Search results for: Environmental Modeling

Authors: C. S. Tsai, Yung-Chang Lin

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In order to upgrade the seismic resistibility of structures and enhance the functionality of an isolator, a new base isolator called the multiple trench friction pendulum system (MTFPS) is proposed in this study. The proposed MTFPS isolator is composed of a trench concave surface and several intermediate sliding plates in two orthogonal directions. Mathematical formulations have been derived to examine the characteristics of the proposed MTFPS isolator possessing multiple intermediate sliding plates. By means of mathematical formulations, it can be inferred that the natural period and damping effect of the MTFPS isolator with several intermediate sliding plates can be altered continually and controllably during earthquakes. Furthermore, results obtained from shaking table tests demonstrate that the proposed isolator provides good protection to structures for prevention of damage from strong earthquakes.

Keywords: Friction Pendulum System, Multiple Friction Pendulum System, Base Isolation, Earthquake Engineering

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Authors: A. Bahmanikashkouli, O.R. Bahadori Nezhad

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In this article, the phenomenon of nonlinear consolidation in saturated and homogeneous clay layer is studied. Considering time-varied drainage model, the excess pore water pressure in the layer depth is calculated. The Generalized Differential Quadrature (GDQ) method is used for the modeling and numerical analysis. For the purpose of analysis, first the domain of independent variables (i.e., time and clay layer depth) is discretized by the Chebyshev-Gauss-Lobatto series and then the nonlinear system of equations obtained from the GDQ method is solved by means of the Newton-Raphson approach. The obtained results indicate that the Generalized Differential Quadrature method, in addition to being simple to apply, enjoys a very high accuracy in the calculation of excess pore water pressure.

Keywords: Generalized Differential Quadrature method, Nonlinear consolidation, Nonlinear system of equations, Time-varied drainage

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Authors: Abiodun M. Aibinu, Momoh J. E. Salami, Amir A. Shafie, Athaur Rahman Najeeb

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In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination.

Keywords: Adaptive Learning rate, Adaptive momentum, Autoregressive, Modeling, Neural Network.

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Authors: Chunshien Li, Jhao-Wun Hu, Tai-Wei Chiang, Tsunghan Wu

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Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybrid learning (HL) approach for the neuro-fuzzy system, the prediction performance is excellent and the speed of learning convergence is much faster than other compared approaches. In the experiments, we use the well-known Mackey-Glass chaos time series. According to the experimental results, the prediction performance and accuracy in time series forecasting by the proposed approach is much better than other compared approaches, as shown in Table IV. Excellent prediction performance by the proposed approach has been observed.

Keywords: forecasting, hybrid learning (HL), Neuro-FuzzySystem (NFS), particle swarm optimization (PSO), recursiveleast-squares estimator (RLSE), time series

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Authors: Hamid Tavakolipour, Mohsen Mokhtarian, Ahmad Kalbasi Ashtari

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In this study, the effect of various drying methods (sun drying, shade drying and industrial drying) on final moisture content, shell splitting degree, shrinkage and color change were studied. Sun drying resulted higher degree of pistachio nuts shell splitting on pistachio nuts relative other drying methods. The ANOVA results showed that the different drying methods did not significantly effects on color change of dried pistachio nut. The results illustrated that pistachio nut dried by industrial drying had the lowest moisture content. After the end of drying process, initially, the experimental drying data were fitted with five famous drying models namely Newton, Page, Silva et al., Peleg and Henderson and Pabis. The results indicated that Peleg and Page models gave better results compared with other models to monitor the moisture ratio’s pistachio nut in industrial drying and open sun (or shade drying) methods, respectively.

Keywords: Industrial drying, Modeling, Pistachio, quality properties, Traditional drying.

