Implantable Fluid Delivery System

An implantable system for delivering fluids , such as drugs , to one or more anatomical structures in a patient \u27 s ( i . e . , human or animal ) body . A number of medical conditions require continual and / or periodic administration of fluids ( e . g . , drugs ) to target regions ( e . g . , anatomic organs ) of the body . Accessibility to those target regions might be limited technically for ex . and not limited to : frequent endoscopic , radiologically guided or surgical approaches . The system delivers the fluid needed in a continual or intermittent fashion to the target region . It controls the amount of fluid delivered to the target region and measures the intended physiologic effect of the fluid delivered

UNAFLOW project: UNsteady Aerodynamics of FLOating Wind turbines

UNAFLOW (UNsteady Aerodynamics for Floating Wind) is a joint EU-IRPWIND founded experiment on wind turbine rotor unsteady aerodynamics. It brings together four different academic contributors: Energy research Centre of the Netherlands (ECN), DTU Wind Energy, University of Stuttgart (USTUTT) and Politecnico di Milano (PoliMi) sharing knowledge both in numerical modelling and in experimental tests design, allowing direct numerical and experimental comparison. The experimental tests carried out for UNAFLOW are of the same type of the ones carried out during the ongoing EU H2020 project LIFES50+ [1], regarding both the unsteady behaviour of the 2d blade section and the entire turbine rotor, although with improved setup and wider test matrix. The project partners are already currently jointly collaborating in the AVATAR project [2], developing and validating numerical models of different accuracy level. The numerical models used in the UNALFOW project range from engineering tool (eg. BEM) to high fidelity CFD methods. Numerical simulations are used both in the design of experiment phase and in the results analysis allowing for an in depth understanding of the experimental findings through advanced modelling approach. The UNAFLOW project, together with a new understanding of the unsteady behaviour of the turbine rotor aerodynamics, will provide also an open database to be shared among the scientific community for future analysis and new models validation

On the aero-elastic design of the DTU 10MW wind turbine blade for the LIFES50+ wind tunnel scale model

This paper illustrates the aero-elastic optimal design, the realization and the verification of the wind tunnel scale model blades for the DTU 10 MW wind turbine model, within LIFES50+ project. The aerodynamic design was focused on the minimization of the difference, in terms of thrust coefficient, with respect to the full scale reference. From the Selig low Reynolds database airfoils, the SD7032 was chosen for this purpose and a proper constant section wing was tested at DTU red wind tunnel, providing force and distributed pressure coefficients for the design, in the Reynolds range 30-250 E3 and for different angles of attack. The aero-elastic design algorithm was set to define the optimal spanwise thickness over chord ratio (t/c), the chord length and the twist to match the first flapwise scaled natural frequency. An aluminium mould for the carbon fibre was CNC manufactured based on B-Splines CAD definition of the external geometry. Then the wind tunnel tests at Politecnico di Milano confirmed successful design and manufacturing approaches

Validation of an Engineered Cell Model for In Vitro and In Vivo HIF-1 alpha Evaluation by Different Imaging Modalities

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Proximal hyperspectral analysis in grape leaves for region and variety identification.

Reflectance measurements of plants of the same species can produce sets of data with differences between spectra, due to factors that can be external to the plant, like the environment where the plant grows, and to internal factors, for measurements of different varieties. This paper reports results of the analysis of radiometric measurements performed on leaves of vines of several grape varieties and on several sites. The objective of the research was, after the application of techniques of dimensionality reduction for the definition of the most relevant wavelengths, to evaluate four machine learning models applied to the observational sample aiming to discriminate classes of region and variety in vineyards. The tested machine learning classification models were Canonical Discrimination Analysis (CDA), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM). From the results, we reported that the LGBM model obtained better accuracy in spectral discrimination by region, with a value the 0.93, followed by the RF model. Regarding the discrimination between grape varieties, these two models also achieved better results, with accuracies of 0.88 and 0.89. The wavelengths more relevant for discrimination were at ultraviolet, followed by those at blue and green spectral regions. This research pointed toward the importance of defining the wavelengths more relevant to the characterization of the reflectance spectra of leaves of grape varieties and revealed the effective capability of discriminating vineyards by their region or grape variety, using machine learning models. Análise hiperespectral proximal em folhas de videiras para identificação de regiões e variedades RESUMO: Medições de refletância de plantas da mesma espécie podem produzir conjuntos de dados com diferenças entre os espectros, devido a fatores que podem ser externos à planta, como o ambiente onde a planta cresce, e fatores internos, para medições com variedades de plantas. Este artigo reporta resultados da análise de medições por espectrorradiometria efetuadas em folhas de vinhas de variedades e em diferentes localidades. O objetivo desta pesquisa foi, após a aplicação de técnicas de redução de dimensionalidade para a definição dos comprimentos de onda mais relevantes, avaliar quatro modelos de aprendizado de máquina aplicados à amostra observacional visando discriminar classes de região e variedade. Os modelos de classificação de aprendizado de máquina testados foram Canonical Discrimination Analysis (CDA), Light Gradient Boosting Machine (LGBM), Random Forest (RF) e Support Vector Machine (SVM). A partir dos resultados, relatamos que o modelo LGBM obteve melhor acurácia na discriminação espectral por região, com valor de 0,93, seguido pelo modelo RF. Relativamente à discriminação entre castas, estes dois modelos também obtiveram melhores resultados, com acurácias de 0,88 e 0,89. Os comprimentos de onda mais importantes para as discriminações procuradas estiveram na região do ultravioleta, seguidos do azul e do verde. Este trabalho aponta para a importância de detectar os comprimentos de onda mais relevantes para a caracterização dos espectros de reflectância das folhas de variedades de vinhas, e revela a capacidade efetiva de discriminar vinhedos por suas regiões ou variedades, usando modelos de aprendizado de máquina. Palavras-chave: Vinhedos, hiperespectral, aprendizagem de máquina

