Evaluation of the HUT modified snow emission model over lake ice using airborne passive microwave measurements

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80 × 100 m were used as ground-truth to evaluate the simulations.The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of −8.4 K and −8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (−5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R = −0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (−4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.     

Effect of Platelet-Rich Plasma on M1/M2 Macrophage Polarization

Osteoarthritis of the knee (OAK) is a chronic degenerative disease and progresses with an imbalance of cytokines and macrophages in the joint. Studies regarding the use of platelet-rich plasma (PRP) as a point-of-care treatment for OAK have reported on its effect on tissue repair and suppression of inflammation but few have reported on its effect on macrophages and macrophage polarization. Based on our clinical experience with two types of PRP kits Cellaid Serum Collection Set P type kit (leukocyte-poor-PRP) and an Autologous Protein Solution kit (APS leukocyte-rich-PRP), we investigated the concentrations of humoral factors in PRPs prepared from the two kits and the effect of humoral factors on macrophage phenotypes. We found that the concentrations of cell components and humoral factors differed between PRPs purified using the two kits; APS had a higher concentration of M1 and M2 macrophage related factors. The addition of PRP supernatants to the culture media of monocyte-derived macrophages and M1 polarized macrophages revealed that PRPs suppressed M1 macrophage polarization and promoted M2 macrophage polarization. This research is the first to report the effect of PRPs purified using commercial kits on macrophage polarization.     

Study on the hydrogen storage properties of the dual active metals Ni and Al doped graphene composites

The graphene nanosheets are synthesized by modified Hummer's method, based on which the dual active metals Ni and Al doped graphene composites are prepared through in-suit reaction and self-assembly with high-temperature reduction process. The molecular structure, morphology and specific surface area of graphene nanosheets are characterized systematically. The phase composition, surface morphology and hydrogen storage properties of dual active metals Ni and Al doped graphene composites are further investigated by X-ray diffraction, scanning electron microscopy and gas reaction controller. Results show that the graphene nanosheets have typical graphene feature, whose transparent graphene edges can be observed clearly, and the specific surface area is as high as 604.2 m² g⁻¹. The Ni and Al doped graphene composites are composed with Ni, Al and C phases, which have high hydrogen storage capacity and excellent hydriding/dehydriding stabilities. The maximum hydrogen storage uptake of such composites is up to 5.7 wt% at 473 K, and the dehydriding efficiency is high as 96%∼97% at the dehydriding temperature of 380 K. The hydrogen adsorption and desorption rate control step of the Ni and Al doped graphene composites is complied to the nucleation and two-dimensional growth mechanism.     

Cathodoluminescence mapping and thermoluminescence of Pr3+ doped in a CaTiO3/CaGa2O4 composite phosphor

In order to probe the properties of the one pot prepared composite phosphor of CaTiO₃ and CaGa₂O₄, cathodoluminescence mapping, thermoluminescence and lifetime measurements were carried out. The structural study showed both irregular and rod shaped particles, which match with CaTiO₃ and CaGa₂O₄, respectively, according the x-ray maps. The cathodoluminescence spectrum showed emission peaks from ³P₀→³H₄ and ¹D₂→³H₄ transitions, with the earlier transition completely absent in CaTiO₃:Pr emission at room temperature. The diffuse reflectance spectrum showed a shifted band of the inter-valence charge transfer from 360 nm to 388 nm, which is not observable for CaTiO₃:Pr³⁺and CaGa₂O₄:Pr³⁺. Additionally, the phosphorescence decay curve was recorded and the presence of electron trapping centers was confirmed using thermoluminescence spectroscopy.     

Hermetic storage for control of common bean weevil,Acanthoscelides obtectus (Say)

This study evaluated hermetic storage as a method of controlling Acanthoscelides obtectus (Coleoptera: Chrysomelidae: Bruchinae) in stored beans. Recently harvested “vermelhinho” cultivar of the common red bean was used, which had already been infested by A. obtectus in the field. Beans with a moisture content of 15.0% wet basis were stored in silo bags (3 kg), plastic bottles (1.5 L), or non-hermetic glass containers (3 L) (control) for 120 days. The packages were stored in an acclimatized chamber at 25 °C with a relative humidity of 70 ± 5%. At time intervals of 0, 30, 60, 90, and 120 days, three packages of each treatment were opened, and analyses were performed to assess the infestation percentage by insect pests, moisture content, density, electrical conductivity, germination percentage, and cooking time. There was no increase in infestation by A. obtectus in the grains stored in the silo bags and plastic bottles during the 120 days of storage; however, there was a significant increase in infestation in the grains in non-hermetic storage (control). The quality of the beans correlated with infestation; it was not altered in the hermetic storage systems and decreased in the control sample. Hermetic storage of common beans is an effective tool in the control of A. obtectus.     

