Search results for: generalized autoregressive conditional heteroskedasticity model

Authors: Nurul Liyana Abdul Aziz, Nur Aidya Hanum Aizam

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Course timetabling problems occur every semester in a university which includes the allocation of resources (subjects, lecturers and students) to a number of fixed rooms and timeslots. The assignment is carried out in a way such that there are no conflicts within rooms, students and lecturers, as well as fulfilling a range of constraints. The constraints consist of rules and policies set up by the universities as well as lecturers’ and students’ preferences of courses to be allocated in specific timeslots. This paper specifically focuses on the preferences of the course timetabling problem in one of the public universities in Malaysia. The demands will be considered into our existing mathematical model to make it more generalized and can be used widely. We have distributed questionnaires to a number of lecturers and students of the university to investigate their demands and preferences for their desired course timetable. We classify the preferences thus converting them to construct one mathematical model that can produce such timetable.

Keywords: university course timetabling problem, integer programming, preferences, constraints

Procedia PDF Downloads 372

Authors: Larry O. Awo, George N. Duru

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There are growing public and mental health concerns over the availability of gambling platforms and shops in Nigeria and the high level of youth involvement in gambling. Early theorizing maintained that gambling involvement was driven by a quest for resource gains. However, evidence shows that the economic model of gambling tends to explain the involvement of the gambling business owners (sport lottery operators: SLOs) as most gamblers lose more than they win. This loss, according to the law of effect, ought to discourage decisions to gamble. However, the quest to recover losses has often initiated prolonged gambling sessions. Therefore, the need to investigate mental contemplations (such as counterfactual reasoning (upward versus downward) of what “would, should, or could” have been, and feeling of the illusion of control; IOC) over gambling outcomes as risk or protective factors in gambling decisions became pertinent. The present study sought to understand the differential contributions and conditional effects of upward versus downward counterfactual reasoning as pathways through which the association between IOC and gambling decisions of Nigerian youths (N = 120, mean age = 18.05, SD = 3.81) could be explained. The study adopted a randomized group design, and data were obtained by means of stimulus material (the Gambling Episode; GE) and self-report measures of IOC and Gambling Decision. One-way analysis of variance (ANOVA) result showed that participants in the upward counterfactual reasoning group (M = 22.08) differed from their colleagues in the downward counterfactual reasoning group (M = 17.33) on the decision to gamble, and this difference was significant [F(1,112) = 23, P < .01]. HAYES PROCESS macro moderation analysis results showed that 1) IOC and upward counterfactual reasoning were positively associated with the decision to gamble (B = 14.21, t = 6.10, p < .01 and B = 7.22, t = 2.07, p <.05, respectively), 2) downward counterfactual reasoning was negatively associated with the decision to gamble more to recover losses (B = 10.03, t = 3.21, p < .01), 3) upward counterfactual reasoning did not moderate the association between IOC and gambling decision (p > .05), and 4) downward counterfactual reasoning negatively moderated the association between IOC and gambling decision (B = 07, t = 2.18, p < .05) such that the association was strong at the low level of downward counterfactual, but wane at high levels of downward counterfactual reasoning. The implication of these findings is that IOC and upward counterfactual reasoning were risk factors and promoted gambling behavior, while downward counterfactual reasoning protects individuals from gambling activities. Thus, it is concluded that downward counterfactual reasoning strategies should be included in gambling therapy and treatment packages as it could diminish feelings of both IOC and negative feelings of missed positive outcomes and the urge to gamble.

Keywords: counterfactual reasoning, gambling cognition, gambling decision, Nigeria, youths

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Authors: Babak Safaei, A. M. Fattahi

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Polymer/carbon nanotube nanocomposites have a wide range of promising applications Due to their enhanced properties. In this work, free vibration analysis of single-walled carbon nanotube-reinforced composite plates is conducted in which carbon nanotubes are embedded in an amorphous polyethylene. The rule of mixture based on various types of plate model namely classical plate theory (CLPT), first-order shear deformation theory (FSDT), and higher-order shear deformation theory (HSDT) was employed to obtain fundamental frequencies of the nanocomposite plates. Generalized differential quadrature (GDQ) method was used to discretize the governing differential equations along with the simply supported and clamped boundary conditions. The material properties of the nanocomposite plates were evaluated using molecular dynamic (MD) simulation corresponding to both short-(10,10) SWCNT and long-(10,10) SWCNT composites. Then the results obtained directly from MD simulations were fitted with those calculated by the rule of mixture to extract appropriate values of carbon nanotube efficiency parameters accounting for the scale-dependent material properties. The selected numerical results are presented to address the influences of nanotube volume fraction and edge supports on the value of fundamental frequency of carbon nanotube-reinforced composite plates corresponding to both long- and short-nanotube composites.

