Georges bank O.C.S. in situ current monitoring program

Author(s):  
E. Brainard ◽  
P. Kelsey ◽  
J. Haustein
2021 ◽  
Vol 239 ◽  
pp. 112274
Author(s):  
Henry Helmer-Smith ◽  
Nicholas Vlachopoulos ◽  
Marc-André Dagenais ◽  
Bradley Forbes

<i>Abstract</i>.—Zooplankton communities perform a critical role as secondary producers in marine ecosystems. They are vulnerable to climate-induced changes in the marine environment, including temperature, stratification, and circulation, but the effects of these changes are difficult to discern without sustained ocean monitoring. The physical, chemical, and biological environment of the Gulf of Maine, including Georges Bank, is strongly influenced by inflow from the Scotian Shelf and through the Northeast Channel, and thus observations both in the Gulf of Maine and in upstream regions are necessary to understand plankton variability and change in the Gulf of Maine. Large-scale, quasi synoptic plankton surveys have been performed in the Gulf of Maine since Bigelow’s work at the beginning of the 20th century. More recently, ongoing plankton monitoring efforts include Continuous Plankton Recorder sampling in the Gulf of Maine and on the Scotian Shelf, U.S. National Marine Fisheries Service’s MARMAP (Marine Resources Monitoring, Assessment, and Prediction) and EcoMon (Ecosystem Monitoring) programs sampling the northeast U.S. Continental Shelf, including the Gulf of Maine, and Fisheries and Oceans Canada’s Atlantic Zone Monitoring Program on the Scotian Shelf and in the eastern Gulf of Maine. Here, we review and compare past and ongoing zooplankton monitoring programs in the Gulf of Maine region, including Georges Bank and the western Scotian Shelf, to facilitate retrospective analysis and broadscale synthesis of zooplankton dynamics in the Gulf of Maine. Additional sustained sampling at greater-than-monthly frequency at selected sites in the Gulf of Maine would be necessary to detect changes in phenology (i.e. seasonal timing of biological events). Sustained zooplankton sampling in critical nearshore fish habitats and in key feeding areas for upper trophic level organisms, such as marine mammals and seabirds, would yield significant insights into their dynamics. The ecosystem dynamics of the Gulf of Maine are strongly influenced by large-scale forcing and variability in upstream inflow. Improved coordination of sampling and data analysis among monitoring programs, effective data management, and use of multiple modeling approaches will all enhance the mechanistic understanding of the structure and function of the Gulf of Maine pelagic ecosystem.


1993 ◽  
Vol 316 ◽  
Author(s):  
E.N. Shauly ◽  
E. Koltin ◽  
I. Munin ◽  
Y. Avrahamov

ABSTRACTIon implantation in semiconductor devices frequently leads to a substantial wafer surface charge build up. Control of this charge during high current implantation is a major process issue, as it may affect the yield and reliability of thin dielectric layers. In addition, the charge build up may affect the ion beam resulting in a non-uniform implant and a reduction in device yield. Control of a specific machine parameter, that will give the charge condition of the ion implanter will enable to neutralize the charge build up.In this study, Disk Current Monitoring (DCM) is shown to be a reliable method for monitoring the Electron Shower (ES) performance in real time. A correlation was found between DCM level and yields, and between DCM level and breakdown voltage, as well as different maintenance activities regarding me ES. A simple 5 steps method is described to achieve a reliable, real time charge monitor, to insure operation within the “High Yield Range”.


2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Zheng Zuo ◽  
Yu Hu ◽  
Qingbin Li ◽  
Liyuan Zhang

Embedded cool-pipes are very important for massive concrete because their cooling effect can effectively avoid thermal cracks. In this study, a data mining approach to analyzing the thermal performance of cool-pipes via in situ monitoring is proposed. Delicate monitoring program is applied in a high arch dam project that provides a good and mass data source. The factors and relations related to the thermal performance of cool-pipes are obtained in a built theory thermal model. The supporting vector machine (SVM) technology is applied to mine the data. The thermal performances of iron pipes and high-density polyethylene (HDPE) pipes are compared. The data mining result shows that iron pipe has a better heat removal performance when flow rate is lower than 50 L/min. It has revealed that a turning flow rate exists for iron pipe which is 80 L/min. The prediction and classification results obtained from the data mining model agree well with the monitored data, which illustrates the validness of the approach.


Author(s):  
Kojiro SUZUKI ◽  
Yoji TANAKA ◽  
Daishi NISHIMURA ◽  
Koji HIOKI ◽  
Hiroyasu NAKADE ◽  
...  

2013 ◽  
Vol 13 (5) ◽  
pp. 1402-1409
Author(s):  
Adam Trescott ◽  
Elizabeth Isenstein ◽  
Mi-Hyun Park

The objective of this study was to develop cyanobacteria remote sensing models using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) as an alternative to shipboard monitoring efforts in Lake Champlain. The approach allowed for estimation of cyanobacteria directly from satellite images, calibrated and validated with 4 years of in situ monitoring data from Lake Champlain's Long-Term Water Quality and Biological Monitoring Program (LTMP). The resulting stepwise regression model was applied to entire satellite images to provide distribution of cyanobacteria throughout the surface waters of Lake Champlain. The results demonstrate the utility of remote sensing for estimating the distribution of cyanobacteria in inland waters, which can be further used for presenting the initiation and propagation of cyanobacterial blooms in Lake Champlain.


Author(s):  
Mohamed Elshaer ◽  
Christopher DeCarlo ◽  
Wade Lein ◽  
Harshdutta Pandya ◽  
Ayman Ali ◽  
...  

Resilient modulus (Mr) is a critical input for pavement design as it is the main property used to evaluate the contribution of subgrade to the overall pavement structure. Considering this, practitioners need simple and accurate ways to determine the Mr of in-situ subgrade without the need for expensive and time-consuming testing. The objective of this study is to develop a generalized regression prediction model for in-situ Mr of subgrades, compare it with established prediction models, and assess the model’s predictions on pavement performance using the Mechanistic-Empirical Pavement Design Guide (Pavement ME). The prediction model was built using field data from 30 pavement sections studied in the Long Term Pavement Performance (LTPP) Seasonal Monitoring Program where backcalculated modulus from falling weight deflectometer testing, in-situ moisture contents, and subgrade material properties were considered in the model. Based on the results, it was found that liquid limit, plasticity index, WPI (the product of percent passing #200 and plasticity index), percent coarse sand, percent fine sand, percent silt, percent clay, moisture content, and their respective interactions were significant predictors of in-situ Mr values. The findings showed that the generalized regression approach was able to predict Mr more accurately than predictions from the Witczak model. To assess the application of the predictive model on pavement performance, three LTPP sections located in New York, South Dakota, and Texas were analyzed to predict the rutting performance based on Mr values obtained from the developed generalized prediction model and those obtained from the current Pavement ME model and then compared with rut depths measured in the field. The findings showed that, for coarse-grained subgrades that have a low degree of plasticity, the generalized regression model predicted rutting performance similar to the embedded Pavement ME model. For fine-grained subgrades, the developed model tends to predict lower rut depths which were closer to the field measured rut depths. Overall, the generalized regression approach was successfully applied to create a simple, practical, cost-effective and accurate Mr prediction model that can be used to estimate the stiffness of subgrades when designing and evaluating pavements.


1999 ◽  
Vol 1999 (1) ◽  
pp. 1265-1267 ◽  
Author(s):  
Nir Barnea ◽  
Roger Laferriere

ABSTRACT SMART (Scientific Monitoring of Advanced Response Technologies) is a new monitoring program designed to provide the Unified Command with real-time field data when in situ burning and dispersants are used during oil spill response. For dispersant monitoring, SMART recommends a three-tiered approach. Tier I has visual observation by trained observers from vessels or from aerial platforms. Tier II combines visual observations with water-column sampling using a fluorometer at a single depth. Tier III expands the fluorometry monitoring to several water depths, and uses a water-quality lab. Water samples for later analysis and correlation of fluorometry readings are taken both in Tier II and Tier III. For in situ burning, SMART recommends deploying three or more monitoring teams, each equipped with a real-time particulate monitor with data-logging capability. The teams deploy downwind of the burn at sensitive locations, and report particulate concentration trends to the Unified Command.


1996 ◽  
Vol 462 ◽  
Author(s):  
John Stewart ◽  
Lome D. Murdock ◽  
Nancy Binnie

ABSTRACTIn order to design a scheme to monitor the state of preservation of iron alloys that are found on historic shipwrecks sunk in fresh water, it was first necessary to identify the predominant form of corrosion found on the wrecks. This was done by visual observation, in situ surface pH measurements, and X-ray powder diffraction of the corrosion layers. Once the form of corrosion was identified as pitting corrosion, a monitoring scheme based on photographic recording of the rust-red tubercles was designed. This scheme is simple and nondestructive, both necessary characteristics for the monitoring of submerged historic resources.


Sign in / Sign up

Export Citation Format

Share Document