Suchergebnisse

In an innovative collaboration between government, university and private industry, researchers at the University of Georgia and Gainesville State College are collaborating with Photo Science, Inc. to acquire, process and quality control check lidar and or-thoimages of forest areas in the Southern Appalachian Mountains of the United States. Funded by the U.S. Geological Survey, this project meets the objectives of the ARRA initiative by creating jobs, preserving jobs and training students for high skill positions in geospatial technology. Leaf-off lidar data were acquired at 1-m resolution of the Tennessee portion of the Great Smoky Mountain National Park (GRSM) and adjacent Foothills Parkway. This 1400-sq. km. area is of high priority for national/global interests due to biodiversity, rare and endangered species and protection of some of the last remaining virgin forest in the U.S. High spatial resolution (30 cm) leaf-off 4-band multispectral orthoimages also were acquired for both the Chattahoochee National Forest in north Georgia and the entire GRSM. The data are intended to augment the National Elevation Dataset and orthoimage database of The National Map with information that can be used by many researchers in applications of LiDAR point clouds, high resolution DEMs and or-thoimage mosaics. Graduate and undergraduate students were involved at every stage of the workflow in order to provide then with high level technical educational and professional experience in preparation for entering the geospatial workforce. This paper will present geospatial workflow strategies, multi-team coordination, distance-learning training and industry-academia partnership.

In an innovative collaboration between government, university and private industry, researchers at the University of Georgia and Gainesville State College are collaborating with Photo Science, Inc. to acquire, process and quality control check lidar and or-thoimages of forest areas in the Southern Appalachian Mountains of the United States. Funded by the U.S. Geological Survey, this project meets the objectives of the ARRA initiative by creating jobs, preserving jobs and training students for high skill positions in geospatial technology. Leaf-off lidar data were acquired at 1-m resolution of the Tennessee portion of the Great Smoky Mountain National Park (GRSM) and adjacent Foothills Parkway. This 1400-sq. km. area is of high priority for national/global interests due to biodiversity, rare and endangered species and protection of some of the last remaining virgin forest in the U.S. High spatial resolution (30 cm) leaf-off 4-band multispectral orthoimages also were acquired for both the Chattahoochee National Forest in north Georgia and the entire GRSM. The data are intended to augment the National Elevation Dataset and orthoimage database of The National Map with information that can be used by many researchers in applications of LiDAR point clouds, high resolution DEMs and or-thoimage mosaics. Graduate and undergraduate students were involved at every stage of the workflow in order to provide then with high level technical educational and professional experience in preparation for entering the geospatial workforce. This paper will present geospatial workflow strategies, multi-team coordination, distance-learning training and industry-academia partnership.

Information is an element of knowledge that can be stored, processed or transmitted. It is linked to concepts of communication, data, knowledge or representation. In a context of steady increase in the mass of information it is difficult to know what information to look for and where to find them. Computer techniques exist to facilitate this research and allow relevant information extraction. Recommendation systems introduced the notions inherent to the recommendation, based, inter alia, information search, filtering, machine learning, collaborative approaches. It also deals with the assessment of such systems and has various applications.

Grey literature, an area of interest to special librarians and information professionals, can be traced back a half-century. However, grey literature as a specialized field in information studies is less than a decade old. At GL'97 in Luxembourg, grey literature was redefined "as information produced on all levels of government, academics, business and industry in electronic and print formats not controlled by commercial publishers (i.e. where publishing is not the primary activity of the producing body)." The subject area was broadened and the need for continuing research and instruction pursued. The results of an online survey carried out in 2004 compared with survey results a decade prior indicate two changes: (1) a move to more specialization in the field of grey literature and (2) a move to more balance in activities related to research and teaching as compared with the processing and distribution of grey literature. It is not that the activities of processing and distribution are today of less concern, but technological advances and the Internet may have made them less labour intensive. The burden that grey literature poised to human resources and budgets appears to have been reduced enough that the benefits of the content of grey literature is discovered. And this discovery of a wealth of knowledge and information is the onset to further research and instruction in the field of grey literature. This research is a follow-up to two projects carried out in 2004. One was a citation analysis based on the published papers in the GL Conference Proceedings and the other was a general survey, which dealt with the response of information professionals to key issues and topics in the field of grey literature. In this study, we seek not only to update and integrate the data from the citation analysis but at the same time we introduce the instrument of an author survey in order to better assess the work and expectations of those who are actually doing research and authoring papers on the topic of grey literature. ...

