF Downloads 487

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

Procedia PDF Downloads 80

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

Procedia PDF Downloads 307

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

Procedia PDF Downloads 145

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

Procedia PDF Downloads 391

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

Procedia PDF Downloads 413

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

Procedia PDF Downloads 81

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)

Procedia PDF Downloads 137

Authors: Rahmah M., Noor Zuhaira Abd Aziz, Farhan M., Mohd Muizz Fahimi M.

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Polybag and mulch film for agricultural field pose environmental wastage upon disposal. Thus a degradable polybag was designed with hybrid sago starch (SS) and polyvinyl alcohol (PVA). Two Different blended composition of SS and PVA Hybrid have been compounded. Then, the hybrids blended are mixed with linear line density polyethylene (LLDPE) resin to fabricate polybag film through conventional film blowing process. Hybrid blends was compounded at different ratios. Samples of LLDPE, SS and PVA hybrid film were exposed to UV light and soil burial. The weight loss were determined during degradation process. Hybrid film by degradation of starch was found to decrease on esterification. However the hybrid film showed greater degradation in soil and uv radiation up to 60% of SS. Weight loss were also determined in control humidity oven with 70% humidity and temperature set up at 30 °C and left in humidity chamber for a month.

Keywords: LLDPE, PVA, sago starch, degradation, soil burial, uv radiation

Procedia PDF Downloads 631

Authors: Rajesh P.P., Md. Tabish Noori, Makarand M. Ghangrekar

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Methanogenic substrate loss is reported to be a major bottleneck in microbial fuel cell which significantly reduces the power production capacity and coulombic efficiency (CE) of microbial fuel cell (MFC). Nitroethane is found to be a potent inhibitor of hydrogenotrophic methanogens in rumen fermentation process. Influence of nitroethane pre-treated sewage sludge inoculum on suppressing the methanogenic activity and enhancing the electrogenesis in MFC was evaluated. MFC inoculated with nitroethane pre-treated anodic inoculum demonstrated a maximum operating voltage of 541 mV, with coulombic efficiency and sustainable volumetric power density of 39.85 % and 14.63 W/m3 respectively. Linear sweep voltammetry indicated a higher electron discharge on the anode surface due to enhancement of electrogenic activity while suppressing methanogenic activity. A 63 % reduction in specific methanogenic activity was observed in anaerobic sludge pre-treated with nitroethane; emphasizing significance of this pretreatment for suppressing methanogenesis and its utility for enhancing electricity generation in MFC.

Keywords: coulombic efficiency, methanogenesis inhibition, microbial fuel cell, nitroethane

Procedia PDF Downloads 317

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

Procedia PDF Downloads 313

Authors: Ju-Hong Lee, Ding-Chen Chung

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The paper deals with the optimal design of two-channel linear-phase (LP) quadrature mirror filter (QMF) banks using a metaheuristic based optimization technique. Based on the theory of two-channel QMF banks using two recursive digital all-pass filters (DAFs), the design problem is appropriately formulated to result in an objective function which is a weighted sum of the group delay error of the designed QMF bank and the magnitude response error of the designed low-pass analysis filter. Through a frequency sampling and a weighted least squares approach, the optimization problem of the objective function can be solved by utilizing a particle swarm optimization algorithm. The resulting two-channel QMF banks can possess approximately LP response without magnitude distortion. Simulation results are presented for illustration and comparison.

Keywords: quadrature mirror filter bank, digital all-pass filter, weighted least squares algorithm, particle swarm optimization

Procedia PDF Downloads 520

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

Procedia PDF Downloads 186

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

Procedia PDF Downloads 74

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

Procedia PDF Downloads 157

Authors: Gaurav Bansod, Swapnil Sutar, Abhijit Patil, Jagdish Patil

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This paper proposes an ultra-lightweight cipher NUX. NUX is a generalized Feistel network. It supports 128/80 bit key length and block length of 64 bit. For 128 bit key length, NUX needs only 1022 GEs which is less as compared to all existing cipher design. NUX design results into less footprint area and minimal memory size. This paper presents security analysis of NUX cipher design which shows cipher’s resistance against basic attacks like Linear and Differential Cryptanalysis. Advanced attacks like Biclique attack is also mounted on NUX cipher design. Two different F function in NUX cipher design results in high diffusion mechanism which generates large number of active S-boxes in minimum number of rounds. NUX cipher has total 31 rounds. NUX design will be best-suited design for critical application like smart grid, IoT, wireless sensor network, where memory size, footprint area and the power dissipation are the major constraints.

Keywords: lightweight cryptography, Feistel cipher, block cipher, IoT, encryption, embedded security, ubiquitous computing

Procedia PDF Downloads 369

Authors: S. A. Naeini, A. Mahigir

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Naturally occurring cohesive soil deposits are inherently anisotropic with respect to different properties amongst which is the shear strength. The anisotropy is primary due to the process of sedimentation followed by predominantly one-dimensional consolidation. However, most soils in their natural states exhibit some anisotropy with respect to shear strength and some non-homogeneity with respect to depth. In this paper the standard Mohr-Coulomb yield criterion was modified to consider the anisotropic shear strength properties. The term non-homogeneity used in this paper refers to only the cohesion intercept which is assumed to vary linearly with depth. The effect of both anisotropy and deterministic non-homogeneity on bearing capacity of shallow foundation was investigated using finite difference method. Result of numerical analysis indicates that the cohesion anisotropy has a significant effect on bearing capacity of shallow foundation. Furthermore, the linear and bilinear heterogeneity affects the bearing capacity in a similar way although the anisotropy issue emerges to be more important as far as shallow foundations are considered.

