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We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possible to spatially map the activity of Twitter users referring to certain papers (e.g., dealing with tuberculosis). The resulting maps reveal whether heavy activity on Twitter is correlated with large numbers of people having certain diseases. In this study, we focus on tuberculosis, human immunodeficiency virus (HIV), and malaria, since the World Health Organization ranks these diseases as the top three causes of death worldwide by a single infectious agent. The results of the social-spatial Twitter maps (and additionally performed regression models) reveal the usefulness of the proposed sensor approach. One receives an impression of how research papers on the diseases have been perceived by people in regions that are especially concerned by these diseases. Our study demonstrates a promising approach for using Twitter data for research evaluation purposes beyond simple counting of tweets.

In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics - by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments) - using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts.

Background Little is known about how adverse events are summarised and reported in trials, as detailed information is usually considered confidential. We have acquired clinical study reports (CSRs) from the European Medicines Agency through the Freedom of Information Act. The CSRs describe the results of studies conducted as part of the application for marketing authorisation for the slimming pill orlistat. The purpose of this study was to study how adverse events were summarised and reported in study protocols, CSRs, and published papers of orlistat trials. Methods and Findings We received the CSRs from seven randomised placebo controlled orlistat trials (4,225 participants) submitted by Roche. The CSRs consisted of 8,716 pages and included protocols. Two researchers independently extracted data on adverse events from protocols and CSRs. Corresponding published papers were identified on PubMed and adverse event data were extracted from this source as well. All three sources were compared. Individual adverse events from one trial were summed and compared to the totals in the summary report. None of the protocols or CSRs contained instructions for investigators on how to question participants about adverse events. In CSRs, gastrointestinal adverse events were only coded if the participant reported that they were “bothersome,” a condition that was not specified in the protocol for two of the trials. Serious adverse events were assessed for relationship to the drug by the sponsor, and all adverse events were coded by the sponsor using a glossary that could be updated by the sponsor. The criteria for withdrawal due to adverse events were in one case related to efficacy (high fasting glucose led to withdrawal), which meant that one trial had more withdrawals due to adverse events in the placebo group. Finally, only between 3% and 33% of the total number of investigator-reported adverse events from the trials were reported in the publications because of post hoc filters, though six of seven papers stated that “all adverse events were recorded.” For one trial, we identified an additional 1,318 adverse events that were not listed or mentioned in the CSR itself but could be identified through manually counting individual adverse events reported in an appendix. We discovered that the majority of patients had multiple episodes of the same adverse event that were only counted once, though this was not described in the CSRs. We also discovered that participants treated with orlistat experienced twice as many days with adverse events as participants treated with placebo (22.7 d versus 14.9 d, p-value < 0.0001, Student’s t test). Furthermore, compared with the placebo group, adverse events in the orlistat group were more severe. None of this was stated in the CSR or in the published paper. Our analysis was restricted to one drug tested in the mid-1990s; our results might therefore not be applicable for newer drugs. Conclusions In the orlistat trials, we identified important disparities in the reporting of adverse events between protocols, clinical study reports, and published papers. Reports of these trials seemed to have systematically understated adverse events. Based on these findings, systematic reviews of drugs might be improved by including protocols and CSRs in addition to published articles., In a document analysis, Jeppe B. Schroll and colleagues seek insights into potential bias and underreporting in documentation of adverse events from the mid-1990s clinical trials of the anti-obesity drug orlistat., Author Summary Why Was This Study Done? Most drugs have adverse effects, or harms, that may become evident in clinical trials. Pharmaceutical companies seeking to market a new drug must report adverse effects observed in trial participants in the Clinical Study Reports (CSRs), which they provide to regulatory authorities. Additionally, investigators may report harms in published reports of their trials. We sought to understand the accuracy, and potential bias, in harms reporting for trials of orlistat, a slimming drug from Roche approved in Europe in 1998 and still marketed in Europe today. What Did The Researchers Do And Find? Using a Freedom of Information Act request to the European Medicines Agency (EMA), we obtained CSRs describing seven clinical trials of orlistat. We studied protocol instructions to investigators for reporting harms, the actual reporting of harms in individual CSR records versus summaries, and the final reporting of harms in published papers. We found that protocol instructions to trial investigators had the potential to dilute the appearance of drug-associated harms. Between 3% and 33% of the total adverse effects from CSR summaries were described in published papers. In one trial, we counted adverse events individually and found that both the number of adverse effects and the number of days with adverse effects in participants taking the drug were understated in the corresponding publication. What Do These Findings Mean? The reporting of trials of orlistat in the 1990s understated harms in the summarised results submitted to the EMA for drug approval and in the published papers. Based on the characteristics of harms observed and reported in these trials, we suggest that reports of harms include duration of adverse effects. We also suggest that systematic reviews of drugs might be improved by including protocols and CSRs in addition to published articles.

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents.

This study establishes a new method for estimating the monthly Average Population Present (APP) in Australian regions. Conventional population statistics, which enumerate people where they usually live, ignore the significant spatial mobility driving short term shifts in population numbers. Estimates of the temporary or ambient population of a region have several important applications including the provision of goods and services, emergency preparedness and serve as more appropriate denominators for a range of social statistics. This paper develops a flexible modelling framework to generate APP estimates from an integrated suite of conventional and novel data sources. The resultant APP estimates reveal the considerable seasonality in small area populations across Australia’s regions alongside the contribution of domestic and international visitors as well as absent residents to the observed monthly variations. The modelling framework developed in the paper is conceived in a manner such that it can be adapted and re-deployed both for use with alternative data sources as well as other situational contexts for the estimation of temporary populations.

How and why customers engage with mobile travel apps is vital to mobile marketing of travel-related companies. This paper discusses the antecedents and consequences of mobile travel app engagement. Specifically, this study aims to understand how travel app attributes stimulate mobile travel app engagement and lead to purchase intention. A research model is established based on the Stimulus-Organism-Response (S-O-R) model and the model is tested by Partial Least Squares Path Modeling (PLS-PM). The results show that ease of use, compatibility, and UI attractiveness positively influence mobile travel app engagement, and in turn, affect purchase intention. Furthermore, a multi-group analysis shows that the attributes affecting mobile travel app engagement differ across different customer groups. This paper discusses some theoretical and practical implications.

