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Bloch Bock posted an update 6 months ago
The basic reproduction number was approximated to be 2.05 (95% CI ); the hospitalized cases arrived at the peak of 29766 (95% CI ) on February 7th (95% CI ). Importantly, we identified that the highest risk group of SARS-Cov-2 was the family of four, which has the biggest probability of degree distributions at such node, suggesting that contact patterns play an important role in curtailing the disease spread.In this work, we study a mathematical model for the interaction of sensitive-resistant bacteria to antibiotics and analyse the effects of introducing random perturbations to this model. We compare the results of existence and stability of equilibrium solutions between the deterministic and stochastic formulations, and show that the conditions for the bacteria to die out are weaker in the stochastic model. Moreover, a corresponding optimal control problem is formulated for the unperturbed and the perturbed system, where the control variable is prophylaxis. The results of the optimal control problem reveal that, depending on the antibiotics, the costs of the prophylaxis, such as implementation, ordering and distribution, have to be much lower than the social costs, to achieve a bacterial resistance effective control.Precise maintenance of acid-base homeostasis is fundamental for optimal functioning of physiological and cellular processes. click here The presence of an acid-base disturbance can affect clinical outcomes and is usually caused by an underlying disease. It is, therefore, important to assess the acid-base status of patients, and the extent to which various therapeutic treatments are effective in controlling these acid-base alterations. In this paper, we develop a dynamic model of the physiological regulation of an HCO3-/CO2 buffering system, an abundant and powerful buffering system, using Henderson-Hasselbalch kinetics. We simulate the normal physiological state and four cardinal acidbase disorders Metabolic acidosis and alkalosis and respiratory acidosis and alkalosis. We show that the model accurately predicts serum pH over a range of clinical conditions. In addition to qualitative validation, we compare the in silico results with clinical data on acid-base homeostasis and alterations, finding clear relationships between primary acid-base disturbances and the secondary adaptive compensatory responses. We also show that the predicted primary disturbances accurately resemble clinically observed compensatory responses. Furthermore, via sensitivity analysis, key parameters were identified which could be the most effective in regulating systemic pH in healthy individuals, and those with chronic kidney disease and distal and proximal renal tubular acidosis. The model presented here may provide pathophysiologic insights and can serve as a tool to assess the safety and efficacy of different therapeutic interventions to control or correct acid-base disorders.With the continuous development of the earth observation technology, the spatial resolution of remote sensing images is also continuously improved. As one of the key problems in remote sensing images interpretation, the classification of high-resolution remote sensing images has been widely concerned by scholars at home and abroad. With the improvement of science and technology, deep learning has provided new ideas for the development of image classification, but it has not been widely used in remote sensing images processing. In the background of remote sensing huge data, the remote sensing images classification based on deep learning proposed in the study has more research significance and application value. The study proposes a high-resolution remote sensing images classification method based on an improved convolutional neural network. The traditional convolutional neural network framework is optimized and the initial structure is added. The actual classification results of radial basis functions and support vector machine are compared horizontally. The classification results of hyperspectral images were presented that the improved method can perform better in overall accuracy and Kappa coefficient. The commission errors of support vector machine classification method are more than 6 times of that of the improved convolutional neural network classification method and the overall accuracy of the improved convolutional neural network classification method has reached 97% above.The human-animal interface plays a vital role in the spread of zoonotic diseases, such as plague, which led to the “Black Death”, the most serious human disaster in medieval Europe. It is reported that more than 200 mammalian species including human beings are naturally infected with plague. Different species acting as different roles construct the transmission net for Yersinia pestis (plague pathogen), in which rodents are the main natural reservoirs. In previous studies, it focused on individual infection of human or animal, rather than cross-species infection. It is worth noting that rodent competition and human-rodent commensalism are rarely considered in the spread of plague. In order to describe it in more detail, we establish a new multi-host mathematical model to reflect the transmission dynamics of plague with wild rodents, commensal rodents and human beings, in which the roles of different species will no longer be at the same level. Mathematical models in epidemiology can clarify the interaction mechanism between plague hosts and provide a method to reflect the dynamic process of plague transmission more quickly and easily. According to our plague model, we redefine the environmental capacity K with interspecific interaction and obtain the reproduction number of zoonotic diseases R Z 0, which is an important threshold value to determine the zoonotic disease to break out or not. At the same time, we analyze the biological implications of zoonotic model, and then study some biological hypotheses that had never been proposed or verified before.The following scenarios, such as complex application requirements, ZB (Zettabyte) order of magnitude of network data, and tens of billions of connected devices, pose serious challenges to the capabilities and security of the three pillars of ICT Computing, network, and storage. Edge computing came into being. Following the design methodology of “description-synthesis-simulation-optimization”, TAC3 (Tile-Architecture Cluster Computing Core) was proposed as the lightweight accelerated ECN (Edge Computing Node). ECN with a Tile-Architecture be designed and simulated through the method of executable description specification and polymorphous parallelism DSE (Design Space Exploration). By reasonable configuration of the edge computing environment and constant optimization of typical application scenarios, such as convolutional neural network and processing of image and graphic, we can meet the challenges of network bandwidth, end-cloud delay and privacy security brought by massive data of the IoE. The philosophy of “Edge-Cloud complements each other, and Edge-AI energizes each other” will become a new generation of IoE behavior principle.
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