Improving Ocean Management Using Insights from Space.

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m (0.49℃) was much lower than that of HYCOM (0.73℃). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, 0.61oC; HYCOM, 0.72oC), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.

Over the past two years, the UNH Coastal Observing Center and the Gulf of Maine Ocean Observing System (GoMOOS) have co-sponsored two week-long teacher workshops that offer curriculum content and hands-on experience in using ocean observation data as a successful teaching tool in the classroom. Teachers ranging from grammar school to high school levels worked to create practical lessons that incorporate ocean observing data into their current classroom settings and are using it today. This paper will explain how the UNH/GOMOOS summer Educator Institutes have been run, what teachers have been learning in these workshops, and how they planned and started using ocean observing data in their classrooms.

Real-world environments are typically dynamic, complex, and multisensory in nature and require the support of top-down attention and memory mechanisms for us to be able to drive a car, make a shopping list, or pour a cup of coffee. Fundamental principles of perception and functional brain organization have been established by research utilizing well-controlled but simplified paradigms with basic stimuli. The last 30 years ushered a revolution in computational power, brain mapping, and signal processing techniques. Drawing on those theoretical and methodological advances, over the years, research has departed more and more from traditional, rigorous, and well-understood paradigms to directly investigate cognitive functions and their underlying brain mechanisms in real-world environments. These investigations typically address the role of one or, more recently, multiple attributes of real-world environments. Fundamental assumptions about perception, attention, or brain functional organization have been challenged-by studies adapting the traditional paradigms to emulate, for example, the multisensory nature or varying relevance of stimulation or dynamically changing task demands. Here, we present the state of the field within the emerging heterogeneous domain of real-world neuroscience. To be precise, the aim of this Special Focus is to bring together a variety of the emerging "real-world neuroscientific" approaches. These approaches differ in their principal aims, assumptions, or even definitions of "real-world neuroscience" research. Here, we showcase the commonalities and distinctive features of the different "real-world neuroscience" approaches. To do so, four early-career researchers and the speakers of the Cognitive Neuroscience Society 2017 Meeting symposium under the same title answer questions pertaining to the added value of such approaches in bringing us closer to accurate models of functional brain organization and cognitive functions.

Chapter 13 - P300-based brain–computer interface for communication and control

Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems

Background:The obstructive sleep apnea (OSA) detection has become a hot research topic because of the high risk of this disease. In this paper, we tested some powerful and low computational signal processing techniques for this task and compared their results with the recent achievements in OSA detection.Methods:The Dual-tree complex wavelet transform (DT-CWT) is used in this paper to extract feature coefficients. From these coefficients, eight non-linear features are extracted and then reduced by the Multi-cluster feature selection (MCFS) algorithm. The remaining features are applied to the hybrid “K-means, RLS” RBF network which is a low computational rival for the Support vector machine (SVM) networks family.Results and Conclusion:The results showed suitable OSA detection percentage near 96% with a reduced complexity of nearly one third of the previously presented SVM based methods.

Are staff in retail companies being adequately trained to cope with the increasingly complex demands being made upon them by the computer and data processing techniques? The DITB carried out a study into the industry to identify areas of specific skills being used in the DP function — a first step in the development of appropriate training of DP staff to a national standard. The study identified the need for impartial training advice from a neutral body which could advise and update firms on computer hard and software. The writer also comments that “firms do not always seem able to obtain the training support from manufacturers and suppliers which they require.” But perhaps increased competition between suppliers will cause them to think more carefully about this — or they may find “consumer resistance” when they look for repeat business.

A review of applications of satellite earth observation data for global societal benefit and stewardship of planet earth

