Search results for: estimation of properties of the model

Authors: P. K. Guha Thakurta, Shouvik Roy, Bhawana Raj

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A congestion control scheme in mobile networks is proposed in this paper through a verification based model. The model proposed in this work is represented through performance metric like buffer Occupancy, latency and packet loss rate. Based on pre-defined values, each of the metric is introduced in terms of three different states. A Markov chain based model for the proposed work is introduced to monitor the occurrence of the corresponding state transitions. Thus, the estimation of the network status is obtained in terms of performance metric. In addition, the improved performance of our proposed model over existing works is shown with experimental results.

Keywords: congestion, mobile networks, buffer, delay, call drop, markov chain

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Authors: Zainab Magaji Musa, Nordin M. A. Rahman, Julaily Aida Jusoh

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The web services applications for digital reference service (WSDRS) of LIS model is an informal model that claims to reduce the problems of digital reference services in libraries. It uses web services technology to provide efficient way of satisfying users’ needs in the reference section of libraries. The formal WSDRS model consists of the Z specifications of all the informal specifications of the model. This paper discusses the formal validation of the Z specifications of WSDRS model. The authors formally verify and thus validate the properties of the model using Z/EVES theorem prover.

Keywords: validation, verification, formal, theorem prover

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Authors: Sie Long Kek, Wah June Leong, Kok Lay Teo

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Linear quadratic Gaussian model is a standard mathematical model for the stochastic optimal control problem. The combination of the linear quadratic estimation and the linear quadratic regulator allows the state estimation and the optimal control policy to be designed separately. This is known as the separation principle. In this paper, an efficient computational method is proposed to solve the linear quadratic Gaussian problem. In our approach, the Hamiltonian function is defined, and the necessary conditions are derived. In addition to this, the output error is defined and the least-square optimization problem is introduced. By determining the first-order necessary condition, the gradient of the sum squares of output error is established. On this point of view, the stochastic approximation approach is employed such that the optimal control policy is updated. Within a given tolerance, the iteration procedure would be stopped and the optimal solution of the linear-quadratic Gaussian problem is obtained. For illustration, an example of the linear-quadratic Gaussian problem is studied. The result shows the efficiency of the approach proposed. In conclusion, the applicability of the approach proposed for solving the linear quadratic Gaussian problem is highly demonstrated.

Keywords: iteration procedure, least squares solution, linear quadratic Gaussian, output error, stochastic approximation

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Authors: M. Khalfa, H. Khachai, F. Chiker, K. Bougherara, R. Khenata, G. Murtaza, M. Harmel

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A theoretical study of structural, elastic, electronic and thermodynamic properties of Fe2VX, (with X = Al and Ga), were studied by means of the full-relativistic version of the full-potential augmented plane wave plus local orbitals method. For exchange and correlation potential we used both generalized-gradient approximation (GGA) and local-density approximation (LDA). Our calculated ground state properties like as lattice constants, bulk modulus and elastic constants appear more accurate when we employed the GGA rather than the LDA approximation, and these results agree very well with the available experimental and theoretical data. Further, prediction of the thermal effects on some macroscopic properties of Fe2VAl and Fe2VGa are given in this paper using the quasi-harmonic Debye model in which the lattice vibrations are taken into account. We have obtained successfully the variations of the primitive cell volume, volume expansion coefficient, heat capacities and Debye temperature with pressure and temperature in the ranges of 0–40 GPa and 0–1500 K.

Keywords: full Heusler, FP-LAPW, electronic properties, thermal properties

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Authors: G. L. C. Yap

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Modelling realized volatility with high-frequency returns is popular as it is an unbiased and efficient estimator of return volatility. A computationally simple model is fitting the logarithms of the realized volatilities with a fractionally integrated long-memory Gaussian process. The Gaussianity assumption simplifies the parameter estimation using the Whittle approximation. Nonetheless, this assumption may not be met in the finite samples and there may be a need to normalize the financial series. Based on the empirical indices S&P500 and DAX, this paper examines the performance of the linear volatility model pre-treated with normalization compared to its existing counterpart. The empirical results show that by including normalization as a pre-treatment procedure, the forecast performance outperforms the existing model in terms of statistical and economic evaluations.

