Search results for: path loss model

Authors: C. Mekala, Indumathi M. Nambi

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Nitrogen pollution in groundwater arising from wastewater and fertilizer application through vadose zone is a major problem and it causes a prime risk to groundwater based drinking water supplies. Nitrogenous compounds namely ammonium, nitrate and nitrite fate and transport in soil subsurface were studied experimentally. The major process like sorption, leaching, biotransformation involving microbial growth kinetics, and biological clogging due to biomass growth were assessed and modeled with advection-dispersion reaction equations for ammonium, nitrate and acetate in a saturated, heterogeneous soil medium. The transport process was coupled with freundlich sorption and monod inhibition kinetics for immobile bacteria and permeability reduction due to biomass growth will be verified and validated with the numerical model. This proposed mathematical model will be very helpful in the development of a management model for a sustainable and safe wastewater reuse strategies such as irrigation and groundwater recharge.

Keywords: nitrogen species transport, transformation, biological clogging, biokinetic parameters, contaminant transport model, saturated soil

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Authors: Khaoula Hidouri, Ali Benhmidene, Bechir Chouachi

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The purification of brackish seawater becomes a necessity and not a choice against demographic and industrial growth especially in third world countries. Two models can be used in this work: simple solar still and simple solar still coupled with a heat pump. In this research, the productivity of water by Simple Solar Distiller (SSD) and Simple Solar Distiller Hybrid Heat Pump (SSDHP) was determined by the orientation, the use of heat pump, the simple or double glass cover. The productivity can exceed 1.2 L/m²h for the SSDHP and 0.5 L/m²h for SSD model. The result of the global efficiency is determined for two models SSD and SSDHP give respectively 30%, 50%. The internal efficiency attained 35% for SSD and 60% of the SSDHP models. Convective heat coefficient can be determined by attained 2.5 W/m²°C and 0.5 W/m²°C respectively for SSDHP and SSD models.

Keywords: productivity, efficiency, convective heat coefficient, SSD model, SSDHPmodel

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

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

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

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Authors: Kehinde Peter Oyeduntan, Kayode Oshinubi

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Nigeria is referred as the ‘Giant of Africa’ due to high population, land mass and large economy. However, it still trails far behind many smaller economies in the continent in terms of maritime operations. As we have seen that the maritime industry is the spark plug for national growth, because it houses the most crucial infrastructure that generates wealth for a nation, it is worrisome that a nation with six seaports lag in maritime activities. In this research, we have studied how the Gross Domestic Product (GDP) of the maritime transport influences the Nigerian economy. To do this, we applied Simple Linear Regression (SLR), Support Vector Machine (SVM), Polynomial Regression Model (PRM), Generalized Additive Model (GAM) and Generalized Linear Mixed Model (GLMM) to model the relationship between the nation’s Total GDP (TGDP) and the Maritime Transport GDP (MGDP) using a time series data of 20 years. The result showed that the MGDP is statistically significant to the Nigerian economy. Amongst the statistical tool applied, the PRM of order 4 describes the relationship better when compared to other methods. The recommendations presented in this study will guide policy makers and help improve the economy of Nigeria in terms of its GDP.

Keywords: maritime transport, economy, GDP, regression, port

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Authors: Charly Julien Nyobe, Eric Jacquelin, Denis Brizard, Alexy Mercier

