Physical Sciences and Mathematics | Open Access Articles

Global Forecasts Of Marine Heatwaves, Michael Jacox

Benefits of Ocean Observing Catalog (BOOC)

Timestamp: 44862.4486656366 Email Address: michael.jacox@noaa.gov Name: Michael Jacox Affiliation: NOAA Southwest Fisheries Science Center and NOAA Physical Sciences Laboratory Program Office/Division: Position Title: Research oceanographer Title of use case: Global forecasts of marine heatwaves Authors or Creators: Jacox, M., Alexander, M., Amaya, D., Becker, B., Bograd, S., Brodie, S., Hazen, E., Pozo Buil, M., Tommasi, D., Hsu, C.-W., Smith, C. Affiliations of Authors or Creators: NOAA Physical Sciences Laboratory; NOAA Southwest Fisheries Science Center; University of Colorado; University of Miami; University of California Santa Cruz Contributors: Affiliation of Contributors: Description: Researchers used climate forecast systems to develop global marine heatwave …

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Impact Of Covid-19 On Security Vulnerabilities Of Learning Management Systems: A Study Towards Security And Sustainability Enhancement, Souheil Abdel-Latif Akacha

Theses

The rapid adoption of Learning Management Systems (LMSs) like Moodle, Chamilo, and Ilias became essential for online education due to the Coronavirus Disease 2019 (COVID-19) pandemic, revolutionizing online learning while exposing security vulnerabilities. This thesis explores security concerns within these LMSs across different pandemic periods. By analyzing existing patches, security measures, and emerging cybersecurity technologies, recommendations are formulated to enhance LMS security against evolving cyber threats, providing actionable insights for educational institutions to ensure secure online education continuity. The numerical findings highlight the increasing need for proactive security measures in Moodle, the fluctuating nature of vulnerabilities in Chamilo, and the …

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Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …

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Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa

All Works

BACKGROUND: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to …

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Forecasting Economic Growth And Movements With Wavelet Transform And Arima Model, Omar Alsinglawi, Omar Alsinglawi, Mohammad Aladwan, Mohammad Aladwan, Saddam Alwadi, Saddam Alwadi

Applied Mathematics & Information Sciences

This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study concludes that GDP is expected to increase with a positive growth rate of around 3.22%, and recommends government agencies to monitor GDP, strengthen existing policies, and adopt necessary economic reforms to support growth. Additionally, the private sector is encouraged to enhance production tools to achieve economic growth that benefits …

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A Simple Vegetation Criterion (Ndf Content) May Account For Diet Choices Of Cattle Between Forages Varying In Maturity Stage And Physical Accessibility, Cécile Ginane, R. Baumont

IGC Proceedings (1997-2023)

The management of extensively grazed pastures requires an understanding and prediction of the diet choices of herbivores grazing on vegetation that is qualitatively (maturity stage) and quantitatively (biomass, sward height) heterogeneous. The Optimal Foraging Theory (OFT, Stephens & Krebs, 1986), bases its predictions on the relative energy intake rate (EIR) of forages. However, as EIRs are difficult to assess at pasture and are subject to wide intra- and inter-individual variations, another vegetation criterion was sought (accessibility, quality), by-passing the animal's influence, to predict cattle diet choices quantitatively.

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Population Modeling With Machine Learning Can Enhance Measures Of Mental Health - Open-Data Replication, Ty Easley, Ruiqi Chen, Kayla Hannon, Rosie Dutt, Janine Bijsterbosch

Statistical and Data Sciences: Faculty Publications

Efforts to predict trait phenotypes based on functional MRI data from large cohorts have been hampered by low prediction accuracy and/or small effect sizes. Although these findings are highly replicable, the small effect sizes are somewhat surprising given the presumed brain basis of phenotypic traits such as neuroticism and fluid intelligence. We aim to replicate previous work and additionally test multiple data manipulations that may improve prediction accuracy by addressing data pollution challenges. Specifically, we added additional fMRI features, averaged the target phenotype across multiple measurements to obtain more accurate estimates of the underlying trait, balanced the target phenotype's distribution …

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Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan

Computer Science Senior Theses

We introduce a framework that combines Gaussian Process models, robotic sensor measurements, and sampling data to predict spatial fields. In this context, a spatial field refers to the distribution of a variable throughout a specific area, such as temperature or pH variations over the surface of a lake. Whereas existing methods tend to analyze only the particular field(s) of interest, our approach optimizes predictions through the effective use of all available data. We validated our framework on several datasets, showing that errors can decline by up to two-thirds through the inclusion of additional colocated measurements. In support of adaptive sampling, …

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Research On Aquifer Water Abundance Evaluation By Borehole Transient Electromagnetic Method Based On Fcnn, Cheng Jiulong, Wang Huijie, Xu Zhongzhong, Huang Qisong, Jiang Guoqing

Coal Geology & Exploration

Predicting the location and water abundance of aquifers by drilling or geophysical methods before tunneling is very important to prevent water disasters in advance for the safety of coal mine production. The use of borehole transient electromagnetic method (BTEM) for advanced detection has obvious advantages. At present, the interpretation method is based on the calculated resistivity to qualitatively analyze rock water abundance, and it is impossible to predict the aquifer water abundance grade. So, the fully convolutional neural network (FCNN) method is proposed to predict the aquifer water abundance grade for BTEM. Firstly, according to the Archie formula, Kozeny-Carman formula, …

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A Simple Vegetation Criterion (Ndf Content) May Account For Diet Choices Of Cattle Between Forages Varying In Maturity Stage And Physical Accessibility, Cécile Ginane, R. Baumont

IGC Proceedings (1997-2023)

The management of extensively grazed pastures requires an understanding and prediction of the diet choices of herbivores grazing on vegetation that is qualitatively (maturity stage) and quantitatively (biomass, sward height) heterogeneous. The Optimal Foraging Theory (OFT, Stephens & Krebs, 1986), bases its predictions on the relative energy intake rate (EIR) of forages. However, as EIRs are difficult to assess at pasture and are subject to wide intra- and inter-individual variations, another vegetation criterion was sought (accessibility, quality), by-passing the animal's influence, to predict cattle diet choices quantitatively.

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Towards An Unsupervised Bayesian Network Pipeline For Explainable Prediction, Decision Making And Discovery, Daniel Mallia

Theses and Dissertations

An unsupervised learning pipeline for discrete Bayesian networks is proposed to facilitate prediction, decision making, discovery of patterns, and transparency in challenging real-world AI applications, and contend with data limitations. We explore methods for discretizing data, and notably apply the pipeline to prediction and prevention of preterm birth.

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Collision Risk Prediction For Small Ships In South Korea Via Optimization Of Wireless Communication Period, So-Ra Kim, Myoung-Ki Lee, Sangwon Park, Dae-Won Kim, Young-Soo Park

Journal of Marine Science and Technology

Since the emergence of COVID-19, there has been a global surge in demand for marine leisure activities. In Korea, the population using marine leisure has risen approximately 192% to 20,406 people, compared to 6,966 people in the year 2000, indicating a continuous growth over the past two decades.. Maritime transportation has become increasingly intricate worldwide due to the development of increasingly autonomous, larger, and faster ships. To effectively address potential hazards in such complex traffic environments, it is imperative to anticipate future scenarios and respond rapidly. However, small vessels account for the highest proportion of marine accidents, exhibit movements that …

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Leveraging Machine Learning To Analyze Sentiment From Covid-19 Tweets: A Global Perspective, Md Mahbubar Rahman, Nafiz Imtiaz Khan, Iqbal H. Sarker, Mohiuddin Ahmed, Muhammad Nazrul Islam

Research outputs 2022 to 2026

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective …

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Biophysical Interactions Control The Progression Of Harmful Algal Blooms In Chesapeake Bay: A Novel Lagrangian Particle Tracking Model With Mixotrophic Growth And Vertical Migration, Jilian Xiong, Jian Shen, Qubin Qin, Michelle C. Tomlinson, Yinglong J. Zhang, Xun Cai, Fei Yi, Linlin Cui, Margaret R. Mulholland

OES Faculty Publications

Climate change and nutrient pollution contribute to the expanding global footprint of harmful algal blooms. To better predict their spatial distributions and disentangle biophysical controls, a novel Lagrangian particle tracking and biological (LPT-Bio) model was developed with a high-resolution numerical model and remote sensing. The LPT-Bio model integrates the advantages of Lagrangian and Eulerian approaches by explicitly simulating algal bloom dynamics, algal biomass change, and diel vertical migrations along predicted trajectories. The model successfully captured the intensity and extent of the 2020 Margalefidinium polykrikoides bloom in the lower Chesapeake Bay and resolved fine-scale structures of bloom patchiness, demonstrating a reliable …

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Physical Sciences and Mathematics Commons^™

Full-Text Articles in Physical Sciences and Mathematics

Global Forecasts Of Marine Heatwaves, Michael Jacox

Benefits of Ocean Observing Catalog (BOOC)

Impact Of Covid-19 On Security Vulnerabilities Of Learning Management Systems: A Study Towards Security And Sustainability Enhancement, Souheil Abdel-Latif Akacha

Theses

Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

All Works

Forecasting Economic Growth And Movements With Wavelet Transform And Arima Model, Omar Alsinglawi, Omar Alsinglawi, Mohammad Aladwan, Mohammad Aladwan, Saddam Alwadi, Saddam Alwadi

Applied Mathematics & Information Sciences

A Simple Vegetation Criterion (Ndf Content) May Account For Diet Choices Of Cattle Between Forages Varying In Maturity Stage And Physical Accessibility, Cécile Ginane, R. Baumont

IGC Proceedings (1997-2023)

Population Modeling With Machine Learning Can Enhance Measures Of Mental Health - Open-Data Replication, Ty Easley, Ruiqi Chen, Kayla Hannon, Rosie Dutt, Janine Bijsterbosch

Statistical and Data Sciences: Faculty Publications

Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan

Computer Science Senior Theses

Research On Aquifer Water Abundance Evaluation By Borehole Transient Electromagnetic Method Based On Fcnn, Cheng Jiulong, Wang Huijie, Xu Zhongzhong, Huang Qisong, Jiang Guoqing

Coal Geology & Exploration

A Simple Vegetation Criterion (Ndf Content) May Account For Diet Choices Of Cattle Between Forages Varying In Maturity Stage And Physical Accessibility, Cécile Ginane, R. Baumont

IGC Proceedings (1997-2023)

Towards An Unsupervised Bayesian Network Pipeline For Explainable Prediction, Decision Making And Discovery, Daniel Mallia

Theses and Dissertations

Collision Risk Prediction For Small Ships In South Korea Via Optimization Of Wireless Communication Period, So-Ra Kim, Myoung-Ki Lee, Sangwon Park, Dae-Won Kim, Young-Soo Park

Journal of Marine Science and Technology

Leveraging Machine Learning To Analyze Sentiment From Covid-19 Tweets: A Global Perspective, Md Mahbubar Rahman, Nafiz Imtiaz Khan, Iqbal H. Sarker, Mohiuddin Ahmed, Muhammad Nazrul Islam

Research outputs 2022 to 2026

OES Faculty Publications