Keywords: nanocomposites, molecular dynamics simulation, free vibration, generalized, differential quadrature (GDQ) method

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Authors: Mansour H. Alkmim, Adriano T. Fabro, Marcus V. G. De Morais

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In this paper, a global direct search optimization algorithm to reduce vibration of a tuned liquid column damper (TLCD), a class of passive structural control device, is presented. The objective is to find optimized parameters for the TLCD under stochastic load from different wind power spectral density. A verification is made considering the analytical solution of an undamped primary system under white noise excitation. Finally, a numerical example considering a simplified wind turbine model is given to illustrate the efficacy of the TLCD. Results from the random vibration analysis are shown for four types of random excitation wind model where the response PSDs obtained showed good vibration attenuation.

Keywords: generalized pattern search, parameter optimization, random vibration analysis, vibration suppression

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Authors: Ahmed Nouainia, Mohamed Hajji, Taoufik Aguili

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In this paper, an electromagnetic analysis is presented for describing the influence of shielding in a rectangular waveguide. A hybridization based on the method of moments combined to the generalized equivalent circuit MoM-GEC is used to model the problem. This is validated by applying the MoM-GEC hybridization to investigate a diffraction structure. It consists of electromagnetic diffraction by an iris in a rectangular waveguide. Numerical results are shown and discussed and a comparison with FEM and Marcuvitz methods is achieved.

Keywords: method MoM-GEC, waveguide, shielding, equivalent circuit

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Birhanu Betela Warssamo

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Background: There is limited evidence on the prevalence and risk factors for hepatitis C virus (HCV) infection among waste handlers in the Sidama region, Ethiopia; however, this knowledge is necessary for the effective prevention of HCV infection in the region. Methods: A cross-sectional study was conducted among randomly selected waste collectors from October 2021 to 30 July 2022 in different public hospitals in the Sidama region of Ethiopia. Serum samples were collected from participants and screened for anti-HCV using a rapid immunochromatography assay. Socio-demographic and risk factor information of waste handlers was gathered by pretested and well-structured questionnaires. The generalized linear model (GLM) was conducted using R software, and P-value < 0.05 was declared statistically significant. Results: From a total of 282 participating waste handlers, 16 (5.7%) (95% CI, 4.2 – 8.7) were infected with the hepatitis C virus. The educational status of waste handlers was the significant demographic variable that was associated with the hepatitis C virus (AOR = 0.055; 95% CI = 0.012 – 0.248; P = 0.000). More married waste handlers, 12 (75%), were HCV positive than unmarried, 4 (25%) and married waste handlers were 2.051 times (OR = 2.051, 95%CI = 0.644 –6.527, P = 0.295) more prone to HCV infection, compared to unmarried, which was statistically insignificant. The GLM showed that exposure to blood (OR = 8.26; 95% CI = 1.878–10.925; P = 0.037), multiple sexual partners (AOR = 3.63; 95% CI = 2.751–5.808; P = 0.001), sharp injury (AOR = 2.77; 95% CI = 2.327–3.173; P = 0.036), not using PPE (AOR = 0.77; 95% CI = 0.032–0.937; P = 0.001), contact with jaundiced patient (AOR = 3.65; 95% CI = 1.093–4.368; P = 0 .0048) and unprotected sex (AOR = 11.91; 95% CI = 5.847–16.854; P = 0.001) remained statistically significantly associated with HCV positivity. Conclusions: The study revealed that there was a high prevalence of hepatitis C virus infection among waste handlers in the Sidama region, Ethiopia. This demonstrated that there is an urgent need to increase preventative efforts and strategic policy orientations to control the spread of the hepatitis C virus.

