Search results for: random effects panel ols model

Authors: Olfa Kaabia, Ilyes Abid, Khaled Guesmi

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This pioneer paper studies whether and how Bitcoin shocks are transmitted to the U.S economy. We employ a new methodology: TVP FAVAR model with stochastic volatility. We use a large dataset of 111 major U.S variables from 1959:m1 to 2016:m12. The results show that Bitcoin shocks significantly impact the U.S. economy. This significant impact is pronounced in a volatile and increasing U.S economy. The Bitcoin has a positive relationship on the U.S real activity, and a negative one on U.S prices and interest rates. Effects on the Monetary Policy exist via the inter-est rates and the Money, Credit and Finance transmission channels.

Keywords: bitcoin, US economy, FAVAR models, stochastic volatility

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Authors: Eleonora Sofilda, Ria Amalia, Muhammad Zilal Hamzah

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Foreign direct investment is one of the sources of financing or capital that important for a country, especially for developing countries. This investment also provides a great contribution to development through the transfer of assets, management improving, and transfer of technology in enhancing the economy of a country. In the other side currently in ASEAN countries emerge the interesting phenomenon where some big producers are re-locate their basic production among those countries. This research is aimed to analyze the factors that affect capital inflows of foreign direct investment into the 6 ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, Philippines, and Vietnam) in period 2004-2012. This study uses panel data analysis to determine the factors that affect of foreign direct investment in 6 ASEAN. The factors that affect of foreign direct investment (FDI) are the gross domestic product (GDP), global competitiveness (GCI), interest rate, exchange rate and trade openness (TO). Result of panel data analysis show that three independent variables (GCI, GDP, and TO) have a significant effect to the FDI in 6 ASEAN Countries.

Keywords: foreign direct investment, the gross domestic product, global competitiveness, interest rate, exchange rate, trade openness, panel data analysis

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Authors: Ke-Jing Lee, Cheng-Jung Lee, Yu-Chi Chang, Li-Wen Wang, Yeong-Her Wang

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To avoid the cross-talk issue of only resistive random access memory (RRAM) cell, one transistor and one resistor (1T1R) architecture with a TiO₂-based RRAM cell connected with solution barium zirconate nickelate (BZN) organic thin film transistor (OTFT) device is successfully demonstrated. The OTFT were fabricated on a glass substrate. Aluminum (Al) as the gate electrode was deposited via a radio-frequency (RF) magnetron sputtering system. The barium acetate, zirconium n-propoxide, and nickel II acetylacetone were synthesized by using the sol-gel method. After the BZN solution was completely prepared using the sol-gel process, it was spin-coated onto the Al/glass substrate as the gate dielectric. The BZN layer was baked at 100 °C for 10 minutes under ambient air conditions. The pentacene thin film was thermally evaporated on the BZN layer at a deposition rate of 0.08 to 0.15 nm/s. Finally, gold (Au) electrode was deposited using an RF magnetron sputtering system and defined through shadow masks as both the source and drain. The channel length and width of the transistors were 150 and 1500 μm, respectively. As for the manufacture of 1T1R configuration, the RRAM device was fabricated directly on drain electrodes of TFT device. A simple metal/insulator/metal structure, which consisting of Al/TiO₂/Au structures, was fabricated. First, Au was deposited to be a bottom electrode of RRAM device by RF magnetron sputtering system. Then, the TiO₂ layer was deposited on Au electrode by sputtering. Finally, Al was deposited as the top electrode. The electrical performance of the BZN OTFT was studied, showing superior transfer characteristics with the low threshold voltage of −1.1 V, good saturation mobility of 5 cm²/V s, and low subthreshold swing of 400 mV/decade. The integration of the BZN OTFT and TiO₂ RRAM devices was finally completed to form 1T1R configuration with low power consumption of 1.3 μW, the low operation current of 0.5 μA, and reliable data retention. Based on the I-V characteristics, the different polarities of bipolar switching are found to be determined by the compliance current with the different distribution of the internal oxygen vacancies used in the RRAM and 1T1R devices. Also, this phenomenon can be well explained by the proposed mechanism model. It is promising to make the 1T1R possible for practical applications of low-power active matrix flat-panel displays.

