Will the use of Less Fecund Cultivars Reduce the Invasiveness of Perennial Plants?

Many invasive species were originally introduced for horticultural purposes, and several continue to be profitable for the green (nursery, horticulture, and landscape) industry. Recently, some plant suppliers have marketed less fecund cultivars of several invasive species, including glossy buckthorn (Frangula alnus), burning bush (Euonymus alatus), and Japanese barberry (Berberis thunbergii), as “safe” alternatives to invasive relatives. We use published matrix population models to simulate the effect of reducing fecundity on the population growth rates of invasive species. We show that large changes in fecundity result in relatively small changes to the population growth rates of long-lived species, which suggests that less fecund cultivars may still provide an invasive threat. Furthermore, many cultivars are clonal selections, and if crossed with other cultivars or selfed, they produce offspring with traits and fecundities that do not resemble the parent plant. On the basis of these two lines of evidence, we suggest that only female sterile cultivars that cannot reproduce asexually should be considered “safe” and noninvasive. Marketing less fecund cultivars as “safe” is premature at this time, and further research is necessary to determine the potential invasiveness of different cultivars

Longitudinal decline in semantic versus letter fluency, but not their ratio, marks incident Alzheimer’s disease in Latinx Spanish-speaking older individuals

Objective: To compare longitudinal verbal fluency performance among Latinx Spanish speakers who develop Alzheimer’s disease to those who do not develop dementia in absolute number of words produced on each task and their ratio to combine both scores. Method: Participants included 833 Latinx Spanish-speaking older adults from a community-based prospective cohort in Manhattan. We performed growth curve modeling to investigate the trajectories of letter and semantic fluency, and their ratio (i.e., ‘semantic index’), between individuals who developed Alzheimer’s disease and those who did not (i.e., controls). The semantic index quantifies the proportion of words generated for semantic fluency in relation to the total verbal fluency performance. Results: Letter fluency performance did not decline in controls; we observed a linear decline in those who developed Alzheimer’s disease. Semantic fluency declined in both groups and showed an increased rate of change over time in the incident Alzheimer’s disease group; in comparison, the control group had a linear and slower decline. There were no group differences in the longitudinal trajectory (intercept and slope) of the semantic index. Conclusion: A decline in letter fluency and a more rapid and accelerating decline over time in semantic fluency distinguished people who developed Alzheimer’s disease from controls. Using the semantic index was not a superior marker of incident Alzheimer’s disease compared to examining the two fluency scores individually. Results suggest the differential decline in verbal fluency tasks, when evaluated appropriately, may be useful for early identification of Alzheimer’s disease in Latinx Spanish speakers, a historically understudied population. Keywords: category fluency, phonemic fluency, language, dementia, Hispani

Comparing genetic diversity in three threatened oaks

Genetic diversity is a critical resource for species’ survival during times of environmental change. Conserving and sustainably managing genetic diversity requires understanding the distribution and amount of genetic diversity (in situ and ex situ) across multiple species. This paper focuses on three emblematic and IUCN Red List threatened oaks (Quercus, Fagaceae), a highly speciose tree genus that contains numerous rare species and poses challenges for ex situ conservation. We compare the genetic diversity of three rare oak species-Quercus georgiana, Q. oglethorpensis, and Q. boyntonii-to common oaks; investigate the correlation of range size, population size, and the abiotic environment with genetic diversity within and among populations in situ; and test how well genetic diversity preserved in botanic gardens correlates with geographic range size. Our main findings are: (1) these three rare species generally have lower genetic diversity than more abundant oaks; (2) in some cases, small population size and geographic range correlate with genetic diversity and differentiation; and (3) genetic diversity currently protected in botanic gardens is inadequately predicted by geographic range size and number of samples preserved, suggesting non-random sampling of populations for conservation collections. Our results highlight that most populations of these three rare oaks have managed to avoid severe genetic erosion, but their small size will likely necessitate genetic management going forward

An intelligent approach to educational data: Performance comparison of the multilayer perceptron and the radial basis function artificial neural networks

