Principles to enable comprehensive national marine ecosystem status assessments from disparate data: The state of the marine environment in Kuwait

This paper presents an approach for preparing a comprehensive national marine ecosystem assessment and its application to the marine and coastal areas of the State of Kuwait. The approach is based on a set of principles to enable diverse data sources, of differing data quality and salience, to be combined into a single coordinated national assessment of marine ecosystem status to support the implementation of ecosystem-based management. The approach enables state assessments for multiple components of the marine ecosystem to be undertaken in a coordinated manner, using differing methods varying from quantitative to qualitative assessments depending on data and indicator availability. The marine ecosystem assessment is structured according to 6 major themes: i) Biodiversity, ii) Commercial Fisheries, iii) Food and Water Quality for Human Health, iv) Environmental Pollution, v) Eutrophication and Harmful Algal Blooms, and vi) Coastal Process and Oceanography. Comprehensive ecosystem assessments are an essential part of implementing the ecosystem approach, however detailed data directly related to clear, specified numerical management targets covering all aspects of a marine ecosystem are rarely available. The development of a State of the Marine Environment Report (SOMER) for Kuwait demonstrate that a coordinated comprehensive ecosystem assessment can be conducted using disparate data, and in relation to partially specified regulatory management objectives. The Kuwait SOMER highlighted the issues of coastal pollution, particularly sewage for human health and the environment. It shows that the rapid urbanization of Kuwait has led to significant changes in the ecology, with clear impacts on coral reef health, the availability of nesting locations for turtles and habitats for migratory birds. Long-term changes in nutrient input, via waste water and modified freshwater inputs is resulting in demonstrable impacts on a range of marine species and habitats within Kuwait marine waters. It also supports the move towards a regional approach required due to transboundary properties of many of the ecosystem components, drivers and pressures

Searches for heavy long-lived charged particles with the ATLAS detector in proton-proton collisions at √s = 8 TeV

Searches for heavy long-lived charged particles are performed using a data sample of 19.1 fb−1 from proton-proton collisions at a centre-of-mass energy of s√ = 8 TeV collected by the ATLAS detector at the Large Hadron Collider. No excess is observed above the estimated background and limits are placed on the mass of long-lived particles in various supersymmetric models. Long-lived tau sleptons in models with gauge-mediated symmetry breaking are excluded up to masses between 440 and 385 GeV for tan β between 10 and 50, with a 290 GeV limit in the case where only direct tau slepton production is considered. In the context of simplified LeptoSUSY models, where sleptons are stable and have a mass of 300 GeV, squark and gluino masses are excluded up to a mass of 1500 and 1360 GeV, respectively. Directly produced charginos, in simplified models where they are nearly degenerate to the lightest neutralino, are excluded up to a mass of 620 GeV. R-hadrons, composites containing a gluino, bottom squark or top squark, are excluded up to a mass of 1270, 845 and 900 GeV, respectively, using the full detector; and up to a mass of 1260, 835 and 870 GeV using an approach disregarding information from the muon spectrometer

An optimized skin texture model using gray-level co-occurrence matrix

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection