Single UV excitation of Hoechst 33342 and propidium iodide for viability assessment of rhesus monkey spermatozoa using flow cytometry

Many fluorescent probes excited by visible light have been used to assess sperm quality by flow cytometry. Developing a viability evaluation method using UV excited stains would be useful for multiparameter analysis of sperm function. This investigation was conducted to determine the efficacy of Hoechst 33342 (H342) and propidium iodide (PI) dual staining for evaluating rhesus monkey sperm viability through use of flow cytometry and excited by a single UV laser. The results showed that the live cells stained only with H342 strongly correlated with expected sperm viability, and flow cytometric analyses were highly correlated with fluorescence microscopic observation. Using H342/PI/SYBR-14 triple staining method, it was found that the live/dead sperm distributions were completely concordant in both H342/PI and SYBR-14/PI assays. In addition, this dual staining was extended with fluorescein isothiocyanate-conjugated peanut agglutinin (FITC-PNA) to simultaneously analyze viability and acrosome integrity of sperm cryopreserved using two different extenders, TTE and TEST, and indicated that TTE offered better Preservation of plasma and acrosome integrity than TEST Therefore, the H342/PI dual staining provides an accurate technique for evaluating viability of rhesus monkey sperm and should be valuable for multiparameter flow cytometric analysis of sperm function.

Assessment of pelagic primary and secondary production in Sefidrud River estuary

Sefid-Rood River Estuary (SRE) is the most important riverine ecosystem in the south Caspian Sea along the Iranian coast lines. The aim of this study was to examine spatial and temporal variability in Phytoplankton and Zooplankton abundance and diversity in SRE. Variability of Chlorophyll a and inorganic nutrient concentration were determined during a year (November 2004– October 2005) in five sampling stations. Primary and secondry production were determined during a year. Total chlorophyll a concentration during the investigation ranged between zero to 22.8 μgl-1 and the highest levels were consistently recorded during summer and the lowest during winter with a annual mean concentration 4.48 μgl-1. Nutrient concentration was seasonally related to river flow with annual mean concentration: NO2 0.05±0.2 mgl-1, NO3 1.13±0.57 mgl-1, NH4 0.51±0.66 mgl-1, total phosphate 0.13±0.1mgl-1 and SiO2 5.68±1.91 mgl-1. Bacillariophytes, Cyanophytes, Chlorophytes, Pyrophytes and Euglenophytes were the dominant phytoplankton groups in this shallow and turbid estuary. The diversity and abundance of phytoplankton had a seasonal pattern while Diatomas and Chrysophytes were dominant throughout the year but Cyanophytes observed only during the summer. Zooplankton community structure was dominated by copepods which 68% of the total zooplankton. In the winter and summer seasons two increased in the number of zooplankton community and usually toward the sea had occurred. Zooplankton also showed a significant spatial and temporal variation. The high turbidity and temperature prime characteristics of SRE seem to be determining factors acting directly on phytoplankton and zooplankton temporal variability and nutrient fluctuations. Everywhere in this estuary nutrients appeared to be in excess of algal requirement and did not influence a phytoplankton and zooplankton composition. Also there was a positive correlation between chlorophyll a and temperature and a negative one with DIN and TP. Primary production determined in this estuary by dark and light butter method and G.P.P. 38.27±34.12 mgcm-2h-1 and N,PP 201.6±289.9 mgcm-2d-1. secondry production determined 15/128 mgc/m3/year. Everywhere in this estuary nutrients appeared to be in excess to algal requirement and did not influence in Chl. a and primary production. The most important factor influence on Chl. a was water temperature.

