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Nation branding, which can be defined as the application of brand strategy principles to states, is still largely perceived as a marketing approach aimed at enhancing the image of countries. It is undoubtedly this reductive perception that has kept cognitive science researchers at a distance from this complex and polysemous concept. However, upon closer analysis, it becomes clear that nation branding is closely related to cognitive science, both in terms of the processes of national brand building and its impact on the perceptions and behaviors of target audiences (residents, tourists, investors, diasporas, etc.). And in a generalized context of multipolar cognitive warfare, nation branding, as well as its "armed wing," public diplomacy, could well prove to be decisive assets, both in terms of the geopolitical impact they can generate in the international arena and the cognitive resilience they contribute to by uniting populations around coherent identity narratives. Finally, it will be interesting to address the theme of sustainability, which nation branding must integrate at a high level in order to effectively structure the economic development of our states, in both the North and the South.
The text highlights the gap between digital, instant time (AI, online answers) and the slow, non-compressible biological time of surgery (healing, bone union, rehab). This mismatch fuels unrealistic patient expectations. Drawing on René Leriche and medical humanities, the author insists that surgery is a human encounter requiring listening and timing. AI can optimize fast steps (diagnosis, planning) but cannot speed up healing; it should clarify, not create false promises.
Dupes, accessible versions of perfumes from major brands, have been a meteorot commercial success since the covid-19 pandemic. Social networks have contributed to this success, making dupes an object of virality. Consumers, especially the youngest, seem to adhere very easily to this style of alternative, but legal consumption, which deconstructs decades of sophisticated marketing and elitist branding. Operating as tangible memes, they seduce and convert consumers in search of purchasing power, but also progressive societal demands (inclusion, accessibility, egalitarianism...). To the point of having an effect on the cultural representations associated with luxury, or even on the processes of human cogni-tion. It would then not be a trade war between the global South (a majority of dupes are made in China) and northern countries that should be guarded, but a cognitive war that could well weaken the system of cultural domination put in place by the West through the cleverly constructed iconicity of its great luxury brands.
This article aims to articulate the epidemiology of EIAS / EIGS and systemic failure models (Reason, Vincent) to guide prevention, recovery, and organizational learning in healthcare, with a particular focus on perioperative care. It outlines the error→incident→accident trajectory driven by cognitive limits, fatigue, stress, and communication failures. Cognitive biases and attentional tunneling are addressed through debiasing tactics. CRM/TEM frames team performance via briefings, adaptive leadership, closed-loop communication, and sterile-cockpit discipline. Checklists (WHO, SURPASS) and structured handovers (I-PASS/SBAR) deliver documented reductions in mortality and complications. The 2024 London Protocol refreshes RCA/ALARM and embeds a just-culture, learning-oriented REX. A Safety-II/HRO stance builds “safe-to-fail” organizations, sensitive to operations and deferent to expertise. Digital enablers (CPOE/CDS, BCMA, emergency manuals, OR Black Box, early-warning/AI) strengthen detection, traceability, and learning.
The growing integration of artificial intelligence into air-combat systems accelerates the collection and fusion of data, yet leaves humans with ultimate responsibility for purely cognitive, high-stakes decisions such as the “shoot / no-shoot” judgment. Because the operator must integrate heterogeneous information within seconds, this decision imposes a substantial cognitive load. The present study aims to improve performance on this task through a training program built from operational feedback and implemented on a lightweight, easily deployable platform. Seventy fighter-squadron aircrew members from the French Air and Space Force participated: an expert group (N = 39) and an intermediate group (N = 31). Intermediates completed a pre-test, a 45-minute training session and a post-test, whereas experts completed only the pre-test. Results on performance (accuracy and response time) show that the training material successfully engages expert knowledge; moreover, intermediates exhibited significant gains after a training session. Transfer of these gains in an operational setting remains to be demonstrated in future work. Nevertheless, the findings support the development of squadron-ready training modules that complement existing instructional tools. Even as AI usage intensifies, humans will remain in the decision loop. Thus, sustained efforts in education and training are essential to keep operators effective, while also understanding how to use AI in an appropriate way to support human decision-making, and to ensure that technological advances do not leave people “behind the aircraft.”
We present the results of a study on a binary classification of emotions, based on data collected through motion sensors and keystrokes of a smartphone and a connected bracelet. To this end, we developed a mobile application to induce emotions through videos and record user interactions. A specific digital self-assessment system was developed based on the Geneva Emotion Wheel to help participants express their emotions. The sensor recordings were labelled according to participants’ statements and video conditions. A method is thus proposed to process the collected temporal data and automatically classify the valence of the declared emotions using machine learning techniques. We tested a general valence classification using all emotions from all individuals and a personalized classification using a subset of emotions from a single individual. The most promising result was obtained with a personalized model, for which we were able to obtain, on average across all participants, two-thirds of correct valence classification, using fused data from different modalities.
