Do We Stand on Common Ground? A Threat Appraisal Model for Terror Alerts Issued by the Department of Homeland Security

The fabric and faces of threat, and the expediency and efficiency in the communication of threat, are examined with a threat appraisal model. This model is empirically tested on an ongoing communication challenge, the issuance of terror alerts by the United States' Department of Homeland Security (DHS), focusing on how threat is appraised by both the conservative and liberal audiences. Findings showed a shared view by the DHS and conservative audiences on the levels and nature of threats; liberal audiences thought otherwise. Though there appeared to be a consensus between the conservative and liberal audiences on the efficacy of threat communication, more internal consistency within DHS is needed to optimise its effectiveness.     

Enforcing Efficient Equilibria in Network Design Games via Subsidies

The efficient design of networks has been an important engineering task that involves challenging combinatorial optimization problems. Typically, a network designer has to select among several alternatives which links to establish so that the resulting network satisfies a given set of connectivity requirements and the cost of establishing the network links is as low as possible. The Minimum Spanning Tree problem, which is well-understood, is a nice example.In this paper, we consider the natural scenario in which the connectivity requirements are posed by selfish users who have agreed to share the cost of the network to be established according to a well-defined rule. The design proposed by the network designer should now be consistent not only with the connectivity requirements but also with the selfishness of the users. Essentially, the users are players in a so-called network design game and the network designer has to propose a design that is an equilibrium for this game. As it is usually the case when selfishness comes into play, such equilibria may be suboptimal. In this paper, we consider the following question: can the network designer enforce particular designs as equilibria or guarantee that efficient designs are consistent with users’ selfishness by appropriately subsidizing some of the network links? In an attempt to understand this question, we formulate corresponding optimization problems and present positive and negative results.     

AdaBoost-based face detection for embedded systems

Face detection is a widely studied topic in computer vision, and recent advances in algorithms, low cost processing, and CMOS imagers make it practical for embedded consumer applications. As with graphics, the best cost-performance ratio is achieved with dedicated hardware. In this paper, we design an embedded face detection system for handheld digital cameras or camera phones. The challenges of face detection in embedded environments include an efficient pipeline design, bandwidth constraints set by low cost memory, a need to find parallelism, and how to utilize the available hardware resources efficiently. In addition, consumer applications require reliability which calls for a hard real-time approach to guarantee that processing deadlines are met. Specifically, the main contributions of the paper include: (1) incorporation of a Genetic Algorithm in the AdaBoost training to optimize the detection performance given the number of Haar features; (2) a complexity control scheme to meet hard real-time deadlines; (3) a hardware pipeline design for Haar-like feature calculation and a system design exploiting several levels of parallelism. The proposed architecture is verified by synthesis to Altera’s low cost Cyclone II FPGA. Simulation results show the system can achieve about 75–80% detection rate for group portraits.     

Synthesis and characterisation of CuS nanomaterials using hydrothermal route

Copper sulphide (CuS) nanomaterials with interesting morphology were synthesised using copper nitrate trihydrate, thiourea and water as a solvent by a simple hydrothermal route. A systematic investigation was carried out to investigate the effect of reaction time (5, 16 and 24?h) at 150°C on the morphology of the materials. Without the use of any template or additives, shape controlled synthesis of CuS nanocrystallites were achieved. The possible mechanism for the formation of the various nanostructures of CuS in this system is discussed. The prepared materials were characterised by X-ray diffraction, field emission scanning electron microscopy (FE-SEM) and DRS-UV–Vis absorption analysis. The UV–Vis spectrum shows that it is the promising material which can absorb in the visible region and hence could be used for photocatalytic applications. In addition, the electrochemical characteristic of the synthesised material was investigated by cyclic voltammetric analysis, which shows that CuS could be used for electrocatalytic applications.     