'Bei der hermeneutisch-klassifikatorischen Inhaltsanalyse werden hermeneutische, interpretative und klassifizierende, quantitative Teile zu einem gemeinsamen Verfahren kombiniert. Im hermeneutischen Teil wird der vom Sprecher intendierte Sinn seiner sprachlichen Äußerung im Diskurs mehrerer Bearbeiter erschlossen. Im quantitativen Teil werden die Sinnelemente mehrdimensional, d.h. im Hinblick auf unterschiedliche Aspekte klassifiziert und in ein EDV-gerechtes Notationssystem überführt. Daran schließt sich eine zweigleisige Auswertungsstrategie an, bei der quantitative und qualitative Analysen mit unterschiedlicher Gewichtung kombiniert werden können. Zunächst werden durch geeignete Auszählverfahren die zentralen Sinnstrukturen rekonstruiert und ihre quantitative Bedeutsamkeit ermittelt. Durch Textidentifikatoren ist es in jeder Phase des Auswertungsprozesses möglich, die betreffenden Textpassagen aufzufinden und die quantitativen Ergebnisse durch qualitative Detailstudien zu ergänzen. Das Verfahren wurde entwickelt, um die bei Leitfadengesprächen anfallenden großen Textmengen in angemessener Weise analysieren zu können.' (Autorenreferat)

Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are the phenomenon of noise, small disjunct samples, and overfitting due to a high imbalance ratio in a dataset. A high level of imbalance ratio and low variance conditions cause the results of synthetic data generation to be collected in narrow areas and conflicting regions among classes and make them susceptible to overfitting during the learning process by machine learning methods. Therefore, this research proposes a combination between Radius-SMOTE and Bagging Algorithm called the IRS-BAG Model. For each sub-sample generated by bootstrapping, oversampling was done using Radius SMOTE. Oversampling on the sub-sample was likely to overcome overfitting problems that might occur. Experiments were carried out by comparing the performance of the IRS-BAG model with various previous oversampling methods using the imbalanced public dataset. The experiment results using three different classifiers proved that all classifiers had gained a notable improvement when combined with the proposed IRS-BAG model compared with the previous state-of-the-art oversampling methods. Doi: 10.28991/ESJ-2023-07-05-04 Full Text: PDF

Abstract
COVID-19 has impacted all aspects of everyday normalcy globally. During the height of the pandemic, people shared their (PI) with one goal—to protect themselves from contracting an "unknown and rapidly mutating" virus. The technologies (from applications based on mobile devices to online platforms) collect (with or without informed consent) large amounts of PI including location, travel, and personal health information. These were deployed to monitor, track, and control the spread of the virus. However, many of these measures encouraged the trade-off on privacy for safety. In this paper, we reexamine the nature of privacy through the lens of safety focused on the health sector, digital security, and what constitutes an infraction or otherwise of the privacy rights of individuals in a pandemic as experienced in the past 18 months. This paper makes a case for maintaining a balance between the benefit, which the contact tracing apps offer in the containment of COVID-19 with the need to ensure end-user privacy and data security. Specifically, it strengthens the case for designing with transparency and accountability measures and safeguards in place as critical to protecting the privacy and digital security of users—in the use, collection, and retention of user data. We recommend oversight measures to ensure compliance with the principles of lawful processing, knowing that these, among others, would ensure the integration of privacy by design principles even in unforeseen crises like an ongoing pandemic; entrench public trust and acceptance, and protect the digital security of people.

