A finite volume scheme with preconditioned Lanczos method for two-dimensional space-fractional reaction–diffusion equations

Fractional differential equations have been increasingly used as a powerful tool to model the non-locality and spatial heterogeneity inherent in many real-world problems. However, a constant challenge faced by researchers in this area is the high computational expense of obtaining numerical solutions of these fractional models, owing to the non-local nature of fractional derivatives. In this paper, we introduce a finite volume scheme with preconditioned Lanczos method as an attractive and high-efficiency approach for solving two-dimensional space-fractional reaction–diffusion equations. The computational heart of this approach is the efficient computation of a matrix-function-vector product f(A)bf(A)b, where A A is the matrix representation of the Laplacian obtained from the finite volume method and is non-symmetric. A key aspect of our proposed approach is that the popular Lanczos method for symmetric matrices is applied to this non-symmetric problem, after a suitable transformation. Furthermore, the convergence of the Lanczos method is greatly improved by incorporating a preconditioner. Our approach is show-cased by solving the fractional Fisher equation including a validation of the solution and an analysis of the behaviour of the model.

Modelling airport passenger group dynamics using an agent-based method

This thesis investigates the influence of passenger group dynamics on passengers' behaviour in an international airport. A simulation model is built to analyse passengers' behaviour during airport departure processes and during an emergency event. Results from the model showed that passengers' group dynamics have significant influences on the performance and utilisation of airport services. The agent-based model also provides a convenient way to investigate the effectiveness of space design and service allocations, which may contribute to the enhancement of passenger airport experiences.

Ultra sensitive label free surface enhanced Raman spectroscopy method for the detection of biomolecules

We present a proof of concept for a novel nanosensor for the detection of ultra-trace amounts of bio-active molecules in complex matrices. The nanosensor is comprised of gold nanoparticles with an ultra-thin silica shell and antibody surface attachment, which allows for the immobilization and direct detection of bio-active molecules by surface enhanced Raman spectroscopy (SERS) without requiring a Raman label. The ultra-thin passive layer (~1.3 nm thickness) prevents competing molecules from binding non-selectively to the gold surface without compromising the signal enhancement. The antibodies attached on the surface of the nanoparticles selectively bind to the target molecule with high affinity. The interaction between the nanosensor and the target analyte result in conformational rearrangements of the antibody binding sites, leading to significant changes in the surface enhanced Raman spectra of the nanoparticles when compared to the spectra of the un-reacted nanoparticles. Nanosensors of this design targeting the bio-active compounds erythropoietin and caffeine were able to detect ultra-trace amounts the analyte to the lower quantification limits of 3.5×10−13 M and 1×10−9 M, respectively.

Two dynamic discrete choice estimation problems and simulation method solutions

This paper considers two problems that frequently arise in dynamic discrete choice problems but have not received much attention with regard to simulation methods. The first problem is how to simulate unbiased simulators of probabilities conditional on past history. The second is simulating a discrete transition probability model when the underlying dependent variable is really continuous. Both methods work well relative to reasonable alternatives in the application discussed. However, in both cases, for this application, simpler methods also provide reasonably good results.

Enhancing Research Productivity of TEFL Academic in China : A Mixed Method Study

As research has become an important indicator of TEFL academics’ overall performance in Chinese higher education institutions, it is critical that TEFL academics are able to meet the expectation of conducting research. This mixed-method study (an initial survey followed by a qualitative collective case study)investigated research productivity of Chinese TEFL academics and associated influences, with the ultimate objective of constructing a framework to help build their research capacity in the future. The findings from this study revealed that the 182 Chinese TEFL academics’ research productivity during 2004-2008 was relatively low. Four influences were identified that impacted on thier research productivity: TEFL disciplinary influences, institutional and departmental research environments, individual characteristics desirable for research, and TEFL academics’ perceptions about research. Drawing upon the above findings, a Framework towards Enhancing Chinese TEFL Academics’ Research Productivity (FECTARP) was constructed. The FECTAR presented a framework for Chinese institutions and TEFL departments to enhance their TEFL academics' research capacity.

