Effects of stocking density on survival and growth indices of common carp (Cyprinus carpio)

The effects of social interactions on survival, growth indices and competitive behavior of common cam (Cyprinus carpio) at four stocking densities (350, 400, 450 and 500 fish per hectare) were investigated. A poly-culture of Chinese carps was implemented for 7 months in 12 Culture ponds each 10 hectares in size at Dikjeh area, Golestan province of Iran. Monthly biometrical characteristics such as total length, weight, and condition factor and growth rate were measured. The introduced common carp weighing on average 45g reached 705g after 7 months. With increase in common can) density up to 450 fish per hectare, growth indices including secondary weight, growth rate, SGR and fish biomass showed no significant differences (P>0.05), whereas 500 common carp individuals per hectare caused secondary weight, growth rate and SGR indices decrease significantly (P<0.05). At this density, fish biomass showed significant increase (P<0.05). Based on the results of this study, we conclude that common carp can be successfully cultured up to a density of 450 fish per hectare

Evaluation of metallothionein protein as a biomarker of Mercury pollution in Scat (Scatophagus argus)

Total Metallothionein (MT) biosynthesis and Mercury bioaccumulation under control & acute Mercury exposure were investigated in Scat (Scatophagus argus). Tissues from liver and gill of samples Scats were exposed to different Mercury concentrations (10, 20, 30μ g/l) for 24, 48, 72 hours. Mercury contents were determined through Cold Vapour Atomic Absorption Spectrometry (CVAAS). Total MT levels were determined by Enzyme-linked Immunosorbent Assay (ELISA) method. Induction of MT during exposure was tissue specific, displaying different response patterns in gill and liver. Mercury accumulated in liver much stronger than gill and the latter also showed lower MT level. Although after exposure to different mercury concentration during different periods, MT biosynthesis in liver showed a significant increase (P<0.05) but in gill did not significantly modify total MT except for 72h exposure at 30 g/l. Nonetheless, the relationship between MT biosynthesis and Mercury bioaccumulation in both tissues was significant. The results suggest that this form of MT presence in S. argus was Hg-inducible and could be extended as a biomarker of Mercury pollution in marine ecosystems and especially in Persian Gulf

Prediction of load-carrying capacity of piles using a support vector machine and improved data collection

Model development for the prediction of the axial load carrying capacity of piles, at least at the model verification stage, relies on the measured data at full scale. Artificial intelligence and machine learning approaches use data in the whole process of model development and verification, making it necessary to incorporate reliable and diverse data. This study aims to develop a more accurate model for predicting pile capacity based on cone penetration test and full scale static pile load test data, employing a support vector machine (SVM) technique. Furthermore, it draws on the concept of support vectors to make suggestions for compiling additional data that are more representative of the problem, leading to enhancing the accuracy of the future models. In fact, in models developed using the SVM technique, those samples within a dataset for which a model shows the greatest uncertainties are detected as support vectors and are the only data that contribute to model development. In previous studies an examination of the distribution of input parameter values and the concentration of support vectors in input intervals were considered as guidelines for collecting further data. Geotechnical problems, however, are more complicated in that the importance of each input in model development and also the interaction of those parameters should be taken into account. Therefore, this study employs a sensitivity analysis of the model input parameters and other statistical analyses to improve the existing support-vector based approaches to data collection.A. Kordjazi, F. Pooya Nejad and M. B. Jaks

The evaluation of ultimate axial-loading capacity of piles using artificial intelligence methods

French title: L’Évaluation de la capacité définitive de la portance des pieux de fondation chargés de manière axiale, qui utilise les techniques d’intelligence artificielle. Abstract also in FrenchTo date, various approaches to predicting load carrying capacity of piles have been adopted; however, there is still a need for methods providing more consistent estimates of this design parameter. Among these approaches, artificial intelligence techniques have become popular in geotechnical research and yielded encouraging results. Although these methods are quite new, various algorithms fall into the category of the artificial intelligence methods, making it necessary to have a clear insight into their performance. This study aims to make a comparison between the application of artificial neural networks (ANNs), a well-established method, and a support vector machine (SVM), a novel machine learning algorithm, in the prediction of the ultimate load carrying capacity of axially loaded piles. Undoubtedly, the capability of these techniques depends on the quality of the data set employed to develop the corresponding models. In this paper the results from the cone penetration test (CPT), which provides more reliable and realistic quantitative information on the characteristics of the in-situ ground conditions, have been employed to meet this criteria. The performance of each model against a testing dataset that was not used in the model development process is evaluated and drawbacks and advantages of these models are discussed. = Jusqu’au présent, diverses démarches ont été adoptées pour prédire la capacité de portance des pieux de fondation ; cependant, il y a encore un grand besoin des méthodes qui fournissent des estimations plus constantes des paramètres de cette conception. Parmi ces démarches, les techniques d’intelligence artificielle se sont révélées très populaires dans le domaine des recherches géotechniques et ont fourni des résultats encourageants. Bien que ces méthodes soient assez récentes, divers algorithmes appartiennent à la catégorie des méthodes d’intelligence artificielle, rendant nécessaire une idée nette de leur performance. Cette étude a pour but de faire une comparaison entre l’application des réseaux de neurones artificiels (ANNs), une méthode bien établie, et une machine à vecteurs de support (SVM), un algorithme d'apprentissage original, dans la prédiction de la capacité de portance définitive des pieux de fondation chargés de manière axiale. Sans aucun doute, la capacité de ces techniques dépend de la qualité de l’ensemble des données employées pour développer les modèles correspondants. Dans cet article, les résultats de l’épreuve du pénétromètre statistique (CPT), qui fourni de l’information quantitative plus solide et plus réaliste sur les caractéristiques des conditions in situ, ont été employé de remplir ce critère. La performance de chaque modèle face à une série de données, qui n’était pas utilisée dans le procédé de développement du modèle, est évaluée et les désavantages et les avantages de ces modèles sont examinés.A. Kordjazi, F. Pooya Nejad and M. B. Jaks

Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data

Abstract not availableA. Kordjazi, F. Pooya Nejad, M.B. Jaks