An exposition on field identification of Clariid catfishes as an important tool in fish breeding and genetics

This paper conducts an exposition on the field identification of Clariid catfishes Heterobranchus, Clarias and their hybrid as an important tool in fish breeding and genetics. The paper explained the classification and aquacultural importance of Clariid catfishes. Parameters necessary for fish identification were highlighted. The identification of Heterobranchus, Clarias, their hybrid and sexual differences were also identified. The paper is of the position that the identification of Heterobranchus, Clarias and their hybrid is important in their genetic conservation and in achieving success in breeding and genetic studies

Synthesis of summer flounder habitat parameters

The summer flounder, Paralichthys dentatus, is overexploited and is currently at very low levels of abundance. This is reflected in the compressed age structure of the population and the low catches in both commercial and recreational fisheries. Declining habitat quantity and quality may be contributing to these declines, however we lack a thorough understanding of the role of habitats in the population dynamics of this species. Stock structure is unresolved and current interpretations, depending on the technique and study area, suggest that there may be two or three spawning populations. If so, these stocks may have differing habitat requirements. In response to this lack of knowledge, this document summarizes and synthesizes the available information on summer flounder habitat in all life history stages (eggs, larvae, juveniles and adults) and identifies areas where further research is needed. Several levels of investigation were conducted in order to produce this document. First, an extensive search for summer flounder habitat information was made, which included both the primary and gray literature as well as unanalyzed data. Second, state and federal fisheries biologists and resource managers in all states within the primary range of summer flounder (Massachusetts to Florida) were interviewed along with a number of fish ecologists and summer flounder experts from the academic and private sectors. Finally, information from all sources was analyzed and synthesized to form a coherent overview. This document first presents an overview of the economic importance and current status of summer flounder (Chapter 1). It then summarizes our present state of knowledge of summer flounder distribution, life history patterns and stock identification (Chapter 2). This is followed by a synopsis of habitat requirements during each life history stage. For convenience, this is presented by general habitat as offshore eggs (Chapter 3), offshore larvae (Chapter 4), estuarine larvae (Chapter 5), estuarine juveniles (Chapter 6), offshore juveniles (Chapter 7) and estuarine and offshore adults (Chapter 8). In several instances, previously undigested data sets are analyzed to provide more detailed information, especially for estuarine juveniles. The information is then discussed in terms of its relevance to resource managers (Chapter 9).

Towards identification of a general model of damping

Characterization of damping forces in a vibrating structure has long been an active area of research in structural dynamics. In spite of a large amount of research, understanding of damping mechanisms is not well developed. A major reason for this is that unlike inertia and stiffness forces it is not in general clear what are the state variables that govern the damping forces. The most common approach is to use `viscous damping' where the instantaneous generalized velocities are the only relevant state variables. However, viscous damping by no means the only damping model within the scope of linear analysis. Any model which makes the energy dissipation functional non-negative is a possible candidate for a valid damping model. This paper is devoted to develop methodologies for identification of such general damping models responsible for energy dissipation in a vibrating structure. The method uses experimentally identified complex modes and complex natural frequencies and does not a-priori assume any fixed damping model (eg., viscous damping) but seeks to determine parameters of a general damping model described by the so called `relaxation function'. The proposed method and several related issues are discussed by considering a numerical example of a linear array of damped spring-mass oscillators.

Identification of causes of Thai pangas mortality in ponds and their control strategies

Mass mortality of Thai pangas (Pangasius hypophthalmus) is reported to be a big threat to monoculture of the species in Bangladesh. Twenty affected and twenty control Thai pangas ponds were investigated around Mymensingh district in order to identify the causes of pangas mortality. Sixty affected and sixty unaffected fish samples were examined and compared to find the fish-level variables associated with the disease. A range of haemorrhagic signs on snout, skin and fins were recorded during examination with naked eyes. Aeromonas spp. and Edwardsiella spp. were isolated from 87% and 80% of the affected fish, respectively. Even 4% of the seemingly healthy fish carried Aeromonas spp. on their skin. Among the four water quality parameters monitored, remarkably higher total ammonia (1.5 ppm) was found in water of the affected ponds compared to that of the unaffected ones (0.4 ppm). High ammonia in affected water caused by excessive organic decomposition and poor pond management might have reduced the immunity of fish, which predisposed them for bacterial invasion and consequent disease outbreak.

