5 resultados para Computational prediction
em Universitat de Girona, Spain
Resumo:
Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculated
Resumo:
The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies
Resumo:
The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics
Resumo:
In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.
Resumo:
Des del seu descobriment, a la molècula C60 se li coneixen una varietat de derivats segons el tipus de funcionalització amb propietats fisicoquímiques específiques de gran interès científic. Una sel·lecció de derivats corresponents a addicions simple o múltiple al C60 s'ha considerat en aquest treball d'investigació. L'estudi a nivell de química computacional de diversos tipus d'addició al C60 s'han portat a terme per tal de poder donar resposta a aspectes que experimentalment no s'entenen o són poc clars. Els sistemes estudiats en referència a l'addició simple al C60 han estat en primer lloc els monoiminoful·lerens, C60NR, (de les dues vies proposades per la seva síntesi, anàlisis cinètic i termodinàmic han ajudat a explicar els mecanismes de formació i justificar l'addició a enllaços tipus [5,6]), i en segon lloc els metanoful·lerens i els hidroful·lerens substituits, C60CHR i C60HR, (raons geomètriques, electròniques, energètiques i magnètiques justifiquen el diferent caràcter àcid ente ambdós derivats tenint en compte una sèrie de substituents R amb diferent caràcter electrònic donor/acceptor). Els fluoroful·lerens, C60Fn, i els epoxid ful·lerens, C60On, (anàlisi sistemàtic dels seus patrons d'addició en base a poder justificar la força que els governa han aportat dades complementàries a les poques que existeixen experimentalment al respecte).