The renormalization group: A perturbation method for the graduate curriculum

In this paper the renormalization group (RG) method of Chen, Goldenfeld, and Oono [Phys. Rev. Lett., 73 (1994), pp.1311-1315; Phys. Rev. E, 54 (1996), pp.376-394] is presented in a pedagogical way to increase its visibility in applied mathematics and to argue favorably for its incorporation into the corresponding graduate curriculum.The method is illustrated by some linear and nonlinear singular perturbation problems. Key word. © 2012 Society for Industrial and Applied Mathematics.

A Combined Renormalization Group-Multiple Scale Method for Singularly Perturbed Problems

This paper introduces a straightforward method to asymptotically solve a variety of initial and boundary value problems for singularly perturbed ordinary differential equations whose solution structure can be anticipated. The approach is simpler than conventional methods, including those based on asymptotic matching or on eliminating secular terms. © 2010 by the Massachusetts Institute of Technology.

Temperature-dependent growth mechanisms of low-dimensional ZnO nanostructures

One-dimensional ZnO nanostructures were successfully synthesized on single-crystal silicon substrates via a simple thermal evaporation and vapour-phase transport method under different process temperatures from 500 to 1000 °C. The detailed and in-depth analysis of the experimental results shows that the growth of ZnO nanostructures at process temperatures of 500, 800, and 1000 °C is governed by different growth mechanisms. At a low process temperature of 500 °C, the ZnO nanostructures feature flat and smooth tips, and their growth is primarily governed by the vapour-solid mechanism. At an intermediate process temperature of 800 °C, the ZnO nanostructures feature cone-shape tips, and their growth is primarily governed by the self-catalyzed and saturated vapour–liquid–solid mechanism. At a high process temperature of 1000 °C, the alloy tip appears on the front side of the ZnO nanostructures, and their growth is primarily governed by the common catalyst-assisted vapour–liquid–solid mechanism. It is also shown that the morphological, structural, optical, and compositional properties of the synthesized ZnO nanostructures are closely related to the process temperature. These results are highly relevant to the development of light-emitting diodes, chemical sensors, energy conversion devices, and other advanced applications.

Compressed sensing using hidden Markov models with application to vision based aircraft tracking

This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

Airport security screeners expertise and implications for interface design

This paper describes research investigating expertise and the types of knowledge used by airport security screeners. It applies a multi method approach incorporating eye tracking, concurrent verbal protocol and interviews. Results show that novice and expert security screeners primarily access perceptual knowledge and experience little difficulty during routine situations. During non-routine situations however, experience was found to be a determining factor for effective interactions and problem solving. Experts were found to use strategic knowledge and demonstrated structured use of interface functions integrated into efficient problem solving sequences. Comparatively, novices experienced more knowledge limitations and uncertainty resulting in interaction breakdowns. These breakdowns were characterised by trial and error interaction sequences. This research suggests that the quality of knowledge security screeners have access to has implications on visual and physical interface interactions and their integration into problem solving sequences. Implications and recommendations for the design of interfaces used in the airport security screening context are discussed. The motivations of recommendations are to improve the integration of interactions into problem solving sequences, encourage development of problem scheme knowledge and to support the skills and knowledge of the personnel that interact with security screening systems.

Death on the front page : “Awesome things” and this thing called humanity

It has been called “the world’s worst recorded natural disaster,” and “the largest earthquake in 40 years,” galvanizing the largest global relief effort in history. For those of us involved in the discipline and/or the practice of communications, we realized that it presented a unique case study from a number of perspectives. Both the media and the public became so enraptured and enmeshed in the story of the tsunami of December 26, 2004, bringing to the fore a piece of geography and a peoples too rarely considered prior to the tragedy, that we felt compelled to examine the phenomenon. The overwhelming significance of this volume comes from its being a combination of both academic scholars and development practitioners in the field. Its poignancy becomes underscored from their wide-ranging perspectives, with 21 chapters representing some 14 different countries. Their realities provide not only credibility but also an unprecedented sensitivity to communication issues. Our approach here considers Tsunami 2004 from five communication perspectives: 1.) Interpersonal/ intercultural, 2.) Mass media, 3.) Telecommunications, 4.) Ethics, philanthropy, and development communication, and; 5.) Personal testimonies and observations. You will learn even more here about the theory and practice of disaster/crisis communication.