Possible fish stock size assessment and available production survey as developed on Lake Kariba

This paper is an outline of methods practically useful for the evaluation of ichthyomass, fish abundance, available production and yield in lakes and rivers. Terms and concepts are reviewed, and difficulties stemming from the use of "predetermined" mathematical models are discussed. Sampling with toxicants in blocked-off areas was found to be the most practical method and is described in detail. For the total estimation of ichthyomass the spatial ranges of fish distribution must be determined; the results of echo-sounding surveys for horizontal, vertical, topographical, seasonal and diel fish distribution are given. Some of the most important methods for computing available production are listed and applied to Lake Kariba as an example. In particular, a method based on the balance between the main predator and prey species is reviewed. The ecological production survey concept is finally stressed as applied to multispecies fish stocks.

Machine vision based concrete surface quality assessment

Manually inspecting concrete surface defects (e.g., cracks and air pockets) is not always reliable. Also, it is labor-intensive. In order to overcome these limitations, automated inspection using image processing techniques was proposed. However, the current work can only detect defects in an image without the ability of evaluating them. This paper presents a novel approach for automatically assessing the impact of two common surface defects (i.e., air pockets and discoloration). These two defects are first located using the developed detection methods. Their attributes, such as the number of air pockets and the area of discoloration regions, are then retrieved to calculate defects’ visual impact ratios (VIRs). The appropriate threshold values for these VIRs are selected through a manual rating survey. This way, for a given concrete surface image, its quality in terms of air pockets and discoloration can be automatically measured by judging whether their VIRs are below the threshold values or not. The method presented in this paper was implemented in C++ and a database of concrete surface images was tested to validate its performance. Read More: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29CO.1943-7862.0000126?journalCode=jcemd4

Defects Detection and Assessment in Concrete Surfaces

Aside from cracks, the impact of other surface defects, such as air pockets and discoloration, can be detrimental to the quality of concrete in terms of strength, appearance and durability. For this reason, local and national codes provide standards for quantifying the quality impact of these concrete surface defects and owners plan for regular visual inspections to monitor surface conditions. However, manual visual inspection of concrete surfaces is a qualitative (and subjective) process with often unreliable results due to its reliance on inspectors’ own criteria and experience. Also, it is labor intensive and time-consuming. This paper presents a novel, automated concrete surface defects detection and assessment approach that addresses these issues by automatically quantifying the extent of surface deterioration. According to this approach, images of the surface shot from a certain angle/distance can be used to automatically detect the number and size of surface air pockets, and the degree of surface discoloration. The proposed method uses histogram equalization and filtering to extract such defects and identify their properties (e.g. size, shape, location). These properties are used to quantify the degree of impact on the concrete surface quality and provide a numerical tool to help inspectors accurately evaluate concrete surfaces. The method has been implemented in C++ and results that validate its performance are presented.

Real Time Concrete Damage Visual Assessment for First Responders

First responders are in danger when they perform tasks in damaged buildings after earthquakes. Structural collapse due to the failure of critical load bearing structural members (e.g. columns) during a post-earthquake event such as an aftershock can make first responders victims, considering they are unable to assess the impact of the damage inflicted in load bearing members. The writers here propose a method that can provide first responders with a crude but quick estimate of the damage inflicted in load bearing members. Under the proposed method, critical structural members (reinforced concrete columns in this study) are identified from digital visual data and the damage superimposed on these structural members is detected with the help of Visual Pattern Recognition techniques. The correlation of the two (e.g. the position, orientation and size of a crack on the surface of a column) is used to query a case-based reasoning knowledge base, which contains apriori classified states of columns according to the damage inflicted on them. When query results indicate the column's damage state is severe, the method assumes that a structural collapse is likely and first responders are warned to evacuate.

Improving Pothole Recognition through Vision Tracking for Automated Pavement Assessment

Pavement condition assessment is essential when developing road network maintenance programs. In practice, pavement sensing is to a large extent automated when regarding highway networks. Municipal roads, however, are predominantly surveyed manually due to the limited amount of expensive inspection vehicles. As part of a research project that proposes an omnipresent passenger vehicle network for comprehensive and cheap condition surveying of municipal road networks this paper deals with pothole recognition. Existing methods either rely on expensive and high-maintenance range sensors, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In our previous work we created a pothole detection method for pavement images. In this paper we present an improved recognition method for pavement videos that incrementally updates the texture signature for intact pavement regions and uses vision tracking to track detected potholes. The method is tested and results demonstrate its reasonable efficiency.

