Laminar to turbulent flow transition measurements using an array of SOI-CMOS MEMS wall shear stress sensors

The successful utilization of an array of silicon on insulator complementary metal oxide semiconductor (SOICMOS) micro thermal shear stress sensors for flow measurements at macro-scale is demonstrated. The sensors use CMOS aluminum metallization as the sensing material and are embedded in low thermal conductivity silicon oxide membranes. They have been fabricated using a commercial 1 μm SOI-CMOS process and a post-CMOS DRIE back etch. The sensors with two different sizes were evaluated. The small sensors (18.5 ×18.5 μm2 sensing area on 266 × 266 μm2 oxide membrane) have an ultra low power (100 °C temperature rise at 6mW) and a small time constant of only 5.46 μs which corresponds to a cut-off frequency of 122 kHz. The large sensors (130 × 130 μm2 sensing area on 500 × 500 μm2 membrane) have a time constant of 9.82 μs (cut-off frequency of 67.9 kHz). The sensors' performance has proven to be robust under transonic and supersonic flow conditions. Also, they have successfully identified laminar, separated, transitional and turbulent boundary layers in a low speed flow. © 2008 IEEE.

The dynamics of industrial emergence

The need to stimulate, identify and nurture new industries is a prominent challenge in advanced economies. While basic science represents a valuable source of new ideas and opportunities, it can often take decades before this science finally finds application in the market. While numerous studies have to date focused on aspects of industrial evolution, (e.g. innovation, internationalisation, new product introduction, technological lifecycles and emerging technologies), far fewer have focused on technology-based industrial emergence. It is clear that if assistance is to be provided to firms and industrial policymakers attempting to navigate industrial emergence then we need an improved understanding of the characteristics and dynamics of this phenomenon. Accordingly, this paper reviews published work from a range of disparate disciplines - evolutionary theory, social construction of technology (SCOT), complexity science, industrial dynamics and technology management - to identify these dynamics. Through this review we conceptualise industrial emergence as a co-evolutionary process in which nonlinear dynamics operate. Industrial emergence is sensitive to the initial availability of resources and the market applications, with growth dependent on the supply-demand coupling, agents' actions to reduce uncertainty and catalytic events. Through synthesizing these key dynamics we go on to propose a conceptual model for industrial emergence. © 2010 IEEE.

Introduction to the development of an information management system for soil mix technology using artificial neural networks

This paper introduces current work in collating data from different projects using soil mix technology and establishing trends using artificial neural networks (ANNs). Variation in unconfined compressive strength as a function of selected soil mix variables (e.g., initial soil water content and binder dosage) is observed through the data compiled from completed and on-going soil mixing projects around the world. The potential and feasibility of ANNs in developing predictive models, which take into account a large number of variables, is discussed. The main objective of the work is the management and effective utilization of salient variables and the development of predictive models useful for soil mix technology design. Based on the observed success in the predictions made, this paper suggests that neural network analysis for the prediction of properties of soil mix systems is feasible. © ASCE 2011.

Synchronous operation control of the Brushless Doubly-Fed Machine

The paper presents a novel vector control structure for the Brushless Doubly-Fed Machine (BDFM) which is derived based on the machine synchronous operation. In fact, the synchronous operation of the BDFM provides an efficient approach for determining the required reference angle in the machine vector control structure. The utilization of such reference angle makes the vector control structure presented in this paper different from and, in fact, more effective than the existing rotor flux and stator flux orientation schemes proposed for the machine. The results of implementing the vector control scheme in simulations confirm the effectiveness of the proposed approach for the BDFM control. © 2010 IEEE.

Robust estimation of local genetic ancestry in admixed populations using a nonparametric Bayesian approach.

We present a new haplotype-based approach for inferring local genetic ancestry of individuals in an admixed population. Most existing approaches for local ancestry estimation ignore the latent genetic relatedness between ancestral populations and treat them as independent. In this article, we exploit such information by building an inheritance model that describes both the ancestral populations and the admixed population jointly in a unified framework. Based on an assumption that the common hypothetical founder haplotypes give rise to both the ancestral and the admixed population haplotypes, we employ an infinite hidden Markov model to characterize each ancestral population and further extend it to generate the admixed population. Through an effective utilization of the population structural information under a principled nonparametric Bayesian framework, the resulting model is significantly less sensitive to the choice and the amount of training data for ancestral populations than state-of-the-art algorithms. We also improve the robustness under deviation from common modeling assumptions by incorporating population-specific scale parameters that allow variable recombination rates in different populations. Our method is applicable to an admixed population from an arbitrary number of ancestral populations and also performs competitively in terms of spurious ancestry proportions under a general multiway admixture assumption. We validate the proposed method by simulation under various admixing scenarios and present empirical analysis results from a worldwide-distributed dataset from the Human Genome Diversity Project.

Continuous Diffraction Patterns from Circular Arrays of Carbon Nanotubes

We report the remarkable diffraction effects produced from circular patterned arrays of multiwalled carbon nanotubes (MWCNTs). Highly ordered circular arrays of multiwalled carbon nanotubes (with inter-nanotube spacings of 633 nm) display optical dispersion effects similar to compact discs. These arrays display remarkable diffraction patterns in the far field which are spatially continuous. High quality diffraction patterns were obtained experimentally which are in excellent agreement with the theoretical calculations. The achieved continuous diffraction patterns pave the way towards the utilization of engineered carbon nanotube arrays in applications like three dimensional holograms.