Introduction The role of gender norms in shaping education and work opportunities, distribution of power and resources, and health and wellbeing is well recognised. However, rigorous studies in low- and middle-income countries on when and how norms change over time and what factors shape adolescents’ and young adult’s gender attitudes are limited. This paper explores the factors that determine adolescents’ gender attitudes, as well as patterns in gender attitude shifts over time among younger and older adolescent boys and girls in India. Data and methods Data presented in this paper were drawn from a unique longitudinal study of adolescents aged 10–19 (Understanding the lives of adolescent and young adults–UDAYA study) in the states of Bihar and Uttar Pradesh in India, conducted during 2015–2016 (wave 1) and 2018–2019 (wave 2). The analysis presented in this paper drew on data from 4,428 boys and 7,607 girls who were aged 10–19 and unmarried at wave 1 and interviewed at both rounds of the survey. We used univariate and bivariate analyses to examine changes in adolescents’ gender role attitudes over time and the association between explanatory variables and gender role attitudes. We also used linear fixed effects regression models to identify factors that shape adolescents’ gender role attitudes. Results Gender role attitudes became more egalitarian over time among boys and girls, except among the older cohort of boys in our study. Among both younger and older cohorts, girls/young women held more egalitarian views than boys/young men and this pattern held over time for both cohorts. Factors that influenced gender role attitudes differed for younger and older adolescents, particularly among boys. While some predictors differed for boys and girls, there were substantial similarities as well. Gender attitudes were affected by factors at the individual, family, peer, and societal levels, as well as by community engagement. Conclusions Our findings show that it is possible to shift gender attitudes toward greater equity and, in so doing, contribute to improved health and rights.

This study provides an overview of science from the Wikipedia perspective. A methodology has been established for the analysis of how Wikipedia editors regard science through their references to scientific papers. The method of co-citation has been adapted to this context in order to generate Pathfinder networks (PFNET) that highlight the most relevant scientific journals and categories, and their interactions in order to find out how scientific literature is consumed through this open encyclopaedia. In addition to this, their obsolescence has been studied through Price index. A total of 1 433 457 references available at Altmetric.com have been initially taken into account. After pre-processing and linking them to the data from Elsevier's CiteScore Metrics the sample was reduced to 847 512 references made by 193 802 Wikipedia articles to 598 746 scientific articles belonging to 14 149 journals indexed in Scopus. As highlighted results we found a significative presence of "Medicine" and "Biochemistry, Genetics and Molecular Biology" papers and that the most important journals are multidisciplinary in nature, suggesting also that high-impact factor journals were more likely to be cited. Furthermore, only 13.44% of Wikipedia citations are to Open Access journals., Comment: Preprint paper published in PLOS ONE

What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.

In this study, it was investigated whether early tweets counts could differentially benefit female and male (first, last) authors in terms of the later citation counts received. The data for this study comprised 47,961 articles in the research area of Life Sciences & Biomedicine from 2014-2016, retrieved from Web of Science's Medline. For each article, the number of received citations per year was downloaded from WOS, while the number of received tweets per year was obtained from PlumX. Using the hurdle regression model, I compared the number of received citations by female and male (first, last) authored papers and then I investigated whether early tweet counts could predict the later citation counts received by female and male (first, last) authored papers. In the regression models, I controlled for several important factors that were investigated in previous research in relation to citation counts, gender or Altmetrics. These included journal impact (SNIP), number of authors, open access, research funding, topic of an article, international collaboration, lay summary, F1000 Score and mega journal. The findings showed that the percentage of papers with male authors in first or last authorship positions was higher than that for female authors. However, female first and last-authored papers had a small but significant citation advantage of 4.7% and 5.5% compared to male-authored papers. The findings also showed that irrespective of whether the factors were included in regression models or not, early tweet counts had a weak positive and significant association with the later citations counts (3.3%) and the probability of a paper being cited (21.1%). Regarding gender, the findings showed that when all variables were controlled, female (first, last) authored papers had a small citation advantage of 3.7% and 4.2% in comparison to the male authored papers for the same number of tweets.

Background Self-management is a promising approach to improve quality of life after stroke. However, evidence for the appropriateness and effectiveness of self-management for stroke survivors with aphasia is limited. This article reports on the process used to develop a supported self-management intervention for stroke survivors with aphasia (SSWA) using co-production and behaviour change theory. Preparatory research included systematic reviews, and qualitative interviews and focus groups with SSWA, family members and speech and language therapists (SLTs). Materials and methods We conducted six, 2 hour long intervention development workshops with key stakeholders. The workshops were informed by principles of co-production and the intervention development process outlined by the Behaviour Change Wheel (BCW). We also incorporated the findings of our preparatory research within workshops. Each workshop included an introduction, 1–2 co-production tasks and time for feedback at the end of the session. Data were analysed on an ongoing basis so that findings could be used to feed in to subsequent workshops and intervention development. Results Workshop participants (n = 12) included; SSWA (n = 5), family members (n = 3) and SLTs (n = 4). Together, participants engaged with accessible and participatory co-production tasks which aligned with the BCW framework. Participants engaged in discussion to define self-management in behavioural terms (behavioural diagnosis) and to identify what needed to change to support self-management. Participant’s co-produced solutions for supporting self-management and discussed options to implement these in practice. Prototype materials were generated by the research team and evaluated by participants. Intervention functions and behaviour change techniques (BCTs) were mapped to the solutions generated by participants by the research team, after the final workshop. A supported self-management intervention for SSWA was developed which will be delivered by SLTs through community stroke services. Conclusions This paper reports the process we used to integrate co-production work with behaviour change theory to develop a complex self-management intervention. This is of relevance for researchers looking to harness the strengths of co-production methods and theory in intervention design. Future research will feasibility test the supported self-management intervention developed. This paper provides transparency to our intervention development process which will help others to better interpret the findings of our feasibility work.

The objective of the paper is to diagnose organisational culture of selected universities and analyse its impact on the innovation processes within them. The subject matter of the study was organisational culture and innovation at universities. The subjects were four selected universities in Poland, Austria, Germany, and Ukraine. The paper provided a definition of organisational culture and its typology. It further discussed the organisational culture of universities and the relationships between organisational culture and innovativeness. The literature review provided foundations for building a model for the formation of a type of organisational culture at universities that is innovation-friendly, which is the added value of the paper. It offers actions worth taking to shape innovation-friendly culture at universities. It is particularly important during difficult time of changing labour market, when universities greatly impact the attitudes of young people. The knowledge of how to shape innovation-friendly organisational culture at universities is necessary for academia to profile future employees in times of continuous changes. To investigate the relationship between organisational culture and the innovativeness of universities, we designed an original survey questionnaire [S1 File]. Organisational culture was diagnosed with the Organizational Culture Assessment Instrument by K.S. Cameron and R.E. Quinn. The analyses were conducted in Dell Statistica v. 13.1 (StatSoft Polska). We normalised data from the Likert rating scale using Kaufman’s and Rousseeuw’s formula. We used Spearman’s correlation coefficient and Kendall’s W to calculate correlations. The research shows that the investigated Polish and Austrian universities are dominated by hierarchy and market cultures. On the other hand, the German and Ukrainian universities host all cultures, but clan and adhocracy dominate there. Moreover, the analyses demonstrated that although the adhocracy culture was the least visible in the investigated organisations, it contributes to university innovativeness the most. The conclusions were used to build a model for promoting innovation-friendly organisational culture at universities. The model contains answers to the research questions. In addition, it offers guidelines for shaping organisational culture to bolster innovation at universities. The research identified relationships between organisational culture and university innovativeness and components that create innovation opportunities at universities as its contribution to management theory. When applied in practice, the guidelines can help form the university’s organisational culture bottom-up.

