scholarly journals The Henetus wave forecast system in the Adriatic Sea

2011 ◽  
Vol 11 (11) ◽  
pp. 2965-2979 ◽  
Author(s):  
L. Bertotti ◽  
P. Canestrelli ◽  
L. Cavaleri ◽  
F. Pastore ◽  
L. Zampato
Keyword(s):  
Adriatic Sea ◽  
Model Performance ◽  
Venice Lagoon ◽  
Wind Fields ◽  
Forecast System ◽  
Meteorological Model ◽  
Wave Forecast ◽  
Constant Quality ◽  
Operational Range

Abstract. We describe the Henetus wave forecast system in the Adriatic Sea. Operational since 1996, the system is continuously upgraded, especially through the correction of the input ECMWF wind fields. As these fields are of progressively improved quality with the increasing resolution of the meteorological model, the correction needs to be correspondingly updated. This ensures a practically constant quality of the Henetus results in the Adriatic Sea since 1996. After suitable and extended validation of the quality of the results at different forecast ranges, the operational range has been recently extended to five days. The Henetus results are used also to improve the tidal forecast on the Venetian coasts and the Venice lagoon, particularly during the most severe events. Extensive statistics on the model performance are provided, both as analysis and forecast, by comparing the model results versus both satellite and buoy data.

scholarly journals Modelling the Quality of Bathing Waters in the Adriatic Sea

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1525
Author(s):  
Christian Ferrarin ◽  
Pierluigi Penna ◽  
Antonella Penna ◽  
Vedrana Spada ◽  
Fabio Ricci ◽  
...  
Keyword(s):  
Adriatic Sea ◽  
Management Tool ◽  
Negative Consequences ◽  
Sea Temperature ◽  
Bathing Water Directive ◽  
Bathing Waters ◽  
Diffusion Dynamics ◽  
Study Sites

The aim of this study is to develop a relocatable modelling system able to describe the microbial contamination that affects the quality of coastal bathing waters. Pollution events are mainly triggered by urban sewer outflows during massive rainy events, with relevant negative consequences on the marine environment and tourism and related activities of coastal towns. A finite element hydrodynamic model was applied to five study areas in the Adriatic Sea, which differ for urban, oceanographic and morphological conditions. With the help of transport-diffusion and microbial decay modules, the distribution of Escherichia coli was investigated during significant events. The numerical investigation was supported by detailed in situ observational datasets. The model results were evaluated against water level, sea temperature, salinity and E. coli concentrations acquired in situ, demonstrating the capacity of the modelling suite in simulating the circulation in the coastal areas of the Adriatic Sea, as well as several main transport and diffusion dynamics, such as riverine and polluted waters dispersion. Moreover, the results of the simulations were used to perform a comparative analysis among the different study sites, demonstrating that dilution and mixing, mostly induced by the tidal action, had a stronger effect on bacteria reduction with respect to microbial decay. Stratification and estuarine dynamics also play an important role in governing microbial concentration. The modelling suite can be used as a beach management tool for improving protection of public health, as required by the EU Bathing Water Directive.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Modelling Exposure by Spraying Activities—Status and Future Needs

2021 ◽  
Vol 18 (15) ◽  
pp. 7737
Author(s):  
Stefan Hahn ◽  
Jessica Meyer ◽  
Michael Roitzsch ◽  
Christiaan Delmaar ◽  
Wolfgang Koch ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Model Performance ◽  
Relevant Information ◽  
Systematic Evaluation ◽  
Model Accuracy ◽  
Efficient Manner ◽  
User Perspective ◽  
Model Predictions ◽  
Spray Applications

Spray applications enable a uniform distribution of substances on surfaces in a highly efficient manner, and thus can be found at workplaces as well as in consumer environments. A systematic literature review on modelling exposure by spraying activities has been conducted and status and further needs have been discussed with experts at a symposium. This review summarizes the current knowledge about models and their level of conservatism and accuracy. We found that extraction of relevant information on model performance for spraying from published studies and interpretation of model accuracy proved to be challenging, as the studies often accounted for only a small part of potential spray applications. To achieve a better quality of exposure estimates in the future, more systematic evaluation of models is beneficial, taking into account a representative variety of spray equipment and application patterns. Model predictions could be improved by more accurate consideration of variation in spray equipment. Inter-model harmonization with regard to spray input parameters and appropriate grouping of spray exposure situations is recommended. From a user perspective, a platform or database with information on different spraying equipment and techniques and agreed standard parameters for specific spraying scenarios from different regulations may be useful.