Proposal of a method to clarify and enhance decision-making at the front-end of innovation

Decision-making at the front-end of innovation is critical for the success of companies. This paper presents a simple visual method, called DMCA (Decision-Making Criteria Assessment), which was created to clarify and improve decision-making at the front-end of innovation. The method maps the uncertainty of project information and importance of decision criteria, compiling a measure that indicates whether the decision is highly uncertain, what information interferes with it, and what criteria are actually being considered. The DMCA method was tested in two projects that faced decision-making issues, and the results confirm the benefits of using this method in decision-making at the front-end. © 2012 IEEE.

A classification of data quality assessment methods

Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad: from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods exist, show where new methods are required and can guide future research and DQ tool development.

A body force-based method for prediction of multiple-pure-tone noise: Validation

Emissions, fuel burn, and noise are the main drivers for innovative aircraft design. Embedded propulsion systems, such as for example used in hybrid-wing body aircraft, can offer fuel burn and noise reduction benefits but the impact of inlet flow distortion on the generation and propagation of turbomachinery noise has yet to be assessed. A novel approach is used to quantify the effects of non-uniform flow on the creation and propagation of multiple pure tone (MPT) noise. The ultimate goal is to conduct a parametric study of S-duct inlets to quantify the effects of inlet design parameters on the acoustic signature. The key challenge is that the effects of distortion transfer, noise source generation and propagation through the non-uniform flow field are inherently coupled such that a simultaneous computation of the aerodynamics and acoustics is required to capture the mechanisms at play. The technical approach is based on a body force description of the fan blade row that is able to capture the distortion transfer and the blade-to-blade flow variations that cause the MPT noise while reducing computational cost. A single, 3-D full-wheel CFD simulation, in which the Euler equations are solved to second-order spatial and temporal accuracy, simultaneously computes the MPT noise generation and its propagation in distorted inlet flow. A new method of producing the blade-to-blade variations in the body force field for MPT noise generation has been developed and validated. The numerical dissipation inherent to the solver is quantified and used to correct for non-physical attenuation in the far-field noise spectra. Source generation, acoustic propagation and acoustic energy transfer between modes is examined in detail. The new method is validated on NASA's Source Diagnostic Test fan and inlet, showing good agreement with experimental data for aerodynamic performance, acoustic source generation, and far-field noise spectra. The next steps involve the assessment of MPT noise in serpentine inlet ducts and the development of a reduced order formulation suitable for incorporation into NASA's ANOPP framework. © 2010 by Jeff Defoe, Alex Narkaj & Zoltan Spakovszky.

Discriminative spoken language understanding using word confusion networks

Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output. © 2012 IEEE.

The vibro-acoustic analysis of built-up systems using a hybrid method with parametric and non-parametric uncertainties

An existing hybrid finite element (FE)/statistical energy analysis (SEA) approach to the analysis of the mid- and high frequency vibrations of a complex built-up system is extended here to a wider class of uncertainty modeling. In the original approach, the constituent parts of the system are considered to be either deterministic, and modeled using FE, or highly random, and modeled using SEA. A non-parametric model of randomness is employed in the SEA components, based on diffuse wave theory and the Gaussian Orthogonal Ensemble (GOE), and this enables the mean and variance of second order quantities such as vibrational energy and response cross-spectra to be predicted. In the present work the assumption that the FE components are deterministic is relaxed by the introduction of a parametric model of uncertainty in these components. The parametric uncertainty may be modeled either probabilistically, or by using a non-probabilistic approach such as interval analysis, and it is shown how these descriptions can be combined with the non-parametric uncertainty in the SEA subsystems to yield an overall assessment of the performance of the system. The method is illustrated by application to an example built-up plate system which has random properties, and benchmark comparisons are made with full Monte Carlo simulations. © 2012 Elsevier Ltd. All rights reserved.

