Online public opinion evaluation through the functional resonance analysis method and deep analysis

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.

S-palmitoylation and sterol interactions mediate antiviral specificity of IFITM isoforms

Interferon-induced transmembrane proteins (IFITM1, 2 and 3) are important antiviral proteins that are active against many viruses, including influenza A virus (IAV), dengue virus (DENV), Ebola virus (EBOV), Zika virus (ZIKV) and severe acute respiratory syndrome coronavirus (SARS-CoV). IFITMs exhibit isoform-specific activity, but their distinct mechanisms of action and regulation are unclear. Since S-palmitoylation and cholesterol homeostasis are crucial for viral infections, we investigated IFITM interactions with cholesterol by molecular dynamic stimulations, nuclear magnetic resonance analysis in vitro and photoaffinity crosslinking in mammalian cells. These studies suggest that cholesterol can alter the conformation of IFITMs in membrane bilayers and directly interact with S-palmitoylated IFITMs in cells. Notably, we discovered that the S-palmitoylation levels regulate differential IFITM isoform interactions with cholesterol in mammalian cells and specificity of antiviral activity towards IAV, SARS-CoV-2 and EBOV. Our studies suggest that modulation of IFITM S-palmitoylation levels and cholesterol interaction may influence host susceptibility to different viruses.

Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation

Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents. Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This paper, therefore, argues in favour of Resilience Engineering using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario on a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation. Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework. Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes show that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation. In conclusion, the FRAM analysis supports decision-makers in enhancing safety enriched by the identification of non-linear and complex risks.

1H-Nuclear Magnetic Resonance Analysis of Urine as Diagnostic Tool for Organic Acidemias and Aminoacidopathies

The utility of low-resolution 1H-NMR analysis for the identification of biomarkers provided evidence for rapid biochemical diagnoses of organic acidemia and aminoacidopathy. 1H-NMR, with a sensitivity expected for a field strength of 400 MHz at 64 scans was used to establish the metabolomic urine sample profiles of an infant population diagnosed with small molecule Inborn Errors of Metabolism (smIEM) compared to unaffected individuals. A qualitative differentiation of the 1H-NMR spectral profiles of urine samples obtained from individuals affected by different organic acidemias and aminoacidopathies was achieved in combination with GC–MS. The smIEM disorders investigated in this study included phenylalanine metabolism; isovaleric, propionic, 3-methylglutaconicm and glutaric type I acidemia; and deficiencies in medium chain acyl-coenzyme and holocarboxylase synthase. The observed metabolites were comparable and similar to those reported in the literature, as well as to those detected with higher-resolution NMR. In this study, diagnostic marker metabolites were identified for the smIEM disorders. In some cases, changes in metabolite profiles differentiated post-treatments and follow-ups while allowing for the establishment of different clinical states of a biochemical disorder. In addition, for the first time, a 1H-NMR-based biomarker profile was established for holocarboxylase synthase deficiency spectrum.

Optimising the Performance of Complex Sociotechnical Systems in High-Stress, High-Speed Environments: The Formula 1 Pit Stop Test Case

In analysing the performance of complex sociotechnical systems, of particular interest is the inevitable and inherent variability that these systems exhibit, but can normally tolerate, in successfully operating in the real world. Knowing how that variability propagates and impacts the total function mix then allows an understanding of emergent behaviours. This interdependence, however, is not readily apparent from normal linear business process flow diagrams. An alternative approach to exploring the operability of complex systems, that addresses these limitations, is the functional resonance analysis method (FRAM). This is a way of visualising a system’s behaviour, by defining it as an array of functions, with all the interactions and interdependencies that are needed for it to work successfully. Until now this methodology has mainly been employed as a qualitative mind map. This paper describes a new development of the FRAM visualisation software that allows the quantification of the extent and effects of this functional variability. It then sets out to demonstrate its application in a practical, familiar test case. The example chosen is the complex sociotechnical system involved in a Formula 1 pit stop. This has shown the potential of the application and provided some interesting insights into the observed performances.

Can using the functional resonance analysis method, as an intervention, improve patient safety in hospitals?: a stepped wedge design protocol

Background Healthcare professionals are sometimes forced to adjust their work to varying conditions leading to discrepancies between hospital protocols and daily practice. We will examine the discrepancies between protocols, ‘Work As Imagined’ (WAI), and daily practice ‘Work As Done’ (WAD) to determine whether these adjustments are deliberate or accidental. The discrepancies between WAI and WAD can be visualised using the Functional Resonance Analysis Method (FRAM). FRAM will be applied to three patient safety themes: risk screening of the frail older patients; the administration of high-risk medication; and performing medication reconciliation at discharge. Methods A stepped wedge design will be used to collect data over 16 months. The FRAM intervention consists of constructing WAI and WAD models by analysing hospital protocols and interviewing healthcare professionals, and a meeting with healthcare professionals in each ward to discuss the discrepancies between WAI and WAD. Safety indicators will be collected to monitor compliance rates. Additionally, the potential differences in resilience levels among nurses before and after the FRAM intervention will be measured using the Employee Resilience Scale (EmpRes) questionnaire. Lastly, we will monitor whether gaining insight into differences between WAI and WAD has led to behavioural and organisational change. Discussion This article will assess whether using FRAM to reveal possible discrepancies between hospital protocols (WAI) and daily practice (WAD) will improve compliance with safety indicators and employee resilience, and whether these insights will lead to behavioural and organisational change. Trial registration Netherlands Trial Register NL8778; https://www.trialregister.nl/trial/8778. Registered 16 July 2020. Retrospectively registered.