Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Environmental Health
Title: An ontology-based healthcare monitoring system in the Internet of Things
Abstract: Continuous health monitoring is a hopeful solution that can efficiently provide health-related services to elderly people suffering from chronic diseases. The emergence of the Internet of Things (IoT) technologies have led to their adoption in the development of new healthcare systems for efficient healthcare monitoring, diagnosis and treatment. This paper presents a healthcare-IoT based system where an ontology is proposed to provide semantic interoperability among heterogeneous devices and users in healthcare domain. Our work consists on integrating existing ontologies related to health, IoT domain and time, instantiating classes, and establishing reasoning rules. The model created has been validated by semantic querying. The results show the feasibility and efficiency of the proposed ontology and its capability to grow into a more understanding and specialized ontology for health monitoring and treatment.
Extracted statement — Has evaluation: by semantic querying
Research field: Environmental Health
Title: HealthIoT Ontology for Data Semantic Representation and Interpretation Obtained from Medical Connected Objects
Abstract: Internet of Things (IoT) covers a variety of applications including the Healthcare field. Consequently, medical objects become connected to each other with the purpose to share and exchange health data. These medical connected objects raise issues on how to ensure the analysis, interpretation and semantic interoperability of the extensive obtained health data with the purpose to make an appropriate decision. This paper proposes a HealthIoT ontology for representing the semantic interoperability of the medical connected objects and their data; while an algorithm alleviates the analysis of the detected vital signs and the decision-making of the doctor. The execution of this algorithm needs the definition of several SWRL rules (Semantic Web Rule Language).
Extracted statement — Has result: make an appropriate decision
Research field: Environmental Health
Title: HealthIoT Ontology for Data Semantic Representation and Interpretation Obtained from Medical Connected Objects
Abstract: Internet of Things (IoT) covers a variety of applications including the Healthcare field. Consequently, medical objects become connected to each other with the purpose to share and exchange health data. These medical connected objects raise issues on how to ensure the analysis, interpretation and semantic interoperability of the extensive obtained health data with the purpose to make an appropriate decision. This paper proposes a HealthIoT ontology for representing the semantic interoperability of the medical connected objects and their data; while an algorithm alleviates the analysis of the detected vital signs and the decision-making of the doctor. The execution of this algorithm needs the definition of several SWRL rules (Semantic Web Rule Language).
Extracted statement — Has method: Several SWRL
Research field: Environmental Sciences
Title: Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method
Abstract: In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
Extracted statement — Has method: Denoising Method
Research field: Environmental Sciences
Title: Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method
Abstract: In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
Extracted statement — Has method: discrete wavelet transform (DWT)
Research field: Environmental Sciences
Title: Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method
Abstract: In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
Extracted statement — Has method: Fourier transform (FT)
Research field: Environmental Sciences
Title: Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method
Abstract: In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
Extracted statement — Has method: fractional-order derivative (FOD)
Research field: Environmental Sciences
Title: Extraction of built-up area using multi-sensor data—A case study based on Google earth engine in Zhejiang Province, China
Abstract: ABSTRACT Accurate and up-to-date built-up area mapping is of great importance to the science community, decision-makers, and society. Therefore, satellite-based, built-up area (BUA) extraction at medium resolution with supervised classification has been widely carried out. However, the spectral confusion between BUA and bare land (BL) is the primary hindering factor for accurate BUA mapping over large regions. Here we propose a new methodology for the efficient BUA extraction using multi-sensor data under Google Earth Engine cloud computing platform. The proposed method mainly employs intra-annual satellite imagery for water and vegetation masks, and a random-forest machine learning classifier combined with auxiliary data to discriminate between BUA and BL. First, a vegetation mask and water mask are generated using NDVI (normalized differenced vegetation index) max in vegetation growth periods and the annual water-occurrence frequency. Second, to accurately extract BUA from unmasked pixels, consisting of BUA and BL, random-forest-based classification is conducted using multi-sensor features, including temperature, night-time light, backscattering, topography, optical spectra, and NDVI time-series metrics. This approach is applied in Zhejiang Province, China, and an overall accuracy of 92.5% is obtained, which is 3.4% higher than classification with spectral data only. For large-scale BUA mapping, it is feasible to enhance the performance of BUA mapping with multi-temporal and multi-sensor data, which takes full advantage of datasets available in Google Earth Engine.