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The performance of road side safety barriers depends largely on the dynamic interactions between post and soil. These interactions play a key role in the response of barriers to crash testing. In the literature, soil-post interaction is modeled in crash test simulations using three approaches. Many researchers have initially used the finite element approach, in which the post is embedded in a continuum soil modelled by solid finite elements. This method represents a more comprehensive and detailed approach, employing a mesh-based continuum to model the soil’s behavior and its interaction with the post. Although this method takes all soil properties into account, it is nevertheless very costly in terms of simulation time. In the second approach, all the points of the post located at a predefined depth are fixed. Although this approach reduces CPU computing time, it overestimates soil-post stiffness. The third approach involves modeling the post as a beam supported by a set of nonlinear springs in the horizontal directions. For support in the vertical direction, the posts were constrained at a node at ground level. This approach is less costly, but the literature does not provide a simple procedure to determine the constitutive law of the springs The aim of this study is to propose a simple and low-cost procedure to obtain the constitutive law of nonlinear springs that model the soil-post interaction. To achieve this objective, we will first present a procedure to obtain the constitutive law of nonlinear springs thanks to the simulation of a soil compression test. The test consists in compressing the soil contained in the tank by a rigid solid, up to a vertical displacement of 200 mm. The resultant force exerted by the ground on the rigid solid and its vertical displacement are extracted and, a force-displacement curve was determined. The proposed procedure for replacing the soil with springs must be tested against a reference model. The reference model consists of a wooden post embedded into the ground and impacted with an impactor. Two simplified models with springs are studied. In the first model, called Kh-Kv model, the springs are attached to the post in the horizontal and vertical directions. The second Kh model is the one described in the literature. The two simplified models are compared with the reference model according to several criteria: the displacement of a node located at the top of the post in vertical and horizontal directions; displacement of the post's center of rotation and impactor velocity. The results given by both simplified models are very close to the reference model results. It is noticeable that the Kh-Kv model is slightly better than the Kh model. Further, the former model is more interesting than the latter as it involves less arbitrary conditions. The simplified models also reduce the simulation time by a factor 4. The Kh-Kv model can therefore be used as a reliable tool to represent the soil-post interaction in a future research and development of road safety barriers.

Keywords: crash tests, nonlinear springs, soil-post interaction modeling, constitutive law

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Authors: Abubakar Sadiq Mensah

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The Knowledge of the population growth of a nation is paramount to national planning. The population of a place is studied and a model developed over a period of time, Matrices is used to form model for population growth. The eigenvalue ƛ of the matrix A and its corresponding eigenvector X is such that AX = ƛX is calculated. The stable age distribution of the population is obtained using the eigenvalue and the characteristic polynomial. Hence, estimation could be made using eigenvalues and eigenvectors.

Keywords: eigenvalues, eigenvectors, population, growth/stability

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Authors: K. Indra Gandhi

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Wireless Sensor Networks (WSNs) have always been characterized for application-specific sensing, relaying and collection of information for further analysis. However, software development was not considered as a separate entity in this process of data collection which has posed severe limitations on the software development for WSN. Software development for WSN is a complex process since the components involved are data-driven, network-driven and application-driven in nature. This implies that there is a tremendous need for the separation of concern from the software development perspective. A layered approach for developing data acquisition design based on Model Driven Development (MDD) has been proposed as the sensed data collection process itself varies depending upon the application taken into consideration. This work focuses on the layered view of the data acquisition process so as to ease the software point of development. A metamodel has been proposed that enables reusability and realization of the software development as an adaptable component for WSN systems. Further, observing users perception indicates that proposed model helps in improving the programmer's productivity by realizing the collaborative system involved.

Keywords: data acquisition, model-driven development, separation of concern, wireless sensor networks

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Authors: Sreeparna Majee, G. C. Shit

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A study on targeted drug delivery is carried out in an unsteady flow of blood infused with magnetic NPs (nanoparticles) with an aim to understand the flow pattern and nanoparticle aggregation in a diseased arterial segment having stenosis. The magnetic NPs are supervised by the magnetic field which is significant for therapeutic treatment of arterial diseases, tumor and cancer cells and removing blood clots. Coupled thermal energy have also been analyzed by considering dissipation of energy because of the application of the magnetic field and the viscosity of blood. Simulation technique used to solve the mathematical model is vorticity-stream function formulations in the diseased artery. An elevation in SLP (Specific loss power) is noted in the aortic bloodstream when the agglomeration of nanoparticles is higher. This phenomenon has potential application in the treatment of hyperthermia. The study focuses on the lowering of WSS (Wall Shear Stress) with increasing particle concentration at the downstream of the stenosis which depicts the vigorous flow circulation zone. These low shear stress regions prolong the residing time of the nanoparticles carrying drugs which soaks up the LDL (Low Density Lipoprotein) deposition. Moreover, an increase in NP concentration enhances the Nusselt number which marks the increase of heat transfer from the arterial wall to the surrounding tissues to destroy tumor and cancer cells without affecting the healthy cells. The results have a significant influence in the study of medicine, to treat arterial diseases such as atherosclerosis without the need for surgery which can minimize the expenditures on cardiovascular treatments.