The purpose of this paper is to analyse the global Sales and Operations Planning (S&OP) process and investigate the steps to support consolidated business planning in worldwide operations and large-scale supply chains. The paper conducts a case study at a multinational manufacturing company applying an abductive approach. It combines the deductive logic from theory and the inductive logic from field observation in an attempt to elaborate further on theory on global S&OP. The analysis is structured and guided by a novel framework for global S&OP, which is developed based on the theoretical background and the case study findings. The research findings characterise the S&OP process for global operations and identify challenges related to the need to synchronise the subsidiaries’ S&OP efforts worldwide to deal with different contingencies of these subsidiaries, and to manage and analyse a large amount of information gathered. The research reveals how the subsidiaries’ performance is analysed by top executives along the global S&OP process, feeding strategic initiatives in the organisation and identifying business opportunities like benchmarking among subsidiaries, synergies with other management practices, and global gains. This paper offers a novel investigation of the global steps on S&OP in a real-life setting, offering a well-documented characterisation of the process that goes beyond the traditional local approach. Moreover, it is the first study to reveal challenges and expected outcomes of such a global perspective for S&OP. The theoretical advancements of S&OP research offered herein aid scholars, opening avenues for middle-range theorising, highlighting the cross-disciplinary nature of the domain, and discussing the use of concepts from related disciplines like Economics, Psychology, and Information Systems. The research findings can also assist executives, especially from multinational manufacturers, in their efforts to consolidate global planning.

There is increasing scrutiny around how science is communicated to the public. For instance, a Twitter account @justsaysinmice (with 70.4K followers in January 2021) was created to call attention to news headlines that omit that mice, not humans, are the ones for whom the study findings apply. This is the case of many headlines reporting on Alzheimer disease (AD) research. AD is characterized by a degeneration of the human brain, loss of cognition, and behavioral changes, for which no treatment is available. Around 200 rodent models have been developed to study AD, even though AD is an exclusively human condition that does not occur naturally in other species and appears impervious to reproduction in artificial animal models, an information not always disclosed. It is not known what prompts writers of news stories to either omit or acknowledge, in the story’s headlines, that the study was done in mice and not in humans. Here, we raised the hypothesis that how science is reported by scientists plays a role on the news reporting. To test this hypothesis, we investigated whether an association exists between articles’ titles and news’ headlines regarding the omission, or not, of mice. To this end, we analyzed a sample of 623 open-access scientific papers indexed in PubMed in 2018 and 2019 that used mice either as models or as the biological source for experimental studies in AD research. We found a significant association (p < 0.01) between articles’ titles and news stories’ headlines, revealing that when authors omit the species in the paper’s title, writers of news stories tend to follow suit. We also found that papers not mentioning mice in their titles are more newsworthy and significantly more tweeted than papers that do. Our study shows that science reporting may affect media reporting and asks for changes in the way we report about findings obtained with animal models used to study human diseases., This study reveals that when scientists omit from the papers’ title that research findings were obtained using mice, as opposed to humans, the media tend to replicate the trend by omitting the use of mice from the news stories headlines that report on these papers, thereby perpetuating the misleading omission.

By utilizing cognitive mapping and leveraging georeferenced text data, this paper aims to suggest a new visualization method that combines the advantages of both conventional and state-of-the-art research techniques to depict the collective identity of place in a single image. The study addressed two research questions: (1) Can crowd-sourced text data be utilized in representing place identity? (2) Can collective place identity be expressed in the form of a cognitive map? By confirming that text data gathered from social media effectively demonstrate people’s behaviors and perceptions related to places, we propose a novel method to create a visual representation of urban identity–a “crowd-sourced cognitive map”. In particular, to improve the conventional cognitive mapping method to depict the collective identity of a city, we draw cognitive maps of Bundang and Ilsan developed in the 1990s, as well as Songdo and Dongtan developed in the 2000s, just outside of the administrative boundaries of Seoul in Korea, through a computational method based on crowd-sourced opinions collected from social media. We open the possibility for the use of social media text data to capture the identity of cities and suggest a graphical image through which people without prior information could also easily apprehend the overall image of a city. The work in this paper is expected to provide a methodological technique for appropriate decision-making and the evaluation of urban identity to shape a more unique and imageable city.

This paper examines the market for initial coin offerings (ICOs). ICOs are smart contracts based on blockchain technology that are designed for entrepreneurs to raise external finance by issuing tokens without an intermediary. Unlike existing mechanisms for early-stage finance, tokens potentially provide investors with rapid opportunities thanks to liquid trading platforms. The marketability of tokens offers novel insights into entrepreneurial finance, which I explore in this paper. First, I document that investors earn on average 8.2% on the first day of trading. However, about 40% of all ICOs destroy investor value on the first day of trading. Second, I explore the determinants of market outcomes and find that management quality and the ICO profile are positively correlated with the funding amount and returns, whereas highly visionary projects have a negative effect. Among the 21% of all tokens that get delisted from a major exchange platform, highly visionary projects are more likely to fail, which investors anticipate. Third, I explore the sensitivity of the ICO market to adverse industry events such as China's ban of ICOs, the hack of leading ledgers, and the marketing ban on FaceBook. I find that the ICO market is highly susceptible to such environmental shocks, resulting in substantial welfare losses for investors.