This book contains a selection of articles from The 2015 World Conference on Information Systems and Technologies (WorldCIST'15), held between the 1st and 3rd of April in Funchal, Madeira, Portugal, a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges of modern Information Systems and Technologies research, technological development and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Intelligent and Decision Support Systems; Big Data Analytics and Applications; Software Systems, Architectures, Applications and Tools; Multimedia Systems and Applications; Computer Networks, Mobility and Pervasive Systems; Human-Computer Interaction; Health Informatics; Information Technologies in Education; Information Technologies in Radiocommunications.

Indonesia is the largest sugar importer country in the world, this is contrary to the government's desire to realize sugar self-sufficiency. To overcome the dependence on sugar imports in order to support national food sovereignty, geographic information system technology (GIS) can be used to present information as material for consideration by the government in determining policies on the management of sugar cane land resources. The K-means algorithm is used to group regions according to production level, while the Matching method is for evaluating the suitability of sugarcane land. Presentation of data in the form of map visualization on the web using a new model in processing land data, where this model processes production grouping data, and land suitability class data in the form of GeoJSON then mapped with the help of Leaflets. This new model enables dynamic land data processing and visualization in the form of interactive maps. The results of the EUCS test for GIS mapping of Land Suitability and Cane Production are 3.23 (Satisfied) of the total score of 4, so this system can be accepted by the user.

Abstract
Purpose
We generated methods for evaluating clinical outcomes including treatment response in oncology using the unstructured data from electronic health records (EHR) in Japanese language.

Methods
This retrospective analysis used medical record database and administrative data of University of Miyazaki Hospital in Japan of patients with lung/breast cancer. Treatment response (objective response [OR], stable disease [SD] or progressive disease [PD]) was adjudicated by two evaluators using clinicians' progress notes, radiology reports and pathological reports of 15 patients with lung cancer (training data set). For assessing key terms to describe treatment response, natural language processing (NLP) rules were created from the texts identified by the evaluators and broken down by morphological analysis. The NLP rules were applied for assessing data of other 70 lung cancer and 30 breast cancer patients, who were not adjudicated, to examine if any difference in using key terms exist between these patients.

Results
A total of 2,039 records in progress notes, 131 in radiology reports and 60 in pathological reports of 15 patients, were adjudicated. Progress notes were the most common primary source data for treatment assessment (60.7%), wherein, the most common key terms with high sensitivity and specificity to describe OR were "reduction/shrink", for SD were "(no) remarkable change/(no) aggravation)" and for PD were "(limited) effect" and "enlargement/grow". These key terms were also found in other larger cohorts of 70 patients with lung cancer and 30 patients with breast cancer.

Conclusion
This study demonstrated that assessing response to anticancer therapy using Japanese EHRs is feasible by interpreting progress notes, radiology reports and Japanese key terms using NLP.

GHGA aims to address the needs for establishing a national genome archive for human genome data while tackling challenges in data access, sharing, and privacy. GHGA aims provide a national infrastructure for secure and safe storage, access management, dissemination, and analysis of human omics data under a coherent ethical-legal framework. The core objectives of GHGA are as follows: To build a platform for long-term FAIR data archival of human omics data that are being generated at the major omics centers in Germany Provide Controlled access management and community-centric data sharing platforms Provide an ethico-legal and data use framework for data sharing, protection and analysis Enable distributed analytics to democratize data processing and research reuse Establish strong interfaces with international genome initiatives and become THE german node in the new Federated EGA

The European Forest Fire Information System (EFFIS) provides timely and reliable information on wildland firesin Europe since 1998. As a Copernicus Emergency Management Service, EFFIS is supporting agencies in chargeof the protection of forests within the European Union. The services comprise the daily analysis and publication ofweather based fire danger information, as well as the timely delivery of active fire locations and recently occuredburnt areas.The German Aerospace Center (DLR) / Department of Geo-Risks and Civil Security has been entrustedwith the task of the analysis, derivation and delivery of burnt areas from Sentinel-3 and MODIS data. . In thisregard, a methodology has been developed, as well as implemented and tested, to efficiently derive reliableand accurate burnt area perimeters for different optical earth observation satellites. The procedure is based onwell-known vegetation related differential indices, such as dNDVI, dBAI and dMSAVI , but primarily uses theActive Contour Level Set method for accurate differentiation between burnt and non-burnt areas via an energyfunction. The fully automated processing chain comprises the pre-processing of satellite data, as well as histogrammatching, mosaic generation and functions for the tracking of burnt area growth over time.Hotspot data from DLR's TET-1 and BIROS satellites are used to verify the validity and accuracy of thederived perimeters.

