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Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. [https://www.selleckchem.com/CDK.html Selleck CDK inhibitor] This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson's disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE-GSO-ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE-GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE-GSO-ANFIS over the similar methods in predicting medical disorders.Along with the COVID-19 outbreak, which has been a global threat for public health, the unconfirmed information about the pandemic in circulation has become another threat. Hence, it has become important to improve public understanding of science with a focus on explaining the nature of uncertainty in science and its impacts. The goal of the present study was to explore pre-service teachers' analysis of claims related to the COVID-19 pandemic throughout an 8-week online implementation of a pre-service teachers' analysis task, focus group interviews, and instructor's feedback to this analysis in a course focusing on critical and analytical thinking. In order to achieve this purpose, the researchers used the claims that contain fallacies, conspiracy theories, and scientific arguments related to the COVID-19 pandemic as an assessment tool. The researchers developed and used a rubric consisting of the high, moderate, and low levels of analysis in three different categories including evaluation of claims, demarcation of fallacies and conspiracy theories from scientific arguments, and judgment of the credibility of sources. The findings indicate that the participants analyzed the claims rarely at a high level before the focus group interviews. However, after the focus group interviews, almost every participant's analysis scores of evaluation, demarcation, and judgment increased. The results also revealed their commitment to various fallacies and conspiracy theories while arguing the claims. Concluding remarks are made for the further implications of teaching critical evaluation of claims based on evidence.The current COVID-19 pandemic raises reflection on the new roles of science education in citizen education in a world characterized by civilization risks, derived from the current socioeconomic development. This specific type of risk is treated as a manufactured risk as proposed by the sociologist Ulrich Beck. In this paper, we report a document analysis starting from Beck's risk society theory, followed by notions of reflexive modernity, risk perception, and the Cynefin decision-making model for complex problems. COVID-19 pandemic is characterized as a manufactured risk. We state that students are unable to deal with manufactured risk because of the type of problems they are usually prepared to solve at school and the limited risk perception they have. In order to acquire better science education, we propose the integration of wicked problems in science programs alongside the use of a multidimensional schema, the so-called amplified risk perception space, a tool to locate students' risk perception. We hope to contribute to prepare citizens for a world of global and complex events, such as the current pandemic.Simulations of neural networks can be used to study the direct effect of internal or external changes on brain dynamics. However, some changes are not immediate but occur on the timescale of weeks, months, or years. Examples include effects of strokes, surgical tissue removal, or traumatic brain injury but also gradual changes during brain development. Simulating network activity over a long time, even for a small number of nodes, is a computational challenge. Here, we model a coupled network of human brain regions with a modified Wilson-Cowan model representing dynamics for each region and with synaptic plasticity adjusting connection weights within and between regions. Using strategies ranging from different models for plasticity, vectorization and a different differential equation solver setup, we achieved one second runtime for one second biological time.Executive functioning (EF) is defined as a set of top-down processes used in reasoning, forming goals, planning, concentrating, and inhibition. It is widely believed that these processes are critical to self-control and, therefore, that performance on behavioral task measures of EF should be associated with individual differences in everyday life outcomes. The purpose of the present study was to test this core assumption, focusing on the EF facet of inhibition. A sample of 463 undergraduates completed five laboratory inhibition tasks, along with three self-report measures of self-control and 28 self-report measures of life outcomes. Results showed that although most of the life outcome measures were associated with self-reported self-control, only one of the outcomes was associated with inhibition task performance at the latent-variable level, and this association was in the unexpected direction. Furthermore, few associations were found at the individual task level. These findings challenge the criterion validity of lab-based inhibition tasks. More generally, when considered alongside the known lack of convergent validity between inhibition tasks and self-report measures of self-control, the findings cast doubt on the task's construct validity as measures of self-control processes. Potential methodological and theoretical reasons for the poor performance of laboratory-based inhibition tasks are discussed.