Keywords: magnetic nanoparticles, blood flow, atherosclerosis, hyperthermia

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Authors: Naeim Akbari Shahkhosravi, Reza Kakavand, Helen M. S. Davies, Amin Komeili

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Equine locomotion and performance are significantly affected by hoof health. One of the most critical diseases of the hoof is laminitis, which can lead to horse lameness in a severe condition. This disease exhibits the mechanical properties degradation of the laminar junction tissue within the hoof. Therefore, it is essential to investigate the biomechanics of the hoof, focusing specifically on excessive and cumulatively accumulated stresses within the laminar junction tissue. For this aim, the current study generated a novel equine hoof Finite Element (FE) model under dynamic physiological loading conditions and employing a hyperelastic material model. Associated tissues of the equine hoof were segmented from computed tomography scans of an equine forelimb, including the navicular bone, third phalanx, sole, frog, laminar junction, digital cushion, and medial- dorsal- lateral wall areas. The inner tissues were connected based on the hoof anatomy, and the hoof was under a dynamic loading over cyclic strides at the trot. The strain distribution on the hoof wall of the model was compared with the published in vivo strain measurements to validate the model. Then the validated model was used to study the development of laminitis. The ultimate stress tolerated by the laminar junction before rupture was considered as a stress threshold. The tissue damage was simulated through iterative reduction of the tissue’s mechanical properties in the presence of excessive maximum principal stresses. The findings of this investigation revealed how damage initiates from the medial and lateral sides of the tissue and propagates through the hoof dorsal area.

Keywords: horse hoof, laminitis, finite element model, continuous damage

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Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

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A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

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Authors: Benjamin Kaufmann, Michael Hofstätter, Nadine Raidl, Peter Supancic

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ZnO varistors are the leading overvoltage protection elements in today’s electronic industry. Their highly non-linear current-voltage characteristics, very fast response times, good reliability and attractive cost of production are unique in this field. There are challenges and questions unsolved. Especially, the urge to create even smaller, versatile and reliable parts, that fit industry’s demands, brings manufacturers to the limits of their abilities. Although, the varistor effect of sintered ZnO is known since the 1960’s, and a lot of work was done on this field to explain the sudden exponential increase of conductivity, the strict dependency on sinter parameters, as well as the influence of the complex microstructure, is not sufficiently understood. For further enhancement and down-scaling of varistors, a better understanding of the microscopic processes is needed. This work attempts a microscopic approach to investigate ZnO varistor performance. In order to cope with the polycrystalline varistor ceramic and in order to account for all possible current paths through the material, a preferably realistic model of the microstructure was set up in the form of three-dimensional networks where every grain has a constant electric potential, and voltage drop occurs only at the grain boundaries. The electro-thermal workload, depending on different grain size distributions, was investigated as well as the influence of the metal-semiconductor contact between the electrodes and the ZnO grains. A number of experimental methods are used, firstly, to feed the simulations with realistic parameters and, secondly, to verify the obtained results. These methods are: a micro 4-point probes method system (M4PPS) to investigate the current-voltage characteristics between single ZnO grains and between ZnO grains and the metal electrode inside the varistor, micro lock-in infrared thermography (MLIRT) to detect current paths, electron back scattering diffraction and piezoresponse force microscopy to determine grain orientations, atom probe to determine atomic substituents, Kelvin probe force microscopy for investigating grain surface potentials. The simulations showed that, within a critical voltage range, the current flow is localized along paths which represent only a tiny part of the available volume. This effect could be observed via MLIRT. Furthermore, the simulations exhibit that the electric power density, which is inversely proportional to the number of active current paths, since this number determines the electrical active volume, is dependent on the grain size distribution. M4PPS measurements showed that the electrode-grain contacts behave like Schottky diodes and are crucial for asymmetric current path development. Furthermore, evaluation of actual data suggests that current flow is influenced by grain orientations. The present results deepen the knowledge of influencing microscopic factors on ZnO varistor performance and can give some recommendations on fabrication for obtaining more reliable ZnO varistors.