Keywords: one transistor and one resistor (1T1R), organic thin-film transistor (OTFT), resistive random access memory (RRAM), sol-gel

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Authors: Abidemi C. Adegboye, Nosakhare Ikponmwosa, Rogers A. Akinsokeji

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For many resource-rich developing countries, fiscal policy has become a key tool used for short-run fiscal management since it is considered as playing a critical role in injecting part of resource rents into the economies. However, given its instability, reliance on revenue from commodity exports renders fiscal management, budgetary planning and the efficient use of public resources difficult. In this study, the linkage between commodity prices and fiscal operations among a sample of commodity-exporting countries in sub-Saharan Africa (SSA) is investigated. The main question is whether commodity price fluctuations affects the effectiveness of fiscal policy as a macroeconomic stabilization tool in these countries. Fiscal management effectiveness is considered as the ability of fiscal policy to react countercyclically to output gaps in the economy. Fiscal policy is measured as the ratio of fiscal deficit to GDP and the ratio of government spending to GDP, output gap is measured as a Hodrick-Prescott filter of output growth for each country, while commodity prices are associated with each country based on its main export commodity. Given the dynamic nature of fiscal policy effects on the economy overtime, a dynamic framework is devised for the empirical analysis. The panel cointegration and error correction methodology is used to explain the relationships. In particular, the study employs the panel ECM technique to trace short-term effects of commodity prices on fiscal management and also uses the fully modified OLS (FMOLS) technique to determine the long run relationships. These procedures provide sufficient estimation of the dynamic effects of commodity prices on fiscal policy. Data used cover the period 1992 to 2016 for 11 SSA countries. The study finds that the elasticity of the fiscal policy measures with respect to the output gap is significant and positive, suggesting that fiscal policy is actually procyclical among the countries in the sample. This implies that fiscal management for these countries follows the trend of economic performance. Moreover, it is found that fiscal policy has not performed well in delivering macroeconomic stabilization for these countries. The difficulty in applying fiscal stabilization measures is attributable to the unstable revenue inflows due to the highly volatile nature of commodity prices in the international market. For commodity-exporting countries in SSA to improve fiscal management, therefore, fiscal planning should be largely decoupled from commodity revenues, domestic revenue bases must be improved, and longer period perspectives in fiscal policy management are the critical suggestions in this study.

Keywords: commodity prices, ECM, fiscal policy, fiscal procyclicality, fully modified OLS, sub-saharan africa

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Authors: Hamid Goharnejad, Milad Sadeghpoor Moalem, Mahmood Zakeri Niri, Leili Sadeghi Khalegh Abadi

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While there are many benefits to using reservoir dams, their break leads to destructive effects. From the viewpoint of International Committee of Large Dams (ICOLD), dam break means the collapse of whole or some parts of a dam; thereby the dam will be unable to hold water. Therefore, studying dam break phenomenon and prediction of its behavior and effects reduces losses and damages of the mentioned phenomenon. One of the most common types of reservoir dams is embankment dam. Overtopping in embankment dams occurs because of flood discharge system inability in release inflows to reservoir. One of the most important issues among managers and engineers to evaluate the performance of the reservoir dam rim when sliding into the storage, creating waves is large and long. In this study, the effects of floods which caused the overtopping of the dam have been investigated. It was assumed that spillway is unable to release the inflow. To determine outflow hydrograph resulting from dam break, numerical model using Flow-3D software and empirical equations was used. Results of numerical models and their comparison with empirical equations show that numerical model and empirical equations can be used to study the flood resulting from dam break.

Keywords: embankment dam break, empirical equations, Taleghan dam, Flow-3D numerical model

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Authors: J. Kosikova, B. Vacenovska, M. Vyhnankova

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The paper reports on the subject of recycling and further use of secondary raw materials obtained from solar panels, which is becoming a very up to date topic in recent years. Recycling these panels is very difficult and complex, and the use of resulting secondary raw materials is still not fully resolved. Within the research carried out at the Brno University of Technology, new polymer materials used for industrial floors are being developed. Secondary raw materials are incorporated into these polymers as fillers. One of the tested filler materials was glass obtained from solar panels. The following text describes procedures and results of the tests that were performed on these materials, confirming the possibility of the use of solar panel glass in industrial polymer flooring systems.

