The approach is based on two successive steps, the former being a filtering scheme typically used in Content-Based Image Retrieval, whereas the latter is an evolutionary algorithm able to classify and, at the same time, automatically extract explicit knowledge under the form of a set of IF-THEN rules. This approach is tested on a set of chest X-ray images aiming at assessing the presence of COVID-19.The unrelenting trend of doctored narratives, content spamming, fake news and rumour dissemination on social media can lead to grave consequences that range from online intimidating and trolling to lynching and riots in real- life. It has therefore become vital to use computational techniques that can detect rumours, do fact-checking and inhibit its amplification. In this paper, we put forward a model for rumour detection in streaming data on social platforms. The proposed CanarDeep model is a hybrid deep neural model that combines the predictions of a hierarchical attention network (HAN) and a multi-layer perceptron (MLP) learned using context-based (text + meta-features) and user-based features, respectively. The concatenated context feature vector is generated using feature-level fusion strategy to train HAN. Eventually, a decision-level late fusion strategy using logical OR combines the individual classifier prediction and outputs the final label as rumour or non-rumour. The results demonstrate improved performance to the existing state-of-the-art approach on the benchmark PHEME dataset with a 4.45% gain in F1-score. The model can facilitate well-time intervention and curtail the risk of widespread rumours in streaming social media by raising an alert to the moderators.Corona Virus continues to harms its effects on the people lives across the globe. The screening of infected persons has to be identified is a vital step because it is a fast and low-cost way. Certain above mentioned things can be recognized by chest X-ray images that plays a significant role and also used for examining in detection of CORONA VIRUS(COVID-19). Here radiological chest X-rays are easily available with low cost only. In this survey paper, Convolutional Neural Network(CNN) based solution that will benefit in detection of the Covid-19 positive patients using radiography chest X-Ray images. To test the efficiency of the solution, using data sets of publicly available X-Ray images of Corona virus positive cases and negative cases. Images of positive Corona Virus patients and pictures of healthy person images are divided into testing images and trainable images. The solution which are providing the good results with classification accuracy within the test set-up. Then GUI based application supports for medical examination areas. This GUI application can be used on any computer and performed by any medical examiner or technician to determine Corona Virus positive patients using radiography X-ray images. The result will be precisely obtaining the Covid-19 Patient analysis through the chest X-ray images and also results may be retrieve within a few seconds.After wind and solar energy, tidal energy presents the most prominent opportunity for generating energy from renewable sources. However, due to the harsh environment that tidal turbines are deployed in, a number of design and manufacture challenges are presented to engineers. As a consequence of the harsh environment, the loadings on the turbine blades are much greater than that on wind turbine blades and, therefore, require advanced solutions to be able to survive in this environment. In order to avoid issues with corrosion, tidal turbine blades are mainly manufactured from fibre reinforced polymer composite material. As a result, the main design and manufacture challenges are related to the main structural aspects of the blade, which are the spar and root, and the connection between the blade and the turbine hub. Therefore, in this paper, a range of advanced manufacturing technologies for producing a 1 MW tidal turbine blade are developed. The main novelty in this study comes with the challenges that are overcome due to the size of the blade, resulting in thickness composite sections (> 130 mm in places), the fast changes in geometry over a short length that isn't the case for wind blades and the required durability of the material in the marine environment. These advances aim to increase the likelihood of survival of tidal turbine blades in operation for a design life of 20 + years.This study investigates the impact of both economic policy uncertainty (EPU) and business cycles on the fine wine market. We use a nonlinear autoregressive distributed lag model to measure the influence of these two variables on three major Liv-ex indices over the period 2005M01-2020M12. Our results are multiple. First, fine wine prices are relatively unaffected asymmetrically by EPU, while the economic cycle has a more pronounced asymmetric effect, especially in the short run. Second, uncertainty in Europe and the USA affect fine wine prices more than in China. Third, in the short term, fine wine prices react more strongly to changes in business cycles than to uncertainty. Finally, prices of the five first growths of Bordeaux are asymmetrically influenced by EPU, unlike of the rest of the most prestigious Bordeaux wines. The study also has implications for investment. We argue that a strong and professional strategic intelligence watch would help stakeholders in the secondary wine market to improve their returns, especially when European and US wines are involved. While short-runners should focus on information relative to changes in the business cycle, long-term investors would find it more interesting to closely monitor policy decisions liable to have long-term effects on wine prices (such as taxation, monetary