In the context of a left-right symmetric model, we introduce one full generation of vectorlike lepton doublets (both left- and right-handed) together with their mirror doublets. We show that the lightest vectorlike neutrino in the model is right-handed, and can serve as the dark matter candidate. We find that the relic density as well as the direct and indirect DM detection bounds are satisfied for a large range of the parameter space of the model. In accordance with the parameter space, we then explore the possibility of detecting signals of the model at both the LHC and the ILC, in the pair production of the associated vectorlike charged leptons which decay into final states including dark matter. A comprehensive analysis of signal and backgrounds shows that the signals at the ILC, especially with polarized beams, are likely to be visible for light vectorlike leptons, even with low luminosity, rendering our model highly predictable and experimentally testable.

There is a need to improve communication for patients and relatives who belong to cultural minority communities in intensive care units (ICUs). As a matter of fact, language barriers negatively impact patient safety and family participation in the care of critically ill patients, as well as recruitment to clinical trials. Recent studies indicate that Google Translate and ChatGPT are not accurate enough for advanced medical terminology. Therefore, developing and implementing an ad hoc machine translation tool is essential for bridging language barriers. This tool would enable language minority communities to access advanced healthcare facilities and innovative research in a timely and effective manner, ensuring they receive the comprehensive care and information they need. Method: Key factors that facilitate access to advanced health services, in particular ICUs, for language minority communities are reviewed. Results: The existing digital communication tools in emergency departments and ICUs are reviewed. To the best of our knowledge, no AI English/French translation app has been developed for deployment in ICUs. Patient privacy and data confidentiality are other important issues that should be addressed. Conclusions: Developing an artificial intelligence-driven translation tool for intensive care units (AITIC) which uses language models trained with medical/ICU terminology datasets could offer fast and accurate real-time translation. An AITIC could support communication, and consolidate and expand original research involving language minority communities.

In the context of a left-right symmetric model, we introduce one full generation of vectorlike lepton doublets (both left and right-handed) together with their mirror doublets. We show that the lightest vectorlike neutrino in the model is right-handed, and can serve as the dark matter candidate. We find that the relic density, as well as the direct and indirect DM detection bounds, are satisfied for a large range of the parameter space of the model. In accordance with the parameter space, we then explore the possibility of detecting signals of the model at both the LHC and the ILC, in the pair production of the associated vectorlike charged leptons which decay into final states including dark matter. A comprehensive analysis of signal and backgrounds shows that the signals at the ILC, especially with polarized beams are likely to be visible for light vectorlike leptons, even with low luminosity, rendering our model highly predictable and experimentally testable.

There is a need to improve communication for patients and family members who belong to cultural minority communities in the Intensive Care units (ICU). As a matter of fact, language barriers negatively impact patient safety, family participation in the care of the critically ill patients as well as recruitment in clinical trial. Recent studies indicate that Google translate and ChatGPT are not accurate enough for advanced medical terminology. Therefore, developing and implementing an Artificial Intelligence-driven language translation tool is essential for bridging language barriers. This tool will enable language minority communities to access advanced healthcare facilities and innovative research in a timely and effective manner, ensuring they receive the comprehensive care and information they need. Method: Key factors that facilitate access to advanced health services, in particular ICUs for language minority communities are reviewed. Results: The existing digital communication tools in emergency and the ICU are reviewed. To the best of our knowledge, no Al translation app has been developed for deployment in ICUs. Patient privacy and data confidentiality are other important issues that should be addressed. Conclusions: Developing AITC which uses language models trained with medical/ICU terminology dataset could offer fast and accurate real-time translation. AITIC could support communication, consolidate and expand original research involving language minority communities.