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule's interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release. ; The IntAct database and EMBL-EBI-based authors received funding from EMBL core funding and Open Targets (grant agreement OTAR-044). The DIP database is funded by NIH grant R01GM123126. MINT received support from ERC grant 'DEPTH project of the European Research Council (grant agreement 322749)'. The British Heart Foundation-University College of London (BHF-UCL) curation team is funded with the British Heart Foundation grant RG/13/5/30112. UniProt curation activities at EMBL-EBI and the Swiss Institute of Bioinformatics are funded by the National Eye Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of General Medical Sciences, and National Institute of Mental Health of the National Institutes of Health under Award Number [U24HG007822], National Human Genome Research Institute under Award Numbers [U41HG007822 and U41HG002273], and the National Institute of General Medical Sciences under Award Numbers [R01GM080646, P20GM103446 and U01GM120953] (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health); Swiss Federal Government through the State Secretariat for Education, Research and Innovation; and aforementioned British Heart Foundation grants and EMBL core funding. DisGeNET is supported with EU-FP7 funds from ISCIII-FEDER (CP10/00524, CPII16/00026), IMI-JU (grant agreement no. 116030, TransQST) and EFPIA companies in kind contribution, and the EU H2020 Programme 2014-2020 (grant agreements no. 634143, MedBioinformatics and no. 676559, Elixir-Excelerate). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I+D+i 2013-2016, funded by ISCIII and FEDER. The DCEXS is a 'Unidad de Excelencia María de Maeztu', funded by the MINECO (ref: MDM-2014-0370). I.J. and group supported in part by Krembil Foundation, Ontario Research Fund (#34876), and Canada Foundation for Innovation (CFI #225404, #30865, #33536). The authors would like to thank Marco Galardini, Luz García-Alonso, Denes Turei and Martin Krallinger for valuable discussions when designing the data set output format; Iain Moal for providing key information about SKEMPI 2.0; Danish Memon for his help pre-processing cBioPortal data; and Luz García-Alonso as the creator of the Python scripts we used to parse UniProt mutagenesis annotations.

Factor Analysis is a genetic term for a somewhat vaguely delimited set of techniques for data processing, mainly applicable to the social and biological sciences. These techniques have been developed for the analysis of mutual relationships among a number of measurements made on a number of measurable entities. In the broad sense, factor analysis comprises a number of statistical models which yield testable hypotheses -- hypotheses that may confirm or disconfirm in terms of the usual statistical procedures for making tests of significance. It also comprises a number of simplifying procedures f

Kurze Reaktionszeiten auf Produktänderungen zu gewährleisten, ist aktuell eine große Herausforderung im Maschinenbau. Als Lösungen werden zunehmend Metamodelle entwickelt, die einzelne Fertigungsprozesse abbilden und eine schnelle Datenverarbeitung ermöglichen. In diesem Beitrag werden Anforderungen und Lösungsansätze vorgestellt, die eine Verknüpfung von Metamodellen einzelner Prozesse zu übergreifenden Metamodellen für die ganzheitliche Prozessfolgenoptimierung ermöglichen.
 
Ensuring short reaction times to product adjustments is currently a great challenge in mechanical engineering. Therefore, metamodels which illustrate individual manufacturing processes and enable fast data processing are increasingly developed as solutions. This paper presents requirements and solution approaches that allow linking metamodels of individual processes to generate cross-technology metamodels for the holistic optimization of process sequences.