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Authors: Shweta Kumari, Parmjit S. Panesar, Manab B. Bera

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The hydrolysis of lactose using β-galactosidase is one of the most promising biotechnological applications, which has wide range of potential applications in food processing industries. However, due to intracellular location of the yeast enzyme, and expensive extraction methods, the industrial applications of enzymatic hydrolysis processes are being hampered. The use of permeabilization technique can help to overcome the problems associated with enzyme extraction and purification of yeast cells and to develop the economically viable process for the utilization of whole cell biocatalysts in food industries. In the present investigation, standardization of permeabilization process of novel yeast isolate was carried out using a statistical model approach known as Response Surface Methodology (RSM) to achieve maximal b-galactosidase activity. The optimum operating conditions for permeabilization process for optimal β-galactosidase activity obtained by RSM were 1:1 ratio of toluene (25%, v/v) and ethanol (50%, v/v), 25.0 ^oC temperature and treatment time of 12 min, which displayed enzyme activity of 1.71 IU /mg DW.

Keywords: β-galactosidase, optimization, permeabilization, response surface methodology, yeast.

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Authors: Mahdi Sadeghian, Mahmoud Hassanlourad, Alireza Ardakani, Reza Dinarvand

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In this research, the behavior of monopiles, under lateral loads, was investigated with vertical and oblique piles by Finite Element Method. In engineering practice when soil-pile interaction comes to the picture some simplifications are applied to reduce the design time. As a simplified replacement of soil and pile interaction analysis, pile could be replaced by a column. The height of the column would be equal to the free length of the pile plus a portion of the embedded length of it. One of the important factors studied in this study was that columns with an equivalent length (free length plus a part of buried depth) could be used instead of soil and pile modeling. The results of the analysis show that the more internal friction angle of the soil increases, the more the bearing capacity of the soil is achieved. This additional length is 6 to 11 times of the pile diameter in dense soil although in loose sandy soil this range might increase.

Keywords: Lateral bearing capacity, pile group, oblique pile, soil-structure interaction, depth of fixity.

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Authors: B. A. Mir, Asim Malik

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Bottom ash is a by-product of the combustion process of coal in furnaces in the production of electricity in thermal power plants. In India, about 75% of total power is produced by using pulverized coal. The coal of India has a high ash content which leads to the generation of a huge quantity of bottom ash per year posing the dual problem of environmental pollution and difficulty in disposal. This calls for establishing strategies to use this industry by-product effectively and efficiently. However, its large-scale utilization is possible only in geotechnical applications, either alone or with soil. In the present investigation, bottom ash was collected from National Capital Power Station Dadri, Uttar Pradesh, India. Test samples of bottom ash admixed with 20% clayey soil were prepared and treated with different cement content by weight and subjected to various laboratory tests for assessing its suitability as an engineered construction material. This study has shown that use of 10% cement content is a viable chemical additive to enhance the mechanical properties of bottom ash, which can be used effectively as an engineered construction material in various geotechnical applications. More importantly, it offers an interesting potential for making use of an industrial waste to overcome challenges posed by bottom ash for a sustainable environment.

Keywords: Bottom ash, environmental pollution, solid waste, sustainable environment, waste utilization.

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Authors: Suparman

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Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

Keywords: Piecewise, Bayesian, reversible jump MCMC, segmentation.

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Authors: Chansopheak Seang, Afia David Kouadri, Eric Ragneau

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Three dimensional analysis of thermal model in laser full penetration welding, Nd:YAG, by transparent mode DP600 alloy steel 1.25mm of thickness and gap of 0.1mm. Three models studied the influence of thermal dependent temperature properties, thermal independent temperature and the effect of peak value of specific heat at phase transformation temperature, AC1, on the transient temperature. Another seven models studied the influence of discretization, meshes on the temperature distribution in weld plate. It is shown that for the effects of thermal properties, the errors less 4% of maximum temperature in FZ and HAZ have identified. The minimum value of discretization are at least one third increment per radius for temporal discretization and the spatial discretization requires two elements per radius and four elements through thickness of the assembled plate, which therefore represent the minimum requirements of modeling for the laser welding in order to get minimum errors less than 5% compared to the fine mesh.