Residual stress and diffraction line-broadening analysis of Al2O3/Y-TZP ceramic composites by neutron diffraction measurement

Time-of-flight neutron diffraction and Rietveld analysis have been used to investigate the residual stresses, crystallite structure and microstructural features in Al₂O₃/Y-TZP ceramic composites fabricated by two different green processing techniques (a novel tape casting and conventional slip casting) and with different zirconia content (5 and 40 vol.% Y-TZP). The change in lattice parameters, individual peak shifts and peak-broadening were analyzed, in order to calculate the uniform residual stress field (mean phase stresses and peak-specific stresses) and the non-uniform microstrains. Peak-specific residual stresses were calculated for different hkl reflections both for the Al₂O₃ matrix and the Y-TZP particulates. The sign and the magnitude of the peak-specific residual stress are highly dependent on the individual hkl reflection studied and on the volume fraction of zirconia. Peak broadening was observed in the Y-TZP reflections, due to non-uniform microstrains. Both the mean phase stress field and the non-uniform microstrains were mainly influenced by the Y-TZP content in the studied Al₂O₃/Y-TZP composites, irrespective of the measured direction and the fabrication process.     

Characterising the loading direction sensitivity of 3D woven composites: Effect of z-binder architecture

Three different architectures of 3D carbon fibre woven composites (orthogonal, ORT; layer-to-layer, LTL; angle interlock, AI) were tested in quasi-static uniaxial tension. Mechanical tests (tensile in on-axis of warp and weft directions as well as 45° off-axis) were carried out with the aim to study the loading direction sensitivity of these 3D woven composites. The z-binder architecture (the through-thickness reinforcement) has an effect on void content, directional fibre volume fraction, mechanical properties (on-axis and off-axis), failure mechanisms, energy absorption and fibre rotation angle in off-axis tested specimens. Out of all the examined architectures, 3D orthogonal woven composites (ORT) demonstrated a superior behaviour, especially when they were tested in 45° off-axis direction, indicated by high strain to failure (∼23%) and high translaminar energy absorption (∼40 MJ/m³). The z-binder yarns in ORT architecture suppress the localised damage and allow larger fibre rotation during the fibre “scissoring motion” that enables further strain to be sustained by the in-plane fabric layers during off-axis loading.     

Data classification with binary response through the Boosting algorithm and logistic regression

The task of classifying is natural to humans, but there are situations in which a person is not best suited to perform this function, which creates the need for automatic methods of classification. Traditional methods, such as logistic regression, are commonly used in this type of situation, but they lack robustness and accuracy. These methods do not not work very well when the data or when there is noise in the data, situations that are common in expert and intelligent systems. Due to the importance and the increasing complexity of problems of this type, there is a need for methods that provide greater accuracy and interpretability of the results. Among these methods, is Boosting, which operates sequentially by applying a classification algorithm to reweighted versions of the training data set. It was recently shown that Boosting may also be viewed as a method for functional estimation. The purpose of the present study was to compare the logistic regressions estimated by the maximum likelihood model (LRMML) and the logistic regression model estimated using the Boosting algorithm, specifically the Binomial Boosting algorithm (LRMBB), and to select the model with the better fit and discrimination capacity in the situation of presence(absence) of a given property (in this case, binary classification). To illustrate this situation, the example used was to classify the presence (absence) of coronary heart disease (CHD) as a function of various biological variables collected from patients. It is shown in the simulations results based on the strength of the indications that the LRMBB model is more appropriate than the LRMML model for the adjustment of data sets with several covariables and noisy data. The following sections report lower values of the information criteria AIC and BIC for the LRMBB model and that the Hosmer–Lemeshow test exhibits no evidence of a bad fit for the LRMBB model. The LRMBB model also presented a higher AUC, sensitivity, specificity and accuracy and lower values of false positives rates and false negatives rates, making it a model with better discrimination power compared to the LRMML model. Based on these results, the logistic model adjusted via the Binomial Boosting algorithm (LRMBB model) is better suited to describe the problem of binary response, because it provides more accurate information regarding the problem considered.