Keywords: metal-semiconductor contact, Schottky diode, varistor, zinc oxide

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Authors: Vijay Kumar Kukreja, Ravneet Kaur

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In this paper, one dimensional advection diffusion model is analyzed using finite difference method based on Crank-Nicolson scheme. A practical problem of filter cake washing of chemical engineering is analyzed. The model is converted into dimensionless form. For the grid Ω × ω = [0, 1] × [0, T], the Crank-Nicolson spatial derivative scheme is used in space domain and forward difference scheme is used in time domain. The scheme is found to be unconditionally convergent, stable, first order accurate in time and second order accurate in space domain. For a test problem, numerical results are compared with the analytical ones for different values of parameter.

Keywords: Crank-Nicolson scheme, Lax-Richtmyer theorem, stability, consistency, Peclet number, Greschgorin circle

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Authors: A. Naamane, M. Hasnaoui

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Numerical modeling of fluid flows, whether compressible or incompressible, laminar or turbulent presents a considerable contribution in the scientific and industrial fields. However, the development of an approximate model of a supersonic flow requires the introduction of specific and more precise techniques and methods. For this purpose, the object of this paper is modeling a supersonic flow of inviscid fluid around a dihedral airfoil. Based on the thin airfoils theory and the non-dimensional stationary Steichen equation of a two-dimensional supersonic flow in isentropic evolution, we obtained a solution for the downstream velocity potential of the oblique shock at the second order of relative thickness that characterizes a perturbation parameter. This result has been dealt with by the asymptotic analysis and characteristics method. In order to validate our model, the results are discussed in comparison with theoretical and experimental results. Indeed, firstly, the comparison of the results of our model has shown that they are quantitatively acceptable compared to the existing theoretical results. Finally, an experimental study was conducted using the AF300 supersonic wind tunnel. In this experiment, we have considered the incident upstream Mach number over a symmetrical dihedral airfoil wing. The comparison of the different Mach number downstream results of our model with those of the existing theoretical data (relative margin between 0.07% and 4%) and with experimental results (concordance for a deflection angle between 1° and 11°) support the validation of our model with accuracy.

Keywords: asymptotic modelling, dihedral airfoil, supersonic flow, supersonic wind tunnel

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Authors: El Hansali Hasnaa, Bennani Mohammed

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Hexapod legged robot’s missions, particularly in irregular and dangerous areas, require high stability and high precision. In this paper, we consider the rectangular architecture body of legged robots with six legs distributed symmetrically along two sides, each leg contains three degrees of freedom for greater mobility. The aim of this work is planning tripod gait trajectory, based on the computing of the kinematic model to determine the joint variables in the lifting and the propelling phases. For this, appropriate coordinate frames are attached to the body and legs in order to obtain clear representation and efficient generation of the system equations. A simulation in MATLAB software platform is developed to confirm the kinematic model and various trajectories to the tripod gait adopted by the hexapod robot in its locomotion.

Keywords: hexapod legged robot, inverse kinematic model, simulation in MATLAB, tripod gait

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Authors: Yolina A. Petrova, Georgi I. Petkov

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The paper presents a symbolic model for category learning and categorization (called RoleMap). Unlike the other models which implement learning in a separate working mode, role-governed category learning and categorization emerge in RoleMap while it does its usual reasoning. The model is based on several basic mechanisms known as reflecting the sub-processes of analogy-making. It steps on the assumption that in their everyday life people constantly compare what they experience and what they know. Various commonalities between the incoming information (current experience) and the stored one (long-term memory) emerge from those comparisons. Some of those commonalities are considered to be highly important, and they are transformed into concepts for further use. This process denotes the category learning. When there is missing knowledge in the incoming information (i.e. the perceived object is still not recognized), the model makes anticipations about what is missing, based on the similar episodes from its long-term memory. Various such anticipations may emerge for different reasons. However, with time only one of them wins and is transformed into a category member. This process denotes the act of categorization.

