User Access Mode Research Articles

Six years of activity of the Italian vaccine portal "VaccinarSì": a web traffic evaluation using Google Analytics.

Despite the scientific consensus about vaccines safety and effectiveness, there is still a discrepancy between scientific evidence and perception by the general population. The "VaccinarSì" portal was created in 2013 by Italian specialists in Public Health to provide evidence-based information regarding vaccination. The purpose of this study was to analyse the web traffic on "VaccinarSì" platform during a six-year period (May 8, 2013-May 8, 2019). Moreover, we compared the first six-month with the last six-month period of the website activity, to identify potential areas of improvements. This is a descriptive study using Google Analytics data. We collected data regarding the following: total number of sessions to the portal, total number of pages viewed, total number of users and the number of new visitors, geographical locations and demography of users as well as user access mode (type of device used and way of access). We also collected some data that were informative about the possibility to infer the level of visitors' engagement with the portal, such as thee number of returning users, bounce rate, number of pages visited per session and mean session duration. Throughout the relevant period, the portal has consistently increased its popularity, with a remarkable increment of monthly connections (ending up to more than 80,000/month) from all over Italy. Visitors were mainly female (71.1%), aged between 25 and 44 years (64.7%). Healthcare professionals were responsible for a considerable proportion of accesses (50.6%). The mobile has become the dominant device used to access the portal, accounting for 77.8% of total connection in the last six months. Similarly, in the last period, organic search accounted for 92% of all connections. Measles and MPR vaccine, as well as chickenpox and hexavalent vaccine, have remained the most appealing topics of interest among visitors over the years. The page that attracted more visitors over the six years was "real risks and benefits of vaccination", accounting for 5.67% of total sessions with a high mean duration spent of 05:08 minutes. During the six years of activity, overall, the level of users' engagement with the portal has dropped with an increased bounce rate and a lower average number of pages visited per session and a lower mean duration of each connection. The lowest engagement involved connections accessed through mobile devices. Results helped "Vaccinarsi" developers to speculate about future strategies to further increase the platform popularity and optimize visitors' engagement.

Assessing the JEOL CRYO ARM 300 for high-throughput automated single-particle cryo-EM in a multiuser environment.

Single-particle cryo-EM has become an indispensable structural biology method. It requires regular access to high-resolution electron cryogenic microscopes. To fully utilize the capacity of the expensive high-resolution instruments, the time used for data acquisition and the rate of data collection have to be maximized. This in turn requires high stability and high uptime of the instrument. One of the first 300 kV JEOL CRYO ARM 300 microscopes has been installed at the cryo-EM facility BECM at VIB-VUB, Brussels, where the microscope is used for continuous data collection on multiple projects. Here, the suitability and performance of the microscope is assessed for high-throughput single-particle data collection. In particular, the properties of the illumination system, the stage stability and ice contamination rates are reported. The microscope was benchmarked using mouse heavy-chain apoferritin which was reconstructed to a resolution of 1.9 Å. Finally, uptime and throughput statistics of the instrument accumulated during the first six months of the facility operation in user access mode are reported.

Joint User Access Mode Selection and Content Popularity Prediction in Non-Orthogonal Multiple Access-Based F-RANs

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals’ evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs.

A Process Evaluation of a Web-Based Mental Health Portal (WalkAlong) Using Google Analytics.

