Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

Research and Practice of Online Service Hall in Colleges and Universities

The needs of teachers and students, the requirements of educational modernization and informatization and the development of information technology promote the online service hall to become an important part of the smart campus. The online service hall is also a hot spot of information construction in universities in recent years. Taking China University of Geosciences (Beijing) for example, this paper probes into the problems and causes of the current difficulties of teachers and students in complicated school’s business process and puts forward the construction goal and ideas of the online service hall. A four-layer architecture which includes data layer, business service support layer, business service layer and presentation layer for service e-hall is designed and the organization and implementation steps of the online service hall are expounded. In addition, the extended applications such as integrated self-service printing terminal and electronic signature are considered. This paper can be used for reference for the construction and implementation of online service hall in other universities.

Analysis and Prediction of Content Popularity for Online Video Service:A Youku Case Study

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.

ITMA ASIA + CITME 2010 Exhibition Space Sold Out Visitors offered convenient online registration and badge printing service

The reputation of ITMA ASIA+ClTME 2010 as Asia’s premier textile industry platform has been given a further boost following an announcement from the show organizers that all exhibition space has now been sold. Over 1,100 textile and garment machinery manufacturers have applied successfully to exhibit at the second combined show,taking up 100,000 square meters of Shanghai New International Expo Centre.

Performance Prediction Based Workload Scheduling in Co-Located Cluster

Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.

基于患者视角的公立医院互联网医院发展路径

目的从患者视角出发,以上海市儿童医院互联网医院为例,通过调查患者对互联网医院的知晓、使用和满意情况,了解公立医院互联网医院的运行现状和存在的问题,有针对性地提出优化方案和发展路径,从而推动公立医院互联网医院持续健康发展。方法在文献研究和专家访谈的基础上,设计调查问卷并对上海市儿童医院线下门诊患儿家长进行调查。调查内容包括个人基本信息,对互联网医院的知晓度、利用度和满意度等情况,共有560人参与并完成了调查。应用SPSS 26.0软件对数据进行统计分析。结果53.21%的受调查者知晓上海市儿童医院开通了互联网医院;知晓并已使用该院互联网医院的受调查者对预约挂号(96.64%)、在线缴费(56.04%)和报告查询(44.97%)等服务功能的利用度较高,而对在线咨询、在线复诊、配药到家等医患互动服务的利用率较低。98.21%的患者对互联网医院总体表示满意,并希望增加高级专家问诊、诊前导诊、商业保险在线理赔等线上服务。结论目前公立医院互联网医院在运行过程中主要存在着宣传力度不足、高级专家少、问诊响应时间长等问题,患者的线上就医过程仍不便利,需要通过加强宣传推广、优化服务方式、丰富服务功能、打通线上线下医疗服务流程等举措,推动公立医院互联网医院高质量发展,不断提升患者就医体验。

The effects of an online inquiry service on gatekeeping systems with heterogeneous patients

An online inquiry service(OIS)is an innovative service designed to make medical re-sources more accessible,especially in remote areas.An OIS provides patients with an alternative method of consulting a physician.This study develops a game-theoretical queueing model to examine the impact of OISs on gatekeeping systems.In such sys-tems,patients are heterogeneous in terms of their travel costs.Here,we compare the performance of gatekeeping systems with and without an OIS.As such,we show that,owing to physicians’reduced diagnostic ability when working online,an OIS reduces pa-tients’travel costs,increases the flow of patients to specialists,increases the total waiting time in the system,and decreases the total health surplus of patients.Moreover,we demonstrate that when patients are not sensitive to delays,introducing an OIS always reduces their total cost.These findings are consistent with the intuition that patients are better off when more options are available.Interestingly,when patients are sensitive to delays,introducing an OIS may increase their total cost.This paradoxical result occurs when patients’sensitivity to delays is high,because those who consult a physician online impose negative externalities on the system by increasing the waiting cost in both the online and offline channels(which is proportional to patients’delay sensitivity).Finally,we numerically illustrate that the benefit of introducing an OIS is non-monotonic in the system parameters;thus,caution is required when designing policies to regulate OISs.

Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://materialsdata-mining.com/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://materials-datamining.com/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).

