Detecting change and dealing with uncertainty in imperfect evolutionary environments

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high

Etude méthodologique : durée et équivalence énergétique d’un exercice aérobie Continu Modéré vs Intermittent Intense.

International audienc

Which characteristics and circumstances of exercise do expose to hypo/hyperglycaemic risk and glycaemic variability in children and adolescents with type 1 diabetes: a daily life observational study controlling for confounding impact of food intake and insulin administration

International audienc

Impact of 4-month training on glycaemic excursions in daily life in adults living with type 1 diabetes

International audienceContext and aim:Physical acNvity is associated with decreased risk of cardiovascular disease, improved qualityof life and type 1 diabetes (T1D) management. In order to individualise recommendaNonswhen implemented chronic exercise intervenNons, it seems important to beSer idenNfy theglycaemic excursions (hypo and hyperglycaemia) occurring the days including supervisedexercise training sessions compared with the days without training. Whether these glycaemicprofiles are changing throughout several months of training and whether differences appearaccording to the type of treatment (MDI or CSII) is also quesNonable.Method:FiWeen adults with T1D parNcipated to four-month combined (aerobic and strength) trainingprogram. Glycaemic excursion (i.e. glycaemic variability MAGE and CV, Nme spent in hypo- (180 and >250 mg.dL-1) and normoglycemia weremeasured on both inacNvity and training days throughout the 4-month intervenNon.Results:Compared with training days, sedentary days led to a significant increase in Nme spent insevere hypoglycaemia (250mg.dL-1 : e : +1.02 p180mg.dL-1 : e : -0,497, p250mg.dL-1 : e : -0,28 p180mg.dL-1 : e : -1,267 p <0,05; MAGE : e : -2,23p<0,05 vs. MDI).Conclusion:These results mays suggest that individuals with T1D are more afraid of hypoglycaemic risk thedays with training vs. the inacNve days, thus controlling more their glycaemia and henceanNcipaNng extreme glycaemic excursions

Risks of glycaemic excursions around exercise sessions and the days without exercise when implementing a 2-month combined

International audienceAims: Implementing exercise programs in individuals with type 1 diabetes (T1D) may precipitate glycaemic fluctuations. A better understanding of these fluctuations is essential for developing appropriate glucose management strategies. We aimed to assess glycaemic excursions and their progression during a 2-month training program, comparing fluctuations around exercise sessions with those of non-exercising days.Methods: Nineteen (13 female) adults with T1D participated in 2-3 supervised 90-minute combined (aerobic/strength) exercise sessions per week, over 2 months. Glycaemic excursions (continuous glucose monitoring) were measured during specific periods (24-hour, nocturnal; periods before, during, after exercise sessions) and compared between exercise and non-exercise days (linear mixed models, logistic regressions).Results: Nights following exercise sessions showed a reduced risk of hyperglycaemia (>10.0 mmol⋅L⁻¹) vs. non-exercise nights. This difference diminished over the weeks of training, alongside a progressive increase in the risk of time >16.7 mmol⋅L⁻¹ during the early and late recovery phases of exercise. Overall, regardless of exercise session occurrence, level 1 and 2 nocturnal hypoglycaemic risk increased as the training program progressed.Conclusions: Initially, acute exercise sessions reduced nocturnal hyperglycaemia without increasing hypoglycaemia. However, over time, the risk of nocturnal hypoglycaemia increased, highlighting the need for vigilant glycaemic supervision, particularly at night, even on non-exercise days

Identification of Jupiter's magnetic equator within H3+ ionospheric emission

Our understanding of Jupiter’s magnetic field has been developed through a combination of spacecraft measurements at distances >1.8 RJ and images of the aurora (1–7). These models all agree on the strength and direction of the jovian dipole magnetic moments, but, because higher order magnetic moments decay more strongly with distance from the planet, past spacecraft measurements could not easily resolve them. In the past two years, the Juno mission has measured very close to the planet (>1.05 RJ), observing a strongly enhanced localized magnetic field in some orbits (8-9) and resulting in models that identify strong hemispheric asymmetries at mid-to-high latitudes (10, 11). These features could be better resolved by identifying changes in ionospheric density caused by interactions with the magnetic field, but past observations have been unable to spatially resolve such features (12–14). In this study, we identify a dark sinusoidal ribbon of weakened H3+ emission near the jovigraphic equator, which we show to be an ionospheric signature of Jupiter’s magnetic equator. We also observe complex structures in Jupiter’s mid-latitude ionosphere, including one dark spot that is coincident with a localized enhancement in Jupiter’s radial magnetic field observed recently by Juno (10). These features reveal evidence of complex localized interactions between Jupiter’s ionosphere and its magnetic field. Our results provide ground-truth for Juno spacecraft observations and future ionospheric and magnetic field model