Keywords: FEA, welding, discretization, ABAQUS user subroutine DFLUX

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: EIoT, machine learning, anomaly detection, environment monitoring.

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Authors: Totok R. Biyanto, Sonny Irawan, Hiskia J. Ginting, Matradji, Ya’umar, A. I. Fitri

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Steam injection is the most suitable of Enhanced Oil Recovery (EOR) methods to recover high viscosity oil. This is due to the capabilities of steam to reduce oil viscosity and increase the sweep capability of oil from the injection well toward the production well. Oil operating conditions in production should be match well with the operating condition target at the bottom of the production well. It is influenced by oil properties and reservoir rock properties. Hence, the operating condition should be optimized. Optimization requires three components i.e., objective function, model, and optimization technique. In this paper, the objective function is to obtain the optimum operating condition at the production well. The model was built using Darcy equation and mass-energy balance. The optimization technique utilizes Duelist Algorithm due to the effectiveness of its algorithm to obtain the desirable optimization results at the optimum operating condition.

Keywords: Enhanced oil recovery, steam injection, operating conditions, modeling, optimization, Duelist algorithm.

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Authors: Parisa Ahmadi Oliaei, Seyed Abolhassan Naeini

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The knowledge of soil–reinforcement interaction parameters is particularly important in the design of reinforced soil structures. The pull-out test is one of the most widely used tests in this regard. The results of tensile tests may be very sensitive to boundary conditions, and more research is needed for a better understanding of the pull-out response of reinforcement, so numerical analysis using the finite element method can be a useful tool for the understanding of the pull-out response of soil-geogrid interaction. The main objective of the present study is to compare the numerical and experimental results of a pull-out test on geogrid-reinforced sandy soils interactions. Plaxis 2D finite element software is used for simulation. In the present study, the pull-out test modeling has been done on sandy soil. The effect of geogrid hardness was also investigated by considering two different types of geogrids. The numerical results curve had a good agreement with the pull-out laboratory results.

Keywords: Plaxis, pull-out test, sand, soil-geogrid interaction.

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Authors: Emad Askar, Eldesoky Elsoaly, Mohamed Kamel, Hisham Kamel

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The objective of this article is to improve the passive vibration damping of solar array (SA) used in space structures, by the effective application of numerical optimization. A case study of a SA is used for demonstration. A finite element (FE) model was created and verified by experimental testing. Optimization was then conducted by implementing the FE model with the genetic algorithm, to find the optimal placement of aluminum circular patches, to suppress the first two bending mode shapes. The results were verified using experimental testing. Finally, a parametric study was conducted using the FE model where patch locations, material type, and shape were varied one at a time, and the results were compared with the optimal ones. The results clearly show that through the proper application of FE modeling and numerical optimization, passive vibration damping of space structures has been successfully achieved.

Keywords: Damping optimization, genetic algorithm optimization, passive vibration damping, solar array vibration damping.

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Authors: R. N. Patel

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This paper deals with the tuning of parameters for Automatic Generation Control (AGC). A two area interconnected hydrothermal system with PI controller is considered. Genetic Algorithm (GA) and Particle Swarm optimization (PSO) algorithms have been applied to optimize the controller parameters. Two objective functions namely Integral Square Error (ISE) and Integral of Time-multiplied Absolute value of the Error (ITAE) are considered for optimization. The effectiveness of an objective function is considered based on the variation in tie line power and change in frequency in both the areas. MATLAB/SIMULINK was used as a simulation tool. Simulation results reveal that ITAE is a better objective function than ISE. Performances of optimization algorithms are also compared and it was found that genetic algorithm gives better results than particle swarm optimization algorithm for the problems of AGC.

Keywords: Area control error, Artificial intelligence, Automatic generation control, Genetic Algorithms and modeling, ISE, ITAE, Particle swarm optimization.