Keywords: Hepatitis C virus, risk factors, waste handlers, prevalence, Sidama Ethiopia

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Authors: Hany Elsaid Fawaz Abdallah

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This study presents a new artificial neural network(ANN) model to predict the Nusselt Number and pressure drop for the turbulent flow in a triangular corrugated plate heat exchanger for forced air and turbulent water flow. An experimental investigation was performed to create a new dataset for the Nusselt Number and pressure drop values in the following range of dimensionless parameters: The plate corrugation angles (from 0° to 60°), the Reynolds number (from 10000 to 40000), pitch to height ratio (from 1 to 4), and Prandtl number (from 0.7 to 200). Based on the ANN performance graph, the three-layer structure with {12-8-6} hidden neurons has been chosen. The training procedure includes back-propagation with the biases and weight adjustment, the evaluation of the loss function for the training and validation dataset and feed-forward propagation of the input parameters. The linear function was used at the output layer as the activation function, while for the hidden layers, the rectified linear unit activation function was utilized. In order to accelerate the ANN training, the loss function minimization may be achieved by the adaptive moment estimation algorithm (ADAM). The ‘‘MinMax’’ normalization approach was utilized to avoid the increase in the training time due to drastic differences in the loss function gradients with respect to the values of weights. Since the test dataset is not being used for the ANN training, a cross-validation technique is applied to the ANN network using the new data. Such procedure was repeated until loss function convergence was achieved or for 4000 epochs with a batch size of 200 points. The program code was written in Python 3.0 using open-source ANN libraries such as Scikit learn, TensorFlow and Keras libraries. The mean average percent error values of 9.4% for the Nusselt number and 8.2% for pressure drop for the ANN model have been achieved. Therefore, higher accuracy compared to the generalized correlations was achieved. The performance validation of the obtained model was based on a comparison of predicted data with the experimental results yielding excellent accuracy.

Keywords: artificial neural networks, corrugated channel, heat transfer enhancement, Nusselt number, pressure drop, generalized correlations

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Authors: Septimia Sarbu

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The entropy rate of a stochastic process is a fundamental concept in information theory. It provides a limit to the amount of information that can be transmitted reliably over a communication channel, as stated by Shannon's coding theorems. Recently, researchers have focused on developing new measures of information that generalize Shannon's classical theory. The aim is to design more efficient information encoding and transmission schemes. This paper continues the study of generalized entropy rates, by deriving a closed-form solution to the Sharma-Mittal entropy rate for Gaussian processes. Using the squeeze theorem, we solve the limit in the definition of the entropy rate, for different values of alpha and beta, which are the parameters of the Sharma-Mittal entropy. In the end, we compare it with Shannon and Rényi's entropy rates for Gaussian processes.

Keywords: generalized entropies, Sharma-Mittal entropy rate, Gaussian processes, eigenvalues of the covariance matrix, squeeze theorem

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Authors: Fawzi Srairi, Abderrahmane Dib

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Recently, the desire for a self-powered micro and nanodevices has attracted a great interest of using sustainable energy sources. Further, the ultimate goal of nanogenerator is to harvest energy from the ambient environment in which a self-powered device based on these generators is needed. With the development of nanogenerator-based circuits design and optimization, the building of new device simulator is necessary for the study and the synthesis of electromechanical parameters of this type of models. In the present article, both numerical modeling and optimization of piezoelectric nanogenerator based on zinc oxide have been carried out. They aim to improve the electromechanical performances, robustness, and synthesis process for nanogenerator. The proposed model has been developed for a systematic study of the nanowire morphology parameters in stretching mode. In addition, heuristic optimization technique, namely, particle swarm optimization has been implemented for an analytic modeling and an optimization of nanogenerator-based process in stretching mode. Moreover, the obtained results have been tested and compared with conventional model where a good agreement has been obtained for excitation mode. The developed nanogenerator model can be generalized, extended and integrated into simulators devices to study nanogenerator-based circuits.

