Bacteria are important examples of active or self-propelled colloids. Because of their directed motion, they accumulate near interfaces. There, they can become trapped and swim adjacent to the interface via hydrodynamic interactions, or they can adsorb directly and swim in an adhered state with complex trajectories that differ from those in bulk in both form and spatiotemporal implications. We have adopted the monotrichous bacterium Pseudomonas aeruginosa PA01 as a model species and have studied its motion at oil–aqueous interfaces. We have identified conditions in which bacteria swim persistently without restructuring the interface, allowing detailed and prolonged study of their motion. In addition to characterizing the ensemble behavior of the bacteria, we have observed a gallery of distinct trajectories of individual swimmers on and near fluid interfaces. We attribute these diverse swimming behaviors to differing trapped states for the bacteria in the fluid interface. These trajectory types include Brownian diffusive paths for passive adsorbed bacteria, curvilinear trajectories including curly paths with radii of curvature larger than the cell body length, and rapid pirouette motions with radii of curvature comparable to the cell body length. Finally, we see interfacial visitors that come and go from the interfacial plane. We characterize these individual swimmer motions. This work may impact nutrient cycles for bacteria on or near interfaces in nature. This work will also have implications in microrobotics, as active colloids in general and bacteria in particular are used to carry cargo in this burgeoning field. Finally, these results have implications in engineering of active surfaces that exploit interfacially trapped self-propelled colloids.

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Purpose: In line with a recent call for side effects research in education, this article aims to synthesize the major concerns that have been raised in the literature concerning large-scale assessments (LSAs) in education. Design/Approach/Methods: The researchers endeavored to complete a deep review of the literature on LSAs to synthesize the reported side effects. The review was synthesized thematically to understand and report the consequences of the ongoing push for the use of LSA in education. Findings: Thematic analysis indicated overarching side effects of LSA in education. We discuss why negative side effects exist and present evidence of the most commonly observed side effects of LSA in education, including distorting education, exacerbating inequity and injustice, demoralization of professionals, ethical corruption, and stifling of innovation in education. Originality/Value: While concerns about the use and misuse of LSA in education are not new and have been discussed widely in the literature, rarely have they been discussed as inherent qualities and consequences of LSAs that can do harm to education.

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The conversion of N2 and CO2 into urea through photocatalytic C–N coupling reaction under ambient condition serves as a novel green avenue for urea synthesis. However, the poor adsorption and...

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Intensive care unit (ICU) readmission of patients following liver transplantation (LT) is associated with poor outcomes. However, its risk factors remain unclarified. Nowadays, machine learning methods are widely used in many aspects of medical health. This study aims to develop a reliable prognostic model for ICU readmission for post-LT patients using machine learning methods. In this paper, a single center cohort ([Formula: see text]) was studied, of which 5.9% ([Formula: see text]) were readmitted to the ICU during hospitalization for LT. A retrospective review of baseline and perioperative factors possibly related to ICU readmission was performed. Three feature selection techniques were used to detect the best features influencing ICU readmission. Moreover, seven machine learning classifiers were proposed and compared to detect the risk of ICU readmission. Alanine transaminase (ALT) at hospital admission, intraoperative fresh frozen plasma (FFP) and red blood cell (RBC) transfusion, and N-Terminal pro-brain natriuretic peptide (NT-proBNP) after LT were found to be essential features for ICU readmission risk prediction. And the stacking model produced the best performance, identifying patients that were readmitted to the ICU after LT at an accuracy of 97.50%, precision of 96.34%, recall of 96.32%, and F1-score of 96.32%. RBC transfusion is the most crucial feature of the stacking classification model, which produced the best performance with overall accuracy, precision, recall, and F1-score of 88.49%, 88.66%, 76.01%, and 81.84%, respectively.

With the advent of the digital age, China's e-commerce platform has developed rapidly. Affected by the COVID-19 epidemic in 2020, the enthusiasm of short video live e-commerce continues to rise, and this brand business faces many threats and challenges. Brand marketing needs new growth points, and short video and live broadcast marketing give brand marketing a new exploration trend. Compared with commercial stars, the influential grassroots stars have gradually transformed into the product of modern science and technology-"online celebrity effect". While the "online celebrity effect" has brought huge fans and traffic economy, the realization of traffic has become an economic model in modern society. Taking "Tiktok" as an example, by studying its existing marketing strategy, this paper analyzes its problems in the "short video+live broadcast" e-commerce marketing strategy from the objective and various data indicators, and provides optimization suggestions for its problems. This paper takes the Tiktok short video APP software as an example to analyze the red IP traffic problem of modern social networks, as well as the analysis and solutions to its problems, using the methods of investigation and research.