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In this study, the cointegration relationship between human capital and economic growth is examined by using five different economic growth measures and it is tried to estimate the long-term effect of human capital on economic growth for Turkey. The sample covers the period of 1961-2019, and all analyzes were carried out with the ARDL technique. The cointegration test findings obtained from the ARDL bounds test indicate that human capital and economic growth in Turkey are cointegrated and therefore act together in the long run. This common movement remains valid in all four models. In addition, it is seen that there is a statistically significant positive relationship between human capital and economic growth for the four models in the long run. In conclusion, the diagnostic test results show that none of the four different models suffer from autocorrelation and varying variance problems, and each model is stable in terms of parameter stability. [ABSTRACT FROM AUTHOR]

In this study we analyze the long-run relationship between openness and military expenditure in Turkey by employing an annual data set running from 1960 to 2018 and ARDL estimation technique. We conducted our analyses by using two different measures of military expenditure to check the validity and robustness of the findings. Co-integration test results show that openness and military expenditure are co-integrated; thus, they move together in the long-run in Turkey. Long-run coefficient estimation results disclose that openness has statistically significant positive effect on military expenditure. In other words, one percent increase in openness cause to a jump in military expenditure by 1.55% and 1.18% for ARDL (1,1) and ARDL (2,1) models respectively in Turkey. On the other hand, openness has a negative influence on military expenditure in the short-run. Meanwhile diagnostic test results indicate that our models do not contain autocorrelation, heteroskedasticity, model misspecification, and parameter instability problems. [ABSTRACT FROM AUTHOR]

This study examines the long-term relationship between inflation on fertility rate in Turkey for the periods of 1960-2019 by employing two different measures of inflation. In that sense our hypothesis asserts that higher inflation rate lowers fertility rate in Turkey. Based on the findings of KPSS stationarity test, our series are integrated order either zero or one, and thus we used ARDL boundary test approach for cointegration analyses. According to cointegration test results, fertility rate is co-integrated with consumer price index and GDP deflator, and thus they move together in the long-run in Turkey. We found a negative statistically significant long-run relationship between inflation and fertility rate. More explicitly, if consumer price index goes up by %1 then fertility rate goes down by %0.035673 and if GDP deflator increases by %1 then fertility rate drops by %0.034813 in the long-run in Turkey. Meantime, except autocorrelation problem, the both models do not suffer from heteroskedasticity and non-normality problems and are stable based on several diagnostic tests. [ABSTRACT FROM AUTHOR]

As a well-established computational framework, probabilistic Boolean networks (PBNs) are widely used for modelling, simulation, and analysis of biological systems. To analyze the steady-state dynamics of PBNs is of crucial importance to explore the characteristics of biological systems. However, the analysis of large PBNs, which often arise in systems biology, is prone to the infamous state-space explosion problem. Therefore, the employment of statistical methods often remains the only feasible solution. We present ${\mathsf{ASSA-PBN}}$ , a software toolbox for modelling, simulation, and analysis of PBNs. ${\mathsf{ASSA-PBN}}$ provides efficient statistical methods with three parallel techniques to speed up the computation of steady-state probabilities. Moreover, particle swarm optimisation (PSO) and differential evolution (DE) are implemented for the estimation of PBN parameters. Additionally, we implement in-depth analyses of PBNs, including long-run influence analysis, long-run sensitivity analysis, computation of one-parameter profile likelihoods, and the visualization of one-parameter profile likelihoods. A PBN model of apoptosis is used as a case study to illustrate the main functionalities of ${\mathsf{ASSA-PBN}}$ and to demonstrate the capabilities of ${\mathsf{ASSA-PBN}}$ to effectively analyse biological systems modelled as PBNs. [ABSTRACT FROM AUTHOR]

This study analyzed the determinants of urban rail supply in Tokyo using time series data for the postwar period. It modeled urban rail supply, measured by vehicle-kilometers of the urban rail service, incorporating demand for urban rail travel, urban rail travel speed, conditions of alternative transportation modes, land use patterns, and socioeconomic conditions as explanatory variables. The model adopted a lag structure for urban rail supply in line with the planning horizon. It was then estimated using the Bayesian model averaging approach, which provided robust estimation results based on our multivariate time series data. The results showed that investments in the urban rail network in Tokyo were primarily driven by the increase in demand for urban rail travel. In effect, demand growth in the Tokyo rail network has clearly translated to supply growth through the planning process; this induced supply has worked as one of the critical components in the establishment of a rail-oriented transportation system in Tokyo. Additionally, the negative effects of bus/tram travel speed on urban rail supply were estimated. [ABSTRACT FROM AUTHOR]

