Influential factors on the levels of cation exchange capacity in sediment at Langat river.

An exploratory study was carried out at 22 sampling stations along the Langat River, Selangor in order to investigate on the vitality of cation exchange capacity (CEC) in sediment (0–5 cm). Parameters such as pH, Eh, salinity, and electrical conductivity (EC) were determined. The CEC in sediment has been calculated by the determination of Ca2+, Na+, Mg2+, and K+ using the flame atomic absorption spectrophotometer, while the organic matter content in sediment was ascertained using the loss on ignition method. The characteristic of the sediment shows that pH (3.09–7.46), salinity (0.02–10.71 ppt), EC (3.39–517 μS/cm) and Eh (−16.20–253.10 mV) were substantially high in variation. This study also revealed that exchangeable Ca2+ and Mg2+ were controlled by organic matter contents, while exchangeable Na+ and K+ were influenced by salinity. Salinity was observed to play a major part in controlling all the exchangeable cations, as it gives strong significant correlations with Na+, K+, Mg2+, CEC, and organic matter at p < 0.01. The presence of seawater, clay mineralogy, and organic matter proves that it does play an important role in determining the CEC and soon relates to the pollution magnitude in the sediment

Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods

Depending on the method used, measuring the specific surface area (SSA) can be expensive and time consuming and limited numbers of studies have been conducted to predict SSA from soil properties. In this study, 127 soil sample data were gathered from the available literature. The data set included SSA values and some of the soil physical and chemical index properties. At the first step, linear regression, non-linear regression, regression trees, artificial neural networks, and a multi-objective group method of data handling were used to develop seven pedotransfer functions (PTFs) for the purpose of finding the best method in predicting SSA. Results showed that the artificial neural networks performed better than the other methods used in the development and validation of PTFs. At the second step, to find the best set of SSA for predicting input variables and to investigate the importance of the input parameters, the artificial neural networks were further used and 25 models were developed. The results showed that the PTF, containing the input variables of sand%, clay%, plastic limit, liquid limit, and free swelling index performed better than the other PTEs. This can be attributed to the close relation between the free swelling index and Atterberg limits with the soil clay mineralogy, which is one of the most important factors controlling SSA. The sensitivity analysis showed that the greatest sensitivity coefficients were found for the cation exchange capacity, clay content, liquid limit, and plasticity index in different models. Overall, the artificial neural networks method was proper to predict SSA from soil variables. (C) 2015 Elsevier B.V. All rights reserved

Incentive motivation in pet dogs - preference for constant vs varied food rewards.

Recently, there has been a move towards positive reinforcement using food rewards in animal training. By definition, rewards function as reinforcers if they increase or maintain the frequency of behaviour that they follow. However, in operant conditioning tasks animals frequently show systematic changes in performance - in particular a reduction in responding over time. One suggested strategy to avoid such performance decrements is to provide a variety of food rewards, rather than the same food reward in all trials. The enhancement of appetitive behaviour and consumption by reward variation is referred to as 'variety effect'. We investigated whether dogs preferred a variable or a constant food reward in a concurrent two-choice test. Of 16 dogs, six subjects showed a significant preference for the varied food reward and six for the constant food reward, while four dogs exhibited no significant preference for either option. At the group level, there was a significant effect of block: preference for the varied food reward increased across six blocks of ten trials each. Thus, although some individuals may prefer a single, favourite food reward in the short term, introducing variation in reward types may maintain dogs' motivation in operant tasks over a longer time period