Search results for: generalized linear mixed model (GLMM)

Authors: Nancy Bartekian, Kaelan Vazquez, Christine Reyna

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Immigrants coming into the United States of America may justify the American system (political, economic, healthcare, criminal justice) and see it as functional. This may be explained because they may come from countries that are even more unstable than the U.S. and/or come here to benefit from the promise of the “American dream” -a narrative that they might be more likely to believe in if they were willing to undergo the costly and sometimes dangerous process to immigrate. Conversely, native-born Americans, as well as immigrants who may have lived in America for a longer period of time, would have more experiences with the various broken systems in America that are dysfunctional, fail to provide adequate services equitably, and/or are steeped in systemic racism and other biases that disadvantage lower-status groups. Thus, our research expects that system justification would decrease, and condemnation would increase with more time spent in the U.S. for immigrant groups. We predict that a) those not born in the U.S. will be more likely to justify the system, b) they will also be less likely to condemn the system, and c) the longer an immigrant has been in the U.S. the less likely they will to justify, and more they will to condemn the system. We will use a mixed-model multivariate analysis of covariance (MANCOVA) and control for race, income, and education. We will also run linear regression models to test if there is a relationship between the length of time in the United States and a decrease in system justification, and length of time and an increase in system condemnation for those not born in the U.S. We will also conduct exploratory analyses to see if the predicted patterns are more likely within certain systems over other systems (political, economic, healthcare, criminal justice).

Keywords: immigration, system justification, system condemnation, system qualification

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Authors: Faisal Salah, Z. A. Aziz, K. K. Viswanathan

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In this article, the effects of g-jitter induced and combined with heat and mass transfer by mixed convection of MHD Maxwell fluid in microgravity situation is investigated for a simple system. This system consists of two heated vertical parallel infinite flat plates held at constant but different temperatures and concentrations. By using modified Darcy’s law, the equations governing the flow are modelled. These equations are solved analytically for the induced velocity, temperature and concentration distributions. Many interesting available results in the relevant literature (i.e. Newtonian fluid) is obtained as the special case of the present general analysis. Finally, the graphical results for the velocity profile of the oscillating flow in the channel are presented and discussed for different values of the material constants.

Keywords: g-jitter, heat and mass transfer, mixed convection, Maxwell fluid, porous medium

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Authors: Mustafa Ekici

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Natural convection is a basic process which is important in a wide variety of practical applications. In essence, a heated fluid expands and rises from buoyancy due to decreased density. Numerous papers have been written on natural or mixed convection in vertical ducts heated on the side. These equations have been proved to be valuable tools for the modelling of many phenomena such as fluid dynamics. Finding solutions to such equations or system of equations are in general not an easy task. We propose a method, which is called differential transform method, of solving a non-linear equations and compare the results with some of the other techniques. Illustrative examples shows that the results are in good agreement.

Keywords: differential transform method, momentum, energy equation, boundry value problem

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Authors: Prashant G. Metri

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A numerical model is developed to study nano liquid film flow over an unsteady stretching sheet in the presence of hydromagnetic have been investigated. Similarity transformations are used to convert unsteady boundary layer equations to a system of non-linear ordinary differential equations. The resulting non-linear ordinary differential equations are solved numerically using Runge-Kutta-Fehlberg and Newton-Raphson schemes. A relationship between film thickness β and the unsteadiness parameter S is found, the effect of unsteadiness parameter S, and the hydromagnetic parameter S, on the velocity and temperature distributions are presented. The present analysis shows that the combined effect of magnetic field and viscous dissipation has a significant influence in controlling the dynamics of the considered problem. Comparison with known results for certain particular cases is in excellent agreement.

