Trace Elements Distribution in Red Soils under Semiarid Mediterranean Environment

This study states the potential trace elements (TE’s) content of red soils located at the centre region of Spain, characterized by low rainfall and slight acidity over prolonged weathering periods. For this purpose, three soil profiles from a catena were described, sampled and analyzed. The most notable characteristics are the low organic matter content and the predominantly acidic pH. Illite and kaolinite are the predominant clay minerals. The fertility of the soils is sufficient to provide most of the nutrients required, with very suitable potassium levels. The geochemical characters of this soil are: only few elements remain almost invariable across the profiles and over time, however the majority of them were directly linked with the clay content. These soils are characterized by relatively low levels of some trace elements such as Sr (64.35 mg?kg–1), Ba (303.67 mg?kg–1) and Sc (13.14 mg?kg–1); high levels of other trace elements such as V (103.92 mg?kg–1), Cr (79.9 mg?kg–1), Cu (15.18 mg?kg–1), Hf (10.26 mg?kg–1), Ni (38 mg?kg–1) and Zr (337 mg?kg–1); while the levels for rare earth elements (REE’s) such as La (48.36 mg?kg–1), Ce (95.07 mg?kg–1), Th (13.33 mg?kg–1) and Nd (42.65 mg?kg–1) are significantly high. The distribution of mayor and trace elements was directly re- lated to weathering processes, parent material and anthropogenic activities.

Profiling the temperature distribution in AlGaN/GaN HEMTs with nanocrystalline diamond heat spreading layers

Reduced performance in Gallium Nitride (GaN) based high electron mobility transistors (HEMTs) as a result of self-heating has been well-documented. A new approach, termed “diamond-before-gate" is shown to improve the thermal budget of the deposition process and enables large area diamond without degrading the gate metal NCD capped devices had a 20% lower channel temperature at equivalent power dissipation.

Impact of N on the atomic-scale Sb distribution in quaternary GaAsSbN-capped InAs quantum dots

The use of GaAsSbN capping layers on InAs/GaAs quantum dots (QDs) has recently been proposed for micro- and optoelectronic applications for their ability to independently tailor electron and hole confinement potentials. However, there is a lack of knowledge about the structural and compositional changes associated with the process of simultaneous Sb and N incorporation. In the present work, we have characterized using transmission electron microscopy techniques the effects of adding N in the GaAsSb/InAs/GaAs QD system. Firstly, strain maps of the regions away from the InAs QDs had revealed a huge reduction of the strain fields with the N incorporation but a higher inhomogeneity, which points to a composition modulation enhancement with the presence of Sb-rich and Sb-poor regions in the range of a few nanometers. On the other hand, the average strain in the QDs and surroundings is also similar in both cases. It could be explained by the accumulation of Sb above the QDs, compensating the tensile strain induced by the N incorporation together with an In-Ga intermixing inhibition. Indeed, compositional maps of column resolution from aberration-corrected Z-contrast images confirmed that the addition of N enhances the preferential deposition of Sb above the InAs QD, giving rise to an undulation of the growth front. As an outcome, the strong redshift in the photoluminescence spectrum of the GaAsSbN sample cannot be attributed only to the N-related reduction of the conduction band offset but also to an enhancement of the effect of Sb on the QD band structure.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Application of active heat pulse method with fiber optic temperature sensing for estimation of wetting bulbs and water distribution in drip emitters.

Through the use of the Distributed Fiber Optic Temperature Measurement (DFOT) method, it is possible to measure the temperature in small intervals (on the order of centimeters) for long distances (on the order of kilometers) with a high temporal frequency and great accuracy. The heat pulse method consists of applying a known amount of heat to the soil and monitoring the temperature evolution, which is primarily dependent on the soil moisture content. The use of both methods, which is called the active heat pulse method with fiber optic temperature sensing (AHFO), allows accurate soil moisture content measurements. In order to experimentally study the wetting patterns, i.e. shape, size, and the water distribution, from a drip irrigation emitter, a soil column of 0.5 m of diameter and 0.6 m high was built. Inside the column, a fiber optic cable with a stainless steel sheath was placed forming three concentric helixes of diameters 0.2 m, 0.4 m and 0.6 m, leading to a 148 measurement point network. Before, during, and after the irrigation event, heat pulses were performed supplying electrical power of 20 W/m to the steel. The soil moisture content was measured with a capacitive sensor in one location at depths of 0.1 m, 0.2 m, 0.3 m and 0.4 m during the irrigation. It was also determined by the gravimetric method in several locations and depths before and right after the irrigation. The emitter bulb dimensions and shape evolution was satisfactorily measured during infiltration. Furthermore, some bulb's characteristics difficult to predict (e.g. preferential flow) were detected. The results point out that the AHFO is a useful tool to estimate the wetting pattern of drip irrigation emitters in soil columns and show a high potential for its use in the field.

Residual stress distribution in ferritic-austenitic steel joints made by laser welding

The present investigation addresse the influence of laser welding process-ing parameters used for joining dis-similar metals (ferritic to austenitic steel), on the induced residual stress field. Welding was performed on a Nd:YAG laser DY033 (3300 W) in a continuous wave (CW), keyhole mode. The base metals (BM) employed in this study are AISI 1010 carbon steel (CS) and AISI 304L austenitic stainless steel (SS). Pairs of dissimilar plates of 200 mm x 45 mm x 3 mm were butt joined by laser welding. Different sets of parameters were used to engineer the base metals apportionment at joint formation, namely distinct dilution rates. Residual strain scanning, carried out by neutron diffraction was used to assess the joints. Through-thickness residual stress maps were determined for the laser welded samples of dis-similar steels using high spatial reso-lution. As a result, an appropriate set of processing parameters, able to mi-nimize the local tensile residual stress associated to the welding process, was found.

