Planning and Reporting Effective Web-Based RAND/UCLA Appropriateness Method Panels: Literature Review and Preliminary Recommendations.

BACKGROUND: The RAND/UCLA Appropriateness Method (RAM), a variant of the Delphi Method, was developed to synthesize existing evidence and elicit the clinical judgement of medical experts on the appropriate treatment of specific clinical presentations. Technological advances now allow researchers to conduct expert panels on the internet, offering a cost-effective and convenient alternative to the traditional RAM. For example, the Department of Veterans Affairs recently used a web-based RAM to validate clinical recommendations for de-intensifying routine primary care services. A substantial literature describes and tests various aspects of the traditional RAM in health research; yet we know comparatively less about how researchers implement web-based expert panels. OBJECTIVE: The objectives of this study are twofold: (1) to understand how the web-based RAM process is currently used and reported in health research and (2) to provide preliminary reporting guidance for researchers to improve the transparency and reproducibility of reporting practices. METHODS: The PubMed database was searched to identify studies published between 2009 and 2019 that used a web-based RAM to measure the appropriateness of medical care. Methodological data from each article were abstracted. The following categories were assessed: composition and characteristics of the web-based expert panels, characteristics of panel procedures, results, and panel satisfaction and engagement. RESULTS: Of the 12 studies meeting the eligibility criteria and reviewed, only 42% (5/12) implemented the full RAM process with the remaining studies opting for a partial approach. Among those studies reporting, the median number of participants at first rating was 42. While 92% (11/12) of studies involved clinicians, 50% (6/12) involved multiple stakeholder types. Our review revealed that the studies failed to report on critical aspects of the RAM process. For example, no studies reported response rates with the denominator of previous rounds, 42% (5/12) did not provide panelists with feedback between rating periods, 50% (6/12) either did not have or did not report on the panel discussion period, and 25% (3/12) did not report on quality measures to assess aspects of the panel process (eg, satisfaction with the process). CONCLUSIONS: Conducting web-based RAM panels will continue to be an appealing option for researchers seeking a safe, efficient, and democratic process of expert agreement. Our literature review uncovered inconsistent reporting frameworks and insufficient detail to evaluate study outcomes. We provide preliminary recommendations for reporting that are both timely and important for producing replicable, high-quality findings. The need for reporting standards is especially critical given that more people may prefer to participate in web-based rather than in-person panels due to the ongoing COVID-19 pandemic.

Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform.

For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

Does e-shopping service quality enhance customers' e-shopping adoption? An extended perspective of unified theory of acceptance and use of technology.

This research advances the knowledge in customer behavior literature by adding new exogenous and moderating variables to the UTAUT framework. It explores the relationships among e-shopping service quality (an exogenous variable), e-shopping drivers (performance expectancy, effort expectation, social influence, and facilitating conditions), e-shopping intention, and e-shopping adoption with the moderating role of offline brand trust in an e-shopping context. Structure equation modeling was performed to confirm the distinctiveness of variables and path analysis based on a sample size of 356 e-shoppers in Pakistan. The outcomes demonstrate that e-shopping drivers are influenced by e-shopping service quality. Moreover, e-shopping intention and e-shopping adoption are led by e-shopping drivers. Furthermore, the relationship between e-shopping drivers and e-shopping intention is moderated by offline brand trust. The discussion of theoretical and practical implications and study limitations are also presented.

Prevalence of Computer Vision Syndrome and Associated Factors among Instructors in Ethiopian Universities: A Web-Based Cross-Sectional Study.

BACKGROUND: In this globalized and high-tech era, the computer has become an integral part of daily life. A constant use of computer for 3 hours and more per day can cause computer vision syndrome (CVS), which is one of the leading occupational hazards of the 21st century. The visual difficulties are the most common health problems associated with excessive computer use. Therefore, this study aimed to assess the prevalence and associated factors of CVS among instructors working in Ethiopian universities. METHODS: A web-based cross-sectional study was conducted among 422 university instructors in Ethiopia from February 02 to March 24, 2021. A structured and self-administered questionnaire prepared by Google Forms was shared among instructors through their e-mail addresses, Facebook, and Telegram accounts. Data cleanup and cross-checking were done before analysis using SPSS version 23. A multivariable logistic regression was applied to identify factors associated with CVS using p value <0.05 and 95% confidence interval. RESULTS: Of the total 416 participants, about 293 (70.4%) were reported to have CVS (95% CI: 65.9-74.5%), of which 54.6% were aged 24-33 years. Blurred vision, pain in and around the eye, and eye redness were the main symptoms reported. Working in third-established universities (AOR = 8.44, 95% CI: 5.47-21.45), being female (AOR = 2.69, 95% CI: 1.28-5.64), being 44 years old and above (AOR = 2.73, 95% CI: 1.31-5.70), frequently working on the computer (AOR = 5.51, 95% CI: 2.05-14.81), and sitting in bent back position (AOR = 8.10, 95% CI: 2.42-23.45) were the factors associated with computer vision syndrome. CONCLUSIONS: In this study, nearly seven-tenths of instructors in Ethiopian universities reported having symptoms of computer vision syndrome. Working in third-generation universities, being female, age, frequently working on the computer, and sitting in bent back position were statistically significant predictors in computer vision syndrome. Therefore, optimizing exposure time, addressing ergonomic hazards associated with computer usage through on-the-job and off-the-job training, and making the safety guidelines accessible for all university instructors would be critical to address the problem.