Keywords: boundary layer flow, nanoliquid, thin film, unsteady stretching sheet

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Authors: Hossein Tavallali, Mohammad Ali Karimi, Gohar Deilamy-Rad

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The proposed method for speciation, preconcentration and determination of Fe(II) and Fe(III) in pharmaceutical products was developed using of alumina-coated magnetite nanoparticles (Fe₃O₄/Al₂O₃ NPs) as solid phase extraction (SPE) sorbent in magnetic mixed hemimicell solid phase extraction (MMHSPE) technique followed by flame atomic absorption spectrometry analysis. The procedure is based on complexation of Fe(II) with 1, 10-phenanthroline (OP) as complexing reagent for Fe(II) that immobilized on the modified Fe₃O₄/Al₂O₃ NPs. The extraction and concentration process for pharmaceutical sample was carried out in a single step by mixing the extraction solvent, magnetic adsorbents under ultrasonic action. Then, the adsorbents were isolated from the complicated matrix easily with an external magnetic field. Fe(III) ions determined after facility reduced to Fe(II) by added a proper reduction agent to sample solutions. Compared with traditional methods, the MMHSPE method simplified the operation procedure and reduced the analysis time. Various influencing parameters on the speciation and preconcentration of trace iron, such as pH, sample volume, amount of sorbent, type and concentration of eluent, were studied. Under the optimized operating conditions, the preconcentration factor of the modified nano magnetite for Fe(II) 167 sample was obtained. The detection limits and linear range of this method for iron were 1.0 and 9.0 - 175 ng.mL⁻¹, respectively. Also the relative standard deviation for five replicate determinations of 30.00 ng.mL^-1 Fe²⁺ was 2.3%.

Keywords: Alumina-Coated magnetite nanoparticles, Magnetic Mixed Hemimicell Solid-Phase Extraction, Fe(ΙΙ) and Fe(ΙΙΙ), pharmaceutical sample

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

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

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

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Authors: Sepehr Moradi, Mehdi Asadi Rad

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The aim of this study is to evaluate and compare chest weight of pure and mixed races Kabood 30-day Pigeons to investigate about their sex, race, and some auxiliary variables. In this paper, 62 pieces of pigeons as 31 male and female pairs with equal age are studied randomly. A natural incubation was done from each pair. All produced chickens were slaughtered at 30 days age after 12 hours hunger. Then their chests were weighted by a scale with one gram precision. A covariance analysis was used since there were many auxiliary variables and unequal observations. SAS software was used for statistical analysis. Mean weight of chests in pure race (Kabood-Kabood) with 8 records, 123.8±32.3g and mixed races of Kabood-Namebar, Kabood-Parvazy, Kabood-Tizpar, Namebar-Kabood, Tizpar-Kabood, and Parvazi-Kabood with 8, 8, 6, 12, 10, and 10 records were 139.4±23.5, 7/122±23.8, 124.7±30.1, 50.3±29.3, 51.4±26.4, and 137±28.6 gr, respectively. Mean weight of 30-day chests in male and female sex were 87.3±2.5 and 82.7±2.6g, respectively. Difference chest weight of 30-day chests of Kabood-Kabood race with Kabood-Namebar, Kabood-Parvazi, Tizpar-Kabood, Kabood-Tizpar, Namebar-Kabood and Parvazi-Kabood mixed races was not significant. Effect of sex was also significant in 5% level (P<0.05), but mutual effect of sex and race was not significant. Auxiliary variable of father weight was significant in 1% level (p < 0.01), but auxiliary variable of mother weight was not significant. The results showed that most and least weights belonged to Kabood-Namebar and Namebar-Kabood.

Keywords: squab, Kabood race, 30-day chest weight, pigeons

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Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

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Authors: Reni M. Sagayaraj, Anand Gnana S. Selvam, Reynald R. Susainathan

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In this paper, we consider stochastic queueing models for Steady-state behavior of a multi-phase M/M/1 queue in random evolution subject to catastrophe failure. The arrival flow of customers is described by a marked Markovian arrival process. The service times of different type customers have a phase-type distribution with different parameters. To facilitate the investigation of the system we use a generalized phase-type service time distribution. This model contains a repair state, when a catastrophe occurs the system is transferred to the failure state. The paper focuses on the steady-state equation, and observes that, the steady-state behavior of the underlying queueing model along with the average queue size is analyzed.