Study of seasonal plankton communities changes in the Karoun dam reservoir and their relationship with water quality parameters

This study was under taken at Karoun Lake Dam No.4. (Southwest of Iran). Water samples were collected from March 2012 to February 2013 in three selected silts. Environmental parameters and chlorophyll a concentration were measured, as well as identification and abundance of Phytoplankton communities were studied. According to this study, 30 species of Phytoplankton were identified at four seasons. Most abundance was related to the phyla Bcillariphyta (17 species), Chlorophyta (6 species), Crysophyra (4 species), Dinophyta (2 species) and Cyanophyta (1 species) respectively. The results showed, the maximum rate of chlorophyll a concentration was measured in the warm with minimum level measured in the cold months. The rate of chlorophyll a concentration showed an oligotrophic condition in the lake of karoon 4 dam. positive significant correlation were seen between the parameters of COD,NO3,temperature, pH, turbidity, chlorophyll a and phytoplankton abundance (P<0.01). The chlorophyll a concentration and phytoplankton community had a significant negative correlation with transparency (-P < 0.01). According to this research, 4 phyla of zooplankton was identified, include Rotifera, Protozoa, Cladocera and Copepoda. Overal 43 species were identified at four seasons. Most abundance was related to the phyla Rotifera (27 species), Copepoda (7 species), Cladocera (5 species) and Protozoa (4 species) respectively. The chlorophyll a concentration, amount of phosphate and zooplankton indicator spesies, showed an oligotrophic condition in the lake of karoon 4 dam. A positive significant correlation was seen between all groups of zooplanktons abundance and temperature, as well as chlorophyll a concentration. (P<0.01) , whereas, there was negative correlation whith no significant between DO and zooplankton communities (P>0.05).

Identification of the steering control behaviour of five test subjects following a randomly curving path in a driving simulator

The paper is concerned with the identification of theoretical preview steering controllers using data obtained from five test subjects in a fixed-base driving simulator. An understanding of human steering control behaviour is relevant to the design of autonomous and semi-autonomous vehicle controls. The driving task involved steering a linear vehicle along a randomly curving path. The theoretical steering controllers identified from the data were based on optimal linear preview control. A direct-identification method was used, and the steering controllers were identified so that the predicted steering angle matched as closely as possible the measured steering angle of the test subjects. It was found that identification of the driver's time delay and noise is necessary to avoid bias in identification of the controller parameters. Most subjects' steering behaviour was predicted well by a theoretical controller based on the lateral/yaw dynamics of the vehicle. There was some evidence that an inexperienced driver's steering action was better represented by a controller based on a simpler model of the vehicle dynamics, perhaps reflecting incomplete learning by the driver. Copyright © 2014 Inderscience Enterprises Ltd.

Bayesian parameter identification for vibro-acoustic models with nonparametric uncertainty

Vibration and acoustic analysis at higher frequencies faces two challenges: computing the response without using an excessive number of degrees of freedom, and quantifying its uncertainty due to small spatial variations in geometry, material properties and boundary conditions. Efficient models make use of the observation that when the response of a decoupled vibro-acoustic subsystem is sufficiently sensitive to uncertainty in such spatial variations, the local statistics of its natural frequencies and mode shapes saturate to universal probability distributions. This holds irrespective of the causes that underly these spatial variations and thus leads to a nonparametric description of uncertainty. This work deals with the identification of uncertain parameters in such models by using experimental data. One of the difficulties is that both experimental errors and modeling errors, due to the nonparametric uncertainty that is inherent to the model type, are present. This is tackled by employing a Bayesian inference strategy. The prior probability distribution of the uncertain parameters is constructed using the maximum entropy principle. The likelihood function that is subsequently computed takes the experimental information, the experimental errors and the modeling errors into account. The posterior probability distribution, which is computed with the Markov Chain Monte Carlo method, provides a full uncertainty quantification of the identified parameters, and indicates how well their uncertainty is reduced, with respect to the prior information, by the experimental data. © 2013 Taylor & Francis Group, London.

