Evolution of structure of SiO2 nanoparticles upon cooling from the melt

Evolution of structure of spherical SiO2 nanoparticles upon cooling from the melt has been investigated via molecular-dynamics (MD) simulations under non-periodic boundary conditions (NPBC). We use the pair interatomic potentials which have weak Coulomb interaction and Morse type short-range interaction. The change in structure of SiO2 nanoparticles upon cooling process has been studied through the partial radial distribution functions (PRDFs), coordination number and bond-angle distributions at different temperatures. The core and surface structures of nanoparticles have been studied in details. Our results show significant temperature dependence of structure of nanoparticles. Moreover, temperature dependence of concentration of structural defects in nanoparticles upon cooling from the melt toward glassy state has been found and discussed.Comment: 12 pages, 6 figure

Phase field modeling and computer implementation: A review

This paper presents an overview of the theories and computer implementation aspects of phase field models (PFM) of fracture. The advantage of PFM over discontinuous approaches to fracture is that PFM can elegantly simulate complicated fracture processes including fracture initiation, propagation, coalescence, and branching by using only a scalar field, the phase field. In addition, fracture is a natural outcome of the simulation and obtained through the solution of an additional differential equation related to the phase field. No extra fracture criteria are needed and an explicit representation of a crack surface as well as complex track crack procedures are avoided in PFM for fracture, which in turn dramatically facilitates the implementation. The PFM is thermodynamically consistent and can be easily extended to multi-physics problem by 'changing' the energy functional accordingly. Besides an overview of different PFMs, we also present comparative numerical benchmark examples to show the capability of PFMs

Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps

With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. The analysis of complex morphological features in the spatial domain is a particularly important task. Here we discuss the challenges of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project and introduce a proper transfer mechanism via quantile random forest regression. By using parallelized rotation and flipping invariant Kohonen-maps, image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio continuum and WISE infrared all sky surveys are first projected down to a two-dimensional embedding in an unsupervised way. This embedding can be seen as a discretised space of shapes with the coordinates reflecting morphological features as expressed by the automatically derived prototypes. We find that these prototypes have reconstructed physically meaningful processes across two channel images at radio and infrared wavelengths in an unsupervised manner. In the second step, images are compared with those prototypes to create a heat-map, which is the morphological fingerprint of each object and the basis for transferring the user generated labels. These heat-maps have reduced the feature space by a factor of 248 and are able to be used as the basis for subsequent ML methods. Using an ensemble of decision trees we achieve upwards of 85.7% and 80.7% accuracy when predicting the number of components and peaks in an image, respectively, using these heat-maps. We also question the currently used discrete classification schema and introduce a continuous scale that better reflects the uncertainty in transition between two classes, caused by sensitivity and resolution limits

A Model for Spheroid Versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-\u3cem\u3ePGJ\u3c/em\u3e\u3csub\u3e2\u3c/sub\u3e

Researchers have observed that response of tumor cells to treatment varies depending on whether the cells are grown in monolayer, as in vitro spheroids or in vivo. This study uses data from the literature on monolayer treatment of SK-N-SH neuroblastoma cells with 15-deoxy-PGJ2 and couples it with data on growth rates for untreated SK-N-SH neuroblastoma cells grown as multicellular spheroids. A linear model is constructed for untreated and treated monolayer data sets, which is tuned to growth, death, and cell cycle data for the monolayer case for both control and treatment with 15-deoxy-PGJ2. The monolayer model is extended to a five-dimensional nonlinear model of in vitro tumor spheroid growth and treatment that includes compartments of the cell cycle (G1,S,G2/M) as well as quiescent (Q) and necrotic (N) cells. Monolayer treatment data for 15-deoxy-PGJ2 is used to derive a prediction of spheroid response under similar treatments. For short periods of treatment, spheroid response is less pronounced than monolayer response. The simulations suggest that the difference in response to treatment of monolayer versus spheroid cultures observed in laboratory studies is a natural consequence of tumor spheroid physiology rather than any special resistance to treatment

MODELING AND SIMULATION OF SHRIMP DISEASES PROPAGATION IN RIVER NETWORKS AND INSIDE POND

ABSTRACTIn this paper we study and apply modeling on agent-based simulation for simulating the mechanism of shrimp infection inside shrimp-ponds and on the river networks in the region of Mekong Delta, Vietnam. The disease-propagation rules were established based on the way that the pathogens multiply in water-flows together withother infecting agents such as aquatic or migrating living things. Inside a shrimp-pond, besides the factors mentioned above, the infecting mechanism also depends on the density of shrimps and the initial number of the pathogens. In this study, we constructed two on-river-infecting scenarios for cases of the occurrence or non-occurrence of the pathogens and two inside one shrimp-pond infecting scenarios. All of the results are written in GAML and executed on GAMA platform.Keywords.  modeling, agent-based simulation, mechanism of disease-propagation, GAMA platform

Size dependent tunneling and optical spectroscopy of CdSe quantum rods

Photoluminescence excitation spectroscopy and scanning tunneling spectroscopy are used to study the electronic states in CdSe quantum rods that manifest a transition from a zero dimensional to a one dimensional quantum confined structure. Both optical and tunneling spectra show that the level structure depends primarily on the rod diameter and not on length. With increasing diameter, the band-gap and the excited state level spacings shift to the red. The level structure was assigned using a multi-band effective-mass model, showing a similar dependence on rod dimensions.Comment: Accepted to PRL (nearly final version). 4 pages in revtex, 4 figure

Dataset of the phospholipidome and transcriptome of Campylobacter jejuni under different growth conditions

The membrane phospholipid composition is not a stable bacterial characteristic but can change in response to altered environmental conditions. Here we provide the dataset of the phospholipidome and transcriptome of the microaerophilic human pathogen Campylobacter jejuni under different environmental conditions. These data have been used in Cao (2020), The unique phospholipidome of the enteric pathogen C. jejuni: Lysolipids are required for motility at low oxygen availability. Here the abundance of each phospholipid is shown during the growth of C. jejuni for 0-108 h under low and high oxygen conditions (0.3 vs 10% O2). The phospholipid data were obtained by applying high performance liquid chromatography tandem-mass spectrometry (LC-MS/MS). The transcriptomic data obtained by RNA-seq show the differential expressed genes between logarithmic and stationary grown bacteria. In addition, our data might serve as a reference information for further in-depth investigation to understand the relation between specific phospholipids and the activity of membrane associated proteins

Development of a radio detection array for the observation of showers induced by UHE Tau neutrinos

International audienceDevelopment of a radio detection array for the observation of showers induced by UHE Tau neutrino

Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era

We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting PINK, a self-organising map algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using $894,415$ images from the FIRST and WISE surveys centred towards positions described by the FIRST catalogue. We produce a set of catalogues that complement FIRST and describe 802,646 objects, including their radio components and their corresponding AllWISE infrared host galaxy. Using these data products we (i) demonstrate the ability to identify objects with rare and unique radio morphologies (e.g. 'X'-shaped galaxies, hybrid FR-I/FR-II morphologies), (ii) can identify the potentially resolved radio components that are associated with a single infrared host and (iii) introduce a "curliness" statistic to search for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant radio galaxies between 700-1100 kpc. As we require no training labels, our method can be applied to any radio-continuum survey, provided a sufficiently representative SOM can be trained