Search results for: biomedical study models

Authors: Chokri Slim

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The relative failure of simultaneous equation models in the seventies has led researchers to turn to other approaches that take into account the dynamics of economic and financial systems. In this paper, we use an approach based on vector autoregressive model that is widely used in recent years. Their popularity is due to their flexible nature and ease of use to produce models with useful descriptive characteristics. It is also easy to use them to test economic hypotheses. The standard econometric techniques assume that the series studied are stable over time (stationary hypothesis). Most economic series do not verify this hypothesis, which assumes, when one wishes to study the relationships that bind them to implement specific techniques. This is cointegration which characterizes non-stationary series (integrated) with a linear combination is stationary, will also be presented in this paper. Since the work of Johansen, this approach is generally presented as part of a multivariate analysis and to specify long-term stable relationships while at the same time analyzing the short-term dynamics of the variables considered. In the empirical part, we have applied these concepts to study the dynamics of of the exchange rate in Tunisia, which is one of the most important economic policy of a country open to the outside. According to the results of the empirical study by the cointegration method, there is a cointegration relationship between the exchange rate and its determinants. This relationship shows that the variables have a significant influence in determining the exchange rate in Tunisia.

Keywords: stationarity, cointegration, dynamic models, causality, VECM models

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Authors: Anthony Usoro

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In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data.

Keywords: autoregressive model, bilinear autoregressive model, bilinear moving average model, moving average model

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Authors: H. Taylan Selamlar, I. Yavuz, G. Yapar

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It is difficult to make predictions especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme. In this paper, the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher order ES methods for additive, multiplicative, additive damped and multiplicative damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics.

Keywords: accuracy, exponential smoothing, forecasting, initial value

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Authors: Saih Mohamed, Rouijaa Hicham, Ghammaz Abdelilah

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This paper presents circuit models to analyze the conducted susceptibility of multiconductor shielded cables in frequency domains using Branin’s method, which is referred to as the method of characteristics. These models, Which can be used directly in the time and frequency domains, take into account the presence of both the transfer impedance and admittance. The conducted susceptibility is studied by using an injection current on the cable shield as the source. Two examples are studied, a coaxial shielded cable and shielded cables with two parallel wires (i.e., twinax cables). This shield has an asymmetry (one slot on the side). Results obtained by these models are in good agreement with those obtained by other methods.

Keywords: circuit models, multiconductor shielded cables, Branin’s method, coaxial shielded cable, twinax cables

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Authors: Hyunwon Moon

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It is necessary to realize new biomedical wireless communication systems which send the signals collected from various bio sensors located at human body in order to monitor our health. Also, it should seamlessly connect to the existing wireless communication systems. A 5.8 GHz ISM band low power RF front-end receiver for a biomedical wireless communication system is implemented using a 0.5 µm SiGe BiCMOS process. To achieve low power RF front-end, the current optimization technique for selecting device size is utilized. The implemented low noise amplifier (LNA) shows a power gain of 9.8 dB, a noise figure (NF) of below 1.75 dB, and an IIP3 of higher than 7.5 dBm while current consumption is only 6 mA at supply voltage of 2.5 V. Also, the performance of a down-conversion mixer is measured as a conversion gain of 11 dB and SSB NF of 10 dB.

Keywords: biomedical, LNA, mixer, receiver, RF front-end, SiGe

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Authors: Idrissa Kayijuka

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The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

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Authors: Katja Ignatieva, Patrick Wong

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We investigated the dynamics of high frequency energy prices, including crude oil and electricity prices. The returns of underlying quantities are modelled using various parametric models such as stochastic framework with jumps and stochastic volatility (SVCJ) as well as non-parametric alternatives, which are purely data driven and do not require specification of the drift or the diffusion coefficient function. Using different statistical criteria, we investigate the performance of considered parametric and nonparametric models in their ability to forecast price series and volatilities. Our models incorporate possible seasonalities in the underlying dynamics and utilise advanced estimation techniques for the dynamics of energy prices.

