-
Garrett Mayer posted an update 6 months ago
Behavioral changes related to proximity were also observed, with an increase in people walking to small urban gardens nearby (e.g. in Italy) or tree-lined streets (e.g. in Spain, Israel), and people traveling by car to green areas outside the city (e.g. in Lithuania). What the respondents missed the most about UGS during the pandemic was “spending time outdoors” and “meeting other people” – highlighting that during the COVID-19 isolation, UGS was important for providing places of solace and respite, and for allowing exercise and relaxation. Respondents expressed the need for urban greenery even when legally mandated access was limited – and many proposed concrete suggestions for improved urban planning that integrates green spaces of different sizes within the fabric of cities and neighborhoods, so that all residents have access to UGS.Microarray data analysis is a major challenging field of research in recent days. Machine learning-based automated gene data classification is an essential aspect for diagnosis of gene related any malfunctions and diseases. As the size of the data is very large, it is essential to design a suitable classifier that can process huge amount of data. Deep learning is one of the advanced machine learning techniques to mitigate these types of problems. Due the presence of more number of hidden layers, it can easily handle the big amount of data. We have presented a method of classification to understand the convergence of training deep neural network (DNN). The assumptions are taken as the inputs do not degenerate and the network is over-parameterized. Also the number of hidden neurons is sufficiently large. Authors in this piece of work have used DNN for classifying the gene expressions data. The dataset used in the work contains the bone marrow expressions of 72 leukemia patients. A five-layer DNN classifier is designed for classifying acute lymphocyte (ALL) and acute myelocytic (AML) samples. The network is trained with 80% data and rest 20% data is considered for validation purpose. Proposed DNN classifier is providing a satisfactory result as compared to other classifiers. Two types of leukemia are classified with 98.2% accuracy, 96.59% sensitivity, and 97.9% specificity. The different types of computer-aided analyses of genes can be helpful to genetic and virology researchers as well in future generation.
The purpose of this study is to quantify the motion dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Three physical models of Newton’s and Stokes’s laws with(out) air resistance in the calm air are used to determine the falling time and velocity regimes of SARS-CoV-2 with(out) a respiratory water droplet of 1 to 2000 micrometers (µm) in diameter of an infected person of 0.5 to 2.6m in height.
The horizontal distance travelled by SARS-CoV-2 in free fall from 1.7m was 0.88m due to breathing or talking and 2.94m due to sneezing or coughing. According to Newton’s laws of motion with air resistance, its falling velocity and time from 1.7m were estimated at 3.95 × 10
ms
and 43s, respectively. Large droplets > 100µm reached the ground from 1.7m in less than 1.6s, while the droplets ≥ 30µm fell within 4.42s regardless of the human height. Based on Stokes’s law, the falling time of the droplets encapsulating SARS-CoV-2 ranged from 4.26 × 10
to 8.83 × 10
s as a function of the droplet size and height.
The spread dynamics of the COVID-19 pandemic is closely coupled to the falling dynamics of SARS-CoV-2 for which Newton’s and Stokes’s laws appeared to be applicable mostly to the respiratory droplet size ≥ 237.5µm and ≤ 237.5µm, respectively. An approach still remains to be desired so as to better quantify the motion of the nano-scale objects.
The spread dynamics of the COVID-19 pandemic is closely coupled to the falling dynamics of SARS-CoV-2 for which Newton’s and Stokes’s laws appeared to be applicable mostly to the respiratory droplet size ≥ 237.5 µm and ≤ 237.5 µm, respectively. Protoporphyrin IX order An approach still remains to be desired so as to better quantify the motion of the nano-scale objects.The selfish life-cycle model or hypothesis is, together with the dynasty or altruism model, the most widely used theoretical model of household behavior in economics, but does this model apply in the case of a country like Japan, which is said to have closer family ties than other countries? In this paper, we first provide a brief exposition of the simplest version of the selfish life-cycle model and then survey the literature on household saving and bequest behavior in Japan in order to answer this question. The paper finds that almost all of the available evidence suggests that the selfish life-cycle model applies to at least some extent in all countries but that there is more consistent support for this model in Japan than in the United States and other countries. It then explores possible explanations for why the life-cycle model is more consistently supported in Japan than in other countries, attributing this finding to government policies, institutional factors, economic factors, demographic factors, and cultural factors. Finally, it shows that the findings of the paper have many important implications for economic modeling and for government tax and expenditure policies.In this work, a new unsupervised classification approach is proposed for the biomedical image segmentation. The proposed method will be known as Fuzzy Electromagnetism Optimization (FEMO). As the name suggests, the proposed approach is based on the electromagnetism-like optimization (EMO) method. The EMO method is extended, modified, and combined with the modified type 2 fuzzy C-Means algorithm to improve its efficiency especially for biomedical image segmentation. The proposed FEMO method uses fuzzy membership and the electromagnetism-like optimization method to locate the optimal positions for the cluster centers. The proposed FEMO approach does not have any dependency on the initial selection of the cluster centers. Moreover, this method is suitable for the biomedical images of different modalities. This method is compared with some standard metaheuristics and evolutionary methods (e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Electromagnetism-like optimization (EMO), Ant Colony Optimization (ACO), etc.
Please follow & like us :)
All content contained on CatsWannaBeCats.Com, unless otherwise acknowledged,is the property of CatsWannaBeCats.Com and subject to copyright.