Search results for: generalised linear model

Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

Procedia PDF Downloads 375

Authors: Homa Ghave, Parmis Shahmaleki

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This paper presents a new multi-criteria stochastic mathematical model for a single machine scheduling with outsourcing allowed. There are multiple jobs processing in batch. For each batch, all of job or a quantity of it can be outsourced. The jobs have stochastic processing time and lead time and deterministic due dates arrive randomly. Because of the stochastic inherent of processing time and lead time, we use the chance constrained programming for modeling the problem. First, the problem is formulated in form of stochastic programming and then prepared in a form of deterministic mixed integer linear programming. The objectives are considered in the model to minimize the maximum tardiness and outsourcing cost simultaneously. Several procedures have been developed to deal with the multi-criteria problem. In this paper, we utilize the concept of satisfaction functions to increases the manager’s preference. The proposed approach is tested on instances where the random variables are normally distributed.

Keywords: single machine scheduling, multi-criteria mathematical model, outsourcing strategy, uncertain lead times and processing times, chance constrained programming, satisfaction function

Procedia PDF Downloads 247

Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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Authors: O. Poleshchuk, E. Komarov

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This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.

Keywords: interval type-2 fuzzy sets, fuzzy regression, weighted interval

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Authors: Abtin Farokhipanah

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In recent years, analysis of structures is based on ductility design in contradictory to strength design in surveying earthquake effects on structures. ASCE07-10 code offers to intensify relative drifts calculated from a linear analysis with Cd which is called (Deflection Amplification Factor) to obtain the real relative drifts which can be calculated using nonlinear analysis. This lateral drift should be limited to the code boundaries. Calculation of this amplification factor for different structures, comparing with ASCE07-10 code and offering the best coefficient are the purposes of this research. Following our target, short and tall building steel structures with various earthquake resistant systems in linear and nonlinear analysis should be surveyed, so these questions will be answered: 1. Does the Response Modification Coefficient (R) have a meaningful relation to Deflection Amplification Factor? 2. Does structure height, seismic zone, response spectrum and similar parameters have an effect on the conversion coefficient of linear analysis to real drift of structure? The procedure has used to conduct this research includes: (a) Study on earthquake resistant systems, (b) Selection of systems and modeling, (c) Analyzing modeled systems using linear and nonlinear methods, (d) Calculating conversion coefficient for each system and (e) Comparing conversion coefficients with the code offered ones and concluding results.

Keywords: ASCE07-10 code, deflection amplification factor, earthquake engineering, lateral displacement of structures, response modification coefficient

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Authors: Zhou Jianhong

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Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again.

Keywords: model averaging, poission regression, Kullback-Leibler distance, statistics

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Authors: Jude K. Safo

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Knowledge Graphs KG) and their relation to Graph Embeddings (GE) represent a unique data structure in the landscape of machine learning (relative to image, text and acoustic data). Unlike the latter, GEs are the only data structure sufficient for representing hierarchically dense, semantic information needed for use-cases like supply chain data and protein folding where the search space exceeds the limits traditional search methods (e.g. page-rank, Dijkstra, etc.). While GEs are effective for compressing low rank tensor data, at scale, they begin to introduce a new problem of ’data retreival’ which we observe in Large Language Models. Notable attempts by transE, TransR and other prominent industry standards have shown a peak performance just north of 57% on WN18 and FB15K benchmarks, insufficient practical industry applications. They’re also limited, in scope, to next node/link predictions. Traditional linear methods like Tucker, CP, PARAFAC and CANDECOMP quickly hit memory limits on tensors exceeding 6.4 million nodes. This paper outlines a topological framework for linear mapping between concepts in KG space and GE space that preserve cardinality. Most importantly we introduce a traceable framework for composing dense linguistic strcutures. We demonstrate performance on WN18 benchmark this model hits. This model does not rely on Large Langauge Models (LLM) though the applications are certainy relevant here as well.