Keywords: stochastic volatility, affine jump-diffusion models, high frequency data, model specification, markov chain monte carlo

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Madad Ali

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In the contemporary landscape of international travel, the infusion of digital technologies has given rise to innovative business models that are reshaping the dynamics of tourism. This research delves into the transformative potential of these novel business models within the realm of digital tourism and their multifaceted impact on local businesses, residents' quality of life, and the overall travel experience. The study focuses on the captivating backdrop of Yunnan Province, China, renowned for its rich cultural heritage and diverse ethnic minorities, to uncover the intricate nuances of this phenomenon. The primary objectives of this research encompass the identification and categorization of emerging business models facilitated by digital technologies, their implications on tourist engagement, and their integration into the operations of local businesses. By employing a mixed-methods approach, blending qualitative techniques like interviews and content analysis with quantitative tools such as surveys and data analysis, the study provides a comprehensive evaluation of these business models' effects on various dimensions of the tourism landscape. The distinctiveness of this research lies in its exclusive focus on Yunnan Province, China. By concentrating on Yunnan Province, the research contributes exceptional insights into the interplay between digital tourism, ethnic diversity, cultural heritage, and sustainable development. The study's outcomes hold significance for both scholarly discourse and the stakeholders involved in shaping the region's tourism strategies.

Keywords: business model, digital tourism, international travel, local businesses, quality of life

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Authors: Sabiu Bala Muhammad, Rosli Saad

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Multiple linear regression (MLR) models for fast estimation of true subsurface resistivity from apparent resistivity field measurements are developed and assessed in this study. The parameters investigated were apparent resistivity (ρₐ), horizontal location (X) and depth (Z) of measurement as the independent variables; and true resistivity (ρₜ) as the dependent variable. To achieve linearity in both resistivity variables, datasets were first transformed into logarithmic domain following diagnostic checks of normality of the dependent variable and heteroscedasticity to ensure accurate models. Four MLR models were developed based on hierarchical combination of the independent variables. The generated MLR coefficients were applied to another data set to estimate ρₜ values for validation. Contours of the estimated ρₜ values were plotted and compared to the observed data plots at the colour scale and blanking for visual assessment. The accuracy of the models was assessed using coefficient of determination (R²), standard error (SE) and weighted mean absolute percentage error (wMAPE). It is concluded that the MLR models can estimate ρₜ for with high level of accuracy.

Keywords: apparent resistivity, depth, horizontal location, multiple linear regression, true resistivity

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Authors: Jerome D. Donovan

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The pandemic has resulted in a dramatic re-consideration of the reliance on international student fees to support university models in Australia. A key resulting initiative for the Australian Federal Government has been shifting the way universities consider their research model, emphasising the importance of commercialising research. This study specifically examines this shift from the perspective of a university spin-off, examining how university support structures and incubation models have assisted in the translation of fundamental research into a high-growth university spin-off. A focused case study approach is adopted in this study, using an auto-ethnographic research method to document the experiences and insights drawn from being a co-founder in a university spin-off in a time where research commercialisation has emerged as a central focus in Australian universities.

Keywords: research commercialisation, spin-offs, university incubation, entrepreneurship

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Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar

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Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.

Keywords: NLP, multilingual, sentiment analysis, texts

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Authors: Maxim A. Kadatskiy, Konstantin V. Khishchenko

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Iron alloys are widespread components in various types of structural materials which are exposed to intensive thermal and mechanical loads. Various quantum-statistical cell models with the approximation of self-consistent field can be used for the prediction of the behavior of these materials under extreme conditions. The application of these models is even more valid, the higher the temperature and the density of matter. Results of Hugoniot calculation for iron alloys in the framework of three quantum-statistical (the Thomas–Fermi, the Thomas–Fermi with quantum and exchange corrections and the Hartree–Fock–Slater) models are presented. Results of quantum-statistical calculations are compared with results from other reliable models and available experimental data. It is revealed a good agreement between results of calculation and experimental data for terra pascal pressures. Advantages and disadvantages of this approach are shown.

Keywords: alloy, Hugoniot, iron, terapascal pressure

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Authors: John P. O’Sullivan, Thomas M. P. Ratouis, Michael J. O’Sullivan

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As the use of geothermal energy grows internationally more effort is required to monitor and protect areas with rare and important geothermal surface features. A number of approaches are presented for developing and calibrating numerical geothermal reservoir models that are capable of accurately representing geothermal surface features. The approaches are discussed in the context of cases studies of the Rotorua geothermal system and the Orakei-korako geothermal system, both of which contain important surface features. The results show that models are able to match the available field data accurately and hence can be used as valuable tools for predicting the future response of the systems to changes in use.

