The proliferation and development of social media platforms in recent years has contributed significantly to the spread of disinformation. Police Authorities around Europe have observed that harmful or criminal behaviour, stemming from social unrest, hate speech, and violent disorder are regularly preceded by disinformation campaigns. This begs the question: How can practitioners be better prepared for the real-world consequences of malign disinformation activities and to potentially even mitigate any criminal consequences? The first step in properly countering disinformation is to enhance the understanding of the complex phenomenon. Therefore, this article puts forth a new theoretical framework, called the ‘C5 Interaction Model’, that explains the creation, spread and impact of disinformation, synthesising academic theory to provide practical guidance on disinformation dynamics. The multidisciplinary model represents a lifecycle and contains five main elements: Context, Causes, Content, Consequences, and Cycle of Amplification. They are each organised into two further layers of (sub)factors, which were developed to provide a comprehensive overview and breakdown of the important elements of disinformation. The C5 Interaction Model represents one of the first concerted efforts to bring diverse insights together into a comprehensive integrative framework. The complexity of the model shows that this process is non-liner and that there are a multitude of factors determining the lifecycle of disinformation, making it a highly complex phenomenon to research. A key contribution of this article is the focus on the interaction between different elements that influence the process of disinformation—from creation to consequences. Importantly, the lifecycle route is predominantly influenced by the social context in which it exists.
In the world of work, it’s also important to maintain motivation in the long term and to enjoy what we are doing. It would be important for companies to better understand the process of flow and how it works. If the people who work for the company are happier and experience flow more often, the company can also benefit from it. In our pilot research, we conducted in-depth personal interviews in the service sector, in an organisation with nearly 250 employees. We investigated whether a position at a managerial level is directly proportional to the frequency of experiencing flow. That is, the more senior the position, the more often the flow experience is experienced in the workplace. The exact opposite result was obtained, i.e., the higher the position held, the less frequent the flow experience at work. Newly hired employees experience flow more often in their daily work. There is no significant difference between Generation Y and Generation Z in terms of the experience of flow and the frequency with which it is achieved. All of the respondents agreed that working at home office meant far fewer interruptions from colleagues and fewer distractions. They all rated it as a major positive and confirmed that the flow experience is more frequent in a home environment.
A TANULMÁNY CÉLJAI A mélyülő, globális léptékű ökológiai válság meghatározó előidézőinek egyike a fenntarthatatlan termelési és fogyasztási módok terjedése. Az elmozdulás a fenntartható életmód felé szükségszerű, azonban számos nehézségbe ütközhet. Jelen kutatás a tényleges viselkedésre és annak változására fókuszálva mutatja be a közösség jelentőségét a fenntartható életmód ösztönzésére, miközben hangsúlyozza a részvételi kutatásban rejlő lehetőségeket. ALKALMAZOTT MÓDSZERTAN Részvételi kutatást végeztünk nagyvárosi egyetemista és friss diplomás fiatalok körében, úgynevezett ökoklubok segítségével. Kvalitatív elemzésünk adatforrásai a fenntarthatóságra törekvő viselkedésről készített előzetes felmérés, az ökoklub találkozók online videofelvételei és azok leiratai, a fenntartható életmódhoz kapcsolódó heti mérések adatai, valamint egyéni reflexiós szövegek és utólagos mélyinterjúk voltak. LEGFONTOSABB EREDMÉNYEK Kutatási eredményeink megerősítették a közösség fontosságát a fenntartható életmód felé való elmozdulásban. Az ökoklub elméleti és gyakorlati tudást adott a résztvevőknek, miközben a fogyasztói felhatalmazódás eszközeként is szolgált. Emellett kutatásunk feltárta a fenntartható szándék és a magatartás közötti eltérés jellemző okait, valamint azokat a tényezőket, amelyek segítik és akadályozzák a fenntartható életmód követését. Eredményeink egyértelműen jelzik a részvételi kutatás hasznosságát a fenntarthatóbb életmód és fogyasztás vizsgálatában. GYAKORLATI JAVASLATOK Eredményeink szerint a fenntartható életmód elsajátításához segítségre van szüksége a fogyasztóknak, amely érkezhet az állami, a civil és a vállalati szféra szereplőitől egyaránt. Kutatásunk eredményei a fenntarthatósággal foglalkozó közösségeken keresztül kapott támogatás hasznossága mellett érvelnek. Kutatásmódszertani szempontból pedig javasoljuk a részvételi kutatás használatát a téma mélyebb megértéséhez.
Industrial software often has many parameters that critically impact performance. Frequently, these are left in a sub-optimal configuration for a given application because searching over possible configurations is costly and, except for developer instinct, the relationships between parameters and performance are often unclear and complex. While there have been significant advances in automated parameter tuning approaches recently, they are typically black-box. The high-quality solutions produced are returned to the user without explanation. The nature of optimisation means that, often, these solutions are far outside the well-established settings for the software, making it difficult to accept and use them. To address the above issue, a systematic approach to software parameter optimization is presented. Several well-established techniques are followed in sequence, each underpinning the next, with rigorous analysis of the search space. This allows the results to be explainable to both end users and developers, improving confidence in the optimal solutions, particularly where they are counter-intuitive. The process comprises statistical analysis of the parameters; single-objective optimization for each target objective; functional ANOVA to explain trends and inter-parameter interactions; and a multi-objective optimization seeded with the results from the single-objective stage. A case study demonstrates application to business-critical software developed by the international airline Air France-KLM for measuring flight schedule robustness. A configuration is found with a run-time of 80% that of the tried-and-tested configuration, with no loss in predictive accuracy. The configuration is supplemented with detailed analysis explaining the importance of each parameter, how they interact with each other, how they influence run-time and accuracy, and how the final configuration was reached. In particular, this explains why the configuration included some parameter settings that were outwith the usually recommended range, greatly increasing developer confidence and encouraging adoption of the new configuration.
One of the challenges of Condition-Based Maintenance (CBM) is to combine health monitoring and predictions with efficient scheduling tools.However, the majority of literature is focusing on the assessment of prognostics algorithms performance.In fact, the added value of these algorithms can only be assessed when considering their impact on maintenance decision process.Furthermore, in practice, when considering the scenario of an aircraft fleet with multiple monitored components, it is hard for a human decision-maker to translate and identify the effect of probabilistic results from all prognostics models from all systems on the maintenance schedule.Therefore, to support the implementation of CBM, the prognostics algorithms have to be integrated within a scheduling framework.Our paper proposes this integration in order to evaluate the impact of different level of prognostics accuracy and uncertainty on the aircraft fleet maintenance scheduling level.First, a Support Vector Regression (SVR) model is used to predict the Remaining Useful Life (RUL) distributions of the monitored components.Second, the maintenance scheduling problem is solved within a Reinforcement Learning (RL) approach incorporating a state-of-the-art Partially Observable Monte Carlo algorithm.Implementing a rolling horizon approach, our proposed framework is applied to a fleet of 10 aircraft, each equipped with multiple monitored systems.A case study with multiple different prediction accuracy and uncertainty scenarios is performed to assess the impact of prognostics uncertainty on optimal maintenance scheduling.The performed analysis aims to guide the development and assessment of prognostic models in terms of accuracy and uncertainty in the context of CBM.
