Convergence patterns in the world economy: exploring the nonlinearity hypothesis

Semiparametric regression is a regression model consisting of two components, namely the parametric and the nonparametric component. The parametric component is a component with curves are known and the nonparametric component is a component with unknown shape of the curve. Bi-response semiparametric regression is regression analysis which has two response variables with the combination of parametric and nonparametric curves. The purpose of this research is to compare the bi-response semiparametric regression model using spline truncated with component parametric linear and quadratic. In this research, the method that used for the estimation parameter is WLS (Weighted Least Square) and the selection of optimal knot points is done by looking at the minimum GCV (Generalized Cross-Validation) value. WLS is minimizing partial derivative based on a parameter in the model. Each the linear bi-responsese semiparametric regression and quadratic bi-responsese semiparametric regression using spline truncated has two parameters that are parameter for parametric and nonparametric components. The difference between the two models contained in the parametric component of the predictor variable and parameter matrix of parametric component is estimator linear bi-response semiparametric regression model and is estimator quadratic bi-response semiparametric regression model using spline truncated.

Russia is facing one of the most severe demographic problems – low fertility rate. At the same time, fertility dynamics differ significantly across the regions of the country. Subsequently, the birth dynamics of first, second, and subsequent orders also differ. Our study identifies the types of regions that have the same or very similar fertility trends differentiated by birth order. For this purpose, we applied hierarchical cluster analysis based on Ward’s method and squared Euclidean distance. We used official Russian statistics and calculated three regional indicators as clustering variables, including the percentage change of the total fertility rate of first, second, and third and subsequent births from 2018 to 2022. We profiled clusters based on economic indicators (gross regional product [GRP], income, and housing provision) and population indicators (women’s age at first, second, and third and subsequent births), testing convergent and divergent trends in the dynamics of regional birth rates. Cluster analysis revealed four clusters of Russian regions with similar fertility trends differentiated by birth order. Cluster 1 includes 10 Russian regions and can be considered the most depressive cluster. Cluster 2 includes 18 Russian regions and can be called the driver of Russian population dynamics. Cluster 3, with 38 regions, and Cluster 4, with 19 regions, occupy intermediate positions between the first two clusters. We found no statistically significant differences between the clusters concerning the birth rates that prevailed in 2018; thus, their starting positions did not determine the regional fertility changes in 2018–2022. The analysis revealed that the clusters did not differ in the age of women at first, second, and third and subsequent births. We also did not find statistically significant differences among clusters in terms of the dynamics of economic indicators such as gross regional product, income, and housing provision. The results did not confirm that traditionally recognized economic determinants defined order-differentiated birth dynamics. Russian regions exhibited no convergent or divergent trends in order-differentiated birth dynamics. Our results established new research areas related to the analysis of birth rate determinants, such as informational support for regional programs to increase birth rates. The approach can form the foundation for developing a segmented state information policy in the demographic sphere. It also indicates the need for continuous monitoring of the reasons behind declining birth rates differentiated by birth order and the related stereotypes concerning various sociodemographic population groups.

The increasing rate of geopolitical changes, technological breakthroughs, and socio-economic changes has redefined the conception, design, and implementation of public policy among states. Law as the main means of governance is no longer a product used as the unit of regulation, but it is more and more the role to set the values in society, to act as the mediator of global forces, or to drive social change. This paper explores the interaction between the state, the law and changing social environments in the modern global phenomena. It takes into account the dynamics of globalization, digitalization, human rights standards, global security issues, and economic restructuring to determine the policy paths and legal changes. Across an interdisciplinary approach the research evaluates the way different jurisdictions implement the same pressures whilst traveling through the localized political cultures, institutional capacity and socio-economic limitations. Convergence and divergence trends among the policy design process, increasing role of the international organizations and transnational norms and conflicts between the domestic sovereignty and the global governance are also patterns of convergence and divergence identified in the paper. The results suggest that contemporary legal reforms can be traced to global discourses, nevertheless, its local delivery is disproportionate and heavily influenced by domestic politics and social demands. Some of the recommendations include consolidating the adaptive governance, building on policy coherence, advancing legal harmonization of the critical sectors and institutionalization of inclusive public participation.

Geographically Weighted Regression Principal Components Analysis (GWRPCA) is a combination of method of Principal Components Analysis (PCA) and Geographically Weighted Regression (GWR). PCA is used to eliminate the multicollinearity effect in regression analysis. GWR is a local form of regression and a statistical method used to analyze the spatial data. In GWRPCA predictor variables is a principal components of the PCA result. Estimates of parameters of the GWRPCA model can use Weighted Least Square (WLS). Selection of the optimum bandwidth use Cross Validation (CV) method. Conformance testing PCA regression and GWRPCA models approximated by the F distribution, while the partial identification of the model parameters using the t distribution. In PCA obtained variables that affect the percentage of severe children malnutrition in Central Java in 2012 can be represented or replaced with PC 1 and PC 2 which can explain the total variance of data is 78.43%. Application GWRPCA models at the percentage of severe children malnutrition in Central Java in 2012 showed every regency locations have different model with global coefficient of determination is 0.6313309 and the largest local coefficient of determination is 0.72793026 present in Batang regency, while the smallest local coefficient of determination is 0.03519539 present in Sukoharjo regency. K eywords : Severe Malnutrition, Multicollinearity, Geographically Weighted Regression Principal Components Analysis, Weighted Least Square,Coefficient of Determination.