Keywords: Gaussian process, long-memory, normalization, value-at-risk, volatility, Whittle estimator

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Authors: Felix Aberu

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The high rate of greenhouse gas (CO₂) emission and increased concentration of Carbon Dioxide in the atmosphere are not unconnected to both human and natural activities. This has caused climate change and global warming in the world. The adverse effect of these climatic changes has no doubt threatened human existence. Hence, this study examined the effects of environmental and climate influence on mortality and morbidity rates, with particular reference to the world’s leading CO₂ emission countries, using both the pre-estimation, estimation, and post-estimation techniques for more dependable outcomes. Hence, the System Generalized Method of Moments (SGMM) was adopted as the main estimation technique for the data analysis from 1996 to 2023. The coefficient of carbon emissions confirmed a positive and significant relationship among CO₂ emission, mortality, and morbidity rates in the world’s leading CO₂ emissions countries, which implies that carbon emission has contributed to mortality and morbidity rates in the world. Therefore, significant action should be taken to facilitate the expansion of environmental protection and sustainability initiatives in any CO₂ emissions nations of the world.

Keywords: environmental, mortality, morbidity, health outcomes, carbon emissions

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Authors: Rithvik Reddy Adapa, Xin Wang

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Phasor Measurement Units (PMUs) are very sophisticated measuring devices that find amplitude, phase and frequency of various voltages and currents in a power system. Particle filter is a state estimation technique that uses Bayesian inference. Particle filters are widely used in pose estimation and indoor navigation and are very reliable. This paper studies and compares four different particle filters as PMUs namely, generic particle filter (GPF), genetic algorithm particle filter (GAPF), particle swarm optimization particle filter (PSOPF) and adaptive particle filter (APF). Two different test signals are used to test the performance of the filters in terms of responsiveness and correctness of the estimates.

Keywords: phasor measurement unit, particle filter, genetic algorithm, particle swarm optimisation, state estimation

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Authors: Zaid Almahmoud, Rana Mahmoud

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To mitigate the environmental impacts of throwing away plastic waste, there has been a recent interest in manufacturing and producing biodegradable plastics. Here, we study a new class of biodegradable plastics which are mixed with external natural additives, including catalytic additives that lead to a successful degradation of the resulting material. To recommend the best alternative among multiple materials, we propose a multi-objective AI model that evaluates the material against multiple objectives given the material properties. As a proof of concept, the AI model was implemented in an expert system and evaluated using multiple materials. Our findings showed that Polyethylene Terephalate is potentially the best biodegradable plastic substitute based on its material properties. Therefore, it is recommended that governments shift the attention to the use of Polyethylene Terephalate in the manufacturing of bottles to gain a great environmental and sustainable benefits.

Keywords: plastic bottles, expert systems, multi-objective model, biodegradable substitutes

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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Authors: Ekaterina M. Myasnikova, Andrey A. Makashov, Alexander V. Spirov

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First manifestation of a segmentation pattern in the early Drosophila development is the formation of expression domains (along with the main embryo axis) of genes belonging to the trunk gene class. Highly variable expression of genes from gap family in early Drosophila embryo is strongly reduced by the start of gastrulation due to the gene cross-regulation. The dynamics of gene expression is described by a gene circuit model for a system of four gap genes. It is shown that for the formation of a steep and stationary border by the model it is necessary that there existed a nucleus (modeling point) in which the gene expression level is constant in time and hence is described by a stationary equation. All the rest genes expressed in this nucleus are in a dynamic equilibrium. The mechanism of border formation associated with the existence of a stationary nucleus is also confirmed by the experiment. An important advantage of this approach is that properties of the system in a stationary nucleus are described by algebraic equations and can be easily handled analytically. Thus we explicitly characterize the cross-regulation properties necessary for the robustness and formulate the conditions providing this effect through the properties of the initial input data. It is shown that our formally derived conditions are satisfied for the previously published model solutions.

