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The Evolution of HCV-Hepatitis C-virus plays the vital cause for liver-related complications in humans, global-wide. But also, specific tools were employed to eliminate the impacts of virus in humans. This phenomena does not rely in the prior stage diagnosis of the virus and in the treatment phases. In this study, the implementation of Ada-Boost algorithm and Random-forest algorithm in the framing the HCV-Hepatitis-C-Virus prediction design prevailing in humans. According to the methodology, Random-forest algorithm were utilized as the weak-learner for choosing the instances of the weight to enhance the stability factors, accuracy-factors and to decrease the outfitting complications. The paper aims to design the secured framework in the improvisation of the user’s privacy. Hence for this purpose, The techniques PPDM-Privacy-Preserving-Data mining approach were developed to keep preserve of the personal data of the userscattered in Distributed-data-mining operational system and also enhancing the security in centralized systems of data-mining as well. The data-set were subjected to the comparison of the ECC-encryption algorithm and the RSA-based encryption along with the blend of SHA-algorithm. The algorithms were evaluated with various sizes of key and proceeded with the decryption process. The Decrypted data, applied with Ada-boost and Random-Forest algorithm in qualitative predictions. Data were partitioned as the test and training sets by the classifiers. The overall-efficiency of the proposed hybrid framework were assessed by the aid of performance-metrics such as accuracy factor, time-factor and specificity-factor. The experimental analysis of the framework, establishes the classifier and various merged classifiers in the predictions of HCV-Stains. The outcomes of the framework exhibited higher accuracy rate in comparison with the other approaches, thus enabling the efficient predictions of HCV.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy called VALID in 364 Chinese cities. VALID is pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12- 2021/01), VALID consists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers' behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants' smartphones under their consent as a virtual infrastructure to design, build, deploy, and operate VALID from in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers' behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.

The paper studies the possibility of using mobile apps for quality control of the urban environment, road networks and transport servicing of the population. We analyze Russian and foreign technical solutions and specify the set of functional user features of the developed application. Based on an online survey in Google Forms, we determine the demand for the application along with the preferable set of functions. iOS is selected as an operational system to run the mobile app. To develop the app based on the iOS platform, Clean Swift architecture is chosen. Marker criteria are developed and selected to evaluate the quality of the urban environment and road networks. The Scrum model is chosen as a life cycle model. The app is developed and tested in the X-Code environment with Swift. The data analysis module is written in Python_3. The app is tested in the TestFlight environment. Guidelines for further development of the developed tools within intelligent transport systems and Smart City are identified.

The development of coupled atmosphere-ocean prediction systems with utility on the short-range Numerical Weather Prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean-atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office, and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a coupled NWP system at the Met Office.

The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preserving sharing of sensitive medical data in compliance with stringent regulations (e.g., HIPAA, GDPR) is now more urgent than ever. In this work, we introduce MedCo, the first operational system that enables a group of clinical sites to federate and collectively protect their data in order to share them with external investigators without worrying about security and privacy concerns. MedCo uses (a) collective homomorphic encryption to provide trust decentralization and end-to-end confidentiality protection, and (b) obfuscation techniques to achieve formal notions of privacy, such as differential privacy. A critical feature of MedCo is that it is fully integrated within the i2b2 (Informatics for Integrating Biology and the Bedside) framework, currently used in more than 300 hospitals worldwide. Therefore, it is easily adoptable by clinical sites. We demonstrate MedCo’s practicality by testing it on data from The Cancer Genome Atlas in a simulated network of three institutions. Its performance is comparable to the ones of SHRINE (networked i2b2), which, in contrast, does not provide any data protection guarantee.

Bengali, is not only a major language in two states West Bengal, and Tripura of India, but it is the main language of an Entire country Bangladesh. Having such large application areas of Bengali language, and with the evolution of automation, machine learning, and trending technological development, it is also high time to make a complete product which can easily and efficiently recognize Bengali handwritten characters. Research in this area is attracting attention day by day, and there are the obvious reasons. Application area of such Bengali handwritten character recognition system is undoubtedly very high, but unfortunately, there is not such success in research, which can produce a complete operational system that can be commercialized or practically implemented in day to day life. The current work is a study of the different tasks to be done along with achievements throughout the last decade for different tasks in the field of handwritten Bengali character recognition.

The sample path generated by a stochastic simulation often exhibits significant variability within each replication, revealing periods of good and poor performance alike. As such, traditional summaries of aggregate performance measures overlook the more fine-grained insights into the operational system behavior. In this paper, we take a simulation analytics view of output analysis, turning to machine learning methods to uncover key insights from the dynamic sample path. We present a k nearest neighbors model on system state information to facilitate real-time predictions of a stochastic performance measure. This model is built on the premise of a system-specific measure of similarity between observations of the state, which we inform via metric learning. An evaluation of our approach is provided on a stochastic activity network and a wafer fabrication facility, both of which give us confidence in the ability of metric learning to provide interpretation and improved predictive performance.