Keywords: representation theory, large language models, graph embeddings, applied algebraic topology, applied knot theory, combinatorics

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Authors: O. Benabdeljelil, A. Karioun, S. Amami, R. Rouger, M. Hamidine

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A model for preventing the risks related to climate conditions in the agricultural sector is presented. It will determine the yearly optimum premium to be paid by a producer in order to reach his required turnover. The model is based on both climatic stability and 'soft' responses of usually grown species to average climate variations at the same place and inside a safety ball which can be determined from past meteorological data. This allows the use of linear regression expression for dependence of production result in terms of driving meteorological parameters, the main ones of which are daily average sunlight, rainfall and temperature. By simple best parameter fit from the expert table drawn with professionals, optimal representation of yearly production is determined from records of previous years, and yearly payback is evaluated from minimum yearly produced turnover. The model also requires accurate pricing of commodity at N+1. Therefore, a pricing model is developed using 3 state variables, namely the spot price, the difference between the mean-term and the long-term forward price, and the long-term structure of the model. The use of historical data enables to calibrate the parameters of state variables, and allows the pricing of commodity. Application to beet sugar underlines pricer precision. Indeed, the percentage of accuracy between computed result and real world is 99,5%. Optimal premium is then deduced and gives the producer a useful bound for negotiating an offer by insurance companies to effectively protect its harvest. The application to beet production in French Oise department illustrates the reliability of present model with as low as 6% difference between predicted and real data. The model can be adapted to almost any agricultural field by changing state parameters and calibrating their associated coefficients.

Keywords: agriculture, production model, optimal price, meteorological factors, 3-factor model, parameter calibration, forward price

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Authors: Wael Elsaady, S. Olutunde Oyadiji, Adel Nasser

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The non-linear flow characteristics of magnetorheological (MR) fluids in MR dampers are studied via a coupled numerical approach that incorporates a two-phase flow model. The approach couples the Finite Element (FE) modelling of the damper magnetic circuit, with the Computational Fluid Dynamics (CFD) analysis of the flow field in the damper. The two-phase flow CFD model accounts for the effect of fluid compressibility due to the presence of liquid and gas in the closed domain of the damper. The dynamic mesh model included in ANSYS/Fluent CFD solver is used to simulate the movement of the MR damper piston in order to perform the fluid excitation. The two-phase flow analysis is studied by both Volume-Of-Fluid (VOF) model and mixture model that are included in ANSYS/Fluent. The CFD models show that the hysteretic behaviour of MR dampers is due to the effect of fluid compressibility. The flow field shows the distributions of pressure, velocity, and viscosity contours. In particular, it shows the high non-Newtonian viscosity in the affected fluid regions by the magnetic field and the low Newtonian viscosity elsewhere. Moreover, the dependence of gas volume fraction on the liquid pressure inside the damper is predicted by the mixture model. The presented approach targets a better understanding of the complicated flow characteristics of viscoplastic fluids that could be applied in different applications.

Keywords: viscoplastic fluid, magnetic FE analysis, computational fluid dynamics, two-phase flow, dynamic mesh, user-defined functions

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Authors: Mihai Caramihai, Dan Vasilescu

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The main research objective of the experimental bioprocess analyzed in this paper was to obtain large biomass quantities. The bioprocess is performed in 100 L Bioengineering bioreactor with 42 L cultivation medium made of peptone, meat extract and sodium chloride. The reactor was equipped with pH, temperature, dissolved oxygen, and agitation controllers. The operating parameters were 37 oC, 1.2 atm, 250 rpm and air flow rate of 15 L/min. The main objective of this paper is to present a case study to demonstrate that intelligent control, describing the complexity of the biological process in a qualitative and subjective manner as perceived by human operator, is an efficient control strategy for this kind of bioprocesses. In order to simulate the bioprocess evolution, an intelligent control structure, based on fuzzy logic has been designed. The specific objective is to present a fuzzy control approach, based on human expert’ rules vs. a modeling approach of the cells growth based on bioprocess experimental data. The kinetic modeling may represent only a small number of bioprocesses for overall biosystem behavior while fuzzy control system (FCS) can manipulate incomplete and uncertain information about the process assuring high control performance and provides an alternative solution to non-linear control as it is closer to the real world. Due to the high degree of non-linearity and time variance of bioprocesses, the need of control mechanism arises. BIOSIM, an original developed software package, implements such a control structure. The simulation study has showed that the fuzzy technique is quite appropriate for this non-linear, time-varying system vs. the classical control method based on a priori model.

