Multi-rotor with suspended load: System dynamics and control toolbox

There is an increasing demand for Unmanned Aerial Systems (UAS) to carry suspended loads as this can provide significant benefits to several applications in agriculture, law enforcement and construction. The load impact on the underlying system dynamics should not be neglected as significant feedback forces may be induced on the vehicle during certain flight manoeuvres. The constant variation in operating point induced by the slung load also causes conventional controllers to demand increased control effort. Much research has focused on standard multi-rotor position and attitude control with and without a slung load. However, predictive control schemes, such as Nonlinear Model Predictive Control (NMPC), have not yet been fully explored. To this end, we present a novel controller for safe and precise operation of multi-rotors with heavy slung load in three dimensions. The paper describes a System Dynamics and Control Simulation Toolbox for use with MATLAB/SIMULINK which includes a detailed simulation of the multi-rotor and slung load as well as a predictive controller to manage the nonlinear dynamics whilst accounting for system constraints. It is demonstrated that the controller simultaneously tracks specified waypoints and actively damps large slung load oscillations. A linear-quadratic regulator (LQR) is derived and control performance is compared. Results show the improved performance of the predictive controller for a larger flight envelope, including aggressive manoeuvres and large slung load displacements. The computational cost remains relatively small, amenable to practical implementations.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

In search of good probability assessors: An experimental comparison of elicitation rules for confidence judgments

In this paper, we use an experimental design to compare the performance of elicitation rules for subjective beliefs. Contrary to previous works in which elicited beliefs are compared to an objective benchmark, we consider a purely subjective belief framework (confidence in one’s own performance in a cognitive task and a perceptual task). The performance of different elicitation rules is assessed according to the accuracy of stated beliefs in predicting success. We measure this accuracy using two main factors: calibration and discrimination. For each of them, we propose two statistical indexes and we compare the rules’ performances for each measurement. The matching probability method provides more accurate beliefs in terms of discrimination, while the quadratic scoring rule reduces overconfidence and the free rule, a simple rule with no incentives, which succeeds in eliciting accurate beliefs. Nevertheless, the matching probability appears to be the best mechanism for eliciting beliefs due to its performances in terms of calibration and discrimination, but also its ability to elicit consistent beliefs across measures and across tasks, as well as its empirical and theoretical properties.

A robust optimization approach for index tracking problem

Index tracking is an investment approach where the primary objective is to keep portfolio return as close as possible to a target index without purchasing all index components. The main purpose is to minimize the tracking error between the returns of the selected portfolio and a benchmark. In this paper, quadratic as well as linear models are presented for minimizing the tracking error. The uncertainty is considered in the input data using a tractable robust framework that controls the level of conservatism while maintaining linearity. The linearity of the proposed robust optimization models allows a simple implementation of an ordinary optimization software package to find the optimal robust solution. The proposed model of this paper employs Morgan Stanley Capital International Index as the target index and the results are reported for six national indices including Japan, the USA, the UK, Germany, Switzerland and France. The performance of the proposed models is evaluated using several financial criteria e.g. information ratio, market ratio, Sharpe ratio and Treynor ratio. The preliminary results demonstrate that the proposed model lowers the amount of tracking error while raising values of portfolio performance measures.

Implementing process views in the web service environment

Web service and business process technologies are widely adopted to facilitate business automation and collaboration. Given the complexity of business processes, it is a sought-after feature to show a business process with different views to cater for the diverse interests, authority levels, etc., of different users. Aiming to implement such flexible process views in the Web service environment, this paper presents a novel framework named FlexView to support view abstraction and concretisation of WS-BPEL processes. In the FlexView framework, a rigorous view model is proposed to specify the dependency and correlation between structural components of process views with emphasis on the characteristics of WS-BPEL, and a set of rules are defined to guarantee the structural consistency between process views during transformations. A set of algorithms are developed to shift the abstraction and concretisation operations to the operational level. A prototype is also implemented for the proof-of-concept purpose. © 2010 Springer Science+Business Media, LLC.

Connected Viewing: Selling, Streaming and Sharing Media in the Digital Era

As patterns of media use become more integrated with mobile technologies and multiple screens, a new mode of viewer engagement has emerged in the form of connected viewing, which allows for an array of new relationships between audiences and media texts in the digital space. This exciting new collection brings together twelve original essays that critically engage with the socially-networked, multi-platform, and cloud-based world of today, examining the connected viewing phenomenon across television, film, video games, and social media. The result is a wide-ranging analysis of shifting business models, policy matters, technological infrastructure, new forms of user engagement, and other key trends affecting screen media in the digital era. Connected Viewing contextualizes the dramatic transformations taking place across both media industries and national contexts, and offers students and scholars alike a diverse set of methods and perspectives for studying this critical moment in media culture.

Assessing acculturation over time: A four-year prospective study of Asian American young adults

Acculturation is commonly defined as a dynamic and multidimensional process in which individuals and groups change over time when coming into contact with another culture. Despite the emphasis on acculturation as a process of change over time, few researchers have directly assessed this hypothesis. The current study first identifies and then examines "stable" and "dynamic" dimensions of acculturation within a 4-year prospective study of 433 first- and second-generation Chinese- and Korean-American college students. Separate growth model analyses revealed significant linear change for first-generation students toward greater U.S. acculturation. In comparison, tests of linear and quadratic change for second-generation students were not significant. When stratifying by gender, acculturation increased for women but there was no significant change in acculturation for men. While all students reported increases in alcohol consumption over the study period, changes in acculturation predicted changes in alcohol consumption only for women. Chinese men showed greater increases in alcohol consumption than Korean men but there was no effect for ethnicity among women. There was significant individual variability in the models, which underscores the importance of examining change prospectively through within and between person analyses. The findings highlight the importance of examining acculturation changes over time for different migrant groups with implications for further development of acculturation measures, research methodologies, and health interventions. More prospective research designs of acculturation are needed to examine changes in health behavior and overall adaptation across migrant groups at varying stages of development.