Keywords: M/G/1 queuing system, multi-phase, random evolution, steady-state equation, catastrophe failure

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Jose Juan Peña, J. Morales, J. García-Ravelo

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In the search of potential models applied in the theoretical treatment of diatomic molecules, some of them have been constructed by using standard hyperbolic functions as well as from the so-called q-deformed hyperbolic functions (sc q-dhf) for displacing and modifying the shape of the potential under study. In order to transcend the scope of hyperbolic functions, in this work, a kind of generalized q-deformed hyperbolic functions (g q-dhf) is presented. By a suitable transformation, through the q deformation parameter, it is shown that these g q-dhf can be expressed in terms of their corresponding standard ones besides they can be reduced to the sc q-dhf. As a useful application of the proposed approach, and considering a class of exactly solvable multi-parameter exponential-type potentials, some new q-deformed quantum interactions models that can be used as interesting alternative in quantum physics and quantum states are presented. Furthermore, due that quantum potential models are conditioned on the q-dependence of the parameters that characterize to the exponential-type potentials, it is shown that many specific cases of q-deformed potentials are obtained as particular cases from the proposal.

Keywords: diatomic molecules, exponential-type potentials, hyperbolic functions, q-deformed potentials

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Authors: A. Boulanoir, B. Ould Bouamama, A. Debiane, N. Achour

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The paper deals with robust Fault Detection and isolation with respect to parameter uncertainties based on linear fractional transformation form (LFT) Bond graph. The innovative interest of the proposed methodology is the use only one representation for systematic generation of robust analytical redundancy relations and adaptive residual thresholds for sensibility analysis. Furthermore, the parameter uncertainties are introduced graphically in the bond graph model. The methodology applied to the nonlinear industrial Electro-Mechanical Actuators (EMA) used in avionic systems, has determined first the structural monitorability analysis (which component can be monitored) with given instrumentation architecture with any need of complex calculation and secondly robust fault indicators for online supervision.

Keywords: bond graph (BG), electro mechanical actuators (EMA), fault detection and isolation (FDI), linear fractional transformation (LFT), mechatronic systems, parameter uncertainties, avionic system

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Authors: Xizhen Zhou, Yanjie Ji

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The charging behavior of drivers provides a reference for the planning and management of charging facilities. Based on the real trajectory data of electric vehicles, this study explored the influence of the surrounding environments of the parking spot on charging decisions. The built environment, the condition of vehicles, and the nearest charging station were all considered. And the mixed binary logit model was used to capture the impact of unobserved heterogeneity. The results show that the number of fast chargers in the charging station, parking price, dwell time, and shopping services all significantly impact the charging decision, while the leisure services, scenic spots, and mileage since the last charging are opposite. Besides, factors related to unobserved heterogeneity include the number of fast chargers, parking and charging prices, residential areas, etc. The interaction effects of random parameters further illustrate the complexity of charging choice behavior. The results provide insights for planning and managing charging facilities.

Keywords: charging decision, trajectory, electric vehicle, infrastructure, mixed logit

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Authors: Marco Furinghetti, Alberto Pavese, Michele Rinaldi

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Concave Surface Slider devices have been more and more used in real applications for seismic protection of both bridge and building structures. Several research activities have been carried out, in order to investigate the lateral response of such a typology of devices, and a reasonably high level of knowledge has been reached. If radial analysis is performed, the frictional force is always aligned with respect to the restoring force, whereas under bidirectional seismic events, a bi-axial interaction of the directions of motion occurs, due to the step-wise projection of the main frictional force, which is assumed to be aligned to the trajectory of the isolator. Nonetheless, if non-linear time history analyses have to be performed, standard codes provide precise rules for the definition of an averagely spectrum-compatible set of accelerograms in radial conditions, whereas for bidirectional motions different combinations of the single components spectra can be found. Moreover, nowadays software for the adjustment of natural accelerograms are available, which lead to a higher quality of spectrum-compatibility and to a smaller dispersion of results for radial motions. In this endeavor a simplified design procedure is defined, for building structures, base-isolated by means of Concave Surface Slider devices. Different case study structures have been analyzed. In a first stage, the capacity curve has been computed, by means of non-linear static analyses on the fixed-base structures: inelastic fiber elements have been adopted and different direction angles of lateral forces have been studied. Thanks to these results, a linear elastic Finite Element Model has been defined, characterized by the same global stiffness of the linear elastic branch of the non-linear capacity curve. Then, non-linear time history analyses have been performed on the base-isolated structures, by applying seven bidirectional seismic events. The spectrum-compatibility of bidirectional earthquakes has been studied, by considering different combinations of single components and adjusting single records: thanks to the proposed procedure, results have shown a small dispersion and a good agreement in comparison to the assumed design values.