Keywords: intelligent, control, fuzzy model, bioprocess optimization

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Authors: K. N. Kiran, S. Anish

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It is well known that secondary flow loses account about one third of the total loss in any axial turbine. Modern gas turbine height is smaller and have longer chord length, which might lead to increase in secondary flow. In order to improve the efficiency of the turbine, it is important to understand the behavior of secondary flow and device mechanisms to curtail these losses. The objective of the present work is to understand the effect of a stream wise end-wall fence on the aerodynamics of a linear turbine cascade. The study is carried out computationally by using commercial software ANSYS CFX. The effect of end-wall on the flow field are calculated based on RANS simulation by using SST transition turbulence model. Durham cascade which is similar to high-pressure axial flow turbine for simulation is used. The aim of fencing in blade passage is to get the maximum benefit from flow deviation and destroying the passage vortex in terms of loss reduction. It is observed that, for the present analysis, fence in the blade passage helps reducing the strength of horseshoe vortex and is capable of restraining the flow along the blade passage. Fence in the blade passage helps in reducing the under turning by 7⁰ in comparison with base case. Fence on end-wall is effective in preventing the movement of pressure side leg of horseshoe vortex and helps in breaking the passage vortex. Computations are carried for different fence height whose curvature is different from the blade camber. The optimum fence geometry and location reduces the loss coefficient by 15.6% in comparison with base case.

Keywords: boundary layer fence, horseshoe vortex, linear cascade, passage vortex, secondary flow

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Authors: WonHo Lee, Hyo-Gyoung Kwak

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A numerical model considering slip behavior of steel-concrete composite structure is introduced. This model is based on a linear bond stress-slip relation along the interface. Single node was considered at the interface of steel and concrete member in finite element analysis, and it improves analytical problems of model that takes double nodes at the interface by adopting spring elements to simulate the partial interaction. The slip behavior is simulated by modifying material properties of steel element contacting concrete according to the derived formulation. Decreased elastic modulus simulates the slip occurrence at the interface and decreased yield strength simulates drop in load capacity of the structure. The model is verified by comparing numerical analysis applying this model with experimental studies. Acknowledgment—This research was supported by a grant(13SCIPA01) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport(MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement(KAIA) and financially supported by Korea Ministry of Land, Infrastructure and Transport(MOLIT) as U-City Master and Doctor Course Grant Program.

Keywords: bond-slip, composite structure, partial interaction, steel-concrete structure

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Authors: M. Seguini, D. Nedjar

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An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability

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Authors: Rabab Allouzi, Amer Alkloub

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As the complex shaped buildings become a popular trend for architects, this paper is presented to investigate the performance of reinforced concrete flat plate-inclined column connection. The studies on the inclined column and flat plate connections are not sufficient in comparison to those on the conventional structures. The effect of column angle of inclination on the punching shear strength is found significant and studied herein. This paper presents a non-linear finite element based modeling approach to estimate behavior of RC flat plate inclined column connection. Results from simulations of RC flat plate-straight column connection show good agreement with experimental response of specimens tested by other researchers. The model is further used to study the response of inclined columns to punching at various ranges of inclination angles. The inclination angle can be included in the punching shear strength provisions provided by ACI 318-14 to account for the effect of column inclination.

Keywords: punching shear, non-linear finite element, inclined columns, reinforced concrete connection

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Authors: Nader H. Ghareeb, Mohamed S. Gaith, Sayed M. Soleimani

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In modern engineering, weight optimization has a priority during the design of structures. However, optimizing the weight can result in lower stiffness and less internal damping, causing the structure to become excessively prone to vibration. To overcome this problem, active or smart materials are implemented. The coupled electromechanical properties of smart materials, used in the form of piezoelectric ceramics in this work, make these materials well-suited for being implemented as distributed sensors and actuators to control the structural response. The smart structure proposed in this paper is composed of a cantilevered steel beam, an adhesive or bonding layer, and a piezoelectric actuator. The static deflection of the structure is derived as function of the piezoelectric voltage, and the outcome is compared to theoretical and experimental results from literature. The relation between the voltage and the piezoelectric moment at both ends of the actuator is also investigated and a reduced finite element model of the smart structure is created and verified. Finally, a linear controller is implemented and its ability to attenuate the vibration due to the first natural frequency is demonstrated.