Keywords: electrical potential, heuristic algorithms, numerical modeling, nanogenerator

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Authors: Sachin Singh, Thomas Penzel, Dinesh Nandan

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Accurate identification of Sleep Apnea Hypopnea Syndrom Diagnosis is difficult problem for human expert because of variability among persons and unwanted noise. This paper proposes the diagonosis of Sleep Apnea Hypopnea Syndrome (SAHS) using airflow, ECG, Pulse and SaO2 signals. The features of each type of these signals are extracted using statistical methods and ANN learning methods. These extracted features are used to approximate the patient's Apnea Hypopnea Index(AHI) using sample signals in model. Advance signal processing is also applied to snore sound signal to locate snore event and SaO2 signal is used to support whether determined snore event is true or noise. Finally, Apnea Hypopnea Index (AHI) event is calculated as per true snore event detected. Experiment results shows that the sensitivity can reach up to 96% and specificity to 96% as AHI greater than equal to 5.

Keywords: neural network, AHI, statistical methods, autoregressive models

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Authors: Timothy Kayode Samson, Adedoyin Isola Lawal

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The past five years have shown a sharp increase in public interest in the crypto market, with its market capitalization growing from $100 billion in June 2017 to $2158.42 billion on April 5, 2022. Despite the outrageous nature of the volatility of cryptocurrencies, the use of skewed error innovation distributions in modelling the volatility behaviour of these digital currencies has not been given much research attention. Hence, this study models the volatility of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, Binance coin, and USD Coin) using four variants of GARCH models (GJR-GARCH, sGARCH, EGARCH, and APARCH) estimated using three skewed error innovation distributions (skewed normal, skewed student- t and skewed generalized error innovation distributions). Daily closing prices of these currencies were obtained from Yahoo Finance website. Finding reveals that the Binance coin reported higher mean returns compared to other digital currencies, while the skewness indicates that the Binance coin, Tether, and USD coin increased more than they decreased in values within the period of study. For both Bitcoin and Ethereum, negative skewness was obtained, meaning that within the period of study, the returns of these currencies decreased more than they increased in value. Returns from these cryptocurrencies were found to be stationary but not normality distributed with evidence of the ARCH effect. The skewness parameters in all best forecasting models were all significant (p<.05), justifying of use of skewed error innovation distributions with a fatter tail than normal, Student-t, and generalized error innovation distributions. For Binance coin, EGARCH-sstd outperformed other volatility models, while for Bitcoin, Ethereum, Tether, and USD coin, the best forecasting models were EGARCH-sstd, APARCH-sstd, EGARCH-sged, and GJR-GARCH-sstd, respectively. This suggests the superiority of skewed Student t- distribution and skewed generalized error distribution over the skewed normal distribution.

Keywords: skewed generalized error distribution, skewed normal distribution, skewed student t- distribution, APARCH, EGARCH, sGARCH, GJR-GARCH

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Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

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Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

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Authors: I. Toumi, Y. Abed, A. Bouafia

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This paper presents a methodology for identifying the cohesive soil parameters that takes into account different constitutive equations. The procedure, applied to identify the parameters of generalized Prager model associated to the Drucker & Prager failure criterion from a pressuremeter expansion curve, is based on an inverse analysis approach, which consists of minimizing the function representing the difference between the experimental curve and the simulated curve using a simplex algorithm. The model response on pressuremeter path and its identification from experimental data lead to the determination of the friction angle, the cohesion and the Young modulus. Some parameters effects on the simulated curves and stresses path around pressuremeter probe are presented. Comparisons between the parameters determined with the proposed method and those obtained by other means are also presented.

Keywords: cohesive soils, cavity expansion, pressuremeter test, finite element method, optimization procedure, simplex algorithm

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Authors: Siphat Lim

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This study used Autoregressive Distributed Lag (ARDL) approach to cointegration. In the long-run the general price level and exchange rate have a positively significant effect on domestic output. The estimated result further revealed that fiscal stimulus help stimulate domestic output in the long-run, but not in the short-run, while monetary expansion help to stimulate output in both short-run and long-run. The result is complied with the theory which is the macroeconomic policies, fiscal and monetary policy; help to stimulate domestic output in the long-run. The estimated result of the Vector Error Correction Model (VECM) has indicated more clearly that the consumer price index has a positive effect on output with highly statistically significant. Increasing in the general price level would increase the competitiveness among producers than increase in the output. However, the exchange rate also has a positive effect and highly significant on the gross domestic product. The exchange rate depreciation might increase export since the purchasing power of foreigners has increased. More importantly, fiscal stimulus would help stimulate the domestic output in the long-run since the coefficient of government expenditure is positive. In addition, monetary expansion would also help stimulate the output and the result is highly significant. Thus, fiscal stimulus and monetary expansionary would help stimulate the domestic output in the long-run in Cambodia.

