Search results for: random effects panel ols model

Authors: M. Z. Raqab, Debasis Kundu, M. A. Meraou

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In this paper, we have analyzed maximum precipitation data during a particular period of time obtained from different stations in the Global Historical Climatological Network of the USA. One important point to mention is that some stations are shut down on certain days for some reason or the other. Hence, the maximum values are recorded by excluding those readings. It is assumed that the number of stations that operate follows zero-truncated Poisson random variables, and the daily precipitation follows a lognormal random variable. We call this model a compound truncated Poisson lognormal model. The proposed model has three unknown parameters, and it can take a variety of shapes. The maximum likelihood estimators can be obtained quite conveniently using Expectation-Maximization (EM) algorithm. Approximate maximum likelihood estimators are also derived. The associated confidence intervals also can be obtained from the observed Fisher information matrix. Simulation results have been performed to check the performance of the EM algorithm, and it is observed that the EM algorithm works quite well in this case. When we analyze the precipitation data set using the proposed model, it is observed that the proposed model provides a better fit than some of the existing models.

Keywords: compound Poisson lognormal distribution, EM algorithm, maximum likelihood estimation, approximate maximum likelihood estimation, Fisher information, skew distribution

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Authors: Po-Wen Chen, Jin-Yu Wu, Md. Manirul Ali, Yang Peng, Chen-Te Chang, Der-Jun Jan

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Dynamics of cathode spot has become a major part of vacuum arc discharge with its high academic interest and wide application potential. In this article, using a three-dimensional statistical model, we simulate the distribution of the ignition probability of a new cathode spot occurring in different magnetic pressure on old cathode spot surface and at different arcing time. This model for the ignition probability of a new cathode spot was proposed in two typical situations, one by the pure isotropic random walk in the absence of an external magnetic field, other by the retrograde motion in external magnetic field, in parallel with the cathode surface. We mainly focus on developed relationship between the ignition probability density distribution of a new cathode spot and the external magnetic field.

Keywords: cathode spot, vacuum arc discharge, transverse magnetic field, random walk

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Authors: Hong-Ming Chen

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This work considers the resolution of the Hull and White interest rate model with the jump process. A deterministic process is adopted to model the random behavior of interest rate variation as deterministic perturbations, which is depending on the time t. The Brownian motion and jumps uncertainty are denoted as the integral functions piecewise constant function w(t) and point function θ(t). It shows that the interest rate function and the yield function of the Hull and White interest rate model with jump process can be obtained by solving a nonlinear semi-infinite programming problem. A relaxed cutting plane algorithm is then proposed for solving the resulting optimization problem. The method is calibrated for the U.S. treasury securities at 3-month data and is used to analyze several effects on interest rate prices, including interest rate variability, and the negative correlation between stock returns and interest rates. The numerical results illustrate that our approach essentially generates the yield functions with minimal fitting errors and small oscillation.

Keywords: optimization, interest rate model, jump process, deterministic

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: G. C. Surya Bahadur, Ranjana Kothari

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The paper attempts to analyze linkages between corporate governance and firm performance in India. The study employs a panel data of 50 Nifty companies from 2008 to 2012. Using LSDV panel data model and 2SLS model the study reveals that that good corporate governance practices adopted by companies is positively related with financial performance. Board independence, number of board committees and executive compensation are found to have positive relationship while ownership by promoters and financial leverage have negative relationship with performance. There is existence of bi-directional relationship between corporate governance and financial performance. Companies with sound financial performance are more likely to conform to corporate governance norms and standards and implement sound corporate governance system. The findings indicate that companies can enhance business performance and sustainability by embracing sound corporate governance practices.