Comparison of VIDAS and radioimmunoassay methods for measurement of cortisol concentration in bovine serum

Radioimmunoassay (RIA) is the "gold standard" method for evaluation of serum cortisol concentration. The VIDAS cortisol test is an enzyme-linked fluorescent assay designed for the MiniVidas system. The aim of this study was to compare the VIDAS method with RIA for measurement of bovine serum cortisol concentration. Cortisol concentrations were evaluated in 40 cows using both VIDAS and RIA methods, the latter as the reference method. A paired Student's t-test, Pearson's correlation analysis, Bland-Altman plot, and Deming regression analysis were used to compare the two methods. There was no statistically significant difference between mean serum cortisol concentrations measured by VIDAS or RIA methods (P = 0.6570). Both methods were able to detect significant differences in mean low and high cortisol concentrations (P < 0.00014 RIA and P < 0.0016 VIDAS). The correlation coefficient was low, but a Bland-Altman plot and Deming regression analysis show neither constant nor proportional error. The VIDAS method produced slightly higher values than RIA, but the difference was small and in no case did the mean value move the normal range. Results suggest that VIDAS method is suitable for the determination of bovine serum cortisol concentration in studies of large numbers of animals

Effects of storage time on total protein and globulin concentrations in bovine fresh frozen plasma obtained for transfusion

To evaluate the effects of storage conditions on total protein (TP) and globulin fractions in fresh frozen bovine plasma units prepared and stored for transfusion, TP and globulin fractions were evaluated in fresh plasma and at 1 month and 6 and 12 months after blood collection in plasma stored at -20\ub0C. Significant differences in concentrations were found in the median concentration of total protein (P = 0.0336), between 0 months and 1 month (P = 0.0108), 0 and 6 months (P = 0.0023), and 0 and 12 months (P = 0.0027), in mean concentration (g/dL) of albumin (P = 0.0394), between 0 months and 1 month (P = 0.0131), 0 and 6 months (P = 0.0035), and 0 and 12 months (P = 0.0038), and beta-2 fraction (P = 0.0401), between 0 and 6 months (P = 0.0401) and 0 and 12 months (P = 0.0230). This study suggests that total gamma globulin concentration in bovine frozen plasma is stable for 12 months at -20\ub0C. Total protein, ALB, and beta-2 fraction have significantly different concentrations (g/dL) when compared to prestorage. This study has shown IgG protein fraction stability in bovine fresh frozen plasma collected for transfusion; therefore, bovine fresh frozen plasma seems to be suitable for the treatment of hypogammaglobulinemia (failure of passive transfer) in calves when stored for 12 months at -20\ub0C

Effects of Storage Time on Total Protein and Globulin Concentrations in Bovine Fresh Frozen Plasma Obtained for Transfusion

To evaluate the effects of storage conditions on total protein (TP) and globulin fractions in fresh frozen bovine plasma units prepared and stored for transfusion, TP and globulin fractions were evaluated in fresh plasma and at 1 month and 6 and 12 months after blood collection in plasma stored at −20 ∘ C. Significant differences in concentrations were found in the median concentration of total protein ( = 0.0336), between 0 months and 1 month ( = 0.0108), 0 and 6 months ( = 0.0023), and 0 and 12 months ( = 0.0027), in mean concentration (g/dL) of albumin ( = 0.0394), between 0 months and 1 month ( = 0.0131), 0 and 6 months ( = 0.0035), and 0 and 12 months ( = 0.0038), and beta-2 fraction ( = 0.0401), between 0 and 6 months ( = 0.0401) and 0 and 12 months ( = 0.0230). This study suggests that total gamma globulin concentration in bovine frozen plasma is stable for 12 months at −20 ∘ C. Total protein, ALB, and beta-2 fraction have significantly different concentrations (g/dL) when compared to prestorage. This study has shown IgG protein fraction stability in bovine fresh frozen plasma collected for transfusion; therefore, bovine fresh frozen plasma seems to be suitable for the treatment of hypogammaglobulinemia (failure of passive transfer) in calves when stored for 12 months at −20 ∘ C