Vibroacoustic Problems in High Speed Trains

Passengers comfort in terms of acoustic noise levels is a key train design parameter, especially relevant in high speed trains, where the aerodynamic noise is dominant. The aim of the work, described in this paper, is to make progress in the understanding of the flow field around high speed trains in an open field, which is a subject of interest for many researchers with direct industrial applications, but also the critical configuration of the train inside a tunnel is studied in order to evaluate the external loads arising from noise sources of the train. The airborne noise coming from the wheels (wheelrail interaction), which is the dominant source at a certain range of frequencies, is also investigated from the numerical and experimental points of view. The numerical prediction of the noise in the interior of the train is a very complex problem, involving many different parameters: complex geometries and materials, different noise sources, complex interactions among those sources, broad range of frequencies where the phenomenon is important, etc. During recent years a research plan is being developed at IDR/UPM (Instituto de Microgravedad Ignacio Da Riva, Universidad Politécnica de Madrid) involving both numerical simulations, wind tunnel and full-scale tests to address this problem. Comparison of numerical simulations with experimental data is a key factor in this process.

Effects of Dietary Fibre (Pectin) and/or Increased Protein (Casein or Pea) on Satiety, Body Weight, Adiposity and Caecal Fermentation in High Fat Diet-Induced Obese Rats

Funding: This work was funded by the Scottish Government Rural and Environment Science and Analytical Services Division. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Role of aromatic residues in stabilization of the [Fe4S4] cluster in high-potential iron proteins (HiPIPs): physical characterization and stability studies of Tyr-19 mutants of Chromatium vinosum HiPIP.

The functional role of residue Tyr-19 of Chromatium vinosum HiPIP has been evaluated by site-directed mutagenesis experiments. The stability of the [Fe4S4] cluster prosthetic center is sensitive to side-chain replacements. Polar residues result in significant instability, while nonpolar residues (especially with aromatic side chains) maintain cluster stability. Two-dimensional NMR data of native and mutant HiPIPs are consistent with a model where Tyr-19 serves to preserve the structural rigidity of the polypeptide backbone, thereby maintaining a hydrophobic barrier for exclusion of water from the cluster cavity. Solvent accessibility results in more facile oxidation of the cluster by atmospheric oxygen, with subsequent rapid hydrolysis of the [Fe4S4]3+ core.

Late-Holocene Mass Movements in High Arctic East Lake, Melville Island (Western Canadian Arctic Archipelago)

East Lake, located at Cape Bounty (Melville Island, Canadian High Arctic), was mapped using a high-resolution swath bathymetric sonar and a 12 kHz sub-bottom profiler, allowing for the first time the imaging of widespread occurrence of mass movement deposits (MMDs) in a Canadian High Arctic Lake. Mass movements occurred mostly on steep slopes located away from deltaic sedimentation. The marine to lacustrine transition in the sediment favours the generation of mass movements where the underlying massive mud appears to act as a gliding surface for the overlying varved deposits. Based on acoustic stratigraphy, we have identified at least two distinct events that triggered failures in the lake during the last 2000 years. The synchronicity of multiple failures and their widespread distribution suggest a seismic origin that could be related to the nearby Gustaf-Lougheed Arch seismic zone. Further sedimentological investigations on the MMDs are however required to confirm their age and origin.

Lake-effect snow as the dominant control of mesic-forest distribution in Michigan, USA

1Recent studies demonstrated the sensitivity of northern forest ecosystems to changes in the amount and duration of snow cover at annual to decadal time scales. However, the consequences of snowfall variability remain uncertain for ecological variables operating at longer time scales, especially the distributions of forest communities. 2The Great Lakes region of North America offers a unique setting to examine the long-term effects of variable snowfall on forest communities. Lake-effect snow produces a three-fold gradient in annual snowfall over tens of kilometres, and dramatic edaphic variations occur among landform types resulting from Quaternary glaciations. We tested the hypothesis that these factors interact to control the distributions of mesic (dominated by Acer saccharum, Tsuga canadensis and Fagus grandifolia) and xeric forests (dominated by Pinus and Quercus spp.) in northern Lower Michigan. 3We compiled pre-European-settlement vegetation data and overlaid these data with records of climate, water balance and soil, onto Landtype Association polygons in a geographical information system. We then used multivariate adaptive regression splines to model the abundance of mesic vegetation in relation to environmental controls. 4Snowfall is the most predictive among five variables retained by our model, and it affects model performance 29% more than soil texture, the second most important variable. The abundance of mesic trees is high on fine-textured soils regardless of snowfall, but it increases with snowfall on coarse-textured substrates. Lake-effect snowfall also determines the species composition within mesic forests. The weighted importance of A. saccharum is significantly greater than of T. canadensis or F. grandifolia within the lake-effect snowbelt, whereas T. canadensis is more plentiful outside the snowbelt. These patterns are probably driven by the influence of snowfall on soil moisture, nutrient availability and fire return intervals. 5Our results imply that a key factor dictating the spatio-temporal patterns of forest communities in the vast region around the Great Lakes is how the lake-effect snowfall regime responds to global change. Snowfall reductions will probably cause a major decrease in the abundance of ecologically and economically important species, such as A. saccharum.