Various applications of electrochemical sensors and biosensors have been reported in many fields. These include pharmaceuticals, drug detection, cancer detection, and analysis of toxic elements in tap water. Electrochemical sensors are characterised by their low cost, ease of manufacture, rapid analysis, small size and ability to detect multiple elements simultaneously. They also allow the reaction mechanisms of analytes, such as drugs, to be taken into account, giving a first indication of their fate in the body or their pharmaceutical preparation. Several materials are used in the construction of sensors, such as graphene, fullerene, carbon nanotubes, carbon graphite, glassy carbon, carbon clay, graphene oxide, reduced graphene oxide, and metals. This review covers the most recent progress in electrochemical sensors used to analyze drugs and metabolites in pharmaceutical and biological samples. We have highlighted carbon paste electrodes (CPE), glassy carbon electrodes (GCE), screen-printed carbon electrodes (SPCE) and reduced graphene oxide electrodes (rGOE). The sensitivity and analysis speed of electrochemical sensors can be improved by modifying them with conductive materials. Different materials used for modification have been reported and demonstrated, such as molecularly imprinted polymers, multiwalled carbon nanotubes, fullerene (C60), iron(III) nanoparticles (Fe3O4NP), and CuO micro-fragments (CuO MF). Manufacturing strategies and the detection limit of each sensor have been reported.
With the large increase in the amount of heterogeneous, complex, and unstructured data issued from Web sources, there is an emerging need to develop Big Data technologies and tools to extract and manage this data. In this context, Web scraping for Big Data is a technique that has gained importance because of its rapidity and efficiency in gathering data for Big Data technology. This study was conducted to propose a Web Scraping framework for descriptive analysis of meteorological Big Data. The introduced framework makes it possible to extract a set of data to process it, present it in a form that is easier to analyze and understand, and use it for decision-making purposes about weather forecasts. The study employed descriptive analysis of available big data as it allows one to easily make quality and effective decisions. The web scraping process takes place in several stages, including data extraction, data archiving in a data warehouse, and finally data filtering and analysis. To test its applicability, the proposed web scraping framework was implemented and tested in the meteorological context to extract and present meteorological data issued from a specialized web source. The proposed system makes it possible to restore the data in the form of statistical models published in a dashboard. The results of the study revealed that the predictive models provided by the system are capable of predicting certain weather-related variables, such as humidity, precipitation, and temperature. The opportunities and implications to leverage the results of this study are many, including weather forecasting and decision support.
This paper deals with modeling and fitting for epidemic models and their applications to the field of plants disease. For this purpose, two models are proposed that are expressed as a blend of two functions which reflect the effect of the temperature and the wetness. In addition, we provide an original method to _t the proposed models by employing simple techniques that can constitute an easy-to-use tool for simulation, prediction and/or control. Moreover, the method accuracy and efficiency are evaluated for some reported works in the literature. Computational results are provided to show the validity and effectiveness of the proposed epidemic models for some plant infections.
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