Nowadays, location based service (LBS) is one of the most popular mobile apps and following with humongous of location data been produced. The publishing of location data can provide benefit for promoting the quality of service, optimizing the commercial environment as well as harmonizing the infrastructure construction. However, as location data may contain some sensitive or confidential information, the publishing may reveal privacy and bring hazards. So the published data had to be disposed to protect the privacy. In order to cope with this problem, a number of algorithms based on the strategy of k-anonymity were proposed, but this is not enough for the privacy protection, as the correlation between the sensitive region and the background knowledge can be used to infer the real location. Thus, consider about this condition, in this paper a ε-sensitive correlation privacy protection scheme is proposed, and provides correlation indistinguishable to the location data. In this scheme, entropy is first used to determine the location centroid of each cell to build up the voronoi diagram. Then the coordinate of the untreated location data that is located in the cell is transferred into the centroid vicinity. Accordingly, the sensitive correlation is destroyed by the coordinate of each published data. The process of transferring the location data is determined by metrics of ε-sensitive correlation privacy, and is rigorous in mathematical justification. At last, security analysis is proposed in this paper to verify the privacy ability of our proposed algorithm based on voronoi diagram and entropy, and then we utilize the comparative experiment to further affirm the advantage of this algorithm in the location data privacy protection as well as the availability of published data.

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970-2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship.

Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research.

Public transport (PT) is a key element in most major cities around the world. With the development of smartphones, available journey planning information is becoming an integral part of the PT system. Each traveler has specific preferences when undertaking a trip, and these preferences can also be reflected on the smartphone. This paper considers transit assignment in urban public-transport networks in which the passengers receive smartphone-based information containing elements that might influence the travel decisions in relation to line loads, as well as passenger benefits, and the paper discusses the transition from the current widespread choosing approach to a personalized decision-making approach based on smartphone information. The approach associated with smartphone guidance that considers passengers’ preference on travel time, waiting time and transfer is proposed in the process of obtaining his/her preferred route from the potential travel routes generated by the Deep First Search (DFS) method. Two other approaches, based on the scenarios reflecting reality, include passengers with access to no real time information, and passengers that only have access to the arrival time at the platform are used as comparisons. For illustration, the same network proposed by Spiess and Florian is utilized on the experiments in an agent-based model. Two experiments are conducted respectively according to whether each passenger’s choosing method is consistent. As expected, the results in the first experiment showed that the travel for consistent passengers with smartphone guidance was clearly shorter and that it can reduce travel time exceeding 15% and weighted cost exceeding 20%, and the average saved time approximated 3.88 minutes per passenger. The second experiment presented that travel cost, as well as cost savings, gradually decreased by employing smartphone guidance, and the maximum cost savings accounted for 14.2% of the total weighted cost.

With more and more political candidates using social media for campaigning, researchers are looking at measuring the effectiveness of this medium. Most research, however, concentrates on the bare count of likes (or twitter mentions) in an attempt to correlate social media presence and winning. In this paper, we propose a novel method, Interaction Strength Plot (IntS) to measure the passive interactions between a candidate's posts on Facebook and the users (liking the posts). Using this method on original Malaysian General Election (MGE13) and Australian Federal Elections (AFE13) Facebook Pages (FP) campaign data, we label an FP as performing well if both the posting frequency and the likes gathered are above average. Our method shows that over 60% of the MGE13 candidates and 85% of the AFE13 candidates studied in this paper had under-performing FP. Some of these FP owners would have been identified as popular based on bare count. Thus our performance chart is a vital step forward in measuring the effectiveness of online campaigning.

Social networks influence health-related behaviors, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behaviors, it is less understood how the structure or topology of the network, in itself, impacts an individual's health behaviors and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual's health and wellness. We develop a model called the Network-Driven health predictor (NetCARE) that leverages features representative of social network structure. Using a large longitudinal data set of students enrolled in the NetHealth study at the University of Notre Dame, we show that the NetCARE model improves the overall prediction performance over the baseline models -- that use demographics and physical attributes -- by 38%, 65%, 55%, and 54% for the wellness states -- stress, happiness, positive attitude, and self-assessed health -- considered in this paper., Comment: 11 pages, 10 display items(figure and tables)

The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when “perfect” nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches., Author summary It has been demonstrated in retrospective settings that flu forecasting can be improved by augmenting forecasting models with internet-based nowcasts (i.e., one-week-ahead forecasts generated with internet data). The improvement of internet-assisted forecasting models, however, has not translated to overall improvement in flu forecasting of public health-relevant targets. This is partially due to the relatively simple forecasting models that nowcasts are augmenting and partially because nowcasts do not directly inform all public health-relevant targets, such as the start, the peak timing, and the peak intensity of the flu season (i.e., seasonal targets). This paper conducts an experiment to provide clarity on the benefits and limitations of augmenting flu forecasting models with internet-based nowcasts in practice. We show that 1) nowcasts can improve flu forecasting across all geographic regions, flu seasons, and public health-relevant targets, including seasonal targets, and 2) the benefits of nowcasting are uneven, as even in the perfect nowcasting setting, only marginal improvements to forecasts of seasonal targets are possible relative to short-term forecasts. This is because nowcasts provide direct information about short-term targets but only indirect information about seasonal targets. This paper supports previous findings that nowcasts can improve flu forecasting but clarifies the limitations of those improvements, both in terms of magnitude of possible improvement as well as which targets can be improved. The continued advancement of flu forecasting hinges on the development of model improvements separate from nowcasting.