Aiming to solve the problem that the significant error between the actual joint torque and the calculated joint torque of a welding robot leads to the vibration of the end-effector, which in turn affects the stability of the end-effector, this paper proposes a identification algorithm based on the Weighted Least Squares Genetic Algorithm (WLS-GA) to construct and solve the dynamical model to obtain the accurate dynamical parameters. Firstly, a linear model of welding robot dynamics is derived. The fifth-order optimal Fourier series excitation trajectory is designed to collect experimental data such as joint torque. Then, a rough solution of the parameters to be recognized is obtained by solving the dynamics model through the Weighted Least Squares (WLS) method, the search space is determined based on the rough solution, and the optimal solution is obtained by using the Genetic Algorithm (GA) to perform a quadratic search in the search space. Finally, the identification data obtained from the algorithm is analyzed and compared with the experimental data. The results show that the error between the identification data obtained using the WLS-GA identification algorithm and the experimental data is relatively small. The results show that the identification data obtained using the WLS-GA identification algorithm have less error than the experimental data, taking the Root Mean Square (RMS) value of the joint torque error obtained using the weighted least squares algorithm as a criterion. The accuracy of the WLS-GA identification algorithm can be improved by up to 66.85% compared with that of the weighted least squares algorithm and by up to 78.0% compared with that of the Ordinary Least Squares (OLS) algorithm. In summary, the WLS-GA identification algorithm can accurately identify the dynamic parameters of the welding robot and more accurately construct a dynamic model to solve the effect of joint torque error on the control characteristics of the welding robot. It can improve the stability of the end-effector of the welding robot to ensure the quality of the automobile body and beam welding and welding speed.

Statistical evaluation of analytical curves for quantification of pesticides in bananas

The paper deals with assessing the common trends in business cycle similarity and convergence in Europe. The main goal of the paper is to identify common cyclical co-movements and trends in convergence among the European countries so that the emerging European business cycle could be identified. Concerning the factors of business cycle, the research question of the paper is based on assumption that the integration effects are so dominant to bring the European cycle into existence. Also a potential influence of the global crisis on European and world business cycles is examined in the paper. The industrial production index is used to approximate business cycles. Hodrick-Prescott filter, Christiano-Fitzgerald filter and first differencing were used to dissect the cyclical components and identify the cycles in the data. The co-movements, trends of convergence and divergence of business cycles are identified using correlation analysis. Particularly, actual cross correlation and historical correlation in separated subsequent periods is applied in the analysis. Also an original measure of the European business cycle emergence was applied. The results do not provide an evidence of emerging European business cycle contrary to US cycle. The global economic crises was identified as a kind of negative symmetric shock pushing all major economies towards the recession phase of the cycle und thus increasing similarity. The results also shed some light on an influence of different detrending techniques when dissecting the cycles from the input macroeconomic time series.

Regression analysis is the study of the relationship between dependent variable and one or more independent variables. One of the important assumption that must be fulfilled to get the regression coefficient estimator Best Linear Unbiased Estimator (BLUE) is homoscedasticity. If the homoscedasticity assumption is violated then it is called heteroscedasticity. The consequences of heteroscedasticity are the estimator remain linear and unbiased, but it can cause estimator haven‘t a minimum variance so the estimator is no longer BLUE. The purpose of this study is to analyze and resolve the violation of heteroscedasticity assumption with Weighted Least Square(WLS) and Quantile Regression. Based on the results of the comparison between WLS and Quantile Regression obtained the most precise method used to overcome heteroscedasticity in this research is the WLS method because it produces that is greater (98%).

This study focuses on the design and evaluation of corrosion-resistant aluminum alloys using galvanostatic test and quadratic regression analysis. Al-Zr and Al-Mn-Zr alloys were evaluated through both galvanostatic and immersion tests. The galvanostatic test effectively simulated localized corrosion mechanisms, reducing testing duration from hundreds or thousands of hours to just tens of hours. Scanning Kelvin Probe Force Microscopy (SKPFM) and SEM/EDS analyses revealed that alloys with higher potential differences between precipitates and the Al matrix showed increased susceptibility to localized corrosion. A quadratic regression model was developed to describe the relationship between maximum pit depth and the contents of Zr and Mn, achieving a high coefficient of determination (R² = 0.975). The model identified an optimal alloy composition of 0.16 wt.% Zr and 0.41 wt.% Mn, resulting in a minimized pit depth of 24.40 μm. This research demonstrates that combining galvanostatic test with regression analysis shorten the required time for development of corrosion-resistant aluminum alloys, providing a framework for optimizing compositions and predicting corrosion resistance, thereby significantly reducing design time.