Keywords: board structure, corporate governance, executive compensation, ownership structure

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Authors: Anwar U. Haque, Waqar Asrar, Ashraf Ali Omar, Erwin Sulaeman, Jaffer Sayed Mohamed Ali

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Due to the interference effects, the intrinsic aerodynamic parameters obtained from the individual component testing are always fundamentally different than those obtained for complete model testing. Consideration and limitation for such testing need to be taken into account in any design work related to the component buildup method. In this paper, the scaled model of a straight rectangular canard of a hybrid buoyant aircraft is tested at 50 m/s in IIUM-LSWT (Low-Speed Wind Tunnel). Model and its attachment with the balance are kept rigid to have results free from the aeroelastic distortion. Based on the velocity profile of the test section’s floor; the height of the model is kept equal to the corresponding boundary layer displacement. Balance measurements provide valuable but limited information of the overall aerodynamic behavior of the model. Zero lift coefficient is obtained at -2.2o and the corresponding drag coefficient was found to be less than that at zero angles of attack. As a part of the validation of low fidelity tool, the plot of lift coefficient plot was verified by the experimental data and except the value of zero lift coefficient, the overall trend has under-predicted the lift coefficient. Based on this comparative study, a correction factor of 1.36 is proposed for lift curve slope obtained from the panel method.

Keywords: wind tunnel testing, boundary layer displacement, lift curve slope, canard, aerodynamics

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Authors: Nelson Bii, Christopher Ouma, John Odhiambo

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Non-response is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random non-response using auxiliary data. In this study, it is assumed that random non-response occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random non-response. In particular, the auxiliary information is used via an improved Nadaraya-Watson kernel regression technique to compensate for random non-response. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at a 95% coverage rate. The results obtained in this study are useful, for instance, in choosing efficient estimators of the finite population mean in demographic sample surveys.

Keywords: mean squared error, random non-response, two-stage cluster sampling, confidence interval lengths

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Authors: Aleš Florian, Lenka Ševelová

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Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.

Keywords: concrete, FEM, pavement, sensitivity, simulation

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Authors: Terefe Alemu

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This study is intended to examine Ethiopia’s trade flow determinants and trade potential with G20 countries whether it was overtraded or there is/are trade potential by using trade gravity model. The sources of panel data used were IMF, WDI, United Nations population division, The Heritage Foundation, Washington's No. 1 think tank online website database, online distance calculator, and others for the duration of 2010 to 2019 for 10 consecutive years. The empirical data analyzing tool used was Random effect model (REM), which is effective in estimation of time-invariant data. The empirical data analyzed using STATA software result indicates that Ethiopia has a trade potential with seven countries of G20, whereas Ethiopia overtrade with 12 countries and EU region. The Ethiopia’s and G20 countries/region bilateral trade flow statistically significant/ p<0.05/determinants were the population of G20 countries, growth domestic products of G20 countries, growth domestic products of Ethiopia, geographical distance between Ethiopia and G20 countries. The top five G20 countries exported to Ethiopia were china, United State of America, European Union, India, and South Africa, whereas the top five G20 countries imported from Ethiopia were EU, China, United State of America, Saudi Arabia, and Germany, respectively. Finally, the policy implication were Ethiopia has to Keep the consistence of trade flow with overtraded countries and improve with under traded countries through trade policy revision, and secondly, focusing on the trade determinants to improve trade flow is recommended.

Keywords: trade gravity model, trade determinants, G20, international trade, trade potential

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Authors: Sungzoon Cho, Hye Jin Lee, Sungwhan Jeon, Dongyoung Min, Sungwon Lyu

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Major public corporations worldwide use conference calls to report their quarterly earnings. These 'earnings' conference calls allow for questions from stock analysts. We investigated if it is possible to identify sentiment from the call script and use it to predict stock price movement. We analyzed call scripts from six companies, two each from Korea, China and Indonesia during six years 2011Q1 – 2017Q2. Random forest with Frequency-based sentiment scores using Loughran MacDonald Dictionary did better than control model with only financial indicators. When the stock prices went up 20 days from earnings release, our model predicted correctly 77% of time. When the model predicted 'up,' actual stock prices went up 65% of time. This preliminary result encourages us to investigate advanced sentiment scoring methodologies such as topic modeling, auto-encoder, and word2vec variants.