Keywords: fillers, industrial floors, recycling, secondary raw material, solar panel

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Laurent Maurin, Mervi Toivanen

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This paper estimates euro area banks’ internal target capital ratios and investigates whether banks’ adjustment to the targets have an impact on credit supply and holding of securities during the financial crisis in 2005-2011. Using data on listed banks and country-specific macro-variables a partial adjustment model is estimated in a panel context. The results indicate, firstly, that an increase in the riskiness of banks’ balance sheets influences positively on the target capital ratios. Secondly, the adjustment towards higher equilibrium capital ratios has a significant impact on banks’ assets. The impact is found to be more size-able on security holdings than on loans, thereby suggesting a pecking order.

Keywords: Euro area, capital ratios, credit supply, partial adjustment model

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Authors: Eva Kotlánová

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After recession that began in 2007 in the United States and subsequently spilled over the Europe we could expect recovery of economic growth. According to the last estimation of economic progress of European countries, this recovery is not strong enough. Among others, it will depend on economic policy, where and in which way, the economic indicators will proceed. Economic theories postulate that the economic subjects prefer stably, continual economic policy without repeated and strong fluctuations. This policy is perceived as support of economic growth. Mostly in crises period, when the government must cope with consequences of recession, the economic policy becomes unpredictable for many subjects and economic policy uncertainty grows, which have negative influence on economic growth. The aim of this paper is to use panel regression to prove or disprove this hypothesis on the example of five largest European economies in the period 2008–2012.

Keywords: economic crises in Europe, economic policy, uncertainty, panel analysis regression

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Authors: Reinhold Kosfeld, Andreas Gohs

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In the reduction of CO₂ emissions, the transition to environmentally friendly transport modes has a high significance. In Germany, the climate protection programme 2030 includes various measures for promoting electromobility. Although electric cars at present hold a market share of just over one percent, its stock more than doubled in the past two years. Special measures like tax incentives and a buyer’s premium have been put in place to promote the shift towards electric cars and boost their diffusion. Knowledge of the future expansion of electric cars is required for planning purposes and adaptation measures. With a view of these objectives, we particularly investigate the effect of spatial spillovers on forecasting performance. For this purpose, time series econometrics and panel econometric models are designed for pure electric cars and hybrid cars for Germany. Regional forecasting models with spatial interactions are consistently estimated by using spatial econometric techniques. Regional data on the stocks of electric cars and their determinants at the district level (NUTS 3 regions) are available from the Federal Motor Transport Authority (Kraftfahrt-Bundesamt) for the period 2017 - 2019. A comparative examination of aggregated regional and national predictions provides quantitative information on accuracy gains by allowing for spatial spillovers in forecasting electric mobility.

Keywords: electric mobility, forecasting market diffusion, regional panel data model, spatial interaction

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Authors: Ruangdech Sirikit

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The purposes of this research were to study the variables affecting the quality assessment of mathematics subject in Thailand by using value-added analysis on TIMSS 2011. The data used in this research is the secondary data from the 2011 Trends in International Mathematics and Science Study (TIMSS), collected from 6,124 students in 172 schools from Thailand, studying only mathematics subjects. The data were based on 14 assessment tests of knowledge in mathematics. There were 3 steps of data analysis: 1) To analyze descriptive statistics 2) To estimate competency of students from the assessment of their mathematics proficiency by using MULTILOG program; 3) analyze value added in the model of quality assessment using Value-Added Model with Hierarchical Linear Modeling (HLM) and 2 levels of analysis. The research results were as follows: 1. Student level variables that had significant effects on the competency of students at .01 levels were Parental care, Resources at home, Enjoyment of learning mathematics and Extrinsic motivation in learning mathematics. Variable that had significant effects on the competency of students at .05 levels were Education of parents and self-confident in learning mathematics. 2. School level variable that had significant effects on competency of students at .01 levels was Extra large school. Variable that had significant effects on competency of students at .05 levels was medium school.