Background The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) is a consortium that aims to understand complex hydrologic, biogeochemical, and microbial connections within river corridors experiencing perturbations such as dam operations, floods, and droughts. For one ongoing WHONDRS sampling campaign, surface water metabolite and microbiome samples are collected through a global survey to generate knowledge across diverse river corridors. Metabolomics analysis and a suite of geochemical analyses have been performed for collected samples through the Environmental Molecular Sciences Laboratory (EMSL). The obtained knowledge and data package inform mechanistic and data-driven models to enhance predictions of outcomes of hydrologic perturbations and watershed function, one of the most critical components in model-data integration. To support efforts of the multi-domain integration and make the ever-growing data package more accessible for researchers across the world, a Shiny/R Graphical User Interface (GUI) called WHONDRS-GUI was created. Results The web application can be run on any modern web browser without any programming or operational system requirements, thus providing an open, well-structured, discoverable dataset for WHONDRS. Together with a context-aware dynamic user interface, the WHONDRS-GUI has functionality for searching, compiling, integrating, visualizing and exporting different data types that can easily be used by the community. The web application and data package are available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1484811, which enables users to simultaneously obtain access to the data and code and to subsequently run the web app locally. The WHONDRS-GUI is also available for online use at Shiny Server (https://xmlin.shinyapps.io/whondrs/).

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping.

Purpose Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling. Design/methodology/approach A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated. Findings (1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com). Research limitations One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts. Practical implications The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations. Originality/value The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods, automated planning, coordination algorithms, etc.). The combination of such methods in an operational system is a technical challenge that requires efficient architectural solutions. In a robotic engineering context, where productivity is important, it is also important to minimize the effort for the development of new systems. As a response to these needs, this paper presents Aerostack, an open-source software framework for the development of aerial robotic systems. This framework facilitates the creation of UAS by providing a set of reusable components specialized in functional tasks of aerial robotics (trajectory planning, self localization, etc.) together with an integration method in a multi-layered cognitive architecture based on five layers: reactive, executive, deliberative, reflective and social. Compared to other software frameworks for UAS, Aerostack can provide higher degrees of autonomy and it is more versatile to be applied to different types of hardware (aerial platforms and sensors) and different types of missions (e.g. multi robot swarm systems). Aerostack has been validated during four years (since February 2013) by its successful use on many research projects, international competitions and public exhibitions. As a representative example of system development, this paper also presents how Aerostack was used to develop a system for a (fictional) fully autonomous indoors search and rescue mission.

The National Oceanic and Atmospheric Administration (NOAA) has pioneered the development and application of high-performance meteorological radar for public weather warning and forecasting services. The current operational system, the dual-polarization WSR-88D weather surveillance radar, has led to significant capability enhancements, for example, increased accuracy and lead time for severe weather and flash flood warnings. This paper summarizes research to determine whether the eventual replacement for the WSR-88D should exploit rapidly maturing phased array radar (PAR) technology. Key benefits for the rapid, adaptive scanning so enabled have been identified and work continues to mature associated data processing and numerical weather prediction (NWP) assimilation techniques. In parallel, the considerable engineering challenges associated with meteorological PAR are being addressed through modeling, hardware demonstration and data analysis. The Advanced Technology Demonstrator (ATD) - an S-band, dual polarization PAR - is described in this paper. ATD puts NOAA in a position to fully expose operational benefits and substantially advance the technical readiness level of meteorological PAR.

Digital technologies are facilitating our daily activities, and thus leading to the social transformation with the upcoming 5G communications and the Internet of Things. However, mainstream and sophisticated attacks are remaining a threat, both for individuals and organisations. Cyber Range emerges as a promising solution to effectively train people in cybersecurity aspects. A Training Programme is considered adequate only if it can adapt to the scope of the attacks they cover and if the trainees apply the learning material to the operational system. Therefore, this study introduces the model-driven CYber Range Assurance platform (CYRA). The solution allows a trainee to be trained for known and new cyber-attacks by adapting to the continuously evolving threat landscape and examines if the trainees transfer the acquired knowledge to the working environment. Furthermore, this paper presents a use case on an operational backend ICT system, showing how the CYRA platform was utilised to increase the security posture of the organisation.

The use of new technologies in business models and infrastructure has been driven in part by the Internet and globalization. The next trend of innovations is likely to come from humans' ability to connect to machines and the data that comes from these connections. The IBM Intelligent Operation Center (IOC) is a system of systems that is not intended to replace an existing physical infrastructure that gathers raw data. Instead, it is intended to extract only the data necessary to optimize the operations of the organization. The types of data and integration into the IOC make efficient problem solving solutions readily available to city authorities. The user interface and standard operating procedure and the resource processing capabilities of the IOC indicate that this system is optimal for smart cities of the future with regard to improvement of quality of life and ease of navigation. The need for smart cities, universities, campuses, citizens, and students to drive growth of urban and regional economies is evident. In this article, a thorough analysis of the architectural design of an intelligent operational system is completed to present a smart solution for cities to unify departments and agencies under one umbrella. IOC is a system of systems that is intended to optimize an organization.IOC makes efficient problem solving solutions available to city authorities.Smart cities, universities, and citizens to drive growth of economies.An intelligent operational system to drive a smart solution for cities.