Background Tobacco industry interference has been identified as the greatest obstacle to the implementation of evidence-based measures to reduce tobacco use. Understanding and addressing industry interference in public health policy-making is therefore crucial. Existing conceptualisations of corporate political activity (CPA) are embedded in a business perspective and do not attend to CPA’s social and public health costs; most have not drawn on the unique resource represented by internal tobacco industry documents. Building on this literature, including systematic reviews, we develop a critically informed conceptual model of tobacco industry political activity. Methods and Findings We thematically analysed published papers included in two systematic reviews examining tobacco industry influence on taxation and marketing of tobacco; we included 45 of 46 papers in the former category and 20 of 48 papers in the latter (n = 65). We used a grounded theory approach to build taxonomies of “discursive” (argument-based) and “instrumental” (action-based) industry strategies and from these devised the Policy Dystopia Model, which shows that the industry, working through different constituencies, constructs a metanarrative to argue that proposed policies will lead to a dysfunctional future of policy failure and widely dispersed adverse social and economic consequences. Simultaneously, it uses diverse, interlocking insider and outsider instrumental strategies to disseminate this narrative and enhance its persuasiveness in order to secure its preferred policy outcomes. Limitations are that many papers were historical (some dating back to the 1970s) and focused on high-income regions. Conclusions The model provides an evidence-based, accessible way of understanding diverse corporate political strategies. It should enable public health actors and officials to preempt these strategies and develop realistic assessments of the industry’s claims., In this thematic analysis, Selda Ulucanlar and colleagues develop taxonomies and an overall model to describe the political strategies used by the tobacco industry to influence policy around tobacco taxation and marketing., Author Summary Why Was This Study Done? Interference by the tobacco industry in government policy-making is known to be an important reason for governments’ failure to adopt proven measures to reduce tobacco consumption. Our study aimed to systematically review tobacco industry political activity from a critical societal perspective using in-depth empirical evidence. We set out to build a widely applicable model that governments in different countries could use to identify and preempt industry interference in policy. What Did the Researchers Do and Find? We analysed 65 papers included in previous systematic reviews that examined tobacco industry political activity in two policy areas: taxation and marketing of tobacco products. Using constructivist grounded theory, we identified industry arguments and techniques and then grouped these under the more general heading of strategies, finally developing a taxonomy and model of tobacco industry political activity. According to our Policy Dystopia Model, the industry produces an alarmist narrative that proposed policies will fail and lead to a great number of undesirable social and economic consequences (outlined in our taxonomy of discursive strategies). The industry also uses different methods (outlined in our taxonomy of instrumental strategies) to disseminate this narrative and persuade decision-makers in order to block policies. What Do These Findings Mean? Public health actors and policy-makers can use our model and taxonomies to better promote tobacco-related policies and to render ineffective tobacco industry opposition and political activity. In particular, they need to pay attention to alliance formation, information provision, interdisciplinary working, and transparency in policy-making.

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

The popularity of online information generally experiences a rising and falling evolution. This paper considers the "burst", "peak", and "fade" key events together as a representative summary of popularity evolution. We propose a novel prediction task-predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify "burst", "peak", and "fade" in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution.

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research., Comment: Submitted to PLOS ONE; currently under review. Figures not included

This paper seeks to examine the extent to which technological advances can enhance inter-organizational information sharing in disaster relief. Our case is the Virtual OSOCC (On-Site Operations Coordination Centre) which is a part of the Global Disaster Alert and Coordination System (GDACS) under the United Nations Office for Coordination of Humanitarian Affairs (UN OCHA). The online platform, which has been developing for more than a decade, provides a unique insight into coordination behaviour among disaster management agencies and individual actors. We build our study on the analysis of a complete database of user interaction including more than 20,000 users and 11,000 comments spread across approximately 300 disaster events. Controlling for types and severities of the events, location-specific vulnerabilities, and the overall trends, we find that the introduction of new features have led to increases in user activity.We supplement the data-driven approach with evidence from semi-structured interviews with administrators and key users, as well as a survey among all users specifically designed to capture and assess the elements highlighted by both interviews and data analysis. This paper seeks to examine the extent to which technological advances can enhance inter-organizational information sharing in disaster relief. Our case is the Virtual OSOCC (On-Site Operations Coordination Centre) which is a part of the Global Disaster Alert and Coordination System (GDACS) under the United Nations Office for Coordination of Humanitarian Affairs (UN OCHA). The online platform, which has been developing for more than a decade, provides a unique insight into coordination behaviour among disaster management agencies and individual actors. We build our study on the analysis of a complete database of user interaction including more than 20,000 users and 11,000 comments spread across approximately 300 disaster events. Controlling for types and severities of the events, location-specific vulnerabilities, and the overall trends, we find that the introduction of new features have led to increases in user activity.We supplement the data-driven approach with evidence from semi-structured interviews with administrators and key users, as well as a survey among all users specifically designed to capture and assess the elements highlighted by both interviews and data analysis.

When disaster events capture global attention users of Twitter form transient interest communities that disseminate information and other messages online. This paper examines content related to Typhoon Haiyan (locally known as Yolanda) as it hit the Philippines and triggered international humanitarian response and media attention. It reveals how Twitter conversations about disasters evolve over time, showing an issue attention cycle on a social media platform. The paper examines different functions of Twitter and the information hubs that drive and sustain conversation about the event. Content analysis shows that the majority of tweets contain information about the typhoon or its damage, and disaster relief activities. There are differences in types of content between the most retweeted messages and posts that are original tweets. Original tweets are more likely to come from ordinary users, who are more likely to tweet emotions, messages of support, and political content compared with official sources and key information hubs that include news organizations, aid organization, and celebrities. Original tweets reveal use of the site beyond information to relief coordination and response.

As science becomes increasingly interdisciplinary, we are expected to acquire both breadth of knowledge and depth of expertise. In bioinformatics, this is especially true. Keeping up to date with major techniques across multiple specialisations can be daunting, but you need not face it alone. A journal club is an excellent way to take in the scientific literature, keep up with developments in your field, and hone your communication and analytical skills. In general, a journal club is a group of people who meet regularly to discuss one or more scientific papers. The structure of such a club can vary. In the more traditional format, an individual studies a paper and then presents it to the group, usually in the form of PowerPoint slides, with time for questions. In some institutions, the journal club is for students only, designed to fulfill the requirements of a course or postgraduate program; attendance is obligatory, the scope of the literature is narrow, and the format is prescribed. The preparation of slides and a lecture may be required. Other kinds of journal clubs are just lab meetings in disguise, with the usual lab head and group members in attendance and one member nominated to present the paper. A formal style often fits well within an established academic structure—but what if your discipline is emerging? Consider the field of bioinformatics. Expertise may be spread across departments and institutions, and there may not be an obvious place or critical mass in any one lab for a traditional journal club. Research students, “pet bioinformaticians,” [1] and those interested in bioinformatics from adjoining fields all need a place to gather. We are pleased to offer an alternative structure to address this situation—an informal journal club, designed to bring together a diversity of backgrounds and career stages to discuss bioinformatics while building a network of like-minded peers. Additional benefits of such a journal club may include friendship and breakfast (see Rule 2)! We thoroughly recommend it to anyone who asks (as well as those who don’t). While this advice is drawn from our experiences in the Parkville Bioinformatics Journal Club, it is applicable to developing informal journal clubs of all disciplines. The advice contained in these rules will also help those who want to spice up their existing formal format. So don’t be a “lonely bioinformatician”[1], create a journal club! Follow these Ten Simple Rules to find out how.

