Deformation and stability regression models for soil nail walls

Lateral displacement and global stability are the two main stability criteria for soil nail walls. Conventional design methods do not adequately address the deformation behaviour of soil nail walls, owing to the complexity involved in handling a large number of influencing factors. Consequently, limited methods of deformation estimates based on empirical relationships and in situ performance monitoring are available in the literature. It is therefore desirable that numerical techniques and statistical methods are used in order to gain a better insight into the deformation behaviour of soil nail walls. In the present study numerical experiments are conducted using a 2 4 factorial design method. Based on analysis of the maximum lateral deformation and factor-of-safety observations from the numerical experiments, regression models for maximum lateral deformation and factor-of-safety prediction are developed and checked for adequacy. Selection of suitable design factors for the 2 4 factorial design of numerical experiments enabled the use of the proposed regression models over a practical range of soil nail wall heights and in situ soil variability. It is evident from the model adequacy analyses and illustrative example that the proposed regression models provided a reasonably good estimate of the lateral deformation and global factor of safety of the soil nail walls.

Oxide-based bifunctional oxygen electrode for rechargeable metal/air batteries

Statistical methods for optimizing the morphology of oxide-based, bifunctional oxygen electrodes for use in rechargeable metal/air batteries are examined with regard to binder composition, compaction time, and compaction load. Results show that LaNiO3 with PTFE binder in a nickel mesh envelope provides a satisfactory electrode.

Comparison of North American universities with and without medical schools and their technology transfer initiatives

Empirical research available on technology transfer initiatives is either North American or European. Literature over the last two decades shows various research objectives such as identifying the variables to be measured and statistical methods to be used in the context of studying university based technology transfer initiatives. AUTM survey data from years 1996 to 2008 provides insightful patterns about the North American technology transfer initiatives, we use this data in our paper. This paper has three sections namely, a comparison of North American Universities with (n=1129) and without Medical Schools (n=786), an analysis of the top 75th percentile of these samples and a DEA analysis of these samples. We use 20 variables. Researchers have attempted to classify university based technology transfer initiative variables into multi-stages, namely, disclosures, patents and license agreements. Using the same approach, however with minor variations, three stages are defined in this paper. The first stage is to do with inputs from R&D expenditure and outputs namely, invention disclosures. The second stage is to do with invention disclosures being the input and patents issued being the output. The third stage is to do with patents issued as an input and technology transfers as outcomes.

Impact of heavy metals on bacterial communities from mangrove soils of the Mahanadi Delta (India)

This study aimed to assess soil nutrient status and heavy metal content and their impact on the predominant soil bacterial communities of mangroves of the Mahanadi Delta. Mangrove soil of the Mahanadi Delta is slightly acidic and the levels of soil nutrients such as carbon, nitrogen, phosphorous and potash vary with season and site. The seasonal average concentrations (g/g) of various heavy metals were in the range: 14810-63370 (Fe), 2.8-32.6 (Cu), 13.4-55.7 (Ni), 1.8-7.9 (Cd), 16.6-54.7 (Pb), 24.4-132.5 (Zn) and 13.3-48.2 (Co). Among the different heavy metals analysed, Co, Cu and Cd were above their permissible limits, as prescribed by Indian Standards (Co=17g/g, Cu=30 g/g, Cd=3-6 g/g), indicating pollution in the mangrove soil. A viable plate count revealed the presence of different groups of bacteria in the mangrove soil, i.e. heterotrophs, free-living N-2 fixers, nitrifyers, denitrifyers, phosphate solubilisers, cellulose degraders and sulfur oxidisers. Principal component analysis performed using multivariate statistical methods showed a positive relationship between soil nutrients and microbial load. Whereas metal content such as Cu, Co and Ni showed a negative impact on some of the studied soil bacteria.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Phase Field Simulation of Equiaxed Microstructure Formation during Semi-solid Processing of A380 Al Alloy