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Authors: Hafizuddin W. Yussof, Syamsutajri S. Bahri, Adam P. Harvey

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Strong anion exchange resins with QN+OH-, have the potential to be developed and employed as heterogeneous catalyst for transesterification, as they are chemically stable to leaching of the functional group. Nine different SIERs (SIER1-9) with QN+OH-were prepared by suspension polymerization of vinylbenzyl chloridedivinylbenzene (VBC-DVB) copolymers in the presence of n-heptane (pore-forming agent). The amine group was successfully grafted into the polymeric resin beads through functionalization with trimethylamine. These SIERs are then used as a catalyst for the transesterification of triacetin with methanol. A set of differential equations that represents the Langmuir-Hinshelwood-Hougen- Watson (LHHW) and Eley-Rideal (ER) models for the transesterification reaction were developed. These kinetic models of LHHW and ER were fitted to the experimental data. Overall, the synthesized ion exchange resin-catalyzed reaction were welldescribed by the Eley-Rideal model compared to LHHW models, with sum of square error (SSE) of 0.742 and 0.996, respectively.

Keywords: Anion exchange resin, Eley-Rideal, Langmuir-Hinshelwood-Hougen-Watson, transesterification.

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Authors: V. Karamzadeh, J. Adhvaryu, A. Chandrasekaran, M. Packirisamy

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In Bulk Acoustic Wave (BAW) microfluidics, the throughput of particle sorting is dependent on the complex interplay between the geometric configuration of the channel, the size of the particles, and the properties of the fluid medium, which therefore calls for a detailed modeling and understanding of the fluid-particle interaction dynamics under an acoustic field, prior to designing the system. In this work, we propose a simplified Bulk acoustophoretic system that can be used for size dependent particle sorting. A Finite Element Method (FEM) based analytical model has been developed to study the dependence of particle sizes on channel parameters, and the sorting efficiency in a given fluid medium. Based on the results, the microfluidic system has been designed to take into account all the variables involved with the underlying physics, and has been fabricated using an additive manufacturing technique employing a commercial 3D printer, to generate a simple, cost-effective system that can be used for size sensitive particle sorting.

Keywords: 3D printing, 3D microfluidic chip, acoustophoresis, cell separation, MEMS, microfluidics.

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Authors: M.R. Mustafa, M.H. Isa, R.B. Rezaur

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Prediction of highly non linear behavior of suspended sediment flow in rivers has prime importance in the field of water resources engineering. In this study the predictive performance of two Artificial Neural Networks (ANNs) namely, the Radial Basis Function (RBF) Network and the Multi Layer Feed Forward (MLFF) Network have been compared. Time series data of daily suspended sediment discharge and water discharge at Pari River was used for training and testing the networks. A number of statistical parameters i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the models. Both the models produced satisfactory results and showed a good agreement between the predicted and observed data. The RBF network model provided slightly better results than the MLFF network model in predicting suspended sediment discharge.

Keywords: ANN, discharge, modeling, prediction, suspendedsediment,

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Authors: P. C. Tewari, Parveen Kumar

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The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: Markov process, milling system, availability modeling, rice milling plant.

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Authors: I. Hosni, L. Bennaceur Farah, N. Saber, R Bennaceur

