text:
COVID-19-Related Hospitalizations in 13 States, by Community-Level Income, 2020
STATISTICAL BRIEF #273
Pamela L. Owens, Ph.D.
This Healthcare Cost and Utilization Project (HCUP) Statistical Brief presents statistics on COVID-19-related hospitalizations using 2019 State Inpatient Databases (SID) and 2020 quarterly inpatient data from 13 States. Differences in monthly hospitalizations by community-level income from April to September 2020 are compared with the same months in the prior year. Community-level income is based on the median household income of the patient's ZIP Code of residence and is categorized by quartiles. Variation in utilization, average length of stay, and in-hospital mortality is illustrated. Because of the large sample size of the HCUP data, small differences can be statistically significant but not meaningful. Thus, only differences greater than or equal to 10 percent are discussed in the text.
This analysis is limited to discharges for patients treated in community, nonrehabilitation hospitals in 13 States (Colorado, Georgia, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, New Jersey, Ohio, South Carolina, and Vermont) for which HCUP data were available for April-September 2019 and April-September 2020. These States account for 24.7 percent of the resident U.S. population in 2019.1,2 All information contained in this Statistical Brief (except age-adjusted rates) can be found in the HCUP Summary Trend Tables.3 The Summary Trend Tables, accessed as downloadable tables, provide State-specific monthly trends in hospital utilization for the most recent HCUP data available. These tables will be updated as more quarterly data become available. The analysis in this Statistical Brief is based on data available as of March 2021.
Differences in COVID-19-related hospitalizations, by community-level income, April-September 2020
Figure 1 displays the distribution of COVID-19-related hospitalizations in 13 States in each of 6 months, April, May, June, July, August, and September 2020, by community-level income of the patient (lowest income quartile, middle income, and highest income quartile). As a reference, the distribution of all hospitalizations in the same States during April-September 2019 is also shown.
Compared with the distribution of all hospitalizations in April-September 2019, patients from the highest income communities in the 13 States accounted for a larger share of COVID-19-related hospitalizations in April and May 2020, whereas patients from the lowest income communities accounted for a larger share each month from June to September 2020.
Between April 2020 and July 2020, the observed (unadjusted) COVID-19-related in-hospital mortality rate declined 55 percent for patients from the lowest income communities and 64 percent for patients from the highest income communities.
In each month from July to September 2020, the COVID-19-related age-adjusted in-hospital mortality rate for patients from the lowest income communities was more than 10 percent higher than the rate for patients from the highest income communities.
Age-adjusted in-hospital mortality rates varied by month, community-level income, and State in which the patient was hospitalized.
Figure 1. Distribution of COVID-19-related hospitalizations in April-September 2020 compared with all hospitalizations in April-September 2019, by community-level income, 13 States
Source: Agency for Healthcare Research and Quality (AHRQ), Healthcare Cost and Utilization Project (HCUP), 2019 State Inpatient Databases (SID) and 2020 quarterly data from 13 States (CO, GA, IA, KY, MD, MI, MN, MO, MS, NJ, OH, SC, and VT) (available as of March 2021)
Bar chart that shows the community-level income distribution of COVID-19-related hospitalizations in 13 States in April-September 2020, along with the distribution of all hospitalizations in those States in April-September 2019. April-September 2019, all hospitalizations (N=4.5M): quartile 1 (lowest income) = 32.2%; quartiles 2 and 3 (middle income) = 48.4%; quartile 4 (highest income) = 18.6%. April 2020 COVID-19-related hospitalizations (N=53,305): quartile 1 = 31.4%; quartiles 2 and 3 = 41.4%; quartile 4 = 26.4%. May 2020 COVID-19-related hospitalizations (N=35,321): quartile 1 = 31.7%; quartiles 2 and 3 = 44.8%; quartile 4 = 22.6%. June 2020 COVID-19-related hospitalizations (N=20,560): quartile 1 = 37.5%; quartiles 2 and 3 = 44.3%; quartile 4 = 17.2%. July 2020 COVID-19-related hospitalizations (N=34,542): quartile 1 = 41.9%; quartiles 2 and 3 = 44.5%; quartile 4 = 12.5%. August 2020 COVID-19-related hospitalizations (N=32,744): quartile 1 = 41.4%; quartiles 2 and 3 = 44.9%; quartile 4 = 12.6%. September 2020 COVID-19-related hospitalizations (N=24,012): quartile 1 = 39.7%; quartiles 2 and 3 = 46.6%; quartile 4 = 12.6%.
Compared with the distribution of all hospitalizations in April-September 2019, patients from the highest income communities in these 13 States accounted for a larger share of COVID-19-related hospitalizations in April and May 2020, whereas patients from the lowest income communities accounted for a larger share each month from June to September 2020.
In April 2020, more than one in four COVID-19-related hospitalizations were for patients from the highest income communities (26.4 percent). By September 2020, only one in eight COVID-19-related hospitalizations were for patients from these communities (12.6 percent).
In April 2020, patients from the lowest income communities accounted for 31.4 percent of all COVID-19-related hospitalizations. The share of COVID-19-related hospitalizations for patients from the lowest income communities increased from 31.4 percent in April 2020 to 39.7-41.9 percent in July-September 2020.
In April 2020, COVID-19-related hospitalizations were 1.2 times more likely to be for patients from the lowest income communities than the highest income communities (31.4 vs. 26.4 percent, respectively). In September 2020, COVID-19-related hospitalizations were more than three times more likely to be for patients from the lowest income communities than the highest income communities (39.7 vs. 12.6 percent, respectively).
Differences in average length of COVID-19-related hospitalizations, by community-level income, April-September 2020
Figure 2 presents the average length of COVID-19-related hospitalizations in April-September 2020 across 13 States, by community-level income of the patient (lowest income quartile, middle income, and highest income quartile). As a reference, the average length of all hospitalizations during April-September 2019 across the 13 States is also shown.
Figure 2. Average length of stay for COVID-19-related hospitalizations in April-September 2020 compared with all hospitalizations in April-September 2019, by community-level income, 13 States
Bar chart that shows the average length of stay (in days) of COVID-19-related hospitalizations in 13 States in April-September 2020, along with the average length of all hospitalizations in those States in April-September 2019, by community-level income. April-September 2019, all hospitalizations (N=4.5M): all income quartiles = 4.7; quartile 1 (lowest income) = 4.9; quartiles 2 and 3 (middle income) = 4.6; quartile 4 (highest income) = 4.5. April 2020 COVID-19-related hospitalizations (N=53,305): all income quartiles = 7.5; quartile 1 = 7.7; quartiles 2 and 3 = 7.5; quartile 4 = 7.4. May 2020 COVID-19-related hospitalizations (N=35,321): all income quartiles = 8.6; quartile 1 = 8.6; quartiles 2 and 3 = 8.5; quartile 4 = 8.7. June 2020 COVID-19-related hospitalizations (N=20,560): all income quartiles = 8.0; quartile 1 = 7.8; quartiles 2 and 3 = 8.0; quartile 4 = 8.4. July 2020 COVID-19-related hospitalizations (N=34,542): all income quartiles = 6.5; quartile 1 = 6.6; quartiles 2 and 3 = 6.5; quartile 4 = 6.5. August 2020 COVID-19-related hospitalizations (N=32,744): all income quartiles = 7.3; quartile 1 = 7.6; quartiles 2 and 3 = 7.2; quartile 4 = 7.0. September 2020 COVID-19-related hospitalizations (N=24,012): all income quartiles = 7.2; quartile 1 = 7.5; quartiles 2 and 3 = 7.0; quartile 4 = 7.0.