Keywords: earnings call script, random forest, sentiment analysis, stock price prediction

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Authors: S. Raja, K. M. Parammasivam, V. Aghilesh

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Fluid-structure interaction is a crucial consideration in the design of many engineering systems such as flight vehicles and bridges. Aircraft lifting surfaces and turbine blades can fail due to oscillations caused by fluid-structure interaction. Hence, it is focussed to study the fluid-structure interaction in the present research. First, the effect of free vibration over the panel is studied. It is well known that the deformation of a panel and flow induced forces affects one another. The selected panel has a span 300mm, chord 300mm and thickness 2 mm. The project is to study, the effect of cross-sectional area and the stiffener location is carried out for the same panel. The stiffener spacing is varied along both the chordwise and span-wise direction. Then for that optimal location the ideal stiffener length is identified. The effect of stiffener cross-section shapes (T, I, Hat, Z) over flutter velocity has been conducted. The flutter velocities of the selected panel with two rectangular stiffeners of cantilever configuration are estimated using MSC NASTRAN software package. As the flow passes over the panel, deformation takes place which further changes the flow structure over it. With increasing velocity, the deformation goes on increasing, but the stiffness of the system tries to dampen the excitation and maintain equilibrium. But beyond a critical velocity, the system damping suddenly becomes ineffective, so it loses its equilibrium. This estimated in NASTRAN using PK method. The first 10 modal frequencies of a simple panel and stiffened panel are estimated numerically and are validated with open literature. A grid independence study is also carried out and the modal frequency values remain the same for element lengths less than 20 mm. The current investigation concludes that the span-wise stiffener placement is more effective than the chord-wise placement. The maximum flutter velocity achieved for chord-wise placement is 204 m/s while for a span-wise arrangement it is augmented to 963 m/s for the stiffeners location of ¼ and ¾ of the chord from the panel edge (50% of chord from either side of the mid-chord line). The flutter velocity is directly proportional to the stiffener cross-sectional area. A significant increment in flutter velocity from 218m/s to 1024m/s is observed for the stiffener lengths varying from 50% to 60% of the span. The maximum flutter velocity above Mach 3 is achieved. It is also observed that for a stiffened panel, the full effect of stiffener can be achieved only when the stiffener end is clamped. Stiffeners with Z cross section incremented the flutter velocity from 142m/s (Panel with no stiffener) to 328 m/s, which is 2.3 times that of simple panel.

Keywords: stiffener placement, stiffener cross-sectional area, stiffener length, stiffener cross sectional area shape

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Authors: Labar Hocine, Kelaiaia Mounia Samira, Mesbah Tarek, Kelaiaia Samia

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Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point(MPP) which depends on panels temperature and on irradiance conditions. The main drawback of P&O is that, the operating point oscillates around the MPP giving rise to the waste of some amount of available energy; moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper, it is shown that in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such initial set parameters is also carried out. The fast convergence of the proposal is proven.

Keywords: P&O, Taylor’s series, MPPT, photovoltaic panel

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Authors: Karima Kimouche

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In this paper, we consider the asymptotic problems in spectral analysis of stationary causal random fields. We impose conditions only involving (conditional) moments, which are easily verifiable for a variety of nonlinear random fields. Limiting distributions of periodograms and smoothed periodogram spectral density estimates are obtained and applications to the spectral domain bootstrap are given.