Keywords: active linear control, lyapunov stability theorem, piezoelectricity, smart structure, static deflection

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Authors: Nurul Aini Abdul Wahab, Nor Syamim Halidin, Sayidatina Aisah Masnan, Nur Izzati Romli

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There are many applications of principle component method for reducing the large set of variables in various fields. Fisher’s Discriminant function is also a popular tool for classification. In this research, the researcher focuses on studying the performance of Principle Component-Fisher’s Discriminant function in helping to classify rice kernels to their defined classes. The data were collected on the smells or odour of the rice kernel using odour-detection sensor, Cyranose. 32 variables were captured by this electronic nose (e-nose). The objective of this research is to measure how well a combination model, between principle component and linear discriminant, to be as a classification model. Principle component method was used to reduce all 32 variables to a smaller and manageable set of components. Then, the reduced components were used to develop the Fisher’s Discriminant function. In this research, there are 4 defined classes of rice kernel which are Aromatic, Brown, Ordinary and Others. Based on the output from principle component method, the 32 variables were reduced to only 2 components. Based on the output of classification table from the discriminant analysis, 40.76% from the total observations were correctly classified into their classes by the PC-Discriminant function. Indirectly, it gives an idea that the classification model developed has committed to more than 50% of misclassifying the observations. As a conclusion, the Fisher’s Discriminant function that was built on a 2-component from PCA (2-PC-D) is not satisfying to classify the rice kernels into its defined classes.

Keywords: classification model, discriminant function, principle component analysis, variable reduction

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Authors: Mohammad R. Jalali, Mohammad M. Jalali

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The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.

Keywords: Green–Naghdi equations, nonlinearity, numerical prediction, sloshing waves, solitary waves

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Authors: Atheer Al-Nuaimi, Harry Evdorides

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Every day many people die or get disabled or injured on roads around the world, which necessitates more specific treatments for transportation safety issues. International road assessment program (iRAP) model is one of the comprehensive road safety models which accounting for many factors that affect road safety in a cost-effective way in low and middle income countries. In iRAP model road safety has been divided into five star ratings from 1 star (the lowest level) to 5 star (the highest level). These star ratings are based on star rating score which is calculated by iRAP methodology depending on road attributes, traffic volumes and operating speeds. The outcome of iRAP methodology are the treatments that can be used to improve road safety and reduce fatalities and serious injuries (FSI) numbers. These countermeasures can be used separately as a single countermeasure or mix as multiple countermeasures for a location. There is general agreement that the adequacy of a countermeasure is liable to consistent losses when it is utilized as a part of mix with different countermeasures. That is, accident diminishment appraisals of individual countermeasures cannot be easily added together. The iRAP model philosophy makes utilization of a multiple countermeasure adjustment factors to predict diminishments in the effectiveness of road safety countermeasures when more than one countermeasure is chosen. A multiple countermeasure correction factors are figured for every 100-meter segment and for every accident type. However, restrictions of this methodology incorporate a presumable over-estimation in the predicted crash reduction. This study aims to adjust this correction factor by developing new models to calculate the effect of using multiple countermeasures on the number of fatalities for a location or an entire road. Regression models have been used to establish relationships between crash frequencies and the factors that affect their rates. Multiple linear regression, negative binomial regression, and Poisson regression techniques were used to develop models that can address the effectiveness of using multiple countermeasures. Analyses are conducted using The R Project for Statistical Computing showed that a model developed by negative binomial regression technique could give more reliable results of the predicted number of fatalities after the implementation of road safety multiple countermeasures than the results from iRAP model. The results also showed that the negative binomial regression approach gives more precise results in comparison with multiple linear and Poisson regression techniques because of the overdispersion and standard error issues.

