ZTE Chairman Bullish on Telecom Business Opportunities in Developing and Densely Populated Nations

ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, recently

Analysing light pipe two-side aperture in dense population housing to improve the visual and thermal condition

Daylight is one of the essential criteria for decent housing.However,in an urban kampung,access to daylight is limited due to the high-density population in urban Kampung Kota.This study aims to improve the visual and thermal conditions by modifying the aperture of the light pipe based on visual daylight distribution,illuminance level,and thermal conditions in multi-storey houses in hot-humid climates.Light pipe was developed experi-mentally through the simulation model to investigate the performance and impact on the visual and thermal conditions like operational temperature and RH in densely populated Kampung Kota without vertical apertures.According to the results,the two-aperture light pipe residential space enhanced the current situation but did not fulfill the standards.The two aperture light pipe visually improved daylight distribution by 1.05%-31.36%and illumination level by 8.4%-14.8%.We also found that light pipe also impacts thermal conditions with a 10.92%RH reduction while at the same time increasing temperature up to 10.57%.Therefore,it can be concluded that a two-aperture light pipe has the potential to be applied to actual conditions in hot-humid climates.

TransCode:Uncovering COVID-19 transmission patterns via deep learning

Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine-scale transmission patterns via deep learning.Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.First,in Hong Kong,China,we construct the mobility trajectories of confirmed cases using their visiting records.Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution.Integrating the spatial and temporal information,we represent the TransCode via spatiotemporal transmission networks.Further,we propose a deep transfer learning model to adapt the TransCode of Hong Kong,China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises:New York City,San Francisco,Toronto,London,Berlin,and Tokyo,where fine-scale data are limited.All the data used in this study are publicly available.Results The TransCode of Hong Kong,China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns(e.g.,the imported and exported transmission intensities)at the district and constituency levels during different COVID-19 outbreaks waves.By adapting the TransCode of Hong Kong,China to other data-limited densely populated metropolises,the proposed method outperforms other representative methods by more than 10%in terms of the prediction accuracy of the disease dynamics(i.e.,the trend of case numbers),and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.Conclusions The fine-scale transmission patterns due to the metapopulation level mobility(e.g.,travel across different districts)and contact behaviors(e.g.,gathering in social-economic centers)are one of the main contributors to the rapid spread of the virus.Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

Optimal resource allocation with spatiotemporal transmission discovery for efective disease control

Background:The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world,especially in highly populated regions.The healthcare capacity(especially the testing resources,vaccination coverage,and hospital capacity)is becoming extremely insufcient as the demand will far exceed the supply.To address this time-critical issue,we need to answer a key question:How can we efectively infer the daily transmission risks in diferent districts using machine learning methods and thus lay out the corresponding resource prioritization strategies,so as to alleviate the impact of the Omicron outbreaks?Methods:We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant.We collect the publicly available data from the ofcial website of the Hong Kong Special Administrative Region(HKSAR)Government and the study period in this paper is from December 27,2021 to July 17,2022(including a period for future prediction).First,we construct the spatiotemporal transmission intensity matrices across diferent districts based on infection case records.With the constructed cross-district transmission matrices,we forecast the future risks of various locations daily by means of the Gaussian process.Finally,we develop a transmission-guided resource prioritization strategy that enables efective control of Omicron outbreaks under limited capacity.Results:We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong,China.The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks,making it difcult for the public health authority to foresee the outbreaks and plan the responses accordingly.With the guidance of the inferred transmission risks,the developed prioritization strategy enables the optimal testing resource allocation for integrative case management(including case detection,quarantine,and further treatment),i.e.,with the 300,000 testing capacity per day;it could reduce the infection peak by 87.1% compared with the population-based allocation strategy(case number reduces from 20,860 to 2689)and by 24.2% compared with the case-based strategy(case number reduces from 3547 to 2689),signifcantly alleviating the burden of the healthcare system.Conclusions:Computationally characterizing spatiotemporal transmission patterns allows for the efective risk mapping and resource prioritization;such adaptive strategies are of critical importance in achieving timely outbreak control under insufcient capacity.The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants.Moreover,the investigation conducted in Hong Kong,China provides useful suggestions on how to achieve efective disease control with insufcient capacity in other highly populated countries and regions.