Evaluation Criteria for Real-Time Specification Languages

1992 ◽  
Author(s):  
Paul C. Clements ◽  
Carolyn E. Gasarch ◽  
Ralph D. Jeffords
Keyword(s):  
Real Time ◽  
Evaluation Criteria ◽  
Specification Languages

scholarly journals Statistical post-processing of ensemble forecasts of the height of new snow

2019 ◽  
Vol 26 (3) ◽  
pp. 339-357 ◽  
Author(s):  
Jari-Pekka Nousu ◽  
Matthieu Lafaysse ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
Guillaume Evin ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Heterogeneous Model ◽  
The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Météo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the French Alps and Pyrenees. The calibration is tested at the station scale and the massif scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

Recommender Systems in Healthcare

2018 ◽  
pp. 323-346 ◽  
Author(s):  
Adekunle Oluseyi Afolabi ◽  
Pekka Toivanen
Keyword(s):  
Real Time ◽  
Recommender Systems ◽  
Recommender System ◽  
Evaluation Criteria ◽  
Upward Trend ◽  
Empirical Results ◽  
Care Giving

In this chapter the appropriateness of any recommender system in healthcare, which lies in its ability to provide capabilities for meeting the challenges of modern care giving, is examined. The impacts of over two decades of research in and implementation of recommender systems in healthcare are extensively examined in two consecutive periods: first to examine empirical results and practical implementations while the second focuses on validating the earlier findings and justifying the propositions made. Although the result indicates an optimistic progress and upward trend in both the research and implementation, there are compelling reasons to invest more efforts at harmonizing evaluation criteria and metrics. In addition, in order to appropriately, adequately, and effectively meet the challenges of modern care, the rapidly evolving trends, and changing technologies, a novel solution with potential for these capabilities is proposed: a solution to provide real-time recommendations and make them available for sharing among stakeholders in real time.

scholarly journals Adequacy of Existing Surveillance Systems to Monitor Racism, Social Stigma and COVID Inequities: A Detailed Assessment and Recommendations

2021 ◽  
Vol 18 (24) ◽  
pp. 13099
Author(s):  
Chandra L. Ford ◽  
Bita Amani ◽  
Nina T. Harawa ◽  
Randall Akee ◽  
Gilbert C. Gee ◽  
...  
Keyword(s):  
Public Health ◽  
Critical Race Theory ◽  
Real Time ◽  
Health Systems ◽  
Social Stigma ◽  
Evaluation Criteria ◽  
Surveillance Systems ◽  
Related Factors ◽  
Public Health Systems ◽  
Detailed Assessment

The populations impacted most by COVID are also impacted by racism and related social stigma; however, traditional surveillance tools may not capture the intersectionality of these relationships. We conducted a detailed assessment of diverse surveillance systems and databases to identify characteristics, constraints and best practices that might inform the development of a novel COVID surveillance system that achieves these aims. We used subject area expertise, an expert panel and CDC guidance to generate an initial list of N > 50 existing surveillance systems as of 29 October 2020, and systematically excluded those not advancing the project aims. This yielded a final reduced group (n = 10) of COVID surveillance systems (n = 3), other public health systems (4) and systems tracking racism and/or social stigma (n = 3, which we evaluated by using CDC evaluation criteria and Critical Race Theory. Overall, the most important contribution of COVID-19 surveillance systems is their real-time (e.g., daily) or near-real-time (e.g., weekly) reporting; however, they are severely constrained by the lack of complete data on race/ethnicity, making it difficult to monitor racial/ethnic inequities. Other public health systems have validated measures of psychosocial and behavioral factors and some racism or stigma-related factors but lack the timeliness needed in a pandemic. Systems that monitor racism report historical data on, for instance, hate crimes, but do not capture current patterns, and it is unclear how representativeness the findings are. Though existing surveillance systems offer important strengths for monitoring health conditions or racism and related stigma, new surveillance strategies are needed to monitor their intersecting relationships more rigorously.

scholarly journals OTHR-06. PACS Lesion Tracking Tool provides real time automatic information on brain tumor metastasis growth curves and RECIST criteria

2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii15-iii15
Author(s):  
Benjamin Jang ◽  
MingDe Lin ◽  
Randy Owens ◽  
Khaled Bousabarah ◽  
Amit Mahajan ◽  
...  
Keyword(s):  
Real Time ◽  
Patient Management ◽  
Brain Mri ◽  
Evaluation Criteria ◽  
Growth Curves ◽  
Complete Response ◽  
Lesion Size ◽  
Intracranial Metastasis ◽  
Radiology Reports ◽  
Clinical Difference