Amid the Coronavirus Disease 2019 (COVID-19) pandemic, preprints in the biomedical sciences are being posted and accessed at unprecedented rates, drawing widespread attention from the general public, press, and policymakers for the first time. This phenomenon has sharpened long-standing questions about the reliability of information shared prior to journal peer review. Does the information shared in preprints typically withstand the scrutiny of peer review, or are conclusions likely to change in the version of record? We assessed preprints from bioRxiv and medRxiv that had been posted and subsequently published in a journal through April 30, 2020, representing the initial phase of the pandemic response. We utilised a combination of automatic and manual annotations to quantify how an article changed between the preprinted and published version. We found that the total number of figure panels and tables changed little between preprint and published articles. Moreover, the conclusions of 7.2% of non-COVID-19–related and 17.2% of COVID-19–related abstracts undergo a discrete change by the time of publication, but the majority of these changes do not qualitatively change the conclusions of the paper., During the COVID-19 pandemic, preprints in the biomedical sciences are being posted and accessed at unprecedented rates, but can we trust them? This study reveals that the majority of preprints published within the first 4 months of the pandemic are comparable to their published version.

Self-control can be assisted by using self-control strategies rather than relying solely on willpower to resist tempting situations and to make more goal-consistent decisions. To understand how self-control strategies can aid financial goals, we conducted a meta-analysis (Study 1) to aggregate the latest research on self-control strategies in the financial domain and to estimate their overall effectiveness for saving and spending outcomes. Across 29 studies and 12 different self-control strategies, strategies reduced spending and increased saving significantly with a medium effect size (d = 0.57). Proactive and reactive strategies were equally effective. We next examined whether these strategies studied in the academic literature were present in a media sample of websites (N = 104 websites with 852 strategies) and in individuals’ personal experiences (N = 939 participants who listed 830 strategies). About half the strategies identified in the meta-analysis were present in the media sample and about half were listed by lay participants as strategies they personally use. In sum, this paper provides a comprehensive overview of the self-control strategies that have been studied in the empirical literature to date and of the strategies promoted in the media and used in daily life, identifying gaps between these perspectives.

The decision-making relating to effective marketing is less supported by operational methodologies and optimizing methods in high education sectors than in the companies. This paper presents an application of goal programming as an aid for the optimizing marketing mix elements. It describes a project undertaken at one of the Universities in Poland considering the marketing mix includes all 7Ps elements at University. The constraints are first identified based on interviews with academic experts and survey. The analytic hierarchy process method is used to select the forms of promotional activities and Activity-Based Costing method are performed to determine the costs of the activities. Then, based on constraints, the multiple-criteria-programming model is built and applied to construct the marketing mix model at university, and it was solved using LP-Solve software AMPL. A comparison between the newly designed marketing mix and the existing one in terms of each of the criteria shows that the overall objective function could be greatly improved, optimized value can be obtained, and the model can be easily applied in any other high education sectors. The consequence of using the model is the optimal selection (with existing limitations) of promotional activities, taking into account their impact on the perception of the image of the university, significant from the perspective of students and impact on profitability, which is important from the perspective of the university management.

Online messaging app Telegram has increased in popularity in recent years surpassing Twitter and Snapchat by the number of active monthly users in late 2020. The messenger has also been crucial to protest movements in several countries in 2019-2020, including Belarus, Russia and Hong Kong. Yet, to date only few studies examined online activities on Telegram and none have analyzed the platform with regard to the protest mobilization. In the present study, we address the existing gap by examining Telegram-based activities related to the 2019 protests in Hong Kong. With this paper we aim to provide an example of methodological tools that can be used to study protest mobilization and coordination on Telegram. We also contribute to the research on computational text analysis in Cantonese-one of the low-resource Asian languages,-as well as to the scholarship on Hong Kong protests and research on social media-based protest mobilization in general. For that, we rely on the data collected through Telegram's API and a combination of network analysis and computational text analysis. We find that the Telegram-based network was cohesive ensuring efficient spread of protest-related information. Content spread through Telegram predominantly concerned discussions of future actions and protest-related on-site information (i.e., police presence in certain areas). We find that the Telegram network was dominated by different actors each month of the observation suggesting the absence of one single leader. Further, traditional protest leaders-those prominent during the 2014 Umbrella Movement,-such as media and civic organisations were less prominent in the network than local communities. Finally, we observe a cooldown in the level of Telegram activity after the enactment of the harsh National Security Law in July 2020. Further investigation is necessary to assess the persistence of this effect in a long-term perspective.

This paper offers an economic model of the operation of multi-level marketing (MLM) firms in competitive and non-competitive markets. The model takes a recursive approach to analyse decision making at the distributor level in order to understand basic issues in the MLM market and firm structure. Specifically, it is shown that under reasonable assumptions MLM firms will have a limited structure. In cases where commissions increase with the number of levels, MLM firms will include no more than six to nine levels in equilibrium. In cases of fixed commissions, market conditions dictate a cap on the number of distributors. These conditions imply a limited “multi-level” structure. They also imply that the revenues of the median distributor are mainly a result of direct sales and not a result of commissions. The model also suggests that MLM firms will only arise where marketing costs are substantial, and that it is primarily individuals with small outside offers who choose to become distributors. Finally, the model provides a formula that calculates market prices for a monopoly MLM firm.

Introduction Community engagement (CE) is an effective public health strategy for improving health outcomes. There is limited published knowledge about effective approaches to CE in ensuring effective responses to COVID-19 throughout lockdowns, travel restrictions and social distancing. In this paper, we contribute to bridging this gap by highlighting experience of CE in Vietnam, specifically focusing on migrant workers in Vietnam. Methods A cross-sectional qualitative study design was used with qualitative data collection was carried out during August-October 2020. Two districts were purposefully selected from two large industrial zones. Data was collected using in-depth interviews (n = 36) with individuals and households, migrants and owners of dormitories, industrial zone factory representatives, community representatives and health authorities. Data was analyzed using thematic analysis approach. The study received ethics approval from the Hanoi University Institutional Review Board. Results The government’s response to COVID-19 was spearheaded by the multi-sectoral National Steering Committee for the Prevention and Control of COVID-19, chaired by the Vice Prime Minister and comprised different members from 23 ministries. This structure was replicated throughout the province and local levels and all public and private organizations. Different activities were carried out by local communities, following four key principles of infection control: early detection, isolation, quarantine and hospitalization. We found three key determinants of engagement of migrant workers with COVID-19 prevention and control: availability of resources, appropriate capacity strengthening, transparent and continuous communication and a sense of trust in government legitimacy. Discussion and conclusion Our results support the current literature on CE in infection control which highlights the importance of context and suggests that future CE should consider five key components: multi-sectoral collaboration with a whole-of-community approach to strengthen governance structures with context-specific partnerships; mobilization of resources and decentralization of decision making to encourage self-reliance and building of local capacity; capacity building through training and supervision to local institutions; transparent and clear communication of health risks and sensitization of local communities to improve compliance and foster trust in the government measures; and understanding the urgent needs ensuring of social security and engaging all parts of the community, specifically the vulnerable groups.