Extracted statement — Softwares: Google Earth Engine
Research field: Environmental Sciences
Title: The ACCESS coupled model: description, control climate and evaluation
Abstract: 4OASIS3.2–5 coupling framework. The primary goal of the ACCESS-CM development is to provide the Australian climate community with a new generation fully coupled climate model for climate research, and to participate in phase five of the Coupled Model Inter-comparison Project (CMIP5). This paper describes the ACCESS-CM framework and components, and presents the control climates from two versions of the ACCESS-CM, ACCESS1.0 and ACCESS1.3, together with some fields from the 20 th century historical experiments, as part of model evaluation. While sharing the same ocean sea-ice model (except different setups for a few parameters), ACCESS1.0 and ACCESS1.3 differ from each other in their atmospheric and land surface components: the former is configured with the UK Met Office HadGEM2 (r1.1) atmospheric physics and the Met Office Surface Exchange Scheme land surface model version 2, and the latter with atmospheric physics similar to the UK Met Office Global Atmosphere 1.0 includ ing modifications performed at CAWCR and the CSIRO Community Atmosphere Biosphere Land Exchange land surface model version 1.8. The global average annual mean surface air temperature across the 500-year preindustrial control integrations show a warming drift of 0.35 °C in ACCESS1.0 and 0.04 °C in ACCESS1.3. The overall skills of ACCESS-CM in simulating a set of key climatic fields both globally and over Australia significantly surpass those from the preceding CSIRO Mk3.5 model delivered to the previous coupled model inter-comparison. However, ACCESS-CM, like other CMIP5 models, has deficiencies in various as pects, and these are also discussed.
Extracted statement — Earth System Model: Land Surface
Research field: Environmental Sciences
Title: The NCEP Climate Forecast System Reanalysis
Abstract: The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice m...
Extracted statement — Earth System Model: Land Surface
Research field: Environmental Sciences
Title: The NCEP Climate Forecast System Reanalysis
Abstract: The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice m...
Extracted statement — Earth System Model: Sea Ice
Research field: Environmental Sciences
Title: A Modified Dynamic Framework for the Atmospheric Spectral Model and Its Application
Abstract: This paper describes a dynamic framework for an atmospheric general circulation spectral model in which a reference stratified atmospheric temperature and a reference surface pressure are introduced into the governing equations so as to improve the calculation of the pressure gradient force and gradients of surface pressure and temperature. The vertical profile of the reference atmospheric temperature approximately corresponds to that of the U.S. midlatitude standard atmosphere within the troposphere and stratosphere, and the reference surface pressure is a function of surface terrain geopotential and is close to the observed mean surface pressure. Prognostic variables for the temperature and surface pressure are replaced by their perturbations from the prescribed references. The numerical algorithms of the explicit time difference scheme for vorticity and the semi-implicit time difference scheme for divergence, perturbation temperature, and perturbation surface pressure equation are given in detail. The modified numerical framework is implemented in the Community Atmosphere Model version 3 (CAM3) developed at the National Center for Atmospheric Research (NCAR) to test its validation and impact on simulated climate. Both the original and the modified models are run with the same spectral resolution (T42), the same physical parameterizations, and the same boundary conditions corresponding to the observed monthly mean sea surface temperature and sea ice concentration from 1971 to 2000. This permits one to evaluate the performance of the new dynamic framework compared to the commonly used one. Results show that there is a general improvement for the simulated climate at regional and global scales, especially for temperature and wind.
Extracted statement — Earth System Model: Sea Ice
Research field: Environmental Sciences
Title: Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method
Abstract: In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
Extracted statement — Has method: singular value decomposition (SVD)
Research field: Epidemiology
Title: Diseases and Health Outcomes Registry Systems in I.R. Iran: Successful Initiative to Improve Public Health Programs, Quality of Care, and Biomedical Research
Abstract: Registration systems for diseases and other health outcomes provide important resource for biomedical research, as well as tools for public health surveillance and improvement of quality of care. The Ministry of Health and Medical Education (MOHME) of Iran launched a national program to establish registration systems for different diseases and health outcomes. Based on the national program, we organized several workshops and training programs and disseminated the concepts and knowledge of the registration systems. Following a call for proposals, we received 100 applications and after thorough evaluation and corrections by the principal investigators, we approved and granted about 80 registries for three years. Having strong steering committee, committed executive and scientific group, establishing national and international collaboration, stating clear objectives, applying feasible software, and considering stable financing were key components for a successful registry and were considered in the evaluation processes. We paid particulate attention to non-communicable diseases, which constitute an emerging public health problem. We prioritized establishment of regional population-based cancer registries (PBCRs) in 10 provinces in collaboration with the International Agency for Research on Cancer. This initiative was successful and registry programs became popular among researchers and research centers and created several national and international collaborations in different areas to answer important public health and clinical questions. In this paper, we report the details of the program and list of registries that were granted in the first round.
Extracted statement — Executive: Ministry of Health and Medical Education