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Revisão de escopo que visa mapear e sumarizar as incapacidades geradas pela migrânea em pacientes adultos, identificando a possibilidade de classificar estes pacientes baseado na Classificação Internacional de Funcionalidade, Incapacidade e Saúde (CIF)

Perma.cc archive of https://www.fda.gov/RegulatoryInformation/Guidances/ucm258946.htm created on 2019-10-14 14:37:57+00:00.

Purpose: This scoping review aims to comprehensively identify, map and synthesise the current evidence on the use of robotics in occupational therapy research, practice, and education. The review will scope the current literature and focus upon the use, classification, and role of healthcare robots to provide a baseline from which to inform future research priorities and practice guidelines. Methods: Arksey and O'Malley's (2005) scoping review framework will guide the review process and will be reported using the PRISMA-SCR (2018) guidelines. MEDLINE, CINAHL, PsycINFO, IEEE Xplore, OT Seeker, Web of Science, Scopus, and ScienceDirect will be searched from inception using predefined search terms related to 'occupational therapy' and 'robotics'. Grey literature will include handsearching of key occupational therapy, engineering and robotics journals. Peer-reviewed, English-language articles addressing robotics in occupational therapy practice, research, or education will be included. No restrictions will be placed upon year of publication, study design, or participant populations. Two independent reviewers will screen titles, abstracts, and full-texts for inclusion. Data will be extracted using a piloted form and will be narratively synthesised. Results: Findings will be summarised using the proposed classification of healthcare robots by Huang (2022) to categorise the main characteristics of the robots. The role to which they are being used will be considered within a five-point checklist: improving precision and accuracy, increasing efficiency, enhancing patient engagement, improving safety, and personalising treatment plans. Conclusions: This scoping review will provide a broad landscape from which to understand the current evidence on the use, classification, and role of robotics in occupational therapy, identifying key opportunities and challenges to guide future research, education, and practice in this rapidly evolving field.

Perma.cc archive of https://www.fia.com/sites/default/files/decision-document/2021 Portuguese Grand Prix - Provisional Race Classification.pdf created on 2021-12-13 02:09:46+00:00.

quora-insincere-questions-classification test data for educational purpose Credits: https://www.kaggle.com/c/quora-insincere-questions-classification/

Compact tension specimen with four sensors, in 1145 Studio.

Well we're back, and in true aussie fashion we are fucking late. Oh well, I'm sure our one fan will be consoled by the fact that this may possibly be our most Australian podcast yet, if only for the mention of Stack Hats. If you don't know what they are you led a deprived and sad life. Anyways, on with the show! Shit we talk about: - STACKHATS!! - We love old school games - Silent Hill Homecoming, it aint no 5 - Sam and Max = God - Scratch that, Mike Patton = God - Duke Forever, RIP - Ahhhh, [url=http://gamingisstupid.com/2009/05/06/the-chair-story-revival/]that chair story[/url]. - We get pissy about Ghostbusters, then at eachother - Where the fuck is our MvC2 demo? - The RC Spotlight: Tits in games. Seriously. - Dillinger Drops his phone number again. Somebody call the bastard. - Listener questions yaaaaayyyy! - AnimuCast! - I know too much about Luchadores Intro Music: Bionic Commando: Rearmed OST - Power Plant Break Music: Selkies the endless obsessions - Between The Buried And Me. 8Bit Version by [url=http://www.youtube.com/user/xTwilightxxPrincessx]xtwilightxxprincessx[/url] Outro Music: Bulb - Illustrated 8 Test

Perma.cc archive of https://www.wipo.int/classifications/ipc/en/ created on 2022-04-22 19:05:56.384607+00:00.

This article is from PLoS ONE , volume 9 . Abstract The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.

This article is from PLoS ONE , volume 9 . Abstract The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.

