Due to the advent of technology, humans now live in the modern age of information and data. In this form of world, different objects are interlinked to data sources, and every aspect of human’s lives are recorded in a digital form. For example, the present electronic globe has an abundance of distinct forms of data e.g., health data, social media fata, smartphone data, business data, smart city data, cybersecurity data and Internet of Things (IoT) data, including Covid-19 data. Data can be unstructured, semi-structured and structured, and this is increasing on a daily basis. Machine Learning (ML) is significantly employed in different aspects of real-life e.g., Congestion Control (CC). This paper provides an evaluation of the aspect ML employed in CC. CC has emerged as a fundamental viewpoint in communications system infrastructure in the recent years, since network operations, and network capacity have enhanced at a rapid rate.
Deep neural complexity theory has recently received new attention, particularly in the study of climate and the environment. According to the majority of the research on urban climate resilience, cities are complex adaptive systems, and as such, urban governance and design should take cues from the study of complex adaptive systems. This means that climate change governance, in order to mitigate the problems presented by climate change's unpredictability, has to be flexible, participatory, and adaptive. This article provides a critical literature review on the topic of Complex Urban Systems, i.e., climate change governance in the context of complexity theory. The paper argues that the current hype around complexity theory exaggerates the theory's relevance. Complexity theory falls short in explaining urbanization and environmental change since they are highly contested social phenomena. However, it serves a significant purpose in bringing attention to the uncertainty realities in the process of policy-making, which are certainly fundamental in the context of climate change, including the changing ecologies on which cities rely. Many critics of complexity theory point out that it tends to showcase urban developments are happening through neutral evolutionary forces, which can be comprehended, and governed by individuals engaged in governance for a particular objective.
An Intelligent Agent (IA) is a type of autonomous entity in the field of Artificial Intelligence (AI) that gathers information about its surroundings using sensors, takes action in response to that information using actuators ("agent" part), and guides its behavior to achieve predetermined results (i.e. it is rational). Agents that are both intelligent and able to learn or utilize information to accomplish their tasks would be ideal. Similar to how economists study agents, cognitive scientists, ethicists, philosophers of practical reason and researchers in a wide range of other disciplines study variations of the IAmodel used in multidisciplinary socio-cognitive modelling and computer social simulation models. In this article, the term "Multi-Agent System" (MAS) has been used to refer to a system in which two or more autonomous entities communicate with one another. The key objective of this research is to provide a critical analysis of MAS and its applications in power systems. A case study to define the application of MAS in power system is also provided, using a critical implementation of fuzzy logic controllers.
This paper provides a review of analytical tools and clinical application in the field of 4D flow MRI. The convention of Magnetic Resonance Imaging (MRI) in clinical practice for valuation of affected role with cardiovascular disease is now commonplace. Two-dimensional stage contrast MRI has remained cast-off to amount local plasma movement in the heart and arteries since the late 1980s. Recently time determined stage contrast magnetic timbre imaging (PC-MRI) with speed programming in all three movement instructions and three dimensional (3D) anatomic handling (sometimes referred to as "4D flow MRI") has remained industrialized and cast-off to measure cardiovascular hemodynamics in various human organs. MRIoffers for dimension complicated blood stream patterns with unparalleled precision and detail due to its capacity to observe blood flow in three dimensions and quantify it retrospectively, in four dimensions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.