+1$\sigma$ Expected 95% CL for the 1SFH $e$ Dirac model.

NMC ran the MRF every 24 h (00 UTC) with 12 h forecasts up to 240 h at a standard resolution of 200 km. The data cutoff for the model runs was 6 h and the output was ON85. There are 18 sigma levels. The output includes pressure, geopotential altitude, u and v wind components, virtual temperature, and relative humidity, among others.

The design and synthesis of a series of 2,7-diazaspiro[4.4]nonane derivatives as potent sigma receptor (SR) ligands, associated with analgesic activity, are the focus of this work. In this study, affinities at S1R and S2R were measured, and molecular modeling studies were performed to investigate the binding pose characteristics. The most promising compounds were subjected to in vitro toxicity testing and subsequently screened for in vivo analgesic properties. Compound 9d (AD258) exhibited negligible in vitro cellular toxicity and a high binding affinity to both SRs (KiS1R = 3.5 nM, KiS2R = 2.6 nM), but not for other pain-related targets, and exerted high potency in a model of capsaicin-induced allodynia, reaching the maximum antiallodynic effect at very low doses (0.6–1.25 mg/kg). Functional activity experiments showed that S1R antagonism is needed for the effects of 9d and that it did not induce motor impairment. In addition, 9d exhibited a favorable pharmacokinetic profile.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for SIGMA on 2025-12-01. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

Notes on OpenData for "On the prediction and the formation of the sigma phase in CrMnCoFeNix high entropy alloys"

This is dataset is a single zip file within which there are three folders that contain the data presented in the associated publication.

The DSC folder contains Differential Scanning Calorimetry data. These can be plotted in any standardised graphic tool. The results were analysed following the NIST guidelines.

The SEM folder data includes the results from scanning electron microscopy and energy-dispersive x-ray spectroscopy (EDX) maps and spot analysis for compositions.

The XRD folder contains the raw XRD data as well as the fitted data using the Pawley procedure in the TOPAS academic software.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for SIGMA on 2025-11-25. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

`*.npz` and `*.asdf` files containing visibilities are in the TMS format (opposite that of CASA).

logo_cube.noise.npz visibilities have been rescaled such that data - model / sigma follows the expected Gaussian envelope.

HD 143006 continuum visibilities have flagged outliers removed and weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.

AS 209 continuum visibilities have been averaged across frequency and have their weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.

Data archived here are the external iron input data and model output data discussed in a paper entitled "Slowly sinking particles underlie dissolved iron transport across the Pacific Ocean" submitted to Global Biogeochemical Cycles. The model used in this study was developed by coupling Regional Ocean Modeling System (Shchepetkin and McWilliams, 2005) and Biogeochemical Elemental Cycling model (Moore et al., 2013). The model covers the whole North Pacific Ocean. The model horizontal resolution was set to 1/4° mesh. The external iron input data are iron fluxes due to atmospheric deposition and dissolution from seabed sediments. The model output data are dissolved iron concentrations simulated by the model and were only presented for the data in the intermediate layer (26.6-27.4 sigma-theta divided by 0.02 sigma-theta). The simulated data were regridded 1° mesh to reduce the size of the data. The model was calculated for 100 years and the simulated dissolved iron concentration are in quasi-steady state. For more details about the individual archived data, please refer to README.pdf included in the data. […]

+2$\sigma$ Expected 95% CL for the 1SFH $e$ Dirac model.

+1$\sigma$ Expected 95% CL for the 2QDH NH Dirac model.

+1$\sigma$ Expected 95% CL for the 2QDH IH Dirac model.

The files contain outputs from numerical simulations using a combination of the numerical models Geospace Environment Model of Ion-Neutral Interactions (GEMINI) and Satellite-beacon Ionospheric- scintillation Global Model of the upper Atmosphere (SIGMA). The outputs provide simulated time series of Global Positioning System (GPS) scintillations through density structures generated by the Kelvin Helmholtz instability (KHI), as explained in detail in the publication. The numerical codes used to generate the outputs are described in the following publications: • Zettergren, M., Semeter, J., & Dahlgren, H. (2015). “Dynamics of density cavities generated by frictional heating: Formation, distortion, and instability”. Geophysical Research Letters, 42(23). [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066806 ] • Deshpande, K. B., Bust, G. S., Clauer, C. R., Rino, C. L., & Carrano, C. S. (2014). “Satellite-beacon Ionospheric-scintillation Global Model of the upper Atmosphere (SIGMA) I: High latitude sensitivity study of the model parameters”. Journal of Geophysical Research: Space Physics, 119, 4026-4043. doi:10.1002/2013JA019699811. [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013JA019699 ] • Deshpande, K. B., & Zettergren, M. D. (2019). “Satellite-Beacon Ionospheric Scintillation Global Model of the Upper Atmosphere (SIGMA) III: Scintillation Simulation Using A Physics-Based Plasma Model”. Geophysical Research Letters, 46(9), 4564-4572. doi:10.1029/2019GL082576. [Available at https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL082576 ] GEMINI is free, open-source software and can be downloaded from GitHub at https://github.com/gemini3d/. Build instructions, example simulations, and documentation are also included on this website. Uploaded by A.S.

Dataset Documentation

  Overview

Dataset Name: sigma_dataset Short Description: This dataset consists of meteorological (time series) and geophysical (catchment attributes) data of 85 basins of Kazakhstan. It is intended for use in weather forecasting or modeling, as well as flood prediction based on the attributes provided. Long Description: We developed basin scale hydrometeorological forcing data for 85 basins in the conterminous Kazakhstan basin subset. Retrospective… See the full description on the dataset page: https://huggingface.co/datasets/floodpeople/sigma_dataset.