Impact of different sampling rates on precise point positioning performance using online processing service

In this study,the effect of different sampling rates(i.e.observation recording interval)on the Precise Point Positioning(PPP)solutions in terms of accuracy was investigated.For this purpose,a field test was carried out inÇorum province,Turkey,on 11 September 2019.Within this context,a Geodetic Point(GP)was established and precisely coordinated.A static GNSS measurement was occupied on the GP for about 4-hour time at 0.10 second(s)/10 Hz measurement intervals with the Trimble R10 geodetic grade GNSS receiver.The original observation file was converted to RINEX format and then decimated into the different data sampling rates as 0.2 s,0.5 s,1 s,5 s,10 s,30 s,60 s,and 120 s.All these RINEX observation files were submitted to the Canadian Spatial Reference System-Precise Point Positioning(CSRS-PPP)online processing service the day after the data collection date by choosing both static and kinematic processing options.In this way,PPP-derived static coordinates,and the kinematic coordinates of each measurement epoch were calculated.The PPP-derived coordinates obtained from each decimated sampling intervals were compared to known coordinates of the GP for northing,easting,2D position,and height components.According to the static and kinematic processing results,high data sampling rates did not change the PPP solutions in terms of accuracy when compared to the results obtained using lower sampling rates.The results of this study imply that it was not necessary to collect GNSS data with high-rate intervals for many surveying projects requiring cm-level accuracy.

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Different types of pandemics that have appeared from time to time have changed many aspects of daily life.Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown.The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store.A huge number of reviews are written daily by users to express their opinions,which include significant information to improve these applications.The manual processing and extracting of information from users’reviews is an extremely difficult and time-consuming task.Therefore,the use of intelligent methods is necessary to analyse users’reviews and extract issues that can help in improving these apps.This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods.To the best of our knowledge,this is the first study to explore users’opinions about governmental apps in Saudi Arabia.The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases.A new dataset used for this research includes 8000 user reviews gathered from social media,Google Play and the app store.Different methods are applied to the dataset,and the results show that the k nearest neighbourhood(KNN)method generates the highest accuracy compared to other implemented methods.

Pregnancy and Birth Service Online

我曾服用过含有激素成分的药2个月,现在停药了想怀孕,听说激素类药在体内代谢十分缓慢,短期怀孕会对胎儿有影响,请问我在何时怀孕比较合适? 广东顺德

On Desideratum for B2C E-Commerce Reputation Systems

This paper reviews existing approaches to reputation systems, their constraints as well as available solutions. Furthermore, it presents and evaluates a novel and comprehensive reputation model devoted to the distributed reputation system for Business-to-Consumer （B2C） E-commerce applications that overcomes the discussed drawbacks. The algorithm offers a comprehensive approach as it considers a number of issues that have a bearing on trust and reputation such as age of ratings, transaction value, credibility of referees, number of malicious incidents, collusion and unfair ratings. Moreover, it also extends the existing frameworks based on information about past behaviour, with other aspects affecting online trading decisions which relate to the characteristic of the providers, such as existence of trustmark seals, payment intermediaries, privacy statements, security/privacy strategies, purchase protection/insurance, alternative dispute resolutions as well as the existence of first party information.

Analyzing the dynamics of online video popularity

Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects： overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion popularity distributions of individual videos are carefully studied. of active days is proposed, and the per-day and per-hour Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.

Roundtable： Research Opportunities and Challenges for Emerging Software Systems

For this special section on software systems special section, discuss important issues that will shape several research leaders in software systems, as guest editors for this this field＇s future directions. The essays included in this roundtable article cover research opportunities and challenges for emerging software systems such as data processing programs （Xiangyu Zhang） and online services （Dongmei Zhang）, with new directions of technologies such as unifications in software testing （Yves Le Traon）, data-driven and evidence-based software engineering （Qing Wang）, and dynamic analysis of multiple traces （Lu Zhang）. Tao Xie, Leading Editor of Special Section on Softwaare Svstem.

Predicting protein subchloroplast locations:the 10th anniversary

Chloroplast is a type of subcellular organelle in green plants and algae.It is the main subcellular organelle for conducting photosynthetic process.The proteins,which localize within the chloroplast,are responsible for the photosynthetic process at molecular level.The chloroplast can be further divided into several compartments.Proteins in different compartments are related to different steps in the photosynthetic process.Since the molecular function of a protein is highly correlated to the exact cellular localization,pinpointing the subchloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic process.Experimental process for determining protein subchloroplast location is always costly and time consuming.Therefore,computational approaches were developed to predict the protein subchloroplast locations from the primary sequences.Over the last decades,more than a dozen studies have tried to predict protein subchloroplast locations with machine learning methods.Various sequence features and various machine learning algorithms have been introduced in this research topic.In this review,we collected the comprehensive information of all existing studies regarding the prediction of protein subchloroplast locations.We compare these studies in the aspects of benchmarking datasets,sequence features,machine learning algorithms,predictive performances,and the implementation availability.We summarized the progress and current status in this special research topic.We also try to figure out the most possible future works in predicting protein subchloroplast locations.We hope this review not only list all existing works,but also serve the readers as a useful resource for quickly grasping the big picture of this research topic.We also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.