Experimental investigation of dynamic response of acoustically forced turbulent premixed CH4/CO2/air flames

Increasing demand for energy and continuing increase in environmental as well as financial cost of use of fossil fuels drive the need for utilization of fuels from sustainable sources for power generation. Development of fuel-flexible combustion systems is vital in enabling the use of sustainable fuels. It is also important that these sustainable combustion systems meet the strict governmental emission legislations. Biogas is considered as one of the viable sustainable fuels that can be used to power modern gas turbines: However, the change in chemical, thermal and transport properties as well as change in Wobbe index due to the variation of the fuel constituents can have a significant effect on the performance of the combustor. It is known that the fuel properties have strong influence on the dynamic flame response; however there is a lack of detailed information regarding the effect of fuel compositions on the sensitivity of the flames subjected to flow perturbations. In this study, we describe an experimental effort investigating the response of premixed biogas-air turbulent flames with varying proportions of CH4 and CO2 to velocity perturbations. The flame was stabilized using a centrally placed conical bluff body. Acoustic perturbations were imposed to the flow using loud speakers. The flame dynamics and the local heat release rate of these acoustically excited biogas flames were studied using simultaneous measurements of OH and H2CO planar laser induced fluorescence. OH* chemiluminescence along with acoustic pressure measurements were also recorded to estimate the total flame heat release modulation and the velocity fluctuations. The measurements were carried out by keeping the theoretical laminar flame speed constant while varying the bulk velocity and the fuel composition. The results indicate that the flame sensitivity to perturbations increased with increased dilution of CH4 by CO2 at low amplitude forcing, while at high amplitude forcing conditions the magnitude of the flame response was independent of dilution.

Preliminary evaluation of a nuclear scenario involving innovative gas cooled reactors

In order to guarantee a sustainable supply of future energy demand without compromising the environment, some actions for a substantial reduction of CO 2 emissions are nowadays deeply analysed. One of them is the improvement of the nuclear energy use. In this framework, innovative gas-cooled reactors (both thermal and fast) seem to be very attractive from the electricity production point of view and for the potential industrial use along the high temperature processes (e.g., H 2 production by steam reforming or I-S process). This work focuses on a preliminary (and conservative) evaluation of possible advantages that a symbiotic cycle (EPR-PBMR-GCFR) could entail, with special regard to the reduction of the HLW inventory and the optimization of the exploitation of the fuel resources. The comparison between the symbiotic cycle chosen and the reference one (once-through scenario, i.e., EPR-SNF directly disposed) shows a reduction of the time needed to reach a fixed reference level from ∼170000 years to ∼1550 years (comparable with typical human times and for this reason more acceptable by the public opinion). In addition, this cycle enables to have a more efficient use of resources involved: the total electric energy produced becomes equal to ∼630 TWh/year (instead of only ∼530 TWh/year using only EPR) without consuming additional raw materials. © 2009 Barbara Vezzoni et al.

Rationales of holonic manufacturing systems in leather industry

This paper presents an insight into leather manufacturing processes, depicting peculiarities and challenges faced by leather industry. An analysis of this industry reveals the need for a new approach to optimize the productivity of leather processing operations, ensure consistent quality of leather, mitigate the adverse health effects in tannery workers exposed to chemicals and comply with environmental regulation. Holonic manufacturing systems (HMS) paradigm represent a bottom-up distributed approach that provides stability, adaptability, efficient use of resources and a plug and operate functionality to the manufacturing system. A vision of how HMS might operate in a tannery is illustrated presenting the rationales behind its application in this industry. © 2013 Springer-Verlag.

Modelling of free-space optoelectronic switching with MPLS for storage area networks (SANs)

Low-cost optical switches based on SLMs have conventionally been considered unsuitable for packet switching due to slow reconfiguration time. In this paper, we demonstrate that the constraint of SLM reconfiguration time in a hybrid three-stage electronic/optical switching node architecture can be compensated through the utilization of MPLS label switching mechanism to achieve the best performance for SAN applications. © 2012 SEE.

Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (∼10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance. © 2013 IEEE.

Physicochemical characteristics and droplet impact dynamics of superhydrophobic carbon nanotube arrays.

The physicochemical and droplet impact dynamics of superhydrophobic carbon nanotube arrays are investigated. These superhydrophobic arrays are fabricated simply by exposing the as-grown carbon nanotube arrays to a vacuum annealing treatment at a moderate temperature. This treatment, which allows a significant removal of oxygen adsorbates, leads to a dramatic change in wettability of the arrays, from mildly hydrophobic to superhydrophobic. Such change in wettability is also accompanied by a substantial change in surface charge and electrochemical properties. Here, the droplet impact dynamics are characterized in terms of critical Weber number, coefficient of restitution, spreading factor, and contact time. Based on these characteristics, it is found that superhydrophobic carbon nanotube arrays are among the best water-repellent surfaces ever reported. The results presented herein may pave a way for the utilization of superhydrophobic carbon nanotube arrays in numerous industrial and practical applications, including inkjet printing, direct injection engines, steam turbines, and microelectronic fabrication.