Keywords: geothermal reservoir models, surface features, monitoring, TOUGH2

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Authors: Sélima Baccar, Ephraim Clark

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This paper can be considered as a new perspective to analyse credit spreads. A comprehensive empirical analysis of conditional variance of credit spreads indices is performed using various GARCH models. Based on a comparison between traditional and asymmetric GARCH models with alternative functional forms of the conditional density, we intend to identify what macroeconomic and financial factors have driven daily changes in the US Dollar credit spreads in the period from January 2011 through January 2013. The results provide a strong interdependence between credit spreads and the explanatory factors related to the conditions of interest rates, the state of the stock market, the bond market liquidity and the exchange risk. The empirical findings support the use of asymmetric GARCH models. The AGARCH and GJR models outperform the traditional GARCH in credit spreads modelling. We show, also, that the leptokurtic Student-t assumption is better than the Gaussian distribution and improves the quality of the estimates, whatever the rating or maturity.

Keywords: corporate bonds, default risk, credit spreads, asymmetric garch models, student-t distribution

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Authors: Gonzalez Carlos, Martinez Fransisco

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There are various types of analysis that allow us to involve seismic phenomena that cause strong requirements for structures that are designed by society; one of them is a probabilistic analysis which works from prediction equations that have been created based on metadata seismic compiled in different regions. These equations form models that are used to describe the 5% damped pseudo spectra response for the various zones considering some easily known input parameters. The biggest problem for the creation of these models requires data with great robust statistics that support the results, and there are several places where this type of information is not available, for which the use of alternative methodologies helps to achieve adjustments to different models of seismic prediction.

Keywords: GMPM, 5% damped pseudo-response spectra, models of seismic prediction, PSHA

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Authors: Yasaman Tohidi, Rouzbeh Riazi, Shidvash Vakilipour, Masoud Mohammadi

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The objective of the present study is to numerically investigate the effect of CO₂ replacement of N₂ in air stream on the flame characteristics of the CH₄ turbulent diffusion flame. The Open source Field Operation and Manipulation (OpenFOAM) has been used as the computational tool. In this regard, laminar flamelet and modified k-ε models have been utilized as combustion and turbulence models, respectively. Results reveal that the presence of CO₂ in air stream changes the flame shape and maximum flame temperature. Also, CO₂ dilution causes an increment in CO mass fraction.

Keywords: CH4 diffusion flame, CO2 dilution, OpenFOAM, turbulent flame

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Authors: Shashwat Mookherjee, Shruti Dutta

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This study explores linguistic distinctions among American, Indian, and Irish English dialects and assesses various Language Models (LLMs) in their ability to generate British English translations from these dialects. Using cosine similarity analysis, the study measures the linguistic proximity between original British English translations and those produced by LLMs for each dialect. The findings reveal that Indian and Irish English translations maintain notably high similarity scores, suggesting strong linguistic alignment with British English. In contrast, American English exhibits slightly lower similarity, reflecting its distinct linguistic traits. Additionally, the choice of LLM significantly impacts translation quality, with Llama-2-70b consistently demonstrating superior performance. The study underscores the importance of selecting the right model for dialect translation, emphasizing the role of linguistic expertise and contextual understanding in achieving accurate translations.

Keywords: cross-dialect translation, language models, linguistic similarity, multilingual NLP

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Ruxandra Barbulescu, Daniel Ioan, Gabriela Ciuprina

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The saltatory conduction is the way the action potential is transmitted along a myelinated axon. The potential diffuses along the myelinated compartments and it is regenerated in the Ranvier nodes due to the ion channels allowing the flow across the membrane. For an efficient simulation of populations of neurons, it is important to use reduced order models both for myelinated compartments and for Ranvier nodes and to have control over their accuracy and inner parameters. The paper presents a reduced order model of this neural system which allows an efficient simulation method for the saltatory conduction in myelinated axons. This model is obtained by concatenating reduced order linear models of 1D myelinated compartments and nonlinear 0D models of Ranvier nodes. The models for the myelinated compartments are selected from a series of spatially distributed models developed and hierarchized according to their modeling errors. The extracted model described by a nonlinear PDE of hyperbolic type is able to reproduce the saltatory conduction with acceptable accuracy and takes into account the finite propagation speed of potential. Finally, this model is again reduced in order to make it suitable for the inclusion in large-scale neural circuits.