This study aims to develop Arabic language teaching media based on mobile learning and to determine the feasibility and influence of the developed media on student learning outcomes. Hannafin and Peck's development model is the model used in this study. Needs analysis, design, development and implementation are the stages in this research. This study involved material experts and media experts as validators. This study produces Arabic language teaching media products with mufrodat material based on mobile learning for Madrasah Tsanawiyah (MTs) with an offline operational system using a smartphone (Android). The feasibility of this product is classified in the very feasible category with a percentage score of 93% by material experts, 96.25% by media experts, 93% one on one trial, 86% small group trials, 92% by large group trials (field trials). Based on the results of calculations with the T-test, the value obtained t0 = 62.5> t table = 2.093 with a difference in learning outcomes reaching 38.75. Thus the media for teaching Arabic lessons on the basis of mobile learning, the developed mufrodat material is very effective and also very suitable for use in learning Arabic.

Aim The aim of this study was to evaluate the reliability and accuracy of documentation in FinnHEMS database, which is a nationwide helicopter emergency service (HEMS) clinical quality registry. Methods This is a nationwide study based on written fictional clinical scenarios. Study subjects were HEMS physicians and paramedics, who filled in the clinical quality registry based on the clinical scenarios. The inter-rater -reliability of the collected data was analyzed with percent agreement and free-marginal multi-rater kappa. Results Dispatch coding had a percent agreement of 91% and free-marginal multi-rater kappa value of 0.83. Coding for transportation or mission cancellation resulted in an agreement of 84% and free-marginal kappa value of 0.68. An agreement of 82% and a kappa value of 0.73 for dispatcher coding was found. Mission end, arrival at hospital and HEMS unit dispatch -times had agreements from 80 to 85% and kappa values from 0.61 to 0.73. The emergency call to dispatch centre time had an agreement of 71% and kappa value of 0.56. The documentation of pain had an agreement of 73% on both the first and second measurements. All other vital parameters had less than 70% agreement and 0.40 kappa value in the first measurement. The documentation of secondary vital parameter measurements resulted in agreements from 72 to 91% and kappa values from 0.43 to 0.64. Conclusion Data from HEMS operations can be gathered reliably in a national clinical quality registry. This study revealed some inaccuracies in data registration and data quality, which are important to detect to improve the overall reliability and validity of the HEMS clinical quality register.

The National Meteorological Center initially completed the construction of the Medium-Extended-Range Weather Forecast (MERWF) operational system in 2018. The system uses browser/server system architecture to support concurrent operation of hundreds of terabyte real-time and historical data, through the introduction of large data core technologies such as distributed storage and distributed computing. The key technical problem of MERWF, which is the fusion of real-time data and historical data, is solved. It greatly improves the efficiency of data access and display, and realizes the development of MERWF technology products based on the big data analysis and the effective extraction of predictable information. Based on big data analyses, an application technology system of MERWF is then established for the first time in national business department, to meet the objective and intelligent needs of modern meteorological business.

Aedes aegypti is a vector for Chikungunya, Dengue and Zika viruses in Latin America and is therefore a large public health problem for the region. For this reason, several inter-institutional and multidisciplinary efforts have been made to support vector control actions through the use of geospatial technologies. This study presents the development of an operational system for the application of free access to remotely sensed products capable of assessing the oviposition activity of Ae. aegypti in all of Argentina?s northern region with the specific aim to improve the current Argentine National Dengue risk system. Temporal modelling implemented includes remotely sensed variables like the normalized difference vegetation index, the normalized difference water index, day and night land surface temperature and precipitation data available from NASA?s tropical rainfall measuring mission and global precipitation measurement. As a training data set, four years of weekly mosquito oviposition data from four different cities in Argentina were used. A series of satellite-generated variables was built, downloading and resampling the these products both spatially and temporally. From an initial set of 41 variables chosen based on the correlation between these products and the oviposition series, a subset of 11 variables were preserved to develop temporal forecasting models of oviposition using a lineal multivariate method in the four cities. Subsequently, a general model was generated using data from the cities. Finally, in order to obtain a model that could be broadly used, an extrapolation method using the concept of environmental distance was developed. Although the system was oriented towards the surveillance of dengue fever, the methodology could also be applied to other relevant vector-borne diseases as well as other geographical regions in Latin America. Fil: Espinosa, Manuel. Fundación Mundo Sano; Argentina Fil: Di Fino, Eliana Marina Alvarez. Fundación Mundo Sano; Argentina. Universidad Nacional de Córdoba; Argentina Fil: Abril, Marcelo. Fundación Mundo Sano; Argentina Fil: Lanfri, Mario. Centro Espacial Teófilo Tabanera; Argentina Fil: Periago, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación Mundo Sano; Argentina Fil: Scavuzzo, Carlos Marcelo. Centro Espacial Teófilo Tabanera; Argentina

