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<span>In the health sector, the computer-aided diagnosis (CAD) system is a rapidly growing technology because medical diagnostic systems make a huge change as compared to the traditional system. Now a day huge availability of medical data and it needs a proper system to extract them into useful knowledge. Heart disease accounts to be the leading cause of death worldwide. Heuristic algorithms have been exposed to be operative in supporting making decisions and classification from the large quantity of data produced by the healthcare sector. Classification is a prevailing heuristic approach which is commonly used for classification purpose some heuristic algorithm predicts accurate result according to the marks whereas some others exhibit limited accuracy. This paper is used to categorize the attendance of heart disease with a compact number of aspects. Original, 13 attributes are involved in classifying heart disease. A reasonable analysis of these techniques was done to conclude how the cooperative techniques can be applied for improving prediction accuracy in heart disease. Four main classifiers used to construct heart disease prediction based on the experimental results demonstrate that support vector machine, artificial bee colony (ABC), Bat algorithm, and memory-based learner (MBL) provide efficient results. The accuracy differs between 13 features and 8 features in the training dataset is 1.9% and in the validation, dataset is 0.92% of vector machine which is the most accurate heuristic algorithm. </span>

(Uploaded by Plazi for the Bat Literature Project) Climate change is predicted to result in changes in the geographic ranges and local prevalence of infectious diseases, either through direct effects on the pathogen, or indirectly through range shifts in vector and reservoir species. To better understand the occurrence of monkeypox virus (MPXV), an emerging Orthopoxvirus in humans, under contemporary and future climate conditions, we used ecological niche modeling techniques in conjunction with climate and remote-sensing variables. We first created spatially explicit probability distributions of its candidate reservoir species in Africa's Congo Basin. Reservoir species distributions were subsequently used to model current and projected future distributions of human monkeypox (MPX). Results indicate that forest clearing and climate are significant driving factors of the transmission of MPX from wildlife to humans under current climate conditions. Models under contemporary climate conditions performed well, as indicated by high values for the area under the receiver operator curve (AUC), and tests on spatially randomly and non-randomly omitted test data. Future projections were made on IPCC 4(th) Assessment climate change scenarios for 2050 and 2080, ranging from more conservative to more aggressive, and representing the potential variation within which range shifts can be expected to occur. Future projections showed range shifts into regions where MPX has not been recorded previously. Increased suitability for MPX was predicted in eastern Democratic Republic of Congo. Models developed here are useful for identifying areas where environmental conditions may become more suitable for human MPX; targeting candidate reservoir species for future screening efforts; and prioritizing regions for future MPX surveillance efforts.

Youth is increasingly learning in non-conventional contexts, such as social networks or video platforms, courses, and tutorials. This research aims to diagnose the role of participatory culture, digital resources, social networks, and, specifically, YouTube, in learning processes and the acquisition of Science, Technology, Engineering, and Mathematics (STEM) skills, specifically in mathematics, also analyzing the role of youtubers and, in contrast, teachers, both learning leaders in the formation of these skills. In order to accomplish this, mixed methods (quantitative and qualitative) were used, based on a survey applied to 4845 Italian adolescents, as well as a content analysis of the videos and YouTube channel of the Italian educational influencer Elia Bombardelli, one of the most followed and best rated in this country. Also, an in-depth interview has been applied to 12 Italian secondary school teachers. Among the main findings, it is highlighted that all adolescents value YouTube videos as a key resource to improve their school performance, rating youtubers better than teachers. However, it is remarkable that in the processes of learning and acquisition of STEM competencies, they prefer to interact with teachers rather than with youtubers.

This paper reports on the “water-in-salt” electrolyte concept, applying CsF as an ionic compound in a symmetric electrochemical capacitor. CsF has been selected as a highly soluble inorganic salt that allows the amount of free water in the electrolyte to be significantly reduced. Several activated carbons with various microtextural parameters have been investigated to identify the most suitable electrode material. A wide range of electrolyte concentrations have been investigated in terms of conductivity, pH and viscosity. The electrochemical characteristics have been determined for three electrolyte concentrations and indicate critical changes in the electrolyte physicochemical properties. It seems that ion-solvent interactions, residual water content, electrolyte viscosity and charging/discharging conditions remarkably affect the electrochemical performance of capacitors with elevated concentrations of electrolyte. In addition to the capacitance increase, a positive impact on the self-discharge and charge/discharge efficiency has been observed for higher CsF concentrations.

The large number of commentaries in this special issue reflect the need that so many people have to express themselves as a way of releasing the anxieties and integrating the hopes that the COVID-1...

Abstract Biomass gasification is a promising technology to produce secondary fuels or heat and power, offering considerable advantages over fossil fuels. An important aspect in the usage of producer gas is the removal of harmful contaminants from the raw syngas. Thus, the object of this study is the development of a simulation model for a gasifier including gas clean-up, for which a fluidized-bed gasifier for biomass-derived syngas production was considered, based on a quasi-equilibrium approach through Gibbs free energy minimisation, and including an innovative hot gas cleaning, constituted by a combination of catalyst sorbents inside the gasification reactor, catalysts in the freeboard and subsequent sorbent reactors, by using Aspen Plus software. The gas cleaning chain simulates the raw syngas clean-up for several organic and inorganic contaminants, i.e. toluene, benzene, naphthalene, hydrogen sulphide, hydrogen chloride and ammonia. The tar and inorganic contaminants final values achieved are under 1 g/Nm3 and 1 ppm respectively.