Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data

We tested the utility of imaging spectroscopy and neural networks to map phosphorus concentration in savanna grass using airborne HyMAP image data. We also sought to ascertain the key wavelengths for phosphorus prediction using hyperspectral remote sensing. The remote sensing of foliar phosphorus has received very little attention as compared to nitrogen, yet it plays an equally important role in explaining the distribution and feeding patterns of herbivores. Band depths from two continuum‐removed absorption features as well as the red edge position (REP) were input into a backpropagation neural network. Following a series of experiments to ascertain the optimum wavelengths, the best trained neural network was used to predict and ultimately to map grass phosphorus concentration in the Kruger National Park. The results indicate that the best trained neural network could predict phosphorus distribution with a coefficient of determination of 0.63 and a root mean square error (RMSE) of 0.07 (28% of the mean observed phosphorus concentration) on an independent test data set. Our results also show that the absorption feature located in the shortwave infrared (R _2015–2199) contains more information on phosphorus distribution, a region that has hardly been explored before in most spectroscopic experiments for phosphorus as compared to the visible bands. Overall, the study demonstrates the potential of imaging spectroscopy in mapping grass phosphorus concentration in savanna rangelands.     

Impact on the chlorophyll concentration in the Bay of Bengal and Arabian Sea during Indian Ocean dipole mode

The chlorophyll (Chl) concentration in the seawater of the Bay of Bengal (BoB) and the Arabian Sea (AS) changes most during the Indian Ocean dipole (IOD) mode event. The empirical orthogonal function (EOF) of several oceanographic and atmospheric data was studied, using 9 years (1998–2006) of reanalysed satellite data. The variation of Chl during the period of September to November (SON) over the 9 years has been studied in this article. It has been found that significant enhancement of Chl in the BoB takes place during IOD years owing to the surface wind action, that is, wind-stress curl (WC), which favours the upwelling process, whereas the AS shows a decline in concentration of Chl owing to a reduced open ocean upwelling process.     

Parallel Godunov smoothed particle hydrodynamics (SPH) with improved treatment of Boundary Conditions and an application to granular flows

Smoothed Particle Hydrodynamics has been successfully used for various fluid-dynamics problems, such as breaking-waves, flooding etc., since it was originally proposed. While the Lagrangian approach is naturally suitable for free-surface flows, enforcing boundary conditions and poor approximations in the presence of discontinuities in the solution are major difficulties with the method. In this paper we present an enhanced conservative Godunov SPH based on the work of Inutsuka [S. Inutsuka, Reformulation of smoothed particle hydrodynamics with Riemann solver, Journal of Computational Physics 179 (2002) 238–267] that accurately resolves discontinuities without the need to use artificial viscosity, preserves partition of unity everywhere in the domain, correctly and flexibly enforces necessary essential and frictional slip boundary conditions to approximately solve free-surface granular flows. The development is motivated by the need to improve upon depth averaged grid based models of large scale debris flows and avalanches often characterized as granular flows. Simple validation of the results is obtained by comparison to table-top experiments.     

Broadcast stochastic receding horizon control of multi-agent systems

Optimal regulation of stochastically behaving agents is essential to achieve a robust aggregate behavior in a swarm of agents. How optimally these behaviors are controlled leads to the problem of designing optimal control architectures. In this paper, we propose a novel broadcast stochastic receding horizon control architecture as an optimal strategy for stabilizing a swarm of stochastically behaving agents. The goal is to design, at each time step, an optimal control law in the receding horizon control framework using collective system behavior as the only available feedback information and broadcast it to all agents to achieve the desired system behavior. Using probabilistic tools, a conditional expectation based predictive model is derived to represent the ensemble behavior of a swarm of independently behaving agents with multi-state transitions. A stochastic finite receding horizon control problem is formulated to stabilize the aggregate behavior of agents. Analytical and simulation results are presented for a two-state multi-agent system. Stability of the closed-loop system is guaranteed using the supermartingale theory. Almost sure (with probability 1) convergence of the closed-loop system to the desired target is ensured. Finally, conclusions are presented.     