Keywords: fiscal policy, monetary policy, ARDL, VECM

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Authors: Muhammad Nouman Qureshi, Muhammad Hanif

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Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper.

Keywords: auxiliary information, adaptive cluster sampling, clustered populations, Hansen-Hurwitz estimation

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Authors: Michael Anton Kraus, Miriam Schuster, Geralt Siebert, Jens Schneider

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Polymers increasingly gained interest in construction materials over the last years in civil engineering applications. As polymeric materials typically show time- and temperature dependent material behavior, which is accounted for in the context of the theory of linear viscoelasticity. Within the context of this paper, the authors show, that some polymeric interlayers for laminated glass can not be considered as thermorheologically simple as they do not follow a simple TTSP, thus a methodology of identifying the thermorheologically complex constitutive bahavioir is needed. ‘Dynamical-Mechanical-Thermal-Analysis’ (DMTA) in tensile and shear mode as well as ‘Differential Scanning Caliometry’ (DSC) tests are carried out on the interlayer material ‘Ethylene-vinyl acetate’ (EVA). A navoel Bayesian framework for the Master Curving Process as well as the detection and parameter identification of the TTSPs along with their associated Prony-series is derived and applied to the EVA material data. To our best knowledge, this is the first time, an uncertainty quantification of the Prony-series in a Bayesian context is shown. Within this paper, we could successfully apply the derived Bayesian methodology to the EVA material data to gather meaningful Master Curves and TTSPs. Uncertainties occurring in this process can be well quantified. We found, that EVA needs two TTSPs with two associated Generalized Maxwell Models. As the methodology is kept general, the derived framework could be also applied to other thermorheologically complex polymers for parameter identification purposes.

Keywords: bayesian parameter identification, generalized Maxwell model, linear viscoelasticity, thermorheological complex

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Authors: Milica Ž. Karadžić, Lidija R. Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Z. Kovačević, Anamarija I. Mandić, Katarina Penov-Gaši, Andrea R. Nikolić, Aleksandar M. Oklješa

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This study considered selection of the in silico molecular descriptors and the models for newly synthesized steroid derivatives description and their characterization using chemometric techniques. Multiple linear regression (MLR) models were established and gave the best molecular descriptors for quantitative structure-retention relationship (QSRR) modeling of the retention of the investigated molecules. MLR models were without multicollinearity among the selected molecular descriptors according to the variance inflation factor (VIF) values. Used molecular descriptors were ranked using generalized pair correlation method (GPCM). In this method, the significant difference between independent variables can be noticed regardless almost equal correlation between dependent variable. Generated MLR models were statistically and cross-validated and the best models were kept. Models were ranked using sum of ranking differences (SRD) method. According to this method, the most consistent QSRR model can be found and similarity or dissimilarity between the models could be noticed. In this study, SRD was performed using average values of experimentally observed data as a golden standard. Chemometric analysis was conducted in order to characterize newly synthesized steroid derivatives for further investigation regarding their potential biological activity and further synthesis. This article is based upon work from COST Action (CM1105), supported by COST (European Cooperation in Science and Technology).

Keywords: generalized pair correlation method, molecular descriptors, regression analysis, steroids, sum of ranking differences

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Authors: Aziz Sezgin

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We consider the problem of stabilization of an unstable heat equation in a 2-D, 3-D and generally n-D domain by deriving a generalized backstepping boundary control design methodology. To stabilize the systems, we design boundary backstepping controllers inspired by the 1-D unstable heat equation stabilization procedure. We assume that one side of the boundary is hinged and the other side is controlled for each direction of the domain. Thus, controllers act on two boundaries for 2-D domain, three boundaries for 3-D domain and ”n” boundaries for n-D domain. The main idea of the design is to derive ”n” controllers for each of the dimensions by using ”n” kernel functions. Thus, we obtain ”n” controllers for the ”n” dimensional case. We use a transformation to change the system into an exponentially stable ”n” dimensional heat equation. The transformation used in this paper is a generalized Volterra/Fredholm type with ”n” kernel functions for n-D domain instead of the one kernel function of 1-D design.

