In this article, generalized differential quadrature method (GDQM) is used to study the free vibrational behavior of variable cross section nano beams. Eringen's nonlocal elastic theory is taken into account to model the small scale effects and nonuniformity is assumed by exponentially varying the width of nano beam. Governing equation of motion is solved using generalized differential quadrature method with different numbers of sampling points. Effects of increasing the sampling points in reaching more accurate results for first three frequency parameters are presented and it is shown that after a specific number of sampling points, results merge to a certain accurate number. It is concluded that generalized differential quadrature method is able to reach the correct answers comparing to analytical results. Moreover, due to the stiffness softening behavior of small-scale structures, necessity of using Eringen's nonlocal elastic theory to model the small scale effects due to the frequency variation is observed. |
This article concerns with the construction of the analytical traveling wave so- lutions for the Generalized-Zakharov System by the Riccati-Bernoulli Sub- ODE technique. Also, we will discuss this technique in random case by using random traveling wave trans- formation in order to find what is the effect of the randomness input for this technique. We presented the Generalized-Zakharov System as an example to show the difference effect between the deterministic and stochastic Riccati-Bernoulli Sub-ODE technique. The first moment of random solution is computed for different statistical probability distributions.
The xanthorrhiza species of the genus Arracacia belongs to the Apiaceae family and is known for its ability to generate tuberous reservoir roots that are harvested annually and marketed fresh in South American countries such as Colombia, Brazil, Venezuela, Peru, Bolivia and Ecuador. In Colombia, arracacha is planted mainly in 15 departments and the regional cultivars are differentiated by the color of the leaves, petiole and tuberous root, the best known being amarilla común or paliverde, yema de huevo, and cartagenera. There are studies that have characterized regional materials by applying a limited number of descriptors, but they do not allow knowing the morphology and phenotypic differentiation of each one; therefore, their definition and characterization constitute a support in breeding programs that allow the efficient use of the genetic potential and increase the knowledge about the diversity of cultivars. Phenotypic characterization and description of three cultivars was performed during two production cycles (2016 and 2018) in two phases (vegetative and productive) applying 74 morphological variables (42 qualitative and 32 quantitative) organized in seven groups of variables: plant, leaf, leaflet, petiole, propagule, stock and tuberous root. A factorial analysis for mixed data (FAMD) was performed, which incorporated a multivariate analysis with all variables and identified 11 discriminant variables, 8 qualitative and 3 quantitative, which can be used in processes of characterization of arracacha materials. A morphological description of each cultivar was made, which means that this is the first complete characterization study of regional arracacha materials in Colombia.
In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.
The financial inclusion program in Asia has begun to be carried out intensively, focusing on increasing public access, especially for people who have yet to enjoy banking services. This makes financial inclusion one of the development focuses in the financial sector in various countries, especially in the Asian region. This study compares the financial inclusion level and socioeconomic variables’ influence on financial inclusion in Asian countries in 2010–2022. To compare the level of financial inclusion in several Asian countries, the Index of Financial Inclusion (IFI) analysis method was used, while to examine the relationship between socioeconomic variables on financial inclusion, the Ordinary Least Square (OLS) method was used with an estimation technique, in the Fixed Effects Model approach. The results of this study indicate that, in general, financial inclusion in several Asian countries is mainly influenced by the usability dimension. In addition, only the variable GDP per capita is partially influential. While other variables, namely, the unemployment rate and population in rural areas, significantly influence the financial inclusion index.
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