This data set contains vector polygons representing the boundaries of all the hardcopy cartographic products produced as part of the Environmental Sensitivity Index (ESI) for Louisiana, as well as digital data extents. This data set comprises a portion of the ESI data for Louisiana. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the data layers, LG_INDEX (Large Index Polygons) and INDEX (Index Polygons), part of the larger Louisiana ESI database, for additional boundary information.

San Marino SM:(GDP) Gross Domestic ProductVolume Index data was reported at 88.225 2010=100 in 2014. This records a decrease from the previous number of 89.104 2010=100 for 2013. San Marino SM:(GDP) Gross Domestic ProductVolume Index data is updated yearly, averaging 85.308 2010=100 from Dec 1997 (Median) to 2014, with 14 observations. The data reached an all-time high of 128.288 2010=100 in 2007 and a record low of 44.666 2010=100 in 1997. San Marino SM:(GDP) Gross Domestic ProductVolume Index data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s San Marino – Table SM.IMF.IFS: Gross Domestic Product: Deflator and Volume Index: Annual.

Graph and download economic data for NASDAQ Global Ex United States Sm Cap Index (NASDAQNQGXUSSC) from 2001-03-30 to 2025-08-08 about NASDAQ and indexes.

San Marino SM:(GDP) Gross Domestic ProductVolume Index: YoY% data was reported at 1.000 % in 2016. This records an increase from the previous number of 0.509 % for 2015. San Marino SM:(GDP) Gross Domestic ProductVolume Index: YoY% data is updated yearly, averaging 1.650 % from Dec 1998 (Median) to 2016, with 19 observations. The data reached an all-time high of 8.998 % in 1999 and a record low of -12.786 % in 2009. San Marino SM:(GDP) Gross Domestic ProductVolume Index: YoY% data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s San Marino – Table SM.IMF.IFS: Gross Domestic Product: Deflator and Volume Index: Annual.

Graph and download economic data for NASDAQ Global Ex United States Sm Cap NTR Index (NASDAQNQGXUSSCN) from 2001-03-30 to 2025-08-14 about NASDAQ and indexes.

San Marino Industrial Production Index data was reported at 105.842 2010=100 in 2015. This records an increase from the previous number of 103.607 2010=100 for 2014. San Marino Industrial Production Index data is updated yearly, averaging 94.275 2010=100 from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 105.842 2010=100 in 2015 and a record low of 69.223 2010=100 in 2000. San Marino Industrial Production Index data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s San Marino – Table SM.IMF.IFS: Production Index: Annual.

SM: Consumer Price Index: % Change over Previous Period data was reported at 1.046 % in 2017. This records an increase from the previous number of 0.574 % for 2016. SM: Consumer Price Index: % Change over Previous Period data is updated yearly, averaging 1.890 % from Dec 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 4.293 % in 2008 and a record low of 0.146 % in 2015. SM: Consumer Price Index: % Change over Previous Period data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s San Marino – Table SM.IMF.IFS: Consumer and Producer Price Index: Annual.

The drought index (DI) is computed using SMOS derived root zone soil moisture (RZSM) monthly fields. The concept is to consider 13 years of SMOS data (2010-2022). The RZSM monthly mean and max over the period is iused to compute a soil water deficit to remove seasonal variability. from teh soil water deficit a soil moisture drought index is computed .The range of values for SMDI lies between -4 to +4, with -4 representing extreme dry conditions and +4 representing extreme wet conditions.

This feature layer contains points representing bivalve quest observations in Boston Harbor Islands National and State Park, Massachusetts, 2021. The data is current as of 2021 and is maintained by BOHA Science and Stewardship Partnerships.

San Marino Consumer Price Index data was reported at 110.632 2010=100 in 2017. This records an increase from the previous number of 109.486 2010=100 for 2016. San Marino Consumer Price Index data is updated yearly, averaging 100.000 2010=100 from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 110.632 2010=100 in 2017 and a record low of 84.734 2010=100 in 2003. San Marino Consumer Price Index data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s San Marino – Table SM.IMF.IFS: Consumer and Producer Price Index: Annual.

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.

SM Stock: Future is Bright, But the Stock May Be Overvalued

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

Analysed liquid soil moisture profile index for four different soil layers (covering the root zone from the surface to ~ 3 metres) generated at ECMWF by the dedicated H SAF soil moisture assimilation system at 24 hour time steps. The analysed soil moisture fields are based on the assimilation of ASCAT-derived surface soil moisture. They are then re-scaled soil wetness index by normalising by the saturated soil moisture value as a function of soil type. SM-DAS-2 is produced daily at 00 UTC with a timeliness of 12-36 hours.

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.

SM Energy (SM) Stock: A Drilling Down into Growth Potential

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

This feature layer contains points representing bivalve quest observations in Boston Harbor Islands National and State Park, Massachusetts, 2019. The data is current as of 2019 and is maintained by BOHA Science and Stewardship Partnerships.