In the Northeast and Great Lakes regions of the United States, the influence of multiple lakes on overlying air can greatly affect lake-effect snowfall over downwind communities. To assess the impact of Lake Huron on snowfall downwind of Lake Erie, we simulated a lake-effect snow event which occurred from 1-6 January 2010 using the Regional Atmospheric Modeling System (RAMS). We found that the presence of Lake Huron enhances snowfall downwind of Lake Erie by almost 20\% and leads to much heavier local snowfall totals than when Lake Huron is not present. This increase in snowfall is due to a lake-to-lake (L2L) convective band, as secondary circulations associated with lake-effect convection form over Lake Huron and persist overland between the lakes before re-intensifying over Lake Erie. As these secondary circulations move over Lake Erie, low-level convergence from the secondary circulation induces mechanical lifting which accelerates the development of convection within the L2L band. S..., This dataset was produced by simulating a lake-effect snow event which occurred between 1-3 January 2010 with the Regional Atmospheric Modeling System (RAMS), an open-source, nonhydrostatic numerical weather model. The model was initiatlized and forced at lateral boundaries with ERA5 reanalysis data. Three simulations were conducted: A CONTROL simulation, without any modifications to the reanalysis, and which used initial water temperatures from a 1-degree horizontal resolution Reynolds-averaged global dataset, a NLH simulation in which the surface of Lake Huron was changed from water to mixed forest and in which initial soil and snow data over the former area of Lake Huron were adjusted to match those of neighboring Michigan, and a VARTEMP simulation which was identical to CONTROL except that data from the Great Lakes Environmental Research Laboratory (GLERL) were used for the initial water temperatures over the Great Lakes. The model output files use a terrain-following sigma-z vertic..., , # Selected large model output files and Buffalo sounding data from: Lake Huron enhances snowfall downwind of Lake Erie: a modeling study of the 2010 near year’s Lake-effect snowfall event

https://doi.org/10.5061/dryad.2z34tmpwj

Description of the data and file structure

This data was collected for the paper entitled "Lake Huron Enhances Snowfall Downwind of Lake Erie: A Modeling Study of the 2010 New Year's Lake-Effect Snowfall Event." With the exception of the file containing radiosonde data from Buffalo Niagara International Airport, which was obtained from the Integrated Global Radiosonde Archive from the National Oceanic and Atmospheric Administration (NOAA), all other files are either raw or post-processed output from the Regional Atmospheric Modeling System (RAMS) simulations of the lake-effect snow event which occurred over the North American Great Lakes from 1-3 January 2010.

This data is not meant to replace the repository for f...

The Hybrid Coordinate Ocean Model (HYCOM) is a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model. The subset of HYCOM data hosted in EE contains the variables salinity, temperature, velocity, and elevation. They have been interpolated to a uniform 0.08 degree lat/long grid between 80.48°S and 80.48°N. The salinity, temperature, and velocity variables have been interpolated to 40 standard z-levels.

NRL HYCOM 1/25 deg model output, Gulf of Mexico, 10.04 Expt 31.0, 2009-2014, At Surface The HYCOM consortium is a multi-institutional effort sponsored by the National Ocean Partnership Program (NOPP), as part of the U. S. Global Ocean Data Assimilation Experiment (GODAE), to develop and evaluate a data-assimilative hybrid isopycnal-sigma-pressure (generalized) coordinate ocean model (called HYbrid Coordinate Ocean Model or HYCOM).

As the number of sequenced bacterial genomes increases, the need for rapid and reliable tools for the annotation of functional elements (e.g., transcriptional regulatory elements) becomes more desirable. Promoters are the key regulatory elements, which recruit the transcriptional machinery through binding to a variety of regulatory proteins (known as sigma factors). The identification of the promoter regions is very challenging because these regions do not adhere to specific sequence patterns or motifs and are difficult to determine experimentally. Machine learning represents a promising and cost-effective approach for computational identification of prokaryotic promoter regions. However, the quality of the predictors depends on several factors including: i) training data; ii) data representation; iii) classification algorithms; iv) evaluation procedures. In this work, we create several variants of E. coli promoter data sets and utilize them to experimentally examine the effect of these factors on the predictive performance of E. coli σ70 promoter models. Our results suggest that under some combinations of the first three criteria, a prediction model might perform very well on cross-validation experiments while its performance on independent test data is drastically very poor. This emphasizes the importance of evaluating promoter region predictors using independent test data, which corrects for the over-optimistic performance that might be estimated using the cross-validation procedure. Our analysis of the tested models shows that good prediction models often perform well despite how the non-promoter data was obtained. On the other hand, poor prediction models seems to be more sensitive to the choice of non-promoter sequences. Interestingly, the best performing sequence-based classifiers outperform the best performing structure-based classifiers on both cross-validation and independent test performance evaluation experiments. Finally, we propose a meta-predictor method combining two top performing sequence-based and structure-based classifiers and compare its performance with some of the state-of-the-art E. coli σ70 promoter prediction methods.

+2$\sigma$ Expected 95% CL for the 2QDH IH Majorana model.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SGML Sigma Lithium Corporation Common Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

+2$\sigma$ Expected 95% CL for 2QDH NH Majorana model.