Keywords: concave surface slider, spectrum-compatibility, bidirectional earthquake, base isolation

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Authors: Vivek Hokam, Vishrut Landge

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An ideal maintenance program for an industrial road network is one that would maintain all sections at a sufficiently high level of functional and structural conditions. However, due to various constraints such as budget, manpower and equipment, it is not possible to carry out maintenance on all the needy industrial road sections within a given planning period. A rational and systematic priority scheme needs to be employed to select and schedule industrial road sections for maintenance. Priority analysis is a multi-criteria process that determines the best ranking list of sections for maintenance based on several factors. In priority setting, difficult decisions are required to be made for selection of sections for maintenance. It is more important to repair a section with poor functional conditions which includes uncomfortable ride etc. or poor structural conditions i.e. sections those are in danger of becoming structurally unsound. It would seem therefore that any rational priority setting approach must consider the relative importance of functional and structural condition of the section. The maintenance priority index and pavement performance models tend to focus mainly on the pavement condition, traffic criteria etc. There is a need to develop the model which is suitably used with respect to limited budget provisions for maintenance of pavement. Linear programming is one of the most popular and widely used quantitative techniques. A linear programming model provides an efficient method for determining an optimal decision chosen from a large number of possible decisions. The optimum decision is one that meets a specified objective of management, subject to various constraints and restrictions. The objective is mainly minimization of maintenance cost of roads in industrial area. In order to determine the objective function for analysis of distress model it is necessary to fix the realistic data into a formulation. Each type of repair is to be quantified in a number of stretches by considering 1000 m as one stretch. A stretch considered under study is having 3750 m length. The quantity has to be put into an objective function for maximizing the number of repairs in a stretch related to quantity. The distress observed in this stretch are potholes, surface cracks, rutting and ravelling. The distress data is measured manually by observing each distress level on a stretch of 1000 m. The maintenance and rehabilitation measured that are followed currently are based on subjective judgments. Hence, there is a need to adopt a scientific approach in order to effectively use the limited resources. It is also necessary to determine the pavement performance and deterioration prediction relationship with more accurate and economic benefits of road networks with respect to vehicle operating cost. The infrastructure of road network should have best results expected from available funds. In this paper objective function for distress model is determined by linear programming and deterioration model considering overloading is discussed.

Keywords: budget, maintenance, deterioration, priority

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Adil A. Mohammed, Seif-Eddeen K. Fateen, Tamer S. Ahmed, Tarek M. Moustafa

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Falling film were widely used for gas-liquid absorption and reaction process. Modeling of falling film for oligomerization of ethylene reaction to linear alpha olefins is developed. Although there are many researchers discuss modeling of falling film in many processes, there has been no publish study the simulation of falling film for the oligomerization of ethylene reaction to produce linear alpha olefins. The Comsol multiphysics software was used to simulate the mass transfer with chemical reaction in falling film absorption process. The effect of concentration profile absorption of the products through falling thickness is discussed. The effect of catalyst concentration, catalyst/co-catalyst ratio, and temperature is also studied. For the effect of the temperature, as it increase the concentration of C4 increase. For catalyst concentration and catalyst/co-catalyst ratio as they increases the concentration of C4 increases, till it reached almost constant value.

Keywords: falling film, oligomerization, comsol mutiphysics, linear alpha olefins

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Authors: Pirom Thangchitpianpol, Suttipong Jumroonrut

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This research investigates the distribution of food demand for animal food and the optimum amount of that food production at minimum cost. The data consist of customer purchase orders for the food of laying hens, price of food for laying hens, cost per unit for the food inventory, cost related to food of laying hens in which the food is out of stock, such as fine, overtime, urgent purchase for material. They were collected from January, 1990 to December, 2013 from a factory in Nakhonratchasima province. The collected data are analyzed in order to explore the distribution of the monthly food demand for the laying hens and to see the rate of inventory per unit. The results are used in a stochastic linear programming model for aggregate planning in which the optimum production or minimum cost could be obtained. Programming algorithms in MATLAB and tools in Linprog software are used to get the solution. The distribution of the food demand for laying hens and the random numbers are used in the model. The study shows that the distribution of monthly food demand for laying has a normal distribution, the monthly average amount (unit: 30 kg) of production from January to December. The minimum total cost average for 12 months is Baht 62,329,181.77. Therefore, the production planning can reduce the cost by 14.64% from real cost.