The longest average length of COVID-19-related hospitalizations across these 13 States occurred in May 2020, regardless of community-level income. The shortest average length of COVID-19-related hospitalizations occurred in July 2020, regardless of community-level income. There was less than a 5 percent difference in average length of hospitalizations across the community-level income groups.
Differences in in-hospital mortality for COVID-19-related hospitalizations, by community-level income, April-September 2020
Figure 3 displays the observed (unadjusted) in-hospital mortality rate for COVID-19-related hospitalizations for each month during April-September 2020 across 13 States, by community-level income of the patient (lowest income quartile, middle income, and highest income quartile). As a reference, the all-cause observed (unadjusted) in-hospital mortality rates across the 13 States during April-September 2019 are also shown. The observed in-hospital mortality rates do not account for differences in the distribution of the population by age.
Figure 3. Observed COVID-19-related in-hospital mortality rate in April-September 2020 compared with the observed all-cause in-hospital mortality rate in April-September 2019, by community-level income, 13 States
Bar chart that shows the in-hospital mortality rate (observed) of COVID-19-related hospitalizations in 13 States in April-September 2020, along with the all-cause in-hospital mortality rate (observed) of all hospitalizations in those States in April-September 2019, by community-level income. April-September 2019, all hospitalizations (N=4.5M): all income quartiles = 1.9%; quartile 1 (lowest income) = 2.0%; quartiles 2 and 3 (middle income) = 1.8%; quartile 4 (highest income) = 1.8%. April 2020 COVID-19-related hospitalizations (N=53,305): all income quartiles = 21.4%; quartile 1 = 21.3%; quartiles 2 and 3 = 19.9%; quartile 4 = 24.0%. May 2020 COVID-19-related hospitalizations (N=35,321): all income quartiles = 16.1%; quartile 1 = 15.8%; quartiles 2 and 3 = 15.4%; quartile 4 = 17.9%. June 2020 COVID-19-related hospitalizations (N=20,560): all income quartiles = 10.9%; quartile 1 = 10.9%; quartiles 2 and 3 = 10.7%; quartile 4 = 11.3%. July 2020 COVID-19-related hospitalizations (N=34,542): all income quartiles = 9.1%; quartile 1 = 9.5%; quartiles 2 and 3 = 8.8%; quartile 4 = 8.6%. August 2020 COVID-19-related hospitalizations (N=32,744): all income quartiles = 11.4%; quartile 1 = 12.7%; quartiles 2 and 3 = 10.8%; quartile 4 = 9.4%. September 2020 COVID-19-related hospitalizations (N=24,012): all income quartiles = 11.1%; quartile 1 = 11.9%; quartiles 2 and 3 = 10.8%; quartile 4 = 9.6%.
The observed (unadjusted) COVID-19-related in-hospital mortality rate for patients from the lowest income communities dropped from 21.3 percent in April 2020 to 9.5 percent in July 2020, a 55 percent decline in the observed in-hospital mortality rate.
Similarly, the observed (unadjusted) COVID-19-related in-hospital mortality rate for patients from the highest income communities dropped from 24.0 percent in April 2020 to 8.6 percent in July 2020, a 64 percent decline in the in-hospital mortality rate.
To account for the differences in in-hospital mortality due to differences in the age distribution among the income groups, Figure 4 displays the age-adjusted in-hospital mortality rate for COVID-19-related hospitalizations in April-September 2020 across the 13 States, by community-level income of the patient (lowest income quartile, middle income, and highest income quartile). The magnitude of the rates presented in this figure is dependent on the standard population used to adjust for age. In this case, the standard population is the nationwide hospitalized population in 2018. These age-adjusted in-hospital mortality rates are useful for comparisons across groups or time after accounting for the age distribution of the population. As a reference, the all-cause age-adjusted in-hospital mortality rates across the 13 States during April-September 2019 are also shown.
Figure 4. COVID-19-related age-adjusted in-hospital mortality rate in April-September 2020 compared with the all-cause in-hospital mortality rate in April-September 2019, by community-level income, 13 States
Note: In-hospital mortality rates were adjusted for age based on the national age distribution of hospitalizations for patients in the 2018 Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS).
Bar chart that shows the age-adjusted in-hospital mortality rate of COVID-19-related hospitalizations in 13 States in April-September 2020, along with the all-cause age-adjusted in-hospital mortality rate of all hospitalizations in those States in April-September 2019, by community-level income. April-September 2019, all hospitalizations (N=4.5M): all income quartiles = 1.9%; quartile 1 (lowest income) = 2.0%; quartiles 2 and 3 (middle income) = 1.8%; quartile 4 (highest income) = 1.7%. April 2020 COVID-19-related hospitalizations (N=53,305): all income quartiles = 16.2%; quartile 1 = 17.4%; quartiles 2 and 3 = 15.2%; quartile 4 = 16.0%. May 2020 COVID-19-related hospitalizations (N=35,321): all income quartiles = 12.8%; quartile 1 = 12.9%; quartiles 2 and 3 = 12.9%; quartile 4 = 12.4%. June 2020 COVID-19-related hospitalizations (N=20,560): all income quartiles = 9.2%; quartile 1 = 9.3%; quartiles 2 and 3 = 9.3%; quartile 4 = 8.7%. July 2020 COVID-19-related hospitalizations (N=34,542): all income quartiles = 7.8%; quartile 1 = 8.1%; quartiles 2 and 3 = 7.6%; quartile 4 = 6.9%. August 2020 COVID-19-related hospitalizations (N=32,744): all income quartiles = 9.0%; quartile 1 = 10.0%; quartiles 2 and 3 = 8.6%; quartile 4 = 7.2%. September 2020 COVID-19-related hospitalizations (N=24,012): all income quartiles = 8.3%; quartile 1 = 8.9%; quartiles 2 and 3 = 8.1%; quartile 4 = 7.1%.
In April 2020, the COVID-19-related age-adjusted in-hospital mortality rate for patients from the lowest income communities was 13 percent higher than the rate for patients from the middle-income communities and 8 percent higher than the rate for patients from the highest income communities (17.4 vs. 15.2 for the middle-income group and 16.0 for the highest income group per 100 standard hospitalized population).
Figure 5 displays the State variation in the rate ratio (RR) of COVID-19-related age-adjusted in-hospital mortality rates in April-September 2020, by community-level income of the patient. As a reference, age-adjusted in-hospital mortality RRs for all hospitalizations during April-September 2019 in each State are shown. Each dot in the figure represents one State's age-adjusted in-hospital mortality RR for a specific community-level income group (i.e., age-adjusted in-hospital mortality rate for a specific community-level income group in a State divided by the age-adjusted in-hospital mortality rate for patients from the highest income communities). If there were fewer than 30 discharges in a State-specific community-level income category, the corresponding age-adjusted State-specific in-hospital mortality RR is suppressed.
Figure 5. State-specific COVID-19-related age-adjusted in-hospital mortality rate ratios in April-September 2020 compared with the State-specific all-cause in-hospital mortality rate ratios in April-September 2019, by community-level income, 13 States
Abbreviation: REF, reference group