After fifty years of almost continuous economic growth in Thailand, it is now possible to reevaluate the developmental process of the education system. Until now, the structural indicators of education development that have been mainly used are the level and pace of the increases in public expenditure on education, the effect of increasing enrolment on social mobility, and the private and public distribution of investment in education. The impact of these factors undeniably offers a better understanding of the quantitative advances in education. However, the dynamics of the education system nowadays encounter structural limits related to both the integration of what is now widespread education within the social structures and Thailand's contribution to globalization. As a result, the contribution of education to the growth regime is increasingly questioned. The aim of this paper is to use a historical approach to explore this evolution. Theoretical and historical perspectives are combined within a quantitative history methodology, drawing on new time-series.

After fifty years of almost continuous economic growth in Thailand, it is now possible to re-evaluate the developmental process of the education system. Until now, the structural indicators of education development that have been mainly used are the level and pace of the increases in public expenditure on education, the effect of increasing enrolment on social mobility, and the private and public distribution of investment in education. The impact of these factors undeniably offers a better understanding of the quantitative advances in education. However, the dynamics of the education system nowadays encounter structural limits related to both the integration of what is now widespread education within the social structures and Thailand's contribution to globalization. As a result, the contribution of education to the growth regime is increasingly questioned. The aim of this paper is to use a historical approach to explore this evolution. Theoretical and historical perspectives are combined within a quantitative history methodology, drawing on new time-series. [ABSTRACT FROM AUTHOR]

In this paper, we examine how the complexities introduced by trends in agricultural productivity and climate change affect the future of global food security. We use a partial equilibrium model of global agriculture incorporating a food security module that links changes in the average dietary energy intake to shifts in the full caloric distribution, allowing us to compute changes in the incidence, headcount and average depth of malnutrition. After validating the model against an historical period, we implement a series of future scenarios to understand the impacts of key exogenous drivers on selected food security outcomes. Our results show improvements in global food security for the period 2006-2050. Despite growing population and increased biofuel demand, baseline income growth, coupled with projected increases in agricultural productivity lead to a 24 per cent rise in global average dietary energy intake. Consequently, the incidence of malnutrition falls by 84 per cent, lifting more than half a billion people out of extreme hunger. However, these results hinge heavily on agricultural productivity growth. Without such growth, there could be a substantial setback on food security improvements. Climate change adds uncertainty to these projections, depending critically on the crop yield impacts of increasing CO2 concentrations in the atmosphere. [ABSTRACT FROM AUTHOR]

Most important characteristics of wireless ad hoc networks, such as link distance distribution, connectivity, and network capacity are dependent on the long-run properties of the mobility profiles of communicating terminals. Therefore, the analysis of the mobility models proposed for these networks becomes crucial. The contribution of this paper is to provide an analytical framework that is generalized enough to perform the analysis of realistic random movement models over two-dimensional regions. The synthetic scenarios that can be captured include hotspots where mobiles accumulate with higher probability and spend more time, and take into consideration location and displacement dependent speed distributions. By the utilization of the framework to the random waypoint mobility model, we derive an approximation to the spatial distribution of terminals over rectangular regions. We validate the accuracy of this approximation via simulation, and by comparing the marginals with proven results for one-dimensional regions, we find out that the quality of the approximation is insensitive to the proportion between dimensions of the terrain. [ABSTRACT FROM AUTHOR]

In wireless ad hoc networks, the ability to analytically characterize the spatial distribution of terminals plays a key role in understanding fundamental network QoS measures such as throughput per source to destination pair, probability of successful transmission, and connectivity. Consequently, mobility models that are general enough to capture the major characteristics of a realistic movement profile, and yet are simple enough to formulate its long-run behavior, are highly desirable. We propose a generalized random mobility model capable of capturing several mobility scenarios and give a mathematical framework for its exact analysis over one-dimensional mobility terrains. The model provides the flexibility to capture hotspots where mobiles accumulate with higher probability and spend more time. The selection process of hotspots is random and correlations between the consecutive hotspot decisions are successfully modeled. Furthermore, the times spent at the destinations can be dependent on the location of destination point, the speed of movement can be a function of distance that is being traveled, and the acceleration characteristics of vehicles can be incorporated into the model. Our solution framework formulates the model as a semi-Markov process using a special discretization technique. We provide long-run location and speed distributions by closed-form expressions for one-dimensional regions (e.g., a highway). [ABSTRACT FROM AUTHOR]