Effect of Free-Fall Height in Water on Performance of Flowable Concrete

Concrete placed under water should be proportioned to flow readily into place with minimum materials separation. Unlike concrete cast for deep tremie seals, the use of concrete in repairs often necessitates some free fall of the mixture through water. Such placement conditions lead to greater risk of water erosion and segregation, and should be addressed in proportioning highly flowable underwater concrete. This paper evaluates the effect of free-fall height (FFH) of concrete through water on resulting in-place properties. Concrete was cast in blocks measuring 0.54 x 0.44 x 1 m with the initial FFH in water ranging between 0.25 and 0.60 m. In-place compressive and splitting tensile strengths, unit weight, and depth of washed-out and sedimentation materials were determined. In total, 24 highly flowable mixtures with slump flows greater than 500 mm were investigated. The evaluated mixtures were prepared with various hydraulic binders, including conventional Type 10 cement, a binary mixture with 10% of silica fume (SF), and a ternary binder incorporating 20% of fly ash (FA) and 6% of SF. The mixtures were proportioned with water-binder ratios (w/b) ranging between 0.41 and 0.47. Test results show that the increase of FFH of fresh concrete in water can greatly decrease the residual strength and significantly increase the thickness of washed out and sedimentation materials. The incorporation of 10% of SF, or 20% of FA and 6% of SF, and the reduction of the w/b from 0.47 to 0.41 can, however, lead to a significant increase in washout resistance and residual strength. A relationship between residual strength and the coupled factor of free-fall drop of concrete in water and washout resistance is established.

A preliminary structural design procedure for laser beam welded airframe stiffened panels

Initial sizing procedures for aircraft stiffened panels that include the influence of welding fabrication residual process effects are missing. Herein, experimental and Finite Element analyses are coupled to generate knowledge to formulate an accurate and computationally efficient sizing procedure which will enable designers to routinely consider panel fabrication, via welding, accounting for the complex distortions and stresses induced by this manufacturing process. Validating experimental results demonstrate the need to consider welding induced material property degradation, residual stresses and distortions, as these can reduce static strength performance. However, results from fuselage and wing trade-studies, using the validated sizing procedure, establish that these potential reductions in strength performance may be overcome through local geometric tailoring during initial sizing, negating any weight penalty for the majority of design scenarios.

Thin-film oxygen sensors using a luminescent polynuclear gold(I) complex

Robust thin-film oxygen sensors were fabricated by encapsulating a lipophilic, polynuclear gold(I) complex, bis{m-(bis(diphenylphosphino)octadecylamine-P,P')}dichlorodigold(I), in oxygen permeable polystyrene and ormosil matrices. Strong phosphorescence, which was quenched by gaseous and dissolved oxygen, was observed from both matrices. The polystyrene encapsulated dye exhibited downward-turning Stern-Volmer plots which were well fitted by a two-site model. The ormosil trapped complex showed linear Stern-Volmer plots for dissolved oxygen quenching but was downward turning for gaseous oxygen. No leaching was observed when the ormosil based sensors were immersed in flowing water over an 8 h period. Both films exhibited fully reversible response and recovery to changing oxygen concentration with rapid response times. (C) 2011 Elsevier B.V. All rights reserved.

EFFECTIVE LABORATORY MONITORING FOR THE ABUSE OF THE BETA-AGONIST CLENBUTEROL IN CATTLE

The use of the beta-agonist clenbuterol (CBL) as a growth promoter has been outlawed in European meat production. The detection of its illegal use is dependent on CBL residues persisting in animal tissues for longer than the withdrawal times given by abusers. A comparison of urine, bile and liver matrices indicated that analysis of the liver offered the best possibility for CBL detection. However, an experimental study showed that CBL detection following withdrawal could be further extended (up to 56 d) if the retina was used as the target tissue. Analysis of 703 retina and liver samples from cattle suspected of CBL medication revealed that 96 cattle had CBL residues present in their retinas, only 46 of these were liver positive. There were no instances of liver CBL residues being detected without the associated retina also being positive.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

A statistical process control approach for automatic anti-islanding detection using synchrophasors

Anti-islanding protection is becoming increasingly important due to the rapid installation of distributed generation from renewable resources like wind, tidal and wave, solar PV, bio-fuels, as well as from other resources like diesel. Unintentional islanding presents a potential risk for damaging utility plants and equipment connected from the demand side, as well as to public and personnel in utility plants. This paper investigates automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through a wide area measurement system, which is based on Phasor Measurement Unit (PMU) technology. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real system and the results have confirmed that the proposed method can correctly identify the fault and islanding site.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

Progress in the development of immunoanalytical methods incorporating recombinant antibodies to small molecular weight biotoxins

Rapid immunoanalytical screening of food and environmental samples for small molecular weight (hapten) biotoxin contaminations requires the production of antibody reagents that possess the requisite sensitivity and specificity. To date animal-derived polyclonal (pAb) and monoclonal (mAb) antibodies have provided the binding element of the majority of these assays but recombinant antibodies (rAb) isolated from in vitro combinatorial phage display libraries are an exciting alternative due to (1) circumventing the need for experimental animals, (2) speed of production in commonly used in vitro expression systems and (3) subsequent molecular enhancement of binder performance. Short chain variable fragments (scFv) have been the most commonly employed rAb reagents for hapten biotoxin detection over the last two decades but antibody binding fragments (Fab) and single domain antibodies (sdAb) are increasing in popularity due to increased expression efficiency of functional binders and superior resistance to solvents. rAb-based immunochromatographic assays and surface plasmon resonance (SPR) biosensors have been reported to detect sub-regulatory levels of fungal (mycotoxins), marine (phycotoxins) and aquatic biotoxins in a wide range of food and environmental matrices, however this technology has yet to surpass the performances of the equivalent mAb- and pAb-based formats. As such the full potential of rAb technology in hapten biotoxin detection has yet to be achieved, but in time the inherent advantages of engineered rAb are set to provide the next generation of ultra-high performing binder reagents for the rapid and specific detection of hapten biotoxins.