Keywords: drosophila, gap genes, reaction-diffusion model, robustness

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Authors: Kanchan Mondal, Dasharath Koulage, Dattatray Manerikar, Asmita Ghate

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This paper presents an additive reliability model using warranty data and Finite Element Analysis (FEA) data. Warranty data for any product gives insight to its underlying issues. This is often used by Reliability Engineers to build prediction model to forecast failure rate of parts. But there is one major limitation in using warranty data for prediction. Warranty periods constitute only a small fraction of total lifetime of a product, most of the time it covers only the infant mortality and useful life zone of a bathtub curve. Predicting with warranty data alone in these cases is not generally provide results with desired accuracy. Failure rate of a mechanical part is driven by random issues initially and wear-out or usage related issues at later stages of the lifetime. For better predictability of failure rate, one need to explore the failure rate behavior at wear out zone of a bathtub curve. Due to cost and time constraints, it is not always possible to test samples till failure, but FEA-Fatigue analysis can provide the failure rate behavior of a part much beyond warranty period in a quicker time and at lesser cost. In this work, the authors proposed an Additive Weibull Model, which make use of both warranty and FEA fatigue analysis data for predicting failure rates. It involves modeling of two data sets of a part, one with existing warranty claims and other with fatigue life data. Hazard rate base Weibull estimation has been used for the modeling the warranty data whereas S-N curved based Weibull parameter estimation is used for FEA data. Two separate Weibull models’ parameters are estimated and combined to form the proposed Additive Weibull Model for prediction.

Keywords: bathtub curve, fatigue, FEA, reliability, warranty, Weibull

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Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

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Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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An estimating equation technique is an alternative method of the widely used maximum likelihood methods, which enables us to ease some complexity due to the complex characteristics of time-varying covariates. In the situations, when both the time-varying covariates and left-truncation are considered in the model, the maximum likelihood estimation procedures become much more burdensome and complex. To ease the complexity, in this study, the modified estimating equations those have been given high attention and considerations in many researchers under semiparametric transformation model was proposed. The purpose of this article was to develop the modified estimating equation under flexible and general class of semiparametric transformation models for left-truncated and right censored survival data with time-varying covariates. Besides the commonly applied Cox proportional hazards model, such kind of problems can be also analyzed with a general class of semiparametric transformation models to estimate the effect of treatment given possibly time-varying covariates on the survival time. The consistency and asymptotic properties of the estimators were intuitively derived via the expectation-maximization (EM) algorithm. The characteristics of the estimators in the finite sample performance for the proposed model were illustrated via simulation studies and Stanford heart transplant real data examples. To sum up the study, the bias for covariates has been adjusted by estimating density function for the truncation time variable. Then the effect of possibly time-varying covariates was evaluated in some special semiparametric transformation models.

Keywords: EM algorithm, estimating equation, semiparametric transformation models, time-to-event outcomes, time varying covariate

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Sandeep Das

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Terminal post is one of the most important features of a Battery. The design and manufacturing of post are very much critical especially when threaded inserts (Bolt-on type) are used since all the collected energy is delivered from the lead part to the threaded insert (Cu or Cu alloy). Any imperfection at the interface may cause Voltage drop, high resistance, high heat generation, etc. This may be because of sudden change of material properties from lead to Cu alloys. To avoid this problem, a scheme of material gradation is proposed for achieving continuous variation of material properties for the Post used in commercially available lead acid battery. The Functionally graded (FG) material for the post is considered to be composed of different layers of homogeneous material. The volume fraction of the materials used corresponding to each layer is calculated by considering its variation along the direction of current flow (z) according to a power law. Accordingly, the effective properties of the homogeneous layers are estimated and the Post composed of this FG material is modeled using the commercially available ANSYS software. The solid 186 layered structural solid element has been used for discretization of the model of the FG Post. A thermal electric analysis is performed on the layered FG model. The model developed has been validated by comparing the results of the existing Post model& experimental analysis

