An Overview of Interactive Immersive Services

Immersive services are the typical emerging services in current IMT-2020 network.With the development of network evolution,real-time interactive applications emerge one after another.This article provides an overview on immersive services which focus on real-time interaction.The scenarios,framework,requirements,key technologies,and issues of interactive immersive service are presented.

Prevalence and control of hypertension in the elderly:results from China hypertension survey,2012-2015

Objective To evaluate the prevalence,awareness,treatment and control rate of hypertension among elder population in China.Methods Data form a cross-sectional stratified multistage random sampling survey conducted from 2012 to 2015 were used to analyzed.Finally,a total of 134,397 participants aged≥60 years were enrolled in our study.Hypertension was defined as systolic BP≥140 mmHg,and/or diastolic BP≥90 mmHg,and/or use of antihypertensive medicine within 2 weeks.Among participants with hypertension,control rate of hypertension was defined as the participant presenting as hypertensive,but with a systolic BP measure less than 140 mm Hg and diastolic BP measure less than 90 mm Hg.

The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li_(10)GeP_(2)S_(12)

Solid-state batteries have received increasing attention in scientific and industrial communities,which benefits from the intrinsically safe solid electrolytes(SEs).Although much effort has been devoted to designing SEs with high ionic conductivities,it is extremely difficult to fully understand the ionic diffusion mechanisms in SEs through conventional experimental and theoretical methods.Herein,the temperature-dependent concerted diffusion mechanism of ions in SEs is explored through machinelearning molecular dynamics,taking Li_(10)GeP_(2)S_(12) as a prototype.Weaker diffusion anisotropy,more disordered Li distributions,and shorter residence time are observed at a higher temperature.Arrhenius-type temperature dependence is maintained within a wide temperature range,which is attributed to the linear temperature dependence of jump frequencies of various concerted diffusion modes.These results provide a theoretical framework to understand the ionic diffusion mechanisms in SEs and deepen the understanding of the chemical origin of temperature-dependent concerted diffusions in SEs.

Rapid subsurface damage detection of SiC using inductivity coupled plasma

This paper proposes a method for the rapid detection of subsurface damage(SSD)of Si C using atmospheric inductivity coupled plasma.As a plasma etching method operated at ambient pressure with no bias voltage,this method does not introduce any new SSD to the substrate.Plasma diagnosis and simulation are used to optimize the detection operation.Assisted by an Si C cover,a taper can be etched on the substrate with a high material removal rate.Confocal laser scanning microscopy and scanning electron microscope are used to analyze the etching results,and scanning transmission electron microscope(STEM)is adopted to confirm the accuracy of this method.The STEM result also indicates that etching does not introduce any SSD,and the thoroughly etched surface is a perfectly single crystal.A rapid SSD screening ability is also demonstrated,showing that this method is a promising approach for the rapid detection of SSD.

Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space

Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface(PES)is the philosopher's stone for unraveling the nature of matter via atomistic simulation.This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES.In this work,we develop an accurate PES model for the Al-Cu-Mg system by employing deep potential(DP),a neural network based representation of the PES,and DP generator(DP-GEN),a concurrent-learning scheme that generates a compact set of ab initio data for training.The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties,including formation energy,equilibrium volume,equation of state,interstitial energy,vacancy and surface formation energy,as well as elastic moduli.Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration.

The prevalence of hypertension overweight/obesity and its relation with cardiovascular disease among middle-aged Chinese population

Objective To investigate the prevalence of hypertension overweight/obesity and the combined effect on the incidence of cardiovascular disease(CVD).Methods The study population(aged from 35 to 64)were selected from 9 regions of China by cluster sampling method.The baseline was conducted in 2010,and the follow-up survey was done in 2017.Participants with 24≤BMI28 kg/m^2 was defined as overweight,BMI≥28 kg/m^2 was defined as obesity.

Effect of comprehensive intervention on hypertension control among employees in the Chinese stated-owned enterprise:a cluster controlled trial

Objective To evaluate the effect of a workplace-based comprehensive intervention strategy on the improvement of blood pressure (BP) control.Methods A cluster controlled trail, with workplaces (clusters)assigned to either the intervention or control group. Totally, 30 statedowned enterprises across China were included, among which 20were allocated to the intervention group and 10 to the control group.

Effectiveness of a workplace-based multicomponent intervention to improve hypertension control among Chinese working population:a cluster-controlled trial

Objective To determine whether a workplace-based multicomponent intervention strategy could improve BP control among Chinese working population.Methods A cluster-controlled trail,with workplaces assigned to either the intervention or control group.60 workplaces across 20 urban regions of China were selected.4,548 hypertensive employees aged 18-60 years were assigned intervention(n=3,470)or control(n=1,078),of whom 4,205(92.5%;intervention,n=3,209;control,n=996)were included in this analysis.