Note: If there were fewer than 30 discharges in a State-specific community-level income category, the corresponding age-adjusted State-specific in-hospital mortality rate ratio is suppressed. In-hospital mortality rates were adjusted for age based on the national age distribution of hospitalizations for patients in the 2018 Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS).
Source: Agency for Healthcare Research and Quality (AHRQ), Healthcare Cost and Utilization Project (HCUP), 2019 State Inpatient Databases (SID) and 2020 quarterly data from 12 States (CO, GA, IA, KY, MD, MI, MN, MO, MS, NJ, OH, and SC) (available as of March 2021)
Scatter plot that shows the range of rate ratios of COVID-19-related age-adjusted in-hospital mortality rates in 13 States, by community-level income, in April-September 2020, along with the in-hospital mortality rate ratios for all hospitalizations in those States in April-September 2019. Quarter 4 (highest income) is the reference category. April-September 2019, all hospitalizations (N=4.5M): quartile 1 (lowest income) = 1.0-1.5; quartiles 2 and 3 (middle income) = 1.0-1.3. April 2020 COVID-19-related hospitalizations (N=53,305): quartile 1 = 0.9-2.0; quartiles 2 and 3 = 0.8-1.4. May 2020 COVID-19-related hospitalizations (N=35,321): quartile 1 = 0.8-1.5; quartiles 2 and 3 = 0.9-1.3. June 2020 COVID-19-related hospitalizations (N=20,560): quartile 1 = 0.9-2.3; quartiles 2 and 3 = 0.9-1.8. July 2020 COVID-19-related hospitalizations (N=34,542): quartile 1 = 0.6-2.3; quartiles 2 and 3 = 0.7-1.9. August 2020 COVID-19-related hospitalizations (N=32,744): quartile 1 = 0.8-1.9; quartiles 2 and 3 = 0.8-2.0. September 2020 COVID-19-related hospitalizations (N=24,012): quartile 1 = 0.8-2.1; quartiles 2 and 3 = 0.8-1.7.
State-specific COVID-19-related age-adjusted in-hospital mortality RRs varied by community-level income of the patient and across time, indicating that age-adjusted in-hospital mortality rates also varied by month, community-level income, and the State in which the patient was hospitalized.
More variation was noted in the COVID-19-related age-adjusted in-hospital mortality RR for patients from the lowest income communities (relative to those from the highest income communities) than for patients from the middle-income communities in each month from May to September 2020, except August 2020.
Between April and September 2020, most States had higher monthly age-adjusted in-hospital mortality rates for patients from the lowest income and middle-income communities than for patients from the highest income communities (RR > 1.0).
1 Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to July 1, 2019 (NC-EST2019-SR11H). U.S. Census Bureau, Population Division. Release Date: June 2020. www.census.gov/newsroom/press-kits/2020/population-estimates-detailed.html. Accessed March 1, 2021.
2 Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin for Colorado, Georgia, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, New Jersey, Ohio, South Carolina, and Vermont: April 1, 2010 to July 1, 2019 (NC-EST2019-SR11H-nn). U.S. Census Bureau, Population Division. Release Date: June 2020. www.census.gov/data/tables/time-series/demo/popest/2010s-state-detail.html. Accessed March 1, 2021.
3 Agency for Healthcare Research and Quality. HCUP Summary Trend Tables. Healthcare Cost and Utilization Project (HCUP). Agency for Healthcare Research and Quality. Updated December 2020. www.hcup-us.ahrq.gov/reports/trendtables/summarytrendtables.jsp. Accessed February 10, 2021.
About Statistical Briefs
Healthcare Cost and Utilization Project (HCUP) Statistical Briefs provide basic descriptive statistics on a variety of topics using HCUP administrative healthcare data. Topics include hospital inpatient, ambulatory surgery, and emergency department use and costs, quality of care, access to care, medical conditions, procedures, and patient populations, among other topics. The reports are intended to generate hypotheses that can be further explored in other research; the reports are not designed to answer in-depth research questions using multivariate methods.
The estimates in this Statistical Brief are based upon data from the HCUP 2019 State Inpatient Databases (SID) and 2020 quarterly inpatient data. Information based on quarterly data should be considered preliminary, as additional quarterly data may become available over time. This analysis is limited to patients treated in community, nonrehabilitation hospitals in 13 States (Colorado, Georgia, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, New Jersey, Ohio, South Carolina, and Vermont) for which HCUP data were available for April-September 2019 and April-September 2020. These States account for 24.7 percent of the total U.S. population.1,2 All of the information contained in this Statistical Brief (except age-adjusted rates) can be found in the HCUP Summary Trend Tables at www.hcup-us.ahrq.gov/reports/trendtables/summarytrendtables.jsp.
The HCUP inpatient data contain the universe of the inpatient discharge abstracts in the participating HCUP States, translated into a uniform format to facilitate multistate comparisons and analyses. In the aggregate, the inpatient data encompass more than 95 percent of all U.S. community hospital discharges. The inpatient data can be used to investigate questions unique to one State, to compare data from two or more States, to conduct market-area variation analyses, and to identify State-specific trends in inpatient care utilization, access, charges, and outcomes.
Types of hospitals included in HCUP State Inpatient Databases (and quarterly inpatient data)
This analysis used SID and quarterly inpatient data limited to information from community hospitals, which are defined as short-term, non-Federal, general, and other hospitals, excluding hospital units of other institutions (e.g., prisons). Community hospitals include obstetrics and gynecology, otolaryngology, orthopedic, cancer, pediatric, public, and academic medical center hospitals. Excluded for this analysis are long-term care facilities such as rehabilitation, psychiatric, and alcoholism and chemical dependency hospitals. However, if a patient received long-term care, rehabilitation, or treatment for a psychiatric or chemical dependency condition in a community hospital, the discharge record for that stay was included in the analysis.
Diagnoses and ICD-10-CM
The principal diagnosis is that condition established after study to be chiefly responsible for the patient's admission to the hospital. Secondary diagnoses are conditions that coexist at the time of admission that require or affect patient care treatment received or management, or that develop during the inpatient stay. All-listed diagnoses include the principal diagnosis plus the secondary conditions.
ICD-10-CM is the International Classification of Diseases, Tenth Revision, Clinical Modification. There are over 70,000 ICD-10-CM diagnosis codes.
Case definition
COVID-19-related hospitalizations are identified by any-listed ICD-10-CM code of U07.1 (2019 novel coronavirus disease) on the discharge record. Per coding guidelines,a the use of U07.1 is based on documentation by the provider or documentation of a positive COVID-19 test result. The ICD-10-CM code for COVID-19 was implemented beginning April 1, 2020. As such, there may be some measurement error in the identification of cases.
The unit of analysis is the hospital discharge (i.e., the hospital stay), not a person or patient. This means that a person who is admitted to the hospital multiple times in 1 year will be counted each time as a separate discharge from the hospital.
Community-level income