Abstract Objective Communicating metastatic brain treatment response can be complicated. A widely used method to assess clinical response is called response evaluation criteria in solid tumors or RECIST. In our study, we use a PACS Lesion Tracking Tool (TT) to assess intracranial metastasis using RECIST criteria. We predict that the TT will be superior to the standard radiology reports. Methods Nuance ® mPowerTM was used to identify 30 patients with brain metastasis who received brain MRI from 4/2020–4/2021. Patient’s first brain MRI with metastasis was set as baseline and subsequent 3 brain MRI studies were examined. All lesions were measured on post-gadolinium sequence and defined as target lesions or new lesions. The TT was used to measure lesion size over time with creation of growth curves and RECIST outcomes, which include stable disease, progressive disease, partial response, or complete response. Subsequently, RECIST evaluations were compared with radiologic impressions for discrepancy, and further evaluations were made to see if it made a clinical difference in patient management and/or provide additional useful information. These evaluations were given a rating of agree/yes, equivocal, or disagree/no. They were assessed by 3 neuroradiologists. Results Number of lesions ranged from 1–27. The assessments from 3 neuroradiologists were averaged. Comparing impression versus RECIST evaluation, the results demonstrated the following: 8/30 disagreement, 4/30 equivocal, and 18/30 agreement. Using more stringent criteria, assessing whether the TT would result in either change in patient management or provide additional useful information, the results were the following: 6/30 yes, 4/30 equivocal, and 20/30 no. Discussion In addition to providing real time RECIST criteria evaluations and visually descriptive lesion growth tables, the TT was easy to use. Interpretation of these additional data provided more clarity and was found to be superior to standard radiology report.

scholarly journals Reducing the Unit Costs of Supplying Drilling Rigs with Spare Parts Based on Information Provided by the Neural Network Classifier

2019 ◽  
Vol 105 ◽  
pp. 03017 ◽  
Author(s):  
Fares Abu-Abed
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Monitoring System ◽  
Evaluation Criteria ◽  
Spare Parts ◽  
Neural Network Classifier ◽  
Drilling Rig ◽  
Unit Costs ◽  
The Neural Network ◽  
Drilling Rigs

Minimizing costs when organizing the supply of drilling rigs with spare parts is an important task. To do this, it is necessary to develop evaluation criteria or rely on information obtained from the monitoring system in real time. The paper proposes a model for processing the output of a drilling rig, depending on the possible strategy for operating the equipment. The work is an integral part of previously published developments presented in the materials of articles in 2-nd and 3-rd International innovative mining symposiums (2017-2018).

scholarly journals Development of a methodology for real-time retrieving and viewing of spatial data in emergency scenarios

2021 ◽  
Author(s):  
Ahmed Alamouri ◽  
Mohammad Hassan ◽  
Markus Gerke
Keyword(s):  
Real Time ◽  
Data Sharing ◽  
Spatial Data ◽  
Evaluation Criteria ◽  
Emergency Situation ◽  
Free Access ◽  
Assessment Procedure ◽  
Quick Response ◽  
Joint Research ◽  
Joint Research Project

AbstractQuick response in emergency situations is crucial, because any delay can result in dramatic consequences and potentially human losses. Therefore, many institutions/authorities are relying on development of strategies for emergency management, specially to have a quick response process using modern technologies like unmanned aerial vehicles. A key factor affecting this process is to have a quick geo-situation report of the emergency in real time, which reflects the current emergency situation and supports in right decision-making. Providing such geo-reports is still not an easy task because—in most cases—a priori known spatial data like map data (raster/vector) or geodatabases are outdated, and anyway would not provide an overview on the current situation. Therefore, this paper introduces a management methodology of spatial data focusing on enabling a free access and viewing the data of interest in real time and in situ to support emergency managers. The results of this work are twofold: on the one hand, an automated mechanism for spatial data synchronization and streaming was developed and on the other hand, a spatial data sharing concept was realized using web map tile service. For results assessment, an experimental framework through the joint research project ANKommEn (English acronym: Automated Navigation and Communication for Exploration) was implemented. The assessment procedure was achieved based on specific evaluation criteria like time consumption and performance and showed that the developed methodology can help in overcoming some of existing challenges and addressing the practically relevant questions concerning on the complexity in spatial data sharing and retrieval.

scholarly journals Statistical post-processing of ensemble forecasts of the height of new snow

2019 ◽  
Author(s):  
Jari-Pekka Nousu ◽  
Matthieu Lafaysse ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
Guillaume Evin ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Heterogeneous Model ◽  
The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, roads viability, ski resorts management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool is refining the elevation resolution, and the Crocus snowpack model is representing the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on Nonhomogeneous Regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24-hour HN in French Alps and Pyrenees. The calibration is tested at the station-scale and the massif-scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

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