Breakdown probabilities for thin heterostructure avalanche photodiodes

The recurrence theory for the breakdown probability in avalanche photodiodes (APDs) is generalized to heterostructure APDs that may have multiple multiplication layers. The generalization addresses layer-boundary effects such as the initial energy of injected carriers as well as the layer-dependent profile of the dead space in the multiplication region. Reducing the width of the multiplication layer serves to both downshift and sharpen the breakdown probability curve as a function of the applied reverse-bias voltage. In structures where the injected carriers have an initial energy that is comparable to the ionization threshold energy, the transition from linear mode to Geiger-mode is more abrupt than in structures in which such initial energy is negligible. The theory is applied to two recently fabricated Al/sub 0.6/Ga/sub 0.4/As-GaAs heterostructure APDs and to other homostructure thin GaAs APDs and the predictions of the breakdown-voltage thresholds are verified.     

A One‐Kilobit PQR‐CMOS Smart Pixel Array

The photonic quantum ring (PQR) laser is a three dimensional whispering gallery (WG) mode laser and has anomalous quantum wire properties, such as microampere to nanoampere range threshold currents and √T‐dependent thermal red shifts. We observed uniform bottom emissions from a 1‐kb smart pixel chip of a 32×32 InGaAs PQR laser array flip‐chip bonded to a 0.35 µm CMOS‐based PQR laser driver. The PQR‐CMOS smart pixel array, now operating at 30 MHz, will be improved to the GHz frequency range through device and circuit optimization.     

Estimation of Aeroelastic Parameters of Bridge Decks Using Neural Networks

A new method of estimating flutter derivatives using artificial neural networks is proposed. Unlike other computational fluid dynamics based numerical analyses, the proposed method estimates flutter derivatives utilizing previously measured experimental data. One of the advantages of the neural networks approach is that they can approximate a function of many dimensions. An efficient method has been developed to quantify the geometry of deck sections for neural network input. The output of the neural network is flutter derivatives. The flutter derivatives estimation network, which has been trained by the proposed methodology, is tested both for training sets and novel testing sets. The network shows reasonable performance for the novel sets, as well as outstanding performance for the training sets. Two variations of the proposed network are also presented, along with their estimation capability. The paper shows the potential of applying neural networks to wind force approximations.     

The role of modelling in identifying drug targets for diseases of the cell cycle.

The cell cycle is implicated in diseases that are the leading cause of mortality and morbidity in the developed world. Until recently, the search for drug targets has focused on relatively small parts of the regulatory network under the assumption that key events can be controlled by targeting single pathways. This is valid provided the impact of couplings to the wider scale context of the network can be ignored. The resulting depth of study has revealed many new insights; however, these have been won at the expense of breadth and a proper understanding of the consequences of links between the different parts of the network. Since it is now becoming clear that these early assumptions may not hold and successful treatments are likely to employ drugs that simultaneously target a number of different sites in the regulatory network, it is timely to redress this imbalance. However, the substantial increase in complexity presents new challenges and necessitates parallel theoretical and experimental approaches. We review the current status of theoretical models for the cell cycle in light of these new challenges. Many of the existing approaches are not sufficiently comprehensive to simultaneously incorporate the required extent of couplings. Where more appropriate levels of complexity are incorporated, the models are difficult to link directly to currently available data. Further progress requires a better integration of experiment and theory. New kinds of data are required that are quantitative, have a higher temporal resolution and that allow simultaneous quantitative comparison of the concentration of larger numbers of different proteins. More comprehensive models are required and must accommodate not only substantial uncertainties in the structure and kinetic parameters of the networks, but also high levels of ignorance. The most recent results relating network complexity to robustness of the dynamics provide clues that suggest progress is possible.     

Strength and fracture toughness of nano and micron-silica particles bidispersed epoxy composites: evaluated by fragility parameter

In this study, we discuss the composition effect of 240 nm and 1.56 μm-silica particles on strength and fracture toughness by examining two parameters, fragility and glass transition temperature, that were derived from the thermo-viscoelasticity measurements. Experimental results showed that the composites had a lower fragility with higher strength and fracture toughness as the content of nanoparticles was increased regardless of glass transition temperature. The improvement in mechanical properties from adding nanoparticles was definitely explained by the fragility represented the heterogeneity in polymer matrix, and this was related to the interaction between particles and matrix. The fragility was found to be an effective parameter for evaluating strength and fracture toughness of epoxy composite containing a bidispersion of nano and micron-silica particles.     

Estimating the costs of occupational injury in the United States

This article estimates workplace injury costs in the U.S. These costs have been studied in less detail than most injury costs. Our methods, which mostly use regularly published data, produce order-of-magnitude estimates. Overall, workplace injuries cost the U.S. an estimated $140 billion annually. This estimate includes $17 billion in medical and emergency services, $60 billion in lost productivity, $5 billion in insurance costs, and $62 billion in lost quality of life. One sixth of the societal costs result from the 3% of workplace injuries in motor vehicle crashes. Motor vehicle costs per injury are almost 6 times the workplace injury average.     

Energy storage devices for future hybrid electric vehicles

Powertrain hybridization as well as electrical energy management are imposing new requirements on electrical storage systems in vehicles. This paper characterizes the associated vehicle attributes and, in particular, the various levels of hybrids. New requirements for the electrical storage system are derived, including: shallow-cycle life, high dynamic charge acceptance particularly for regenerative braking and robust service life in sustained partial-state-of-charge usage. Lead/acid, either with liquid or absorptive glass-fibre mat electrolyte, is expected to remain the predominant battery technology for 14 V systems, including micro-hybrids, and with a cost-effective battery monitoring system for demanding applications. Advanced AGM batteries may be considered for mild or even medium hybrids once they have proven robustness under real-world conditions, particularly with respect to cycle life at partial-states-of-charge and dynamic charge acceptance. For the foreseeable future, NiMH and Li-ion are the dominating current and potential battery technologies for higher-functionality HEVs. Li-ion, currently at development and demonstration stages, offers attractive opportunities for improvements in performance and cost. Supercapacitors may be considered for pulse power applications. Aside from cell technologies, attention to the issue of system integration of the battery into the powertrain and vehicle is growing. Opportunities and challenges for potential “battery pack” system suppliers are discussed.     

KWTA networks and their applications

Winner-Take-All (WTA) or K-Winner-Take-All (KWTA) networks have been frequently used as the basic building blocks of complex neural networks. This paper introduces a new selection rule for network connections that implements stable KWTA networks. To widen the applications of WTA networks, a new class of WTA networks is proposed, and their efficient design methods are presented. We demonstrate the properties of the generalized class of WTA networks, through three application examples.