This article is from Advanced Biomedical Research , volume 2 . Abstract Background:: Up to date different methods have been used in order to dimensions reduction, classification, clustering and prediction of cancers based on gene expression profiling. The aim of this study is extracting most significant genes and classifying of Diffuse Large B-cell Lymphoma (DLBCL) patients on the basis of their gene expression profiles. Materials and Methods:: We studied 40 DLBCL patients and 4026 genes. We utilized Artificial Neural Network (ANN) for classification of patients in two groups: Germinal center and Activated like. As we were faced with low number of patients (40) and numerous genes (4026), we tried to deploy one optimum network and achieve to minimum error. Moreover we used signal to noise (S/N) ratio as a main tool for dimension reduction. We tried to select suitable training data and so to train just one network instead of 26 networks. Finally, we extracted two most significant genes. Result:: In this study two most significant genes based on their S/N ratios were selected. After selection of suitable training samples, the training and testing error were 0 and 7% respectively. Conclusion:: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.

This article is from Advanced Biomedical Research , volume 2 . Abstract Background:: Up to date different methods have been used in order to dimensions reduction, classification, clustering and prediction of cancers based on gene expression profiling. The aim of this study is extracting most significant genes and classifying of Diffuse Large B-cell Lymphoma (DLBCL) patients on the basis of their gene expression profiles. Materials and Methods:: We studied 40 DLBCL patients and 4026 genes. We utilized Artificial Neural Network (ANN) for classification of patients in two groups: Germinal center and Activated like. As we were faced with low number of patients (40) and numerous genes (4026), we tried to deploy one optimum network and achieve to minimum error. Moreover we used signal to noise (S/N) ratio as a main tool for dimension reduction. We tried to select suitable training data and so to train just one network instead of 26 networks. Finally, we extracted two most significant genes. Result:: In this study two most significant genes based on their S/N ratios were selected. After selection of suitable training samples, the training and testing error were 0 and 7% respectively. Conclusion:: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.

This article is from Advanced Biomedical Research , volume 2 . Abstract Background:: Up to date different methods have been used in order to dimensions reduction, classification, clustering and prediction of cancers based on gene expression profiling. The aim of this study is extracting most significant genes and classifying of Diffuse Large B-cell Lymphoma (DLBCL) patients on the basis of their gene expression profiles. Materials and Methods:: We studied 40 DLBCL patients and 4026 genes. We utilized Artificial Neural Network (ANN) for classification of patients in two groups: Germinal center and Activated like. As we were faced with low number of patients (40) and numerous genes (4026), we tried to deploy one optimum network and achieve to minimum error. Moreover we used signal to noise (S/N) ratio as a main tool for dimension reduction. We tried to select suitable training data and so to train just one network instead of 26 networks. Finally, we extracted two most significant genes. Result:: In this study two most significant genes based on their S/N ratios were selected. After selection of suitable training samples, the training and testing error were 0 and 7% respectively. Conclusion:: We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.

Problem of Classification in Pakistan Punjab

To identify and synthesise literature on theories that inform technology enhanced learning in nursing education.

This review will analyze the effect of KDIGO reclassification in observation and experimental studies that directly compare the CKD-EPI 2009 eGFR equation which contains a race coefficient to the CKD-EPI 2021 equation which eliminates this race factor. The review will analyze CKD reclassification and treatment eligibility changes in Black populations for which the two eGFR equations have been used to estimate kidney function.

-unordered movie set -no direct search to avoid problems with rights -was to free up space -it's not spam -is archived as unique data -The visualization of these data is very little and has no economic effect or damage to the author, but it is integrated to save the data. -This collection is not made by the author, nor the one who makes this note. -They are a data set without connection and from an unknown author (the collection)

-unordered movie set -no direct search to avoid problems with rights -was to free up space -it's not spam -is archived as unique data -The visualization of these data is very little and has no economic effect or damage to the author, but it is integrated to save the data. -This collection is not made by the author, nor the one who makes this note. -They are a data set without connection and from an unknown author (the collection)

Perma.cc archive of https://www.law.cornell.edu/wex/suspect_classification created on 2021-11-14 22:23:56+00:00.