Potential applications of shape memory alloys in seismic retrofitting of exterior RC beam-column joints

Shape memory alloys (SMAs) have the ability to undergo large deformations with minimum residual strain and also the extraordinary ability to undergo reversible hysteretic shape change known as the shape memory effect. The shape memory effect of these alloys can be utilised to develop a convenient way of actively confine concrete sections to improve their shear strength, flexural ductility and ultimate strain. Most of the previous work on active confinement of concrete using SMA has been carried out on circular sections. In this study retrofitting strategies for active confinement of non-circular sections have been proposed. The proposed schemes presented in this paper are conceived with an aim to seismically retrofit beam-column joints in non-seismically designed reinforced concrete buildings. SMAs are complex materials and their material behaviour depends on number of parameters. Depending upon the alloying elements, SMAs exhibit different behaviour in different conditions and are highly sensitive to variation in temperature, phase in which it is used, loading pattern, strain rate and pre-strain conditions. Therefore, a detailed discussion on the behaviour of SMAs under different thermo-mechanical conditions is presented first.

Shape Memory Alloy Features for Seismic Retrofitting of External RC Beam-Column Joint

Shape memory alloys (SMAs) have the ability to undergo large deformations with minimum residual strain and also the extraordinary ability to undergo reversible hysteretic shape change known as the shape memory effect. The shape memory effect of these alloys can be utilised to develop a convenient way of actively confining concrete sections to improve their shear strength, flexural ductility and ultimate strain capacity. Most of the previous work on active confinement of concrete using SMA has been carried out on circular sections. In this study retrofitting strategies for active confinement of non-circular sections have been proposed. The proposed schemes presented in this paper are conceived with an aim to seismically retrofit a beam-column joint in non-seismically designed reinforced concrete buildings.

The complex material behaviour of SMAs depends on number of parameters. Depending upon the alloying elements, SMAs exhibit different behaviour in different conditions and are highly sensitive to variation in temperature, phase in which it is used, loading pattern, strain rate and pre-strain conditions. Therefore, a detailed discussion on the behaviour of SMAs under different thermo-mechanical conditions is presented first in this paper.

High Performance Real-time Pre-Processing for Fixed-Complexity Sphere Decoder

Pre-processing (PP) of received symbol vector and channel matrices is an essential pre-requisite operation for Sphere Decoder (SD)-based detection of Multiple-Input Multiple-Output (MIMO) wireless systems. PP is a highly complex operation, but relative to the total SD workload it represents a relatively small fraction of the overall computational cost of detecting an OFDM MIMO frame in standards such as 802.11n. Despite this, real-time PP architectures are highly inefficient, dominating the resource cost of real-time SD architectures. This paper resolves this issue. By reorganising the ordering and QR decomposition sub operations of PP, we describe a Field Programmable Gate Array (FPGA)-based PP architecture for the Fixed Complexity Sphere Decoder (FSD) applied to 4 × 4 802.11n MIMO which reduces resource cost by 50% as compared to state-of-the-art solutions whilst maintaining real-time performance.

Advances in the Diagnosis of Human Schistosomiasis

Schistosomiasis is a major neglected tropical disease that afflicts more than 240 million people, including many children and young adults, in the tropics and subtropics. The disease is characterized by chronic infections with significant residual morbidity and is of considerable public health importance, with substantial socioeconomic impacts on impoverished communities. Morbidity reduction and eventual elimination through integrated intervention measures are the focuses of current schistosomiasis control programs. Precise diagnosis of schistosome infections, in both mammalian and snail intermediate hosts, will play a pivotal role in achieving these goals. Nevertheless, despite extensive efforts over several decades, the search for sensitive and specific diagnostics for schistosomiasis is ongoing. Here we review the area, paying attention to earlier approaches but emphasizing recent developments in the search for new diagnostics for schistosomiasis with practical applications in the research laboratory, the clinic, and the field. Careful and rigorous validation of these assays and their cost-effectiveness will be needed, however, prior to their adoption in support of policy decisions for national public health programs aimed at the control and elimination of schistosomiasis.

Quadratic Forms on Complex Random Matrices and Multiple-Antenna Systems

This research published in the foremost international journal in information theory and shows interplay between complex random matrix and multiantenna information theory. Dr T. Ratnarajah is leader in this area of research and his work has been contributed in the development of graduate curricula (course reader) in Massachusetts Institute of Technology (MIT), USA, By Professor Alan Edelman. The course name is "The Mathematics and Applications of Random Matrices", see http://web.mit.edu/18.338/www/projects.html