The mission of the Earth System Grid Federation (ESGF) is to provide the worldwide climate-research community with access to the data, information, model codes, analysis tools, and intercomparison capabilities required to make sense of enormous climate data sets. Its specific goals are to (1) provide an easy-to-use and secure web-based data access environment for data sets; (2) add value to individual data sets by presenting them in the context of other data sets and tools for comparative analysis; (3) address the specific requirements of participating organizations with respect to bandwidth, access restrictions, and replication; (4) ensure that the data are readily accessible through the analysis and visualization tools used by the climate research community; and (5) transfer infrastructure advances to other domain areas. For the ESGF, the U.S. Department of Energy's (DOE's) Earth System Grid Center for Enabling Technologies (ESG-CET) team has led international development and delivered a production environment for managing and accessing ultra-scale climate data. This production environment includes multiple national and international climate projects (such as the Community Earth System Model and the Coupled Model Intercomparison Project), ocean model data (such as the Parallel Ocean Program), observation data (Atmospheric Radiation Measurement Best Estimate, Carbon Dioxide Information and Analysis Center, Atmospheric Infrared Sounder, etc.), and analysis and visualization tools, all serving a diverse user community. These data holdings and services are distributed across multiple ESG-CET sites (such as ANL, LANL, LBNL/NERSC, LLNL/PCMDI, NCAR, and ORNL) and at unfunded partner sites, such as the Australian National University National Computational Infrastructure, the British Atmospheric Data Centre, the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Max Planck Institute for Meteorology, the German Climate Computing Centre, the National Aeronautics and Space Administration Jet Propulsion Laboratory, and the National Oceanic and Atmospheric Administration. The ESGF software is distinguished from other collaborative knowledge systems in the climate community by its widespread adoption, federation capabilities, and broad developer base. It is the leading source for present climate data holdings, including the most important and largest data sets in the global-climate community, and - assuming its development continues - we expect it to be the leading source for future climate data holdings as well. Recently, ESG-CET extended its services beyond data-file access and delivery to include more detailed information products (scientific graphics, animations, etc.), secure binary data-access services (based upon the OPeNDAP protocol), and server-side analysis. The latter capabilities allow users to request data subsets transformed through commonly used analysis and intercomparison procedures. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users seeking to understand, process, extract value from, visualize, and/or communicate it to others. This ongoing effort, though daunting in scope and complexity, will greatly magnify the value of numerical climate model outputs and climate observations for future national and international climate-assessment reports. The ESG-CET team also faces substantial technical challenges due to the rapidly increasing scale of climate simulation and observational data, which will grow, for example, from less than 50 terabytes for the last Intergovernmental Panel on Climate Change (IPCC) assessment to multiple Petabytes for the next IPCC assessment. In a world of exponential technological change and rapidly growing sophistication in climate data analysis, an infrastructure such as ESGF must constantly evolve if it is to remain relevant and useful. Regretfully, we submit our final report at the end of project funding. To continue to serve the climate-science community, we are currently seeking additional funding. Such funding would allow us to maintain and enhance ESGF production and operation of this vital endeavor of cataloging, serving, and analyzing ultra-scale climate science data. At this time, the entire ESG-CET team would like to take this opportunity to sincerely thank our funding agencies in the DOE Scientific Discovery through Advanced Computing (SciDAC) program and the Office of Biological and Environmental Research (OBER) - as well as our national and international collaborators, stakeholders, and partners - for allowing us to work with you and serve the community these past several years.

A labeled-tree approach to semantic and structural data interoperability applied in hydrology domain

Over the last three decades, Windhoek, Namibia has grown substantially. In line with the general urban growth, Windhoek’s informal settlements have expanded, making it ever more pressing to secure basic services and suitable housing for residents of these settlements. This paper, drawing from the author’s PhD completed at the University of Manchester in 2013, documents how one social movement, the Shack Dwellers Federation of Namibia (SDFN), has challenged and collaborated with the City of Windhoek to improve living conditions. The analysis draws on varied data sources – including extensive interviews with government and federation representatives, the SDFN’s own data, and maps showing the spatial transformation of the city – to trace the recent history of co-production in Windhoek. The continuing marginalization of the city’s informal settlement dwellers points to a need for reimagining Windhoek’s residential development and integrating this into planning, as well as working with social movements like the SDFN to ensure that reforms are grounded in local realities and reflect the aspirations of those on the city’s northwest fringe.

We read with great interest the article by Jensen et al. (Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics 13, 395–405)1. This was a well-written Review that summarized a large, complex and topical subject. To augment the article, and in particular to augment Table 1, we would like to point out that one of the earliest and most successful research databases that integrated diverse data sources with electronic health records (EHRs) is the Utah Population Database (UPDB) at the University of Utah, USA. Its earliest success was the identification of families with a high incidence of breast cancer; this research led to the discovery of the breast cancer genes BRCA1 and BRCA2 (Refs 2,3,4,5). A crucial component of the UPDB — one that allowed it to probe genetic inheritance long before gene sequencing was widely available — was the linking of diverse data sources with family pedigrees that were originally supplied by the Utah Genealogical Society and that were later updated by probabilistic matching with vital records from the Utah Department of Health (records such as birth, death and marriage certificates).

The fifth industrial revolution is characterized by the rise of innovative technologies and the ongoing collaboration between humans and machines. In this context, journalism increasingly integrates Artificial Intelligence (AI) technologies and Semantic Web (SW) services to enhance the efficiency of news production. Accordingly, Semantic Knowledge Graphs have become key assets, enabling access to diverse data sources used in investigative reporting, yet their relationship with other journalistic forms, such as data journalism, has not been adequately examined. This paper aims to examine the relationship between data journalism and semantic knowledge graphs, focusing on their applications in news production and highlighting emerging opportunities for innovation in communication. The methodology is based on the analysis of two semantic knowledge graphs (case studies) used by media organizations that specialize in data journalism. The findings reveal that both case studies are applied in news production to access, organize, and analyze diverse data sources, uncovering hidden patterns and connections within complex information. They also highlight how both knowledge graphs leverage artificial intelligence to drive innovation in communication, enhancing the organization and presentation of complex data through automated categorization and visualization. Lastly, the discussion provides research directions based on the study’s analysis and findings.

Fusion of diverse data sources for flood extent mapping and risk assessment in Sindh: A comparative study of inundation mapping approaches

This work is aimed at using the marine data of the Shared Use Centre (SUC) “IKI-Monitoring” in the variational assimilation procedures of the Informational Computational System (ICS) “INM RAS - Black Sea”. SUC “IKI - Monitoring” is a tool for obtaining remote sensing observations on the Earth state. In the paper observation data information is given, data processing procedures are described, algorithms for the assimilation of the information received and several specific features of the numerical model used are presented. Results of the variational assimilation of two sets of observation data are presented and discussed. Numerical experiments have confirmed the possibility of using incomplete data from satellites in the problems of modelling the sea area.