Spatio-temporal characteristics of the novel coronavirus attention network and its influencing factors in China.

The outbreak of a novel coronavirus pneumonia (COVID-19), wherein more than 200 million people have been infected and millions have died, poses a great threat to achieving the United Nations 2030 sustainable development goal (SDGs). Based on the Baidu index of 'novel coronavirus', this paper analyses the spatial and temporal characteristics of and factors that influenced the attention network for COVID-19 from January 9, 2020, to April 15, 2020. The study found that (1) Temporally, the attention in the new coronavirus network showed an upward trend from January 9 to January 29, with the largest increase from January 23 to January 29 and a peak on January 29, and then a slow downward trend. The level of attention in the new coronavirus network was basically flat when comparing January 22 and March 4. (2) Spatially, first, from the perspective of regional differences, the network attention in the eastern and central regions decreased in turn. The network users in the eastern region exhibited the highest attention to the new coronavirus, especially in Guangdong, Shandong, Jiangsu and other provinces and cities. The network attention in Tibet, Xinjiang, Qinghai and Ningxia in the western region was the lowest in terms of the national network attention. Second, from the perspective of interprovincial differences, the attention in the new coronavirus network was highly consistent with the Hu Huanyong line of China's population boundary. The east of the Hu Huanyong line is densely populated, and the network showed high concern, mostly ranking at the third to fifth levels. (3) The number of Internet users in the information technology field, the population, and the culture and age characteristics of individuals are important factors that influence the novel coronavirus attention network.

Power of the collective: A review of multimodal internet-based surgical education resources in the 21st century.

Electronic resources have changed surgical education in the 21st century. Resources spanning from digital textbooks to multiple choice question banks, online society meetings, and social media can facilitate surgical education. The COVID pandemic drastically changed the paradigm for education. The ramifications of Zoom lectures and online surgical society meetings will last into the future. Educators and learners can be empowered by the many available electronic resources to enhance surgical training and education.

Research on the degree of coupling of the Internet development level and agricultural--Ecological efficiency based on 2009-2018 data from 13 major grain-producing areas in China.

Agricultural informatization and agricultural green development are important components of modern agricultural development, and coordination between the two is an important foundation for achieving sustainable agricultural development. This paper uses data from the 13 major grain producing areas in China from 2009 to 2018 to analyze the coordination of the Internet development level and the agricultural ecological-efficiency, and it further investigates the degree of coupling and coordination between the two. The results of theis study are as follows.(1) The Internet development level of China's 13 main grain production areas has been continuously improving. The average Internet penetration rate increased from 0.25 in 2008 to 0.54 in 2018. (2) The agricultural ecological efficiency of China's main grain production areas has gradually improved. The average value of agricultural ecological efficiency increased from 0.45 in 2009 to 0.79 in 2018. (3) The Internet development level in China's main grain production areas and the continuous improvement of coordination and degree of coupling of the agricultural ecological efficiency show that the interaction between them has led to continuous improvements in the agricultural informatization development and agricultural green development. However, the coordination between the two still has significant room for growth, and there is a certain gap between the different regions.

(Mis)informed about what? What it means to be a science-literate citizen in a digital world.

Science literacy is often held up as crucial for avoiding science-related misinformation and enabling more informed individual and collective decision-making. But research has not yet examined whether science literacy actually enables this, nor what skills it would need to encompass to do so. In this report, we address three questions to outline what it should mean to be science literate in today's world: 1) How should we conceptualize science literacy? 2) How can we achieve this science literacy? and 3) What can we expect science literacy's most important outcomes to be? If science literacy is to truly enable people to become and stay informed (and avoid being misinformed) on complex science issues, it requires skills that span the "lifecycle" of science information. This includes how the scientific community produces science information, how media repackage and share the information, and how individuals encounter and form opinions on this information. Science literacy, then, is best conceptualized as encompassing three dimensions of literacy spanning the lifecycle: Civic science literacy, digital media science literacy, and cognitive science literacy. Achieving such science literacy, particularly for adults, poses many challenges and will likely require a structural perspective. Digital divides, in particular, are a major structural barrier, and community literacy and building science literacy into media and science communication are promising opportunities. We end with a discussion of what some of the beneficial outcomes could be-and, as importantly, will likely not be-of science literacy that furthers informed and critical engagement with science in democratic society.

