Integrated scientific modeling and lab automation (keynote)

2021 ◽  
Author(s):  
Luca Cardelli
Keyword(s):  
2021 ◽  
pp. 1-23
Author(s):  
Muhmmad Saeed ◽  
Muhmmad Ahsan ◽  
Atiqe Ur Rahman ◽  
Muhammad Haris Saeed ◽  
Asad Mehmood

Brain tumors are one of the leading causes of death around the globe. More than 10 million people fall prey to it every year. This paper aims to characterize the discussions related to the diagnosis of tumors with their related problems. After examining the side effects of tumors, it encases similar indications, and it is hard to distinguish the precise type of tumors with their seriousness. Since in practical assessment, the indeterminacy and falsity parts are frequently dismissed, and because of this issue, it is hard to notice the precision in the patient’s progress history and cannot foresee the period of treatment. The Neutrosophic Hypersoft set (NHS) and the NHS mapping with its inverse mapping has been design to overcome this issue since it can deal with the parametric values of such disease in more detail considering the sub-parametric values; and their order and arrangement. These ideas are capable and essential to analyze the issue properly by interfacing it with scientific modeling. This investigation builds up a connection between symptoms and medicines, which diminishes the difficulty of the narrative. A table depending on a fuzzy interval between [0, 1] for the sorts of tumors is constructed. The calculation depends on NHS mapping to adequately recognize the disease and choose the best medication for each patient’s relating sickness. Finally, the generalized NHS mapping is presented, which will encourage a specialist to extricate the patient’s progress history and to foresee the time of treatment till the infection is relieved.


2017 ◽  
Vol 105 ◽  
pp. 44-56 ◽  
Author(s):  
Chia-Yu Liu ◽  
Chao-Jung Wu ◽  
Wing-Kwong Wong ◽  
Yunn-Wen Lien ◽  
Tsung-Kai Chao

Author(s):  
Julie Keane ◽  
Laura A. Zangori ◽  
Troy D. Sadler ◽  
Patricia J. Friedrichsen

Socio-scientific issues (SSI) are widely advocated as a productive context for promoting scientific literacy that aims to prepare responsible citizens who can use science in their daily lives. However, many teachers find it challenging to enact SSI and consider SSI and discipline-based instruction as mutually exclusive approaches to science teaching. In this chapter, the authors present their framework for SSI instruction, socio-scientific issue and model-based learning (SIMBL), that emphasizes both disciplinary knowledge and its social implications. In particular, the authors argue that the integration of scientific modeling and socio-scientific reasoning (SSR) can advance students' competencies in both areas, thus promoting students' scientific literacy. The authors use an illustrative example from their work with elementary students to demonstrate the connection between students' modeling practice and their SSR. The authors conclude the chapter by introducing the epistemic tools developed to support students' modeling practice and SSR as well as implications for classroom enactments.


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