scholarly journals Bering-Chukchi-Beaufort Seas bowhead whale (Balaena mysticetus) abundance estimate from the 2019 ice-based survey

2021 ◽  
Vol 22 (1) ◽  
pp. 61-73
Author(s):  
Geof Givens ◽  
J. Craig George ◽  
Robert Suydam ◽  
Barbara Tudor
Keyword(s):  
Confidence Interval ◽  
Health Assessment ◽  
Health Condition ◽  
Bowhead Whale ◽  
Migration Route ◽  
Balaena Mysticetus ◽  
Abundance Estimate ◽  
Management Procedure ◽  
Detection Probabilities ◽  
Ice Conditions

An ice-based visual survey of the Bering-Chukchi-Beaufort Seas stock of bowhead whales (Balaena mysticetus) was conducted in spring 2019 near Utqiaġvik (formerly Barrow), Alaska. A Horvitz-Thompson-type estimator is used to estimate population abundance from the resulting data, correcting for detection probabilities, whale availability within visual range, and whale passage during periods of missed effort. Analytical methods mirror those used by Givens et al. (2016) for the 2011 survey as much as possible; however, unlike 2011, no simultaneous acoustic monitoring was conducted in 2019, so the availability correction factor had to be estimated from past years. The estimated abundance was 12,505 with 95% confidence interval of (7,994, 19,560) and a CV of 0.228. This estimated abundance is markedly lower than the 2011 estimate of 16,820, but the 2019 confidence interval wholly encompasses the 2011 interval. We do not interpret this finding as evidence of a decline for many reasons including: highly unusual ice conditions, an unusual migration route that was sometimes too distant from observers to detect whales, failure to conduct watch because of closed leads during the early weeks of the migration when numerous whales likely passed, an unusually short perch, and hunters’ heavy use of powered skiffs near the observation perch which likely disturbed the whales during the survey. Furthermore, bowhead health assessment information for 2019 suggests that harvested bowheads did not exhibit obvious reductions in health condition, and aerial surveys in summer 2019 indicated high calf production (Stimmelmayr et al. 2020). Despite the challenges of the 2019 survey, the estimate is adequate for use with the International Whaling Commission’s management procedure and complies with the survey requirements of the Aboriginal Whaling Scheme.

Abundance estimate of the Okhotsk Sea population of the bowhead whale (Balaena mysticetus Linnaeus, 1758)

2017 ◽  
Vol 477 (1) ◽  
pp. 236-238
Author(s):  
O. V. Shpak ◽  
I. G. Meschersky ◽  
D. M. Kuznetsova ◽  
A. N. Chichkina ◽  
A. Yu. Paramonov ◽  
...  
Keyword(s):  
Bowhead Whale ◽  
Okhotsk Sea ◽  
Balaena Mysticetus ◽  
Abundance Estimate

Listen2Cough

2021 ◽  
Vol 5 (1) ◽  
pp. 1-22
Author(s):  
Xuhai Xu ◽  
Ebrahim Nemati ◽  
Korosh Vatanparvar ◽  
Viswam Nathan ◽  
Tousif Ahmed ◽  
...  
Keyword(s):  
Health Assessment ◽  
Ground Truth ◽  
Health Condition ◽  
Fine Tuning ◽  
Detection Model ◽  
Assessment Tasks ◽  
Augmentation Techniques ◽  
In The Wild ◽  
The Rich ◽  
Lung Health

The prevalence of ubiquitous computing enables new opportunities for lung health monitoring and assessment. In the past few years, there have been extensive studies on cough detection using passively sensed audio signals. However, the generalizability of a cough detection model when applied to external datasets, especially in real-world implementation, is questionable and not explored adequately. Beyond detecting coughs, researchers have looked into how cough sounds can be used in assessing lung health. However, due to the challenges in collecting both cough sounds and lung health condition ground truth, previous studies have been hindered by the limited datasets. In this paper, we propose Listen2Cough to address these gaps. We first build an end-to-end deep learning architecture using public cough sound datasets to detect coughs within raw audio recordings. We employ a pre-trained MobileNet and integrate a number of augmentation techniques to improve the generalizability of our model. Without additional fine-tuning, our model is able to achieve an F1 score of 0.948 when tested against a new clean dataset, and 0.884 on another in-the-wild noisy dataset, leading to an advantage of 5.8% and 8.4% on average over the best baseline model, respectively. Then, to mitigate the issue of limited lung health data, we propose to transform the cough detection task to lung health assessment tasks so that the rich cough data can be leveraged. Our hypothesis is that these tasks extract and utilize similar effective representation from cough sounds. We embed the cough detection model into a multi-instance learning framework with the attention mechanism and further tune the model for lung health assessment tasks. Our final model achieves an F1-score of 0.912 on healthy v.s. unhealthy, 0.870 on obstructive v.s. non-obstructive, and 0.813 on COPD v.s. asthma classification, outperforming the baseline by 10.7%, 6.3%, and 3.7%, respectively. Moreover, the weight value in the attention layer can be used to identify important coughs highly correlated with lung health, which can potentially provide interpretability for expert diagnosis in the future.

Olfaction and brain size in the bowhead whale (Balaena mysticetus)

2010 ◽  
Vol 27 (2) ◽  
pp. 282-294 ◽  
Author(s):  
J. G. M. Thewissen ◽  
John George ◽  
Cheryl Rosa ◽  
Takushi Kishida
Keyword(s):  
Brain Size ◽  
Bowhead Whale ◽  
Balaena Mysticetus

scholarly journals Health state of the citizens of Nothern Kosovska Mitrovica

2010 ◽  
Vol 138 (11-12) ◽  
pp. 746-751
Author(s):  
Momcilo Mirkovic ◽  
Snezana Simic ◽  
Jelena Marinkovic ◽  
Sladjana Djuric
Keyword(s):  
Health Survey ◽  
Health Assessment ◽  
Population Level ◽  
Cross Sectional Study ◽  
Health Condition ◽  
Health State ◽  
Cross Sectional ◽  
Significance Level ◽  
Self Rated Health ◽  
Significant Difference

Introduction. For health assessment, beside the data of routine health statistics, it is necessary to include and data obtained by a health survey of the citizens. Objective. The aim of this study was to establish how northern Kosovska Mitrovica adults assess their health and which diseases are most common among the population, as well as to investigate differences in relation to demographic and socioeconomic characteristics, the characteristics of social interaction and health behavior and habits. Methods. The research was conducted as a cross-sectional study conducted on the representative sample of adult citizens in northern Kosovska Mitrovica in 2006. Two hundred-eighteen respondents were included in the survey. In the research we used a questionnaire identical to the Health Survey conducted in Serbia in 2006. The significance of differences in responses about self-rated health and chronic diseases in relation to the characteristics of respondents? responses were determined by X2-test with the significance level of 0.05. Results. Over half of the respondents (54.7%) assessed their health condition as good or very good. There was a significant difference in self-rated health in relation to the respondents? age (?2=202.036; p=0.000), education (?2=72.412; p=0.000), social support (?2=12.416; p=0.015), smoking (?2=11.675; p=0.020) and physical activity (?2=61.842; p=0.000). The leading health problems among the respondents were high blood pressure, rheumatologic diseases of joints, ulcer of the duodenal or gastric ulcer, gall bladder disease and high blood fat. Conclusion. Adult residents of northern Kosovska Mitrovica assessed their health as better than the residents of Serbia without Kosovo and Metohia. The diseases in which stress plays the major role among etiological factors are in the leading position. The obtained data on the population level of specific areas represent the basis in the planning of health education and health promotion activities.

MICROFLORA ASSOCIATED WITH THE SKIN OF THE BOWHEAD WHALE (BALAENA MYSTICETUS)

1990 ◽  
Vol 26 (3) ◽  
pp. 351-359 ◽  
Author(s):  
E. B. Shotts ◽  
T. F. Albert ◽  
R. E. Wooley ◽  
J. Brown
Keyword(s):  
Bowhead Whale ◽  
Balaena Mysticetus

Evidence for simultaneous sound production in the bowhead whale (Balaena mysticetus)

2011 ◽  
Vol 130 (4) ◽  
pp. 2257-2262 ◽  
Author(s):  
Outi M. Tervo ◽  
Mads Fage Christoffersen ◽  
Susan E. Parks ◽  
Reinhardt Møbjerg Kristensen ◽  
Peter Teglberg Madsen
Keyword(s):  
Sound Production ◽  
Bowhead Whale ◽  
Balaena Mysticetus

scholarly journals Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
A. Romero ◽  
Y. Lage ◽  
S. Soua ◽  
B. Wang ◽  
T.-H. Gan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Health Assessment ◽  
Field Trials ◽  
Health Condition ◽  
Normal Operation ◽  
Wind Turbine Gearbox ◽  
Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

scholarly journals Classification of Body Constitution Based on TCM Philosophy and Deep Learning

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 803 ◽  
Author(s):  
Yung-Hui Li ◽  
Muhammad Saqlain Aslam ◽  
Kai-Lin Yang ◽  
Chung-An Kao ◽  
Shin-You Teng
Keyword(s):  
Deep Learning ◽  
Health Assessment ◽  
Assessment Method ◽  
Health Condition ◽  
The Body ◽  
Convolutional Network ◽  
Non Invasive ◽  
Body Constitution ◽  
Perfect Symmetry ◽  
Psychological Strength

There is a growing demand for alternative or complementary medicine in health care disciplines that uses a non-invasive instrument to evaluate the health status of various organs inside the human body. In this regard, we proposed a real-time, non-invasive, and painless technique to assess an individual’s health condition. Our approach is based on the combination of iridology and the philosophy of traditional Chinese medicine (TCM). The iridology chart presents perfect symmetry between the left and right eyes, and such a unique representation reveals the body constitution based on TCM philosophy, which classifies the aforementioned body constitution into a combination of nine categories to describe the varieties of genomic traits. In addition, we applied a deep-learning method along with the combination of iridology and TCM to predict the possible physiological or psychological strength or weakness of the subjects and give advice to them about how to take care of their health according to the body constitution assessment. We used several pre-trained convolutional neural networks (CNNs, or ConvNet), such as a residual neural network (ResNet50), InceptionV3, and dense convolutional network (DenseNet201), to classify the body constitution using iris images. In the experiments, the CASIA-Iris-Thousand database was used to perform this task. The experimental results showed that the proposed iris-based health assessment method achieved an 82.9% accuracy.

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