Keywords: international road assessment program, negative binomial, road multiple countermeasures, road safety

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Authors: Hossein Panahi, Firouz Fallahi, Sima Nasibparast

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Background & Aim: Unnecessary growth in health expenditures of developing countries in recent decades, and also the importance of physicians’ behavior in health market, have made the theory of physician-induced demand (PID) as one of the most important issues in health economics. Therefore, the main objective of this study is to investigate the hypothesis of induced demand among mental patients who receive services from either psychologists or psychiatrists in East Azerbaijan province. Methods: Using data from questionnaires in 2020 and employing the theoretical model of Jaegher and Jegers (2000) and hierarchical linear modeling (HLM), this study examines the PID hypothesis of selected psychologists and psychiatrists. The sample size of the study, after removing the questionnaires with missing data, is 45 psychologists and 203 people of their patients, as well as 30 psychiatrists and 160 people of their patients. Results: The results show that, although psychiatrists are ‘profit-oriented physicians’, there is no evidence of inducing unnecessary demand by them (PID), and the difference between the behavior of employers and employee doctors is due to differences in practice style. However, with regard to psychologists, the results indicate that they are ‘profit-oriented’, and there is a PID effect in this sector. Conclusion: According to the results, it is suggested that in order to reduce competition and eliminate the PID effect, the admission of students in the field of psychology should be reduced, patient information on mental illness should be increased, and government monitoring and control over the national health system must be increased.

Keywords: physician-induced demand, national health system, hierarchical linear modeling methods, multilevel modela

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Authors: Sunil Mittal, Sukhpreet Singh, Hardeep Kaur

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The affinity of thiol group with heavy metals is a well-established phenomenon. The present investigation has been focused on electrochemical response of cysteine and thioredoxin against arsenite (As III) on indium tin oxide (ITO) electrodes. It was observed that both the compounds produce distinct response in free and immobilised form at the electrode. The SEM, FTIR, and impedance studies of the modified electrode were conducted for characterization. Various parameters were optimized to achieve As (III) effect on the reduction potential of the compounds. Cyclic voltammetry and linear sweep voltammetry were employed as the analysis techniques. The optimum response was observed at neutral pH in both the cases, at optimum concentration of 2 mM and 4.27 µM for cysteine and thioredoxin respectively. It was observed that presence of As (III) increases the reduction current of both the moieties. The linear range of detection for As (III) with cysteine was from 1 to 10 mg L⁻¹ with detection limit of 0.8 mg L⁻¹. The thioredoxin was found more sensitive to As (III) and displayed a linear range from 0.1 to 1 mg L⁻¹ with detection limit of 10 µg L⁻¹.

Keywords: arsenite, cyclic voltammetry, cysteine, thioredoxin

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Authors: Dhyaalddin Bahaalddin Noori Zangana

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This study involves the behavior of reinforced sand under a square footing. A series of bearing capacity tests were performed on a small-scale laboratory model, which filled with a poorly-graded homogenous bed of sand, which was placed in a medium dense state using sand raining technique. The sand was reinforced with 40 mm wide household aluminum foil strips. The main studied parameters was to consider the effect of reinforcing strip length, with various linear density of reinforcement, number of reinforcement layers and depth of top layer of reinforcement below the footing, on load-settlement behavior, bearing capacity ratio and settlement reduction factor. The relation of load-settlement generally showed similar trend in all the tests. Failure was defined as settlement equal to 10% of the footing width. The recommended optimum reinforcing strip length, linear density of reinforcement, number of reinforcement layers and depth of top layer of reinforcing strips that give the maximum bearing capacity improvement and minimum settlement reduction factor were presented and discussed. Different bearing capacity ration versus length of the reinforcing strips and settlement reduction factor versus length of the reinforcing strips relations at failure were showed improvement of bearing capacity ratio by a factor of 3.82 and reduction of settlement reduction factor by a factor of 0.813. The optimum length of reinforcement was found to be 7.5 times the footing width.

