On some factors influencing dissolved silicon distribution over the northwest African shelf

Nitrate concentrations in the upwelling source waters observed near Cabo Corbeiro during the JOINT-I experiment were more than twice as high as dissolved silicon concentrations, but dissolved silicon concentrations in the surface layers fell below 0.5 µg-atoms..

The Thoracic Morphology of Archostemata and the Relationships of the Extant Suborders of Coleoptera (Hexapoda)

Thoracic structures of Tetraphalerus bruchi are described in detail. The results were compared with features found in other representatives of Archostemata and other coleopteran suborders. Differences between thoracic structures of Tetraphalerus and members of other archostematan subgroups are discussed. External and internal characters of larval and adult representatives of 37 genera of the coleopteran suborders are outlined, coded and analysed cladistically, with four groups of Neuropterida as outgroup taxa. The results strongly suggest the branching pattern Archostemata + [Adephaga + (Myxophaga + Polyphaga)]. Coleoptera excluding Archostemata are supported with a high Bremer support. Important evolutionary changes linked with this branching event are simplifications of the thoracic skeleton resulting in reduced degrees of freedom (i.e. a restricted movability, especially at the leg bases), and a distinct simplification of the muscle system. This development culminates in Polyphaga, which are also strongly supported as a clade. Internalization of the partly reduced propleura, further muscle losses, and the fusion of the mesoventrites and metaventrites—with reversal in Scirtoidea and Derodontidae—are autapomorphies of Polyphaga. Archostemata is a small relict group in contrast to highly successful xylobiontic groups of Polyphaga. The less efficient thoracic locomotor apparatus, the lack of cryptonephric Malpighian tubules, and the rise of angiosperms with beetle groups primarily adjusted to them may have contributed to the decline of Archostemata.Organismic and Evolutionary Biolog

NASA-JSC antenna near-field measurement system

Work was completed on the near-field range control software. The capabilities of the data processing software were expanded with the addition of probe compensation. In addition, the user can process the measured data from the same computer terminal used for range control. The design of the laser metrology system was completed. It provides precise measruement of probe location during near-field measurements as well as position data for control of the translation beam and probe cart. A near-field range measurement system was designed, fabricated, and tested

Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements

Modern manufacturing systems can benefit from the use of digital tools to support both short- and long-term decisions. Meanwhile, such systems reached a high level of complexity and are frequently subject to modifications that can quickly make the digital tools obsolete. In this context, the ability to dynamically generate models of production systems is essential to guarantee their exploitation on the shop-floors as decision-support systems. The literature offers approaches for generating digital models based on real-time data streams. These models can represent a system more precisely at any point in time, as they are continuously updated based on the data. However, most approaches consider only isolated aspects of systems (e.g., reliability models) and focus on a specific modeling purpose (e.g., material flow identification). The research challenge is therefore to develop a novel framework that systematically enables the combination of models extracted through different process mining algorithms. To tackle this challenge, it is critical to define the requirements that enable the emergence of automated modeling and simulation tasks. In this paper, we therefore derive and define data requirements for the models that need to be extracted. We include aspects such as the structure of the manufacturing system and the behavior of its machines. The paper aims at guiding practitioners in designing coherent data structures to enable the coupling of model generation techniques within the digital support system of manufacturing companies

A subsurface particle maximum layer and enhanced microbial activity in the secondary nitrite maximum of the northeastern tropical Pacific Ocean

Profiles of light transmission, dissolved oxygen, dissolved nutrients, electron transport system (ETS) activity, temperature and salinity were made in the northeastern tropical Pacific Ocean. A particle maximum at 150–300 m within the oxygen minimum and secondary nitrite maximum was associated with the salinity maximum of Subtropical Subsurface Water. A subsurface maximum in ETS activity was also found to be associated with the secondary nitrite maximum and the particle maximum. Persistence of these features at a constant depth and their location within a minimum in vertical static stability suggest an advective and/or in situ origin for the particles and an in situ development of the associated chemical and biochemical extremes

A First Comparison of the responses of a He4-based fast-neutron detector and a NE-213 liquid-scintillator reference detector

A first comparison has been made between the pulse-shape discrimination characteristics of a novel $^{4}$He-based pressurized scintillation detector and a NE-213 liquid-scintillator reference detector using an Am/Be mixed-field neutron and gamma-ray source and a high-resolution scintillation-pulse digitizer. In particular, the capabilities of the two fast neutron detectors to discriminate between neutrons and gamma-rays were investigated. The NE-213 liquid-scintillator reference cell produced a wide range of scintillation-light yields in response to the gamma-ray field of the source. In stark contrast, due to the size and pressure of the $^{4}$He gas volume, the $^{4}$He-based detector registered a maximum scintillation-light yield of 750~keV$_{ee}$ to the same gamma-ray field. Pulse-shape discrimination for particles with scintillation-light yields of more than 750~keV$_{ee}$ was excellent in the case of the $^{4}$He-based detector. Above 750~keV$_{ee}$ its signal was unambiguously neutron, enabling particle identification based entirely upon the amount of scintillation light produced.Comment: 23 pages, 7 figures, Nuclear Instruments and Methods in Physics Research Section A review addresse

Machine learning dihydrogen activation in the chemical space surrounding Vaska’s complex

Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand–ligand interactions. The classic example of Vaska\u27s complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska\u27s complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol−1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol−1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H2-activation barrier were identified

Note on SLE and logarithmic CFT

It is discussed how stochastic evolutions may be linked to logarithmic conformal field theory. This introduces an extension of the stochastic Loewner evolutions. Based on the existence of a logarithmic null vector in an indecomposable highest-weight module of the Virasoro algebra, the representation theory of the logarithmic conformal field theory is related to entities conserved in mean under the stochastic process.Comment: 10 pages, LaTeX, v2: version to be publishe