Keywords: animal food, stochastic linear programming, aggregate planning, production planning, demand uncertainty

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Authors: Amirhossein Chambari

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This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

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Authors: Seyed Abolhasan Naeini, Eisa Aliagahei

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Today, the use of geosynthetic materials in geotechnical activities is increasing significantly. One of the main uses of these materials is to increase the compressive strength of clay reinforced by geotextile layers. In the present study, the effect of clay reinforcement by geotextile layers in increasing the compressive strength of clay has been investigated using modeling in ABAQUS 6.11.3 software. For this purpose, the modified Drager Prager model has been chosen to simulate the stress-strain behavior of soil layers and the linear elastic model for the geotextile layer. Unreinforced samples and reinforced samples are modeled by geotextile layers (1, 2 and 3 geotextile layers) by software. In order to validate the results, an article in the same field was used and the numerical modeling results were calibrated with the laboratory results. Based on the obtained results, the software has a suitable capability for modeling and the results of the numerical model overlap with the laboratory results to a very acceptable extent, by increasing the number of geotextile layers, the error between the results of the laboratory sample and the software model increases. The highest amount of error is related to the sample reinforced with three layers of geotextile and is 7.3%.

Keywords: Abaqus, cap model, clay, geotextile layer, reinforced soil

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Authors: Tugce Caliskan

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The gentrification literature in Turkey provided important insights regarding the analysis of the socio-spatial change in İstanbul mostly through the existing gentrification theories which were produced in Anglo-American literature. Yet early researches focused on the classical gentrification while failing to notice other place-specific forms of the phenomena. It was only after the mid-2000s that scholarly attention shifted to the recent discussions in the mainstream such as the neoliberal urban policies, government involvement, and resistance. Although these studies have considerable potential to contribute to the geography of gentrification, it seems that copying the linear timeline of Anglo-American conceptualization limited the space to introduce contextually nuanced way of process in Turkey. More specifically, the gentrification literature in Turkey acknowledged the linear timeline of the process drawing on the mainstream studies, and, made the spontaneous classical gentrification as the starting point in İstanbul at the expense of contextually specific forms of the phenomenon that took place in the same years. This paper is an attempt to understand place-specific forms of gentrification through the abandonment of the linear understanding of time. In this vein, this paper approaches the process as moving both linear and cyclical rather than the waves succeeded each other. Maintaining a dialectical relationship between the cyclical and the linear time, this paper investigates how the components of gentrification have been taken place in the cyclical timeline while becoming bolder in the linear timeline. This paper argues that taking the (re)investment in the secondary circuit of capital and class transformation as the core characteristics of gentrification, and accordingly, searching for these components beyond the linear timeline provide strategic value to decenter the perspectives, not merely for Turkish studies. In this vein, this strategy revealed that Western experience of gentrification did not travel, adopted or copied in Turkey but gentrification -as an abstract and general concept- has emerged as a product of different contextual, historical and temporal forces which must be considered within the framework of state-led urbanization as early as 1980 differing from the Global North trajectories.

Keywords: comparative urbanism, geography of gentrification, linear and cyclical timeline, state-led gentrification

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Authors: Hung-Sheng Lin, Cheng-Hsuan Li

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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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Authors: B. X. Tchomeni, A. A. Alugongo, T. B. Tengen

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In this paper, de Laval rotor system has been characterized by a hinge model and its transient response numerically treated for a dynamic solution. The effect of the ensuing non-linear disturbances namely rub and breathing crack is numerically simulated. Subsequently, three analysis methods: Orbit Analysis, Fast Fourier Transform (FFT) and Wavelet Transform (WT) are employed to extract features of the vibration signal of the faulty system. An analysis of the system response orbits clearly indicates the perturbations due to the rotor-to-stator contact. The sensitivities of WT to the variation in system speed have been investigated by Continuous Wavelet Transform (CWT). The analysis reveals that features of crack, rubs and unbalance in vibration response can be useful for condition monitoring. WT reveals its ability to detect non-linear signal, and obtained results provide a useful tool method for detecting machinery faults.