BackgroundDespite the increasing amount of research on Web-based mental health interventions with proven efficacy, high attrition rates decrease their effectiveness. Continued process evaluations should be performed to maximize the target population’s engagement. Google Analytics has been used to evaluate various health-related Web-based programs and may also be useful for Web-based mental health programs.ObjectiveThe objective of our study was to evaluate WalkAlong.ca, a youth-oriented mental health web-portal, using Google Analytics to inform the improvement strategy for the platform and to demonstrate the use of Google Analytics as a tool for process evaluation of Web-based mental health interventions.MethodsGoogle Analytics was used to monitor user activity during WalkAlong’s first year of operation (Nov 13, 2013-Nov 13, 2014). Selected Google Analytic variables were overall website engagement including pages visited per session, utilization rate of specific features, and user access mode and location.ResultsThe results included data from 3076 users viewing 29,299 pages. Users spent less average time on Mindsteps (0 minute 35 seconds) and self-exercises (1 minute 08 seconds), which are important self-help tools, compared with that on the Screener tool (3 minutes 4 seconds). Of all visitors, 82.3% (4378/5318) were desktop users, followed by 12.7 % (677/5318) mobile phone and 5.0% (263/5318) tablet users. Both direct traffic (access via URL) and referrals by email had more than 7 pages viewed per session and longer than average time of 6 minutes per session. The majority of users (67%) accessed the platform from Canada.ConclusionsEngagement and feature utilization rates are higher among people who receive personal invitations to visit the site. Low utilization rates with specific features offer a starting place for further exploration of users in order to identify the root cause. The data provided by Google Analytics, although informative, can be supplemented by other evaluation methods (ie, qualitative methods) in order to better determine the modifications required to improve user engagement. Google Analytics can play a vital role in highlighting the preferences of those using Web-based mental health tools.

Study on Replica Strategy Based on Access Pattern Mining in Smart City Cloud Storage System

The replica strategy in traditional distributed file system, which creates a copy mainly from the perspective of internal resources while changes in external demand are ignored. However, this strategy is not suitable for deployment in a service-based, resource-rich internal storage “smart city” in cloud storage center. This paper proposes a replica strategy, which combines data security (the minimum amount of copies) together with service needs (best copy volume). The strategy predicts file popularity based on access pattern mining algorithms. What’s more, the number of copies of the cloud adjusts itself dynamically according to the popularity of file and system resources. Mining algorithm is based on the analysis of the characteristics of spatio-temporal data in smart cities. The algorithm first maps the historical user access request to the spatio-temporal attribute domain. Then according to the geographical area grid and association rules, the correlation analysis and evolution rule identification of access requests are carried out in the domain of spatio-temporal attributes. Finally dig out the user access mode and predict the user’s access request, calculate the file popularity according to the request. The simulation results show that the popularity of the file calculated by the access pattern mining algorithm in this paper is simple and efficient, and the prediction accuracy of the popularity can reach 84%. The dynamic replica mechanism based on popularity has a significant advantage in coping with sudden large-scale concurrent accesses. Meanwhile, compared with the conventional dynamic replicas based on access frequency, the proposed strategy consumes less storage resources.

An Evolutionary Game for User Access Mode Selection in Fog Radio Access Networks

The fog radio access network (F-RAN) is a promising paradigm to provide high spectral efficiency and energy efficiency. Characterizing users to select an appropriate communication mode in F-RANs is critical for performance optimization. With evolutionary game theory, a dynamic mode selection is proposed for F-RANs, in which the competition among the groups of potential users' space is formulated as a dynamic evolutionary game, and the game is solved by an evolutionary equilibrium. Stochastic geometry tool is used to derive the proposals' payoff expressions for both fog access point and device-to-device users by considering node location, cache sizes, as well as the delay cost. The analytical results for the proposed game model and the corresponding solution are evaluated, which show that the evolutionary game-based access mode selection algorithm has a better payoff than the max rate-based algorithm.

Web Data mining-A Research area in Web usage mining

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. The data mining technology normally adopts data integration method to generate data warehouse, on which to gather all data into a central site, and then run an algorithm against that data to extract the useful module prediction and knowledge evaluation. Web usage mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. The motive of mining is to find users' access models automatically and quickly from the vast Web log data, such as frequent access paths, frequent access page groups and user clustering. Through web usage m i n i ng , the se rve r l og , registration information and other relative information left by user access can be mined with the user access mode which will provide foundation for decision making of organizations. This article provi de s a survey and analysis of current Web usage mining systems and technologies. Keywords: Web log, Session model, path completion.

A Study of the Relationship between Park User's Behavioral Charateristics and Access Mode in Large-Scale Urban Parks

A Study of the Relationship between Park User's Behavioral Charateristics and Access Mode in Large-Scale Urban Parks