Monitoring well-being and changing environmental conditions in coastal communities: development of an assessment method

The intersection of social and environmental forces is complex in coastal communities. The well-being of a coastal community is caught up in the health of its environment, the stability of its economy, the provision of services to its residents, and a multitude of other factors. With this in mind, the project investigators sought to develop an approach that would enable researchers to measure these social and environmental interactions. The concept of well-being proved extremely useful for this purpose. Using the Gulf of Mexico as a regional case study, the research team developed a set of composite indicators to be used for monitoring well-being at the county-level. The indicators selected for the study were: Social Connectedness, Economic Security, Basic Needs, Health, Access to Social Services, Education, Safety, Governance, and Environmental Condition. For each of the 37 sample counties included in the study region, investigators collected and consolidated existing, secondary data representing multiple aspects of objective well-being. To conduct a longitudinal assessment of changing wellbeing and environmental conditions, data were collected for the period of 2000 to 2010. The team focused on the Gulf of Mexico because the development of a baseline of well-being would allow NOAA and other agencies to better understand progress made toward recovery in communities affected by the Deepwater Horizon oil spill. However, the broader purpose of the project was to conceptualize and develop an approach that could be adapted to monitor how coastal communities are doing in relation to a variety of ecosystem disruptions and associated interventions across all coastal regions in the U.S. and its Territories. The method and models developed provide substantial insight into the structure and significance of relationships between community well-being and environmental conditions. Further, this project has laid the groundwork for future investigation, providing a clear path forward for integrated monitoring of our nation’s coasts. The research and monitoring capability described in this document will substantially help counties, local organizations, as well state and federal agencies that are striving to improve all facets of community well-being.

Phone-level pronunciation scoring and assessment for interactive language learning

This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilizes a likelihood-based `Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native confidence scores and on rejection statistics provided by human judges. Further improvements are obtained by incorporating models of the subject's native language and by augmenting the recognition networks to include expected pronunciation errors. The various GOP measures are assessed using a specially recorded database of non-native speakers which has been annotated to mark phone-level pronunciation errors. Since pronunciation assessment is highly subjective, a set of four performance measures has been designed, each of them measuring different aspects of how well computer-derived phone-level scores agree with human scores. These performance measures are used to cross-validate the reference annotations and to assess the basic GOP algorithm and its refinements. The experimental results suggest that a likelihood-based pronunciation scoring metric can achieve usable performance, especially after applying the various enhancements.

Study of inter species diversity and population structure by molecular genetic method in Iranian Artemia

Artemia is a small crustacean that adapted to live in brine water and has been seen in different brine water sources in Iran. Considering the importance of genetic studies manifest inter population differences in species, to estimate genetic structure, detect difference at molecular level and separate different Artemia populations of Iran, also study of phylogenic relationships among them, samples of Artemia were collected from nine region: Urmia lake in West Azerbaijan, Shoor and Inche-Borun lakes in Golestan, Hoze-Soltan and Namak lakes in Qom, Maharloo and Bakhteghan lakes in Fars, Nough pool in Kerman and Mighan pool in Markazi and DNA extracted by phenol-chloroform method. Primers designed on a ribosomal fragment (16s rRNA) of mt DNA sequence and PCR was done. Digestion of the 1566 bp segment PCR product by 10 restriction endonuclease (Alu I, EcoR I, Eco47 I, Hae III, Hind III, Hinf I, Mbo I, Msp I, Rsa I, TaqI) showed 25 different haplotypes: 9 in Urmia, 4 in Shoor and Inche- Borun, 1 in Namak and Hoze-Soltan, 3 in Mighan, 1 in Bakhtegan Maharlo, 3 in Maharloo and 4 in Nough. Measurement of haplotype and nucleotide diversity intra population and nucleotide diversity and divergence inter populations and evolutionary distance between haplotypes showed a high diversity in mitochondrial genome of Artemia in studied regions whose results are similar to those explained for highly geographic expansion organism. In addition, results showed considerable heterogeneity between different populations and there are enough evidences in haplotypic level for separation of studied samples and division of Iranian Artemia to seven populations including Urmia, Shoor and Inche-Borun, Hoze-Soltan and Namak, Maharloo, Bakhteghan, Nough and Mighan. Phylogenetic analysis of the 16S rRNA data set resulted strict consensus and neighbor joining distance trees, demonstrated that all samples were monophyletic and parthenogenetic form derivation from bisexual populations and genetically high resemblance to those of A. urmiana. Study of 270 specimens from different region showed the genus Artemia in Iran clustered into three clades including: 1- Shoor, Inche-Burun, Hoze-Soltan, Namak, Bakhtegan and Maharloo 2- Nough and Mighan 3- Urmia. Totally, obtained results indicated to ability of used techniques for study of inter species diversity, population structure, reveal of phylogenic relationship and dividing of different populations of Artemia in Iran.

