Which middleware platform should you choose for your next remote service?

Due to the shift from software-as-a-product to software-as-a-service, software components that were developed to run in a single address space must increasingly be accessed remotely across the network. Distribution middleware is frequently used to facilitate this transition. Yet a range of middleware platforms exist, and there are few existing guidelines to help the programmer to choose an appropriate middleware platform to achieve desired goals for performance, conciseness, intuitiveness, and reliability. To address this limitation, in this article, we describe a case study of transitioning an Open Service Gateway Initiative service from local to remote access. In our case study, we evaluate five remote versions of this service, constructed using different distribution middleware platforms. These platforms are implemented by widely-used commercial technologies or have been proposed as improvements on the state of the art. In particular, we implemented a service-oriented version of our own Remote Batch Invocation abstraction. We compare and contrast these implementations in terms of their respective performance, conciseness, complexity, and reliability. Our results can help remote service programmers make informed decisions when choosing middleware platforms for their applications.     

A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000-2004, and was validated using observed GPP over the period 2005-2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km × 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr^− 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.     

Development of proteome-wide binding reagents for research and diagnostics

Alongside MS, antibodies and other specific protein-binding molecules have a special place in proteomics as affinity reagents in a toolbox of applications for determining protein location, quantitative distribution and function (affinity proteomics). The realisation that the range of research antibodies available, while apparently vast is nevertheless still very incomplete and frequently of uncertain quality, has stimulated projects with an objective of raising comprehensive, proteome-wide sets of protein binders. With progress in automation and throughput, a remarkable number of recent publications refer to the practical possibility of selecting binders to every protein encoded in the genome. Here we review the requirements of a pipeline of production of protein binders for the human proteome, including target prioritisation, antigen design, ‘next generation’ methods, databases and the approaches taken by ongoing projects in Europe and the USA. While the task of generating affinity reagents for all human proteins is complex and demanding, the benefits of well-characterised and quality-controlled pan-proteome binder resources for biomedical research, industry and life sciences in general would be enormous and justify the effort. Given the technical, personnel and financial resources needed to fulfil this aim, expansion of current efforts may best be addressed through large-scale international collaboration.     

Database Query Processing Using Finite Cursor Machines

We introduce a new abstract model of database query processing, finite cursor machines, that incorporates certain data streaming aspects. The model describes quite faithfully what happens in so-called “one-pass” and “two-pass query processing”. Technically, the model is described in the framework of abstract state machines. Our main results are upper and lower bounds for processing relational algebra queries in this model, specifically, queries of the semijoin fragment of the relational algebra.     

Power-aware microarchitecture: design and modeling challenges fornext-generation microprocessors

The ability to estimate power consumption during early-stage definition and trade-off studies is a key new methodology enhancement. Opportunities for saving power can be exposed via microarchitecture-level modeling, particularly through clock-gating and dynamic adaptation. In this paper we describe the approach of using energy-enabled performance simulators in early design. We examine some of the emerging paradigms in processor design and comment on their inherent power-performance characteristics     

Increasing Reading Comprehension and Engagement Through Concept-Oriented Reading Instruction.

Based on an engagement perspective of reading development, we investigated the extent to which an instructional framework of combining motivation support and strategy instruction (Concept-Oriented Reading Instruction--CORI) influenced reading outcomes for third-grade children. In CORI, five motivational practices were integrated with six cognitive strategies for reading comprehension. In the first study, we compared this framework to an instructional framework emphasizing Strategy Instruction (SI), but not including motivation support. In the second study, we compared CORI to SI and to a traditional instruction group (TI), and used additional measures of major constructs. In both studies, class-level analyses showed that students in CORI classrooms were higher than SI and/or TI students on measures of reading comprehension, reading motivation, and reading strategies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The sleep and performance of train drivers during an extended freight-haul operation

The sleep and performance of train drivers was monitored across a 106-h rail operation between the Australian cities of Adelaide and Perth. The drivers worked alternating 8-h shift rotations across the operation and rested in specially equipped, crew-van carriages during non-work periods. The crew-van rest opportunities were associated with shorter bedtime spans, less total sleep time, and poorer sleep efficiency than sleeps initiated at home. The duration of crew-van sleeps was primarily dependent on the time of day at which the rest opportunities occurred. Overall, drivers incurred a significant cumulative sleep loss across the duration of the operation. Despite the deficit, drivers were able to sustain vigilance performance across the operation.     

Joint maximum likelihood estimation of unit root testing equations and GARCH processes: Some finite-sample issues

In recent research [B. Seo, Distribution theory for unit root tests with conditional heteroskedasticity, J. Econometrics 91 (1999) 113–144] has suggested that the examination of the unit root hypothesis in series exhibiting GARCH behaviour should proceed via joint maximum likelihood (ML) estimation of the unit root testing equation and GARCH process. The results presented show the asymptotic distribution of the resulting ML t-test to be a mixture of the Dickey–Fuller and standard normal distributions. In this paper, the relevance of these asymptotic arguments is considered for the finite samples encountered in empirical research. In particular, the influences of sample size, alternative values of the parameters of the GARCH process and the use of the Bollerslev–Wooldridge covariance matrix estimator upon the finite-sample distribution of the ML t-statistic are explored. It is shown that the resulting critical values for the ML t-statistic are similar to those of the Dickey–Fuller distribution rather than the standard normal, unless a large sample size and empirically unrealistic values of the volatility parameter of the GARCH process are considered. Use of the Bollerslev–Wooldridge standard covariance matrix estimator exaggerates this finding, causing a leftward shift in the finite-sample distribution of the ML t-statistic. The results of the simulation analysis are illustrated via an application to U.S. short term interest rates.     

Human Activity Recognition and Pattern Discovery

Activity recognition and activity pattern discovery hold significant promise to enabling many applications in the domain of human behavior monitoring, understanding and alteration. However, concurrent, interleaved and highly similar activities challenge the utility of existing approaches. In this installment of the Standards and Emerging Technologies column we survey the state of the art and point to crucial limitations worthy of additional research and investigations.