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Every year about one million people die due to diseases transmitted by mosquitoes. The infection is transmitted to a person when an infected mosquito stings, injecting the saliva into the human body. The best possible way to prevent a mosquito-borne infection till date is to save the humans from exposure to mosquito bites. This study proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes such as the Aedes and Culex. The proposed system will effectively aid in epidemiology to design evidence-based policies and decisions by analyzing the risks and transmission. The study proposes an effective methodology for the classification of mosquitoes using ML and CNN models. The novel RIFS has been introduced which integrates two types of feature selection techniques – the ROI-based image filtering and the wrappers-based FFS technique. Comparative analysis of various ML and deep learning models has been performed to determine the most appropriate model applicable based on their performance metrics as well as computational needs. Results prove that ETC outperformed among the all applied ML model by providing 0.992 accuracy while VVG16 has outperformed other CNN models by giving 0.986 of accuracy.

A number of numeric measures like rough inclusion functions (RIFs) are used in general rough sets and soft computing. But these are often intrusive by definition, and amount to making unjustified assumptions about the data. The contamination problem is also about recognizing the domains of discourses involved in this, specifying errors and reducing data intrusion relative to them. In this research, weak quasi rough inclusion functions (wqRIFs) are generalized to general granular operator spaces with scope for limiting contamination. New algebraic operations are defined over collections of such functions, and are studied by the present author. It is shown by her that the algebras formed by the generalized wqRIFs are ordered hemirings with additional operators. By contrast, the generalized rough inclusion functions lack similar structure. This potentially contributes to improving the selection (possibly automatic) of such functions, training methods, and reducing contamination (and data intrusion) in applications. The underlying framework and associated concepts are explained in some detail, as they are relatively new., 33 Pages

This article investigates crowd dynamics in co-creation settings. In particular, it aims to provide an understanding of how those dynamics eventually shape collective intelligence in co-creation activities, either enabling or bounding their capacity of scaling. Furthermore, different value perspectives are questioned through the shapes that co-creation practices may assume once moving from local groups and communities to a population assuming the dimension of the crowd. Thus, the article aims to contribute to the research on cooperative strategies involving users as innovators, with a specific focus on high tech industries and co-creation in experimental settings, suitable to lead to innovation related to emergent technologies. To this end, the article considers the early insights from the specific case of a robotics innovation facility based in Italy, where an ethnography has been carried out in 2018-2019 and presents the results from an early analysis of the data.

Though mitochondrial research has become more prominent in the last forty years, most methodologies to study mitochondria have been focused on cell and animal models. Respirometry, the assessment of mitochondrial function measured as oxygen consumption by Complex IV of the electron transport chain, is the “gold standard” for mitochondrial assessment, yet it is rarely utilized in human samples. This is due to two fundamental methodological challenges: (1) samples for respirometry must be processed and ran within hours of acquisition on specialized and complex equipment, and (2) human sampling for respirometry must be acquired using invasive tissue biopsies. While we as a field realize the importance of oxygen consumption as a measurement of mitochondrial function, the current methodologies are now limiting the types of biological and clinical questions we can ask and answer. Addressing the first challenge preventing widespread adoption of respirometry, the recently developed RIFS (Respirometry In previously-Frozen Specimen) assay allows for the measurement of maximal respiratory capacity from cells or tissues which have been previously frozen. This allows samples to be processed and then stored or biobanked for later respirometry assessment, ultimately making respirometry methodologies significantly easier, cheaper, and more accessible. To the second challenge though, there has been minimal breakthrough.In this thesis we will cover novel methodologies overcoming challenges which are paramount to the expansion of respirometry methodologies for clinical applications. We will discuss the RIFS protocol in detail, describe the methodologies developed to perform a first-of-its-kind high-throughput and high-resolution respirometry study of the human brain, and will detail the development of a technique to measure mitochondrial respiration in cells human salivary leukocytes. Together, these chapters present novel methodologies designed to overcome the primary limitations of performing respirometry in human studies.