Keywords: backstepping, boundary control, 2-D, 3-D, n-D heat equation, distributed parameter systems

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Authors: Gholamreza Ghodrati Amiri, Mojtaba Jafarian Abyaneh, Ali Zare Hosseinzadeh

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This paper presents an effective model updating strategy for damage localization and quantification in frames by defining damage detection problem as an optimization issue. A generalized version of the Modal Residual Force (MRF) is employed for presenting a new damage-sensitive cost function. Then, Grey Wolf Optimization (GWO) algorithm is utilized for solving suggested inverse problem and the global extremums are reported as damage detection results. The applicability of the presented method is investigated by studying different damage patterns on the benchmark problem of the IASC-ASCE, as well as a planar shear frame structure. The obtained results emphasize good performance of the method not only in free-noise cases, but also when the input data are contaminated with different levels of noises.

Keywords: frame, grey wolf optimization algorithm, modal residual force, structural damage detection

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: R. Pilafkan, M. Kaffash Irzarahimi, S. F. Asbaghian Namin

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The biaxial buckling behavior of single-layered graphene sheets (SLGSs) is studied in the present work. To consider the size-effects in the analysis, Eringen’s nonlocal elasticity equations are incorporated into classical plate theory (CLPT). A Generalized Differential Quadrature Method (GDQM) approach is utilized and numerical solutions for the critical buckling loads are obtained. Then, molecular dynamics (MD) simulations are performed for a series of zigzag SLGSs with different side-lengths and with various boundary conditions, the results of which are matched with those obtained by the nonlocal plate model to numerical the appropriate values of nonlocal parameter relevant to each type of boundary conditions.

Keywords: biaxial buckling, single-layered graphene sheets, nonlocal elasticity, molecular dynamics simulation, classical plate theory

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Authors: Lubov Magerramova, Vladimir Isakov, Michail Petrov

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A methodology for calculating and modeling technological modes of laser 3D printing of Ti6Al4V powder alloy samples has been developed. ProXDPM320 3D printer was used. The technological model that takes into account the multifactorial influence of modes and conditions of additive cultivation on characteristics and strength properties of titanium samples has been created. Process control parameters and an order parameter, to which the others are subordinate, were established. Using the iterative method, the optimal technological parameters for the additive growth of cylindrical samples were calculated. The calculations were combined with data obtained during virtual 3D printing in the Altair Inspire software environment. The samples were subjected to short-term tensile strength tests at normal temperature on a servo-hydraulic machine “LFV-100”. As a result, deformation diagrams were constructed, and mechanical characteristics such as proportionality limit, conditional yield strength, tensile strength, elastic modulus, relative elongation, and stress at break were obtained. Comparison of these characteristics with those for the industrial alloy Ti6Al4V showed acceptable agreement. Some of the synthesized samples were subjected to laser shock treatment to increase fatigue strength. The results obtained were used to validate the mathematical model of 3D printing of titanium alloys.

Keywords: additive technology, titanium alloy, numerical simulation, strength tests

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Authors: Saba Riaz, Syed A. Hussain

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This paper is addressing the estimation method of the unknown population variance of the variable of interest. A new generalized class of estimators of the finite population variance has been suggested using the auxiliary information. To improve the precision of the proposed class, known population variance of the auxiliary variable has been used. Mathematical expressions for the biases and the asymptotic variances of the suggested class are derived under large sample approximation. Theoretical and numerical comparisons are made to investigate the performances of the proposed class of estimators. The empirical study reveals that the suggested class of estimators performs better than the usual estimator, classical ratio estimator, classical product estimator and classical linear regression estimator. It has also been found that the suggested class of estimators is also more efficient than some recently published estimators.

Keywords: study variable, auxiliary variable, finite population variance, bias, asymptotic variance, percent relative efficiency

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Authors: Olga I. Moskalenko, Vladislav A. Khanadeev, Anastasya D. Koloskova, Alexey A. Koronovskii, Anatoly A. Pivovarov