Community-level income is based on the median household income of the patient's ZIP Code of residence. Quartiles are defined so that the total U.S. population is evenly distributed. Cut-offs for the quartiles are determined annually using ZIP Code demographic data obtained from Claritas, a vendor that produces population estimates and projections based on data from the U.S. Census Bureau.b The value ranges for the income quartiles vary by year. Patients in the first quartile are designated as having the lowest income, patients in the middle two quartiles are designated as having middle income, and patients in the highest quartile are designated as having the highest income. The income quartile is missing for patients who are homeless or foreign.
In-hospital mortality rate, age-adjusted in-hospital mortality rate, and in-hospital mortality rate ratio
The simplest in-hospital mortality rate is the observed in-hospital mortality rate, defined as the total number of COVID-19-related deaths in the hospital divided by the number of patients hospitalized with COVID-19. However, the observed in-hospital mortality rate does not account for the age distribution of the population.
Because in-hospital mortality rates generally increase with age, another important measure is the age-adjusted mortality rate. The age-adjusted in-hospital mortality rate (in-hospital mortality per 100 standard population) is the proportion of patients with COVID-19 who died while in the hospital, standardized using the direct method and the age distribution of a standard population. The standard population is the nationwide hospitalized population in 2018, using data from the 2018 HCUP National Inpatient Sample (NIS). The rates represent the expected in-hospital mortality rates if the observed age-specific rates were applied to the same standard population.
The age-adjusted in-hospital mortality rate ratio is defined as the age-adjusted in-hospital mortality rate of one group divided by the age-adjusted in-hospital mortality rate of the comparison, or reference, group. State-specific age-adjusted in-hospital mortality rates are suppressed for groups with fewer than 30 discharges.
About HCUP
The Healthcare Cost and Utilization Project (HCUP, pronounced "H-Cup") is a family of healthcare databases and related software tools and products developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ). HCUP databases bring together the data collection efforts of State data organizations, hospital associations, and private data organizations (HCUP Partners) and the Federal government to create a national information resource of encounter-level healthcare data. HCUP includes the largest collection of longitudinal hospital care data in the United States, with all-payer, encounter-level information beginning in 1988. These databases enable research on a broad range of health policy issues, including cost and quality of health services, medical practice patterns, access to healthcare programs, and outcomes of treatments at the national, State, and local market levels.
HCUP would not be possible without the contributions of the following data collection Partners from across the United States:
Alaska Department of Health and Social Services
Alaska State Hospital and Nursing Home Association
Arizona Department of Health Services
Arkansas Department of Health
California Office of Statewide Health Planning and Development
Colorado Hospital Association
Connecticut Hospital Association
Delaware Division of Public Health
District of Columbia Hospital Association
Florida Agency for Health Care Administration
Georgia Hospital Association
Hawaii Laulima Data Alliance
Hawaii University of Hawai'i at Hilo
Indiana Hospital Association
Iowa Hospital Association
Kansas Hospital Association
Kentucky Cabinet for Health and Family Services
Louisiana Department of Health
Maine Health Data Organization
Maryland Health Services Cost Review Commission
Massachusetts Center for Health Information and Analysis
Michigan Health & Hospital Association
Minnesota Hospital Association
Mississippi State Department of Health
Missouri Hospital Industry Data Institute
Montana Hospital Association
Nebraska Hospital Association
Nevada Department of Health and Human Services
New Hampshire Department of Health & Human Services
New Mexico Department of Health
North Carolina Department of Health and Human Services
North Dakota (data provided by the Minnesota Hospital Association)
Ohio Hospital Association
Oregon Association of Hospitals and Health Systems
Oregon Office of Health Analytics
Pennsylvania Health Care Cost Containment Council
Rhode Island Department of Health
South Carolina Revenue and Fiscal Affairs Office
South Dakota Association of Healthcare Organizations
Tennessee Hospital Association
Utah Department of Health
Vermont Association of Hospitals and Health Systems
Virginia Health Information
West Virginia Department of Health and Human Resources, West Virginia Health Care Authority
Wyoming Hospital Association
For information on COVID-19 resources at AHRQ, refer to the AHRQ COVID-19 Resources page at www.ahrq.gov/coronavirus/index.html. For other information on COVID-19 healthcare utilization, refer to the HCUP Statistical Briefs located at www.hcup-us.ahrq.gov/reports/statbriefs/sb_covid.jsp.
For additional HCUP statistics, visit:
HCUP Fast Stats at https://datatools.ahrq.gov/hcup-fast-stats for easy access to the latest HCUP-based statistics for healthcare information topics
HCUPnet, HCUP's interactive query system, at datatools.ahrq.gov/hcupnet
HCUP Summary Trend Tables at www.hcup-us.ahrq.gov/reports/trendtables/summarytrendtables.jsp for monthly information on hospital utilization
For more information about HCUP, visit www.hcup-us.ahrq.gov/.
For a detailed description of HCUP and more information on the design of the State Inpatient Databases (SID), please refer to the following database documentation:
Agency for Healthcare Research and Quality. Overview of the State Inpatient Databases (SID). Healthcare Cost and Utilization Project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality. Updated October 2020. www.hcup-us.ahrq.gov/sidoverview.jsp. Accessed January 22, 2021.
Owens PL (AHRQ). COVID-19-Related Hospitalizations in 13 States, by Community-Level Income, 2020. HCUP Statistical Brief #273. June 2021. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb273-COVID19-Hospital-Income.pdf.
The author would like to acknowledge the contributions of Marguerite Barrett of M.L. Barrett, Inc., in addition to Molly Hensche, Brendan Leonard, Minya Sheng, and Jennifer Welch of IBM Watson Health.
AHRQ welcomes questions and comments from readers of this publication who are interested in obtaining more information about access, cost, use, financing, and quality of healthcare in the United States. We also invite you to tell us how you are using this Statistical Brief and other HCUP data and tools, and to share suggestions on how HCUP products might be enhanced to further meet your needs. Please email us at hcup@ahrq.gov or send a letter to the address below:
Joel W. Cohen, Ph.D., Director
Center for Financing, Access and Cost Trends
This Statistical Brief was posted online on June 14, 2021.
a Centers for Disease Control and Prevention, National Center for Health Statistics. ICD-10-CM Official Guidelines for Coding and Reporting FY 2021 (October 1, 2020 - September 30, 2021). www.cdc.gov/nchs/data/icd/10cmguidelines-FY2021.pdf. Accessed February 10, 2021.
b Claritas. Claritas Demographic Profile by ZIP Code. https://claritas360.claritas.com/mybestsegments/. Accessed February 3, 2020.
Internet Citation: Statistical Brief #273. Healthcare Cost and Utilization Project (HCUP). June 2021. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-us.ahrq.gov/reports/statbriefs/sb273-COVID19-Hospital-Income.jsp.