Keywords: spatial nonlinear processes, spectral estimators, GMC condition, bootstrap method

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Authors: Avishek Chanda, Nam Kyeun Kim, Debes Bhattacharyya

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The use of renewable substitutes in various semi-structural and structural applications has experienced an increase since the last few decades. Sandwich panels have been used for many decades, although research on understanding the effects of the core structures on the panels’ fire-reaction properties is limited. The current work investigates the fire-performance of a corrugated sandwich panel made from renewable, biodegradable, and sustainable material, plywood. The bench-scale fire testing apparatus, cone-calorimeter, was employed to evaluate the required fire-reaction properties of the sandwich core in a panel configuration, with three corrugated layers glued together with face-sheets under a heat irradiance of 50 kW/m². The study helped in documenting a unique heat release trend associated with the fire performance of the 3-layered corrugated sandwich panels and in understanding the structural stability of the samples in the event of a fire. Furthermore, the total peak heat release rate was observed to be around 421 kW/m², which is significantly low compared to many polymeric materials in the literature. The total smoke production was also perceived to be very limited compared to other structural materials, and the total heat release was also nominal. The time to ignition of 21.7 s further outlined the advantages of using the plywood component since polymeric composites, even with flame-retardant additives, tend to ignite faster. Overall, the corrugated plywood sandwich panels had significant fire-reaction properties and could have important structural applications. The possible use of structural panels made from bio-degradable material opens a new avenue for the use of similar structures in sandwich panel preparation.

Keywords: corrugated sandwich panel, fire-reaction properties, plywood, renewable material

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Authors: M. Koray Cetin, Mehmet Mert

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The purpose of the study is to examine impacts on tourism revenues of the exchange rate and country overall security level. There are numerous studies that examine the bidirectional relation between macroeconomic factors and tourism revenues and tourism demand. Most of the studies support the existence of impact of tourism revenues on growth rate but not vice versa. Few studies examine the impact of factors like real exchange rate or purchasing power parity on the tourism revenues. In this context, firstly impact of real exchange rate on tourism revenues examination is aimed. Because exchange rate is one of the main determinants of international tourism services price in guests currency unit. Another determinant of tourism demand for a country is country’s overall security level. This issue can be handled in the context of the relationship between tourism revenues and overall security including turmoil, terrorism, border problem, political violence. In this study, factors are handled for several countries which have tourism revenues on a certain level. With this structure, it is a panel data, and it is evaluated with panel data analysis techniques. Panel data have at least two dimensions, and one of them is time dimensions. The panel data analysis techniques are applied to data gathered from Worldbank data web page. In this study, it is expected to find impacts of real exchange rate and security factors on tourism revenues for the countries that have noteworthy tourism revenues.

Keywords: exchange rate, panel data analysis, security, tourism revenues

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Authors: Igor Velickovski, Aleksandar Stojkov, Ivana Rajkovic

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This paper investigates drivers of shock synchronization using quarterly data for 27 European countries over the period 1999-2013 and taking into account the difference between core (‘the euro area core locomotive’) and peripheral euro area and transition countries (‘the peripheral wagons’). Results from panel error-correction models suggest that core of the euro area has not been strong magnetizer of the shock convergence of periphery and transition countries since the euro inception as a result of the offsetting effects of the various factors that affected the shock convergence process. These findings challenge the endogeneity hypothesis in the optimum currency area framework and rather support the specialisation paradigm which is concerning evidence for the future stability of the euro area.

Keywords: dynamic panel models, shock synchronisation, trade, optimum currency area

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Authors: Mahmoud Hassan, Walid Oueslati, Damien Rousseliere

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This paper explores the channels through which energy taxes may affect economic growth, using a simultaneous equations model for a balanced panel data of 31 OECD countries over the 1994–2013 period. The empirical results reveal a negative impact of energy taxes on physical investment in the short and long term. This impact is negatively sensitive to the existence and level of public debt. Additionally, the results show that energy taxes have an indirect effect on human capital through their impact on polluting emissions. The taxes on energy products are able to reduce both the flux and the stock of polluting emissions that have a negative impact on human capital skills in the short and long term. Finally, we found that energy taxes could encourage eco-innovation in the short and long term.