Keywords: anisotropic ratio, finite difference analysis, bearing capacity, heterogeneity

Procedia PDF Downloads 267

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

Procedia PDF Downloads 270

Authors: M. Capocelli, A. Caputo, M. De Falco, D. Mazzei, V. Piemonte

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This paper presents the experimental results and the related modeling of a thermal energy storage (TES) facility, ideated and realized by ENEA and realizing the thermocline with an innovative geometry. Firstly, the thermal energy exchange model of an equivalent shell & tube heat exchanger is described and tested to reproduce the performance of the spiral exchanger installed in the TES. Through the regression of the experimental data, a first-order thermocline model was also validated to provide an analytical function of the thermocline, useful for the performance evaluation and the comparison with other systems and implementation in simulations of integrated systems (e.g. power plants). The experimental data obtained from the plant start-up and the short-cut modeling of the system can be useful for the process analysis, for the scale-up of the thermal storage system and to investigate the feasibility of its implementation in actual case-studies.

Keywords: CSP plants, thermal energy storage, thermocline, mathematical modelling, experimental data

Procedia PDF Downloads 326

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

Procedia PDF Downloads 133

Authors: Ta-Huang Sun, Ming-Hao Hsieh, Min-Chuan Wang, Der-Jun Jan

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Electrochromism refers to the persistent and reversible change of optical properties by an applied voltage pulse. There are many transition metal oxides exhibiting electrochromism, e.g. oxides of W, Ni, Ir, V, Ti, Co and Mo. Organic materials especially some conducting polymers such as poly(aniline), poly(3, 4-propylene- dioxythiophene) also received much attention for electrochromic (EC) applications. Electrochromic materials attract considerable interest because of their potential applications, such as information displays, smart windows, variable reflectance mirrors, and variable-emittance thermal radiators. In this study, the EC characteristics are investigated on an all-solid-state EC device composed of a-WO₃ and Ni(OH)₂ with a Ta₂O₅ protective layer which is prepared by magnetron sputtering. It is found that the transmittance modulation increases with decreasing the film thickness of Ta₂O₅. On the other hand, the transmittance modulation is 57% as the Ni(OH)₂/ITO is prepared by the linear-sweep potential cycling of the sputter-deposited Ta₂O₅/NiO/ITO in a 0.5 M LiClO₄+H₂O electrolyte. However, when Ni(OH)₂/ITO is prepared by a 0.01 M HCl electrolyte, the transmittance modulation of EC device can be improved to 61%.

Keywords: electrochromic device, tungsten oxide, nickel, Ta₂O₅

Procedia PDF Downloads 290

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

Procedia PDF Downloads 159

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

Procedia PDF Downloads 130

Authors: Kelemewerk Destalem, Joongjae Cho, Jaeseong Lee, Ju H. Park, Joonhyuk Yoo

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Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method.

Keywords: background subtraction, background updating, real time, light weight algorithm, temporal difference

Procedia PDF Downloads 340

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

Procedia PDF Downloads 247

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

Abstract:

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: Jason P. Michaud, Connor P. Speer, David A. Miller, David S. Nobes

Abstract:

An experimental study on finned-tube radiators has been conducted. Three radiators found in desktop computers sized for 120 mm fans were tested in steady and pulsatile flows of ambient air over a Reynolds number range of  50 < Re < 900. Water at 60 °C was circulated through the radiators to maintain a constant fin temperature during the tests. For steady flow, it was found that the heat transfer rate increased linearly with the mass flow rate of air. The pulsatile flow experiments showed that frequency of pulsation had a negligible effect on the heat transfer rate for the range of frequencies tested (0.5 Hz – 2.5 Hz). For all three radiators, the heat transfer rate was decreased in the case of pulsatile flow. Linear heat transfer correlations for steady and pulsatile flow were calculated in terms of Reynolds number and Nusselt number.

Keywords: finned-tube heat exchangers, heat transfer correlations, pulsatile flow, computer radiators

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Authors: Yang-Kuei Lin, Yun-Xi Zhang

Abstract:

This research focuses on the bicriteria correlated parallel machine scheduling problem. The two objective functions considered in this problem are to minimize maximum lateness and total weighted completion time. We first present a mixed integer programming (MIP) model that can find the entire efficient frontier for the studied problem. Next, we have proposed a bicriteria heuristic that can find non-dominated solutions for the studied problem. The performance of the proposed bicriteria heuristic is compared with the efficient frontier generated by solving the MIP model. Computational results indicate that the proposed bicriteria heuristic can solve the problem efficiently and find a set of diverse solutions that are uniformly distributed along the efficient frontier.

Keywords: bicriteria, correlated parallel machines, heuristic, scheduling

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Authors: Mohammad-Ali Latifi, Harry Bouwman

Abstract:

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

Procedia PDF Downloads 119