Interactions Between Forage, Recruitment and Activity Patterns of the Indian Blackbuck

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Cardiac Arrythmia Detection Using Naïve Bayes And Svm Models

Arrhythmia in any case called cardiovascular arrythmia, is a social event of conditions where the heartbeat is inconsistent, unnecessarily fast, or exorbitantly drowsy. Arrhythmia tends to a critical by and large broad ailment, addressing 15–20 % of all passing's. Early acknowledgment and investigation remains the best approach to perseverance, which can be refined by using novel procedures and shrewd development. As of now, arrhythmia ID is refined through ECG signal examination. In ECG signals, QRS structures which address the depolarization of ventricles are analyzed for arrhythmia revelation. The examination of time period of these PQRST waves essentially implies the presence of arrhythmia or commonness. In this assignment, a motorized structure to channel and bit ECG signals using different estimations is created using Python and MATLAB. For division computations, for instance, Two Moving Average are used. Unmistakable Machine Learning models, for instance, Naïve Bayes Model is used for the following examination of the divided ECG signals. Finally, the precision has using diverse division estimations and Machine Learning Models are researched and considered. Through this the most exact and beneficial system is settled. The eventual outcomes of this undertaking can appropriately help as a manual for clinicians for the distinguishing proof of arrhythmia

Assessing the Environmental and Economic Sustainability of Functional Food Ingredient Production Process

Development and application of novel technologies in food processing is vital for ensuring the availability of adequate, safe, and convenient food with the desired quality and functional properties. Environmental and economic sustainability of technologies is essential prior to their application in the food processing sector. The objective of this research is to determine the environmental and economic feasibility of ultrasound-assisted extraction (UAE) for recovering functional food ingredients from seaweed. Experimental data is used to conduct a life cycle assessment (LCA) to investigate the environmental performance with a functional unit (FU) of obtaining 1 g of extracted polyphenols, measured as gallic acid equivalents (mg GAE)/g seaweed. A life cycle impact assessment is performed with ReCiPe 2016 at midpoint. The cost of manufacturing (COM) of phenolic-rich extracts (as functional ingredient, bioactive, or nutraceutical) is estimated using time-driven activity-based costing (TDABC). The environmental profile findings show that across all categories, the UAE has considerably lower impacts than the conventional method, with electricity as the most important impact contributor, followed by solvent production. An economic assessment estimates the COM over a one-year period at a large scale using the UAE to be EUR 1,200,304, EUR 2,368,440, and EUR 4,623,290 for extraction vessel capacities of 0.05, 0.1, and 0.15 m3, respectively. Raw materials (including the type of raw material) and operational labour costs are the primary contributors to the COM. The findings thus present evidence to support the adoption of an environmentally and economically viable technology for functional ingredient production