Keywords: quality assessment, value-added model, TIMSS, mathematics, Thailand

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Authors: Oktofa Y. Sudrajad, Didier V. Caillie

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Association of Southeast Asian Countries Nations (ASEAN) is moving forward to the next level of regional integration by the initiation of ASEAN Economic Community (AEC) which is already started in 2015, 8 years after its declaration for the creation of AEC in 2007. This commitment imposes financial integration in the region is one of the main agenda which will be achieved until 2025. Therefore, the commitment to financial integration including banking integration will bring new landscape in the competition and business model in this region. This study investigates the effect of competition on bank business model using a sample of 324 banks from seven members of Association of Southeast Asian Nations (ASEAN) countries (Cambodia, Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam). We use market power approach and Boone indicator as competition measures, while income diversification and bank funding strategies are employed as bank business model representation. Moreover, we also evaluate bank business model based by grouping the banks based on the main banking characteristics. We use unbalanced bank-specific annual panel data over the period of 2003 – 2015. Our empirical analysis shows that the banking industries in ASEAN countries adapt their business model by increasing non-interest income proportion due to the level of competition increase in the sector.

Keywords: bank business model, banking competition, Boone indicator, market power

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Authors: Hao Chen, Bo Guo, Ping Jiang

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Residual life (RL) estimation based on multi-phase nonlinear Wiener process was studied in this paper, which is significant for complicated products with small samples. Firstly, nonlinear Wiener model with random parameter was introduced and multi-phase nonlinear Wiener model was proposed to model degradation process of products that were nonlinear and separated into different phases. Then the multi-phase RL probability density function based on the presented model was derived approximately in a closed form and parameters estimation was achieved with the method of maximum likelihood estimation (MLE). Finally, the method was applied to estimate the RL of high voltage plus capacitor. Compared with the other three different models by log-likelihood function (Log-LF) and Akaike information criterion (AIC), the results show that the proposed degradation model can capture degradation process of high voltage plus capacitors in a better way and provide a more reliable result.

Keywords: multi-phase nonlinear wiener process, residual life estimation, maximum likelihood estimation, high voltage plus capacitor

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving army, moving convoys etc. The Radar operator selects one of the promising targets into Single Target Tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper we present a technique using mathematical and statistical methods like Fast Fourier Transformation (FFT) and Principal Component Analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, fft, principal component analysis, eigenvector, octave-notes, dsp

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Authors: Erim Koyun

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This paper aim to construct a mathematical model for Underwater vehicles under load variation test conditions. Propeller effects on underwater vehicle are investigated. Body with counter rotating propeller model is analyzed by CFD methods, thus forces and moment are obtained. Propeller effects of vehicle’s hydrodynamic performance under load variation conditions will be investigated. Additionally, pressure contour is examined for differences between different load conditions. Axial force equation is established using hydrodynamic coefficients, which contains resistance, thrust, and additional coefficients occurs due to load variations. Additional coefficients helps to express completely axial force on underwater vehicle. When the vehicle accelerates, additional force occurs besides thrust force increment. This is propeller effect on the body. Hence, mathematical model cover this effect. For CFD analysis, the incompressible, three-dimensional, and unsteady Reynolds Averaged Navier-Stokes equations will be used Numerical results is verified with experimental results for verification. The overall goal of this study is to present complementary mathematical model for body with counter rotating propeller.

Keywords: counter rotating propeller, CFD, hydrodynamic mathematic model, hydrodynamics analysis, thrust deduction

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Authors: Alhusein Almahjoub, Dongyu Qiu

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This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation

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Authors: Huma Khan, Faiza Akhtar

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The present study investigates the effects of burnout toward job performance and job satisfaction with the moderating role of hope and self-efficacy. Findings from 310 frontline employees of Pakistani commercial banks (Lahore, Karachi & Islamabad) disclosed burnout has negative significant effects on job performance and job satisfaction. Simple random sampling technique was used to collect data and inferential statistics were applied to analyzed the data. However, results disclosed no moderation effect of hope on burnout, job performance or with job satisfaction. Moreover, Data significantly supported the moderation effect of self-efficacy. Study further shed light on the development of psychological capital. Importance of the implication of the current finding is discussed.

Keywords: burnout, hope, job performance, job satisfaction, psychological capital, self-efficacy

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Authors: K. Manmi, Q. X. Wang

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This paper investigates microbubble dynamics subject to ultrasound in a weakly viscous fluid near a rigid boundary. The phenomenon is simulated using a boundary integral method. The weak viscous effects are incorporated into the model through the normal stress balance across the bubble surface. The model agrees well with the Rayleigh-Plesset equation for a spherical bubble for several cycles. The effects of the fluid viscosity in the bubble dynamics are analyzed, including jet development, centroid movement and bubble volume.