Keywords: Continuous wavelet, crack, discrete wavelet, high acceleration, low acceleration, nonlinear, rotor-stator, rub

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Authors: Adel Bougamouza, Boualam Remini, Abd El Hadi Ammari, Feteh Sakhraoui

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The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords: artificial neural network, Oued Abiod watershed, feedforward network, generalized regression network, radial basis network, sediment concentration

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Authors: Melvin Ballera, Aldrich Olivar, Mary Soriano

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Digital computers nowadays must be able to have a utility that is capable of generating random numbers. Usually, computer-generated random numbers are not random given predefined values such as starting point and end points, making the sequence almost predictable. There are many applications of random numbers such business simulation, manufacturing, services domain, entertainment sector and other equally areas making worthwhile to design a unique method and to allow unpredictable random numbers. Applying stochastic simulation using linear congruential algorithm, it shows that as it increases the numbers of the seed and range the number randomly produced or selected by the computer becomes unique. If this implemented in an environment where random numbers are very much needed, the reliability of the random number is guaranteed.

Keywords: stochastic simulation, random numbers, linear congruential algorithm, pseudorandomness

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Authors: Asim Laeeq Khan, Mazhar Amjad Gilani, Tayub Raza

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The use of fossil fuels for energy generation leads to the emission of greenhouse gases particularly CO2 into the atmosphere. To date, several techniques have been proposed for the efficient removal of CO2 from flue gas mixtures. Membrane technology is a promising choice due to its several inherent advantages such as low capital cost, high energy efficiency, and low ecological footprint. One of the goals in the development of membranes is to achieve high permeability and selectivity. Mixed matrix membranes comprising of inorganic fillers embedded in polymer matrix are a class of membranes that have showed improved separation properties. One of the biggest challenges in the commercialization if mixed matrix membranes are the removal of non-selective voids existing at the polymer-filler interface. In this work, mixed matrix membranes were prepared using polysulfone as polymer matrix and ordered mesoporous MCM-41 as filler materials. A new approach to removing the interfacial voids was developed by introducing room temperature ionic (RTIL) at the polymer-filler interface. The results showed that the imidazolium based RTIL not only provided wettability characteristics but also helped in further improving the separation properties. The removal of interfacial voids and good contact between polymer and filler was verified by SEM measurement. The synthesized membranes were tested in a custom built gas permeation set-up for the measurement of gas permeability and ideal gas selectivity. The results showed that the mixed matrix membranes showed significantly higher CO2 permeability in comparison to the pristine membrane. In order to have further insight into the role of fillers, diffusion and solubility measurements were carried out. The results showed that the presence of highly porous fillers resulted in increasing the diffusion coefficient while the solubility showed a slight drop. The RTIL filled membranes showed higher CO2/CH4 and CO2/N2 selectivity than unfilled membranes while the permeability dropped slightly. The increase in selectivity was due to the highly selective RTIL used in this work. The study revealed that RTIL filled mixed matrix membranes are an interesting candidate for gas separation membranes.

Keywords: ionic liquids, CO2 separation, membranes, mixed matrix membranes

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Authors: Weera Weerasophon

Abstract:

This research paper is to study the development of a management model for promoting sustainable tourism of Rajabhat universities in Thailand. Mixed Method Research is applied under the said topic. The researcher has developed a management model to promote sustainable tourism. The objectives of the research are 1) to study the readiness in management sustainable tourism of Rajabhat universities in Thailand 2) to develop a management model for promoting sustainable tourism of those universities. The process of this research is organized in two steps according to the objectives. The results of the research are as in the following: 1. Rajabhat universities have the readiness in management for promoting sustainable tourism. The universities can be developed to be sustainable tourist attraction under the admistrators who have vision and realize the importance of tourism, eager to promote sustainable tourism of the universities by specifying obvious policy plans and management. 2. The management model for promoting sustainable tourism of Rajabhat universities is consisted of the main following factors : 2.1 Master plan and policy, 2.2 Rajabhat universities organization management and personnel administration, 2.3 Assignment and authority, leadership, 2.4 Join network, 2.5 Assurance of quality and controlling, 2.6 Budget management, 2.7 Human Resources management, 2.8 Alliance and co-ordination, 2.9 Tool of marketing. There are also other communal factors for promoting sustainable tourism. They are: local communities, local communities, tourism activities, government and private sectors, communicative technology system, history, tourist attractive, art and culture, internal and external environment including local wisdom heritage. The management model for promoting sustainable tourism can be concluded from these main and communal factors mentioned above.