Keywords: square footing, relative density, linear density of reinforcement, bearing capacity ratio, load-settlement behaviour

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Authors: L. Madouni, M. Ould Ouali, N. E. Hannachi

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Two constitutive models for concrete are available in ABAQUS/Explicit, the Brittle Cracking Model and the Concrete Damaged Plasticity Model, and their suitability and limitations are well known. The aim of the present paper is to implement a damage-friction concrete constitutive model and to evaluate the performance of this model by comparing the predicted response with experimental data. The constitutive formulation of this material model is reviewed. In order to have consistent results, the parameter identification and calibration for the model have been performed. Several numerical simulations are presented in this paper, whose results allow for validating the capability of the proposed model for reproducing the typical nonlinear performances of concrete structures under different monotonic and cyclic load conditions. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated model.

Keywords: Abaqus, concrete, constitutive model, numerical simulation

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Authors: Abdel-Razzaq Mugdadi, Ruqayyah Sani

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The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data.

Keywords: estimation, bandwidth, mean square error, cumulative distribution function

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Authors: Shivani Sharma, Prashant Saxena, Inamul Hasan Madar

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Since the time of Darwin, it has been considered that genetic change is the direct indicator of variation in phenotype. But a few studies in system biology in the past years have proposed that epigenetic developmental processes also affect the phenotype thus shifting the focus from a linear genotype-phenotype map to a non-linear G-P map. In this paper, we attempt at explaining the evolution of colour vision in mammals by taking LWS/ Long-wave sensitive gene under consideration.

Keywords: evolution, phenotypes, epigenetics, LWS gene, G-P map

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Authors: Getachew Tilahun, Oluwole Makinde, David Malonza

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We propose and analyze a non-linear mathematical model for the transmission dynamics of pneumonia disease in a population of varying size. The deterministic compartmental model is studied using stability theory of differential equations. The effective reproduction number is obtained and also the local and global asymptotically stability conditions for the disease free and as well as for the endemic equilibria are established. The model exhibit a backward bifurcation and the sensitivity indices of the basic reproduction number to the key parameters are determined. Using Pontryagin’s maximum principle, the optimal control problem is formulated with three control strategies; namely disease prevention through education, treatment and screening. The cost effectiveness analysis of the adopted control strategies revealed that the combination of prevention and treatment is the most cost effective intervention strategies to combat the pneumonia pandemic. Numerical simulation is performed and pertinent results are displayed graphically.

Keywords: cost effectiveness analysis, optimal control, pneumonia dynamics, stability analysis, numerical simulation

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Authors: Tomas Menard

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The synthesis of output-feedback control law has been investigated by many researchers since the last century. While many results exist for the case of Linear Time Invariant systems whose measurements are continuously available, nowadays, control laws are usually implemented on micro-controller, then the measurements are discrete-time by nature. This fact has to be taken into account explicitly in order to obtain a satisfactory behavior of the closed-loop system. One considers here a general class of systems corresponding to an observability normal form and which is subject to uncertainties in the dynamics and sampling of the output. Indeed, in practice, the modeling of the system is never perfect, this results in unknown uncertainties in the dynamics of the model. We propose here an output feedback algorithm which is based on a linear state feedback and a continuous-discrete time observer. The main feature of the proposed control law is that only discrete-time measurements of the output are needed. Furthermore, it is formally proven that the state of the closed loop system exponentially converges toward the origin despite the unknown uncertainties. Finally, the performances of this control scheme are illustrated with simulations.

Keywords: dynamical systems, output feedback control law, sampling, uncertain systems

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Authors: Reji Thankachan, Varsha PS

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Both image capturing devices and human visual systems are nonlinear. Hence nonlinear filtering methods outperforms its linear counterpart in many applications. Linear methods are unable to remove impulsive noise in images by preserving its edges and fine details. In addition, linear algorithms are unable to remove signal dependent or multiplicative noise in images. This paper presents an approach to denoise and smoothen the Bipolar impulse noised images and videos using improved Kuwahara filter. It involves a 2 stage algorithm which includes a noise detection followed by filtering. Numerous simulation demonstrate that proposed method outperforms the existing method by eliminating the painting like flattening effect along the local feature direction while preserving edge with improvement in PSNR and MSE.