Class 2C. Tree Selection. Keen Ponderosa Pine Tree Classification. Photo by: F.P. Keen Date: September 1934 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Bureau of Entomology Collection; La Grande, Oregon. Image: BUR-8712 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth The following information about the Keen Classification system is excerpted from Wickman, Boyd E. 2005. Harry E. Burke and John M. Miller, pioneers in Western forest entomology. Gen. Tech. Rep. PNW-GTR-638. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 163 p.: "Keen’s research on tree classification continued in Oregon and was called the “Keen Ponderosa Pine Tree Classification.” The Salman-Bongberg approach was called the “California Pine Risk-Rating System.” Both systems evolved from research on tree susceptibility to bark beetle attack that had been conducted in the 1920s by Miller, Keen, Patterson, Person, and Struble. Tree growth rates were a particularly important area of the studies, with Miller and Person proposing that slower growing mature ponderosa pine were more susceptible to attack by the western pine beetle than younger more vigorous trees (Smith et al. 1981). The rating systems resulted in a silvicultural approach to managing the western pine beetle instead of the direct control method of felling infested trees, then peeling and burning the bark. This was a breakthrough for forest managers that allowed them to capture the economic value of susceptible trees before they were killed by bark beetles, and at the same time resulted in decreased beetle populations or at least seemed to prevent outbreaks. The Salman- Bongberg California risk-rating system identified susceptible trees by assigning penalty points to crown and stem characteristics of a tree. This was followed up by a logging practice called sanitation salvage (Smith et al. 1981). The Keen system, involved four classes based on age and four vigor classes within each age class. It was oriented more toward identifying the susceptibility of individual trees to insect attack. Keen also assigned penalty points in his system. It was slightly more complex to use and was not directly tied to the sanitation salvage concept, but a certain level of logging of susceptible trees was generally followed. Keen’s classification was more widely used in ponderosa pine stands of eastern Oregon, and the California system was used for both ponderosa pine and Jeffrey pine stands in eastern California." For more, see: www.fs.fed.us/pnw/publications/pnw_gtr638/ For more about Keen's classifications see: www.fs.fed.us/psw/publications/ecology_of.../psw_1943_kee...

Class 2C. Tree Selection. Keen Ponderosa Pine Tree Classification. Photo by: F.P. Keen Date: September 1934 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Bureau of Entomology Collection; La Grande, Oregon. Image: BUR-8712 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth The following information about the Keen Classification system is excerpted from Wickman, Boyd E. 2005. Harry E. Burke and John M. Miller, pioneers in Western forest entomology. Gen. Tech. Rep. PNW-GTR-638. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 163 p.: "Keen’s research on tree classification continued in Oregon and was called the “Keen Ponderosa Pine Tree Classification.” The Salman-Bongberg approach was called the “California Pine Risk-Rating System.” Both systems evolved from research on tree susceptibility to bark beetle attack that had been conducted in the 1920s by Miller, Keen, Patterson, Person, and Struble. Tree growth rates were a particularly important area of the studies, with Miller and Person proposing that slower growing mature ponderosa pine were more susceptible to attack by the western pine beetle than younger more vigorous trees (Smith et al. 1981). The rating systems resulted in a silvicultural approach to managing the western pine beetle instead of the direct control method of felling infested trees, then peeling and burning the bark. This was a breakthrough for forest managers that allowed them to capture the economic value of susceptible trees before they were killed by bark beetles, and at the same time resulted in decreased beetle populations or at least seemed to prevent outbreaks. The Salman- Bongberg California risk-rating system identified susceptible trees by assigning penalty points to crown and stem characteristics of a tree. This was followed up by a logging practice called sanitation salvage (Smith et al. 1981). The Keen system, involved four classes based on age and four vigor classes within each age class. It was oriented more toward identifying the susceptibility of individual trees to insect attack. Keen also assigned penalty points in his system. It was slightly more complex to use and was not directly tied to the sanitation salvage concept, but a certain level of logging of susceptible trees was generally followed. Keen’s classification was more widely used in ponderosa pine stands of eastern Oregon, and the California system was used for both ponderosa pine and Jeffrey pine stands in eastern California." For more, see: www.fs.fed.us/pnw/publications/pnw_gtr638/ For more about Keen's classifications see: www.fs.fed.us/psw/publications/ecology_of.../psw_1943_kee...

