Investigating the chlorophyll-a variability in the Gulf of Taranto (North-western Ionian Sea) by a multi-temporal analysis of MODIS-Aqua Level 3/Level 2 data

2018 ◽  
Vol 155 ◽  
pp. 34-44 ◽  
Author(s):  
Emanuele Ciancia ◽  
Irina Coviello ◽  
Carmine Di Polito ◽  
Teodosio Lacava ◽  
Nicola Pergola ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Temporal Analysis ◽  
Ionian Sea ◽  
Multi Temporal ◽  
Level 3 ◽  
Level 2 ◽  
North Western

First release of the CMEMS Global coastal OLCI 300 meters Chlorophyll-a Product

2021 ◽  
Author(s):  
Philippe Garnesson ◽  
Antoine Mangin ◽  
Julien Demaria ◽  
Marine Bretagnon ◽  
Odile Hembise Fanton d'Andon
Keyword(s):  
Chlorophyll A ◽  
Spatial Resolution ◽  
End User ◽  
Vicarious Calibration ◽  
Large Matrix ◽  
Merging Procedure ◽  
Level 3 ◽  
Marine Environment Monitoring ◽  
Monitoring Service ◽  
Level 2

<p>The Ocean Colour Instrument (OLCI) on-board the Sentinel-3A and 3B satellites with a 300 m spatial resolution has a major advantage compared to other satellite missions with a typical 1 km spatial resolution. The chlorophyll-a product derived from OLCI’s 300 m measurement facilitates many applications in marine and coastal ecology, from ecosystem modeling, to fisheries management, and monitoring of water quality. The OLCI 300 m chlorophyll-a swath data (Level-2) are operationally disseminated in NRT mode by the EUMETSAT agency. The Copernicus Marine Environment Monitoring Service (CMEMS) eases the usage of these Level-2 (swath data) by providing Level-3 (daily mapped gridded files) at global and regional level.</p><p>This study highlights the first release of a 300 m NRT global daily chlorophyll-a product based on the merging of OLCI S3A and S3B. It will be routinely disseminated in the frame of CMEMS in May 2021. Before this date, the resolution of the CMEMS Chlorophyll products was 4km at global level and 1km over some European regional seas This 300 m product will be based on the Copernicus-GlobColour processor already used by CMEMS for the Global chlorophyll-a product and the regional Atlantic daily interpolated product. The daily image will correspond to a large matrix of 32400x64800 pixels with chlorophyll-a data provided along the coastline (200 km). CMEMS provides to the end-user facilities to extract data on his area and period of interest.</p><p>This new product will take benefit of a new EUMETSAT’s Level-2 product baseline which should be switched operationally in NRT mode mid-February 2021. This new baseline improves mainly the System Vicarious Calibration (SVC) gains of both S3A and S3B and the associated quality flags. The Chlorophyll-a OC4ME algorithm has been also improved with the use of the Colour Index algorithm for clear water. The assessment of this new OC4ME chlorophyll-a product (based on tandem data) shows a very good correlation between S3A and S3B. A regression between a daily S3A and S3B global product provides a R2 of 0.98 with a respective slope and offset of 1.0 and 0.005. However, some limitations concerning the level-2 upstream products have been identified. Details about the merging procedure, inter-comparison with existing product and illustrations of results will be presented.</p>

Historical text archives and prosopography: the COEL database system

1998 ◽  
Vol 10 (1-3) ◽  
pp. 57-72 ◽  
Author(s):  
K. S. B. Keats-Rohan
Keyword(s):  
Research Tool ◽  
Direct Access ◽  
Specific Situation ◽  
Core Data ◽  
Secondary Sources ◽  
The Core ◽  
Level 3 ◽  
Full Discussion ◽  
Available Information ◽  
Level 2

The COEL database and database software, a combined reference and research tool created by historians for historians, is presented here through Screenshots illustrating the underlying theoretical model and the specific situation to which that has been applied. The key emphases are upon data integrity, and the historian's role in interpreting and manipulating what is often contentious data. From a corpus of sources (Level 1) certain core data are extracted for separate treatment at an interpretive level (Level 3), based upon a master list of the core data (Level 2). The core data are interdependent: each record in Level 2 is of interest in itself; and it either could or should be associated with an(other) record(s) as a specific entity. Sometimes the sources are ambiguous and the association is contentious, necessitating a probabilty-coding approach. The entities created by the association process can then be treated at a commentary level, introducing material external to the database, whether primary or secondary sources. A full discussion of the difficulties is provided within a synthesis of available information on the core data. Direct access to the source texts is only ever a mouse click away. Fully query able, COEL is formidable look-up and research tool for users of all levels, who remain free to exercise an alternative judgement on the associations of the core data. In principle, there is no limit on the type of text or core data that could be handled in such a system.

EVALUASI PELATIHAN DI PT. PUPUK KUJANG CIKAMPEK

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Lania Muharsih ◽  
Ratih Saraswati
Keyword(s):  
Training Evaluation ◽  
Work Productivity ◽  
Learning Behavior ◽  
Level 3 ◽  
Level 1 ◽  
Four Levels ◽  
A Company ◽  
Level 2 ◽  
Evaluation Sheet ◽  
Assessment Factor

This study aims to determine the training evaluation at PT. Kujang Fertilizer. PT. Pupuk Kujang is a company engaged in the field of petrochemicals. Evaluation sheet of PT. Fertilizer Kujang is made based on Kirkpatrick's theory which consists of four levels of evaluation, namely reaction, learning, behavior, and results. At level 1, namely reaction, in the evaluation sheet is in accordance with the theory of Kirkpatrick, at level 2 that is learning should be held pretest and posttest but only made scale. At level 3, behavior, according to theory, but on assessment factor number 3, quantity and work productivity should not need to be included because they are included in level 4. At level 4, that is the result, here is still lacking to get a picture of the results of the training that has been carried out because only based on answers from superiors without evidence of any documents.   Keywords: Training Evaluation, Kirkpatrick Theory.    Penelitian ini bertujuan mengetahui evaluasi training di PT. Pupuk Kujang. PT. Pupuk Kujang merupakan perusahaan yang bergerak di bidang petrokimia. Lembar evaluasi PT. Pupuk Kujang dibuat berdasarkan teori Kirkpatrick yang terdiri dari empat level evaluasi, yaitu reaksi, learning, behavior, dan hasil. Pada level 1 yaitu reaksi, di lembar evaluasi tersebut sudah sesuai dengan teori dari Kirkpatrick, pada level 2 yaitu learning seharusnya diadakan pretest dan posttest namun hanya dibuatkan skala. Pada level 3 yaitu behavior, sudah sesuai teori namun pada faktor penilaian nomor 3 kuantitas dan produktivitas kerja semestinya tidak perlu dimasukkan karena sudah termasuk ke dalam level 4. Pada level 4 yaitu hasil, disini masih sangat kurang untuk mendapatkan gambaran hasil dari pelatihan yang sudah dilaksanakan karena hanya berdasarkan dari jawaban atasan tanpa bukti dokumen apapun.   Kata kunci: Evaluasi Pelatihan, Teori Kirkpatrick.

Variation in antibiotic use across intensive care units (ICU): A population-based cohort study in Ontario, Canada

2020 ◽  
Vol 41 (9) ◽  
pp. 1035-1041
Author(s):  
Erika Y. Lee ◽  
Michael E. Detsky ◽  
Jin Ma ◽  
Chaim M. Bell ◽  
Andrew M. Morris
Keyword(s):  
Cohort Study ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Ventilatory Support ◽  
Severity Of Illness ◽  
Antibiotic Use ◽  
Patient Factors ◽  
Days Of Therapy ◽  
Level 3 ◽  
Level 2

AbstractObjectives:Antibiotics are commonly used in intensive care units (ICUs), yet differences in antibiotic use across ICUs are unknown. Herein, we studied antibiotic use across ICUs and examined factors that contributed to variation.Methods:We conducted a retrospective cohort study using data from Ontario’s Critical Care Information System (CCIS), which included 201 adult ICUs and 2,013,397 patient days from January 2012 to June 2016. Antibiotic use was measured in days of therapy (DOT) per 1,000 patient days. ICU factors included ability to provide ventilator support (level 3) or not (level 2), ICU type (medical-surgical or other), and academic status. Patient factors included severity of illness using multiple-organ dysfunction score (MODS), ventilatory support, and central venous catheter (CVC) use. We analyzed the effect of these factors on variation in antibiotic use.Results:Overall, 269,351 patients (56%) received antibiotics during their ICU stay. The mean antibiotic use was 624 (range 3–1460) DOT per 1,000 patient days. Antibiotic use was significantly higher in medical-surgical ICUs compared to other ICUs (697 vs 410 DOT per 1,000 patient days; P < .0001) and in level 3 ICUs compared to level 2 ICUs (751 vs 513 DOT per 1,000 patient days; P < .0001). Higher antibiotic use was associated with higher severity of illness and intensity of treatment. ICU and patient factors explained 47% of the variation in antibiotic use across ICUs.Conclusions:Antibiotic use varies widely across ICUs, which is partially associated with ICUs and patient characteristics. These differences highlight the importance of antimicrobial stewardship to ensure appropriate use of antibiotics in ICU patients.

scholarly journals An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 869
Author(s):  
Xiuguo Zou ◽  
Jiahong Wu ◽  
Zhibin Cao ◽  
Yan Qian ◽  
Shixiu Zhang ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Traffic Accidents ◽  
Feature Sets ◽  
Atmospheric Visibility ◽  
Average Accuracy ◽  
Level 3 ◽  
Visual Characteristics ◽  
Level 1 ◽  
Grading Model ◽  
Level 2

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.

scholarly journals Integrating structure and function: mapping the hierarchical spatial heterogeneity of urban landscapes

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Yuguo Qian ◽  
Weiqi Zhou ◽  
Steward T. A. Pickett ◽  
Wenjuan Yu ◽  
Dingpeng Xiong ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Heterogeneity ◽  
Urban Ecosystem ◽  
Tree Canopy ◽  
Urban Landscapes ◽  
Shenzhen City ◽  
Object Based ◽  
Level 3 ◽  
Industrial Zones ◽  
Level 2

Abstract Background Cities are social-ecological systems characterized by remarkably high spatial and temporal heterogeneity, which are closely related to myriad urban problems. However, the tools to map and quantify this heterogeneity are lacking. We here developed a new three-level classification scheme, by considering ecosystem types (level 1), urban function zones (level 2), and land cover elements (level 3), to map and quantify the hierarchical spatial heterogeneity of urban landscapes. Methods We applied the scheme using an object-based approach for classification using very high spatial resolution imagery and a vector layer of building location and characteristics. We used a top-down classification procedure by conducting the classification in the order of ecosystem types, function zones, and land cover elements. The classification of the lower level was based on the results of the higher level. We used an object-based methodology to carry out the three-level classification. Results We found that the urban ecosystem type accounted for 45.3% of the land within the Shenzhen city administrative boundary. Within the urban ecosystem type, residential and industrial zones were the main zones, accounting for 38.4% and 33.8%, respectively. Tree canopy was the dominant element in Shenzhen city, accounting for 55.6% over all ecosystem types, which includes agricultural and forest. However, in the urban ecosystem type, the proportion of tree canopy was only 22.6% because most trees were distributed in the forest ecosystem type. The proportion of trees was 23.2% in industrial zones, 2.2% higher than that in residential zones. That information “hidden” in the usual statistical summaries scaled to the entire administrative unit of Shenzhen has great potential for improving urban management. Conclusions This paper has taken the theoretical understanding of urban spatial heterogeneity and used it to generate a classification scheme that exploits remotely sensed imagery, infrastructural data available at a municipal level, and object-based spatial analysis. For effective planning and management, the hierarchical levels of landscape classification (level 1), the analysis of use and cover by urban zones (level 2), and the fundamental elements of land cover (level 3), each exposes different respects relevant to city plans and management.

scholarly journals Driver seat comfort for level 3-4 autonomous vehicles

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S111-S118
Author(s):  
Neil J. Mansfield ◽  
Kartikeya Walia ◽  
Aditya Singh
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Case Methods ◽  
Driving Mode ◽  
Full Control ◽  
Seat Comfort ◽  
Autonomous Mode ◽  
Different Types ◽  
Level 3 ◽  
Level 2

BACKGROUND: Autonomous vehicles can be classified on a scale of automation from 0 to 5, where level 0 corresponds to vehicles that have no automation to level 5 where the vehicle is fully autonomous and it is not possible for the human occupant to take control. At level 2, the driver needs to retain attention as they are in control of at least some systems. Level 3-4 vehicles are capable of full control but the human occupant might be required to, or desire to, intervene in some circumstances. This means that there could be extended periods of time where the driver is relaxed, but other periods of time when they need to drive. OBJECTIVE: The seat must therefore be designed to be comfortable in at least two different types of use case. METHODS: This driving simulator study compares the comfort experienced in a seat from a production hybrid vehicle whilst being used in a manual driving mode and in autonomous mode for a range of postures. RESULTS: It highlights how discomfort is worse for cases where the posture is non-optimal for the task. It also investigates the design of head and neckrests to mitigate neck discomfort, and shows that a well-designed neckrest is beneficial for drivers in autonomous mode.

Sign in / Sign up

Export Citation Format

Share Document