2,626 research outputs found
Species-level classification of mangrove forest using AVIRIS-NG hyperspectral imagery
No full textSpecies-level classification of mangroves provides important inputs for conservation, rehabilitation and understanding of ecosystem functions. The hyperspectral sensor, Airborne Visible InfraRed Imaging Spectrometer-New Generation (AVIRIS-NG), holds promises for species-level discrimination by virtue of its coverage across a wider spectrum at very high spatial resolution. Using the continuum removal (CR) technique and absorption band depth (ABD), this study applied Random Forest (RF) model to classify the distribution of three species (Heritiera fomes, Excoecaria agallocha and Avicennia officinalis) and two of their combinations (Heritiera fomes-Excoecaria agallocha and Avicennia officinalis-Excoecaria agallocha). The classified map demonstrated good accuracy (overall accuracy = 88%; kappa coefficient = 0.84) using ABD as an independent variable. The important wavelengths (972, 1172, 1177 nm) identified for mangrove species discrimination correspond to water absorption bands. This characteristic may be replicated for species-level classification of other mangrove forests with similar species
CNNViT. A robust deep neural network for video anomaly detection
No full textDetecting anomalies in videos poses a significant challenge due to the unbounded, infrequent, ambiguous, and irregular nature of abnormal events in real-world scenes. Recently, transformers have shown remarkable modeling capabilities for sequential data. As a result, we endeavor to leverage transformers for video anomaly detection. This paper presents a novel prediction-based method for video anomaly detection called CNNViT by integrating the architectural elements of Convolutional Neural Network (CNN) and Vision Transformer (ViT). The purpose of this fusion is to effectively capture enhanced spatial-temporal information and global features. The effectiveness of the proposed method is evaluated on UCSD Ped2 and CUHK Avenue benchmark datasets. Experimental results demonstrate that the proposed method attains considerably superior performance compared to state-of-the-art techniques
Systematic investigation of trench filling with photo materials
No full textAuthor Amal Dev Raj VilayilMasterarbeit Universität Linz 2022Arbeit auf den öffentlichen PCs in den Bibliotheken der JKU+Medizin abrufba
Corrigendum to Programmed cell death in the lithium pilocarpine model: Evidence for NMDA receptor and ceramide-mediated mechanisms [Brain Dev 30 (2008) 513-519] (DOI:10.1016-j.braindev.2008.01.002)
No full text[No abstract available]Mikati MA, 2008, BRAIN DEV-JPN, V30, P513, DOI 10.1016-j.braindev.2008.01.0020
open-AIMS/ADRIA.jl: v0.7.0-dev.1
No full textWhat's Changed
Update use of functions due to new import approach by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/334
Migrate from SnoopPrecompile to PrecompileTools by @timholy in https://github.com/open-AIMS/ADRIA.jl/pull/335
Add planning horizon factor by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/336
Replace use of area attribute/field with call to function site_area() to ensure correct values by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/337
Remove reference to defunct fields when making factors constant by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/338
Make use of planning horizon in sims by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/340
Changes to support running ADRIA with external model (ReefMod Engine) data by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/341
CompatHelper: add new compat entry for SimpleWeightedGraphs at version 1, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/343
CompatHelper: add new compat entry for OrderedCollections at version 1, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/344
CompatHelper: bump compat for StatsBase to 0.34, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/345
Split ADRIA-mod domain definition by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/346
Add GBR zones by priority by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/347
Exit with error if given path is not a directory by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/350
Make Aviz into an extension package by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/349
CompatHelper: add new compat entry for Reexport at version 1, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/351
CompatHelper: add new compat entry for ImageMagick at version 1, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/352
Update bleaching mortality model to align with published paper by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/354
Add option to use JMcDM functions in ADRIA site selection by @Rosejoycrocker in https://github.com/open-AIMS/ADRIA.jl/pull/348
Address mismatched number of elements under certain conditions by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/358
Remove Copras method due to erroring by @Rosejoycrocker in https://github.com/open-AIMS/ADRIA.jl/pull/359
CompatHelper: bump compat for HypothesisTests to 0.11, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/363
CompatHelper: add new compat entry for JMcDM at version 0.7, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/362
Update documentation by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/361
Scenario discovery docs by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/364
Fix: Metric errors when applied to a single simulation by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/365
Address ranking errors in mcda outputs by @Rosejoycrocker in https://github.com/open-AIMS/ADRIA.jl/pull/367
Add temporal clustering by @Zapiano in https://github.com/open-AIMS/ADRIA.jl/pull/370
Fix incorrect type check by @Zapiano in https://github.com/open-AIMS/ADRIA.jl/pull/371
CompatHelper: add new compat entry for Clustering at version 0.15, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/372
CompatHelper: bump compat for Zarr to 0.9, (keep existing compat) by @github-actions in https://github.com/open-AIMS/ADRIA.jl/pull/373
Update time series clustering and add visualization functionality by @Zapiano in https://github.com/open-AIMS/ADRIA.jl/pull/374
Update scenario viz by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/355
Refactor run_scenarios to improve when running multiple rcps by @Zapiano in https://github.com/open-AIMS/ADRIA.jl/pull/376
Bump version number and add new author by @Zapiano in https://github.com/open-AIMS/ADRIA.jl/pull/378
Update docstrings for growth function by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/375
Add map visualization - displays k-area by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/356
Consider near-term conditions with greater weight than far-future conditions by @ConnectedSystems in https://github.com/open-AIMS/ADRIA.jl/pull/387
Changes to number of species in ADRIA by @Rosejoycrocker in https://github.com/open-AIMS/ADRIA.jl/pull/380
Temporal clustering for spatial data by @Zapiano in https://github.com/open-AIMS/ADRIA.jl/pull/382
New Contributors
@timholy made their first contribution in https://github.com/open-AIMS/ADRIA.jl/pull/335
@Zapiano made their first contribution in https://github.com/open-AIMS/ADRIA.jl/pull/370
Full Changelog: https://github.com/open-AIMS/ADRIA.jl/compare/v0.5.0...v0.7.0-dev.
The Cryosphere Discussions
No full textwww.geosci-model-dev-discuss.net/6/3003/2013/ doi:10.5194/gmdd-6-3003-2013 © Author(s) 2013. CC Attribution 3.0 License
System Safety in Healthcare: The Right and Wrong Ways to Perform Failure Mode and Effects Analysis (FMEA)
No full textThe objective of performing Failure Mode and Effects Analysis (FMEA) is to use sound risk management principles, coupled with innovative solutions that can assure high return on investment (ROI). Quality Guru Philip Crosby wrote in his book, Quality is Free, that quality is free if you do the right things at the right time. Essentially, the savings from avoiding fixes, process changes and lawsuits are much higher than the cost of doing things right. The principles of sound risk management, experienced by this paper’s co-author Dev Raheja as an international engineering management consultant over 30 years, include:
Identifying risks
Assessing risks
Mitigating risks
Orchestrating risk management
Aiming at high ROI without compromising safet
A role for SUMO modification in transcriptional repression and activation
No full textSince the discovery of the SUMO (small ubiquitin-related modifier) family of proteins just over a decade ago, a plethora of substrates have been uncovered including many regulators of transcription. Conjugation of SUMO to target proteins has generally been considered as a repressive modification. However, there are now a growing number of examples where SUMOylation has been shown to activate transcription. Here, we discuss whether there is something intrinsically repressive about SUMOylation, or if the outcome of this modification in the context of transcription will prove to be largely substrate-dependent. We highlight some of the technical challenges that will be faced by attempting to answer this question
Leaf chlorophyll concentration estimation using absorption spectroscopy of AVIRIS-NG for a mangrove forest in India
No full textChlorophyll concentration is one of the important biochemical properties of vegetation as it relates to photosynthetic activity and health. The amount of chlorophyll in a vegetation canopy indicates the physiological status or the health condition. Compared to other terrestrial ecosystems, mangroves are highly productive, so there is a need for a better understanding of the dynamics of carbon sequestration by monitoring their health and nutrition status for ecological conservation and restoration processes. In spite of many ecosystem services, limited research has been conducted concerning mangrove chlorophyll assessment due to the challenges of field sampling. The majority of the chlorophyll assessments in mangroves are being executed with the help of remote sensing data-derived vegetation indices (VIs). However, they are site or species-specific, which prohibits a universal adaptation. Our study quantifies leaf chlorophyll concentration (LCC) distribution using the Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) hyperspectral imagery and field observed dataset for the Bhitarkanika National Park (BNP), a mangrove ecosystem of India. This study aims to predict the LCC utilizing absorption features such as absorption band depth (ABD) as a predictor variable. This was calculated using continuum removal techniques and further predicted using machine learning (Random Forest, RF). This study identifies the red-edge region (676–722 nm) as the prominent part of the electromagnetic spectrum that is useful for predicting LCC. Our model achieved an acceptable accuracy (R2 = 0.82, RMSE = 0.34) and comparable validation statistics (R2 = 0.44, RMSE = 0.38), despite on-field logistic constraints in LCC measurements. This study demonstrated a protocol for a rapid estimate of biochemical variables using (AVIRIS-NG) hyperspectral imagery
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