Building damage assessment for deep excavations in Singapore and the influence of building stiffness

One of the biggest issues for underground construction in a densely built-up urban environment is the potentially adverse impact on buildings adjacent to deep excavations. In Singapore, a building damage assessment is usually carried out using a three-staged approach to assess the risk of damage caused by major underground construction projects. However, the tensile strains used for assessing the risk of building damage are often derived using deflection ratios and horizontal strains under 'greenfield' conditions. This ignores the effects of building stiffness and in many cases may be conservative. This paper presents some findings from a study on the response of buildings to deep excavations. Firstly, the paper discusses the settlement response of an actual building - the Singapore Art Museum - adjacent to a deep excavation. By comparing the monitored building settlement with the adjacent ground settlement markers, the influence of building stiffness in modifying the response to excavation-induced settlements is observed. Using the finite element method, a numerical study on the building response to movements induced by deep excavations found a consistent relationship between the building modification factor and a newly defined relative bending stiffness of the building. This relationship can be used as a design guidance to estimate the deflection ratio in a building from the greenfield condition. By comparing the case study results with the design guidance developed from finite element analysis, this paper presents some important characteristics of the influence of building stiffness on building damages for deep excavations.

Vulnerability assessment of buildings subject to tunnel-induced settlements: the influence of orientation and position of the building

The assessment of settlement induced damage on buildings during the preliminary phase of tunnel excavation projects, is nowadays receiving greater attention. Analyses at different levels of detail are performed on the surface building in proximity to the tunnel, to evaluate the risk of structural damage and the need of mitigation measures. In this paper, the possibility to define a correlation between the main parameters that influence the structural response to settlement and the potential damage is investigated through numerical analysis. The adopted 3D finite element model allows to take into account important features that are neglected in more simplified approaches, like the soil-structure interaction, the nonlinear behaviour of the building, the three dimensional effect of the tunnelling induced settlement trough and the influence of openings in the structure. Aim of this approach is the development of an improved classification system taking into account the intrinsic vulnerability of the structure, which could have a relevant effect on the final damage assessment. Parametric analyses are performed, focusing on the effect of the orientation and the position of the structure with respect to the tunnel. The obtained results in terms of damage are compared with the Building Risk Assessment (BRA) procedure. This method was developed by Geodata Engineering (GDE) on the basis of empirical observations and building monitoring and applied during the construction of different metro lines in urban environment. The comparison shows a substantial agreement between the two procedures on the influence of the analysed parameters. The finite element analyses suggest a refinement of the BRA procedure for pure sagging conditions.

Modelling of turbulent lifted jet flames using flamelets: A priori assessment and a posteriori validation

This study focuses on the modelling of turbulent lifted jet flames using flamelets and a presumed Probability Density Function (PDF) approach with interest in both flame lift-off height and flame brush structure. First, flamelet models used to capture contributions from premixed and non-premixed modes of the partially premixed combustion in the lifted jet flame are assessed using a Direct Numerical Simulation (DNS) data for a turbulent lifted hydrogen jet flame. The joint PDFs of mixture fraction Z and progress variable c, including their statistical correlation, are obtained using a copula method, which is also validated using the DNS data. The statistically independent PDFs are found to be generally inadequate to represent the joint PDFs from the DNS data. The effects of Z-c correlation and the contribution from the non-premixed combustion mode on the flame lift-off height are studied systematically by including one effect at a time in the simulations used for a posteriori validation. A simple model including the effects of chemical kinetics and scalar dissipation rate is suggested and used for non-premixed combustion contributions. The results clearly show that both Z-c correlation and non-premixed combustion effects are required in the premixed flamelets approach to get good agreement with the measured flame lift-off heights as a function of jet velocity. The flame brush structure reported in earlier experimental studies is also captured reasonably well for various axial positions. It seems that flame stabilisation is influenced by both premixed and non-premixed combustion modes, and their mutual influences. © 2014 Taylor & Francis.