Corrosion Behavior of Composite Coatings Obtained by Electrolytic Codeposition of Copper with Al2O3 Nanoparticles

Composite coatings of copper incorporating Al2O3 nanoparticles electrodeposited on carbon steel were obtained and characterized. By using electrochemical methods such as open circuit potential (ocp) measurements, polarization curves and electrochemical impedance spectroscopy, the corrosion behavior of the Al2O3-copper nanocomposite coatings was examined. The corrosion parameters determined from the polarization curves recorded in Na2SO4 solution (pH 3) indicate that the corrosion process on copper-Al2O3 composite surface is slower than on pure copper. The impedance spectra recorded at the ocp showed in all cases an increase of the polarization resistance in time, which may be explained by the development of corrosion products on the electrode surface. Using a (2RC) equivalent electrical circuit, the process parameters were estimated by non-linear regression calculations with a Simplex method. The Al2O3 particles embedded in the electroplated copper, increase the polarization resistance and decrease the corrosion rates as compared with electrodeposited pure copper. The electrochemical results were corroborated with those obtained by SEM and EDX investigations

Wind turbine gearbox condition monitoring based on class of support vector regression models and residual analysis

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models

Road-to-Lab: Validation of the Static Load Test for Predicting On-Road Driving Performance While Using Advanced In-Vehicle Information and Communication Devices

Information, communication, and navigation devices need to beevaluated for ease-of-use and safety while driving. Lab tests, if validated, canevaluate prototype designs faster, more economically, and earlier than on-roadtests. The Static Load Test was evaluated for its ability to predict on-road driverperformance while using in-vehicle devices. In this test, participants performvarious in-vehicle tasks in a lab while viewing a videotaped road scene on amonitor, tapping a brake pedal when a central or peripheral light is observed. Forthe on-road comparison test, the device, tasks, and lights are the same, but theparticipants also drive the vehicle while performing the tasks and responding tothe lights. In both the lab and road tests, ten driver performance variables weremeasured. Our goal was to produce a linear model to predict an on-road variablefrom the lab data with low residual error, high percent variance explained, andfew errors in classifying tasks as meeting or not meeting on-road driverperformance criteria. Separate test data from a replicated Static Load Test at anindependent lab were used to further validate the models. The results indicate asimple, inexpensive, and low-fidelity Static Load Test can accurately predict anumber of on-road driver performance variables suitable for assessing the safetyand ease-of-use of advanced in-vehicle devices while driving

Single-cell lineage tracing in the mammary gland reveals stochastic clonal dispersion of stem/progenitor cell progeny.

The mammary gland undergoes cycles of growth and regeneration throughout reproductive life, a process that requires mammary stem cells (MaSCs). Whilst recent genetic fate-mapping studies using lineage-specific promoters have provided valuable insights into the mammary epithelial hierarchy, the true differentiation potential of adult MaSCs remains unclear. To address this, herein we utilize a stochastic genetic-labelling strategy to indelibly mark a single cell and its progeny in situ, combined with tissue clearing and 3D imaging. Using this approach, clones arising from a single parent cell could be visualized in their entirety. We reveal that clonal progeny contribute exclusively to either luminal or basal lineages and are distributed sporadically to branching ducts or alveoli. Quantitative analyses suggest that pools of unipotent stem/progenitor cells contribute to adult mammary gland development. Our results highlight the utility of tracing a single cell and reveal that progeny of a single proliferative MaSC/progenitor are dispersed throughout the epithelium.This work was supported by a grant from the Medical Research Council programme grant no. MR/J001023/1 (B.L-L. and C.J.W). F.M.D. was funded by a National Health and Medical Research Council CJ Martin Biomedical Fellowship (GNT1071074). O.B.H. was funded by a Wellcome Trust PhD studentship (105377/Z/14/Z)

Evolutionary cognitive therapy versus standard cognitive therapy for depression: a protocol for a blinded, randomized, superiority clinical trial

Background: Depression is estimated to become the leading cause of disease burden globally by 2030. Despite existing efficacious treatments (both medical and psychotherapeutic), a large proportion of patients do not respond to therapy. Recent insights from evolutionary psychology suggest that, in addition to targeting the proximal causes of depression (for example, targeting dysfunctional beliefs by cognitive behavioral therapy), the distal or evolutionary causes (for example, inclusive fitness) should also be addressed. A randomized superiority trial is conducted to develop and test an evolutionary-driven cognitive therapy protocol for depression, and to compare its efficacy against standard cognitive therapy for depression. Methods/design: Romanian-speaking adults (18 years or older) with elevated Beck Depression Inventory (BDI) scores (\u3e13), current diagnosis of major depressive disorder or major depressive episode (MDD or MDE), and MDD with comorbid dysthymia, as evaluated by the Structured Clinical Interview for DSM-IV (SCID), are included in the study. Participants are randomized to one of two conditions: 1) evolutionary-driven cognitive therapy (ED-CT) or 2) cognitive therapy (CT). Both groups undergo 12 psychotherapy sessions, and data are collected at baseline, mid-treatment, post-treatment, and the 3-month follow-up. Primary outcomes are depressive symptomatology and a categorical diagnosis of depression post-treatment. Discussion: This randomized trial compares the newly proposed ED-CT with a classic CT protocol for depression. To our knowledge, this is the first attempt to integrate insights from evolutionary theories of depression into the treatment of this condition in a controlled manner. This study can thus add substantially to the body of knowledge on validated treatments for depression

Chemical Composition and Biological Activities of Beebread – Review

Beebread is a product of the hive obtained from pollen collected by bees, to which they add honey, digestive enzymes and subsequently is stored in the combs. The bees transform the bee pollen in beebread by an anaerobic fermentation process. A proper hive management promotes beebread collection, aimed at marketing it for human consumption since it can be considered a valuable food supplement due to its content of a wide range of nutrients. Its value is given by the content in protein, amino acids, fatty acids, carbohydrates, mineral salts, polyphenols and flavonoids, which depends on the botanical source of bee pollen. The nutritional and functional composition of beebread is widely reported; nevertheless, few studies on transformation processes of the pollen to improve the availability of the compounds present in this product were found. Overall, beebread is a recent collected and consumed bee product and at this stage it can be used as a food supplement

Overview of ¹⁴C release from irradiated zircaloys in geological disposal conditions

Carbon-14 (radiocarbon, 14C) is a long-lived radionuclide (5730 yr) of interest regarding the safety for the management of intermediate level wastes (ILW). The present study gives an overview of the release of 14C from irradiated Zircaloy cladding in alkaline media. 14C is found either in the alloy part of Zircaloy cladding due to the neutron activation of 14N impurities by 14N(n,p)14C reaction, or in the oxide layer (ZrO2) formed at the metal surface by the neutron activation of 17O from UO2 or (U-Pu)O2 fuel and water from the primary circuit in the reactor by 17O(n,α)14C reaction. Various irradiated and unirradiated Zircaloys have been studied. The total 14C inventory has been determined both experimentally and by calculations. The results seem to be in good agreement. Leaching experiments were conducted in alkaline media for several time durations. 14C was mainly released as carboxylic acids. Further, corrosion measurements were performed by using both hydrogen measurements and electrochemical measurements. The corrosion rate (CR) ranges from a few nm/yr to 100 nm/yr depending on the surface conditions and the method used for measurement. From a safety assessment point of view, the instant release fraction (IRF) was determined on irradiated Zircaloy-2. The results showed that the 14C inventory in the oxide was significantly below the 20% commonly used in safety case assessments