Keywords: action potential, myelinated segments, nonlinear models, Ranvier nodes, reduced order models, saltatory conduction

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

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Authors: Syazwani Ramli, Norhidayu Muhamad Zain

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Nowadays, the use of gelatin as biomaterials in tissue engineering are evolving especially in skin graft and wound dressing applications. Towards year 2018, Malaysia is in the way of planning to get the halal certification for biomedical device in order to cater the needs of Muslims and non-Muslims in Malaysia. However, the use of gelatins in tissue engineering are mostly derived from non-halal sources. Currently, gelatin production mostly comes from mammalian gelatin sources. Moreover, within these past years, just a few studies of the uses of gelatin in tissue engineering from halal perspective has been studied. Thus, this paper aims to give overview of the use of gelatin from different sources from halal perspectives. This review also discussing the current status of halal for the emerging biomedical devices. In addition, the different sources of gelatin used in tissue engineering are being identified and provides better alternatives for halal gelatin. Cold- water fish skin gelatin could be an effective alternative to substitute the mammalian sources. Therefore, this review is important because the information about the halal biomedical devices will delighted Muslim consumers and give better insight of halal gelatin in tissue engineering application.

Keywords: biomedical device, gelatin, halal, skin graft, tissue engineering

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Authors: M. Novak, J. Dufourmount, D. Wildi, A. Sutherland, L. Sosa, J. Zimmer, E. Szabo

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The circular economy has grounded itself amongst scholars and practitioners operating across governments and enterprises. The aim of this paper is to augment the circular economy concept by identifying common core and enabling circular business models. To this aim, we have analysed over 150 papers regarding circular activities and identified 8 clusters of business models and enablers. We have mapped and harmonised the most prominent frameworks conceptualising the circular economy. Our findings indicate that circular economy core business models include regenerative in addition to reduce, reuse and recycle activities. We further find enabling activities in design, digital technologies, knowledge development and sharing, multistakeholder collaborations, and extended corporate responsibility initiatives in various forms. We critically contrast the application of these business models across the European and African contexts. Overall, we find that seemingly varied circular economy definitions distill the same conceptual business models. We hope to contribute towards the coherence of the circular economy concept, and the continuous development of practical guidance to select and implement circular strategies.

Keywords: Circular economy, content analysis, business models, definitions, enablers, frameworks

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Authors: Peyman Aela, Lu Zong, Guoqing Jing

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Recently, there has been growing interest in numerical modeling of ballast railway tracks. A commonly used mechanistic modeling approach for ballast is the discrete element method (DEM). Up to now, the effects of the contact model on ballast particle behavior have not been precisely examined. In this regard, selecting the appropriate contact model is mainly associated with the particle characteristics and the loading condition. Since ballast is cohesionless material, different contact models, including the linear spring, Hertz-Mindlin, and Hysteretic models, could be used to calculate particle-particle or wall-particle contact forces. Moreover, the simulation of a dynamic test is vital to investigate the effect of damping parameters on the ballast deformation. In this study, ballast box tests were simulated by DEM to examine the influence of different contact models on the mechanical behavior of the ballast layer under cyclic loading. This paper shows how the contact model can affect the deformation and damping of a ballast layer subjected to cyclic loading in a ballast box.

