Global mass segregation in hydrodynamical simulations of star formation

Recent analyses of mass segregation diagnostics in star forming regions invite a comparison with the output of hydrodynamic simulations of star formation. In this work we investigate the state of mass segregation of 'stars' (i.e. sink particles in the simulations) in the case of hydrodynamical simulations which omit feedback. We first discuss methods to quantify mass segregation in substructured regions, either based on the minimum spanning tree (Allison's Lambda), or through analysis of correlations between stellar mass and local stellar surface number densities. We find that the presence of even a single 'outlier' (i.e. a massive object far from other stars) can cause the Allison Lambda method to describe the system as inversely mass segregated, even where in reality the most massive sink particles are overwhelmingly in the centres of the subclusters. We demonstrate that a variant of the Lambda method is less susceptible to this tendency but also argue for an alternative representation of the data in the plane of stellar mass versus local surface number density. The hydrodynamical simulations show global mass segregation from very early times which continues throughout the simulation, being only mildly influenced during sub-cluster merging. We find that up to approx. 2-3% of the "massive" sink particles (m > 2.5 Msun) are in relative isolation because they have formed there, although other sink particles can form later in their vicinity. Ejections of massive sinks from subclusters do not contribute to the number of isolated massive sink particles, as the gravitational softening in the calculation suppresses this process.Comment: 6 pages, 6 figure

Fuel Consumption Prediction for a Passenger Ferry using Machine Learning and In-service Data: A Comparative Study

As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential. Methods for status monitoring with consideration to the weather condition and forecasting with the use of in-service data from ships requires accurate and complete models for predicting the energy efficiency of a ship. The models need to effectively process all the operational data in real-time. This paper presents models that can predict fuel consumption using in-service data collected from a passenger ship. Statistical and domain-knowledge methods were used to select the proper input variables for the models. These methods prevent over-fitting, missing data, and multicollinearity while providing practical applicability. Prediction models that were investigated include multiple linear regression (MLR), decision tree approach (DT), an artificial neural network (ANN), and ensemble methods. The best predictive performance was from a model developed using the XGboost technique which is a boosting ensemble approach. \rvv{Our code is available on GitHub at \url{https://github.com/pagand/model_optimze_vessel/tree/OE} for future research.Comment: 20 pages, 11 figures, 7 table

Towards automating the sizing process in conceptual (airframe) systems architecting

Presented is a method for automated sizing of airframe systems, ultimately aiming to enable an efficient and interactive systems architecture evaluation process. The method takes as input the logical view of the system architecture. A source-sink approach combined with a Design Structure Matrix (DSM) sequencing algorithm is used to orchestrate the sequence of the sub-system sizing tasks. Bipartite graphs and a maximum matching algorithm are utilized to identify and construct the computational sizing workflows. A recursive algorithm, based on fundamental dimensions of additive physical quantities (e.g., weight, power, etc.) is employed to aggregate variables at the system level. The evaluation, based on representative test cases confirmed the correctness of the proposed method. The results also showed that the proposed approach overcomes certain limitations of existing methods and looks very promising as an initial systems architectural design enabler

Performance of two Askaryan Radio Array stations and first results in the search for ultrahigh energy neutrinos

Ultrahigh energy neutrinos are interesting messenger particles since, if detected, they can transmit exclusive information about ultrahigh energy processes in the Universe. These particles, with energies above 1016 eV, interact very rarely. Therefore, detectors that instrument several gigatons of matter are needed to discover them. The ARA detector is currently being constructed at the South Pole. It is designed to use the Askaryan effect, the emission of radio waves from neutrino-induced cascades in the South Pole ice, to detect neutrino interactions at very high energies. With antennas distributed among 37 widely separated stations in the ice, such interactions can be observed in a volume of several hundred cubic kilometers. Currently three deep ARA stations are deployed in the ice, of which two have been taking data since the beginning of 2013. In this article, the ARA detector “as built” and calibrations are described. Data reduction methods used to distinguish the rare radio signals from overwhelming backgrounds of thermal and anthropogenic origin are presented. Using data from only two stations over a short exposure time of 10 months, a neutrino flux limit of 1.5 × 10−6 GeV=cm2=s=sr is calculated for a particle energy of 1018 eV, which offers promise for the full ARA detector

Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition

The SP theory of computing and cognition, described in previous publications, is an attractive model for intelligent databases because it provides a simple but versatile format for different kinds of knowledge, it has capabilities in artificial intelligence, and it can also function like established database models when that is required. This paper describes how the SP model can emulate other models used in database applications and compares the SP model with those other models. The artificial intelligence capabilities of the SP model are reviewed and its relationship with other artificial intelligence systems is described. Also considered are ways in which current prototypes may be translated into an 'industrial strength' working system

Magnetic characterization of superparamagnetic nanoparticles pulled through model membranes

