Public communication by research institutes compared across countries and sciences: building capacity for engagement or competing for visibility?

Leading academic institutions, governments, and funders of research across the world have spent the last few decades fretting publicly about the need for scientists and research organisations to engage more widely with the public and be open about their research. While a global literature asserts that public communication has changed from a virtue to a duty for scientists in many countries and disciplines, our knowledge about what research institutions are doing and what factors drive their 'going public' is very limited. Here we present the first cross-national study of N = 2,030 research institutes within universities and large scientific organisations in Brazil, Germany, Italy, Japan, the Netherlands, Portugal, the United Kingdom, and the United States of America. We find that institutes embrace communication with non-peers and do so through a variety of public events and traditional news media-less so through new media channels-and we find variation across countries and sciences, yet these are less evident than we expected. Country and disciplinary cultures contribute to the level of this communication, as do the resources that institutes make available for the effort; institutes with professionalised staff show higher activity online. Future research should examine whether a real change in the organisational culture is happening or whether this activity and resource allocation is merely a means to increase institutional visibility

Any Port in a Storm: Vessel Activity and the Risk of IUU-Caught Fish Passing through the World’s Most Important Fishing Ports

This study assesses the risk of fish from illegal, unregulated and unreported (IUU) sources passing through the world’s most important fishing ports and explores the drivers of this risk. Like previous studies it has attempted to rank ports and States based on landings and vessel visits reported by governments by using Automatic Identification System (AIS) positional data transmitted by fishing and fish carrier vessels to identify the locations of ports and rank them based on the frequency of visits by foreign-flagged and domestic-flagged vessels. It advances our thinking in that (i) the analysis includes an estimation of the hold capacity of fishing vessels and is therefore able to rank ports based on the total hold capacity of vessels visiting them and (ii) the profile and the frequency of vessel visits inform an assessment of the relative risks between different ports, and the implications for the implementation of the Port State Measures Agreement (PSMA). The study also assesses the accuracy and utility of AIS-derived data for determining IUU risk globally for all ports, notably by cross-referencing its findings with those of other studies. The study develops a broad suite of indicators that quantify and aggregate the AIS-derived port visit information in conjunction with published and publicly available policy and regulatory information drawn from other sources, such as the compliance record with binding port State measures of regional fisheries management organizations, to raise a global port State IUU Risk Index. The comparison of achieved risk scores with national income, levels of corruption, and geography provides insights into factors driving (aggravating) or modulating (mitigating) risks of IUU-caught seafood passing through a Nation’s fishing ports, and supports a view that States with weaker governance also face higher odds of visits by vessels likely to have engaged in IUU fishing (i.e. higher external risks). Based on an in-depth assessment of 14 individual ports globally, appended as a supplement to this paper, the study finds that overall, and with the possible exception of mandatory advance request procedures for entering ports, the implementation of key provisions of the 2009 PSMA remains severely lacking. The two main areas for improvement are the posting of publicly available PSM-related information on national and/or FAO portals, and the formal designation of ports

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

Advanced Air Quality Management with Machine Learning

Air pollution has been a significant health risk factor at a regional and global scale. Although the present method can provide assessment indices like exposure risks or air pollutant concentrations for air quality management, the modeling estimations still remain non-negligible bias which could deviate from reality and limit the effectiveness of emission control strategies to reduce air pollution and derive health benefits. The current development in air quality management is still impeded by two major obstacles: (1) biased air quality concentrations from air quality models and (2) inaccurate exposure risk estimations Inspired by more available and overwhelming data, machine learning techniques provide promising opportunities to solve the above-mentioned obstacles and bridge the gap between model results and reality. This dissertation illustrates three machine learning applications to strengthen air quality management: (1) identifying heterogeneous exposure risk to air pollutants among diverse urbanization levels, (2) correcting modeled air pollutant concentrations and quantifying the bias of sources from model inputs, and (3) examine nonlinear air pollutant responses to local emissions. This dissertation uses Taiwan as a case study, due to its well-established hospital data, emission inventory, and air quality monitoring network. In conclusion, although ML models have become common in atmospheric and environmental health science in recent years, the modeling processes and output interpretation should rely on interdisciplinary professions and judgment. Except for meeting the basic modeling performance, future ML applications in atmospheric and environmental health science should provide interpretability and explainability in terms of human-environment interactions and interpretable physical/chemical mechanisms. Such applications are expected to feedback to traditional methods and deepen our understanding of environmental science

A Bayesian Functional Methodology for Dengue Risk Mapping in Latin America and the Caribbean

Dengue fever has become one of the most outstanding infectious diseases in the world. Besides, the incidence and prevalence of dengue are increasing in the endemic areas of the tropical and subtropical regions. Space and time disease mapping models are common instruments to explain the patterns of disease counts, where hierarchical Bayesian models constitute a suitable framework for their formulation. These random events reflect interactions between nearby geographic locations, as well as correlations between close temporary instants. Functional data analysis techniques can better describe the evolution of disease mapping. In this paper, the risk of dengue in Mexico, Central and South America is studied from a Functional approach through a Bayesian estimation model focused on Hilbert-valued autoregressive processes combined with the Kalman filtering algorithm. Thus, the temporal functional evolution of spatial geographic patterns of incidence risk in disease mapping during 1998-2018 is approximated. Applying this methodology, the excess of smoothing that occurs with traditional models is avoided and the heterogeneity is conserved across the years. It improves the number of false positives created by noise and the number of false negatives as well. The results obtained with the application of this model are compared with those of previous models, corroborating the preceding statements and obtaining better results in the relative risk estimates, providing greater robustness and stability of disease risk estimates

Relational Benefits, Customer Satisfaction, And Customer Loyalty In Chain Store Restaurants

[[abstract]]This study aims to investigate the structural relationships among relational benefits, customer satisfaction, and customer loyalty in the chain store restaurants. Based on a theoretical background literature review, three types of customer relational benefits were determined: psychological, social, and special treatment benefits. Theoretical relationships among relational benefits, customer satisfaction, and customer loyalty were derived from the review of literature, and a theoretical model was proposed. The proposed model was then tested employing data collected from 267 customers of chain store restaurants. The results of subsequent analysis of the data indicated that relational benefits influence customer loyalty, and customer satisfaction with employees influence customer loyalty. In addition, the impact of which is partially mediated by satisfaction with employees. The managerial implications of these findings are discussed in the latter part of this article.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]電子

Knowledge withholding intentions in teams: the roles of normative conformity, affective bonding, rational choice and social cognition

The decision of members in a knowledge-intensive team to withhold their knowledge may threaten the performance of the team. To address the problem of knowledge resource risk in project teams, we maintain that it is important to understand why team members choose to withhold their knowledge, conceptualized as knowledge-withholding intention. In line with the literature on effort withholding, the research on multifoci relations between justice perceptions and social exchanges, and social cognitive theory, we proposed that the social exchange relationships that individuals form in the workplace, their perceptions of justice, and their knowledge withholding self-efficacy would influence their knowledge-withholding intentions. Through a survey of 227 information system development team workers, we found that all social exchange relationship variables had a significant impact on knowledge-withholding intentions. However, the justice perception variables only indirectly influenced knowledge-withholding intentions through the mediation of social exchange relationships. In addition, one of the task variables, task interdependence, influenced knowledge withholding intention through the mediation of knowledge withholding self-efficacy. Our results contribute to the knowledge management literature by providing a better understanding of the antecedents of knowledge withholding. We also offer suggestions for future research utilizing the framework of Kidwell and Bennett (1993) to study effort and knowledge withholding