PickLock: A Deadlock Prediction Approach under Nested Locking

2015 ◽  
pp. 179-199 ◽  
Author(s):  
Francesco Sorrentino
Keyword(s):  
Prediction Approach

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

An Affect Prediction Approach Through Depression Severity Parameter Incorporation in Neural Networks

2017 ◽  
Author(s):  
Rahul Gupta ◽  
Saurabh Sahu ◽  
Carol Espy-Wilson ◽  
Shrikanth S. Narayanan
Keyword(s):  
Neural Networks ◽  
Depression Severity ◽  
Prediction Approach ◽  
Severity Parameter

A Statistical Seizure Prediction Approach Based on Savitzky-Golay Smoothing

2018 ◽  
Vol 27 (1) ◽  
pp. 53-70
Author(s):  
Ahmed Sedik ◽  
Turky Alotaiby ◽  
Heba El-Khobby ◽  
Mahmoud Atea ◽  
Saleh A. Alshebeili ◽  
...  
Keyword(s):  
Seizure Prediction ◽  
Prediction Approach

Quantitative Evaluation of Drug-Drug Interaction Potentials by in vivo Information- Guided Prediction Approach

2015 ◽  
Vol 15 (8) ◽  
pp. 761-766 ◽  
Author(s):  
Feng Chen ◽  
Zhe-Yi Hu ◽  
Wei-Wei Jia ◽  
Jing-Tao Lu ◽  
Yuan-Sheng Zhao
Keyword(s):  
Drug Interaction ◽  
Quantitative Evaluation ◽  
Interaction Potentials ◽  
Drug Drug Interaction ◽  
Prediction Approach

scholarly journals A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 19550-19563 ◽  
Author(s):  
Ling Huang ◽  
Chang-Dong Wang ◽  
Hong-Yang Chao ◽  
Jian-Huang Lai ◽  
Philip S. Yu
Keyword(s):  
Collaborative Filtering ◽  
Prediction Approach

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

Throughput Performance Prediction Approach for Wi-Fi Site Surveys

2021 ◽  
Author(s):  
Akihiro Tatsuta ◽  
Yasunori Shimazaki ◽  
Teppei Emura ◽  
Takuya Asada ◽  
Taichi Hamabe
Keyword(s):  
Performance Prediction ◽  
Throughput Performance ◽  
Prediction Approach

scholarly journals Current and New Approaches to Predict the Deflections of One-Way Flexural Members with a Focus on Composite Steel Deck Slabs Voided by Circular Tubes

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 421
Author(s):  
Chang-Hwan Lee ◽  
Iman Mansouri ◽  
Jaehoon Bae ◽  
Jaeho Ryu
Keyword(s):  
Structural Function ◽  
Flexural Members ◽  
Long Span ◽  
Deck Slabs ◽  
New Type ◽  
New Perspective ◽  
Flexural Tests ◽  
Prediction Approach ◽  
Steel Deck

A new type of composite voided slab, the TUBEDECK (TD), which utilizes the structural function of profiled steel decks, has recently been proposed. Previous studies have confirmed that the flexural strength of TD slabs can be calculated based on the full composite contribution of the steel deck, but for long-span flexural members, the deflection serviceability requirement is often dominant. Herein, we derived a novel deflection prediction approach using the results of flexural tests on slab specimens, focusing on TD slabs. First, deflection prediction based on modifications of the current code was proposed. Results revealed that TD slabs exhibited smaller long-term deflections and at least 10% longer maximum span lengths than solid slabs, indicating their greater efficiency. Second, a novel rational method was derived for predicting deflections without computing the effective moment of inertia. The ultimate deflections predicted by the proposed method correlated closely with the deflection under maximum bending moments. To calculate immediate deflections, variation functions for the concrete strain at the extreme compression fiber and neutral axis depth were assumed with predictions in good agreement with experiments. The proposed procedure has important implications in highlighting a new perspective on the deflection prediction of reinforced concrete and composite flexural members.

scholarly journals The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Eun Pyo Hong ◽  
Seong Gu Heo ◽  
Ji Wan Park
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Risk Prediction ◽  
Linear Mixed Model ◽  
Threshold Model ◽  
Snp Markers ◽  
Polygenic Risk Score ◽  
Phenotypic Variance ◽  
Prediction Approach

Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT231) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

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