Class 2C. Tree Selection. Keen Ponderosa Pine Tree Classification. Photo by: F.P. Keen Date: September 1934 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Bureau of Entomology Collection; La Grande, Oregon. Image: BUR-8712 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth The following information about the Keen Classification system is excerpted from Wickman, Boyd E. 2005. Harry E. Burke and John M. Miller, pioneers in Western forest entomology. Gen. Tech. Rep. PNW-GTR-638. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 163 p.: "Keen’s research on tree classification continued in Oregon and was called the “Keen Ponderosa Pine Tree Classification.” The Salman-Bongberg approach was called the “California Pine Risk-Rating System.” Both systems evolved from research on tree susceptibility to bark beetle attack that had been conducted in the 1920s by Miller, Keen, Patterson, Person, and Struble. Tree growth rates were a particularly important area of the studies, with Miller and Person proposing that slower growing mature ponderosa pine were more susceptible to attack by the western pine beetle than younger more vigorous trees (Smith et al. 1981). The rating systems resulted in a silvicultural approach to managing the western pine beetle instead of the direct control method of felling infested trees, then peeling and burning the bark. This was a breakthrough for forest managers that allowed them to capture the economic value of susceptible trees before they were killed by bark beetles, and at the same time resulted in decreased beetle populations or at least seemed to prevent outbreaks. The Salman- Bongberg California risk-rating system identified susceptible trees by assigning penalty points to crown and stem characteristics of a tree. This was followed up by a logging practice called sanitation salvage (Smith et al. 1981). The Keen system, involved four classes based on age and four vigor classes within each age class. It was oriented more toward identifying the susceptibility of individual trees to insect attack. Keen also assigned penalty points in his system. It was slightly more complex to use and was not directly tied to the sanitation salvage concept, but a certain level of logging of susceptible trees was generally followed. Keen’s classification was more widely used in ponderosa pine stands of eastern Oregon, and the California system was used for both ponderosa pine and Jeffrey pine stands in eastern California." For more, see: www.fs.fed.us/pnw/publications/pnw_gtr638/ For more about Keen's classifications see: www.fs.fed.us/psw/publications/ecology_of.../psw_1943_kee...

Map 2. Intensity of Blowdown in Areas Covered by The Survey. Classification is based on approximate number of acres per square mile on which 10% or more of the stand is recent blowdown. The data have been generalized for minimum areas of about 3,500 acres from 1"-per-mile scale maps. Map is from: Summary Statement on the 1952 Blowdown-Bark-Beetle Survey in the Douglas-fir Region of Oregon and Washington. Prepared by: Pacific Northwest Forest and Range Experiment Station, US Forest Service; and Portland Forest Insect Laboratory, Bureau of Entomology and Plant Quarantine. Portland, Oregon. 7 pages. Photo by: Unknown Map creation date: 1952 Credit: USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection. Source: Aerial Survey Program digital collection. Image provided by USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Board of Assessors presented on tax classifications at the Select Board meeting on December 3, 2019.

Karnataka Legislative Assembly Debates - Book 728, Pages 193-194

Contains public sector information licensed under the Open Government Licence v3.0. Statistical report on some aspects of farm structure Ford List, 1965-1974 1 Explore this and other items from our digitised collections on the University of Southampton Digital Library.

Contains public sector information licensed under the Open Government Licence v3.0. Statistical report on some aspects of farm structure Ford List, 1965-1974 1 Explore this and other items from our digitised collections on the University of Southampton Digital Library.

Contains public sector information licensed under the Open Government Licence v3.0. Statistical report on some aspects of farm structure Ford List, 1965-1974 1 Explore this and other items from our digitised collections on the University of Southampton Digital Library.

Contains public sector information licensed under the Open Government Licence v3.0. Statistical report on some aspects of farm structure Ford List, 1965-1974 1 Explore this and other items from our digitised collections on the University of Southampton Digital Library.

Contains public sector information licensed under the Open Government Licence v3.0. Statistical report on some aspects of farm structure Ford List, 1965-1974 1 Explore this and other items from our digitised collections on the University of Southampton Digital Library.

Colorado Department of Transportation Research Report

Colorado Department of Transportation Research Report

Colorado Department of Transportation Research Report

109p. ; 24 cm

pages cm

This article is from Neural Regeneration Research , volume 8 . Abstract The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.

This article is from Neural Regeneration Research , volume 8 . Abstract The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.

Declassified US intelligence documents related to China found in the US National Archives.

Declassified US intelligence documents related to China found in the US National Archives.

Automatic classification and disease detection in medical images, aided by machine learning, provide crucial support to prevent overlooked instances and ensure prompt treatment of diseases. Despite impressive achievements in the field of polyp detection from endoscopic images, classification of other diseases, such as reflux esophagitis, esophageal cancer, gastritis, gastric cancer, and duodenal ulcer, is still subject to significant limitations and remains a challenging area of study because of their different and more challenging characteristics. This paper proposes a method to roughly classify the diseases from the whole images by deep learning. In particular, we focus on identifying hard samples from the training dataset and enriching them with some fundamental augmentation techniques. We then employ a cutting edge model, specifically ResNet, for the final classification stage. Additionally, we enhance the original ResNet’s loss function by incorporating another loss function called focal loss. These modifications play a crucial role in boosting the accuracy of the ResNet model. Our proposed method outputs the disease category and corresponding heat map showing the area of interest. It achieved very promising accuracy (99.55%) for the classification of five lesions on our self-collected dataset. It serves a dual purpose. Firstly, it aids in the training of novice endoscopists, enabling them to gain valuable experience. Secondly, it offers a rapid solution for annotating extensive volumes of endoscopic image data at the label level.

Automatic classification and disease detection in medical images, aided by machine learning, provide crucial support to prevent overlooked instances and ensure prompt treatment of diseases. Despite impressive achievements in the field of polyp detection from endoscopic images, classification of other diseases, such as reflux esophagitis, esophageal cancer, gastritis, gastric cancer, and duodenal ulcer, is still subject to significant limitations and remains a challenging area of study because of their different and more challenging characteristics. This paper proposes a method to roughly classify the diseases from the whole images by deep learning. In particular, we focus on identifying hard samples from the training dataset and enriching them with some fundamental augmentation techniques. We then employ a cutting edge model, specifically ResNet, for the final classification stage. Additionally, we enhance the original ResNet’s loss function by incorporating another loss function called focal loss. These modifications play a crucial role in boosting the accuracy of the ResNet model. Our proposed method outputs the disease category and corresponding heat map showing the area of interest. It achieved very promising accuracy (99.55%) for the classification of five lesions on our self-collected dataset. It serves a dual purpose. Firstly, it aids in the training of novice endoscopists, enabling them to gain valuable experience. Secondly, it offers a rapid solution for annotating extensive volumes of endoscopic image data at the label level.

310 p

Document from Tamil Nadu Legislative Assembly Digital Archive

Document from Tamil Nadu Legislative Assembly Digital Archive

Document from Tamil Nadu Legislative Assembly Digital Archive

The Rules and Regulations for the Classification of Ships for Service on the Great Lakes and River St. Lawrence apply to ships which intend to operate within the limits of the Great Lakes and the River St. Lawrence, with the seaward limit, as a straight line drawn from Cap des Rosiers on the Gaspe Peninsula to the west point of Anticosti Island and from Anticosti Island to the north shore of the River St. Lawrence along the meridian of longitude sixty-three degrees west.  

The Rules and Regulations for the Classification of Ships for Service on the Great Lakes and River St. Lawrence apply to ships which intend to operate within the limits of the Great Lakes and the River St. Lawrence, with the seaward limit, as a straight line drawn from Cap des Rosiers on the Gaspe Peninsula to the west point of Anticosti Island and from Anticosti Island to the north shore of the River St. Lawrence along the meridian of longitude sixty-three degrees west.  

The Rules and Regulations for the Classification of Ships for Service on the Great Lakes and River St. Lawrence apply to ships which intend to operate within the limits of the Great Lakes and the River St. Lawrence, with the seaward limit, as a straight line drawn from Cap des Rosiers on the Gaspe Peninsula to the west point of Anticosti Island and from Anticosti Island to the north shore of the River St. Lawrence along the meridian of longitude sixty-three degrees west.