The primary reason is the fact that beneath the background of “one country, two systems” policy, the institutional differences when considering Guangdong, Hong-Kong, and Macao haven’t been effectively connected up and synergetic, Greater Bay region metropolitan agglomeration has not yet however formed a natural whole, additionally the synergy effectation of shared help and promotion is relatively poor. Based on this, we have to seize the fantastic historical possibility associated with construction of Guangdong-Hong Kong-Macao Greater Bay region, accelerate the construction of the mechanism when it comes to synergetic economic development of the three places, accelerate the establishment of an integral market, develop a fair division of labor system and collaborative innovation system, and jointly promote the synergetic economic growth of Greater Bay Area.With the quick development of deep discovering, automated lesion recognition is used commonly in medical evaluating. To resolve the situation that existing deep learning-based cervical precancerous lesion recognition find more algorithms cannot fulfill high category accuracy and quickly working speed at precisely the same time, a ShuffleNet-based cervical precancerous lesion category technique is suggested. With the addition of station awareness of the ShuffleNet, the network overall performance is enhanced. In this study, the image dataset is classified into five categories typical, cervical cancer, LSIL (CIN1), HSIL (CIN2/CIN3), and cervical neoplasm. The colposcopy photos are expanded to solve the issues associated with lack of colposcopy images therefore the uneven circulation of photos from each group. For the test dataset, the accuracy of the suggested CNN models is 81.23% and 81.38%. Our classifier achieved an AUC rating of 0.99. The experimental results reveal that the colposcopy image classification community based on artificial cleverness features great overall performance in category precision and design size, and it has high clinical applicability.In this study, while aiming in the avoidance of fire accidents in underground commercial roads, an underground commercial road is selected as a research item, as well as the building fire is numerically simulated utilizing the PyroSim software. Fire simulation scenarios are split according to various fire zones by analyzing the heat, carbon monoxide (CO) concentration, and presence within the smoke level inside a building. The readily available safe evacuation time is determined based on the critical fire danger judgment circumstances emerging pathology . We found that enough time if the flue gas temperature and CO focus achieved the critical value in the fire website was longer than the time if the exposure achieved the important price relieving and on occasion even avoiding the scatter of smoke through the fire area towards the evacuation stairs provides effective assistance for crowd evacuation. Eventually, the security regarding the building is assessed, and fire avoidance countermeasures are defined based on the actual circumstance and fire numerical simulation results to lower fire occurrence, casualties, and financial losings.Due to the development and application of information technology, a number of modern-day information technologies represented by 5G, huge data, and synthetic cleverness tend to be altering quickly, and people’s demands for video coding standards have become greater. Into the High-Efficiency Video Coding (HEVC) standard, the coding block unit just isn’t flexible enough, and also the prediction mode just isn’t detailed enough. A new generation of Versatile Video Coding (VVC) criteria was born. VVC inherits the crossbreed coding framework used by HEVC, gets better the initial technology of each and every component, introduces a series of new coding technologies, and builds with this considerably improving the coding performance. Compared to HEVC, the block division structure of VVC has withstood great changes, retaining the quad-tree (QT) division method and increasing the multi-type tree (MTT) division strategy, which brings large coding complexity. To cut back the computational complexity of VVC coding block division, a quick decision algorithm for VVC intra-frame coding centered on texture characteristics and device learning is recommended. Initially, we review the traits of the CU partition structure decision and then utilize the texture complexity of the CU partition structure decision to end genetic profiling the CU partition process early; for CUs which do not meet with the very early cancellation of this partition, make use of the global test information, neighborhood test information, and context information. The three-category feature-trained tandem classifier framework predicts the division sort of CU. The experimental outcomes show that when you look at the complete intra mode, compared to the prevailing VTM10.0, the encoding output little bit rate is increased by 1.36%, while the encoding time is conserved by 52.63%.Integrating large intelligent reflecting areas (IRS) into a millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) strategy has been a promising approach to boost the overall performance associated with cordless interaction system with all the channel condition information (CSI). Most existing work assume that ideal channel estimation can be had, however the proposed high-dimensional cascaded MIMO channels and passive reflectors pose a good challenge to those methods.
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