What types of safety nets would be most efficient and effective for protecting small farmers and the poor against volatile food prices?

Smallholder farmers in dryland areas, including the Arab region, face many risks, including those relating to weather and markets. While risk taking is necessary for economic development, many risks are so severe that responses to them entail long-term losses. Social risk management is a family of tools that facilitates efficient risk management and can help eliminate risk-related poverty traps. Safety nets form an important part of the social risk management toolkit; it is therefore important to evaluate their effectiveness. This review has identified several considerations for the design and implementation of safety nets to improve food security among small-scale producers in dryland areas. The main conclusions are: safety nets must be part of an integrated risk management strategy; they should be designed to contribute to long-term agricultural growth; and more effort is needed to establish efficient risk management mechanisms prior to crises.     

Electronic and structural properties of TiB2: Bulk, surface, and nanoscale effects

Titanium diboride (TiB₂), is a widely used hard material that comprises graphene-like layers of B and intercalated Ti atoms. Here we report the results of extensive first-principles calculations on key properties of bulk TiB₂, TiB₂ surfaces, and TiB₂ nanocrystals (NCs). The computational approach is first validated based on the agreement between calculated structural and electronic properties of bulk TiB₂ and available experimental and theoretical data. We then obtain the formation energies for several surface cuts and use these values to construct TiB₂ NCs based on the Wulff theorem. Finally, we demonstrate by studying the adsorption of small molecules that hydrogen and oxygen adatoms can be attached through strongly exothermic chemisorption reactions on TiB₂ surfaces. Likewise, water molecules bind on various TiB₂ surfaces and NC facets, with an energetic preference for the latter. The results are relevant to applications that depend on reactivity-related TiB₂ properties, for example resistance to corrosion and interactions with water-based solutions.     

Estimation of multiple accelerated motions using chirp-Fourier transform and clustering.

Motion estimation in the spatiotemporal domain has been extensively studied and many methodologies have been proposed, which, however, cannot handle both time-varying and multiple motions. Extending previously published ideas, we present an efficient method for estimating multiple, linearly time-varying motions. It is shown that the estimation of accelerated motions is equivalent to the parameter estimation of superpositioned chirp signals. From this viewpoint, one can exploit established signal processing tools such as the chirp-Fourier transform. It is shown that accelerated motion results in energy concentration along planes in the 4-D space: spatial frequencies-temporal frequency-chirp rate. Using fuzzy c-planes clustering, we estimate the plane/motion parameters. The effectiveness of our method is verified on both synthetic as well as real sequences and its advantages are highlighted.     

Studies on some aspects of the biology of Corcyra cephalonica (Stainton) (Lepidoptera: Galleriidae)

The biology of Corcyra cephalonica (Stainton) was studied under controlled conditions in the laboratory. At a constant temperature of 30°C, oviposition was influenced by relative humidity, an average of 148.9 eggs per female being laid at 70% r.h., compared with 131.8 eggs at 50% r.h. More eggs were retained in the ovaries of dead females at the lower r.h. Maize, rice and groundnuts were preferred to cowpea and cocoa for egg deposition. The incubation period was about 4 days at both humidities. Larval development was fastest at 70% r.h. (34.1 days) and slowest at 50% r.h. (43.3 days). Pupal duration was not affected by relative humidity. Development on different forms of a range of food commodities was studied. The larval period was generally shortest on brokens and meal, particularly of maize. Adult moths appeared weakly negatively phototactic and most locomotory activities were observed to take place at dusk.     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.     

Multigrid geometric active contour models.

Geometric active contour models are very popular partial differential equation-based tools in image analysis and computer vision. We present a new multigrid algorithm for the fast evolution of level-set-based geometric active contours and compare it with other established numerical schemes. We overcome the main bottleneck associated with most numerical implementations of geometric active contours, namely the need for very small time steps to avoid instability, by employing a very stable fully 2-D implicit-explicit time integration numerical scheme. The proposed scheme is more accurate and has improved rotational invariance properties compared with alternative split schemes, particularly when big time steps are utilized. We then apply properly designed multigrid methods to efficiently solve the occurring sparse linear system. The combined algorithm allows for the rapid evolution of the contour and convergence to its final configuration after very few iterations. Image segmentation experiments demonstrate the efficiency and accuracy of the method.     

On the electrical resistivity and chemical etching of TiSi2 thin films sputtered from a composite target

The electrical resistivity of TiSi₂ thin films sputtered onto an oxidised Si substrate using a composite alloy target is studied. It is found that the as-deposited films show high resistivity. Annealing the films at an elevated temperature leads to a significant fall in the resistivity. An optimum sheet resistance of 2om tq⁻¹ is obtained after annealing at 800°C for 30 min in argon ambient. The effect of annealing temperature on resistivity is studied. The sheet resistance is also found to be affected by the magnitude of the substrate bias during film deposition. The data are given. The patterning of TiSi₂ thin films by wet chemical etching for device applications is described.