Keywords: ANSYS, functionally graded material, lead-acid battery, terminal post

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Authors: Ya-Mei Chang

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This research is intended to apply spatio-temporal point process methods to the dengue fever data in Tainan. The spatio-temporal intensity function of the dataset is assumed to be separable. The kernel estimation is a widely used approach to estimate intensity functions. The intensity function is very helpful to study the relation of the spatio-temporal point process and some covariates. The covariate effects might be nonlinear. An nonparametric smoothing estimator is used to detect the nonlinearity of the covariate effects. A fitted parametric model could describe the influence of the covariates to the dengue fever. The correlation between the data points is detected by the K-function. The result of this research could provide useful information to help the government or the stakeholders making decisions.

Keywords: dengue fever, spatial point process, kernel estimation, covariate effect

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Authors: Hidir S. Nogay

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In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: cascaded neural network, internal temperature, inverter, three-phase induction motor

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Authors: Adel Belayadi, Ahmed Mougari, Boualem Bourahla

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Molecular junctions are currently considered a promising style in the miniaturization of electronic devices. In this contribution, we provide a tight-binding model to investigate the quantum transport properties across-molecular junctions sandwiched between 2D-graphene nanoribbons in the zigzag direction. We investigate, in particular, the effect of embedded atoms such as Gold and Silicon across the molecular junction. The results exhibit a resonance behavior in terms of incident Fermi levels, depending on the molecular junction type. Additionally, the transport properties under a perpendicular magnetic field exhibit an oscillation for the transmittance versus the magnetic field strength.

Keywords: molecular junction, 2D-graphene nanoribbons, quantum transport properties, magnetic field

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Authors: Augustine Okolie, Johannes Müller, Mirjam Kretzchmar

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We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is the basic reproduction number R0. The estimator is tested in a simulation study and applied to covid-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical covid-19 data, we are able to compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution meet the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency on the reproduction number.

Keywords: stochastic SIR model on graph, contact tracing, branching process, parameter inference

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Authors: Yasemin Kaya, Ahmet N. Eraslan

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In this study, the thermoelastic behavior of a long tube is studied by taking into account the temperature dependency of all mechanical and thermal properties. As the tube is heated slowly, an uncoupled solution procedure is adopted under free and radially constrained boundary conditions. The nonlinear heat conduction equation is solved by a finite element collocation procedure and the corresponding distributions of stress and strain are computed by shooting iterations. The computational model is verified in comparison to the analytical solution by shutting down the temperature dependency of physical properties. In the analysis, experimental data available in the literature is used to describe the coefficient of thermal expansion $\alpha$, the thermal conductivity $k$, the modulus of rigidity $G$, the yield strength $\sigma_{0}$, and the Poisson's ratio $\nu$ of Nickel. Results of the analysis are presented in comparison to those having constant physical properties. As a result of the calculations, the temperature dependency of the material properties should be taken into account at higher temperature ranges.

Keywords: thermoelasticity, long tube, temperature-dependent properties, internal heating

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Authors: Aqeela Mir, Akhtar Nawaz Malik, Javaid Iqbal

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The paper presents a survey based approach in order to observe the level of awareness regarding Mechatronics in society of Pakistan and the factors affecting the future development trend of Mechatronics in Pakistan. With the help of these surveys a new direction for making a Mathematical model for the future development trend of Mechatronics in Pakistan is also suggested.

