Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (OBESINDEX) generated by the association between body fat and body mass index

Background: A Fuzzy Obesity Index (OBESINDEX) for use as an alternative in bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI.

Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (OBESINDEX) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the OBESINDEX, an obesity classification with entirely new classes of obesity in the fuzzy context as well is used for BSI.

Results: There is a gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity.

Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the OBESINDEX

Dynamical Casimir effect with Robin boundary conditions in a three dimensional open cavity

We consider a massless scalar field in 1+1 dimensions inside a cavity composed by a fixed plate, which imposes on the field a Robin BC, and an oscillating one, which imposes on the field a Dirichlet BC. Assuming that the plate moves for a finite time interval, and considering parametric resonance, we compute the total number of created particles inside the cavity. We generalize our results to the case of two parallel plates in 3+1 dimensions.Comment: This work was presented in the Conference QFEXT09, held at the University of Oklahoma, Norman, OK, USA, September 21-25, 2009, and will appear in the proceedings of this conference. It contains 4 figure

Relationship of arterial and exhaled CO2 during elevated artificial pneumoperitoneum pressure for introduction of the first trocar.

The present study evaluated the correlation between arterial CO2 and exhaled CO2 during brief high-pressure pneumoperitoneum. Patients were randomly distributed into two groups: P12 group (n=30) received a maximum intraperitoneal pressure of 12mmHg, and P20 group (n=37) received a maximum intraperitoneal pressure of 20mmHg. Arterial CO2 was evaluated by radial arterial catheter and exhaled CO2 was measured by capnometry at the following time points: before insufflation, once intraperitoneal pressure reached 12mmHg , 5 minutes after intraperitoneal pressure reached 12mmHg for the P12 group or 20mmHg for the P20 group, and 10 minutes after intraperitoneal pressure reached 12mmHg for the P12 group or when intraperitoneal pressure had decreased from 20mmHg to 12mmHg, for the P20 group. During brief durations of very high intraperitoneal pressure (20mmHg), there was a strong correlation between arterial CO2 and exhaled CO2. Capnometry can be effectively used to monitor patients during transient increases in artificial pneumoperitoneum pressure

Active Learning Metamodels for ATM Simulation Modeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values

Active Learning for Air Traffic Management Simulation Metamodeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region

Protocolo para criopreservação do sêmen de tambaqui (Colossoma macropomum).

bitstream/item/39781/1/cot-112.pd

Tecnologia de aplicação de defensivos agrícolas.

Defensivos agrícolas. Alvo biológico e químico. Formulação dos defensivos agrícolas. Vias de intoxicação. Métodos de aplicação de defensivos agrícolas. Fatores fundamentais em uma aplicação de defensivos. Equipamentos de proteção individual (E. P. I.). Preparo e aplicação da calda. Fatores que influenciam nas pulverizações. Regulagem e calibração de um pulverizador de barras. Perdas por deriva. Perdas por evaporação. Manutenção de pulverizadores. Primeiros socorros. Avanços no desenvolvimento de novos equipamentos. Considerações finais.bitstream/CNPAT-2010/10340/1/Dc-102.pd

Explainable Metamodels for ATM Performance Assessment

Fast-time simulation constitutes a well-known and long-established technique within the Air Traffic Management (ATM) community. However, it is often the case that simulation input and output spaces are underutilized, limiting the full understandability, transparency, and interpretability of the obtained results. In this paper, we propose a methodology that combines simulation metamodeling and SHapley Additive exPlanations (SHAP) values, aimed at uncovering the intricate hidden relationships among the input and output variables of a simulated ATM system in a rather practical way. Whereas metamodeling provides explicit functional approximations mimicking the behavior of the simulators, the SHAP-based analysis delivers a systematic framework for improving their explainability. We illustrate our approach using a state-of-the-art ATM simulator across two case studies in which two delay-centered performance metrics are analyzed. The results show that the proposed methodology can effectively make simulation and its results more explainable, facilitating the interpretation of the obtained emergent behavior, and additionally opening new opportunities towards novel performance assessment processes within the ATM research field