Synergistic application of molecular docking and machine learning for improved binding pose

Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design.Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space,while relying on machine-learning approaches may lead to invalid conformations.In this study,we propose a novel strategy that combines molecular docking and machine learning methods.Firstly,the protein-ligand binding poses are predicted using a deep learning model.Subsequently,position-restricted docking on predicted binding poses is performed using Uni-Dock,generating physically constrained and valid binding poses.Finally,the binding poses are re-scored and ranked using machine learning scoring functions.This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking.Evaluation experiments on multiple datasets demonstrate that,compared to using molecular docking or machine learning methods alone,our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions.

Simulation model of self-organizing pedestrian movement considering following behavior

在本文中一种新的力学模型被引入到社会力模型中,用来仿真相向行人流中的跟随行为。这种跟随行为指的是行人通过接近同向行人以避免与反向行人冲突的行为。新的力学模型类似于一种引力模型,在建模过程中考虑了行人的视野范围、自身的运动状态、被跟随行人的运动状态等因素。我们利用新的力学模型对相向行人流进行了仿真,研究了跟随行为对渠化现象、行人间冲突以及双向通道通行效率的影响。仿真结果表明:跟随行为能促进渠化现象形成,并能起到缓解相向行人流拥堵的作用;跟随行为具有降低相向行人流冲突次数的作用,这种作用在入口流量较低时并不明显,但随着行人流量的升高而增强。跟随行为能够提高双向通道的通行效率,并且跟随行为的强度参数越大通道的通行效率越高。

Viscosity in water from first-principles and deep-neural-network simulations

We report on an extensive study of the viscosity of liquid water at near-ambient conditions,performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics(AIMD),based on density-functional theory(DFT).In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy,our ab initio approach is enhanced with deep-neural-network potentials(NNP).This approach is first validated against AIMD results,obtained by using the Perdew–Burke–Ernzerhof(PBE)exchange-correlation functional and paying careful attention to crucial,yet often overlooked,aspects of the statistical data analysis.Then,we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed(SCAN)functional.Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one,our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

Specialising neural network potentials for accurate properties and application to the mechanical response of titanium

Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex phenomena.Machine learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic potentials.Here,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not suffice.As an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal expansion.The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti.The approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.

Esrrb plays important roles in maintaining self-renewal of trophoblast stem cells (TSCs) and reprogramming somatic cells to induced TSCs

Trophoblast stem cells (TSCs), which can be derived from the trophoectoderm of a blastocyst, have the ability to sustain self-renewal and differentiate into various placental trophoblast cell types. Meanwhile, essential insights into the molecular mechanisms controlling the placental development can be gained by using TSCs as the cell model. Esrrb is a transcription factor that has been shown to play pivotal roles in both embryonic stem cell (ESC) and TSC, but the precise mechanism whereby Esrrb regulates TSC-specific transcriptome during differentiation and reprogramming is still largely unknown. In the present study, we elucidate the function of Esrrb in self-renewal and differentiation of TSCs, as well as during the induced TSC (iTSC) reprogramming. We demonstrate that the precise level of Esrrb is critical for stem state maintenance and further trophoblast differentiation of TSCs, as ectopically expressed Esrrb can partially block the rapid differentiation of TSCs in the absence of fibroblast growth factor 4. However, Esrrb depletion results in downregulation of certain key TSC-specific transcription factors, consequently causing a rapid differentiation of TSCs and these Esrrb-deficient TSCs lose the ability of hemorrhagic lesion formation in vivo. This function of Esrrb is exerted by directly binding and activating a core set of TSC-specific target genes including Cdx2, Eomes, Sox2, Fgfr4, and Bmp4. Furthermore, we show that Esrrb overexpression can facilitate the MEF-to-iTSC conversion. Moreover, Esrrb can substitute for Eomes to generate GEsTM-iTSCs. Thus, our findings provide a better understanding of the molecular mechanism of Esrrb in maintaining TSC self-renewal and during iTSC reprogramming.

Deep potentials for materials science

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

第一性原理分子动力学中电子结构的神经网络表达

第一性原理分子动力学模拟过程中产生海量包含丰富电子结构信息的数据,未被充分利用.本文提出一种通过预测晶体材料紧束缚模型的形式,高效、准确地表达第一性原理电子结构的神经网络方法TBworks.TBworks可以结合机器学习分子动力学方法,快速实现复杂体系的分子动力学模拟和电子结构采样,计算电子关联函数以及相应的物理性质.以一维电荷密度波材料碳炔链为应用示例,本文基于TBworks研究了正则系综下的电子谱函数和光电导率,以及孤子-反孤子对湮灭的动力学过程中电子的非绝热演化,发现由于超越波恩-奥本海默近似的动力学效应对电子谱函数的低能模式的修正.该方法为研究动力学体系提供了准确、高效、可重复使用的第一性原理电子结构的代理模型,极大地提高了电子结构的计算效率和模拟尺度,使得对一些由于计算瓶颈无法或者难以进行的新奇性质和现象的研究成为可能.