The article deals with the theoretically and practically relevant problem of the manifestation of convergent and divergent trends in the inclusive development of UTC and regions of Ukraine, which are identified as rural-urban agglomerations. For our country, the specified issue is of crucial importance both from the point of view of the successful completion of the reform of decentralization of public power and management, which began in the pre-war period, and the prospects of her accession to the EU. In view of this, the purpose of the study is to justify the scientific approach to the definition of essential features, which are used to monitor the manifestation of convergent and divergent trends in the inclusive development of ruralurban agglomeration formations. For her implementation, the following tasks were formulated: to reveal the differences between the Ukrainian and international context of convergence; to propose classification signs by which manifestations of convergent and divergent tendencies should be monitored; to characterize the scientific approach to defining the club convergence version of the inclusivity of rural-urban agglomerations. The achievement of the research goal was facilitated by the use of scientific works of Ukrainian and foreign scientists on the specified problem, systemic, institutional and synergistic approaches, of the principle of interdisciplinarity, comparative method, methods of SWOT-analysis, analysis and synthesis. The kinship of the processes of convergence and inclusivity, which take place in a common space, is argued: as a result of inclusive development, contradictions within such paired dichotomies as “alienation (detachment) ‒ involvement” are overcome; “exclusivity (exclusion) ‒ inclusiveness (inclusion)”; “segregation – integration”; thereby manifesting its essential and morphological similarity with convergence and divergen, which in this sense appear as a form of inclusion and exclusion, integration and disintegration. The scientific novelty of the study lies in the defined essence of the club convergence versionof the inclusive development of rural-urban agglomerations according to administrative-territorial, demographic-settlement, resource, industry and socio-spatial feature. Based on the results of the research, certain generalizations were made, practical recommendations were given, and it was noted that the proposed scientific approach opens up new opportunities for identifying convergent-divergent trends of inclusive development at the level of individual UTC and districts.

Occurrence and distribution of emerging contaminants in surface water and biota: An integrated analytical-statistical assessment.

Cumulative adversity, defined as the accumulation of adverse life events across a lifetime, has been associated with worse mental and physical health. Black and Hispanic Americans exhibit increased risk for exposure to adverse experiences, however, these groups have been underrepresented in the development of cumulative adversity assessments. This study aimed to explore the prevalence of adverse life events and the psychometric properties of a cumulative adversity assessment, originally posited by Turner, Wheaton, and Lloyd (1995), in a racially and ethnically diverse sample (N = 260). Factor analysis models were estimated and compared, uncovering a two-factor solution, encompassing unintentional and intentional trauma. Prevalence rates of cumulative adversity were relatively low in the sample, with specific events surrounding parents and spouses being the most common. Significant associations between demographic characteristics and these factors were identified through Weighted Least Squares (WLS) regression analyses, shedding light on race/ethnicity and gender disparities for adversity, as well as relationships with age, household composition, work, and marital status. Findings contribute to a deeper understanding of cumulative lifetime adversity, providing insights by increasing the applicability of the lifetime adversity measurement in diverse populations in future studies.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Helida Nurcahayani, I. Nyoman Budiantara, Ismaini Zain; The curve estimation of bi-response nonparametric regression using truncated spline on East Java SDGs achievement. AIP Conference Proceedings 25 January 2023; 2554 (1): 030007. https://doi.org/10.1063/5.0106527 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Metabolic habitat imaging with hemodynamic heterogeneity predicts individual progression-free survival in high-grade glioma

The global Economic meltdown has effect on all the countries in the world. The Indian economy is also not exception to this situation because world economy cannot perform in seclusion in today's faster growing world. All the economies are interlinked to each other and any major ebb and flow in economic conditions causes numerous impacts for all other economies. The global economic crisis started with Subprime disaster in the US. Many strong economies are suffering from the crisis of credit and liquidity due to the interconnectedness of each other in international trade relations. This situation has the negative impacts on GDP, Employment, exports, FDI and economic development of the nation. Government and Policy makers are always taking precautionary measures for maintaining the economic stability and corrective actions in inflationary and recession situations. The striking balance of Monetary and Fiscal policies may help to overcome the situation to some extent. Monetary and Fiscal policies can be framed and implemented according to the economic situations. Central Banks of countries can influence the economy through the use of monetary policy by controlling the monetary variables such as rate of interest, the money supply and the volume of credit. The fiscal policy can be used to help on economy out of recession or reduce demand pressures in a boom. Monetary and fiscal policies are complementary to each other. Monetary policy influences the level of aggregate income and spending in the economy by influencing the money supply and cost of borrowing. Fiscal policy affects income and spending through its effects on size, composition and timing of government revenue and spending. These two policies can play the role in a perfect balancing for promotion of inclusive growth. This paper will focus on the important and balanced role of monetary and fiscal policy in economy with reference to the current scenario in Indian Economy.