Changing agendas in international research in mathematics education

Handbooks serve an important function for our research community in providing state-of-the-art summations, critiques, and extensions of existing trends in research. In the intervening years between the second and third editions of the Handbook of International Research in Mathematics Education, there have been stimulating developments in research, as well as new challenges in translating outcomes into practice. This third edition incorporates a number of new chapters representing areas of growth and challenge, in addition to substantially updated chapters from the second edition. As such, the Handbook addresses five core themes, namely, Priorities in International Mathematics Education Research, Democratic Access to Mathematics Learning, Transformations in Learning Contexts, Advances in Research Methodologies, and Influences of Advanced Technologies...

Visible light-driven cross-coupling reactions at lower temperatures using a photocatalyst of palladium and gold alloy nanoparticles

Palladium (Pd)-catalyzed cross-coupling reactions are among the most important methods in organic synthesis. We report the discovery of highly efficient and green photocatalytic processes by which cross-coupling reactions, including Sonogashira, Stille, Hiyama, Ullmann, and Buchwald–Hartwig reactions, can be driven with visible light at temperatures slightly above room temperature using alloy nanoparticles of gold and Pd on zirconium oxide, thus achieving high yields. The alloy nanoparticles absorb visible light, and their conduction electrons gain energy, which is available at the surface Pd sites. Results of the density functional theory calculations indicate that transfer of the light excited electrons from the nanoparticle surface to the reactant molecules adsorbed on the nanoparticle surface activates the reactants. When the light intensity was increased, a higher reaction rate was observed, because of the increased population of photoexcited electrons. The irradiation wavelength also has an important impact on the reaction rates. Ultraviolet irradiation can drive some reactions with the chlorobenzene substrate, while visible light irradiation failed to, and substantially improve the yields of the reactions with the bromobenzene substrate. The discovery reveals the possibility of using low-energy and -density sources such as sunlight to drive chemical transformations.

Direct photocatalysis for organic synthesis by using plasmonic-metal nanoparticles irradiated with visible light

Recent advances in direct-use plasmonic-metal nanoparticles (NPs) as photocatalysts to drive organic synthesis reactions under visible-light irradiation have attracted great interest. Plasmonic-metal NPs are characterized by their strong interaction with visible light through excitation of the localized surface plasmon resonance (LSPR). Herein, we review recent developments in direct photocatalysis using plasmonic-metal NPs and their applications. We focus on the role played by the LSPR of the metal NPs in catalyzing organic transformations and, more broadly, the role that light irradiation plays in catalyzing the reactions. Through this, the reaction mechanisms that these light-excited energetic electrons promote will be highlighted. This review will be of particular interest to researchers who are designing and fabricating new plasmonic-metal NP photocatalysts by identifying important reaction mechanisms that occur through light irradiation.

Viable photocatalysts under solar-spectrum irradiation: Nonplasmonic metal nanoparticles

Supported nanoparticles (NPs) of nonplasmonic transition metals (Pd, Pt, Rh, and Ir) are widely used as thermally activated catalysts for the synthesis of important organic compounds, but little is known about their photocatalytic capabilities. We discovered that irradiation with light can significantly enhance the intrinsic catalytic performance of these metal NPs at ambient temperatures for several types of reactions. These metal NPs strongly absorb the light mainly through interband electronic transitions. The excited electrons interact with the reactant molecules on the particles to accelerate these reactions. The rate of the catalyzed reaction depends on the concentration and energy of the excited electrons, which can be increased by increasing the light intensity or by reducing the irradiation wavelength. The metal NPs can also effectively couple thermal and light energy sources to more efficiently drive chemical transformations.

Tracking adversarial targets

We study linear control problems with quadratic losses and adversarially chosen tracking targets. We present an efficient algorithm for this problem and show that, under standard conditions on the linear system, its regret with respect to an optimal linear policy grows as O(log^2 T), where T is the number of rounds of the game. We also study a problem with adversarially chosen transition dynamics; we present an exponentiallyweighted average algorithm for this problem, and we give regret bounds that grow as O(sqtr p T).

Studying change in popular culture: A "middle-range" approach

"This chapter reviews the capacity of the discipline field to account for the velocity and quality of digitally-driven transformations, while making a case for a "middle range" approach that steers between unbridled optimism ("all-change") and determined scepticism ("Continuity") about the potential of such change. The chapter focuses on online screen distribution as a case study, considering the evidence for, and significance of, change in industry structure and the main payers, how content is produced and by whom, the nature of content, and the degree to which online screen distribution has reached thresholds of mainstream popularity."

Transformative times: Australian journalists’ perceptions of changes in their work

Numerous studies have pointed to the fact that journalism in most industrialised societies is undergoing a particularly intensive period of transformation. Yet, while many scholars have studied how news organisations are changing, comparatively fewer studies have inquired into how journalists themselves are experiencing the changes in their work brought on by the technological, economic and cultural transformations. Based on a representative study of Australian journalists, this paper reports on their perceptions of changes in a variety of influences and aspects of their work over the past five years. It finds that journalists say change has been most notable in audience interactions and technological innovation, while economic changes are somewhat less strong. Importantly, they are also very concerned about an increase in sensationalism and a drop in journalistic standards and the credibility of journalism. Results are also compared across different organisational contexts.

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.