Keywords: microbubble dynamics, bubble jetting, viscous effect, boundary integral method

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Authors: Md.Noman Habib Khan, M. S. Tajul Islam, T. S. Gunawan, M. Hasanuzzaman

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The photovoltaic (PV) panel with no galvanic isolation system is well-known technique in the world which is effective and deliver power with enhanced efficiency. The PV generation presented here is for stand-alone system installed in remote areas when as the resulting power gets connected to electronic load installation instead of being tied to the grid. Though very small, even then transformer-less topology is shown to be with leakage in pico-ampere range. By using PWM technique PWM, leakage current in different situations is shown. The results that are demonstrated in this paper show how the pico-ampere current is reduced to femto-ampere through use of inductors and capacitors of suitable values of inductor and capacitors with the load.

Keywords: photovoltaic (PV) panel, duty cycle, pulse duration modulation (PDM), leakage current

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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Authors: Seyedsamad Tahani, Samira Ghorbanpour

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The global warming crisis forced humans to consider their place in the world and the earth's future. In this regard, Electric Vehicles (EVs) are a significant step toward protecting the environment. By identifying factors that influence people's behavior intentions toward using Electric Vehicles (EV), we proposed a theoretical model by extending the Technology Acceptance Model (TAM), including three more concepts, Subjective Norm (SN), Self-Efficacy (SE), and Perceived Behavior Control (PBC). The study was conducted in South Korea, and a random sample was taken at a specific time. In order to collect data, a questionnaire was created in a Google Form and sent via Kakao Talk, a popular social media application used in Korea. There were about 220 participants in this survey. However, 201 surveys were completely done. The findings revealed that all factors in the TAM model and the other added concepts such as subjective norms, self-efficacy and perceived behavior control significantly affect the behavioral intention of using EVs.

Keywords: electric vehicles, behavioral intention, perceived trust, perceived enjoyment, self-efficacy

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Authors: Mustafa Engin Başoğlu, Bekir Çakır

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The most important component affecting the efficiency of photovoltaic power systems are solar panels. Efficiency of these systems are significantly affected because of being low efficiency of solar panel. Therefore, solar panels should be operated under maximum power point conditions through a power converter. In this study, design boost converter with maximum power point tracking (MPPT) operation has been designed and performed with Incremental Conductance (Inc-Cond) algorithm by using direct duty control. Furthermore, it is shown that performance of boost converter with MPPT operation fails under low load resistance connection.

Keywords: boost converter, incremental conductance (Inc-Cond), MPPT, solar panel

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Authors: Amit Gill, Lalith Wijerathne, Sebastian Poledna

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This research presents the details of the implementation of high performance computing (HPC) extension of agent-based economic models (ABEMs) to simulate hundreds of millions of heterogeneous agents. ABEMs offer an alternative approach to study the economy as a dynamic system of interacting heterogeneous agents, and are gaining popularity as an alternative to standard economic models. Over the last decade, ABEMs have been increasingly applied to study various problems related to monetary policy, bank regulations, etc. When it comes to predicting the effects of local economic disruptions, like major disasters, changes in policies, exogenous shocks, etc., on the economy of the country or the region, it is pertinent to study how the disruptions cascade through every single economic entity affecting its decisions and interactions, and eventually affect the economic macro parameters. However, such simulations with hundreds of millions of agents are hindered by the lack of HPC enhanced ABEMs. In order to address this, a scalable Distributed Memory Parallel (DMP) implementation of ABEMs has been developed using message passing interface (MPI). A balanced distribution of computational load among MPI-processes (i.e. CPU cores) of computer clusters while taking all the interactions among agents into account is a major challenge for scalable DMP implementations. Economic agents interact on several random graphs, some of which are centralized (e.g. credit networks, etc.) whereas others are dense with random links (e.g. consumption markets, etc.). The agents are partitioned into mutually-exclusive subsets based on a representative employer-employee interaction graph, while the remaining graphs are made available at a minimum communication cost. To minimize the number of communications among MPI processes, real-life solutions like the introduction of recruitment agencies, sales outlets, local banks, and local branches of government in each MPI-process, are adopted. Efficient communication among MPI-processes is achieved by combining MPI derived data types with the new features of the latest MPI functions. Most of the communications are overlapped with computations, thereby significantly reducing the communication overhead. The current implementation is capable of simulating a small open economy. As an example, a single time step of a 1:1 scale model of Austria (i.e. about 9 million inhabitants and 600,000 businesses) can be simulated in 15 seconds. The implementation is further being enhanced to simulate 1:1 model of Euro-zone (i.e. 322 million agents).

