Acute aortic disease ranks as the 19th leading cause of death with steadily increasing incidence. The prevalence of aneurysms varies depending on the localization along the aorta with a mortality of aortic rupture of around 80%. Traditionally, aortic disease affects men more frequently than women, however, with a varying gender ratio. Nevertheless, in the setting of acute aortic dissection, the International Registry of Acute Aortic Dissections identified significant gender-related differences in the management of both sexes with acute aortic conditions. Current data suggest that women are at an increased risk of both dying from aortic dissection and having aorta-related complications than men. This review aims to report on current evidence of gender impact on natural history, treatment and outcomes in patients with acute aortic dissection.
Associative learning plays a major role in the formation of the internal dynamic engine of an adaptive system or a cognitive robot. Interaction with the environment can provide a sparse and discrete set of sample correlations of input–output incidences. These incidences of associative data points can provide useful hints for capturing underlying mechanisms that govern the system’s behavioral dynamics. In many approaches to solving this problem, of learning system’s input–output relation, a set of previously prepared data points need to be presented to the learning mechanism, as a training data, before a useful estimations can be obtained. Besides data-coding is usually based on symbolic or nonimplicit representation schemes. In this paper, we propose an incremental learning mechanism that can bootstrap from a state of complete ignorance of any representative sample associations. Besides, the proposed system provides a novel mechanism for data representation in nonlinear manner through the fusion of self-organizing maps and Gaussian receptive fields. Our architecture is based solely on cortically-inspired techniques of coding and learning as: Hebbian plasticity and adaptive populations of neural circuitry for stimuli representation. We define a neural network that captures the problem’s data space components using emergent arrangement of receptive field neurons that self-organize incrementally in response to sparse experiences of system–environment interactions. These learned components are correlated using a process of Hebbian plasticity that relates major components of input space to those of the output space. The viability of the proposed mechanism is demonstrated through multiple experimental setups from real-world regression and robotic arm sensory-motor learning problems.
Smart home technologies have accelerated with the internet of things and continue to develop by combining new ideas. Almost all electronic devices used in homes have been affected by this change. Smart refrigerator systems aim to prevent food waste and health problems as they continue to develop day by day. Researchers are working on smart refrigerators and generating new ideas. Users interact with internet-refrigerators using cameras, sensors and object detection, and artificial intelligence applications developed for these devices. Today, using cloud computing technology, robotic, artificial intelligence, and deep learning algorithms, smart refrigerator users can be informed about the expiry date and the amount of food in the refrigerator with warning messages and access the data loaded in the sensors. Recent advances in smart refrigerators have led to new developments using computer vision, image processing, and voice recognition algorithms. This article examines the methods used by researchers in their recent studies of smart refrigerators.
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