7 research outputs found
A Comparative Analysis on Volatility and Scalability Properties of Blockchain Compression Protocols
Increasing popularity of trading digital assets can lead to significant
delays in Blockchain networks when processing transactions. When transaction
fees become miners' primary revenue, an imbalance in reward may lead to miners
adopting deviant mining strategies. Scaling the block capacity is one of the
potential approaches to alleviate the problem. To address this issue, this
paper reviews and evaluates six state-of-the-art compression protocols for
Blockchains. Specifically, we designed a Monte Carlo simulation to simulate two
of the six protocols to observe their compression performance under larger
block capacities. Furthermore, extensive simulation experiments were conducted
to observe the mining behaviour when the block capacity is increased.
Experimental results reveal an interesting trade-off between volatility and
scalability. When the throughput is higher than a critical point, it worsens
the volatility and threatens Blockchain security. In the experiments, we
further analyzed the relationship between volatility and scalability properties
with respect to the distribution of transaction values. Based on the analysis
results, we proposed the recommended maximum block size for each protocol. At
last, we discuss the further improvement of the compression protocols
Descope of the ALIA mission
The present work reports on a feasibility study commissioned by the Chinese
Academy of Sciences of China to explore various possible mission options to
detect gravitational waves in space alternative to that of the eLISA/LISA
mission concept. Based on the relative merits assigned to science and
technological viability, a few representative mission options descoped from the
ALIA mission are considered. A semi-analytic Monte Carlo simulation is carried
out to understand the cosmic black hole merger histories starting from
intermediate mass black holes at high redshift as well as the possible
scientific merits of the mission options considered in probing the light seed
black holes and their coevolution with galaxies in early Universe. The study
indicates that, by choosing the armlength of the interferometer to be three
million kilometers and shifting the sensitivity floor to around one-hundredth
Hz, together with a very moderate improvement on the position noise budget,
there are certain mission options capable of exploring light seed, intermediate
mass black hole binaries at high redshift that are not readily accessible to
eLISA/LISA, and yet the technological requirements seem to within reach in the
next few decades for China
Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence
Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models
Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection
Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of Things, is prospective for computer-aided diagnosis and therapy. In this paper, we focus on the application value of the second-generation sequencing technology in the diagnosis and treatment of pulmonary infectious diseases with the aid of the deep reinforcement learning. Specifically, the rapid, comprehensive, and accurate identification of pathogens is a prerequisite for clinicians to choose timely and targeted treatment. Thus, in this work, we present representative deep reinforcement learning methods that are potential to identify pathogens for lung infection treatment. After that, current status of pathogenic diagnosis of pulmonary infectious diseases and their main characteristics are summarized. Furthermore, we analyze the common types of second-generation sequencing technology, which can be used to diagnose lung infection as well. Finally, we point out the challenges and possible future research directions in integrating deep reinforcement learning with second-generation sequencing technology to diagnose and treat lung infection, which is prospective to accelerate the evolution of smart healthcare with medical Internet of Things and big data