Keywords: mechatronics society survey, future development trend of mechatronics in pakistan, probability estimation, mathematical model

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Authors: Reda Abdel Azim, Tariq Shehab

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The main objective of this study is to develop a subsurface fracture map of naturally fractured reservoirs by overcoming the limitations associated with different data sources in characterising fracture properties. Some of these limitations are overcome by employing a nested neuro-stochastic technique to establish inter-relationship between different data, as conventional well logs, borehole images (FMI), core description, seismic attributes, and etc. and then characterise fracture properties in terms of fracture density and fractal dimension for each data source. Fracture density is an important property of a system of fracture network as it is a measure of the cumulative area of all the fractures in a unit volume of a fracture network system and Fractal dimension is also used to characterize self-similar objects such as fractures. At the wellbore locations, fracture density and fractal dimension can only be estimated for limited sections where FMI data are available. Therefore, artificial intelligence technique is applied to approximate the quantities at locations along the wellbore, where the hard data is not available. It should be noted that Artificial intelligence techniques have proven their effectiveness in this domain of applications.

Keywords: naturally fractured reservoirs, artificial intelligence, fracture intensity, fractal dimension

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Authors: Sahar Jahani, Meryem Ayse Yucel, David Boas, Seyed Kamaledin Setarehdan

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Near-infrared spectroscopy allows monitoring of oxy- and deoxy-hemoglobin concentration changes associated with hemodynamic response function (HRF). HRF is usually affected by natural physiological hemodynamic (systemic interferences) which occur in all body tissues including brain tissue. This makes HRF extraction a very challenging task. In this study, we used Kalman filter based on a general linear model (GLM) of brain activity to define the proportion of systemic interference in the brain hemodynamic. The performance of the proposed algorithm is evaluated in terms of the peak to peak error (Ep), mean square error (MSE), and Pearson’s correlation coefficient (R2) criteria between the estimated and the simulated hemodynamic responses. This technique also has the ability of real time estimation of single trial functional activations as it was applied to classify finger tapping versus resting state. The average real-time classification accuracy of 74% over 11 subjects demonstrates the feasibility of developing an effective functional near infrared spectroscopy for brain computer interface purposes (fNIRS-BCI).

Keywords: hemodynamic response function, functional near-infrared spectroscopy, adaptive filter, Kalman filter

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Authors: Dua K. S. Y. Klaas, Monzur A. Imteaz, Ika Sudiayem, Elkan M. E. Klaas, Eldav C. M. Klaas

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Due to its porous nature, karst terrains – geomorphologically developed from dissolved formations, is vulnerable to water shortage and deteriorated water quality. Therefore, a solid comprehension on sub-surface flow of karst landscape is essential to assess the long-term availability of groundwater resources. In this paper, a single-continuum model using a finite difference model, MODLFOW, was constructed to represent an unconfined carbonate aquifer in a tropical karst island of Rote in Indonesia. The model, spatially discretized in 20 x 20 m grid cells, was calibrated and validated using available groundwater level and atmospheric variables. In the calibration and validation steps, Parameter Estimation (PEST) and geostatistical pilot point methods were employed to estimate hydraulic conductivity and specific yield values. The results show that the model is able to represent the sub-surface flow indicated by good model performances both in calibration and validation steps. The final model can be used as a robust representation of the system for future study on climate and land use scenarios.

Keywords: carbonate aquifer, karst, sub-surface flow, groundwater model

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Authors: Muhammad Ashraf

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It is experimentally conceded reality that education not just promotes social and intellectual abilities yet, in addition, the incomes of people. The present study is directed to investigate the connection between education level and student income. Data of different education levels is acquired from 300 students through field review from four public sector Universities; two from upper Punjab (University of Gujarat and Government college university-Lahore) and two from lower Punjab (Islamia University-Bahawalpur and The University of Sahiwal). Two-phase estimation is based on the Mincerian human capital model. The first stage presents statistical/descriptive investigation, which shows positive linkage among higher education and income of the students. Econometric estimation is estimated in the second stage by applying Ordinary least Square Method (OLS). Econometric examination reaffirms the importance of higher education as the impact of higher education on students’ incomes accelerates as we move from lower-level education to higher-level education. Educational levels, experience, and working hours are sure and noteworthy with student’s income. Econometric estimation additionally investigated that M. Phil and Ph.D. students have a higher income than bachelor students. Concerning the students, the income profile study commended that the Government ought to give part-time jobs or internships to students as indicated to labor market demand.