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Generalized synchronization is one of the most intricate phenomena in nonlinear science. It can be observed both in systems with a unidirectional and mutual type of coupling including the complex networks. Such a phenomenon has a number of practical applications, for example, for the secure information transmission through the communication channel with a high level of noise. Known methods for the secure information transmission needs in the increase of the privacy of data transmission that arises a question about the observation of such phenomenon in systems with a complex topology of chaotic attractor possessing two or more positive Lyapunov exponents. The present report is devoted to the study of such phenomenon in two unidirectionally and mutually coupled dynamical systems being in chaotic (with one positive Lyapunov exponent) and hyperchaotic (with two or more positive Lyapunov exponents) regimes, respectively. As the systems under study, we have used two mutually coupled modified Lorenz oscillators and two unidirectionally coupled time-delayed generators. We have shown that in both cases the generalized synchronization regime can be detected by means of the calculation of Lyapunov exponents and phase tube approach whereas due to the complex topology of attractor the nearest neighbor method is misleading. Moreover, the auxiliary system approaches being the standard method for the synchronous regime observation, for the mutual type of coupling results in incorrect results. To calculate the Lyapunov exponents in time-delayed systems we have proposed an approach based on the modification of Gram-Schmidt orthogonalization procedure in the context of the time-delayed system. We have studied in detail the mechanisms resulting in the generalized synchronization regime onset paying a great attention to the field where one positive Lyapunov exponent has already been become negative whereas the second one is a positive yet. We have found the intermittency here and studied its characteristics. To detect the laminar phase lengths the method based on a calculation of local Lyapunov exponents has been proposed. The efficiency of the method has been verified using the example of two unidirectionally coupled Rössler systems being in the band chaos regime. We have revealed the main characteristics of intermittency, i.e. the distribution of the laminar phase lengths and dependence of the mean length of the laminar phases on the criticality parameter, for all systems studied in the report. This work has been supported by the Russian President's Council grant for the state support of young Russian scientists (project MK-531.2018.2).

Keywords: complex topology of attractor, generalized synchronization, hyperchaos, Lyapunov exponents

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Authors: Mihai Rebenciuc, Simona Mihaela Bibic

Abstract:

Branch of modern mathematics, graphs represent instruments for optimization and solving practical applications in various fields such as economic networks, engineering, network optimization, the geometry of social action, generally, complex systems including contemporary urban problems (path or transport efficiencies, biourbanism, & c.). In this paper is studied the interconnection of some urban network, which can lead to a simulation problem of a digraph through another digraph. The simulation is made univoc or more general multivoc. The concepts of fragment and atom are very useful in the study of connectivity in the digraph that is simulation - including an alternative evaluation of k- connectivity. Rough set approach in (bi)digraph which is proposed in premier in this paper contribute to improved significantly the evaluation of k-connectivity. This rough set approach is based on generalized rough sets - basic facts are presented in this paper.

Keywords: (bi)digraphs, rough set theory, systems of interacting agents, complex systems

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Authors: Alexander Buhr, Klaus Ehrenfried

Abstract:

Trackside induced airflow velocities, also known as slipstream velocities, are an important criterion for the design of high-speed trains. The maximum permitted values are given by the Technical Specifications for Interoperability (TSI) and have to be checked in the approval process. For train manufactures it is of great interest to know in advance, how new train geometries would perform in TSI tests. The Reynolds number in moving model experiments is lower compared to full-scale. Especially the limited model length leads to a thinner boundary layer at the rear end. The hypothesis is that the boundary layer rolls up to characteristic flow structures in the train wake, in which the maximum flow velocities can be observed. The idea is to enlarge the boundary layer using roughness elements at the train model head so that the ratio between the boundary layer thickness and the car width at the rear end is comparable to a full-scale train. This may lead to similar flow structures in the wake and better prediction accuracy for TSI tests. In this case, the design of the roughness elements is limited by the moving model rig. Small rectangular roughness shapes are used to get a sufficient effect on the boundary layer, while the elements are robust enough to withstand the high accelerating and decelerating forces during the test runs. For this investigation, High-Speed Particle Image Velocimetry (HS-PIV) measurements on an ICE3 train model have been realized in the moving model rig of the DLR in Göttingen, the so called tunnel simulation facility Göttingen (TSG). The flow velocities within the boundary layer are analysed in a plain parallel to the ground. The height of the plane corresponds to a test position in the EN standard (TSI). Three different shapes of roughness elements are tested. The boundary layer thickness and displacement thickness as well as the momentum thickness and the form factor are calculated along the train model. Conditional sampling is used to analyse the size and dynamics of the flow structures at the time of maximum velocity in the train wake behind the train. As expected, larger roughness elements increase the boundary layer thickness and lead to larger flow velocities in the boundary layer and in the wake flow structures. The boundary layer thickness, displacement thickness and momentum thickness are increased by using larger roughness especially when applied in the height close to the measuring plane. The roughness elements also cause high fluctuations in the form factors of the boundary layer. Behind the roughness elements, the form factors rapidly are approaching toward constant values. This indicates that the boundary layer, while growing slowly along the second half of the train model, has reached a state of equilibrium.

