Thermal photon production in high-energy nuclear collisions

We use a boost-invariant one-dimensional (cylindrically symmetric) fluid dynamics code to calculate thermal photon production in the central rapidity region of S+Au and Pb+Pb collisions at SPS energy ($\sqrt{s}=20$ GeV/nucleon). We assume that the hot matter is in thermal equilibrium throughout the expansion, but consider deviations from chemical equilibrium in the high temperature (deconfined) phase. We use equations of state with a first-order phase transition between a massless pion gas and quark gluon plasma, with transition temperatures in the range $150 \leq T_c \leq 200$ MeV.Comment: revised, now includes a_1 contribution. revtex, 10 pages plus 4 figures (uuencoded postscript

Medium effect on photon production in ultrarelativistic nuclear collisions

The effect of in-medium vector and axial-vector meson masses on photon production is studied. We assume that the effective mass of a vector meson in hot nuclear matter decreases according to a universal scaling law, while that of an axial-vector meson is given by Weinberg's mass formula. We find that the thermal production rate of photons increases with reduced masses, and is enhanced by an order of magnitude at T=160 MeV with $m_\rho=300$ MeV. Assuming a hydrodynamic evolution, we estimate the effect of the reduced masses on photon production in nucleus-nucleus collisions. The result is compared to experimental data from the WA80/WA98 collaboration.Comment: 21 pages, REVTEX + 9 figures (ps file

Genes in the postgenomic era

We outline three very different concepts of the gene - 'instrumental', 'nominal', and 'postgenomic'. The instrumental gene has a critical role in the construction and interpretation of experiments in which the relationship between genotype and phenotype is explored via hybridization between organisms or directly between nucleic acid molecules. It also plays an important theoretical role in the foundations of disciplines such as quantitative genetics and population genetics. The nominal gene is a critical practical tool, allowing stable communication between bioscientists in a wide range of fields grounded in well-defined sequences of nucleotides, but this concept does not embody major theoretical insights into genome structure or function. The post-genomic gene embodies the continuing project of understanding how genome structure supports genome function, but with a deflationary picture of the gene as a structural unit. This final concept of the gene poses a significant challenge to conventional assumptions about the relationship between genome structure and function, and between genotype and phenotype

The Promise of Prediction Markets

Prediction markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. Using these markets as forecasting tools could substantially improve decision making in the private and public sectors. We argue that U.S. regulators should lower barriers to the creation and design of prediction markets by creating a safe harbor for certain types of small stakes markets. We believe our proposed change has the potential to stimulate innovation in the design and use of prediction markets throughout the economy, and in the process to provide information that will benefit the private sector and government alike.Technology and Industry

Enhancing the early student experience

This paper is concerned with identifying how the early student experience can be enhanced in order to improve levels of student retention and achievement. The early student experience is the focus of this project as the literature has consistently declared the first year to be the most critical in shaping persistence decisions. Programme managers of courses with high and low retention rates have been interviewed to identify activities that appear to be associated with good retention rates. The results show that there are similarities in the way programmes with high retention are run, with these features not being prevalent on programmes with low retention. Recommendations of activities that appear likely to enhance the early student experience are provided

Superposition rule and entanglement in diagonal and probability representations of density states

The quasidistributions corresponding to the diagonal representation of quantum states are discussed within the framework of operator-symbol construction. The tomographic-probability distribution describing the quantum state in the probability representation of quantum mechanics is reviewed. The connection of the diagonal and probability representations is discussed. The superposition rule is considered in terms of the density-operator symbols. The separability and entanglement properties of multipartite quantum systems are formulated as the properties of the density-operator symbols of the system states.Comment: Invited talk presented at the XV Central European Workshop on Quantum Optics (Belgrade, Serbia, 30 May -- 3 June 2008), to appear in Physica Scripta

Giant persistent photoconductivity in monolayer MoS2 field-effect transistors

Monolayer transition metal dichalcogenides (TMD) have numerous potential applications in ultrathin electronics and photonics. The exposure of TMD-based devices to light generates photo-carriers resulting in an enhanced conductivity, which can be effectively used, e.g., in photodetectors. If the photo-enhanced conductivity persists after removal of the irradiation, the effect is known as persistent photoconductivity (PPC). Here we show that ultraviolet light (λ = 365 nm) exposure induces an extremely long-living giant PPC (GPPC) in monolayer MoS2 (ML-MoS2) field-effect transistors (FET) with a time constant of ~30 days. Furthermore, this effect leads to a large enhancement of the conductivity up to a factor of 107. In contrast to previous studies in which the origin of the PPC was attributed to extrinsic reasons such as trapped charges in the substrate or adsorbates, we show that the GPPC arises mainly from the intrinsic properties of ML-MoS2 such as lattice defects that induce a large number of localized states in the forbidden gap. This finding is supported by a detailed experimental and theoretical study of the electric transport in TMD based FETs as well as by characterization of ML-MoS2 with scanning tunneling spectroscopy, high-resolution transmission electron microscopy, and photoluminescence measurements. The obtained results provide a basis for the defect-based engineering of the electronic and optical properties of TMDs for device applications

Segmentation of the C57BL/6J mouse cerebellum in magnetic resonance images

The C57BL mouse is the centerpiece of efforts to use gene-targeting technology to understand cerebellar pathology, thus creating a need for a detailed magnetic resonance imaging (MRI) atlas of the cerebellum of this strain. In this study we present a methodology for systematic delineation of the vermal and hemispheric lobules of the C57BL/6J mouse cerebellum in magnetic resonance images. We have successfully delineated 38 cerebellar and cerebellar-related structures. The higher signal-to-noise ratio achieved by group averaging facilitated the identification of anatomical structures. In addition, we have calculated average region volumes and created probabilistic maps for each structure. The segmentation method and the probabilistic maps we have created will provide a foundation for future studies of cerebellar disorders using transgenic mouse models

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201