213 research outputs found
Phytoplankton and Carbon Dynamics in the Estuarine-Coastal Waters of the Northern Gulf of Mexico from Field Data and Ocean Color Remote Sensing
In this study, phytoplankton community and carbon dynamics were examined in the optically complex estuarine-coastal regions of the northern Gulf of Mexico (nGOM) from field and satellite ocean color observations. As part of this study, bio-optical ocean color algorithms for i) dissolved organic carbon (DOC), ii) phytoplankton pigment composition, iii) adaptive estimation of Chl a and iv) phytoplankton size fractions were developed to facilitate the study of biogeochemical cycling in the nGOM.
The phytoplankton based algorithms were applied to Sentinel 3A/B-OLCI oean color data to assess phytoplankton community dynamics to extreme river discharge conditions as well as hurricanes in the nGOM. This study revealed that the effects of hurricanes on phytoplankton community dynamics were dependent on background nutrient conditions, as well as the intensity, track and translational speed of storms: 1) Strong flooding associated with Hurricane Harvey (2017) shifted the dominance of phytoplankton community in Galveston Bay from cyanobacteria and dinoflagellate to diatom and chlorophyte; 2) high levels of organic matter delivered from estuaries to shelf waters after Hurricane Michael (2018) fueled a red tide mixed with coccolithophore bloom in the nGoM; 3) the physical and chemical environment after hurricanes are favorable for the growth and dominance of coccolithophores in shelf waters. Further, microphytoplankton mainly controlled by freshwater inflows showed dominance in estuaries of the nGoM, with highest/lowest values observed in spring/fall. In comparison, phytoplankton size fraction (PSF) dynamics in the midshelf and offshore waters of the nGoM are strongly influenced by Loop Current (LC) expansion, and eddy shedding with highest picophytoplankton fraction observed in the warm waters of LC.
DOC dynamics was studied using an empirical algorithm that was developed and applied to multiple satellite sensors (Landsat 5 TM and MODIS-Aqua) to assess multi-decadal (1985-2012) DOC trends in Barataria Basin. The linkages between DOC and environmenal variations were investigated. The relationships between satellite-derived DOC and land cover variations (1985â2011) derived from Landsat-5 TM supervised classification indicate soil loss in the salt marsh to be an important DOC source in the wetland-estuary system, and overall strong land use/land loss impact on the long-term DOC trends in the Barataria Basin
A hierarchical semantic segmentation framework for computer vision-based bridge damage detection
Computer vision-based damage detection using remote cameras and unmanned
aerial vehicles (UAVs) enables efficient and low-cost bridge health monitoring
that reduces labor costs and the needs for sensor installation and maintenance.
By leveraging recent semantic image segmentation approaches, we are able to
find regions of critical structural components and recognize damage at the
pixel level using images as the only input. However, existing methods perform
poorly when detecting small damages (e.g., cracks and exposed rebars) and thin
objects with limited image samples, especially when the components of interest
are highly imbalanced. To this end, this paper introduces a semantic
segmentation framework that imposes the hierarchical semantic relationship
between component category and damage types. For example, certain concrete
cracks only present on bridge columns and therefore the non-column region will
be masked out when detecting such damages. In this way, the damage detection
model could focus on learning features from possible damaged regions only and
avoid the effects of other irrelevant regions. We also utilize multi-scale
augmentation that provides views with different scales that preserves
contextual information of each image without losing the ability of handling
small and thin objects. Furthermore, the proposed framework employs important
sampling that repeatedly samples images containing rare components (e.g.,
railway sleeper and exposed rebars) to provide more data samples, which
addresses the imbalanced data challenge
A Review of Hypoxia in the Chesapeake Bay Under Climate Change
The Chesapeake Bay is the largest estuary in the United States, with high ecological and economic values. However, hypoxia occurs in the Chesapeake Bay every summer and threatens the ecosystem of the Bay. The seasonal hypoxia in the Chesapeake Bay is caused by the organic matter decompositions, intensive water stratification, and other biological and physical factors/processes. Under the stress of global climate change, Chesapeake Bay will likely experience severer hypoxia in the future. This case is because climate change affects water temperature, sea level, precipitation, river discharge, and wind strength, and consequently impacts the formation of hypoxia in the Bay. Most of the previous studies explore the effects of climate change on hypoxia in the Chesapeake Bay through qualitative discussions. Few of them quantified and predicted the impacts. This paper attempts to provide recommendations for further studies to quantify and predict the effects of climate change on hypoxia in the Bay. For further studies, it is recommended to use hydrodynamic-biogeochemical models and multimodel climate projections. More studies are needed for investigating the impacts of sea-level rise on hypoxia in the Bay and the wind changes caused by climate change. Studies could explore the effects of climate change from both hypoxic volume and hypoxic duration. Moreover, studies could take atmospheric dissolved inorganic nitrogen (DIN) and coastal DIN as variables into consideration to study the impacts of climate change. More studies are needed for understanding, quantifying, and predicting the impacts of climate change on hypoxia in the Chesapeake Bay, which will help to improve the management of Chesapeake Bay
Effects of Personality on Trading Performance in Social Trading Platforms
Social trading platforms offer opportunities for amateur investors to copy professional tradersâ behavior. However, past studies on behavioral finance have largely neglected the role of personality in shaping tradersâ behavior. To this end, we aim to scrutinize the effects of leader tradersâ personality on their trading behaviors and subsequent performance on social trading platforms. Particularly, we employ the MyersâBriggs Type Indicator (MBTI) personality classification scheme to delineate leader tradersâ personality into the four dimensions of Extraversion-Introversion (E-I), Sensing-Intuition (S-N), Thinking-Feeling (T-F), and Judging-Perceiving (J-P). Next, we draw on machine learning techniques to advance a novel text-based approach for extracting the personality dimensions of leader traders automatically. Analytical results attest to the impact of personality dimensions on trading behavior and that of trading behavior on performance. Findings from this study yield insights for both social trading platforms and followers by identifying profitable leader traders based on their personality
Controlling the Amount of Verbatim Copying in Abstractive Summarization
An abstract must not change the meaning of the original text. A single most
effective way to achieve that is to increase the amount of copying while still
allowing for text abstraction. Human editors can usually exercise control over
copying, resulting in summaries that are more extractive than abstractive, or
vice versa. However, it remains poorly understood whether modern neural
abstractive summarizers can provide the same flexibility, i.e., learning from
single reference summaries to generate multiple summary hypotheses with varying
degrees of copying. In this paper, we present a neural summarization model
that, by learning from single human abstracts, can produce a broad spectrum of
summaries ranging from purely extractive to highly generative ones. We frame
the task of summarization as language modeling and exploit alternative
mechanisms to generate summary hypotheses. Our method allows for control over
copying during both training and decoding stages of a neural summarization
model. Through extensive experiments we illustrate the significance of our
proposed method on controlling the amount of verbatim copying and achieve
competitive results over strong baselines. Our analysis further reveals
interesting and unobvious facts.Comment: AAAI 2020 (Main Technical Track
Research progress on the relationship between fibroblast growth factor 23 and chronic kidney disease
Chronic kidney disease(CKD)is now a global public health problem. In chronic kidney disease(CKD)patientsïŒalmost all have complications such as calcium and phosphorus metabolism disordersïŒhyperparathyroidismïŒcardiovascular diseaseïŒanemiaïŒand inflammationïŒwhich seriously affect the progress and prognosis of CKD. Fibroblast growth factor 23(FGF23) is a bone-derived hormone that regulates the metabolism of phosphate and vitamin D. In the pastïŒFGF23 was generally considered to play only an important role in the regulation of calcium and phosphorus metabolism. In recent years FGF23has been found to be associated with the occurrence or progression of various CKD complications. This opens up new horizons for studying the role of FGF23 in the course of chronic kidney disease. FGF23 is expected to become a new therapeutic target in the futureïŒimproving the prognosis of patients with CKD. This article will review the biological characteristics of FGF23 and its role in the progression of CKD. And briefly discuss its potential future role in chronic kidney disease
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
Current video object detection (VOD) models often encounter issues with
over-aggregation due to redundant aggregation strategies, which perform feature
aggregation on every frame. This results in suboptimal performance and
increased computational complexity. In this work, we propose an image-level
Object Detection Difficulty (ODD) metric to quantify the difficulty of
detecting objects in a given image. The derived ODD scores can be used in the
VOD process to mitigate over-aggregation. Specifically, we train an ODD
predictor as an auxiliary head of a still-image object detector to compute the
ODD score for each image based on the discrepancies between detection results
and ground-truth bounding boxes. The ODD score enhances the VOD system in two
ways: 1) it enables the VOD system to select superior global reference frames,
thereby improving overall accuracy; and 2) it serves as an indicator in the
newly designed ODD Scheduler to eliminate the aggregation of frames that are
easy to detect, thus accelerating the VOD process. Comprehensive experiments
demonstrate that, when utilized for selecting global reference frames, ODD-VOD
consistently enhances the accuracy of Global-frame-based VOD models. When
employed for acceleration, ODD-VOD consistently improves the frames per second
(FPS) by an average of 73.3% across 8 different VOD models without sacrificing
accuracy. When combined, ODD-VOD attains state-of-the-art performance when
competing with many VOD methods in both accuracy and speed. Our work represents
a significant advancement towards making VOD more practical for real-world
applications.Comment: 11 pages, 6 figures, accepted by ACM MM202
Astragalus Polysaccharides Lowers Plasma Cholesterol through Mechanisms Distinct from Statins
To determine the efficacy and underlying mechanism of Astragalus polysaccharides (APS) on plasma lipids in hypercholesterolemia hamsters. The effect of APS (0.25g/kg/d) on plasma and liver lipids, fecal bile acids and neutral sterol, cholesterol absorption and synthesis, HMG-CoA reductase activity, and gene and protein expressions in the liver and small intestine was investigated in twenty-four hypercholesterolemia hamsters. Treatment periods lasted for three months. APS significantly lowered plasma total cholesterol by 45.8%, triglycerides by 30%, and low-density lipoprotein-cholesterol by 47.4%, comparable to simvastatin. Further examinations revealed that APS reduced total cholesterol and triglycerides in the liver, increased fecal bile acid and neutral sterol excretion, inhibited cholesterol absorption, and by contrast, increased hepatic cholesterol synthesis and HMG-CoA reductase activity. Plasma total cholesterol or low-density lipoprotein-cholesterol levels were significantly correlated with cholesterol absorption rates. APS up-regulated cholesterol-7α-hydroxylase and LDL-receptor gene expressions. These new findings identify APS as a potential natural cholesterol lowering agent, working through mechanisms distinct from statins
Unraveling the Relationship between Content Design and Kinesthetic Learning on Communities of Practice Platforms
As a variant of the sharing economy, Communities of Practice (CoP) platforms have allowed kinesthetic learners to acquire skillsets corresponding to their interests for immediate or future use in practice. However, the impact of digital learning content design on kinesthetic learning remains underexplored in the field of information systems. We hence extend prior research by advancing content richness and structure clarity as antecedents affecting kinesthetic learnersâ digestibility of contents, culminating in differential kinesthetic learning effects. To substantiate our arguments, we collected data from a leading Chinese recipe sharing platform. Whereas content richness was measured in terms of readability, verb richness, and prototypicality, structure clarity was operationalized as block structure, block quantity, and block regularity. Employing a machine learning model, we simulated and tested learnersâ digestibility of image content embodied within recipes. Plans for future research beyond the current study are also discussed
- âŠ