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Soil moisture content is a key variable in many environmental sciences. Even though it represents a small proportion of the liquid freshwater on Earth, it modulates interactions between the land surface and the atmosphere, thereby influencing climate and weather. Accurate modeling of the above processes depends on the ability to provide a proper spatial characterization of soil moisture. The measurement of soil moisture content allows assessment of soil water resources in the field of hydrology and agronomy. The second parameter in interaction with the radar signal is the geometric structure of the soil. Most traditional electromagnetic models consider natural surfaces as single scale zero mean stationary Gaussian random processes. Roughness behavior is characterized by statistical parameters like the Root Mean Square (RMS) height and the correlation length. Then, the main problem is that the agreement between experimental measurements and theoretical values is usually poor due to the large variability of the correlation function, and as a consequence, backscattering models have often failed to predict correctly backscattering. In this study, surfaces are considered as band-limited fractal random processes corresponding to a superposition of a finite number of one-dimensional Gaussian process each one having a spatial scale. Multiscale roughness is characterized by two parameters, the first one is proportional to the RMS height, and the other one is related to the fractal dimension. Soil moisture is related to the complex dielectric constant. This multiscale description has been adapted to two-dimensional profiles using the bi-dimensional wavelet transform and the Mallat algorithm to describe more correctly natural surfaces. We characterize the soil surfaces and sub-surfaces by a three layers geo-electrical model. The upper layer is described by its dielectric constant, thickness, a multiscale bi-dimensional surface roughness model by using the wavelet transform and the Mallat algorithm, and volume scattering parameters. The lower layer is divided into three fictive layers separated by an assumed plane interface. These three layers were modeled by an effective medium characterized by an apparent effective dielectric constant taking into account the presence of air pockets in the soil. We have adopted the 2D multiscale three layers small perturbations model including, firstly air pockets in the soil sub-structure, and then a vegetable canopy in the soil surface structure, that is to simulate the radar backscattering. A sensitivity analysis of backscattering coefficient dependence on multiscale roughness and new soil moisture has been performed. Later, we proposed to change the dielectric constant of the multilayer medium because it takes into account the different moisture values of each layer in the soil. A sensitivity analysis of the backscattering coefficient, including the air pockets in the volume structure with respect to the multiscale roughness parameters and the apparent dielectric constant, was carried out. Finally, we proposed to study the behavior of the backscattering coefficient of the radar on a soil having a vegetable layer in its surface structure.

Keywords: Multiscale, bi-dimensional, wavelets, SPM, backscattering, multilayer, air pockets, vegetable.

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Authors: Gh. Saeidifar, F. Raeiszadeh

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BRI-STARS (BRIdge Stream Tube model for Alluvial River Simulation) program was used to investigate the scour depth around bridge piers in some of the major river systems in Iran. Model calibration was performed by collecting different field data. Field data are cataloged on three categories, first group of bridges that their rivers bed are formed by fine material, second group of bridges that their rivers bed are formed by sand material, and finally bridges that their rivers bed are formed by gravel or cobble materials. Verification was performed with some field data in Fars Province. Results show that for wide piers, computed scour depth is more than measured one. In gravel bed streams, computed scour depth is greater than measured scour depth, the reason is due to formation of armor layer on bed of channel. Once this layer is eroded, the computed scour depth is close to the measured one.

Keywords: BRI-STARS, local scour, bridge, computer modeling

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Authors: Y. B. Galerkin, E. Y. Popova, K. V. Soldatova

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There is an evident trend to elevate pressure ratio of a single stage of a turbo compressors - axial compressors in particular. Whilst there was an opinion recently that a pressure ratio 1,9 was a reasonable limit, later appeared information on successful modeling tested of stages with pressure ratio up to 2,8. The authors recon that lack of information on high pressure stages makes actual a study of rational choice of design parameters before high supersonic flow problems solving. The computer program of an engineering type was developed. Below is presented a sample of its application to study possible parameters of the impeller of the stage with pressure ratio 3,0. Influence of two main design parameters on expected efficiency, periphery blade speed and flow structure is demonstrated. The results had lead to choose a variant for further analysis and improvement by CFD methods.

Keywords: Supersonic stage, impeller, efficiency, flow rate coefficient, work coefficient, loss coefficient, oblique shock, direct shock.

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Authors: Miika Toivanen, Jouko Lampinen

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This paper presents an online method that learns the corresponding points of an object from un-annotated grayscale images containing instances of the object. In the first image being processed, an ensemble of node points is automatically selected which is matched in the subsequent images. A Bayesian posterior distribution for the locations of the nodes in the images is formed. The likelihood is formed from Gabor responses and the prior assumes the mean shape of the node ensemble to be similar in a translation and scale free space. An association model is applied for separating the object nodes and background nodes. The posterior distribution is sampled with Sequential Monte Carlo method. The matched object nodes are inferred to be the corresponding points of the object instances. The results show that our system matches the object nodes as accurately as other methods that train the model with annotated training images.