Keywords: bipolar impulse noise, Kuwahara, PSNR MSE, PDF

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Authors: Aristeidis Samitas, Evangelos Vasileiou

Abstract:

The aim of this study is to examine the month and the trading month effect under changing financial trends. We choose the Greek stock market to implement our assumption because there are clear and long term periods of financial growth and recession. Daily financial data from Athens Exchange General Index for the period 2002-2012 are considered. The paper employs several linear and non-linear models, although the TGARCH asymmetry model best fits in this sample and for this reason we mainly present the TGARCH results. Empirical results show that changing economic and financial conditions influences the calendar effects. Especially, the trading month effect totally changes in each fortnight according to the financial trend. On the other hand, in Greece the January effect exists during the growth periods, although it does not exist when the financial trend changes. The findings are helpful to anybody who invest and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, so it may be useful to investors who take into account these anomalies when they draw their investment strategy.

Keywords: month effect, trading month effect, economic cycles, crisis

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Authors: Walid A. M. Ghoneim, Hamdy A. Ashour, Asmaa E. Abdo

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In this paper, linear analysis of a Switched Reluctance Generator (SRG) model is applied on the most common configurations (4/2, 6/4 and 8/6) for both conventional short-pitched and fully-pitched designs, in order to determine the optimum stator/rotor pole angles at which the maximum output voltage is generated per unit excitation current. This study is focused on SRG analysis and design as a proposed solution for renewable energy applications, such as wind energy conversion systems. The world’s potential to develop the renewable energy technologies through dedicated scientific researches was the motive behind this study due to its positive impact on economy and environment. In addition, the problem of rare earth metals (Permanent magnet) caused by mining limitations, banned export by top producers and environment restrictions leads to the unavailability of materials used for rotating machines manufacturing. This challenge gave authors the opportunity to study, analyze and determine the optimum design of the SRG that has the benefit to be free from permanent magnets, rotor windings, with flexible control system and compatible with any application that requires variable-speed operation. In addition, SRG has been proved to be very efficient and reliable in both low-speed or high-speed applications. Linear analysis was performed using MATLAB simulations based on the (Modified generalized matrix approach) of Switched Reluctance Machine (SRM). About 90 different pole angles combinations and excitation patterns were simulated through this study, and the optimum output results for each case were recorded and presented in detail. This procedure has been proved to be applicable for any SRG configuration, dimension and excitation pattern. The delivered results of this study provide evidence for using the 4-phase 8/6 fully pitched SRG as the main optimum configuration for the same machine dimensions at the same angular speed.

Keywords: generalized matrix approach, linear analysis, renewable applications, switched reluctance generator

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Authors: Behrang Tavousi Tehrani, Mohammad-Zaman Kabir

Abstract:

Shape memory alloys (SMA) are often implemented in smart structures as the active components. Their ability to recover large displacements has been used in many applications, including structural stability/response enhancement and active structural acoustic control. SMA wires or fibers can be embedded with composite cylinders to increase their critical buckling load, improve their load-deflection behavior, and reduce the radial deflections under various thermo-mechanical loadings. This paper presents a semi-analytical investigation on the non-linear load-deflection response of SMA-reinforced composite circular cylindrical shells. The cylinder shells are under uniform external pressure load. Based on first-order shear deformation shell theory (FSDT), the equilibrium equations of the structure are derived. One-dimensional simplified Brinson’s model is used for determining the SMA recovery force due to its simplicity and accuracy. Airy stress function and Galerkin technique are used to obtain non-linear load-deflection curves. The results are verified by comparing them with those in the literature. Several parametric studies are conducted in order to investigate the effect of SMA volume fraction, SMA pre-strain value, and SMA activation temperature on the response of the structure. It is shown that suitable usage of SMA wires results in a considerable enhancement in the load-deflection response of the shell due to the generation of the SMA tensile recovery force.

Keywords: airy stress function, cylindrical shell, Galerkin technique, load-deflection curve, recovery stress, shape memory alloy

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