Keywords: boundary layer, high-speed PIV, ICE3, moving train model, roughness elements

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Authors: M. H. Kazemi, D. Salac

Abstract:

A semi-implicit phase field method for droplet evolution is proposed. Using the phase field Cahn-Hilliard equation, we are able to track the interface in multiphase flow. The idea of a semi-implicit finite difference scheme is reviewed and employed to solve two nonlinear equations, including the Navier-Stokes and the Cahn-Hilliard equations. The use of a semi-implicit method allows us to have larger time steps compared to explicit schemes. The governing equations are coupled and then solved by a GMRES solver (generalized minimal residual method) using modified Gram-Schmidt orthogonalization. To show the validity of the method, we apply the method to the simulation of a rising droplet, a leaky dielectric drop and the coalescence of drops. The numerical solutions to the phase field model match well with existing solutions over a defined range of variables.

Keywords: coalescence, leaky dielectric, numerical method, phase field, rising droplet, semi-implicit method

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Authors: Sergio Haram Sarmiento, Arcady Ponosov

Abstract:

Based on appropriate multivariate statistical methodology, we suggest a generic framework for efficient parameter estimation for ordinary differential equations and the corresponding nonlinear models. In this framework classical linear regression strategies is refined into a nonlinear regression by a locally linear modelling technique (known as metamodelling). The approach identifies those latent variables of the given model that accumulate most information about it among all approximations of the same dimension. The method is applied to several benchmark problems, in particular, to the so-called ”power-law systems”, being non-linear differential equations typically used in Biochemical System Theory.

Keywords: principal component analysis, generalized law of mass action, parameter estimation, metamodels

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Authors: Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab

Abstract:

Urban Heat Islands (UHIs) are distinctive urban areas characterized by densely populated central cores surrounded by less densely populated peripheral lands. These areas experience elevated temperatures, primarily due to impermeable surfaces and specific land use patterns. The consequences of these temperature variations are far-reaching, impacting the environment and society negatively, leading to increased energy consumption, air pollution, and public health concerns. This paper emphasizes the need for simplified approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. To illustrate this relationship, the study focuses on the Guilan Plain, utilizing techniques like principal component analysis and generalized additive models. The research centered on mapping land use and land surface temperature in the low-lying area of Guilan province. Satellite data from Landsat sensors for three different time periods (2002, 2012, and 2021) were employed. Using eCognition software, a spatial unit known as a "city block" was utilized through object-based analysis. The study also applied the normalized difference vegetation index (NDVI) method to estimate land surface radiance. Predictive variables for urban land surface temperature within residential city blocks were identified categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Principal Component Analysis (PCA) was used to select significant variables, and a Generalized Additive Model (GAM) approach, implemented using R's mgcv package, modeled the relationship between urban land surface temperature and predictor variables.Notable findings included variations in urban temperature across different years attributed to environmental and climatic factors. Block size, shared boundary, mother polygon area, and perimeter-to-area ratio were identified as main variables for the generalized additive regression model. This model showed non-linear relationships, with block size, shared boundary, and mother polygon area positively correlated with temperature, while the perimeter-to-area ratio displayed a negative trend. The discussion highlights the challenges of predicting urban surface temperature and the significance of block size in determining urban temperature patterns. It also underscores the importance of spatial configuration and unit structure in shaping urban temperature patterns. In conclusion, this study contributes to the growing body of research on the connection between land use patterns and urban surface temperature. Block size, along with block dispersion and aggregation, emerged as key factors influencing urban surface temperature in residential areas. The proposed methodology enhances our understanding of parameter significance in shaping urban temperature patterns across various regions, particularly in Iran.

Keywords: urban heat island, land surface temperature, LST modeling, GAM, Gilan province

Procedia PDF Downloads 81