redpajama_set_name: RedPajamaCommonCrawl


text:
RTE's Sarah McInerney receives good news from a stranger in the gym
The television presenter opened up about a good moment she experienced this morning when she was working out in the gym which she decided to share with her followers
Shauna Bannon Ward
Sarah McInerney receives good news from a stranger in the gym (Image: Instagram)
Sarah McInerney received some good news from a stranger in the gym this morning.
The journalist was busy working out when someone approached her, sharing that they had to tell a person what they just learned.
In an effort to brighten her followers' morning, she posted about the happy encounter.
Read more: RTE's Sarah McInerney wows as she steps out for glamorous red carpet appearance
Sarah explained that she is glad that the stranger decided to speak to her about their news.
She took to social media to write: "A stranger just approached me in the gym to tell me that, after three years of treatment, he got the all clear on his cancer this morning.
"He said he just had to tell someone. I'm so glad he told me. #cancer #CancerResearch".
It turned out that Sarah's followers felt the exact same and commented how happy they were to be part of the moment.
One person said: "Great way to start the day".
A second social media user wrote: "That's fantastic, Can you just imagine how he feels! I'd say it made your day too Sarah".
A third commenter shared: "That's nice, good news on a Gloomy November day".
With another person adding: "Awe that's lovely. Now that would brighten your day".
Sarah recently opened up about how important the gym is to her schedule as she uses it to wind down.
"The gym is like my church, honestly, I go five days a week," she told Irish County Magazine. "When I am in the gym I am generally working so hard concentrating on what I am doing and listening to really loud music, so it just shuts everything else out.
"The added benefit of it is that it helps your body and keep you fit, but it is mostly for my head. It is one of the only places that I really switch off."
The RTE presenter balances a lot, between being the mum to two children, hosting a daily radio show and co-presenting Primetime.
She shared: "My husband sometimes laughs at me, at night time if I am really tired I will say I am going to sleep and he says, 'You are going to teach sleep a lesson. He laughs that I am so determined that I even sleep hard."
"I don't know if it is a good way to be, I have to watch myself because everything I am doing is pushing, pushing... but it is very fulfilling to be honest.
"I am really exhausted a lot of the time, I feel like I don't have the balance right, but at the same time I am very content. I am doing everything I want to do."
RTE's Sarah McInerney shares video of sons Ben and Caelan before they go back to school
RTE's Sarah McInerney has new way of working to suit marriage to husband Thomas and kids Ben and Caelan
RTE's Sarah McInerney jets to London for well deserved holiday during break from TV and radio
Sarah McInerney shares picture of husband Thomas and their son dressed up for Halloween


Know Hersey High Class of 1988 graduates that are NOT on this List? Help us Update the 1988 Class List by adding missing names.
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text:
What is venture philanthropy?
Innovator Spotlight
Benefit-Risk
Management and Accountability
Research and Development System
Value and Coverage
Venture Philanthropy
Foundation-Company Partnerships
Foundation-University Partnerships
Foundations as Collaboration Conveners
The Research Acceleration and Innovation Network (TRAIN) was established by FasterCures in 2005 to recognize organizations that are pioneering principles of venture philanthropy to catalyze development of new therapies and cures, and to encourage more foundations to adopt such approaches to de-risk translation and commercialization of research in their respective disease areas. Organizations participating in TRAIN have found it a valuable platform for learning from the experiences and practices of other nonprofit research funders and for building relationships with stakeholders in industry, academia, government, and finance. There is no cost to participating in or applying to TRAIN.
While the organizations that participate in TRAIN are individually unique, all TRAIN organizations demonstrate an ongoing commitment to accountability, collaboration, research effectiveness, resource building, and patient centeredness. The criteria for participation are based on a FasterCures' publication Measuring and Improving Impact: A Toolkit for Nonprofit Funders of Medical Research, which was intended to provide nonprofit disease research organizations with a self-evaluation framework by which to assess and improve organizational effectiveness.
Through the questions below, please describe and give examples of the ways in which your organization demonstrates an ongoing commitment to the characteristics that make TRAIN a unique network. The examples provided after questions are meant to underscore the variety of activities that meet the requirements of each category. Innovative and new methods of demonstrating commitment to the TRAIN characteristics are welcomed.
We estimate the form will take 20-45 minutes to complete.Once you begin to enter information into the online form, you will not be able to save your work and return to it later. You may want to draft your responses in another format first, then copy and paste them into the form.
If you have any questions, please don't hesitate to contact train@milkeninstitute.org. Thank you for your interest in TRAIN!
© 2022 T R A I N Central Station
FasterCures.org A Center of the Milken Institute