Simultaneous determination of monoamine and amino acid neurotransmitters in rat endbrain tissues by pre-column derivatization with high-performance liquid chromatographic fluorescence detection and mass spectrometric identification

A sensitive and efficient method for simultaneous determination of glutamic acid (Glu), gamma-amino-butyric acid (GABA), dopamine (DA), 5-hydroxytryptamine (5-HT) and 5-hydroxyindole acetic acid (5-HIAA) in rat endbrains was developed by high-performance liquid chromatography (HPLC) with fluorescence detection and on-line mass spectrometric identification following derivatization with 1,2-benzo-3,4-dihydrocarbazole-9-ethyl chloroformate (BCEOC). Different parameters which influenced derivatization and separation were optimized. The complete separation of five neurotransmitter (NT) derivatives was performed on a reversed-phase Hypersil BDS-C-18 column with a gradient elution. The rapid structure identification of five neurotransmitter derivatives was carried out by on-line mass spectrometry with electrospray ionization (ESI) source in positive ion mode, and the BCEOC-labeled derivatives were characterized by easy-to-interpret mass spectra. Stability of derivatives, repeatability, precision and accuracy were evaluated and the results were excellent for efficient HPLC analysis. The quantitative linear range of five neurotransmitters were 2.441-2 x 10(4) nM, and limits of detection were in the range of 0.398-1.258 nM (S/N = 3:1). The changes of their concentrations in endbrains of three rat groups were also studied using this HPLC fluorescence detection method. The results indicated that exhausting exercise could obviously influence the concentrations of neurotransmitters in rat endbrains. The established method exhibited excellent validity, high sensitivity and convenience, and provided a new technique for simultaneous analysis of monoamine and amino acid neurotransmitters in rat brain. (C) 2008 Elsevier B.V. All rights reserved.

Correct identification of polychlorinated biphenyls in temperature-programmed GC with ECD detection

A method has been developed for peak identification of PCBs in GC with ECD detection under different temperature programs and isothermal conditions on two commonly used columns (DB-5 and DB-1701). This was achieved by means of accurate calibration of retention times based on the concept of the relative retention index P-i and retention times of the selected PCB internal standards. The P-i was calculated from the predicted retention times with the database of the retention parameters (A, B) and the migration equations. Through comparison of the calibrated and experimental retention times of PCBs in technical samples, it was shown that the developed method was effective for correct PCB comprehensive, quantitative, congener-specific (CQCS) analyses.

Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.

Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.

Numerical and experimental studies of a substructural identification strategy

For structural health monitoring it is impractical to identify a large structure with complete measurement due to limited number of sensors and difficulty in field instrumentation. Furthermore, it is not desirable to identify a large number of unknown parameters in a full system because of numerical difficulty in convergence. A novel substructural strategy was presented for identification of stiffness matrices and damage assessment with incomplete measurement. The substructural approach was employed to identify large systems in a divide-and-conquer manner. In addition, the concept of model condensation was invoked to avoid the need for complete measurement, and the recovery process to obtain the full set of parameters was formulated. The efficiency of the proposed method is demonstrated numerically through multi-storey shear buildings subjected to random force. A fairly large structural system with 50 DOFs was identified with good results, taking into consideration the effects of noisy signals and the limited number of sensors. Two variations of the method were applied, depending on whether the sensor could be repositioned. The proposed strategy was further substantiated experimentally using an eight-storey steel plane frame model subjected to shaker and impulse hammer excitations. Both numerical and experimental results have shown that the proposed substructural strategy gave reasonably accurate identification in terms of locating and quantifying structural damage.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.

Factorial Design of Modelling Mix Proportion Parameters of Underwater Composite Cement Grouts

There is an increasing need to identify the rheological properties of cement grout using a simple test to determine the fluidity, and other properties of underwater applications such as washout resistance and compressive strength. This paper reviews statistical models developed using a factorial design that was carried out to model the influence of key parameters on properties affecting the performance of underwater cement grout. Such responses of fluidity included minislump and flow time measured by Marsh cone, washout resistance, unit weight, and compressive strength. The models are valid for mixes with 0.35–0.55 water-to-binder ratio (W/B), 0.053–0.141% of antiwashout admixture (AWA), by mass of water, and 0.4–1.8% (dry extract) of superplasticizer (SP), by mass of binder. Two types of underwater grout were tested: the first one made with cement and the second one made with 20% of pulverised fuel ash (PFA) replacement, by mass of binder. Also presented are the derived models that enable the identification of underlying primary factors and their interactions that influence the modelled responses of underwater cement grout. Such parameters can be useful to reduce the test protocol needed for proportioning of underwater cement grout. This paper attempts also to demonstrate the usefulness of the models to better understand trade-offs between parameters and compare the responses obtained from the various test methods that are highlighted.