Modulation and control of three-phase paralleled Z-source inverters for distributed generation applications

This paper presents a modulation and controller design method for paralleled Z-source inverter systems applicable for alternative energy sources like solar cells, fuel cells, or variablespeed wind turbines with front-end diode rectifiers. A modulation scheme is designed based on simple shoot-through principle with interleaved carriers to give enhanced ripple reduction in the system. Subsequently, a control method is proposed to equalize the amount of power injected by the inverters in the grid-connected mode and also to provide reliable supply to sensitive loads onsite in the islanding mode. The modulation and controlling methods are proposed to have modular independence so that redundancy, maintainability, and improved reliability of supply can be achieved. The performance of the proposed paralleled Z-source inverter configuration is validated with simulations carried out using Matlab/Simulink/Powersim. Moreover, a prototype is built in the laboratory to obtain the experimental verifications.

Unbalance identification in large steam turbo-generator unit using a model-based method

Fault identification in industrial machine is a topic of major importance under engineering point of view. In fact, the possibility to identify not only the type, but also the severity and the position of a fault occurred along a shaft-line allows quick maintenance and shorten the downtime. This is really important in the power generation industry where the units are often of several tenths of meters long and where the rotors are enclosed by heavy and pressure-sealed casings. In this paper, an industrial experimental case is presented related to the identification of the unbalance on a large size steam turbine of about 1.3 GW, belonging to a nuclear power plant. The case history is analyzed by considering the vibrations measured by the condition monitoring system of the unit. A model-based method in the frequency domain, developed by the authors, is introduced in detail and it is then used to identify the position of the fault and its severity along the shaft-line. The complete model of the unit (rotor – modeled by means of finite elements, bearings – modeled by linearized damping and stiffness coefficients and foundation – modeled by means of pedestals) is analyzed and discussed before being used for the fault identification. The assessment of the actual fault was done by inspection during a scheduled maintenance and excellent correspondence was found with the identified one by means of authors’ proposed method. Finally a complete discussion is presented about the effectiveness of the method, even in presence of a not fine tuned machine model and considering only few measuring planes for the machine vibration.

Application of a hybrid Entropy–McKinsey Matrix method in evaluating sustainable urbanization : a China case study

Although urbanization can promote social and economic development, it can also cause various problems. As the key decision makers of urbanization, local governments should be able to evaluate urbanization performance, summarize experiences, and find problems caused by urbanization. This paper introduces a hybrid Entropy–McKinsey Matrix method for evaluating sustainable urbanization. The McKinsey Matrix is commonly referred to as the GE Matrix. The values of a development index (DI) and coordination index (CI) are calculated by employing the Entropy method and are used as a basis for constructing a GE Matrix. The matrix can assist in assessing sustainable urbanization performance by locating the urbanization state point. A case study of the city of Jinan in China demonstrates the process of using the evaluation method. The case study reveals that the method is an effective tool in helping policy makers understand the performance of urban sustainability and therefore formulate suitable strategies for guiding urbanization toward better sustainability.

Predicting movie ratings from audience behaviors

We propose a method of representing audience behavior through facial and body motions from a single video stream, and use these features to predict the rating for feature-length movies. This is a very challenging problem as: i) the movie viewing environment is dark and contains views of people at different scales and viewpoints; ii) the duration of feature-length movies is long (80-120 mins) so tracking people uninterrupted for this length of time is still an unsolved problem, and; iii) expressions and motions of audience members are subtle, short and sparse making labeling of activities unreliable. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input. We then utilize motion-history features which capture the subtle movements of a person within a pre-defined volume, and then form a group representation of the audience by a histogram of pair-wise correlations over a small-window of time. Using this group representation, we learn our movie rating classifier from crowd-sourced ratings collected by rottentomatoes.com and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).