IT incident management process requires a correct categorization to attribute incident tickets to the right resolution group and obtain an operational system as quickly as possible, having the lowest possible impact on the business and costumers. In this work, we introduce a module to automatically categorize incident tickets, turning the responsible teams for incident management more productive. This module can be integrated as an extension into an incident ticket system (ITS), which contributes to reduce the time wasted on incident ticket route and reduce the amount of errors on incident categorization. To automate the classification, we use a support vector machine (SVM), obtaining an accuracy of 89%, approximately, on a dataset of real-world incident tickets. info:eu-repo/semantics/acceptedVersion

Geospatial web service of agricultural information has a wide variety of consumers. An operational agricultural service will receive considerable requests and process a huge amount of datasets each day. To ensure the service quality, many strategies have to be taken during developing and deploying agricultural information services. This paper presents a set of methods to build robust geospatial web service for agricultural information extraction and sharing. The service is designed to serve the public and handle heavy-load requests for a long-lasting term with least maintenance. We have developed a web service to validate our approach. The service is used to serve more than 10 TB data product of agricultural drought. The performance is tested. The result shows that the service has an excellent response time and the use of system resources is stable. We have plugged the service into an operational system for global drought monitoring. The statistics and feedbacks show our approach is feasible and efficient in operational web systems.

The Global Ocean Forecast System (GOFS) 3.1 is comprised of the 1/12 HYbrid Coordinate Ocean Model that is two-way coupled to the Community Ice CodE in a daily update cycle with the Navy Coupled Ocean Data Assimilation. Additionally it uses Improved Synthetic OceanProfiles to project surface information downward into the water column. GOFS nowcasts/forecasts the oceans weather, which includes the three-dimensional ocean temperature, salinity and current structure, the surface mixed layer, the location of mesoscale features, and ice concentration, thickness and drift in both hemispheres. It is scheduled to replace GOFS 3.0 for the ocean and the Arctic Cap Nowcast/ForecastSystem (ACNFS) for sea ice, both of which are the existing operational systems at the Naval Oceanographic Office. This report describes the system comparisons against unassimilated observations at both the nowcast time and as a function of forecast length. An ocean scorecard provides the relative performance of GOFS 3.1 vs. GOFS 3.0. Overall, GOFS 3.1 is performing equal to or better than both GOFS 3.0 and ACNFS, and is recommended to become the next global operational system.

Currently the multimedia contents and various terminal devices owned by public agencies or private companies are operated separately by their manufacturer. This can make an immediate action impossible in the event of failure in equipment or contents due to the lack of integrated management, and it can also cause a lot of administrative costs for the maintenance of equipment for each type. In addition, the ineffective management of contents brings about such problems as errors, duplication and inconsistency in the contents. To solve these, construction of an integrated management system as well as an efficient, low-cost operational system is required. The system proposed in this paper integrates and controls the remote terminal devices, and it implemented in a multicast way. For each of many different devices in remote locations such as set-top boxes, kiosks and speakers, functions including broadcast schedule edition, multimedia playback automation and subtitle display configuration are controlled directly at the central system. The system has been developed mainly on the algorithm of Middleware for multicasting large amounts of data and centralized group management. As a result of the test in conjunction with the actual devices, this proposed program could easily integrate the heterogeneous terminals.

The purpose of this study are:1) To find out the implementation of the operational system of data processing of students by using a management information system in the Madrasah Ibtidaiyah Nasrul Haq Makassar . 2) To find out the data processing process of students in the Madrasah Ibtidaiyah Nasrul Haq Makassar. 3) To find out the impact of the use of management information systems on data processing of students in the Madrasah Ibtidaiyah Nasrul Haq Makassar . The type of this study is a qualitative field research using data collection methods, namely interviews, observation and documentation. Data processing of students in Madrasah Ibtidaiyah Nasrul Haq based on management information systems is carried out using the online EMIS application, the process of data processing of students in the Madrasah Ibtidaiyah Nasrul Haq follows four main steps, namely data collection, data input, information storage and retrieval to be used and the use of management information systems in the management of student data in Nasrul Haq Ibtidaiyah Madrasah provides benefits in the form of ease of data storage, information retrieval processes, data security and access to student information wherever and whenever needed.

Jupyter Notebook ― is a web-appendix which allows writing and supplying comments a code to Python in interactive regime. It is an exclusive method to make experiments and studies and intercommunicate with others. Many research people use this calculative medium in their works more often. The main factors of growing popularity of programming language Python and project Jupyter are characterized in brief. The basic of them are: high velocity of development and merit of software; standard library and libraries with open initial code NumPy, SciPy, Matplotlib et al.; simplicity of integration with code to C, C++ and FORTRAN; free distribution; support and numerous assemblage of designers and users. According to the data of TIOBE company, collecting monthly statistics of search inquiries and on the base of data obtained compiles its own visualized rates of programming languages Python ranks the third place in popularity among programming languages. It was chosen as a language of a year in 2007, 2010 and 2018. Aspects of installation of programs, libraries and packets in operational system Windows have been considered. It is recommended to download and install the libraries from the storage of whl-files on the web-page by Christoph Gohlke from the laboratory of fluorescence dynamics of California University. WHL format is supported by all basic platforms (Mac OS X, Linux, Windows). The process of starting the server of Jupyter notebooks from command line has been described in details. The simplicity and effectiveness of scientific calculations in Jupyter Notebook have been demonstrated. Test calculations have been given for solving the problems of linear algebra. It has been shown in particular that the code of calculation of the matrix of 5000Ч5000 size occupies only several lines.