Web resources facilitate drug discovery in treatment of COVID-19.

The infectious disease Coronavirus 2019 (COVID-19) continues to cause a global pandemic and, thus, the need for effective therapeutics remains urgent. Global research targeting COVID-19 treatments has produced numerous therapy-related data and established data repositories. However, these data are disseminated throughout the literature and web resources, which could lead to a reduction in the levels of their use. In this review, we introduce resource repositories for the development of COVID-19 therapeutics, from the genome and proteome to antiviral drugs, vaccines, and monoclonal antibodies. We briefly describe the data and usage, and how they advance research for therapies. Finally, we discuss the opportunities and challenges to preventing the pandemic from developing further.

Predicting innovative firms using web mining and deep learning.

Evidence-based STI (science, technology, and innovation) policy making requires accurate indicators of innovation in order to promote economic growth. However, traditional indicators from patents and questionnaire-based surveys often lack coverage, granularity as well as timeliness and may involve high data collection costs, especially when conducted at a large scale. Consequently, they struggle to provide policy makers and scientists with the full picture of the current state of the innovation system. In this paper, we propose a first approach on generating web-based innovation indicators which may have the potential to overcome some of the shortcomings of traditional indicators. Specifically, we develop a method to identify product innovator firms at a large scale and very low costs. We use traditional firm-level indicators from a questionnaire-based innovation survey (German Community Innovation Survey) to train an artificial neural network classification model on labelled (product innovator/no product innovator) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict whether they are product innovators or not. We then compare these predictions to firm-level patent statistics, survey extrapolation benchmark data, and regional innovation indicators. The results show that our approach produces reliable predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity.

Integrating contextual sentiment analysis in collaborative recommender systems.

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers' feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

Characteristics and influencing factors of Airbnb spatial distribution in China's rapid urbanization process: A case study of Nanjing.

As in other countries, short-term rentals for tourism services are growing rapidly in China's tourist cities, which are mainly operated through Airbnb. This paper explores whether the spatial distribution of Airbnb in China's rapid urbanization process exhibits characteristics, paths, and drivers that are different from those of cities in other countries. Airbnb is a model for the global sharing economy, but it is increasingly influenced by other functions and facilities in cities as it grows. In this paper, the zero-expansion negative binomial regression was used to study the factors affecting the spatial distribution of Airbnb in Nanjing, China. The results showed that the spatial distribution of Airbnb listings was correlated with the distribution of cultural attractions, universities, public transport accessibility, shopping centers, and business apartments. By analyzing the driving forces of Airbnb's development in Nanjing, this paper found that a large number of business apartments developed in cities were essential providers of Airbnb listings, and affected its spatial distribution. The gap between short-term and long-term rentals was also correlated with the distribution of Airbnb. In addition, similar to the previous literature findings, the increase in the proportion of professional hosts changes the original intention of Airbnb for sharing and communication. Our empirical results applies to the current situation of Airbnb in Chinese cities, which is conducive to the government's more intelligent management and effective promotion of the Airbnb market. Our findings also provide positive references for urban renewal policies and public participation methods in China.

Exploring the gender gap in the Spanish Wikipedia: Differences in engagement and editing practices.

Wikipedia's significant gender bias is widely acknowledged. In this paper we analyze the Spanish Wikipedia with the aim of estimating the percentage of women editors and measuring their engagement and editing practices with respect to their men counterparts. To identify the gender of Wikipedia registered users, we analyzed both the information contained in their user profile and the information provided by users about themselves on their personal user pages. Using our own coding procedure, it is possible to identify a greater number of women than by relying only on the gender reported in their user profile. Combining both methods, our results show that the percentage of women is small, a meagre 11.6% of all analyzed editors, though there is still a significant percentage of users whose gender cannot be determined by either method. Men outnumber women in all Wikipedia namespaces in a ratio that is always equal to or greater than 3:1. This fact can be partially explained by the lesser persistence of women editors, who tend to leave Wikipedia much more quickly. There is, however, a small group of veteran women editors who, in some cases, surpass men editors in terms of their editing practices and participation in different Wikipedia namespaces.

Use Internet search data to accurately track state level influenza epidemics.

For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people's Internet search pattern. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases.