Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

Sleeping site selection by the golden-handed tamarin Saguinus midas midas: The role of predation risk, proximity to feeding sites, and territorial defence

Tamarin monkeys, of the genus Saguinus, spend over half their lives at arboreal sleeping sites. The decision as to which site to use is likely to have considerable fitness consequences. These decisions about sleeping sites by three troops of golden-handed tamarin Saguinus midas midas were examined over a 9-mo period at a rainforest site in French Guiana. Data are presented on the physical nature of sleeping sites, their number, position within home ranges, and pattern of use and reuse, aspects of behaviour at retirement and egress, and predation attempts on the study troops. Cumulative plot analysis indicated that a tamarin troop used 30-40 sleeping sites in a 100-day period, approximately half of which were used very infrequently, so that consecutive reuse was never greater than three nights. Sleeping trees were superior in architectural parameters and liana weight to non-sleeping trees. There were no more sleeping sites than expected within the home range boundary region of the tamarins or in areas of overlap with the home ranges of neighbouring troops. Tamarins selected sleeping sites nearest to the last feeding site of the day on 25% of occasions. The study troops engaged in a number of activities that may reduce predation risk; raptor attacks on the study troops over 9 mo were frequent but unsuccessful. Tamarins often visited a sleeping site several hours before arrival, and were more likely to visit a site before use if they had not used it recently. The decision to select a sleeping site therefore involved knowledge of the previous frequency of use of that site.

Effect of genotype on changes in intelligence quotient after dietary relaxation in phenylketonuria and hyperphenylalaninaemia

Background-Associations between genotype and intellectual outcome in patients with phenylketonuria are complicated because intelligence is influenced by many variables, including environmental factors and other genetic determinants. Intellectual changes with age, both on and after relaxation of diet, vary within the patient population. This study aims to determine whether a significant association exists between genotype and change in intelligence after relaxation of diet.