Structure and stability of quasi-two-dimensional boson-fermion mixtures with vortex-antivortex superposed states

We investigate the equilibrium properties of a quasi-two-dimensional degenerate boson-fermion mixture (DBFM) with a bosonic vortex-antivortex superposed state (VAVSS) using a quantum-hydrodynamic model. We show that, depending on the choice of parameters, the DBFM with a VAVSS can exhibit rich phase structures. For repulsive boson-fermion (BF) interaction, the Bose-Einstein condensate (BEC) may constitute a petal-shaped "core" inside the honeycomb-like fermionic component, or a ring-shaped joint "shell" around the onion-like fermionic cloud, or multiple segregated "islands" embedded in the disc-shaped Fermi gas. For attractive BF interaction just below the threshold for collapse, an almost complete mixing between the bosonic and fermionic components is formed, where the fermionic component tends to mimic a bosonic VAVSS. The influence of an anharmonic trap on the density distributions of the DBFM with a bosonic VAVSS is discussed. In addition, a stability region for different cases of DBFM (without vortex, with a bosonic vortex, and with a bosonic VAVSS) with specific parameters is given.Comment: 8 pages,5 figure

Performance of silicon solar cell assemblies

Solar cell assembly current-voltage characteristics, thermal-optical properties, and power performance were determined. Solar cell cover glass thermal radiation, optical properties, confidence limits, and temperature intensity effects on maximum power were discussed

AR-based Technoself Enhanced Learning Approach to Improving Student Engagement

The emerging technologies have expanded a new dimension of self – ‘technoself’ driven by socio-technical innovations and taken an important step forward in pervasive learning. Technology Enhanced Learning (TEL) research has increasingly focused on emergent technologies such as Augmented Reality (AR) for augmented learning, mobile learning, and game-based learning in order to improve self-motivation and self-engagement of the learners in enriched multimodal learning environments. These researches take advantage of technological innovations in hardware and software across different platforms and devices including tablets, phoneblets and even game consoles and their increasing popularity for pervasive learning with the significant development of personalization processes which place the student at the center of the learning process. In particular, augmented reality (AR) research has matured to a level to facilitate augmented learning, which is defined as an on-demand learning technique where the learning environment adapts to the needs and inputs from learners. In this paper we firstly study the role of Technology Acceptance Model (TAM) which is one of the most influential theories applied in TEL on how learners come to accept and use a new technology. Then we present the design methodology of the technoself approach for pervasive learning and introduce technoself enhanced learning as a novel pedagogical model to improve student engagement by shaping personal learning focus and setting. Furthermore we describe the design and development of an AR-based interactive digital interpretation system for augmented learning and discuss key features. By incorporating mobiles, game simulation, voice recognition, and multimodal interaction through Augmented Reality, the learning contents can be geared toward learner's needs and learners can stimulate discovery and gain greater understanding. The system demonstrates that Augmented Reality can provide rich contextual learning environment and contents tailored for individuals. Augment learning via AR can bridge this gap between the theoretical learning and practical learning, and focus on how the real and virtual can be combined together to fulfill different learning objectives, requirements, and even environments. Finally, we validate and evaluate the AR-based technoself enhanced learning approach to enhancing the student motivation and engagement in the learning process through experimental learning practices. It shows that Augmented Reality is well aligned with constructive learning strategies, as learners can control their own learning and manipulate objects that are not real in augmented environment to derive and acquire understanding and knowledge in a broad diversity of learning practices including constructive activities and analytical activities

Characterizations of Pseudo-Codewords of LDPC Codes

An important property of high-performance, low complexity codes is the existence of highly efficient algorithms for their decoding. Many of the most efficient, recent graph-based algorithms, e.g. message passing algorithms and decoding based on linear programming, crucially depend on the efficient representation of a code in a graphical model. In order to understand the performance of these algorithms, we argue for the characterization of codes in terms of a so called fundamental cone in Euclidean space which is a function of a given parity check matrix of a code, rather than of the code itself. We give a number of properties of this fundamental cone derived from its connection to unramified covers of the graphical models on which the decoding algorithms operate. For the class of cycle codes, these developments naturally lead to a characterization of the fundamental polytope as the Newton polytope of the Hashimoto edge zeta function of the underlying graph.Comment: Submitted, August 200

Nuclear matter symmetry energy and the neutron skin thickness of heavy nuclei

Correlations between the thickness of the neutron skin in finite nuclei and the nuclear matter symmetry energy are studied in the Skyrme Hartree-Fock model. From the most recent analysis of the isospin diffusion data in heavy-ion collisions based on an isospin- and momentum-dependent transport model with in-medium nucleon-nucleon cross sections, a value of $L=88\pm 25$ MeV for the slope of the nuclear symmetry energy at saturation density is extracted, and this imposes stringent constraints on both the parameters in the Skyrme effective interactions and the neutron skin thickness of heavy nuclei. Predicted thickness of the neutron skin is $0.22\pm 0.04$ fm for $$Pb, $0.29\pm 0.04$ fm for $^{132}$Sn, and $0.22\pm 0.04$ fm for $$Sn.Comment: 6 pages, 4 figures, 1 table, revised version, to appear in PR