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Satellite images have a very high resolution, which make their automatic processing computationally costly, and they suffer from artifacts making their processing difficult. This paper describes a method for the effective semantic segmentation of satellite images, and compares different object classifiers in terms of accuracy and execution time. In the paper, the image spectrum is used to reduce the computational cost during the segmentation and classification steps. Firstly, artifacts are corrected from the satellite images for facilitating the feature extraction process. After this, semantic representation is used to gather the semantic regions of downscaled images. As the images are very large, this scaling down significantly reduces the computing time with little degradation in the coarse object detection results. A deep neural forest classifier finds potential regions before executing the pixel-based segmentation. Finally, in our experiments, boundary detection and several classifiers are evaluated to find the objects associated with these regions. The paper details the set-up for our tree-based convolutional neural network. The results indicate that this tree-based convolutional neural network outperforms the other surveyed techniques in the literature.

Listening to, and acting on, the voices of children and families during clinical research and innovation is fundamental to ensuring enhanced pediatric health care, medicines development, and technological advances. While this is often discussed as an important step in ensuring patient-centered care, involving children and families across the life cycle of clinical research is not currently routine. The pediatric research community needs to address how to meaningfully involve children and families if they are to succeed in designing clinical research that suits the needs of pediatric patients and their families. This paper describes how an international community working under the umbrella International Children’s Advisory Network (iCAN) and European Young Person’s Advisory Group Network (eYPAGnet) has involved children and families in the design and delivery of pediatric clinical research. It offers practical solutions through various case studies assessed against seven patient engagement quality criteria within the Patient Engagement Quality Guidance (PEQG) tool, highlighting some of the lessons learnt from involving and engaging with children and families across different stages of clinical research, including pediatric trials for drug development programs.

Palliative care must be early applied to all types of advanced chronic and life limited prognosis patients, present in all health and social services. Patients' early identification and registry allows introducing palliative care gradually concomitant with other measures. Patients undergo a systematic and integrated care process, meant to improve their life quality, which includes multidimensional assessment of their needs, recognition of their values and preferences for advance care planning purposes, treatments review, family care, and case management. Leaded by the National Department of Health, a program for the early identification of these patients has been implemented in Catalonia (Spain). Although the overall benefits expected, the program has raised some ethical issues. In order to address these challenges, diverse institutions, including bioethics and ethics committees, have elaborated a proposal for the program's advantages. This paper describes the process of evaluation, elaboration of recommendations, and actions done in Catalonia.

Air pollution from diesel emissions is becoming an increased international concern, and whilst attention has been primarily focused on the automotive industry, concerns have also been raised about emissions from diesel rail vehicles. This paper reports an extensive series of measurements made at the Birmingham New Street station, a major rail interchange in the Midlands of England, with a mix of diesel and electric train movements, which is of particular concern because of the enclosed nature of the platforms. This study was undertaken in collaboration with Network Rail to better understand the environment in and around the station over a longer period to provide a more detailed analysis of the complex environment at the station. The station environment has been considered in terms of the European Union (EU) and Department of Environment, Food and Rural Affairs (DEFRA) limits as part of the monitoring methodology, but it should be noted that these limits do not apply in this environment as the Management of Health and Safety at Work Regulation 1999 and the Control of Substances Hazardous to Health Regulations 2002 are applicable. The monitoring campaign consisted of diffusion tube measurements to measure nitrogen dioxide at a large number of different locations throughout and around the station. These were followed by detailed measurements of oxides of nitrogen, particulate matter, carbon dioxide and black carbon (a diesel tracer) at a smaller number of sites at the platform level. The results are analysed to give concentrations over a wide variety of time scales, and long- and short-term averages. The effects of ambient wind conditions and individual train movements are also considered. Recommendations are made for possible remedial measures and for future work to more fully understand the physical mechanisms involved.

We present the living application, a method to autonomously manage applications on the grid. During its execution on the grid, the living application makes choices on the resources to use in order to complete its tasks. These choices can be based on the internal state, or on autonomously acquired knowledge from external sensors. By giving limited user capabilities to a living application, the living application is able to port itself from one resource topology to another. The application performs these actions at run-time without depending on users or external workflow tools. We demonstrate this new concept in a special case of a living application: the living simulation. Today, many simulations require a wide range of numerical solvers and run most efficiently if specialized nodes are matched to the solvers. The idea of the living simulation is that it decides itself which grid machines to use based on the numerical solver currently in use. In this paper we apply the living simulation to modelling the collision between two galaxies in a test setup with two specialized computers. This simulation switces at run-time between a GPU-enabled computer in the Netherlands and a GRAPE-enabled machine that resides in the United States, using an oct-tree N-body code whenever it runs in the Netherlands and a direct N-body solver in the United States., 26 pages, 3 figures, accepted by IJHPCA