Crassulacean acid metabolism in the Gesneriaceae

The occurrence of the Crassulacean acid metabolism (CAM) was studied in four epiphytic species of the Gesneriaceae: two neotropical species, Codonanthe crassifolia and Columnea linearis, and two paleotropical species, Aoschynanthus pulcher and Saintpaulia ionantha. Gas exchange parameters, enzymology, and leaf anatomy, including mesophyll succulence and rel­ ative percent of the mesophyll volume occupied by airspace, were studied for each species. Codonanthe crassifolia was the only species to show nocturnal CO2 uptake and a diurnal organic acid fluctuation. According to these results, Codonanthe crassifolia shows CAM-cycling under well-watered conditions and when subjected to drought, it switches to CAM-idling. Other characteristics, such as leaf anatomy, mesophyll succulence, and PEP carboxylase and NADP malic enzyme activity, indicate attributes of the CAM pathway. All other species tested showed C3 photosynthesis. The most C3-like species is Columnea linearis, according to the criteria tested in this investigation. The other two species show mesophyll succulence and relative percent of the leaf volume occupied by airspace within the CAM range, but no other characters of the CAM pathway. The leaf structure of certain genera of the Gesneriaceae and of the genus Peperomia in the Piperaceae are similar, both having an upper succulent, multiple epidermis, a medium palisade of one or a few cell layers, and a lower, succulent spongy parenchyma not too unlike CAM photosynthetic tissue. We report ecophysiological similarities between these two distantly related families. Thus, the occurrence of CAM-cycling may be more common among epiphytic species than is currently known

Quantum secret sharing between multi-party and multi-party without entanglement

We propose a quantum secret sharing protocol between multi-party ($m$ members in group 1) and multi-party ($n$ members in group 2) using a sequence of single photons. These single photons are used directly to encode classical information in a quantum secret sharing process. In this protocol, all members in group 1 directly encode their respective keys on the states of single photons via unitary operations, then the last one (the $m^{th}$ member of group 1) sends $1/n$ of the resulting qubits to each of group 2. Thus the secret message shared by all members of group 1 is shared by all members of group 2 in such a way that no subset of each group is efficient to read the secret message, but the entire set (not only group 1 but also group 2) is. We also show that it is unconditionally secure. This protocol is feasible with present-day techniques.Comment: 6 pages, no figur

Dislocation core field. I. Modeling in anisotropic linear elasticity theory

Aside from the Volterra field, dislocations create a core field, which can be modeled in linear anisotropic elasticity theory with force and dislocation dipoles. We derive an expression of the elastic energy of a dislocation taking full account of its core field and show that no cross term exists between the Volterra and the core fields. We also obtain the contribution of the core field to the dislocation interaction energy with an external stress, thus showing that dislocation can interact with a pressure. The additional force that derives from this core field contribution is proportional to the gradient of the applied stress. Such a supplementary force on dislocations may be important in high stress gradient regions, such as close to a crack tip or in a dislocation pile-up

A robust empirical seasonal prediction of winter NAO and surface climate

A key determinant of winter weather and climate in Europe and North America is the North Atlantic Oscillation (NAO), the dominant mode of atmospheric variability in the Atlantic domain. Skilful seasonal forecasting of the surface climate in both Europe and North America is reflected largely in how accurately models can predict the NAO. Most dynamical models, however, have limited skill in seasonal forecasts of the winter NAO. A new empirical model is proposed for the seasonal forecast of the winter NAO that exhibits higher skill than current dynamical models. The empirical model provides robust and skilful prediction of the December-January-February (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions of sea-ice concentration, stratospheric circulation, and sea-surface temperature. The predictability is, for the most part, derived from the relatively long persistence of sea ice in the autumn. The lower stratospheric circulation and sea-surface temperature appear to play more indirect roles through a series of feedbacks among systems driving NAO evolution. This MLR model also provides skilful seasonal outlooks of winter surface temperature and precipitation over many regions of Eurasia and eastern North America

Two-Color Coherent Photodissociation of Nitrogen Oxide in Intense Laser Fields

A simple one-dimensional semi-classical model with a Morse potential is used to investigate the possibility of two-color infrared multi-photon dissociation of vibrationally excited nitrogen oxide. The amplitude ratio effects and adiabatic effects are investigated. Some initial states are found to have thresholds smaller than expected from single-mode considerations and multiple thresholds exist for initial states up to 32. PACS: 42.50.HzComment: 3 pages, old papers, add source files to replace original postscrip

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes