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Numalyze: Numerical Analysis Web Application
No full textNumalyze is an online platform that allows users to run and apply different numerical methods in real time. The application is built using Python and the Flask web framework. It provides an interface where users input mathematical functions and parameters to see the results for root-finding and integration methods, and to also perform reductions on matrices. By using a light-weight web framework and self-coded algorithms which removes dependency on massive external libraries—this application connects theoretical concepts to their practical implementation. It enables students and researchers to visualize the series of steps that each algorithm takes to compute results. Moreover, the application can be packaged into a Docker Image which makes it easy for distribution and deployment. The application, once deployed, is available on any device that supports a web browser, which makes it highly suitable for educational demonstrations, self-study, and quick experimentation
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
ready4show: Author Literate Programs to Share Insights from Applying the Ready4 Framework
No full textready4show provides tools for authoring technical documentation, analysis reports and scientific summaries to showcase insights generated by open, modular mental health system models.
This release corrects citation information.Matthew Hamilton and Glen Wiesner (2022). ready4show: Author Literate Programs to Share Insights from Applying the Ready4 Framework. Version 0.0.0.9098. Zenodo. https://doi.org/10.5281/zenodo.564456
Using cross-model learnings for the Gram Vaani ASR Challenge 2022
No full textIn the diverse and multilingual land of India, Hindi is spoken as a first language by a majority of its population. Efforts are made to obtain data in terms of audio, transcriptions, dictionary, etc. to develop speech-technology applications in Hindi. Similarly, the Gram-Vaani ASR Challenge 2022 provides spontaneous telephone speech, with natural back-ground and regional variations in Hindi. The challenge provides: 100 hours of labeled train-set, 5 hours of labeled dev-set and 1000 hours of unlabeled data-set. For the 'Closed Challenge', we trained an End-to-End (E2E) Conformer model using speed perturbations, SpecAugment techniques and use VTLN to handle any unknown speaker groups in the blind evaluation set. On the dev-set, we achieved a 30.3% WER compared to the 34.8% WER by the Challenge E2E baseline. For the 'Self Supervised Closed Challenge', a semi-supervised learning approach is used. We generate pseudo-transcripts for the unlabeled data using a hybrid TDNN-3gram LM model and trained an E2E model. This is then used as a seed for retraining the E2E model with high confidence data. Cross-model learning and refining of the E2E model gave 25.3% WER on the dev-set compared to ∼33-35% WER by the Challenge baseline that use wav2vec models.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia Computin
Photon-bunching in ground-based submillimeter-wave astronomy
No full textDESHIMA (the Deep Spectroscopic High-Redshift Mapper) is a 347 channel superconducting spectrometer with spectral resolution R =500 that operates in the range of 220GHz to 440GHz and can therefore accurately measure the frequency of spectral lines in order to calculate redshift z.This report investigates the sensitivity of DESHIMA-like spectrometers by investigating photon noise due to Poisson and bunching effects. It gives a broad overview of photon statistics and explains, through an analogous model, that photon bunching occurs due to an underlying change in the probabilistics, rather than the act of detecting itself. After that I investigate photon and quasiparticle recombination noise for a DESHIMA-like spectrometer with Lorentzian filters and find a closed form equation for NEP per channel for a constant power spectral density arriving at the filters.Previously the bandwidth of the filters was assumed to be negligible, resulting in an overestimation of the bunching. Because the photons that are impinging on the detector span a bigger bandwidth, the bunching is a factor of π/2 smaller than previously approximated.This NEPτ is defined at an integration time of τ=0.5s. For other integration times this is scalable, however this will only hold while the integration time is much bigger than the coherence time τ≫tcoh. Because of the correlation between photons arriving shorter than a coherence time apart, the scaling of the NEPτ drops in cases when τ≫̸tcoh.Finally I propose and describe modifications to the sensitivity model DESHIMA uses. The following features have been be improved and added:- Integrate over the entire power spectrum when calculating photon noise- Use arbritatry filter designs loaded from a file- Improve estimations of the quantities that express sensitivityI compare the proposed modifications to the old model, which has previously been compared with measurement results, and use it to validate the changes. Other than the previously mentioned factor of π/2 for the bunching term and the smoothing out in local extrema, the modified simulation results are similar to the old model. This is because the Lorentzian filters have a small bandwidth ν≫Δν, such that the previous narrowband approximation held for most non-extreme cases.https://joristiebosch.github.io/thesis/ Interactive version available https://github.com/deshima-dev/deshima-sensitivity Python model described in ThesisDESHIMAApplied Physic
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