Background/PurposeSarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.Materials/Methods390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the section was auto-selected, and a 2D ResUNet auto-segmented the auto-selected slice. Both the 3D and 2D approaches trained five sub-models (5-fold cross-validation) and combined sub-model predictions on the test set using majority vote ensembling. Model performance was primarily determined using the Dice similarity coefficient (DSC). Predicted SMI was calculated using the auto-segmented SM cross-sectional area. Finally, using established SMI cutoffs, we performed a Kaplan-Meier analysis to determine associations with overall survival.ResultsMean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. Predicted SMI had high correlation to the ground-truth SMI in males and females (r>0.96). Predicted SMI stratified patients for overall survival in males (log-rank p = 0.01) but not females (log-rank p = 0.07), consistent with ground-truth SMI.ConclusionWe developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making in patients with HNC.

Major, trace element concentrations and Nd, Sr isotope ratios were measured in micro-drilled samples of a 2.37 Ga-old, hand-specimen sized spheroidally weathered diabase from southern India. A sample of the un-weathered diabase dike was also analyzed. X-ray micro-CT imaging of the weathered sample shows three dominant mineral phases which are plagioclase, pyroxene, and a Fe-bearing phase (possibly hematite and/or ilmenite). This imaging documents the pervasive nature of two generations of ribbon-like, cross-cutting fractures. The older fracture is sealed while the more recent fracture is open without any in-filling. The values of the Chemical Index of Alteration (CIA) of the samples show a wide range but are less than 50. Despite being a relatively less weathered rock, we observe that concentrations of major, minor and trace elements vary significantly with the percentage relative standard deviation (%RSD) for the elements ranging from 10.2?41.8. The CIA of the samples do not show any trend with the position of the sample in the hand-specimen. Barring Ca and Li, whose concentrations decrease from the core to the rim of the sample, there is no significant spatial trend in the concentrations of the elements. Concentrations of Na, Al, and Sr increase with increasing CIA values while concentrations of Mg, Fe, and Sc decrease with increasing CIA. The strong positive correlations of Na and Al, as well as Na and Sr indicates preferential weathering of plagioclase in the diabase. Na/Ca increases while Mg/Al, Mg/Na, Mg/Ca, Fe/Al and Sc/Sr decrease with increasing CIA values and the un-weathered rock plots in the middle of these trends. Such variations are explained in terms of differential weathering of plagioclase (in samples with lower CIA than the un-weathered rock, W1-type) and pyroxene (in samples with higher CIA than the un-weathered rock, W2-type) which have varying resistance to weathering. At the hand-specimen scale, the variability in the weathering indices like CIA are controlled by differential weathering of minerals and might not accurately reflect the intensity of weathering. Chondrite-normalized La/Sm and Gd/Lu co-vary with CIA values indicating mobility of the REEs during spheroidal weathering even at the hand-specimen scale. The Eu anomaly also increases with increasing CIA values which is explained by differential weathering of pyroxene and plagioclase. We observe large percentage deviations of the Nb-normalized concentrations of elements from the un-weathered rock in specific samples but no spatial trend is observed. Overall, the variations in element concentrations can be explained by varying fluid mobility of the elements, selective weathering of the minerals in the diabase, and ambient environmental conditions. Considerable Nd and Sr isotopic variability is observed at the hand-specimen scale and is explained in terms of weathering-related fractionation of parent/daughter ratios. This elemental fractionation must have happened long time ago to allow for radiogenic decay of the long-lived isotopes of 87Rb and 147Sm. The spread (%RSD) in the initial Sr and Nd isotope compositions of the weathered samples reach a minimum value around 1.2?1.3 Ga which we interpret as the timing of the peak weathering event which led to fractionation of the parent/daughter ratios. For Nd isotopes, the average epsilon-Nd (1.2 Ga) of the weathered samples coincides with the epsilon-Nd (1.2 Ga) of the un-weathered rock. The timing of the weathering event coincides with the timing of the breakup of the Columbia supercontinent and follows wide-spread alkaline volcanism in the Indian subcontinent. This is the first such attempt to determine the timing of a weathering event in rocks using long-lived radioactive isotopes

Business Confidence in the United States decreased to 48 points in July from 49 points in June of 2025. This dataset provides the latest reported value for - United States ISM Purchasing Managers Index (PMI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

San Marino SM:(GDP) Gross Domestic ProductVolume Index: Seasonally Adjusted data was reported at 81.647 2010=100 in 2016. This records an increase from the previous number of 80.838 2010=100 for 2015. San Marino SM:(GDP) Gross Domestic ProductVolume Index: Seasonally Adjusted data is updated yearly, averaging 92.916 2010=100 from Dec 1997 (Median) to 2016, with 20 observations. The data reached an all-time high of 120.250 2010=100 in 2008 and a record low of 75.388 2010=100 in 1997. San Marino SM:(GDP) Gross Domestic ProductVolume Index: Seasonally Adjusted data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s San Marino – Table SM.IMF.IFS: Gross Domestic Product: Deflator and Volume Index: Annual.

Explore the historical Whois records related to sm-plus.com (Domain). Get insights into ownership history and changes over time.

Explore the historical Whois records related to sm-a-p.com (Domain). Get insights into ownership history and changes over time.