IntroductionCommunity-acquired pneumonia (CAP) is a major health concern in the United States (US), with its incidence, severity, and outcomes influenced by social determinants of health, including socioeconomic status. The impact of neighborhood socioeconomic status, as measured by the Distressed Communities Index (DCI), on CAP-related admissions remains understudied in the literature.ObjectiveTo determine the independent association between DCI and CAP-related admissions in Maryland.MethodsWe conducted a retrospective study using the Maryland State Inpatient Database (SID) to collate data on CAP-related admissions from January 2018 to December 2020. The study included adults aged 18–85 years. We explored the independent association between community-level economic deprivation based on DCI quintiles and CAP-related admissions, adjusting for significant covariates.ResultsIn the study period, 61,467 cases of CAP-related admissions were identified. The patients were predominantly White (49.7%) and female (52.4%), with 48.6% being over 65 years old. A substantive association was found between the DCI and CAP-related admissions. Compared to prosperous neighborhoods, patients living in economically deprived communities had 43% increased odds of CAP-related admissions.ConclusionResidents of the poorest neighborhoods in Maryland have the highest risk of CAP-related admissions, emphasizing the need to develop effective public health strategies beneficial to the at-risk patient population.

Botswana's main stock market index, the Gaborone, closed flat at 10428 points on July 11, 2025. Over the past month, the index has climbed 1.16% and is up 11.17% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Botswana. Botswana Stock Market (BSI DCI) - values, historical data, forecasts and news - updated on July of 2025.

Key information about Botswana Domestic Companies (DCI)

• Botswana Domestic Companies (DCI) closed at 10,108.6 points in Feb 2025, compared with 10,075.3 points at the previous month end
• Botswana Equity Market Index: Month End: Domestic Companies (DCI) data is updated monthly, available from Jan 2003 to Feb 2025, with an average number of 7,505.8 points
• The data reached an all-time high of 10,936.4 points in Jul 2015 and a record low of 2,222.7 points in Jun 2003

BackgroundAsthma represents a substantial public health challenge in the United States, affecting over 25 million adults. This study investigates the impact of neighborhood economic deprivation on asthma-associated Emergency Department (ED) visits in Maryland, using the Distressed Communities Index (DCI) for analysis.MethodsA retrospective analysis of Maryland's Emergency Department Databases from January 2018 to December 2020 was conducted, focusing on asthma-associated ED visits.ResultsThe study involved 185,317 ED visits, majority of which were females (56.3%) and non-Hispanic whites (65.2%). A significant association was found between increased neighborhood socioeconomic deprivation and asthma-related ED visits. The poorest neighborhoods showed the highest rates of such visits. Compared to prosperous areas, neighborhoods classified from Comfortable to Distressed had progressively higher odds for asthma-related ED visits (Comfortable: OR = 1.14, Distressed OR = 1.65). Other significant asthma predictors included obesity, female gender, tobacco smoking, and older age.ConclusionThere is a substantive association between higher asthma-related ED visits and high neighborhood economic deprivation, underscoring the impact of socioeconomic factors on health outcomes.Public health implicationsAddressing healthcare disparities and improving access to care in economically distressed neighborhoods is crucial. Targeted interventions, such as community health clinics and asthma education programs, can help mitigate the impact of neighborhood disadvantage.

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

Baseline characteristics of men with castration-resistant prostate cancer (CRPC) in the Uppsala-Örebro PSA cohort (UPSAC) database.

The digital certainty index score which is calculated by aggregating the percentages of all of the Quality Assurance, Accessibility and SEO scores.

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.

(DCI) Dolphin Capital: Navigating the Mediterranean Real Estate Tides

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 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.

Is DCI Stock Buy or Sell? (Stock Forecast)

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

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address icak@dci-kansascity.com..

Results from multi-level logistic regressions on the association between type of residential area and choice of a hospital for delivery.

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.

Does algo trading work? (LON:DCI Stock Forecast)

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

Results from multi-level linear regressions on the association between type of residential area and choice of a hospital for antenatal care.

Properties of 7 postures: frequency (N), improved linearity index (h’), directional consistency index (DCI), coverage (Unknown), unidirectionality (1-Way), bidirectionality (2-Way) and number of ties (Tied), over the last 4 weeks of observations.

Microsoft's Digital Civility Index is an indication of the level of civility in online environments in a particular region. The higher the DCI score, the lower the perceived civility in France. Thus, the DCI has decreased since 2016, reaching an all time low in 2019 before reaching a stable index value of 63 in 2021. This mainly represents the dangers and the online responses of all internet actors and stakeholders.

Long chain alkyl diols form a group of lipids occurring widely in marine environments. Recent studies have suggested several palaeoclimatological applications for proxies based on their distributions, but also revealed uncertainties about their applicability. Here we evaluate the use of long chain 1,14-alkyl diol indices for reconstruction of temperature and upwelling conditions by comparing index values, obtained from a comprehensive set of marine surface sediments, with environmental factors like sea surface temperature (SST), salinity and nutrient concentrations. Previous cultivation efforts indicated a strong effect of temperature on the degree of saturation and the chain length distribution of long chain 1,14-alkyl diols in Proboscia spp., quantified in the diol saturation index (DSI) and diol chain length index (DCI), respectively. However, values of these indices in surface sediments show no relationship with annual mean SST of the overlying water. It remains unknown what determines the DSI, although our data suggests that it may be affected by diagenesis, while the relationship between temperature and DCI may be different for different Proboscia species. In addition, contributions of algae other than Proboscia diatoms may affect both indices, although our data provide no direct evidence for additional long chain 1,14-alkyl diol sources. Two other indices using the abundance of 1,14-diols vs. 1,13-diols and C30 1,15-diols have previously been applied as indicators for upwelling intensity at different locations. The geographical distribution of their values supports the use of 1,14 diols vs. 1,13 diols [C28 + C30 1,14-diols]/[(C28 + C30 1,13-diols) + (C28 + C30 1,14-diols)] as a general indicator for high nutrient or upwelling conditions.

Properties of Lowering of Posture (LoP): frequency (N), improved Landau’s linearity (h’ index), direction consistency (DCI), coverage (Unknown), unidirectionality (1-Way), bidirectionality (2-Way) and number of ties (Tied), over the last 4 weeks of observations.

Properties of 24 behavioural elements: frequencies (N), improved Landau’s linearity (h’ index), direction consistency (DCI), coverage (Unknown), unidirectionality (1-Way), bidirectionality (2-Way) and number of ties (Tied), over the last 4 weeks of observations.

Priority aquatic barriers for removal was based on the Dendritic Connectivity Index (DCI) scores of stream segments and habitat quality in reach contributing areas (Directly Connected Impervious Area (DCIA) as a measure of aquatic habitat quality). Priority barriers are identified by numbers 1 –11.

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name DCI Administrator.