The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The participants’ transcript scores were used as the target variables and the two methods were employed to test the predictors that affected these variables. The results show that the multilayer perceptron artificial neural network outperformed the radial basis artificial neural network in terms of predictive ability. Although the findings suggest that research in quantitative educational science should be conducted by using the former artificial neural network method, additional supporting evidence needs to be collected in related studies. © 2015 EDAM

Predictive abilities of Bayesian regularization and levenberg-marquardt algorithms in artificial neural networks: A comparative empirical study on social data

The objective of this study is to compare the predictive ability of Bayesian regularization with Levenberg-Marquardt Artificial Neural Networks. To examine the best architecture of neural networks, the model was tested with one-, two-, three-, four-, and five-neuron architectures, respectively. MATLAB (2011a) was used for analyzing the Bayesian regularization and Levenberg-Marquardt learning algorithms. It is concluded that the Bayesian regularization training algorithm shows better performance than the Levenberg-Marquardt algorithm. The advantage of a Bayesian regularization artificial neural network is its ability to reveal potentially complex relationships, meaning it can be used in quantitative studies to provide a robust model

Investigating the effect of certain socio-demographic factors on university students’ level of reflective thinking by using regression tree

This research aims to investigate the effect of certain socio-demographic factors upon the reflective thinking of university students. The data set comprised 2,247 university students. In this research, a “Reflective Thinking Questionnaire” was used to determine the reflective thinking levels of university students. According to the findings of this research, the area where they attended primary school, satisfaction with their department, attitude toward taking notes in class, willingness to ask the lecturer to explain details about the topic, current GPA, classes taken, and the number of books read to date were found to be meaningful predictors in the model. The level of parents’ education was not found to be meaningful in the research. © Kamla-Raj 2016

Investigation of Performance of HHO Algorithm in Solving Global Optimization Problems

2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 -- 21 September 2019 through 22 September 2019 -- -- 153040In recent years, the use of meta-heuristic optimization algorithms has been increasing in most areas of science and engineering. These algorithms have advantages and disadvantages to each other. In this study, the performance of the Harris hawks optimization (HHO) algorithm has been verified by performing comparative statistical analysis of the optimal solutions of some well-known benchmark functions. Sphere, Rosenbrock, Schwefel, Ackley, Egg Crate and Easom are the chosen benchmark functions that are commonly used. From the analysis results, it is seen that the HHO algorithm were superior to artificial bee colony (ABC), wind driven optimization (WDO) and atom search optimization (ASO) algorithms. In addition, the results of the statistical boxplot prove the unique performance and efficiency of the HHO algorithm. © 2019 IEEE

Comparison of students with high academic achievement from different socio-economic backgrounds in Turkey

The research problem is comparative portrayals of qualities of students with high academic achievement levels, at economically disadvantaged and advantaged schools (in views of school administrators in the TIMMS-2011 data set, Turkey), in relation to socio-economic background and highlight on the potential relationship between achievements of disadvantaged students and academic resilience despite their poor conditions. The study group of the research that employs comparative survey methods consists of 520 students. When the research findings are holistically considered, it could be suggested that opportunities of economically advantaged and disadvantaged students with high academic achievement vary in terms of almost every indicator. When the fact that being socio-economically disadvantaged increases the risk of academic failure is considered, what is recalled here, as in this research, is that high academic achievement levels of students in insufficient conditions in many aspects could relate to academic resilience. © Kamla-Raj 2016

Recognition of Daily and Sports Activities

Baidu;et al.;Expedia Group;IEEE;IEEE Computer Society;Squirrel AI Learning2018 IEEE International Conference on Big Data, Big Data 2018Since being physically inactive was reported as one of the major risk factor of mortality, classifying daily and sports activities becomes a critical task that may improve human life quality. In this paper, the daily and sports activities dataset was used in order to evaluate and validate the employed approach. In this approach, the statistical features were extracted from the histograms of the local changes in the wearable sensors logs were obtained by one-dimensional local binary patterns. Later, extracted features were classified by extreme learning machines. Results were showed that the proposed approach is enough to recognize the action type, but in order to recognize the actions, or gender, different feature extraction methods must be employed. © 2018 IEEE