DSMC investigation of rarefied gas flow through diverging micro- and nanochannels

Direct simulation Monte Carlo (DSMC) method with simplified Bernoulli-trials (SBT) collision scheme has been used to study the rarefied pressure-driven nitrogen flow through diverging microchannels. The fluid behaviours flowing between two plates with different divergence angles ranging between 0$^{\circ}$ to 17$^{\circ}$ are described at different pressure ratios (1.5${\le}{\prod}{\le}$2.5) and Knudsen numbers (0.03${\le}$Kn${\le}$12.7). The primary flow field properties, including pressure, velocity, and temperature, are presented for divergent microchannels and are compared with those of a microchannel with a uniform cross-section. The variations of the flow field properties in divergent microchannels, which are influenced by the area change, the channel pressure ratio and the rarefication are discussed. The results show no flow separation in divergent microchannels for all the range of simulation parameters studied in the present work. It has been found that a divergent channel can carry higher amounts of mass in comparison with an equivalent straight channel geometry. A correlation between the mass flow rate through microchannels, the divergence angle, the pressure ratio, and the Knudsen number has been suggested. The present numerical findings prove the occurrence of Knudsen minimum phenomenon in micro- and Nano- channels with non-uniform cross-sections.Comment: Accepted manuscript; 25 Pages and 11 Figures; "Microfluidics and Nanofluidics

Role-Reversible Judgments and Related Democratic Objections to AI Judges

In a recent article published by this journal, Kiel Brennan-Marquez and Stephen E. Henderson argue that replacing human judges with AI would violate the role-reversibility ideal of democratic governance. Unlike human judges, they argue, AI judges are not reciprocally vulnerable to the process and effects of their own decisions. I argue that role-reversibility, though a formal ideal of democratic governance, is in the service of substantive ends that may be independently achieved under AI judges. Thus, although role-reversibility is necessary for democratic governance when human judges are on the job, it may not be so when AI judges replace them. One broader implication for normative evaluation of disruptive technologies follows: formal and substantive ideals that often align must be independently examined in the evaluation of disruptive technologies. This is because these formal and substantive ideals may no longer align under the factual circumstances that come to govern when such technologies are deployed

Developing a Prediction Model for Author Collaboration in Bioinformatics Research Using Graph Mining Techniques and Big Data Applications

Nowadays, scientific collaboration has dramatically increased due to web-based technologies, advanced communication systems, and information and scientific databases. The present study aims to provide a predictive model for author collaborations in bioinformatics research output using graph mining techniques and big data applications. The study is applied-developmental research adopting a mixed-method approach, i.e., a mix of quantitative and qualitative measures. The research population consisted of all bioinformatics research documents indexed in PubMed (n=699160). The correlations of bioinformatics articles were examined in terms of weight and strength based on article sections including title, abstract, keywords, journal title, and author affiliation using graph mining techniques and big data applications. Eventually, the prediction model of author collaboration in bioinformatics research was developed using the abovementioned tools and expert-assigned weights. The calculations and data analysis were carried out using Expert Choice, Excel, Spark, and Scala, and Python programming languages in a big data server. Accordingly, the research was conducted in three phases: 1) identifying and weighting the factors contributing to authors’ similarity measurement; 2) implementing co-authorship prediction model; and 3) integrating the first and second phases (i.e., integrating the weights obtained in the previous phases). The results showed that journal title, citation, article title, author affiliation, keywords, and abstract scored 0.374, 0.374, 0.091, 0.075, 0.055, and 0.031. Moreover, the journal title achieved the highest score in the model for the co-author recommender system. As the data in bibliometric information networks is static, it was proved remarkably effective to use content-based features for similarity measures. So that the recommender system can offer the most suitable collaboration suggestions. It is expected that the model works efficiently in other databases and provides suitable recommendations for author collaborations in other subject areas. By integrating expert opinion and systemic weights, the model can help alleviate the current information overload and facilitate collaborator lookup by authors.https://dorl.net/dor/20.1001.1.20088302.2021.19.2.1.