Increasingly, individuals experiencing loneliness are seeking support on online forums—some of which focus specifically on discussions around loneliness (loneliness forums); loneliness may influence how these individuals communicate in other online forums not focused on loneliness (non-loneliness forums). In order to provide effective and appropriate online interventions around loneliness, it is important to understand how users who publish posts in a loneliness forum communicate in the loneliness forum and non-loneliness forums they belong to. In this paper, using language features, the following analyses are conducted: (1) Posts published on an online loneliness forum on Reddit,/r/Lonelyare compared to posts (published by the same users and around the same time period) on two Reddit online forums i.e. an advice seeking forum,/r/AskRedditand a forum focused on discussions around depression (depression forum),/r/depression. (2) Interventions related to loneliness may vary depending on if an individual is lonely and depressed or lonely but not depressed; language use differences in posts published in/r/Lonelyby the following set of users are identified: (a) users who post in both/r/Lonelyand a depression forum and (b) users who post in/r/Lonelybut not in the depression forum. The findings from this work gain new insights, for example: (i)/r/Lonelyusers tend to seek advice/ask questions related to relationships in the advice seeking forum,/r/AskRedditand (ii) users who are members of the loneliness forum but not the depression forum tend to publish posts (on the loneliness forum) on topic themes related to work/job, however, those who are members of the loneliness and depression forums tend to use more words associated with anger, negation, death, and post on topic themes related to affection relative to relationships in their loneliness forum posts. Some of the findings from this work also align with prior work e.g. users who express loneliness in online forums tend to make more reference to self. These findings aid in gaining insights into how users communicate on these forums and their support needs, thereby informing loneliness interventions.

The increase of social media (SM) has led to continuous deviations in how day-to-day entrepreneurial activities can be carried out. Additionally, studies devoted to SM entrepreneurship and SM are relatively limited and fragmented in their focus. However there is growing interest from scholars, practitioners and academia for upcoming studies and exploration within small and medium-sized enterprises (SMEs) context. This research explores the impact of entrepreneurial orientation (EO) on SM adoption and SME performance in developing countries. We employed the resource-based view (RBV) as the foundation for developing the conceptual framework The present study employed a closed-ended questionnaire to collect data from SMEs located in Pakistan. Partial-least-squares-structural equation-modeling (PLS-SEM) was utilized for the analysis of 423 responses. The results proved a direct positive link between EO–SMEs performance, EO–SM adoption, SM adoption–SMEs performance, innovation capabilities (IC), and SME performance. Partial mediation was found between EO and SME performance, and the significant moderation effect of IC was found between SM adoption and SME performance. This paper has implications for practitioners and researchers regarding SM adoption in the SMEs. It builds an empirical, multi-dimensional hypothesized model, including mediating and moderating roles affecting the relationships.

Faced with the increasing demand for blood and greater restrictions on ensuring the safety of transfusions, voluntary donation is currently the only and best alternative for the health system to have a sustainable and safe blood supply. In this context, one of the primary strategies of blood transfusion centres is to increase the satisfaction of their active donors so that they maintain their intention to donate in the future and, in turn, make the necessary recommendations so that third parties can become new donors. That is why this paper raises a question for research concerning, what should the premises be to guide the management of blood transfusion centres to maintain and even increase the satisfaction and loyalty of their active donors? In order to respond to this issue, a change in paradigm is proposed based on a management model supported by donor orientation and service quality as basic pillars, as well as on the development of a number of key social capital resources that explain this orientation toward the donor. In both approaches, the donor becomes the cornerstone of decision-making, moving away from the traditional model which is focused more on achieving targets related to the collection of blood bags. Through the empirical analysis of a sample of 126 employees from various transfusion centres and 26,833 active donors in nine Spanish autonomous communities, we have been able to validate this proposed management model as a useful tool to blood promotion agents in their decision-making processes.

This paper presents the design of a Built-in-self-Test (BIST) implemented Advanced Encryption Standard (AES) cryptoprocessor Application Specific Integrated Circuit (ASIC). AES has been proved as the strongest symmetric encryption algorithm declared by USA Govt. and it outperforms all other existing cryptographic algorithms. Its hardware implementation offers much higher speed and physical security than that of its software implementation. Due to this reason, a number of AES cryptoprocessor ASIC have been presented in the literature, but the problem of testability in the complex AES chip is not addressed yet. This research introduces a solution to the problem for the AES cryptoprocessor ASIC implementing mixed-mode BIST technique, a hybrid of pseudo-random and deterministic techniques. The BIST implemented ASIC is designed using IEEE industry standard Hardware Description Language(HDL). It has been simulated using Electronic Design Automation (EDA)tools for verification and validation using the input-output data from the National Institute of Standard and Technology (NIST) of the USA Govt. The simulation results show that the design is working as per desired functionalities in different modes of operation of the ASIC. The current research is compared with those of other researchers, and it shows that it is unique in terms of BIST implementation into the ASIC chip.

One of the significant challenges in the Internet of Things (IoT) is the provisioning of guaranteed security and privacy, considering the fact that IoT devices are resource-limited. Oftentimes, in IoT applications, remote users need to obtain real-time data, with guaranteed security and privacy, from resource-limited network nodes through the public Internet. For this purpose, the users need to establish a secure link with the network nodes. Though the IPv6 over low-power wireless personal area networks (6LoWPAN) adaptation layer standard offers IPv6 compatibility for resource-limited wireless networks, the fundamental 6LoWPAN structure ignores security and privacy characteristics. Thus, there is a pressing need to design a resource-efficient authenticated key exchange (AKE) scheme for ensuring secure communication in 6LoWPAN-based resource-limited networks. This paper proposes a resource-efficient secure remote user authentication scheme for 6LoWPAN-based IoT networks, called SRUA-IoT. SRUA-IoT achieves the authentication of remote users and enables the users and network entities to establish private session keys between themselves for indecipherable communication. To this end, SRUA-IoT uses a secure hash algorithm, exclusive-OR operation, and symmetric encryption primitive. We prove through informal security analysis that SRUA-IoT is secured against a variety of malicious attacks. We also prove the security strength of SRUA-IoT through formal security analysis conducted by employing the random oracle model. Additionally, we prove through Scyther-based validation that SRUA-IoT is resilient against various attacks. Likewise, we demonstrate that SRUA-IoT reduces the computational cost of the nodes and communication overheads of the network.