Traditionally Processed Beverages in Africa: A Review of the Mycotoxin Occurrence Patterns and Exposure Assessment

African traditional beverages are widely consumed food‐grade liquids processed from single or mixed grains (mostly cereals) by simple food processing techniques, of which fermentation tops the list. These beverages are very diverse in composition and nutritional value and are specific to different cultures and countries. The grains from which home‐processed traditional beverages are made across Africa are often heavily contaminated with multiple mycotoxins due to poor agricultural, handling, and storage practices that characterize the region. In the literature, there are many reports on the spectrum and quantities of mycotoxins in crops utilized in traditional beverage processing, however, few studies have analyzed mycotoxins in the beverages themselves. The available reports on mycotoxins in African traditional beverages are mainly centered on the finished products with little information on the process chain (raw material to final product), fate of the different mycotoxins during processing, and exposure estimates for consumers. Regulations targeting these local beverages are not in place despite the heavy occurrence of mycotoxins in their raw materials and the high consumption levels of the products in many homes. This paper therefore comprehensively discusses for the 1st time the available data on the wide variety of African traditional beverages, the mycotoxins that contaminate the beverages and their raw materials, exposure estimates, and possible consequent effects. Mycotoxin control options and future directions for mycotoxin research in beverage production are also highlighted.     

IR transmission prediction,processing, and characterization of dense La2Ce2O7

High-throughput computation, based on density functional theory (HT-DFT), is used to predict the bounds for optical transparency, from the ultraviolet to the infrared, for materials in the pyrochlore family. The HT-DFT approach adopted here uses an initial screening from Materials-Project database, with millions of calculated properties. Band gaps and phonon spectra were calculated from selected pyrochlore crystal structures taken from the Materials Project database. Short and long wavelength bounds for optical transparency were calculated for chemistries with stable, cubic structures. The calculations predict that La₂Ce₂O₇ has one of the broadest range of transparency for the pyrochlore family. Based on these calculations, dense polycrystalline samples of La₂Ce₂O₇ were produced by sintering and hot-isostatic pressing. Transparency was characterized by methods that did not require large samples with high optical quality, obtaining 7.15 and 7.5 µm at 95% and 90% normalized transmittance, respectively. Bandgap calculations suggest a lower bound of UV transparency cut-off of 0.3 µm. The infrared wavelength cut-off is higher than that reported for other pyrochlores, and higher than for yttria, zirconia, or other common infrared transparent ceramics. We discuss our prediction and characterization methods as well as the suitability of pyrochlores for mid- and far-infrared optical applications.     

An algorithm for the kinetics of tire pyrolysis under different heating rates

Tires exhibit different kinetic behaviors when pyrolyzed under different heating rates. A new algorithm has been developed to investigate pyrolysis behavior of scrap tires. The algorithm includes heat and mass transfer equations to account for the different extents of thermal lag as the tire is heated at different heating rates. The algorithm uses an iterative approach to fit model equations to experimental data to obtain quantitative values of kinetic parameters. These parameters describe the pyrolysis process well, with good agreement (r² > 0.96) between the model and experimental data when the model is applied to three different brands of automobile tires heated under five different heating rates in a pure nitrogen atmosphere. The model agrees with other researchers’ results that frequencies factors increased and time constants decreased with increasing heating rates. The model also shows the change in the behavior of individual tire components when the heating rates are increased above 30 K min⁻¹. This result indicates that heating rates, rather than temperature, can significantly affect pyrolysis reactions. This algorithm is simple in structure and yet accurate in describing tire pyrolysis under a wide range of heating rates (10–50 K min⁻¹). It improves our understanding of the tire pyrolysis process by showing the relationship between the heating rate and the many components in a tire that depolymerize as parallel reactions.     

Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation

This study investigates the processing of sonar signals with ensemble neural networks for robust recognition of simple objects such as plane, corner and trapezium surface. The ensemble neural networks can differentiate the target objects with high accuracy. The simplified fuzzy ARTMAP (SFAM) and probabilistic ensemble simplified fuzzy ARTMAP (PESFAM) are compared in terms of classification accuracy. The PESFAM implements an accurate and effective probabilistic plurality voting method to combine outputs from multiple SFAM classifiers. Five benchmark data sets have been used to evaluate the applicability of the proposed ensemble SFAM network. The PESFAM achieves good accuracy based on the twofold cross-validation results. In addition, the effectiveness of the proposed ensemble SFAM is delineated in sonar target differentiation. The experiments demonstrate the potential of PESFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent classification tool in mobile robot application.