Keywords: energy taxes, economic growth, public debt, simultaneous equations model, multiple imputation

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Chin-Chih Chang

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Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

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Authors: Lu Ren, You-Liang Fang, Yan-Gang Zhao

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In recent years, the application of BIM (Building Information Modelling) to construction schedule plan has been the focus of more and more researchers. In order to assess the reasonable level of the BIM-based construction schedule plan, that is whether the schedule can be completed on time, some researchers have introduced reliability theory to evaluate. In the process of evaluation, the uncertain factors affecting the construction schedule plan are regarded as random variables, and probability distributions of the random variables are assumed to be normal distribution, which is determined using two parameters evaluated from the mean and standard deviation of statistical data. However, in practical engineering, most of the uncertain influence factors are not normal random variables. So the evaluation results of the construction schedule plan will be unreasonable under the assumption that probability distributions of random variables submitted to the normal distribution. Therefore, in order to get a more reasonable evaluation result, it is necessary to describe the distribution of random variables more comprehensively. For this purpose, cubic normal distribution is introduced in this paper to describe the distribution of arbitrary random variables, which is determined by the first four moments (mean, standard deviation, skewness and kurtosis). In this paper, building the BIM model firstly according to the design messages of the structure and making the construction schedule plan based on BIM, then the cubic normal distribution is used to describe the distribution of the random variables due to the collecting statistical data of the random factors influencing construction schedule plan. Next the reliability analysis of the construction schedule plan based on BIM can be carried out more reasonably. Finally, the more accurate evaluation results can be given providing reference for the implementation of the actual construction schedule plan. In the last part of this paper, the more efficiency and accuracy of the proposed methodology for the reliability analysis of the construction schedule plan based on BIM are conducted through practical engineering case.

Keywords: BIM, construction schedule plan, cubic normal distribution, reliability analysis

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Authors: Barbara Gronostajska

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Currently, housing conditions of buildings executed in large panel technology are deteriorating. The article presents modernization solutions implemented throughout the variety of architectural activities (adding of balconies and staircases, connecting apartments) which guarantee very intriguing results that meet the needs and expectations of the modern society.

Keywords: housing estate, apartments, flats, modernization, plate blocks

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Authors: Mahadev Bhise, Nabanita Majumder

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Recent studies have found association between air pollutants and mortality, particularly how concentration of air pollutant explains under-five mortality across the countries. Thus, the present study evaluates the relationship between Carbon dioxide (CO2) emission and under-five mortality, while controlling other well-being determinant of Under-five mortality in 100 countries using panel unbalanced cross sectional data. We have used PCSE and GMM model for the period 1990-2011 to meet our objectives. Our findings suggest that, the positive relationship between lagged periods of carbon dioxide and under-five mortality; the percentage of rural population with access of improved water is negatively associated with under-five mortality, while in case of urban population with access of improved water, is positively related to under-five mortality. Access of sanitation facility, food production index, GDP per capita, and concentration of urban population have significant negative impact on under-five mortality. Further, total fertility rate is significantly associated (positive) with under-five mortality which indicates relative change in fertility is related to relative change in under-five mortality.

Keywords: arbon dioxide (CO2), under-five mortality (0q5), gross domestic product (GDP), urban population, food production, panel corrected standard errors (PCSE), generalized method of moments (GMM)

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

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Authors: Hye Lin Yang, Clifton R. Emery

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Purpose: The purpose of this study is to examine the effects of intimate partner violence on child abuse, whether patriarchal attitude and informal social control moderate the relationship between intimate partner violence and child abuse. This study was conducted with data from The Seoul Families and Neighborhoods Study (SFNS). The SFNS is a representative random probability 3-stage cluster sample of 541 cohabiting couples in Seoul, South Korea collected in 2012. To verify research models, Random effect analysis were used. All analyses were performed using the Stata program. Results: Crucial findings are the following. First, intimate partner violence showed a significantly positive relationship with Child abuse. Second, there are significant moderating effects of informal social control on intimate partner violence - child abuse. Third, there are significant moderating effects of patriarchal attitude on intimate partner violence - child abuse. In other words, Patriarchal attitude is a significant risk factor of child abuse and informal social control is a significant Protection factor of child abuse. Based on results, the policy and practical implications for preventing child abuse, promoting informal social control were discussed.