Keywords: ballast, contact model, cyclic loading, DEM

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Authors: Ahmed Fradi

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Nowadays, recent technological advances in the acquisition, modeling, and processing of three-dimensional (3D) objects data lead to the creation of models stored in huge databases, which are used in various domains such as computer vision, augmented reality, game industry, medicine, CAD (Computer-aided design), 3D printing etc. On the other hand, the industry is currently benefiting from powerful modeling tools enabling designers to easily and quickly produce 3D models. The great ease of acquisition and modeling of 3D objects make possible to create large 3D models databases, then, it becomes difficult to navigate them. Therefore, the indexing of 3D objects appears as a necessary and promising solution to manage this type of data, to extract model information, retrieve an existing model or calculate similarity between 3D objects. The objective of the proposed research is to develop a framework allowing easy and fast access to 3D objects in a CAD models database with specific indexing algorithm to find objects similar to a reference model. Our main objectives are to study existing methods of similarity calculation of 3D objects (essentially shape-based methods) by specifying the characteristics of each method as well as the difference between them, and then we will propose a new approach for indexing and comparing 3D models, which is suitable for our case study and which is based on some previously studied methods. Our proposed approach is finally illustrated by an implementation, and evaluated in a professional context.

Keywords: CAD, 3D object retrieval, shape based retrieval, similarity calculation

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Said Benkaciali, Mourad Haddadi, Abdallah Khellaf, Kacem Gairaa, Mawloud Guermoui

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In this study, many experiments were carried out to assess the influence of the input parameters on the performance of multilayer perceptron which is one the configuration of the artificial neural networks. To estimate the daily global solar radiation on the horizontal surface, we have developed some models by using seven combinations of twelve meteorological and geographical input parameters collected from a radiometric station installed at Ghardaïa city (southern of Algeria). For selecting of best combination which provides a good accuracy, six statistical formulas (or statistical indicators) have been evaluated, such as the root mean square errors, mean absolute errors, correlation coefficient, and determination coefficient. We noted that multilayer perceptron techniques have the best performance, except when the sunshine duration parameter is not included in the input variables. The maximum of determination coefficient and correlation coefficient are equal to 98.20 and 99.11%. On the other hand, some empirical models were developed to compare their performances with those of multilayer perceptron neural networks. Results obtained show that the neural networks techniques give the best performance compared to the empirical models.

Keywords: empirical models, multilayer perceptron neural network, solar radiation, statistical formulas

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Authors: Nima Pirhadi, Yong Bo Shao, Xusheng Wa, Jianguo Lu

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Several models based on the simplified method introduced by Seed and Idriss (1971) have been developed to assess the liquefaction potential of saturated sandy soils. The procedure includes determining the cyclic resistance of the soil as the cyclic resistance ratio (CRR) and comparing it with earthquake loads as cyclic stress ratio (CSR). Of all methods to determine CRR, the methods using shear-wave velocity (Vs) are common because of their low sensitivity to the penetration resistance reduction caused by fine content (FC). To evaluate the capability of the models, based on the Vs., the new data from Bachu-Jianshi earthquake case history collected, then the prediction results of the models are compared to the measured results; consequently, the accuracy of the models are discussed via three criteria and graphs. The evaluation demonstrates reasonable accuracy of the models in the Banchu region.

Keywords: seismic liquefaction, banchu-jiashi earthquake, shear-wave velocity, liquefaction potential evaluation

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Authors: Fares Laouacheria, Said Kechida, Moncef Chabi

Abstract:

The objective of the study was based on the hydrological routing modelling for the continuous monitoring of the hydrological situation in the Moudjar river catchment, especially during floods with Hydrologic Engineering Center–Hydrologic Modelling Systems (HEC-HMS). The HEC-GeoHMS was used to transform data from geographic information system (GIS) to HEC-HMS for delineating and modelling the catchment river in order to estimate the runoff volume, which is used as inputs to the hydrological routing model. Two hydrological routing models were used, namely Muskingum and Muskingum routing models, for conducting this study. In this study, a comparison between the parameters of the Muskingum and Muskingum-Cunge routing models in HEC-HMS was used for modelling flood routing in the Moudjar river catchment and determining the relationship between these parameters and the physical characteristics of the river. The results indicate that the effects of input parameters such as the weighting factor "X" and travel time "K" on the output results are more significant, where the Muskingum routing model was more sensitive to input parameters than the Muskingum-Cunge routing model. This study can contribute to understand and improve the knowledge of the mechanisms of river floods, especially in ungauged river catchments.

Keywords: HEC-HMS, hydrological modelling, Muskingum routing model, Muskingum-Cunge routing model

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Authors: Sarah Beaver, Renee Bryce

Abstract:

Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

Procedia PDF Downloads 49