Optimal band selection from hyperspectral data for Lantana camara discrimination

The primary objective of this research was to determine the optimal hyperspectral wavelengths based on spectroscopy data over the spectral range of 450–2500 nm for the detection of the invasive species Lantana camara L. (lantana) from seven of its co-occurring species. A procedure based on statistical analysis of the reflectance and the first derivative reflectance (FDR) identified 86 and 18 bands, respectively, where lantana significantly differed from its co-occurring species. The effectiveness of the identified optimal bands was then evaluated using Hyperion imagery. The original Hyperion image with 155 bands gave an overall accuracy of 80% compared to 77% and 76% from the 86- and 18-band spectral subsets, respectively. A pairwise comparison of the three error matrices showed no significant difference in the accuracy achieved. The FDR analysis combined with the statistical analysis proved to be a useful procedure for data reduction by refining the discrimination to fewer optimal bands for lantana detection with no adverse impact on classification accuracy.     

Prediction of hopper discharge rate using combined discrete element method and artificial neural network

An Artificial Neural Network (ANN) was developed to predict the mass discharge rate from conical hoppers. By employing Discrete Element Method (DEM), numerically simulated flow rate data from different internal angles (20°–80°) hoppers were used to train the model. Multi-component particle systems (binary and ternary) were simulated and mass discharge rate was estimated by varying different parameters such as hopper internal angle, bulk density, mean diameter, coefficient of friction (particle-particle and particle-wall) and coefficient of restitution (particle-particle and particle-wall). The training of ANN was accomplished by feed forward back propagation algorithm. For validation of ANN model, the authors carried out 22 experimental tests on different mixtures (having different mean diameter) of spherical glass beads from different angle conical hoppers (60° and 80°). It was found that mass discharge rate predicted by the developed neural network model is in a good agreement with the experimental discharge rate. Percentage error predicted by ANN model was less than ±13%. Furthermore, the developed ANN model was also compared with existing correlations and showed a good agreement.     

Performance evaluation of saponin over other organic acid and tap water for removal of chromium in tannery sludge by electrokinetic enhancement

The performance of Saponin over different electrolyte-mediated electrokinetic (EK) process for removal of Cr(III) from tannery sludge was assessed. Saponin exhibited maximum Cr(III) removal efficiency, i.e. 67.6–80.75% as compared to other groups of processing fluids. The possible mechanistic approach for the highest removal efficiency of saponin over other electrolytes may be attributed to synchronized effect of micelle formation and subsequent complexation and sorption of chromium ions. The highest electroosmotic flow rate of saponin indicated low electroosmotic resistance during EK process, which validates the observed trend. Therefore, saponin can be considered as a promising electrolyte for EK treatment of tannery sludge.

Abbreviation: CA: Citric acid; LA: lactic acid; OA: oxalic acid; BOD: biochemical oxygen demand; COD: chemical oxygen demand; EK: electokinetic.     

Performance Analysis of Gate-Stack Dual-Material DG MOSFET Using Work-Function Modulation Technique for Lower Technology Nodes

Silicon - Short channel effects (SCEs) along with mobility degradation has a great impact on CMOS technology below 100 nm. These effects can be overcome by using gate and channel...     

Logistics planning and inventory optimization using swarm intelligence: a third party perspective

The economic and competitive pressures have made it imperative for organizations to focus on third party or outsourcing to reduce costs and improve operating efficiencies. The thrust of global economy drives the organizations to outsource process, parts, and labor, virtually anywhere in the world and get the desired combination of low cost and high quality. However, ineffective utilization of shipment practices in supply chain prevents to achieve the anticipated outsourcing benefits. In this research, a three-stage inventory model is developed to address the outsourcing issues with different shipment policies between manufacturer, exporter, and assembly point for any manufacturing industry. The model provides a measure for establishing an optimal balance between two conflicting objectives viz. net costs involved and the transportation discounts with consideration of holding cost at all the points in the supply chain. Owing to inherent computational complexities of the problem with higher dimensions, various deterministic approaches practically fail. Proposed work, therefore, utilizes a nature-inspired evolutionary algorithm, namely particle swarm optimization, to solve the problem. This paper applies enhanced particle swarm optimization, a variant of particle swarm optimization for solution purpose. Furthermore, the practical benefits and implications of the proposed model are demonstrated. The results obtained delineate efficacy to handle the fluctuations in the possible shipment options and simultaneously deciding the optimal shipment policies.