Automated 3D Structure Inference of Civil Infrastructure Using a Stereo Camera Set

The commercial far-range (>10m) infrastructure spatial data collection methods are not completely automated. They need significant amount of manual post-processing work and in some cases, the equipment costs are significant. This paper presents a method that is the first step of a stereo videogrammetric framework and holds the promise to address these issues. Under this method, video streams are initially collected from a calibrated set of two video cameras. For each pair of simultaneous video frames, visual feature points are detected and their spatial coordinates are then computed. The result, in the form of a sparse 3D point cloud, is the basis for the next steps in the framework (i.e., camera motion estimation and dense 3D reconstruction). A set of data, collected from an ongoing infrastructure project, is used to show the merits of the method. Comparison with existing tools is also shown, to indicate the performance differences of the proposed method in the level of automation and the accuracy of results.

Method for generating maximally entangled states of multiple three-level atoms in cavity QED

We propose a scheme to generate maximally entangled states (MESs) of multiple three-level atoms in microwave cavity QED based on the resonant atom-cavity interaction. In the scheme, multiple three-level atoms initially in their ground states are sequently sent through two suitably prepared cavities. After a process of appropriate atom-cavity interaction, a subsequent measurement on the second cavity field projects the atoms onto the MESs. The practical feasibility of this method is also discussed.

Native deep level defects in ZnO single crystal grown by CVT method - art. no. 68410I

Hall effect, photoluminescence (PL), infrared absorption, deep level transient spectroscopy (DLTS), and Raman scattering have been used to study property and defects of ZnO single crystal grown by a chemical vapor transport method (CVT). As-grown ZnO is N type with free electron density Of 10(16)-10(17)cm(-3). It has a slight increase after 900 degrees C annealing in oxygen ambient. The DLTS measurement revealed four deep level defects with energy at 0.30eV, 0.50eV, 0.68eV and 0.90eV in the as-grown ZnO sample, respectively. After the high temperature annealing, only the 0.5eV defect survive and has a concentration increase. PL results of the as-grown and annealed ZnO indicate that the well-known green emission disappear after the annealing. The result suggests a correlation between the 0.68eV defect and the green PL peak. Results of P-doped ZnO were also compared with the undoped ZnO sample. The nature of the defects and their influence on the material property have been discussed.

SHEAR-DEFORMATION-POTENTIAL CONSTANT OF THE CONDUCTION-BAND MINIMA OF SI - EXPERIMENTAL-DETERMINATION BY THE DEEP-LEVEL CAPACITANCE TRANSIENT METHOD

The shear-deformation-potential constant XI-u of the conduction-band minima of Si has been measured by a method which we called deep-level capacitance transient under uniaxial stress. The uniaxial-stress (F) dependence of the electron emission rate e(n) from deep levels to the split conduction-band minima of Si has been analyzed. Theoretical curves are in good agreement with experimental data for the S0 and S+ deep levels in Si. The values of XI-u obtained by the method are 11.1 +/- 0.3 eV at 148.9 K and 11.3 +/- 0.3 eV at 223.6 K. The analysis and the XI-u values obtained are also valuable for symmetry determination of deep electron traps in Si.

COASTAL SEA-LEVEL AND THE LARGE-SCALE CLIMATE STATE - A DOWNSCALING EXERCISE FOR THE JAPANESE ISLANDS

A major problem which is envisaged in the course of man-made climate change is sea-level rise. The global aspect of the thermal expansion of the sea water likely is reasonably well simulated by present day climate models; the variation of sea level, due to variations of the regional atmospheric forcing and of the large-scale oceanic circulation, is not adequately simulated by a global climate model because of insufficient spatial resolution. A method to infer the coastal aspects of sea level change is to use a statistical ''downscaling'' strategy: a linear statistical model is built upon a multi-year data set of local sea level data and of large-scale oceanic and/or atmospheric data such as sea-surface temperature or sea-level air-pressure. We apply this idea to sea level along the Japanese coast. The sea level is related to regional and North Pacific sea-surface temperature and sea-level air pressure. Two relevant processes are identified. One process is the local wind set-up of water due to regional low-frequency wind anomalies; the other is a planetary scale atmosphere-ocean interaction which takes place in the eastern North Pacific.

