Identifying the Crack Nature Using b-Value Acoustic Emission Signal Analysis

Concrete is an important constituent of structures. The strength performance of the concrete decrease due to several factors. Concrete suffers from deterioration at a later stage. Early and constant identification of concrete deterioration is necessary. Nowadays, non-destructive testing (NDT) is widely used especially on continuous real-time monitoring system with minimum labor involvement. It could also be used to discriminate the different types of damage occurring in reinforced concrete (RC) beam and real structure. In this research was monitored by using Acoustic Emission testing and it have several analysis such as RA-value, b-value, intensity signal analysis and historical index. To determine the acoustic emission signals for concrete structures and cracking identification this research using b-value analysis. b-value signals analysis contain useful information about damage mechanisms. A high b-value arises due to a large number of small AE hits, it representing new crack formation and slow crack growth, whereas a low b-value indicates faster or unstable crack growth accompanied by relatively high amplitude AE in large number. Reinforced concrete beams measuring of size 150 mm 250 mm 1500 mm were used during the acoustic emission test. A four-point load test was carried out on specimens until cracking occurred. The signals generated from the equipment were used for the analysis process, and the values are compared to define and summarise type of cracking and cracking processes

A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI. © 2011 Li et al

Highly sensitive and label-free digital detection of whole cell E. coli with interferometric reflectance imaging

Bacterial infectious diseases are a major threat to human health. Timely and sensitive pathogenic bacteria detection is crucial in identifying the bacterial contaminations and preventing the spread of infectious diseases. Due to limitations of conventional bacteria detection techniques there have been concerted research efforts towards development of new biosensors. Biosensors offering label free, whole bacteria detection are highly desirable over those relying on label based or pathogenic molecular components detection. The major advantage is eliminating the additional time and cost required for labeling or extracting the desired bacterial components. Here, we demonstrate rapid, sensitive and label free E. coli detection utilizing interferometric reflectance imaging enhancement allowing for visualizing individual pathogens captured on the surface. Enabled by our ability to count individual bacteria on a large sensor surface, we demonstrate a limit of detection of 2.2 CFU/ml from a buffer solution with no sample preparation. To the best of our knowledge, this high level of sensitivity for whole E. coli detection is unprecedented in label free biosensing. The specificity of our biosensor is validated by comparing the response to target bacteria E. coli and non target bacteria S. aureus, K. pneumonia and P. aeruginosa. The biosensor performance in tap water also proves that its detection capability is unaffected by the sample complexity. Furthermore, our sensor platform provides high optical magnification imaging and thus validation of recorded detection events as the target bacteria based on morphological characterization. Therefore, our sensitive and label free detection method offers new perspectives for direct bacterial detection in real matrices and clinical samples.First author draf

A rigorous evaluation of crossover and mutation in genetic programming

The role of crossover and mutation in Genetic Programming (GP) has been the subject of much debate since the emergence of the field. In this paper, we contribute new empirical evidence to this argument using a rigorous and principled experimental method applied to six problems common in the GP literature. The approach tunes the algorithm parameters to enable a fair and objective comparison of two different GP algorithms, the first using a combination of crossover and reproduction, and secondly using a combination of mutation and reproduction. We find that crossover does not significantly outperform mutation on most of the problems examined. In addition, we demonstrate that the use of a straightforward Design of Experiments methodology is effective at tuning GP algorithm parameters

Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials

An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community's resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.Comment: Published in at http://dx.doi.org/10.1214/09-STS315 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The effect of prior upper body exercise on subsequent wingate performance

It has been reported previously that the upper body musculature is continually active during high intensity cycle ergometry. The aim of this study was to examine the effects of prior upper body exercise on subsequent Wingate (WAnT) performance. Eleven recreationally active males (20.8 ± 2.2 yrs; 77.7 ± 12.0 kg;  1.79 ± 0.04 m) completed two trials in a randomised order. In one trial participants completed 2 × 30 s WAnT tests (WAnT1 and WAnT2) with a 6 min recovery period; in the other trial, this protocol was preceded with 4 sets of biceps curls to induce localised arm fatigue. Prior upper body exercise was found to have a statistically significant detrimental effect on peak power output (PPO) during WAnT1 (P < 0.05) but no effect was observed for mean power output (MPO) (P > 0.05). Handgrip (HG) strength was also found to be significantly lower following the upper body exercise. These results demonstrate that the upper body  is meaningfully involved in the generation of leg power during intense cycling

Robust Optimization of Biological Protocols

When conducting high-throughput biological experiments, it is often necessary to develop a protocol that is both inexpensive and robust. Standard approaches are either not cost-effective or arrive at an optimized protocol that is sensitive to experimental variations. Here, we describe a novel approach that directly minimizes the cost of the protocol while ensuring the protocol is robust to experimental variation. Our approach uses a risk-averse conditional value-at-risk criterion in a robust parameter design framework. We demonstrate this approach on a polymerase chain reaction protocol and show that our improved protocol is less expensive than the standard protocol and more robust than a protocol optimized without consideration of experimental variation