BAYESIAN DECISION STRATEGIES APPLIED TO PRODUCTION AND MARKETING DECISIONS FOR COW-CALF FARMS IN THE SHENANDOAH AREA OF VIRGINIA

Livestock Production/Industries,

Wobbling excitations at high spins in A~160

We found that in 156Dy and 162Yb the lowest odd spin gamma-vibrational states transform to the wobbling excitations after the backbending, associated with the transition from axially-symmetric to nonaxial shapes. The analysis of quadrupole electric transitions determines uniquely the sign of the gamma-deformation in both nuclei after the transition point.Comment: 6 pages, 4 figure

Multistream dynamic Bayesian network for meeting segmentation

Consonant duration is influenced by a number of linguistic factors such as the consonant s identity, within-word position, stress level of the previous and following vowels, phrasal position of the word containing the target consonant, its syllabic position, identity of the previous and following segments. In our work, consonant duration is predicted from a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the consonant s duration. Interactions between factors are represented as conditional dependency arcs in this graphical model. Given the parameters of the belief network, the duration of each consonant in the test set is then predicted as the value with the maximum probability. We compare the results of the belief network model with those of sums-of-products (SoP) and classification and regression tree (CART) models using the same data. In terms of RMS error, our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. In addition, the Bayesian model reliably predicts consonant duration in cases of missing or hidden linguistic factors

A microscopic derivation of the quantum mechanical formal scattering cross section

We prove that the empirical distribution of crossings of a "detector'' surface by scattered particles converges in appropriate limits to the scattering cross section computed by stationary scattering theory. Our result, which is based on Bohmian mechanics and the flux-across-surfaces theorem, is the first derivation of the cross section starting from first microscopic principles.Comment: 28 pages, v2: Typos corrected, layout improved, v3: Typos corrected. Accepted for publication in Comm. Math. Phy

EPUAP classification system for pressure ulcers: European reliability study

‘The definitive version is available at www3.interscience.wiley.com .' Copyright Blackwell PublishingPeer reviewe

Scenario-based modeling in industrial information systems

This manuscript addresses the creation of scenario-based models to reason about the behavior of existing industrial information systems. In our approach the system behavior is modeled in two steps that gradually introduce detail and formality. This manuscript addresses the first step, where text-based descriptions, in the form of structured rules, are used to specify how the system is or should be regulated. Those rules can be used to create behavioral snapshots, which are collections of scenario-based descriptions that represent different instances of the system behavior. Snapshots are specified in an intuitive and graphical notation that considers the elements from the problem domain and permit designers to discuss and validate the externally observable behavior, together with the domain experts. In the second step (not fully covered in this manuscript), the system behavior is formalized with an executable model. This formal model, which in our approach is specified using the Colored Petri Net (CP-nets) language, allows the system internal behavior to be animated, simulated, and optimized. The insights gained by experimenting with the formal model can be subsequently used for reengineering the existing system

Groundwater recharge influenced by ephemeral river flow and land use in the semiarid Limpopo Province of South Africa

Determining the sustainability of groundwater use in drylands with high climate variability is complex. Central to this determination is an understanding of groundwater recharge and associated processes and controls. Groundwater recharge in drylands can occur by diffuse and focused recharge (focused recharge being associated with intense episodic rainfall events and ephemeral river flow, predicted to increase and intensify with climate change). This study evaluated the relative significance and dominant controls on these two recharge processes. Ten groundwater hydrographs with multidecadal observations were collated from the Limpopo Province, South Africa, based on their proximity to river channels and rain gauges, representing diversity in local climate, landscape, vegetation, and hydrogeological conditions. The hydrographs showed that groundwater-level rises are sensitive to rainfall intensity during the rainy season, with generally larger increases after years with large episodic rainfall events, which disproportionately contribute to groundwater replenishment. Recharge processes and annual recharge volumes were quantified using the water-table fluctuation method and the numerical model HYDRUS-1D. This allowed for the inference of additional recharge contributions from focused recharge in proximity to ephemeral rivers, up to a factor of five relative to diffuse recharge. The analysis revealed synchronicity and linear correlation between annual river discharge and recharge close to the river, substantiating the importance of focused recharge close to the river network. The study showed that recharge in drylands is subject to large spatial and temporal variation and that consideration of focused and episodic recharge is critically important for managing groundwater resources at various scales in these regions

Geometric Phase, Curvature, and Extrapotentials in Constrained Quantum Systems

We derive an effective Hamiltonian for a quantum system constrained to a submanifold (the constraint manifold) of configuration space (the ambient space) by an infinite restoring force. We pay special attention to how this Hamiltonian depends on quantities which are external to the constraint manifold, such as the external curvature of the constraint manifold, the (Riemannian) curvature of the ambient space, and the constraining potential. In particular, we find the remarkable fact that the twisting of the constraining potential appears as a gauge potential in the constrained Hamiltonian. This gauge potential is an example of geometric phase, closely related to that originally discussed by Berry. The constrained Hamiltonian also contains an effective potential depending on the external curvature of the constraint manifold, the curvature of the ambient space, and the twisting of the constraining potential. The general nature of our analysis allows applications to a wide variety of problems, such as rigid molecules, the evolution of molecular systems along reaction paths, and quantum strip waveguides.Comment: 27 pages with 1 figure, submitted to Phys. Rev.

Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling: a case study

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic land-use change model (CLUE-s) for the Paochiao watershed region in Taiwan. Relative operating characteristic curves (ROCs), kappa statistics, multiple resolution validation and landscape metrics were used to assess the ability of the three techniques in estimating the relationship between driving factors and land use and its subsequent application in land-use change models. The validation results illustrate that for this case study ANNs constitute a powerful alternative for the use of logistic regression in empirical modeling of spatial land-use change processes. ANNs provide in this case a better fit between driving factors and land-use pattern. In addition, auto-logistic regression performs better than logistic regression and nearly as well as ANNs. Auto-logistic regression and ANNs are considered especially useful when the performance of more conventional models is not satisfactory or the underlying data relationships are unknown. The results indicate that an evaluation of alternative techniques to specify relationships between driving factors and land use can improve the performance of land-use change models