Combining multiple proximal sensors within a wireless sensor network (WSN) enhances our capacity to monitor vegetation, compared to using a single sensor or non-networked setup. Data from sensors with different spatial and temporal characteristics can provide complementary information. For example, point-based sensors such as multispectral sensors which monitor at high temporal frequency but, at a single point, can be complemented by array-based sensors such as digital cameras which have greater spatial resolution but may only gather data at infrequent intervals. In this article we describe the successful deployment of a prototype system for using multiple proximal sensors (multispectral sensors and digital cameras) for monitoring pastures. We show that there are many technical issues involved in such a deployment, and we share insights relevant for other researchers who may consider using WSNs for an operational deployment for pasture monitoring under often difficult environmental conditions. Although the sensors and infrastructure are important, we found that other issues arise and that an end-to-end workflow is an essential part of effectively capturing, processing and managing the data from a WSN. Our deployment highlights the importance of testing and ongoing monitoring of the entire workflow to ensure the quality of data captured. We demonstrate that the combination of different sensors enhances our ability to identify sensor problems necessary to collect accurate data for pasture monitoring.

By setting up the right service and product for the customer, the company may increase its marketing effectiveness as well as sales. For that purpose, information on company’s product and sales is highly needed in order to help on decision making process, which expectedly enables company to create value and eventually gain competitive advantage. This research attempts to develop a Data warehouse for PT XXX, which is expected to exhibit useful information for the company without disturbing its operational system. The Data warehouse is developed with Oracle 11g. Apart from that, this research also aims to gather information and knowledge with spatial database. Th e data mining is conducted by using WEKA application, which compares the algorithm of DBSCAN, K-Means, and EM clustering. Data mining from the spatial data is expected to create area map which indicates sales rate of PT Pos Indonesia’s products . T hrough this it is hoped that the company may focus more in marketing certain product with particular advantage in one particular area to produce effectiveness. The research outcome indicates that the Data warehouse being developed has already managed to exhibit the receipt report of postal money order (wesel pos), retail stock, as well as retail sales.

With increasing use of weather observation and forecast services, there is an urgent need of an effective and efficient system to generate high-quality weather services and products. For example, weather maps are expected to be submitted not only for decision-making departments but also to websites whose main feature is to provide public service products on a daily or an hourly basis. In this study, first, geoprocessing is successfully extended to handle the entire process of weather map generation from data, and some associated key issues are discussed. Second, based on the distributed computing technology, an application system with multitiered architecture for weather services products generation system is designed and implemented. The system significantly enhances sharing and scalability. Finally, an application case is studied in detail by illustrating modeling, task deployment, and final execution, and it is shown that the operational system meets the requirements of simplicity, easy modeling, and customizable task deployment.

The patient flow in the health centres is one of contributing factors to the dissatisfaction and delay in the delivery of healthcare service. This study aims to explore the pattern of dental patient flow in one centre and to solve the delay reason using computer simulation. Three different scenarios has been experimented using FlexSim 4.0 software to simulate the patient’s flow. Three proposed solutions have been experimented, and the best-proposed scenario successfully improved three from seven observed variables: the patient throughput, fasten service in the dental clinic and the length of stay. The length of stay from real data and from simulation output has been compared. The explored times was the longest at registration area for receiving the service from receptionist. The finding shows that we can customise safe interventions without interfering with the operational system using the simulation software.

Trauma signature (TSIG) analysis is an evidence-based method that examines the interrelationship between population exposure to a disaster, extreme event, or complex emergency, and the inter-related physical and psychological consequences for the purpose of providing timely, actionable guidance for effective mental health and psychosocial support that is organically tailored and targeted to the defining features of the event. A series of TSIG case studies has been published since 2011 and TSIG analyses of recent disasters are in process. Disaster Health intends to expedite and feature novel TSIG research focusing on late-breaking disaster events. At the current stage of development, expert consensus is sought for refining the TSIG methodology using a Delphi process. The overarching goal is to create a fully operational system to provide timely guidance for adapting disaster behavioral health support to the salient psychological risk factors in each disaster.

The five parts of the ISO EN 13606 standard define a means by which health-care records can be exchanged between computer systems. Starting within the European standardisation process, it has now become internationally ratified in ISO. However, ISO standards do not require that a reference implementation be provided, and in order for ISO EN 13606 to deliver the expected benefits, it must be provided not as a document, but as an operational system that is not vendor specific. This article describes the evolution of an Extensible Markup Language (XML) Schema through three iterations, each of which emphasised one particular approach to delivering an executable equivalent to the printed standard. Developing these operational versions and incorporating feedback from users of these demonstrated where implementation compromises were needed and exposed defects in the standard. These are discussed herein. They may require a future technical revision to ISO EN 13606 to resolve the issues identified.

The production of biogas from animal, crops, or urban waste benefits environmental-friendly energy production, and it is also beneficial for sustainable agricultural production through the use of liquid organic fertiliser as a source of organic matter. A number of systems are available for the transportation and application of the liquid organic fertiliser but detailed in-field operations planning tools are necessary to establish safe conclusions on the advantages and disadvantages of each application system. Here, an algorithmic approach for the operational planning of liquid organic fertiliser application using the umbilical system is developed and presented. The approach provides all the key operational parameters necessary for the evaluation of a selected operational scenario, together with the relative significance of the various part operations involved, taking into account attributes of the operating environment. The whole system has been built as a web-based decision support system tool. A set of results demonstrated the use of the tool for supporting decisions on the adoption of the umbilical system as an operational system for the application of the organic fertiliser. A second set of results demonstrated the use of the tool for comparing an umbilical system in terms of cost and time with a tanker system. The comparison between these two systems quantified the benefit of such field operation models to accurately simulate in-field machine activities and provide precise case-based data rather than average norm based evaluations of selected field operation systems.

When an earthquake hits, the dissemination of tsunami warnings can be crucial in saving lives. Unfortunately, some dissemination channels may be insufficient as they may be affected by the earthquake itself (damaged, loss of power or overloaded), may be expensive to build and are not available at certain places. In this research we propose a tsunami warning dissemination system that can serve as an additional channel to the existing systems. The system is lightweight, affordable, and can be powered autonomously. It disseminates messages to the commonly used GSM handsets and may reach numerous at-risk civilians. This paper also reports on the suggested integration plan of our proposed system within the current disaster management operation procedure in Indonesia. A set of experiments were carried out and they showed that the system has adequately met the users' requirements. It also shows the feasibility of the system to be integrated within the existing operational system and its associated early warning dissemination channels.

As the demand and deployment of renewables get better, there comes a growing interest in improved techniques of forecasting the energy generation from those sources. This paper aims at testing and suggesting techniques for input parameter selection and accuracy enhancement in forecasting power output of a PV system. The PV system under study is an operational system in Goldwind smart microgrid in Beijing, China. Historical records of PV power and weather data are utilized while the forecasting models are based on ANN. The paper starts with studying selection of input parameters through correlation analysis, sets of sensitivity analysis techniques and Garson's algorithm. A combination of these methods was able to pick out the most important parameters in deciding the PV power amount. In order to boost accuracy of forecasting, in addition to the recommended strategic input selection method and searching for optimal size of network, options of output processing were tested. The use of more than one network with different training algorithm and using different types of ANN were investigated with both techniques resulting in enhancement in accuracy of forecast.

In this paper, we present an operational system for cyber threat intelligence gathering from various social platforms on the Internet particularly sites on the darknet and deepnet. We focus our attention to collecting information from hacker forum discussions and marketplaces offering products and services focusing on malicious hacking. We have developed an operational system for obtaining information from these sites for the purposes of identifying emerging cyber threats. Currently, this system collects on average 305 high-quality cyber threat warnings each week. These threat warnings include information on newly developed malware and exploits that have not yet been deployed in a cyber-attack. This provides a significant service to cyber-defenders. The system is significantly augmented through the use of various data mining and machine learning techniques. With the use of machine learning models, we are able to recall 92% of products in marketplaces and 80% of discussions on forums relating to malicious hacking with high precision. We perform preliminary analysis on the data collected, demonstrating its application to aid a security expert for better threat analysis., Comment: 6 page paper accepted to be presented at IEEE Intelligence and Security Informatics 2016 Tucson, Arizona USA September 27-30, 2016

This paper introduces the characteristics and the new technologies applied in Fengyun-3 Series Meteorological Satellite Data Archiving and Service System. The system integrates several high-performance servers, high availability disk array, large-scale automated tape library as well as operation system, database, storage software and application software, all of which constitutes archive and service application clusters. The application of configured dynamic load container technology and multi-server processing load balancing and parallel computing technology makes the cluster a highly scalable, available, efficient operational system to realize all levels of satellite data archiving, customization, retrieval, dynamic information release, operation monitoring, management, and other functions. Internet-based full dataset sharing, distributed file system, spatiotemporal integration of database storage, WebGIS global satellite image distribution, visual processing and display technology are adopted in the system to improve data sharing and service. Completion of the system meets the needs of operational meteorological satellite, achieving daily 4 TB data storing and daily 1 TB data download service. The system running in NSMC/CMA is the first one to provide internet-based full archive dataset (online, nearline and offline) sharing service in the area of Chinese civil remote sensing satellite data services.

Background: In the competitive data driven business world, business Intelligence (BI) team converts the raw operational data to information for decision making. Operational system captures the day-to-day operations and BI database refreshes operational data periodically. Methods: A component to create the metadata repository which maintains the current BI database summary by logical data partitioning using range based partition for frequently changing parameter which are critical to business. During different time frequency, using metadata repository component identifies the latest data victim between BI vs operational data and refreshes the modified victim to BI database. Findings: In traditional data loading approach from Operational system to BI database, huge volume of data gets refreshed periodically irrespective of modifications, which leads to higher processing time and cost. To overcome this limitation, this proposed methodology helps to identify the latest data victims present in operational systems instead of bulk data replacement which can minimize the processing time and enables faster data transformation to achieve "Time to Decision" and "Quick to Market" implementation for business enhancements. Also component can be scheduled for data refresh with different time frequency for multiple critical to business as well as frequently changing parameters. Applications: In financial, traffic, weather, e-business, Logistics&stock management transactions, data changes frequently and process big data periodically to gain real-time knowledge discovery for time sensitive decision making.

In the last decade there has been a paradigm shift in creating, viewing and utilizing geospatial data for planning, navigation and traffic management of urban areas. Realistic, three-dimensional information is preferred over conventional two dimensional maps. The paper describes objectives, methodology and results of an operational system being developed for generation of site model from Cartosat-2 multiview images. The system is designed to work in operational mode with varying level of manual interactivity. A rigorous physical sensor model based on collinearity condition models the "step n stare" mode of image acquisition of the satellite. The relative orientation of the overlapping images is achieved using coplanarity condition and conjugate points. A procedure is developed to perform digitization in mono and stereo modes. A technique for refining manually digitized boundaries is developed. The conjugate points are generated by establishing a correspondence between the points obtained on refined edges to analogous points on the images obtained with view angles ±26 deg. It is achieved through geometrically constrained image matching method. The results are shown for a portion of multi-view images of Washington City obtained from Cartosat-2. The scheme is generic to accept very high resolution stereo images from other satellites as input.

Electric vehicles (EVs) present efficiency and environmental advantages over conventional transportation. It is expected that in the next decade this technology will progressively penetrate the market. The integration of plug-in electric vehicles in electric power systems poses new challenges in terms of regulation and business models. This paper proposes a conceptual regulatory framework for charging EVs. Two new electricity market agents, the EV charging manager and the EV aggregator, in charge of developing charging infrastructure and providing charging services are introduced. According to that, several charging modes such as EV home charging, public charging on streets, and dedicated charging stations are formulated. Involved market agents and their commercial relationships are analysed in detail. The paper elaborates the opportunities to formulate more sophisticated business models for vehicle-to-grid applications under which the storage capability of EV batteries is used for providing peak power or frequency regulation to support the power system operation. Finally penetration phase dependent policy and regulatory recommendations are given concerning time-of-use pricing, smart meter deployment, stable and simple regulation for reselling energy on private property, roll-out of public charging infrastructure as well as reviewing of grid codes and operational system procedures for interactions between network operators and vehicle aggregators.

This paper describes an applied document filtering system embedded in an operational watch center that monitors disease outbreaks worldwide. At the initial time of this writing, the system effectively supported monitoring of 23 geographic regions by filtering documents in several thousand daily news sources in 11 different languages. This paper describes the filtering algorithm, statistical procedures for estimating Precision and Recall in an operational environment, summarizes operational performance data and suggests lessons learned for other applications of document filtering technology. Overall, these results are interpreted as supporting the general utility of document filtering and information retrieval technology and offers recommendations for future applications of this technology.

Information fused by multi-sensors is an important factor for obtaining reliable contextual information in smart spaces which use the pervasive and ubiquitous computing techniques. Adaptive fusion improves robust operational system performances then makes a reliable decision by reducing uncertain information. However, these fusion techniques suffer from problems regarding the accuracy of estimation or inference. No commonly accepted approaches exist currently. In this survey, we introduce the advantages and disadvantages of fusion techniques which can be used in specific applications. Second, we categorize well-known models, algorithms, systems, and applications depending on the proposed approaches. Finally, we discuss the related issues for fusion techniques within the smart spaces then suggest research directions for improving the decision-making in uncertain situation.

This paper describes the generic data assimilation software tool DATools. DATools can be used as standalone or within Delft-FEWS. DATools is completely configurable via XML configuration. DATools is built up of three components: a Filter, a Stochastic Modeler, and a Stochastic Observer. Configuration of all these three parts is explained in detail. At the moment two data assimilation filters are available within DATools: (1) ensemble Kalman Filter and (2) the residual resampling filter. Results of a twin experiment with both filters with DATtools show similar results as a previous study performed with custom implementations. It is also shown that DATools can function inside Delft-FEWS software used for operational flood forecasting. Applying EnKF to a 1D hydrodynamic SOBEK-RE model of the river Rhine within the operational system FEWS-NL Rhine and Meuse improves the forecasts at the Lobith gaugin station and downstream of Lobith. DATools has been coupled with the HBV-96, SOBEK, and REW models and will be coupled to MODFLOW, Delft-3D, and the geotechnical model MSetlle in the near future. Uncertainty analysis with this tool is also possible and calibration will be added later this year.