Keywords: Intimate partner violence, child abuse, informal social control, patriarchal attitude

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Authors: S. Benkraouda, Z. Djelloul-Khedda, B. Yagoubi

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we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection.

Keywords: forest fire, forest fire detection, satellite image, normal distribution, theoretical gaussian model, thermal infrared matrix image

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Authors: Aysha Zamir

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This study examines to what extent foreign private investment (FPI) affects the clean industrial environment and sustainable economic growth through developed countries investment in China. Moreover, this study investiage an association among FPI, CO2 emission, energy consumption, and sustainable economic growth. This study uses random effects and generalized least squares (GLS) and panel VAR estimators for data analysis. The results indicate that the Chinese economy has a vastly positive influenced regarding the location and choice of emerging and developed countries’ investment in the domestic market. Furthermore, emerging and developed economies investment increases the contribution among domestic firms, environment sustainability toward the national economy. The further results show that foreign private investment and gross domestic investment have a positive impact on sustainable economic growth.

Keywords: clean industrial environment, energy consumption, CO2 emmission, foreign private investment, developed and emerging economies

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Authors: Mohamed Amarouayache, Badia Amrouche, Gharbi Akila, Boukadoume Mohamed

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This work is devoted to the modeling of DC-DC converter (boost) used for MPPT applications to set conditions of stability. For this, we establish a linear mathematical model of the DC-DC converter with an average small signal model. This model has allowed us to apply conventional linear methods of automation. A mathematical relationship between the duty cycle and the voltage of the panel has been set up. With this relationship we specify the conditions of the stability in closed-loop depending on the system parameters (the elements of storage capacity and inductance, PWM control).

Keywords: MPPT, PWM, stability, criterion of Routh, average small signal model

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Authors: Jasmine Latiolais

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Much of the disaster-related literature finds a positive relationship between the impact of a natural disaster and the growth of wages. Panel datasets are often used to explore these effects. However, natural disasters do not impact a single variable in the economy. Rather, natural disasters affect all facets of the economy, simultaneously, upon impact. It is difficult to control for all factors that would be influenced by the impact of a natural disaster, which can lead to lead to omitted variable bias in those studies employing panel datasets. To address this issue of omitted variable bias, an interrupted time series analysis is used to test the short-run relationship between the impact of Hurricanes Katrina and Rita on parish wage levels in Southern Louisiana, inherently controlling for economic conditions. This study provides evidence that natural disasters do increase wages in the very short term (one quarter following the impact of the hurricane) but that these results are not seen in the longer term and are not robust. In addition, the significance of the coefficients changes depending on the parish. Overall, this study finds that previous literature on this topic may not be robust when considered through a time-series lens.

Keywords: economic recovery, local economies, local wage growth, natural disasters

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

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Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: Samira Cherid, Samir Bentata, Feyza Zahira Meghoufel, Yamina Sefir, Sabria Terkhi, Fatima Bendahma, Bouabdellah Bouadjemi, Ali Zitouni

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Using the transfer-matrix technique and the Kronig Penney model, we numerically and analytically investigate the effect of short-range correlated disorder in random dimer model (RDM) on transmission properties of light in one dimension photonic crystals made of three different materials. Such systems consist of two different structures randomly distributed along the growth direction, with the additional constraint that one kind of these layers appears in pairs. It is shown that the one-dimensional random dimer photonic crystals support two types of extended modes. By shifting of the dimer resonance toward the host fundamental stationary resonance state, we demonstrate the existence of the ballistic response in these systems.

Keywords: photonic crystals, disorder, correlation, transmission

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