text:
SpeakeasyENT: The biggest Oscar snubs EVER
Carina Belles / February 22, 2013
Photo from Mashable.
Oscar night is a bittersweet time for the ENT section. On one hand, we get to see all of our beloved stars, their beautiful outfits and their even more beautiful dates. On the other, we're forced to watch their dreams die, when some undeserving nobody who just happened to land that Word War II biopic about the Titanic survivor undoubtedly takes home their trophy. Or something. Didn't you just want to cry watching Sandra Bullock beat out Carey Mulligan for Best Actress in 2009? No? Just us?
Rant aside, the Academy has a history of picking winners that make absolutely no sense. Ben Affleck and Kathryn Bigelow, both clear favorites to battle it out for Best Director at this year's ceremony, didn't even score a nomination. What the what, Academy? We have a lot of feelings about this.
So without further ado, here are the Oscar snubs that still have us boiling mad.
The films that lost to "Crash," 2005 | Ross Dickerhoof
"Crash" is a prime example of yet another pandering "issue" movie taking home Oscar gold over more subtle, films. Photo from IMDB.
Things I learned about life from "Crash:"
-Spike Lee was right: Everyone is a loudmouth racist. And if you aren't racist now, you will be.
-Dropping the title of your movie in the opening narration isn't tacky or amateurish, it's "deep."
-Ripping off Robert Altman and Paul Thomas Anderson doesn't make you a hack; it makes you a genius.
-Falling down a flight of stairs will cure you of racism. No, really. Go try it.
-Children are magical angelic saviors with no flaws. Get me a bucket.
-Audiences are stupid, so you should repeat the message of your movie over and over, just so they get it.
-Your movie can suck and still win Best Picture, as long as it's supposedly about an Important Thing.
Really, Academy? You'd rather give your most prestigious award to a hyperbolic, hammy hot mess of an after-school special full of unlikable jerks than to a well-acted and subtle movie about homosexuality and society, an intense character study about how fame and infamy twist the stories we tell, one of the best movies about journalism and government fear-mongering ever made or a harrowing and thought-provoking film about terrorism? I'll never understand you.
Well, I guess it's too much to ask that the Best Picture award go to the best film of the year. Now, it goes to the most didactic and thunderingly obvious. You know what? Fine. I'm cool with that. Give the Oscar to "The Impossible" in a surprise upset this year, for all I care. I'm done trying to comprehend the Academy's politics, and I'm done hoping for them to consistently honor artistic merit rather than sledgehammer philosophies. Remember kids, racism and tsunamis are bad, mmmkay?
"Snow White and the Seven Dwarves," Best Picture, 1937 | Nikki Lanka
So what if Snow White cemented all of those horrible princess clichés? Disney classics like "Mulan" and "Hercules" wouldn't exist without her. Photo from FanPop.
The Best Picture winner of 1937 was "The Life of Emile Zola." Zola was a French muckraker involved in uncovering dirt in the Dreyfus Affair, when Captain Alfred Dreyfus was framed for political treason in the 1890's. Zola helped publicize the cover-up and Dreyfus was eventually exonerated.
That's cool and all. But the Academy should have chosen "Snow White and the Seven Dwarves" as Best Picture, or at least nominated it.
The pretty lady/handsome prince/bumbling side-kicks/ugly villain formula is something so cliché that we often forget it had to start somewhere, and that somewhere is "Snow White." It was the world's first full-length animated feature, and the Academy totally dropped the ball by not recognizing just how groundbreaking the film would be.
Future films (most of them Disney's), as well as "The Wizard of Oz" weren't alone in feeling "Snow White's" influence. Disney filmed it on a multiplane camera that was previously only used on a handful of cartoons, which didn't become obsolete until studios began turning digital. And an astounding box-office gross of just under $200 million dollars (in 1937!) proved its success would continue for decades to come. It goes without saying just how much the promise of revenue for animation affected films of the 21st Century.
Though if we're being honest, setting a standard that films can survive as long as a whiny white girl gets saved by a handsome prince while using midgets and animals to do her bidding might not be the most positive influence, but hey. It's Disney, what did you expect?
"Fight Club," Best Sound Editing, 1999 | Isaac Noland
"The Matrix" may have been cool, but "Fight Club" was a work of auditory genius. Photo from IMDB.
1999 was a good year for movies. Specifically, it was a great year for sound in film. "Fight Club" and "The Matrix," those action/philosophy teenage-boy obsessions went head to head at the 2000 Oscars for Best Sound Editing. The Matrix won. It wasn't too much of a surprise, considering the sound effects were as complex as the wire-fu tech used to make those crazy action sequences. The sound of cascading computer code and hovering hunter-killer machines drilled into your brain.
But "Fight Club" should have won.
We know that nontraditional doesn't always mean "good," but in "Fight Club's" case, it meant amazing. Director David Fincher tapped the production-duo Dust Brothers because he wanted a score wholly unique to the film. The result is mind-bending. Scratches coincide with "WTF" moments that pepper the film, wrenching the viewer from the comfort of their own mind. Every moment of psychological disassociation is accompanied by a glitchy blip, a subtly warped sample or some other audio emphasis.
The pinnacle of this audio immersion comes when the narrator, "Jack" (Edward Norton) sits listening to his boss after an early fight night. The boss' voice is muffled and watery. He sounds as far as away as the narrator's thoughts are from that bland corporate world. When he tosses down a set of reports on Jack's desk, there's a light "plip" of water splashing. It is simply beautiful.
Leonardo DiCaprio, Best Actor | Meghan Malone
Leo, honey, it's okay. Those weirdos at the Academy couldn't spot a genius even if he sicced wild dogs on them. Photo from Yahoo Movies.
Leo, Leo, Leo… it's been a long 21 years on the big screen, hasn't it? Your career has been littered with extraordinary ("The Departed," "Catch Me If You Can," "Inception") and not-so-extraordinary movies ("Titanic," "Man in the Iron Mask"—guilty pleasures, yes, good films, no), but despite your great work, the Academy has continually refused to place the Golden Man in your hands. I'm sure you can easily recount each moment of rejection, but let's run through the list for posterity's sake, shall we?
In 1994, you received a nomination for Best Supporting Acting for "What's Eating Gilbert Grape?" Did you win? Nope. Instead the Oscar went to Tommy Lee Jones for "The Fugitive." But that's okay, Leo. It was your first time. There's always next year. Eleven years go by and for the 2005 Academy Awards, you were nominated for Best Actor for your portrayal of Howard Hughes in "The Aviator." Winner this time around? Not even. A big loss to Jamie Foxx (yes, Jamie Foxx) for the movie "Ray." But who knew Jamie could play a blind man so well? Next year, perhaps. In 2007, Best Actor for "Blood Diamond?") Once again, it was a no. And this year, you were snubbed again for your brilliant performance as Calvin Candie in Quentin Tarantino's "Django Unchained."
Leo, we believe that one day you will be given your due. But don't worry, if the Academy refuses to hand over your Oscar, your loyal supporters will honor you with more internet memes.
Which Oscar snub left you furious with the Academy? Do you think anyone will be screwed over this year? Leave us a comment and let us know, or tweet us @SpeakeasyENT!
February 22, 2013 in Blogs, Columns, ENT Blog. Tags: 2013 academy awards, Brokeback Mountain, capote, crash, Django Unchained, fight club, good night and good luck, leonardo dicaprio, oscars 2013, snow white, the matrix
2013 Best Picture: Presidents, tigers and terrorists, oh my!
2019 Movie Fantasy Part 1: The Setup
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One thought on "SpeakeasyENT: The biggest Oscar snubs EVER"
Meghan Malone says:
Reblogged this on Noted.


Browsers use the HTTP to request and transport data between user and server. For usability the browser sends some information to the server. The user mostly doesn't have much possibility to specify what information will be sent. Sometimes the browser sends more than the user would like it to send.
On this page you can see some of the Information. It is just a light service for you.


text:
#include <aws/logs/model/FilterLogEventsRequest.h>
#include <aws/core/utils/json/JsonSerializer.h>

#include <utility>

using namespace Aws::CloudWatchLogs::Model;
using namespace Aws::Utils::Json;
using namespace Aws::Utils;

FilterLogEventsRequest::FilterLogEventsRequest() :
    m_logGroupNameHasBeenSet(false),
    m_logStreamNamesHasBeenSet(false),
    m_startTime(0),
    m_startTimeHasBeenSet(false),
    m_endTime(0),
    m_endTimeHasBeenSet(false),
    m_filterPatternHasBeenSet(false),
    m_nextTokenHasBeenSet(false),
    m_limit(0),
    m_limitHasBeenSet(false),
    m_interleaved(false),
    m_interleavedHasBeenSet(false)
{
}

Aws::String FilterLogEventsRequest::SerializePayload() const
{
  JsonValue payload;

  if(m_logGroupNameHasBeenSet)
  {
   payload.WithString("logGroupName", m_logGroupName);

  }

  if(m_logStreamNamesHasBeenSet)
  {
   Array<JsonValue> logStreamNamesJsonList(m_logStreamNames.size());
   for(unsigned logStreamNamesIndex = 0; logStreamNamesIndex < logStreamNamesJsonList.GetLength(); ++logStreamNamesIndex)
   {
     logStreamNamesJsonList[logStreamNamesIndex].AsString(m_logStreamNames[logStreamNamesIndex]);
   }
   payload.WithArray("logStreamNames", std::move(logStreamNamesJsonList));

  }

  if(m_startTimeHasBeenSet)
  {
   payload.WithInt64("startTime", m_startTime);

  }

  if(m_endTimeHasBeenSet)
  {
   payload.WithInt64("endTime", m_endTime);

  }

  if(m_filterPatternHasBeenSet)
  {
   payload.WithString("filterPattern", m_filterPattern);

  }

  if(m_nextTokenHasBeenSet)
  {
   payload.WithString("nextToken", m_nextToken);

  }

  if(m_limitHasBeenSet)
  {
   payload.WithInteger("limit", m_limit);

  }

  if(m_interleavedHasBeenSet)
  {
   payload.WithBool("interleaved", m_interleaved);

  }

  return payload.WriteReadable();
}

Aws::Http::HeaderValueCollection FilterLogEventsRequest::GetRequestSpecificHeaders() const
{
  Aws::Http::HeaderValueCollection headers;
  headers.insert(Aws::Http::HeaderValuePair("X-Amz-Target", "Logs_20140328.FilterLogEvents"));
  return headers;

}

redpajama_set_name: RedPajamaGithub


Aquamarines are the birthstone for March and in days of old were said to be the treasure of mermaids. The word aquamarine comes from the Latin words for "water" and "the sea." March is an excellent month for sea creatures, whether real or make-believe, and we have a whole host of books that are a perfect fit!
The story of the Mermaid will sound familiar to fans of Goldilocks and the Three Bears, but set off of the coast of Okinawa, Japan. The mermaid, Kiniro, instead finds the seashell house of an octopus family. This beautifully illustrated book gives a new underwater flair to the old classic.
This book tells the story of Kelp, a unicorn from the sea, who is raised by narwhals. He doesn't quite fit in. He winds up on land and learns about his true identity.
Rainbow Fish is proud of his beautiful scales, but he won't share! This classic tale shows the result of being selfish, and that being kind will lead to great friendships.
Minnow, a young and inquisitive mermaid, finds a human shoe and wonders what it could be. She goes up to the surface to discover the truth.