BACKGROUND: To quantitatively compare in-vitro and in vivo membrane transport studies of targeted delivery, one needs characterization of the magnetically-induced mobility of superparamagnetic iron oxide nanoparticles (SPION). Flux densities, gradients, and nanoparticle properties were measured in order to quantify the magnetic force on the SPION in both an artificial cochlear round window membrane (RWM) model and the guinea pig RWM. METHODS: Three-dimensional maps were created for flux density and magnetic gradient produced by a 24-well casing of 4.1 kilo-Gauss neodymium-iron-boron (NdFeB) disc magnets. The casing was used to pull SPION through a three-layer cell culture RWM model. Similar maps were created for a 4 inch (10.16 cm) cube 48 MGOe NdFeB magnet used to pull polymeric-nanoparticles through the RWM of anesthetized guinea pigs. Other parameters needed to compute magnetic force were nanoparticle and polymer properties, including average radius, density, magnetic susceptibility, and volume fraction of magnetite. RESULTS: A minimum force of 5.04 × 10(-16 )N was determined to adequately pull nanoparticles through the in-vitro model. For the guinea pig RWM, the magnetic force on the polymeric nanoparticles was 9.69 × 10(-20 )N. Electron microscopy confirmed the movement of the particles through both RWM models. CONCLUSION: As prospective carriers of therapeutic substances, polymers containing superparamagnetic iron oxide nanoparticles were succesfully pulled through the live RWM. The force required to achieve in vivo transport was significantly lower than that required to pull nanoparticles through the in-vitro RWM model. Indeed very little force was required to accomplish measurable delivery of polymeric-SPION composite nanoparticles across the RWM, suggesting that therapeutic delivery to the inner ear by SPION is feasible

Multiple functional neurosteroid binding sites on GABAA receptors

Neurosteroids are endogenous modulators of neuronal excitability and nervous system development and are being developed as anesthetic agents and treatments for psychiatric diseases. While gamma amino-butyric acid Type A (GABAA) receptors are the primary molecular targets of neurosteroid action, the structural details of neurosteroid binding to these proteins remain ill defined. We synthesized neurosteroid analogue photolabeling reagents in which the photolabeling groups were placed at three positions around the neurosteroid ring structure, enabling identification of binding sites and mapping of neurosteroid orientation within these sites. Using middle-down mass spectrometry (MS), we identified three clusters of photolabeled residues representing three distinct neurosteroid binding sites in the human α1β3 GABAA receptor. Novel intrasubunit binding sites were identified within the transmembrane helical bundles of both the α1 (labeled residues α1-N408, Y415) and β3 (labeled residue β3-Y442) subunits, adjacent to the extracellular domains (ECDs). An intersubunit site (labeled residues β3-L294 and G308) in the interface between the β3(+) and α1(-) subunits of the GABAA receptor pentamer was also identified. Computational docking studies of neurosteroid to the three sites predicted critical residues contributing to neurosteroid interaction with the GABAA receptors. Electrophysiological studies of receptors with mutations based on these predictions (α1-V227W, N408A/Y411F, and Q242L) indicate that both the α1 intrasubunit and β3-α1 intersubunit sites are critical for neurosteroid action

Light with a self-torque: extreme-ultraviolet beams with time-varying orbital angular momentum

Twisted light fields carrying orbital angular momentum (OAM) provide powerful capabilities for applications in optical communications, microscopy, quantum optics and microparticle rotation. Here we introduce and experimentally validate a new class of light beams, whose unique property is associated with a temporal OAM variation along a pulse: the self-torque of light. Self-torque is a phenomenon that can arise from matter-field interactions in electrodynamics and general relativity, but to date, there has been no optical analog. In particular, the self-torque of light is an inherent property, which is distinguished from the mechanical torque exerted by OAM beams when interacting with physical systems. We demonstrate that self-torqued beams in the extreme-ultraviolet (EUV) naturally arise as a necessary consequence of angular momentum conservation in non-perturbative high-order harmonic generation when driven by time-delayed pulses with different OAM. In addition, the time-dependent OAM naturally induces an azimuthal frequency chirp, which provides a signature for monitoring the self-torque of high-harmonic EUV beams. Such self-torqued EUV beams can serve as unique tools for imaging magnetic and topological excitations, for launching selective excitation of quantum matter, and for manipulating molecules and nanostructures on unprecedented time and length scales.Comment: 24 pages, 4 figure

The detection of the imprint of filaments on cosmic microwave background lensing

Galaxy redshift surveys, such as 2dF, SDSS, 6df, GAMA and VIPERS, have shown that the spatial distribution of matter forms a rich web, known as the cosmic web. The majority of galaxy survey analyses measure the amplitude of galaxy clustering as a function of scale, ignoring information beyond a small number of summary statistics. Since the matter density field becomes highly non-Gaussian as structure evolves under gravity, we expect other statistical descriptions of the field to provide us with additional information. One way to study the non-Gaussianity is to study filaments, which evolve non-linearly from the initial density fluctuations produced in the primordial Universe. In our study, we report the first detection of CMB (Cosmic Microwave Background) lensing by filaments and we apply a null test to confirm our detection. Furthermore, we propose a phenomenological model to interpret the detected signal and we measure how filaments trace the matter distribution on large scales through filament bias, which we measure to be around 1.5. Our study provides a new scope to understand the environmental dependence of galaxy formation. In the future, the joint analysis of lensing and Sunyaev-Zel'dovich observations might reveal the properties of `missing baryons', the vast majority of the gas which resides in the intergalactic medium and has so far evaded most observations