Marine litter distribution and composition in the North Sea

Following the development of marine litter surveillance methodologies for use on beaches, this study shows the feasibility of similar surveys at sea. The results indicate widespread distribution of marine litter in the surface waters of the North Sea, especially plastics. Not only does this draw attention to the need for the implementation of Annexe V of the MARPOL Convention, but serious consideration should be given now to the designation of the North Sea as a ‘special area’ such that the dumping of all garbage from ships, except food wastes, should be prohibited.     

Influence of salinity and fish species on PAH uptake from dispersed crude oil

The use of chemical oil dispersants to minimize spill impacts causes a transient increase in hydrocarbon concentrations in water, which increases the risk to aquatic species if toxic components become more bioavailable. The risk of effects depends on the extent to which dispersants enhance the exposure to toxic components, such as polycyclic aromatic hydrocarbons (PAH). Increased salinities can reduce the solubility of PAH and the efficiency of oil dispersants. This study measured changes in the induction of CYP1A enzymes of fish to demonstrate the effect of salinity on PAH availability. Freshwater rainbow trout and euryhaline mummichog were exposed to water accommodated fractions (WAF), and chemically-enhanced water accommodated fractions (CEWAF) at 0 per thousand, 15 per thousand, and 30 per thousand salinity. For both species, PAH exposure decreased as salinity increased whereas dispersant effectiveness decreased only at the highest salinity. Hence, risks to fish of PAH from dispersed oil will be greatest in coastal waters where salinities are low.     

Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs

This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε‐insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd.     

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract

     

A search strategy for finding extraterrestrial radio beacons

The Principle of Anti-Cryptography, together with our knowledge of the structure and properties of the universe, lead to the suggestion that extraterrestrial radio beacons will transmit continuously and omnidirectionally, using binary sense-switched circular polarization modulation. The expected frequency of transmission is the rest frequency of hydrogen, relative to the galactic center. Probable signaling rates are within two orders of magnitude of one second. Methods of implementing the strategy are suggested, from both the receiving and transmitting viewpoints.     

Valuing visitors' economic benefits of public beach access points

An increase in the population of coastal counties and popularity of coastal beaches as tourism destinations create difficulties for management agencies responsible for providing public beach access. The objective of this paper is to determine non-resident visitors to South Carolina beaches economic value for public beach access. Visitors are willing to pay an extra $6.60 per day for additional beach access points and parking or an aggregate of $93 million. Visitors' economic value of public beach access allows decision-makers the ability to better compare management policies in their efforts to provide sufficient public beach access through a target effective strategy.