World History with John Rossi will be cancelled on 3/18. Class will resume on 3/25. The last day of class is 4/1 (1:15-2:15pm President's Room).
World History with John Rossi will continue to take place every Wednesday until 4/1 (1:15-2:15pm President's Room).
Conversational Spanish with Isaias Mercado will continue to take place every Monday until 3/30 (11am-12pm President's Room).
Shakespeare as Historian with Peter Whitehouse will meet on the following Tuesdays 9:30-10:30am in the Adult Lounge: 3/17, 3/31 & 4/7.
Wednesday March 18: Trip to Yeshiva University. Echoes of the Borsht Belt. The buses are leaving at 9:45am from HKC.
Thursday March 19 : Cinema Club and Lunch 12pm – 2:30pm. Magic in the Moonlight. 2014,98 min. Exposing a phony soothsayer proves harder than expected when the debunker (an Englishman) becomes smitten with the purported fraud (a French beauty). This deft romantic comedy unwinds amid the gilt and glamour of the French Riviera in the 1920s. Directed by Woody Allen.
Thursday Dinner/Entertainment: Eddie Gentile with music to get out of your seat on dance on your feet!
Thursday March 26: Picasso & Wine. 2:30-4:30pm. Any participant interested in attending Wine & Art event on March 26 must pay Branny as soon as possible, as the number of spots are limited.


Chrysler 300C Forum: 300C & SRT8 Forums > Regional Forums > UK & Europe Regional Forum > How much do you reckon?
Hi Everyone, I'm back after quite a break. Forgot password and everything but I'm in now.
Good to see some familiar names still here.
I'm looking to sell my late 2010 CRD300 SRT Saloon, Auto Diesel with private plate. J300EXE.
Previously used as a chauffeur car so regularly serviced and looked after.
158,000 miles with a full 12months MOT.
Any suggestions as to what it could be worth?
There are also plenty of ads to be found on the web, which might help.


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text:
Q: click action is not working in selenium I am still new to Selenium test world, and I am currently using the selenium chrome webdriver for testing.
what I want to do is in the below popup dialog:

*

*input a number in text input box

*click the Apply button

*then web application will be triggered to post info to server-side.

my code:
/*
 ....
 a bunch of assert and wait.until function to make sure everything is correct till this step.
 ....
*/
driver.findElement(By.xpath(DIALOGBOX + "/input")).sendKeys("10");
//Thread.sleep(500);
wait.until(ExpectedConditions.elementToBeClickable(By.xpath(APPLY_BUTTON)));
new Actions(driver).moveToElement(driver.findElement(By.xpath(APPLY_BUTTON))).click().perform();

and here is what I discovered till now, If I uncomment Thread.sleep(500), everything will work perfectly. but without Thread.sleep(500), the web application will not make a post call to the server which the Apply button should trigger.
I do not know why it happens, what Thread.sleep(500) made difference to click action, is selenium takes the time to move to the element and performs the click with the mouse not yet reach the button position? and how to fix my cases without using thread sleep?
Update and solution:
first, I forget to mention that the web application is built on GWT(Google web toolkit). I have already make sure the driver has successfully got the button element before click(), so the bug is minimized to click() of Action.
then I tried
new Actions(driver).moveToElement(driver.findElement(By.xpath(APPLY_BUTTON)).sendKeys(Keys.ENTER).perform();

it works perfectly, no need to make the thread sleep.
so the cause of the bug is that selenium's click action is different from how GWT handle the onclick function. If you face the same issue, try using sendKeys and for the client side adding handler for onKeyPress

A: Just because the button is visible and selenium thinks it is clickable doesn't mean it is ready to use. Check with browser inspector and investigate if it is tied to some asynchronous javascript. Meaning, the alert may be waiting on some background process to complete before you can actually click it for an event to occur.
It's possible that upon entering a value into the input field, there is an async script going out to a server, and causing the "Apply" button to do nothing when clicked, until there is a response from the server. And that 500ms delay is just enough time for the ajax communication to complete.
To learn more about ajax: https://www.tutorialspoint.com/ajax/what_is_ajax.htm

A: I will probably try the js.ExecuteScript method as ( below code is for c#)
IJavaScriptExecutor js = driver as IJavaScriptExecutor;
string jsOutput = (string)js.ExecuteScript(String.Format("document.getElementById('{0}').click();", elementId));

where the normal click fails

A: first, I forget to mention that the web application is built on GWT(Google web toolkit). I have already make sure the driver has successfully got the button element before click(), so the bug is minimized to click() of Action. then I tried
new Actions(driver).moveToElement(driver.findElement(By.xpath(APPLY_BUTTON))
    .sendKeys(Keys.ENTER).perform();

it works perfectly, no need to make the thread sleep. so the cause of the bug is that selenium's click action is different from how GWT handle the onclick function. If you face the same issue, try using sendKeys and for the client side adding handler for onKeyPress

redpajama_set_name: RedPajamaStackExchange


Discussion in 'Love & Lifestyles' started by Professur, Oct 6, 2004.
I have a cupboard prestocked with lunch material. Crackers, cookies, meatsticks, tuna lunches, etc. I make myself eat whatever comes out, or is on top. This was on top.
No. I feel my pain.
although I must be getting used to it. It's not as bad as usual. Do you think leaving it in a hot car might have tamed it down a bit?
That, or you burned off all your heat sensors already.
You'll make a great journalist one day, Inky. Always looking for the needle when someone's romping in the hay.
By now, you should be used to that miniscule amount of spice. I thought Scots were made of hardier stock than that.
There's a lot of ground between ability and enjoyment.
Ok, after getting hit, yet again, I called her on it. Seems she likes them, and bought them for herself. But she only likes the Kimchi flavour. Not the kimchi beef or kimchi chicken. So they got slid over to the guy who likes beef and chicken.
It took you this long to confirm the obvious source? 'Fess up, man, you LIKE this stuff, doncha?
How can I like what I can't taste. I pretty much only talk to her first thing in the morning, when she calls to make sure I'm up. I'm afraid I'm not at my best for recalling what yesterday's lunch was at 6am. And calling her from work is a no-no.
Any more spicy soup adventures?
Nope. That stuff is now banned.
Altron was banned too; what's your point?
That it's going to slip back into his life unnoticed and forgotten in 2-3 years?
Not as confused as I am. Until a minute ago, I thought you two were married.
This post still makes me laugh!!!


Peakles took part in her school Harvest Festival recently and wanted to make some more Harvest crafts. Sometimes we make some simple arts and crafts because it is fun and a great way to learn together. We also like to use our creations to decorate our home as well.
With this in mind, we decided to check some of the following ideas out during the half term holidays. Click through on the links to find out how to make these wonderful arts and crafts activities for kids.
We are happy to be sharing these wonderful ideas as part of the Top Ten Blog Hop for October. Click through on links to find out what my colleagues have been writing about this month.
I want to try that button pumpkin – it looks easy but comes out beautiful, too!