Keywords: tourism, sustainable tourism, management, Rajabhat University

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Authors: Zhiwu Zheng, Prakhar Kumar, Sigurd Wagner, Naveen Verma, James C. Sturm

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Soft robots have drawn great interest recently due to a rich range of possible shapes and motions they can take on to address new applications, compared to traditional rigid robots. Large-area electronics (LAE) provides a unique platform for creating soft robots by leveraging thin-film technology to enable the integration of a large number of actuators, sensors, and control circuits on flexible sheets. However, the rich shapes and motions possible, especially when interacting with complex environments, pose significant challenges to forming well-generalized and robust models necessary for robot design and control. In this work, we describe an analytical model for predicting the shape and locomotion of a flexible (steel-foil-based) piezoelectric-actuated 2D robot based on Euler-Bernoulli beam theory. It is nominally (unpowered) lying flat on the ground, and when powered, its shape is controlled by an array of piezoelectric thin-film actuators. Key features of the models are its ability to incorporate the significant effects of gravity on the shape and to precisely predict the spatial distribution of friction against the contacting surfaces, necessary for determining inchworm-type motion. We verified the model by developing a distributed discrete element representation of a continuous piezoelectric actuator and by comparing its analytical predictions to discrete-element robot simulations using PyBullet. Without gravity, predicting the shape of a sheet with a linear array of piezoelectric actuators at arbitrary voltages is straightforward. However, gravity significantly distorts the shape of the sheet, causing some segments to flatten against the ground. Our work includes the following contributions: (i) A self-consistent approach was developed to exactly determine which parts of the soft robot are lifted off the ground, and the exact shape of these sections, for an arbitrary array of piezoelectric voltages and configurations. (ii) Inchworm-type motion relies on controlling the relative friction with the ground surface in different sections of the robot. By adding torque-balance to our model and analyzing shear forces, the model can then determine the exact spatial distribution of the vertical force that the ground is exerting on the soft robot. Through this, the spatial distribution of friction forces between ground and robot can be determined. (iii) By combining this spatial friction distribution with the shape of the soft robot, in the function of time as piezoelectric actuator voltages are changed, the inchworm-type locomotion of the robot can be determined. As a practical example, we calculated the performance of a 5-actuator system on a 50-µm thick steel foil. Piezoelectric properties of commercially available thin-film piezoelectric actuators were assumed. The model predicted inchworm motion of up to 200 µm per step. For independent verification, we also modelled the system using PyBullet, a discrete-element robot simulator. To model a continuous thin-film piezoelectric actuator, we broke each actuator into multiple segments, each of which consisted of two rigid arms with appropriate mass connected with a 'motor' whose torque was set by the applied actuator voltage. Excellent agreement between our analytical model and the discrete-element simulator was shown for both for the full deformation shape and motion of the robot.

Keywords: analytical modeling, piezoelectric actuators, soft robot locomotion, thin-film technology

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

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Authors: Jorge Gonzalez Camus, Valentin Keyantuo, Mahamadi Warma

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In this work, we obtain representation results for solutions of a time-fractional differential equation involving the discrete fractional Laplace operator in terms of generalized Wright functions. Such equations arise in the modeling of many physical systems, for example, chain processes in chemistry and radioactivity. The focus is on the linear problem of the simplified Moore - Gibson - Thompson equation, where the discrete fractional Laplacian and the Caputo fractional derivate of order on (0,2] are involved. As a particular case, we obtain the explicit solution for the discrete heat equation and discrete wave equation. Furthermore, we show the explicit solution for the equation involving the perturbed Laplacian by the identity operator. The main tool for obtaining the explicit solution are the Laplace and discrete Fourier transforms, and Stirling's formula. The methodology mainly is to apply both transforms in the equation, to find the inverse of each transform, and to prove that this solution is well defined, using Stirling´s formula.

Keywords: discrete fractional Laplacian, explicit representation of solutions, fractional heat and wave equations, fundamental

Procedia PDF Downloads 203