The amount, size, complexity, and importance of Knowledge Graphs (KGs) have increased during the last decade. Many different communities have chosen to publish their datasets using Linked Data principles, which favors the integration of this information with many other sources published using the same principles and technologies. Such a scenario requires to develop techniques of Linked Data Summarization. The concept of a class is one of the core elements used to define the ontologies which sustain most of the existing KGs. Moreover, classes are an excellent tool to refer to an abstract idea which groups many individuals (or instances) in the context of a given KG, which is handy to use when producing summaries of its content. Rankings of class importance are a powerful summarization tool that can be used both to obtain a superficial view of the content of a given KG and to prioritize many different actions over the data (data quality checking, visualization, relevance for search engines…). In this paper, we analyze existing techniques to measure class importance and propose a novel approach called ClassRank. We compare the class usage in SPARQL logs of different KGs with the importance ranking produced by the approaches evaluated. Then, we discuss the strengths and weaknesses of the evaluated techniques. Our experimentation suggests that ClassRank outperforms state-of-the-art approaches measuring class importance.

Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and document categorization has led to an emphasis on graph-based keyword extraction techniques because they can capture more structural information compared to other classic text analysis methods. In this paper, we propose a simple unsupervised text mining approach that aims to extract a set of keywords from a given text and analyze its topic diversity using graph analysis tools. Initially, the text is represented as a directed graph using synonym relationships. Then, community detection and other measures are used to identify keywords in the text. The set of extracted keywords is used to assess topic diversity within the text and analyze its sentiment. The proposed approach relies on grouping semantically similar candidate words. This approach ensures that the set of extracted keywords is comprehensive. Differing from other graph-based keyword extraction approaches, the proposed method does not require user parameters during graph construction and word scoring. The proposed approach achieved significant results compared to other keyword extraction techniques.

The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the circumstances of big data, this paper showed the feasibility of vocational portraits driven by job advertisements with data analysis and processing engineering technicians (DAPET) as an example. First, according to the description of occupation in the Chinese Occupation Classification Grand Dictionary, a text similarity algorithm was used to preliminarily choose recruitment data with high similarity. Second, Convolutional Neural Networks for Sentence Classification (TextCNN) was used to further classify the preliminary corpus to obtain a precise occupational dataset. Third, the specialty and skill were taken as named entities that were automatically extracted by the named entity recognition technology. Finally, putting the extracted entities into the occupational dataset, the occupation characteristics of multiple dimensions were depicted to form a profile of the vocation.

This paper explores whether efficacy beliefs can alter the relationship between worry about climate change and personal energy-saving behaviors, controlling for climate change beliefs and socio-demographics. For this purpose, we used data from 23 countries that participated in the European Social Survey Round 8 (N = 44 387). Worry about climate change, personal efficacy, personal outcome expectancy, and collective outcome expectancy were each associated with personal energy-saving behaviors concerning either energy curtailment or energy efficiency. The results further show that outcome expectancies moderate the association between worry about climate change and both types of energy behaviors. Worry was more strongly related to energy curtailment behaviors among those with high levels of personal and collective outcome expectancy. A similar pattern was found for energy efficiency behaviors, which were more strongly predicted by worry about climate change when combined with high levels of collective outcome expectancy. These findings are relevant for climate change communication, especially informational campaigns aiming to lower overall household energy use. publishedVersion

The emergence of a new tech elite in Silicon Valley and beyond raises questions about the economic reach, political influence, and social importance of this group. How do these inordinately influential people think about the world and about our common future? In this paper, we test a) whether members of the tech elite share a common, meritocratic view of the world, b) whether they have a “mission” for the future, and c) how they view democracy as a political system. Our data set consists of information about the 100 richest people in the tech world, according to Forbes, and rests on their published pronouncements on Twitter, as well as on their statements on the websites of their philanthropic endeavors. Automated “bag-of-words” text and sentiment analyses reveal that the tech elite has a more meritocratic view of the world than the general US Twitter-using population. The tech elite also frequently promise to “make the world a better place,” but they do not differ from other extremely wealthy people in this respect. However, their relationship to democracy is contradictory. Based on these results, we conclude that the tech elite may be thought of as a “class for itself” in Marx’s sense—a social group that shares particular views of the world, which in this case means meritocratic, missionary, and inconsistent democratic ideology.

Background Understanding patients’ trust in health information sources is critical to designing work systems in healthcare. Patient-centered communication during the visit might be a major factor in shaping patients’ trust in information sources. Objective The purpose of this paper is to explore relationships between patient ratings of clinician communication during the visit and patient trust in health information sources. Methodology We conducted a secondary analysis of the nationally-representative Health Information National Trends Surveys; HINTS4 Cycle1 (2011), HINTS4 Cycle4 (2014), and HINTS5 Cycle1 (2017), and HINTS5 Cycle2 (2018). We created a composite score of patient-centered communication from five questions and dichotomized at the median. We created multivariable logistic regression models to see how patient-centered communication influenced trust in different information sources across cycles. Consecutively, we used hierarchical analysis for aggregated data. Results We analyzed data from 14,425 individuals. In the adjusted logistic models for each cycle and the hierarchical model, clinicians’ perceived patient-centered communication skills were significantly associated with increased trust in the clinicians as an information source. Conclusion Clinicians still represent an essential source of trustworthy information reinforced by patient-centered communication skills. Given that trust helps build healing relationships that lead to better healthcare outcomes, communication sets an essential foundation to establish necessary trust. Interpreting information from the internet sources for patients is likely to remain a vital clinician function.

Online social networks like Twitter and Facebook are among the most popular sites on the Internet. Most online social networks involve some specific features, including reciprocity, transitivity and degree heterogeneity. Such networks are so called scale-free networks and have drawn lots of attention in research. The aim of this paper is to develop a novel methodology for directed network embedding within the latent space model (LSM) framework. It is known, the link probability between two individuals may increase as the features of each become similar, which is referred to as homophily attributes. To this end, penalized pair-specific attributes, acting as a distance measure, are introduced to provide with more powerful interpretation and improve link prediction accuracy, named penalized homophily latent space models (PHLSM). The proposed models also involve in-degree heterogeneity of directed scale-free networks by embedding with the popularity scales. We also introduce LASSO-based PHLSM to produce an accurate and sparse model for high-dimensional covariates. We make Bayesian inference using MCMC algorithms. The finite sample performance of the proposed models is evaluated by three benchmark simulation datasets and two real data examples. Our methods are competitive and interpretable, they outperform existing approaches for fitting directed networks.

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.