Keywords: analogy-making, categorization, category learning, cognitive modeling, role-governed categories

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Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, Rahmatullah Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in the LFRM. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

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Authors: Chang Seok Lee, Sang Whan Han, Girbo Ko, Debbie Kim

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Existing reinforced concrete structures are designed and built without considering seismic loads. The columns in such buildings generally exhibit widely spaced transverse reinforcements without using seismic hooks. Due to the insufficient reinforcement details in columns, brittle shear failure is expected in columns that may cause pre-mature building collapse mechanism during earthquakes. In order to retrofit those columns, the accurate seismic behavior of the columns needs to be predicted with proper analytical models. In this study, an analytical model is proposed for accurately simulating the cyclic behavior of shear critical columns. The parameters for pinching and cyclic deterioration in strength and stiffness are calibrated using test data of column specimens failed by shear.

Keywords: analytical model, cyclic deterioration, existing reinforced concrete columns, shear failure

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Authors: Francisco Velez, Juan David Gomez

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Dispersion effects on the Scattering for a semicircular canyon in a micropolar continuum are analyzed, by using a computational finite element scheme. The presence of microrotational waves and the dispersive SV waves affects the propagation of elastic waves. Here, a contrast with the classic model is presented, and the dependence with the micropolar parameters is studied.

Keywords: scattering, semicircular canyon, wave dispersion, micropolar medium, FEM modeling

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Authors: G. S. Thakur

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This paper presents a Fuzzy Soft Recruitment Model (FSRM) for faculty selection of MHRD technical institutions. The selection criteria are based on 4-tier flexible structure in the institutions. The Advisory Committee on Faculty Recruitment (ACoFAR) suggested nine criteria for faculty in the proposed FSRM. The model Fuzzy Soft is proposed with consultation of ACoFAR based on selection criteria. The Fuzzy Soft distance similarity measures are applied for finding best faculty from the applicant pool.

Keywords: fuzzy soft set, fuzzy sets, fuzzy soft distance, fuzzy soft similarity measures, ACoFAR

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Authors: Mutaz Dwairy

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Solid tumors have high interstitial fluid pressure (IFP), high mechanical stress, and low oxygen levels. Solid stresses may induce apoptosis, stimulate the invasiveness and metastasis of cancer cells, and lower their proliferation rate, while oxygen concentration may affect the response of cancer cells to treatment. Although tumors grow in a nonhomogeneous environment, many existing theoretical models assume homogeneous growth and tissue has uniform mechanical properties. For example, the brain consists of three primary materials: white matter, gray matter, and cerebrospinal fluid (CSF). Therefore, tissue inhomogeneity should be considered in the analysis. This study established a physical model based on convection-diffusion equations and continuum mechanics principles. The model considers the geometrical inhomogeneity of the brain by including the three different matters in the analysis: white matter, gray matter, and CSF. The model also considers fluid-solid interaction and explicitly describes the effect of mechanical factors, e.g., solid stresses and IFP, chemical factors, e.g., oxygen concentration, and biological factors, e.g., cancer cell concentration, on growing tumors. In this article, we applied the model on a brain tumor positioned within the white matter, considering the brain inhomogeneity to estimate solid stresses, IFP, the cancer cell concentration, oxygen concentration, and the deformation of the tissues within the neoplasm and the surrounding. Tumor size was estimated at different time points. This model might be clinically crucial for cancer detection and treatment planning by measuring mechanical stresses, IFP, and oxygen levels in the tissue.

Keywords: biomechanical model, interstitial fluid pressure, solid stress, tumor microenvironment

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Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

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Authors: O. D. Arbeláez-Echeverri, E. Restrepo-Parra, J. C. Riano-Rojas

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A two dimensional geometric model of Bit Patterned Media is proposed, the model is based on the crystal structure of the materials commonly used to produce the nano islands in bit patterned materials and the possible defects that may arise from the interaction between the nano islands and the matrix material. The dynamic magnetic properties of the material are then computed using time aware integration methods for the multi spin Hamiltonian. The Hamiltonian takes into account both the spatial and topological disorder of the sample as well as the high perpendicular anisotropy that is pursued when building bit patterned media. The main finding of the research was the possibility of replicating the results of previous experiments on similar materials and the ability of computing the switching field distribution given the geometry of the material and the parameters required by the model.

Keywords: nanostructures, Monte Carlo, pattern media, magnetic properties

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Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: Mohd Ibrahim K. Azeez, Mohammed Sani Ibrahim, Rosemawati Mustapa, Maisarah A. Malik, Chandrakala Varatharajoo, Wee Akina Sia Seng Lee

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The leadership of the principals is known to be a key indicator in development and school performance. Therefore, this study was undertaken to assess the extent of the influence of instructional leadership in the field of supervision and curriculum focus on capacity development competence in the field of communication and teamwork. In addition, this study also examines self-efficacy mediator school leadership in the field of self-improvement and self-management of school principals. The study involved 383 guest teachers from 55 secondary schools for leadership in schools. Data was analyzed using SEM aid program AMOS 21. The final result shows partial mediation model was the best model fit to obtain the best goodness of fit of (X2/df = 4.663, CFI = 0.922, GFI = 0.778, TLI = 0914, NFI = 0.903, and RMSEA = 0.098) compared to the direct effect model of the findings (X2/df = 5.319, CFI = 0.908, GFI = 0755, TLI = 0.899, NFI = 0.889, and RMSEA = 0.106). While the findings of the fully mediator model with a self-efficacy refers principals as a mediator as follows (X2/df = 4.838, CFI = 0918, GFI = 0772, TLI = 0.910, NFI = 0.899, and RMSEA = 0.100). Therefore, it can be concluded that the findings clearly demonstrate self-efficacy variables principals become a mediator in the relationship between instructional leadership capacity and competency development.

Keywords: instructional leadership, capacity development, self-efficacy, competency

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Authors: Mohammad Mahdi Mohammadi Dehnavi, Alessandra De Angelis, Maria Rosaria Pecce

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This paper deals with the role of studs in the structural response of steel-concrete composite beams. A tri-linear slip-shear strength law is assumed according to literature and codes provisions for developing a finite element (FE) model of a case study of a composite deck. The variation of the strength and ductility of the connection is implemented in the numerical model carrying out nonlinear analyses. The results confirm the utility of the model to evaluate the importance of the studs capacity, ductility and strength on the global response (ductility and strength) of the structures but also to analyze the trend of slip and shear at interface along the beams.

Keywords: stud connectors, finite element method, slip, shear load, steel-concrete composite bridge

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Authors: Hamed Saremi

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One of the challenges in manufacturing and service companies to provide a product or service is recognized Brand to consumers in target markets. They provide most of their processes under the same capacity. But the constant threat of devastating internal and external resources to prevent a rise Brands and more companies are recognizing the stages are bankrupt. This paper has tried to identify and analyze effective indicators of brand equity and focuses on indicators and presents a model of intelligent create a model to prevent possible damage. In this study identified indicators of brand equity based on literature study and according to expert opinions, set of indicators By techniques DEMATEL Then to used Multi-Step Adaptive Neural-Fuzzy Inference system (ANFIS) to design a multi-stage intelligent system for assessment of brand equity.

Keywords: anfis, dematel, brand, cosmetic product, brand value

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Authors: A. Y. Mukhtar, J. B. Munyakazi, R. Ouifki

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Over the past few decades, the unprecedented increase in mobility has raised considerable concern about the relationship between mobility and vector-borne diseases and malaria in particular. Thus, one can claim that human mobility is one of the contributing factors to the resurgence of malaria. To assess human mobility on malaria burden among hosts, we formulate a movement-based model on a network of patches. We then extend human multi-group SEIAR deterministic epidemic models into a system of stochastic differential equations (SDEs). Our quantitative stochastic model which is expressed in terms of average rates of movement between compartments is fitted to time-series data (weekly malaria data of 2011 for each patch) using the maximum likelihood approach. Using the metapopulation (multi-group) model, we compute and analyze the basic reproduction number. The result shows that human movement is sufficient to preserve malaria disease firmness in the patches with the low transmission. With these results, we concluded that the sensitivity of malaria to the human mobility is turning to be greatly important over the implications of future malaria control in South Sudan.