scholarly journals Development of prognostic models for Health-Related Quality of Life following traumatic brain injury

2021 ◽  
Author(s):  
Isabel R. A. Retel Helmrich ◽  
David van Klaveren ◽  
Simone A. Dijkland ◽  
Hester F. Lingsma ◽  
Suzanne Polinder ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Model Performance ◽  
Prognostic Models ◽  
Health Related Quality ◽  
Mild Tbi ◽  
Related Quality ◽  
Health Related

Abstract Background Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R2) and corrected for optimism with bootstrap procedures. Results 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R2 of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R2 PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%). Conclusion Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI.

Prediction of quality of life in people with ALS

2021 ◽  
Vol 21 (2) ◽  
pp. 5-17
Author(s):  
Anna Markella Antoniadi ◽  
Miriam Galvin ◽  
Mark Heverin ◽  
Orla Hardiman ◽  
Catherine Mooney
Keyword(s):  
Quality Of Life ◽  
Disease Onset ◽  
Model Performance ◽  
Sampling Technique ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Rapid Decline ◽  
Extreme Gradient Boosting ◽  
Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease that causes a rapid decline in motor functions and has a fatal trajectory. ALS is currently incurable, so the aim of the treatment is mostly to alleviate symptoms and improve quality of life (QoL) for the patients. The goal of this study is to develop a Clinical Decision Support System (CDSS) to alert clinicians when a patient is at risk of experiencing low QoL. The source of data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person's overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. We used Extreme Gradient Boosting (XGBoost) for the development of the predictive models, which was compared to a logistic regression baseline model. Additionally, we used Synthetic Minority Over-sampling Technique (SMOTE) to examine if that would increase model performance and SHAP (SHapley Additive explanations) as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately using three features - age at disease onset, ALSFRS-R score for orthopnoea and the caregiver's status pre-caregiving - with a F1-score on the test set equal to 0.81, recall of 0.78, and precision of 0.84. The addition of two extra features (caregiver's age and the ALSFRS-R score for speech) produced similar outcomes (F1-score 0.79, recall 0.70 and precision 0.90).

EHR-based machine learning modeling for preterm birth prediction: A systematic review (Preprint)

2021 ◽  
Author(s):  
Zahra Sharifiheris ◽  
Juho Laitala ◽  
Antti Airola ◽  
Amir M Rahmani ◽  
Miriam Bender
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Preterm Birth ◽  
Model Performance ◽  
Bibliographic Databases ◽  
Complementary Approach ◽  
Prisma Statement ◽  
Comprehensive Search ◽  
First Time

BACKGROUND Preterm birth (PTB) as a common pregnancy complication is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly impacts around 15 million children annually across the world. Conventional approaches to predict PTB may neither be applicable for first-time mothers nor possess reliable predictive power. Recently, machine learning (ML) models have shown the potential as an appropriate complementary approach for PTB prediction. OBJECTIVE In this article we systematically reviewed the literature concerned with PTB prediction using ML modeling. METHODS This systematic review was conducted in accordance with the PRISMA statement. A comprehensive search was performed in seven bibliographic databases up until 15 May 2021. The quality of studies was assessed, and the descriptive information including socio-demographic characteristics, ML modeling processes, and model performance were extracted and reported. RESULTS A total of 732 papers were screened through title and abstract. Of these, 23 studies were screened by full text resulting in 13 papers that met the inclusion criteria. CONCLUSIONS We identified various ML models used for different EHR data resulting in a desirable performance for PTB prediction. However, evaluation metrics, software/package used, data size and type, and selected features, and importantly data management method often varied from study to study threatening the reliability and generalizability of the model. CLINICALTRIAL n.a.