Keywords: education, student’s income, experience, universities

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In robotics literature, the simultaneous localization and mapping (SLAM) is commonly associated with a priori-posteriori problem. The autonomous vehicle needs a neutral map to spontaneously track its local position, i.e., “localization” while at the same time a precise path estimation of the environment state is required for effective route planning and obstacle avoidance. On the other hand, the environmental noise factors can significantly intensify the inherent uncertainties in using odometry information and measurements obtained from the robot’s exteroceptive sensor which in return directly affect the overall performance of the corresponding SLAM. Therefore, the current work is primarily dedicated to provide a diagnostic analysis of six SLAM algorithms including FastSLAM, L-SLAM, GraphSLAM, Grid SLAM and DP-SLAM. A SLAM simulated environment consisting of two sets of landmark locations and robot waypoints was set based on modified EKF and UKF in MATLAB using two separate maps for indoor and outdoor route planning subject to natural and artificial obstacles. The simulation results are expected to provide an unbiased platform to compare the estimation performances of the five SLAM models as well as on the reliability of each SLAM model for indoor and outdoor applications.

Keywords: route planning, obstacle, estimation performance, FastSLAM, L-SLAM, GraphSLAM, Grid SLAM, DP-SLAM

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Authors: I. Benkaddour, Y. Benkaddour, A. Benk Addour

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The structural, elastic, Thermal, electronic, and magnetic properties of intermetallic RMn₂Ge₂ (R= Ca, Y, Nd) are investigated by density functional theory (DFT), using the full potential –linearised augmented plane wave method (FP-LAPW). In this approach, the local-density approximation (LDA) is used for the exchange-correlation (XC) potential. The equilibrium lattice constant and magnetic moment agree well with the experiment. The density of states shows that these phases are conductors, with contribution predominantly from the R and Mn d states. We have determined the elastic constants C₁₁, C₁₂, C₁₃, C₄₄, C₃₃, andC₆₆ at ambient conditions in, which have not been established neither experimentally nor theoretically. Thermal properties, including the relative expansion coefficients and the heat capacity, have been estimated using a quasi-harmonic Debye model.

Keywords: RMn₂Ge₂, intermetallic, first-principles, density of states, mechanical properties

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Authors: Ashwani Kumar

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GPS plays an important role in everyday life for safe and convenient transportation. While pedestrians use hand held devices to know their position in a city, vehicles in intelligent transport systems use relatively sophisticated GPS receivers for estimating their current position. However, in urban areas where the GPS satellites are occluded by tall buildings, trees and reflections of GPS signals from nearby vehicles, GPS position estimation becomes poor. In this work, an exhaustive GPS data is collected at a single point in urban area under different times of day and under dynamic environmental conditions. The data is analyzed and statistical refinement methods are used to obtain optimal position estimate among all the measured positions. The results obtained are compared with publically available datasets and obtained position estimation refinement results are promising.

Keywords: global positioning system, statistical approach, intelligent transport systems, least squares estimation

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Authors: Dong-Ho Kim, Zheng-Xun Jin, Yong-Woon Cha, Su-Sang Lim, Sang-Won Han, Chang-Taek Hyun

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As construction is a labor-intensive industry, it is important to identify and manage labor quantities for accurate progress management of the construction project. However, the progress management that focuses on construction cost calculated based on materials rather than labor quantities has led to a difference in the implementation of cost and progress of the actual construction. In addition, since it is not easy to predict accurate labor quantities in the estimation of labor quantity-based progress rate, there have been limited researches into the progress rate estimation based on labor quantity. Accordingly, this study proposed a process for labor quantity-based progress rate estimation using a standard of estimate to predict accurate progress rate of the construction project in Republic Korea. It is expected that the utilization of the proposed process will help to identify the progress rate closer to that of the actual site management and adjust the workforce in each construction type, thereby contributing to improving construction efficiency.

Keywords: labor based, labor cost, progress management, progress rate, progress payment

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