In this paper we report the development of the immunologically detected uncoupling protein (UCP) in brown adipose tissue during the perinatal period in the rat and its relationship with its functional activity expressed in terms of GDP-binding capacity, GDP-sensitive permeabilities and GDP-sensitive respiration. Immunologically detected UCP increased during the last 2 days of foetal life (under euthermic conditions) and after birth (after postnatal hypothermia) during the early suckling period, reaching its maximum value on day 10 after birth. This increase in UCP is accompanied by parallel increases in the GDP-binding capacity, GDP-sensitive permeabilities to protons and chloride ions and GDP-inhibitable respiration. During the suckling -weaning transition, there was a regression of the parameters related to the functional activity of the UCP (GDP-binding capacity and nucleotide-sensitive permeabilities and respiration) without changes in the immunologically detected UCP. These results suggest that the involution of this tissue in the rat commences in this period; the first parameters affected are those related to the functional activity of the UCP while the UCP is still present in its highest level. This seems to support the idea that, in this period of development of the rat, the UCP may exist in the brown fat mitochondria in a functional (unmasked) form and a non-functional (masked) form.During the foetal -neonatal transition newborn rats face a sudden drop of ambient temperature. To face this change in the extra-uterine environment, it is necessary for newborns to develop endogenous regulatory thermogenic mechanisms. Brown adipose tissue at birth is the main tissue involved in the neonatal thermogenesis [ 1,2]. Accordingly, brown adipose tissue exhibits a very rapid process of morphological and functional maturation perinatally. At the 4th postnatal week, brown fat cells commence a relatively slow process of involution [3, 41. The thermogenic capacity of brown adipose tissue is related to the presence of a tissue-specific protein ( M r 32000) in the inner mitochondrial membrane that catalyzes the regulated re-entry of protons extruded by the respiratory chain, thus dissipating the proton electrochemical gradient as heat. This protein, generally termed 'uncoupling protein' (UCP), is thereby the principal factor regulating the rate of respiration within the brown adipocyte. In addition to protons which are relevant physiologically, the UCP also transports anions such as CI-. Ion transport is inhibited by purine nucleoside di-or triphosphates which bind to the carrier; hence GDP-binding capacity provides a convenient means of quantifying the functional UCP content. Under physiological conditions, fatty acids override the nucleotide inhibition and have been proposed as the physiological regulators of the UCP (see for review [ 5 , 61.The development of the thermogenic capacity of rat brown adipose tissue during the perinatal period, estimated in terms The aim of this work was to study variations in UCP concentra...

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Continuous assessment of wind turbine performance is a key to maximising power generation at a very low cost. A wind turbine power curve is a non-linear function between power output and wind speed and is widely used to approach numerous problems linked to turbine operation. According to the current IEC standard, power curves are determined by a data reduction method, called binning, where hub height, wind speed and air density are considered as appropriate input parameters. However, as turbine rotors have grown in size over recent years, the impact of variations in wind speed, and thus of power output, can no longer be overlooked. Two environmental variables, namely wind shear and turbulence intensity, have the greatest impact on power output. Therefore, taking account of these factors may improve the accuracy as well as reduce the uncertainty of data-driven power curve models, which could be helpful in performance monitoring applications. This paper aims to quantify and analyse the impact of these two environmental factors on wind turbine power curves.Gaussian process (GP) is a data-driven, nonparametric based approach to power curve modelling that can incorporate these two additional environmental factors. The proposed technique's effectiveness is trained and validated using historical 10-minute average supervisory control and data acquisition (SCADA) datasets from variable speed, pitch control, and wind turbines rated at 2.5 MW. The results suggest that (i) the inclusion of the additional environmental parameters increases GP model accuracy and reduces uncertainty in estimating the power curve; (ii) a comparative study reveals that turbulence intensity has a relatively greater impact on GP model accuracy, together with uncertainty as compared to blade pitch angle. These conclusions are confirmed using performance error metrics and uncertainty calculations. The results have practical beneficial consequences for O&M related activities such as early failure detection.

In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate-the key information to prioritize medical care-was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized. INDEX TERMS Clustering, computational intelligence, coronavirus disease 2019 (COVID-19), kernel density estimation (KDE), medical care routing, optimization.