A phase field modelling approach is implemented in the present study towards simulation of microstructure evolution during cooling slope semi solid slurry generation process of A380 Aluminium alloy. First, experiments are performed to evaluate the number of seeds required within the simulation domain to simulate near spherical microstructure formation, occurs during cooling slope processing of the melt. Subsequently, microstructure evolution is studied employing a phase field method. Simulations are performed to understand the effect of cooling rate on the slurry microstructure. Encouraging results are obtained from the simulation studies which are validated by experimental observations. The results obtained from mesoscopic phase field simulations are grain size, grain density, degree of sphericity of the evolving primary Al phase and the amount of solid fraction present within the slurry at different time frames. Effect of grain refinement also has been studied with an aim of improving the slurry microstructure further. Insight into the process has been obtained from the numerical findings, which are found to be useful for process control.

Solution Processed Cu2CoSnS4 Thin Films for Photovoltaic Applications

Earth abundant alternative chalcopyrite Cu2CoSnS4 (CCTS) thin films were deposited by a facile sol-gel process onto larger substrates. Temperature dependence of the process control of deposition and desired phase formations was studied in detail. Films were analyzed for complete transformation from amorphous to polycrystalline, with textured structures for stannite phase, as reflected from the X-ray diffraction and with nearly stoichiometric compositions of Cu:Co:Sn:S = 2:0:1:0:1:0:4:0 from EDAX analysis. Morphological investigations revealed that the CCTS films with larger grains, on the order of its thickness, were synthesized at higher temperature of 500 degrees C. The optimal band gap for application in photovoltaics was estimated to be 1.4 eV. Devices with SLG/CCTS/Al geometry were fabricated for real time demonstration of photoconductivity under A.M 1.5 G solar and 1064 rim infrared laser illuminations. A photodetector showed one order current amplification from similar to 1.9 X 10(-6) A in the dark to 2.2 x 10(-5) A and 9.8 X 10(-6) A under A.M 1.5 G illumination and 50 mW cm(-2) IR laser, respectively. Detector sensitivity, responsivity, external quantum efficiency, and gain were estimated as 4.2, 0.12 A/W, 14.74% and 14.77%, respectively, at 50 mW cm(-2) laser illuminations. An ON and OFF ratio of 2.5 proved that CCTS can be considered as a potential absorber in low cost photovoltaics applications.

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

Adaptive evolution of a novel avian-origin influenza A/H7N9 virus

In China, the recent outbreak of novel influenza A/H7N9 virus has been assumed to be severe, and it may possibly turn brutal in the near future. In order to develop highly protective vaccines and drugs for the A/H7N9 virus, it is critical to find out the selection pressure of each amino acid site. In the present study, six different statistical methods consisting of four independent codon-based maximum likelihood (CML) methods, one hierarchical Bayesian (HB) method and one branch-site (BS) method, were employed to determine if each amino acid site of A/H7N9 virus is under natural selection pressure. Functions for both positively and negatively selected sites were inferred by annotating these sites with experimentally verified amino acid sites. Comprehensively, the single amino acid site 627 of PB2 protein was inferred as positively selected and it function was identified as a T-cell epitope (TCE). Among the 26 negatively selected amino acid sites of PB2, PB1, PA, HA, NP, NA, M1 and NS2 proteins, only 16 amino acid sites were identified to be involved in TCEs. In addition, 7 amino acid sites including, 608 and 609 of PA, 480 of NP, and 24, 25, 109 and 205 of M1, were identified to be involved in both B-cell epitopes (BCEs) and TCEs. Conversely, the function of positions 62 of PA, and, 43 and 113 of HA was unknown. In conclusion, the seven amino acid sites engaged in both BCEs and TCEs were identified as highly suitable targets, as these sites will be predicted to play a principal role in inducing strong humoral and cellular immune responses against A/H7N9 virus. (C) 2014 Elsevier Inc. All rights reserved.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.