Combined R-matrix eigenstate basis set and finite-difference propagation method for the time-dependent Schrodinger equation: The one-electron case

In this work we present the theoretical framework for the solution of the time-dependent Schrödinger equation (TDSE) of atomic and molecular systems under strong electromagnetic fields with the configuration space of the electron’s coordinates separated over two regions; that is, regions I and II. In region I the solution of the TDSE is obtained by an R-matrix basis set representation of the time-dependent wave function. In region II a grid representation of the wave function is considered and propagation in space and time is obtained through the finite-difference method. With this, a combination of basis set and grid methods is put forward for tackling multiregion time-dependent problems. In both regions, a high-order explicit scheme is employed for the time propagation. While, in a purely hydrogenic system no approximation is involved due to this separation, in multielectron systems the validity and the usefulness of the present method relies on the basic assumption of R-matrix theory, namely, that beyond a certain distance (encompassing region I) a single ejected electron is distinguishable from the other electrons of the multielectron system and evolves there (region II) effectively as a one-electron system. The method is developed in detail for single active electron systems and applied to the exemplar case of the hydrogen atom in an intense laser field.

A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs

Three-dimensional reconstruction from volumetric medical images (e.g. CT, MRI) is a well-established technology used in patient-specific modelling. However, there are many cases where only 2D (planar) images may be available, e.g. if radiation dose must be limited or if retrospective data is being used from periods when 3D data was not available. This study aims to address such cases by proposing an automated method to create 3D surface models from planar radiographs. The method consists of (i) contour extraction from the radiograph using an Active Contour (Snake) algorithm, (ii) selection of a closest matching 3D model from a library of generic models, and (iii) warping the selected generic model to improve correlation with the extracted contour.

This method proved to be fully automated, rapid and robust on a given set of radiographs. Measured mean surface distance error values were low when comparing models reconstructed from matching pairs of CT scans and planar X-rays (2.57–3.74 mm) and within ranges of similar studies. Benefits of the method are that it requires a single radiographic image to perform the surface reconstruction task and it is fully automated. Mechanical simulations of loaded bone with different levels of reconstruction accuracy showed that an error in predicted strain fields grows proportionally to the error level in geometric precision. In conclusion, models generated by the proposed technique are deemed acceptable to perform realistic patient-specific simulations when 3D data sources are unavailable.

Earthmoving trucks condition level prediction using neural networks

Purpose: The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model’s performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA).

Design/methodology/approach: An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks.

Findings: Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors.

Originality/value: Earthmoving trucks’ sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.

Constrained Multi-Objective Aerofoil Design Using a Multi-Level Optimisation Strategy

A novel approach for the multi-objective design optimisation of aerofoil profiles is presented. The proposed method aims to exploit the relative strengths of global and local optimisation algorithms, whilst using surrogate models to limit the number of computationally expensive CFD simulations required. The local search stage utilises a re-parameterisation scheme that increases the flexibility of the geometry description by iteratively increasing the number of design variables, enabling superior designs to be generated with minimal user intervention. Capability of the algorithm is demonstrated via the conceptual design of aerofoil sections for use on a lightweight laminar flow business jet. The design case is formulated to account for take-off performance while reducing sensitivity to leading edge contamination. The algorithm successfully manipulates boundary layer transition location to provide a potential set of aerofoils that represent the trade-offs between drag at cruise and climb conditions in the presence of a challenging constraint set. Variations in the underlying flow physics between Pareto-optimal aerofoils are examined to aid understanding of the mechanisms that drive the trade-offs in objective functions.

A microelectrode impedance method to measure interaction of cells

An impedance method was developed to determine how immune system cells (hemocyte) interact with intruder cells (parasites). When the hemocyte cells interact with the parasites, they cause a defensive reaction and the parasites start to aggregate in clusters. The level of aggregation is a measure of the host-parasite interaction, and provides information about the efficiency of the immune system response. The cell aggregation is monitored using a set of microelectrodes. The impedance spectrum is measured between each individual microelectrode and a large reference electrode. As the cells starts to aggregate and settle down towards the microelectrode array the impedance of the system is changed. It is shown that the system impedance is very sensitive to the level of cell aggregation and can be used to monitor in real time the interaction between hemocyte cells and parasites.