Keywords: Bayesian modeling, Gabor filters, Online learning, Sequential Monte Carlo.

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Authors: P.Kumar

Abstract:

Fluids are used for heat transfer in many engineering equipments. Water, ethylene glycol and propylene glycol are some of the common heat transfer fluids. Over the years, in an attempt to reduce the size of the equipment and/or efficiency of the process, various techniques have been employed to improve the heat transfer rate of these fluids. Surface modification, use of inserts and increased fluid velocity are some examples of heat transfer enhancement techniques. Addition of milli or micro sized particles to the heat transfer fluid is another way of improving heat transfer rate. Though this looks simple, this method has practical problems such as high pressure loss, clogging and erosion of the material of construction. These problems can be overcome by using nanofluids, which is a dispersion of nanosized particles in a base fluid. Nanoparticles increase the thermal conductivity of the base fluid manifold which in turn increases the heat transfer rate. In this work, the heat transfer enhancement using aluminium oxide nanofluid has been studied by computational fluid dynamic modeling of the nanofluid flow adopting the single phase approach.

Keywords: Heat transfer intensification, nanofluid, CFD, friction factor

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Authors: Sachin Man Bajimaya, SangChul Park, Gi-Nam Wang

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The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.

Keywords: Artificial Neural Network (ANN), Indirect Extrusion, Finite Element Analysis, MES.

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Authors: A. M. Tarola, R. Preti, A. M. Girelli, P. Campana

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Goat dairy products are gaining popularity worldwide. In developing countries, but also in many marginal regions of the Mediterranean area, goats represent a great part of the economy and ensure food security. In fact, these small ruminants are able to convert efficiently poor weedy plants and small trees into traditional products of high nutritional quality, showing great resilience to different climatic and environmental conditions. In developed countries, goat milk is appreciated for the presence of health-promoting compounds, bioactive compounds such as conjugated linoleic acids, oligosaccharides, sphingolipids and polyammines. This paper focuses on the recent advances in literature on the nutritional properties of goat milk and on innovative techniques to improve its quality as to become a promising functional food. The environmental sustainability of different methodologies of production has also been examined. Goat milk is valued today as a food of high nutritional value and functional properties as well as small environmental footprint. It is widely consumed in many countries due to high nutritional value, lower allergenic potential, and better digestibility when compared to bovine milk, that makes this product suitable for infants, elderly or sensitive patients. The main differences in chemical composition between a cow and goat milk rely on fat globules that in goat milk are smaller and in fatty acids that present a smaller chain length, while protein, fat, and lactose concentration are comparable. Milk nutritional properties have demonstrated to be strongly influenced by animal diet, genotype, and welfare, but also by season and production systems. Furthermore, there is a growing interest in the dairy industry in goat milk for its relatively high concentration of prebiotics and a good amount of probiotics, which have recently gained importance for their therapeutic potential. Therefore, goat milk is studied as a promising matrix to develop innovative functional foods. In addition to the economic and nutritional value, goat milk is considered a sustainable product for its small environmental footprint, as they require relatively little water and land, and less medical treatments, compared to cow, these characteristics make its production naturally vocated to organic farming. Organic goat milk production has becoming more and more interesting both for farmers and consumers as it can answer to several concerns like environment protection, animal welfare and economical sustainment of rural populations living in marginal lands. These evidences make goat milk an ancient food with novel properties and advantages to be valorized and exploited.

Keywords: Goat milk, nutritional quality, bioactive compounds, sustainable production.

