Introduction: In critically ill patients, sleep and circadian rhythms are greatly altered. These disturbances have been associated with adverse consequences, including increased mortality. Factors associated with the ICU environment, such as exposure to inadequate light and noise levels during the day and night or inflexible schedules of daily care activities, have been described as playing an essential role in sleep disturbances. The main objective of this study is to evaluate the impact of the use of a multifaceted environmental control intervention in the ICU on the quantity and quality of sleep, delirium, and post-intensive care neuropsychological impairment in critically ill patients. Methods: This is a prospective, parallel-group, randomized trial in 56 critically ill patients once they are starting to recover from their acute illness. Patients will be randomized to receive a multifaceted intervention of environmental control in the ICU (dynamic light therapy, auditory masking, and rationalization of ICU nocturnal patient care activities) or standard care. The protocol will be applied from enrollment until ICU discharge. Baseline parameters, light and noise levels, polysomnography and actigraphy, daily oscillation of plasma concentrations of Melatonin and Cortisol, and questionnaires for the qualitative evaluation of sleep, will be assessed during the study. In addition, all patients will undergo standardized follow-up before hospital discharge and at 6 months to evaluate neuropsychological impairment. Discussion: This study is the first randomized clinical trial in critically ill patients to evaluate the effect of a multicomponent, non-pharmacological environmental control intervention on sleep improvement in ICU patients. The results will provide data about the potential synergistic effects of a combined multi-component environmental intervention in ICU on outcomes in the ICU and long term, and the mechanism of action. Trial registration: ClinicalTrials.gov, NCT. Registered on January 10, 2023. Last updated on 24 Jan 2023.

Multimorbidity is defined as the presence of two or more chronic medical conditions in a person, whether physical, mental or long-term infectious diseases. This is especially common in older populations, affecting their quality of life and emotionally impacting their caregivers and family. Technology can allow for monitoring, managing, and motivating older adults in their self-care, as well as supporting their caregivers. However, when several conditions are present at once, it may be necessary to manage several types of technologies, or for technology to manage the interaction between conditions. This work aims to understand and describe the technologies that are used to support the management of multimorbidity for older adults. We conducted a systematic review of ten years of scientific literature from four online databases. We reviewed a corpus of 681 research papers, finally including 25 in our review. The technologies used most frequently by older adults with multimorbidity are mobile applications and websites, and they are mostly focused on communication and connectivity. We then propose opportunities for future research on addressing the challenges in the management of several simultaneous health conditions, potentially creating a better approach than managing each condition as if it were independent.

Deep learning, one of the fastest-growing branches of artificial intelligence, has become one of the most relevant research and development areas of the last years, especially since 2012, when a neural network surpassed the most advanced image classification techniques of the time. This spectacular development has not been alien to the world of the arts, as recent advances in generative networks have made possible the artificial creation of high-quality content such as images, movies or music. We believe that these novel generative models propose a great challenge to our current understanding of computational creativity. If a robot can now create music that an expert cannot distinguish from music composed by a human, or create novel musical entities that were not known at training time, or exhibit conceptual leaps, does it mean that the machine is then creative? We believe that the emergence of these generative models clearly signals that much more research needs to be done in this area. We would like to contribute to this debate with two case studies of our own: TimbreNet, a variational auto-encoder network trained to generate audio-based musical chords, and StyleGAN Pianorolls, a generative adversarial network capable of creating short musical excerpts, despite the fact that it was trained with images and not musical data. We discuss and assess these generative models in terms of their creativity and we show that they are in practice capable of learning musical concepts that are not obvious based on the training data, and we hypothesize that these deep models, based on our current understanding of creativity in robots and machines, can be considered, in fact, creative.

Fuzzy logic is an artificial intelligence technique that has applications in many areas, due to its importance in handling uncertain inputs. Despite the great recent success of other branches of AI, such as deep neural networks, fuzzy logic is still a very powerful machine learning technique, based on expert reasoning, that can be of help in many areas of musical creativity, such as composing music, synthesizing sounds, gestural mappings in electronic instruments, parametric control of sound synthesis, audiovisual content generation or sonification. We propose that fuzzy logic is a very suitable framework for thinking and operating not only with sound and acoustic signals but also with symbolic representations of music. In this article, we discuss the application of fuzzy logic ideas to music, introduce the Fuzzy Logic Control Toolkit, a set of tools to use fuzzy logic inside the MaxMSP real-time sound synthesis environment, and show how some fuzzy logic concepts can be used and incorporated into fields, such as algorithmic composition, sound synthesis and parametric control of computer music. Finally, we discuss the composition of Incerta, an acousmatic multichannel composition as a concrete example of the application of fuzzy concepts to musical creation.