Keywords: agent-based economic model, high performance computing, MPI-communication, MPI-process

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Authors: Lukman Oyelami

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Studies have identified trade as a veritable tool for inclusive economic growth and poverty reduction in developing countries. However, contrary to the overwhelming pieces of evidence of the Asian tiger as a success story of beneficial trade, many African countries still experience poverty unabatedly despite active engagement in trade. Consequently, this study seeks to investigate the contributory effect of technological innovation on total export performance and specifically manufacturing exports of African countries. This is with a view to exploring manufacturing exports as a viable option for diversification. To achieve the empirical investigation this study, require Systems Generalized Method of Moments (sys-GMM) estimation technique was adopted based on the econometric realities inherent in the data utilized. However, the static technique of panel estimation of the Fixed Effects (FE) model was utilized for baseline analysis and robustness check. The conclusion from this study is that innovation generally impacts export performance of African countries positively, however, manufacturing export shows more sensitivity to innovation than total export. And, this provides a clear pathway for export diversification for many African countries that run a resource-based economy.

Keywords: innovation, export, GMM, Africa

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Authors: Jiahui Song

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In order to predict or explain a complicated biological process, it is important first to construct mathematical models that can be used to yield analytical solutions. Through numerical simulation, mathematical model results can be used to test scenarios that might not be easily attained in a laboratory experiment, or to predict parameters or phenomena. High-intensity, nanosecond pulse electroporation has been a recent development in bioelectrics. The dynamic pore model can be achieved by including a dynamic aspect and a dependence on the pore population density into pore formation energy equation to analyze and predict such electroporation effects. For greater accuracy, with inclusion of atomistic details, molecular dynamics (MD) simulations were also carried out during this study. Besides inducing pores in cells, external voltages could also be used in principle to modulate action potential generation in nerves. This could have an application in electrically controlled ‘pain management’. Also a simple model-based rate equation treatment of the various cellular bio-chemical processes has been used to predict the pulse number dependent cell survival trends.

Keywords: model, high-intensity, nanosecond, bioelectrics

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Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Eso Archibong

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The creep life of an industrial gas turbine is determined through a physics-based model used to investigate the high pressure temperature (HPT) of the blade in use. A performance model was carried out via the Cranfield University TURBOMATCH simulation software to size the blade and to determine the corresponding stress. Various effects such as radial temperature distortion factor, turbine entry temperature, ambient temperature, blade metal temperature, and compressor degradation on the blade creep life were investigated. The output results show the difference in creep life and the location of failure along the span of the blade enabling better-informed advice for the gas turbine operator.

Keywords: creep, living, performance, degradation

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Authors: Guillaume Leduc

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In this paper, we describe a broad setting under which the error of the approximation can be quantified, controlled, and for which convergence occurs at a speed of n⁻¹ for European and American options. We describe how knowledge of the error allows for arbitrarily fast acceleration of the convergence.

Keywords: random walk approximation, European and American options, rate of convergence, option pricing

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Authors: Shumaila Zeb

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The proposed study aims to investigate insolvency risk of commercial banks of Pakistan. Furthermore, it empirically estimates the effect of already implemented financial regulations on the insolvency risk of banks. To carry out the empirical analysis, a balanced bank-level panel data covering the period 2008-2016 is used. The Z-score is used for calculating the insolvency risk of each bank. The panel regression is used to investigate the relationship between financial regulations and insolvency risk of banks. The empirics reveal that the financial regulations enforced by State Bank of Pakistan have significant impacts on the insolvency risk of banks. The results further indicate that loan ratio and reserve ratio are positively and significantly related to the insolvency risk of banks.

Keywords: insolvency risk, Z-score, financial regulations, banks

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Abdellatif Chebboub

Abstract:

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Keywords: climate change, agriculture, weather change, danger of climate change

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