Keywords: basic reproduction number, malaria, maximum likelihood, movement, stochastic model

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Authors: Grzegorz Baranski, Piotr Kacejko, Konrad Pietrykowski, Mariusz Duk

Abstract:

This paper discusses a model of a bus-driving scheme. Rapid changes in speed result in a constantly changing kinetic energy accumulated in a bus mass and an increased fuel consumption due to hardly recuperated kinetic energy. The model is based on the results achieved from chassis dynamometer, airport and city street researches. The verified model was applied to simulate the mechanical energy recuperation during the Japanese JE05 Emission Test Cycle. The simulations were performed for several values of vehicle mass. The research results show that fuel economy is impacted by kinetic energy recuperation.

Keywords: heavy duty vehicle, city bus, Japanese JE05 test cycle, kinetic energy, simulations

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Authors: Ali Reza Vaezi, Naser Fakori Ivand, Fereshteh Azarifam

Abstract:

Interrill erosion is the detachment and transfer of soil particles between the rills due to the impact of raindrops and the shear stress of shallow surface runoff. This erosion can be affected by some soil properties such as texture, amount of organic matter and stability of soil aggregates. Information on the temporal variation of interrill erosion during a rainfall event and the effect soil properties have on it can help in understanding the process of runoff production and soil loss between the rills in hillslopes. The importance of this study is especially grate in semi-arid regions, where the soil is weakly aggregated and vegetation cover is mostly poor. Therefore, this research was conducted to investigate the temporal variation of surface flow and interrill erosion and the effect of soil properties on it in some semi-arid soils. A field experiment was done in eight different soil textures under simulated rainfalls with uniform intensity. A total of twenty four plots were installed for eight study soils with three replicates in the form of a random complete block design along the land. The plots were 1.2 m (length) × 1 m (width) in dimensions which designed with a distance of 3 m from each other across the slope. Then, soil samples were purred into the plots. The plots were surrounded by a galvanized sheet, and runoff and soil erosion equipment were placed at their outlets. Rainfall simulation experiments were done using a designed portable simulator with an intensity of 60 mm per hour for 60 minutes. A plastic cover was used around the rainfall simulator frame to prevent the impact of the wind on the free fall of water drops. Runoff production and soil loss were measured during 1 hour time with 5-min intervals. In order to study soil properties, such as particle size distribution, aggregate stability, bulk density, ESP and Ks were determined in the laboratory. Correlation and regression analysis was done to determine the effect of soil properties on runoff and interrill erosion. Results indicated that the study soils have lower booth organic matter content and aggregate stability. The soils, except for coarse textured textures, are calcareous and with relatively higher exchangeable sodium percentages (ESP). Runoff production and soil loss didn’t occur in sand, which was associated with higher infiltration and drainage rates. In other study soils, interrill erosion occurred simultaneously with the generation of runoff. A strong relationship was found between interrill erosion and surface runoff (R2 = 0.75, p< 0.01). The correlation analysis showed that surface runoff was significantly affected by some soil properties consisting of sand, silt, clay, bulk density, gravel, hydraulic conductivity (Ks), lime (calcium carbonate), and ESP. The soils with lower Ks such as fine-textured soils, produced higher surface runoff and more interrill erosion. In the soils, Surface runoff production temporally increased during rainfall and finally reached a peak after about 25-35 min. Time to peak was very short (30 min) in fine-textured soils, especially clay, which was related to their lower infiltration rate.

Keywords: erosion plot, rainfall simulator, soil properties, surface flow

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Authors: Suraj Mehrotra

Abstract:

The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

Procedia PDF Downloads 91