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Authors: Balasundaram Prasaant, Ploix Stephane, Delinchant Benoit, Muresan Cristian

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Hardware in Loop (HIL) testing is done to test and validate a particular product especially in building technology. When it comes to building technology, it is more important to test the products for their efficiency. The test rig in the HIL simulator may contribute to some uncertainties on measured efficiency. The uncertainties include physical uncertainties and scenario-based uncertainties. In this paper, a simple uncertainty analysis framework for an HIL setup is shown considering only the physical uncertainties. The entire modeling of the HIL setup is done in Dymola. The uncertain sources are considered based on available knowledge of the components and also on expert knowledge. For the propagation of uncertainty, Monte Carlo Simulation is used since it is the most reliable and easy to use. In this article it is shown how an HIL setup can be modeled and how uncertainty propagation can be performed on it. Such an approach is not common in building energy analysis.

Keywords: Energy in Buildings, Hardware in Loop, Modelica (Dymola), Monte Carlo Simulation, Uncertainty Propagation.

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Authors: Eugene Grichuk, Margarita Kuzmina, Eduard Manykin

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A network of coupled stochastic oscillators is proposed for modeling of a cluster of entangled qubits that is exploited as a computation resource in one-way quantum computation schemes. A qubit model has been designed as a stochastic oscillator formed by a pair of coupled limit cycle oscillators with chaotically modulated limit cycle radii and frequencies. The qubit simulates the behavior of electric field of polarized light beam and adequately imitates the states of two-level quantum system. A cluster of entangled qubits can be associated with a beam of polarized light, light polarization degree being directly related to cluster entanglement degree. Oscillatory network, imitating qubit cluster, is designed, and system of equations for network dynamics has been written. The constructions of one-qubit gates are suggested. Changing of cluster entanglement degree caused by measurements can be exactly calculated.

Keywords: network of stochastic oscillators, one-way quantumcomputations, a beam of polarized light.

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Authors: Julio Kauss, Miguel Cadillo, David Mauricio

Abstract:

E-commerce is a business model that allows companies to optimize the processes of buying, selling, transferring goods and exchanging services through computer networks or the Internet. In Peru, the electronic commerce is used infrequently. This situation is due, in part to the fact that there is no model that allows companies to implement an e-commerce solution, which means that most SMEs do not have adequate knowledge to adapt to electronic commerce. In this work, a reference model is proposed for the implementation of an e-commerce solution in Peruvian SMEs in the retail sector. It consists of five phases: Business Analysis, Business Modeling, Implementation, Post Implementation and Results. The present model was validated in a SME of the Peruvian retail sector through the implementation of an electronic commerce platform, through which the company increased its sales through the delivery channel by 10% in the first month of deployment. This result showed that the model is easy to implement, is economical and agile. In addition, it allowed the company to increase its business offer, adapt to e-commerce and improve customer loyalty.

Keywords: E-commerce, retail, SMEs, reference model.

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Authors: S. M. Ali, A. Salam Khan, A. U. Khan, M. Tariq, M. S. Hussain, B. A. Abbasi, I. Hussain, U. Farid

Abstract:

Environment and surrounding plays a pivotal rule in structuring life-style of the consumers. Living standards intern effect the energy consumption of the consumers. In smart grid paradigm, climate drifts, weather parameter and green environmental directly relates to the energy profiles of the various consumers, such as residential, commercial and industrial. Considering above factors helps policy in shaping utility load curves and optimal management of demand and supply. Thus, there is a pressing need to develop correlation models of load and weather parameters and critical analysis of the factors effecting energy profiles of smart grid consumers. In this paper, we elaborated various environment and weather parameter factors effecting demand of consumers. Moreover, we developed correlation models, such as Pearson, Spearman, and Kendall, an inter-relation between dependent (load) parameter and independent (weather) parameters. Furthermore, we validated our discussion with real-time data of Texas State. The numerical simulations proved the effective relation of climatic drifts with energy consumption of smart grid consumers.

Keywords: Climatic drifts, correlation analysis, energy consumption, smart grid, weather parameter.

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