Introducing a New Test Rig for Film Cooling Measurements With Realistic Hole Inflow Conditions

2017 ◽  
Author(s):  
Marc Fraas ◽  
Tobias Glasenapp ◽  
Achmed Schulz ◽  
Hans-Jörg Bauer
Keyword(s):  
Film Cooling ◽  
Gas Flow ◽  
Test Rig ◽  
Hot Gas ◽  
Cooling Hole ◽  
Measurement Principle ◽  
Density Ratios ◽  
Operational Range ◽  
Film Cooling Holes

Further improvements in film cooling require an in-depth understanding of the influencing parameters. Therefore, a new test rig has been designed and commissioned for the assessment of novel film cooling holes under realistic conditions. The test rig is designed for generic film cooling studies. External hot gas flow as well as internal coolant passage flow are simulated by two individual flow channels connected to each other by the cooling holes. Based on a similarity analysis, the geometry of the test rig is scaled up by a factor of about 20. It furthermore offers the possibility to conduct experiments at high density ratios and realistic approach flow conditions at both cooling hole exit and inlet. The operational range of the new test rig is presented and compared to real engine conditions. It is shown that the important parameters are met and the transfer-ability of the results is ensured. Special effort is put onto the uniformity of the approaching hot gas flow, which will be demonstrated by temperature and velocity profiles. A first measurement of the heat transfer coefficient without film cooling is used to demonstrate the quality of the measurement principle.

scholarly journals BUDGET UAV SYSTEMS FOR THE PROSPECTION OF SMALL- AND MEDIUM-SCALE ARCHAEOLOGICAL SITES

2016 ◽  
Vol XLI-B1 ◽  
pp. 971-977 ◽  
Author(s):  
W. Ostrowski ◽  
K. Hanus
Keyword(s):  
Low Cost ◽  
Planning System ◽  
Archaeological Sites ◽  
Quality Of Data ◽  
Computing Power ◽  
Medium Scale ◽  
The Cost ◽  
User Friendly ◽  
Operational Range

One of the popular uses of UAVs in photogrammetry is providing an archaeological documentation. A wide offer of low-cost (consumer) grade UAVs, as well as the popularity of user-friendly photogrammetric software allowing obtaining satisfying results, contribute to facilitating the process of preparing documentation for small archaeological sites. However, using solutions of this kind is much more problematic for larger areas. The limited possibilities of autonomous flight makes it significantly harder to obtain data for areas too large to be covered during a single mission. Moreover, sometimes the platforms used are not equipped with telemetry systems, which makes navigating and guaranteeing a similar quality of data during separate flights difficult. The simplest solution is using a better UAV, however the cost of devices of such type often exceeds the financial capabilities of archaeological expeditions. <br><br> The aim of this article is to present methodology allowing obtaining data for medium scale areas using only a basic UAV. The proposed methodology assumes using a simple multirotor, not equipped with any flight planning system or telemetry. Navigating of the platform is based solely on live-view images sent from the camera attached to the UAV. The presented survey was carried out using a simple GoPro camera which, from the perspective of photogrammetric use, was not the optimal configuration due to the fish eye geometry of the camera. Another limitation is the actual operational range of UAVs which in the case of cheaper systems, rarely exceeds 1 kilometre and is in fact often much smaller. Therefore the surveyed area must be divided into sub-blocks which correspond to the range of the drone. It is inconvenient since the blocks must overlap, so that they will later be merged during their processing. This increases the length of required flights as well as the computing power necessary to process a greater number of images. <br><br> These issues make prospection highly inconvenient, but not impossible. Our paper presents our experiences through two case studies: surveys conducted in Nepal under the aegis of UNESCO, and works carried out as a part of a Polish archaeological expedition in Cyprus, which both prove that the proposed methodology allows obtaining satisfying results. The article is an important voice in the ongoing debate between commercial and academic archaeologists who discuss the balance between the required standards of conducting archaeological works and economic capabilities of archaeological missions.

Sign in / Sign up

Export Citation Format

Share Document