measures…).This paper compares the implications of tax system and public borrowing limit asymmetries for the welfare cost of business cycles and interregional consumption risk sharing in a two-region fiscal union. We identify the welfare-improving and risk-sharing-improving designs of the regional tax systems and borrowing limits. We find that the choice of public borrowing limits is more consequential than is the choice of a tax regime for union welfare. It also serves as an argument for the harmonization of fiscal policies adopted in the fiscal union, as it would internalize fiscal externalities and improve consumption risk-sharing across the union regions. The key parameter determining the merits of alternative regional tax systems and possible limits to public borrowing in the fiscal union is the productivity of public good. Other aspects of the economy, such as the type of technology process, or the nature of the productivity shock do not affect the union public finance system design significantly. Extensive simulations suggest that if the productivity of public capital lies within the range of plausible empirical estimates, allowing both regions to have flexible borrowing limits and to choose whatever tax system they prefer will reduce the overall welfare costs of business fluctuations. However, for very low productivity of public capital, the welfare-improving regional public finance reforms that would prohibit public borrowing and tax labor income can produce limited benefits.In a monopolistically competitive model with production externalities, where individuals voluntarily provide offsets which compensate for degradation of environmental quality caused by their income earning activities, this paper examines how an increase in the population size affects the equilibrium levels of environmental quality, offsets, and net contributions. Whether labor supply is institutionally constrained or not, as the population size increases, environmental quality decreases and converges to zero. https://www.selleckchem.com/products/fluorofurimazine.html However, since offsets increase and converge to the degradation rate of environmental quality, the carbon neutrality theorem holds net contributions are zero. These results are independent of the specification of the utility function.Science includes the fundamental attributes of durability and uncertainty; hence, we teach about the "tentative yet durable" nature of science. Public discourse can be different, where one hears both confidence about "settled science" and doubts about "just theories." The latter observation gives rise to the possibility that emphasis on learning the tentative nature of science offers some people the actionable option of declining to accept canonical science. Our paper reports the findings from initial and replication exploratory studies involving about 500 preservice, elementary/middle school teacher education students at a large Midwestern public university. Using a survey method that included opportunities for student comments, the study tested hypotheses about confidence in the veracity, durability, tentativeness, and trustworthiness of science. We found that most students embrace noncontroversial science as correct, and that almost all embraced the tentative nature of science regardless of what they thought about controversial topics. However, when asked about the trustworthiness of science, many students were not willing to say that they trust scientific knowledge. Even students strongly supportive of science, including controversial science, responded similarly. And why did they say that science is not trustworthy? The explanation echoed by many students was that scientific knowledge is tentative. Our paper concludes with implications for instruction and research. Our findings suggest that it would be prudent for science educators to increase instructional focus on the relationship between data and evidence that leads to the durability of scientific knowledge. Future research needs to thoroughly investigate the public interpretation of what we teach about the nature and characteristics of science, and for the implications it might have on how scientific knowledge is or is not incorporated in the development and implementation of public policy.Many authors blame postmodernism and studies on Sociology and Anthropology of Science (Science Studies) for the rise of relativism and anti-science movements. Despite such criticism, Science Studies have always been concerned with the construction of the common world (a shared reality), while the anti-science movement goes in the opposite direction, denying science to defend economic and political interests of specific groups. In this sense, the post-truth movement is part of a political agenda and therefore science education will not be able to face the dilemmas of such scenario unless it takes a clear political stance. Thus, our objective is to present a discussion on why we should trust science as well as what it means to trust science precisely from the so-called ontological turn of science studies. We argue that, based on this sociological framework, it is possible to recognize the value of science as a community capable of producing networks and actors that mobilize the world and that respond to day-to-day problems. Next, we discuss the fact that trusting in science does not mean blind trusting specialists. It is necessary to increase the participation of different actors in the construction of the common world, especially by calling into debate those who were made invisible in the process of colonialism. Finally, we argue that recovering confidence in science is a political process, in a way that public opinion can only changed by politically organizing the field of science and science education.