A generic toolbox for data assimilation called COSTA (COmmon Set of Tools for the Assimilation of data) makes it possible to simplify the application of data assimilation to models and to try out various methods for a particular model. Concepts of object oriented programming are used to define building blocks for data assimilation systems that can be exchanged and reused. The main building blocks in COSTA are the model component and the stochastic observer component. These components can be created by wrapping existing code. The LOTOS-EUROS air quality model will be used for operational smog and aerosol forecasts in the Netherlands in the near future. The COSTA framework will be used in this operational environment to implement the data assimilation techniques. As a first steps towards this operational system the model component of LOTOS-EUROS is created and the performance of various Kalman filter based data assimilation techniques are compared for a real live case study. The ensemble Kalman filter and the ensemble square-root filters converged faster than the other tested techniques for the selected case study setup.

At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains. Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising.

The purpose of this study is to find methods for utilizing digital room in public library and its efficient operation by analyzing the status quo of the digital rooms in public libraries, which had been built nation-widely about 5 years ago. It is found that it is urgent to supplement and improve the infrastructure of them. It is also required to build multi-functional digital rooms and an integrated operational system for digital rooms. Implementation of an integrated operational system, first of all, for applicable sections of digital room may be the best alternative. The multi-functional digital rooms can be built, which, in addition to its traditional function, can include a variety of original information resources and function of information creation using application programs, as well as functions such as education, information services, online/offline community creation, and activating information sharing. In the future, the digital rooms in public libraries must play a significant role as online/offline spaces, which are not the places for use and access to internet information resource, but for information/knowledge sharing and collaborative activities. It also must provide a communication channel between librarians and users as well as among users themselves.

Principles of arranging and design features of the WebSonix system consisting of the central website and facilities for communication with spectrometers are considered. The system allows one to reflect the actual statuses of all spectrometer components, view measurements protocols, display acquired spectra, and control the course of the experiment on the spectrometers under control of the Sonix+ software package (Windows XP operational system). The system does not depend on the spectrometer characteristics and permits simple changes of their structure and easy adaptation to special features of spectrometric data representation. The system is based on PHP and Python scripts. The GNU/Linux Debian operational system and Apache 2 web server are installed on the website computer.

Head Mounted Displays (HMDs) have traditionally been and are currently utilized in a variety of applications including training, entertainment, and military operations. Most applications of HMDs seek to provide the user with enriched information, that is, information beyond that of what a human can gather with only biological vision. The Land Warrior is an operational system that utilizes a HMD. For Land Warrior, the HMD provides the Soldier better battle field situational understanding through text by communicating command and control information. Other Land Warrior battlefield tasks include target detection and recognition. This paper evaluates the visual components of the current Ground Soldier System (GSS), Land Warrior HMD, analyzes embedded training requirements (specifically detection ranges for embedded virtual targets and human performance issues associated with HMDs), and proposes an optical model to overcome some of the limitations inherent to the current commercial version of the Land Warrior HMD and other commercially-available HMDs. The design quality of the Land Warrior HMD has a direct impact on a soldier's performance in the field. The varying stringent environment in which LW operates provides HMD visual component design challenges, due to the correlation between the equipment style, information formatting, and use environment. Information formatting affects a Soldier's workload. The current LW HMD visual implementation contains information formatting limitations based on its ability to scale to other resolutions and size linearly. By increasing the performance of the HMD of the Land Warrior System, the potential to improve readiness and survivability also increase. Forty-five thousand sets of Land Warrior equipment will be fielded between 2001 and 2014.

Operational security assurance evaluation requires building security metrics models to express the expected security status of the system, and collecting data from the operational system to express the current state against these models. Many factors impact the confidence we can have in these metrics and their reported status. One major factor is the trust we can put in the provided measurement data. This paper describes the properties of a trusted measurement base, use of secure element functions and different probe form factors, and their impact on defining confidence levels for the measurement data. A way of quantifying this confidence level and using it as part of security metrics models is defined. Cloud computing is used as a domaini¾?to illustrate these concepts and the process of their application. The cloud environment is especially challenging for this type of assurance due to mixed ownership and potentially limited visibility into the infrastructure.

The article focuses on deployment of model based control algorithms which have passed the stage of simulation analysis in Simulink. The target software platform is OROCOS framework within Linux Xenomai — a real-time operational system. First, the algorithm of transforming of an automatically generated code from Simulink scheme into an OROCOS component is presented. Next, the proposed procedure is discussed and evaluated from the perspective of the control engineer's needs.

Layers of Protection Analysis (LOPA) described in the IEC 61511 standard is one of the most popular methods that enables the adequacy of the layers of protection for an industrial process to be assessed. LOPA has found much broader application as a relatively simple risk assessment methodology. However, an important issue faced by risk analysts is how to deal with uncertainties that arise in the elements of the accident scenario like the severity of consequences, frequency data for initiating event, probability of failure of independent protection layers (IPL) and human reliability. (ICAT'14). This paper presents an approach of fuzzy LOPA which takes into account uncertain and/or vague data. A possibilistic representation of data by fuzzy intervals is proposed. Consequence reduced frequency is computed by extended multiplication using a-cuts method. The proposed approach is illustrated by an application to the protection of an operational system which is a heater.