My three-and-a-half year old son, Divyanshu (who became Divi to help people at least pronounce his name), knows the numbers 0 through 10. Last week, I got a set of flash cards to teach him 11 – 25. As an utterly clueless father, I don't know how other children at this age react to learning, but Divi is a very avid learner, with an attention span of five seconds.
Say one and one eleven.
After 25, I started again from zero. In this round, he somehow figured out that if you insert 'teen' after a number, it becomes another number – four-teen, six-teen, seven-teen, eight-teen, and nine-teen. And when I reached 20, two and zero, he said ZERO-TEEN. However, I continued and so did his idea of the new numbers. After zero-teen came one-teen (21), two-teen (22), three-teen (23), four-teen (24) and five-teen (25). I smiled, corrected him, and restarted from 0. To my horror, he didn't identify 10 by 'ten', which he knows but 'zero-teen'. This is when I realized that he had identified a pattern, to suffix the number at units place with a teen. And this round resulted in yet hilarious 'new' numbers. So both 11 and 21 became one-teen, 12 and 22 became two-teen, and so on.
Divi might have developed misconceptions by identifying a pattern in the numbers, but pattern identification is what differentiates an expert from a novice. By pattern, I mean the underlying principles, meaningful arrangements, and logical reasonings.
If a chess board with some randomly placed pieces are shown to a chess expert and a non-expert for a few seconds and then asked to arrange the pieces on another board from memory, both persons fare poorly. However, when the pieces are arranged in a meaningful way, as in an actual game of chess, the expert chess player is able to arrange most of the pieces correctly, while the non-expert fares as poorly as before. The expert, in the second case, is able to identify the pattern of the pieces on the board.
Similarly, expert students are able to identify the appropriate laws of motion in word problems while novices try to plug in variable values in formulas and do not know the underlying principle on which the problem is based.
Divi's is an oversimplified case (and resulting in a misconception) but … you get the idea.


• advertising efficiency researches, etc.
consulting on effective marketing management within the company (from strategic planning to implementation of the elements and technologiesof operating marketing). Development of marketing analytic function in the company, etc.
• revision of the existing plans, etc.
• organization of tenders on brand advertising and PR support, etc.


Scottish Weekend (SW) promotes Scottish Country dancing; provides attendees with instruction in Scottish Country dancing; and provides the opportunity to participate in social Scottish Country dancing programs. The SW Scholarship Program was developed to give financial assistance to academic students, new dancers (6 months experience minimum), more experienced dancers in need of financial assistance to participate, and musicians learning techniques for playing SCD music.
Your application for assistance is due to the SW registrar by August 1: Fred Eustis, 2118 Pine Ave., Windsor Mill, MD 21244. All applications will be reviewed by the SW Scholarship Committee by August 15. Awards will be made based on funds available for scholarships, established need of the applicants, and benefit to both the applicant and the Scottish Country dancing community. Be as complete as possible in filling out this application. In addition you are required to get a letter of recommendation from your Scottish Country dance or music teacher. Please provide your teacher with the following link to the Teacher Recommendation form or a physical copy with a stamped/addressed envelope, and ask her/him to mail the recommendation directly to the SW Registrar, Fred Eustis, 2118 Pine Avenue, Windsor Mill, MD 21244.


I was recently convinced by two very good friends to do a 40 mile charity ride around NYC – the 5Boro Bike Tour. I've been wanting to start biking for years, and charity events are a passion of mine. Plus, I'm always looking to visit one of my very best friends in NY…so, this seemed like a great idea all around. When I committed to the ride in January, May 3rd seemed so far away, and I thought I had plenty of time to train. Then came the infamous 2015 Boston Winter.
Now, I'm sort of an anxious person, so by March, I started thinking I should get moving (keep in mind, I didn't yet own a bike). While I was waiting for my bike to come in after I ordered it, and waiting even longer for the 7 foot snow mounds to melt in my neighborhood, I spent time riding a stationary bike indoors, picked up my running a bit, and concentrated on a well rounded leg and core strengthening routine. All important for building muscular endurance needed for a long ride…especially one with hills. Once the snow did melt, my friend and I started doing longer weekend rides around Boston, and I found time most weeks to do one shorter ride around my neighborhood that included hills.
Get a good pair of shoes. You're going to be racking up a lot of miles, so get fitted properly or even consider getting your gait assessed.
Increase the distance you run or bike by no more than 10% each week.
Include workouts focused on increasing speed also to alternate on the days you increase distance.
Cross train…with other forms of cardio and definitely with strength training.
Don't forget your flexibility. Stretch more than you're used to, or try that new yoga studio you've heard about.
For short events, there's probably no need to change your diet too much. "Carb loading" is certainly not necessary, but do seek out a Nutritionist who specializes in sports nutrition for any particularly long or challenging event.
Eat enough and drink enough water for the days leading up to your event.
I hope you all find time to challenge yourself this Spring and Summer with a new event or workout routine. Once you start training for an event, you'll find motivation and enjoyment in exercise again, and you'll really appreciate how amazing it is to see your goal through. Signing up is the first step…what are you waiting for??


text:
// Copyright (c) ppy Pty Ltd <contact@ppy.sh>. Licensed under the MIT Licence.
// See the LICENCE file in the repository root for full licence text.

using osu.Framework.Allocation;
using osu.Framework.Graphics;
using osu.Framework.Localisation;
using osu.Game.Resources.Localisation.Web;
using osu.Game.Rulesets;

namespace osu.Game.Overlays.BeatmapListing
{
    public class BeatmapSearchRulesetFilterRow : BeatmapSearchFilterRow<RulesetInfo>
    {
        public BeatmapSearchRulesetFilterRow()
            : base(BeatmapsStrings.ListingSearchFiltersMode)
        {
        }

        protected override Drawable CreateFilter() => new RulesetFilter();

        private class RulesetFilter : BeatmapSearchFilter
        {
            [BackgroundDependencyLoader]
            private void load(RulesetStore rulesets)
            {
                AddTabItem(new RulesetFilterTabItemAny());

                foreach (var r in rulesets.AvailableRulesets)
                    AddItem(r);
            }
        }

        private class RulesetFilterTabItemAny : FilterTabItem<RulesetInfo>
        {
            protected override LocalisableString LabelFor(RulesetInfo info) => BeatmapsStrings.ModeAny;

            public RulesetFilterTabItemAny()
                : base(new RulesetInfo())
            {
            }
        }
    }
}


text:
{-# LANGUAGE TemplateHaskell #-}
module UnitTest.ResponseParse.ThreadControl where

import Data.Aeson (Value)
import Data.Yaml.TH (decodeFile)

import Test.Tasty as Tasty
import Web.Facebook.Messenger

import UnitTest.Internal

--------------------
-- THREAD CONTROL --
--------------------

threadControlTests :: TestTree
threadControlTests = Tasty.testGroup "Thread Control Responses"
    [ threadControl
    , secondaryReceivers
    , threadOwnerResponse
    ]

threadControlVal :: Value
threadControlVal = $$(decodeFile "test/json/response/thread_control.json")

threadControl :: TestTree
threadControl = parseTest "Success response" threadControlVal
              $ ThreadControlResponse True

secondaryReceiversVal :: Value
secondaryReceiversVal = $$(decodeFile "test/json/response/secondary_receiver.json")

secondaryReceivers :: TestTree
secondaryReceivers = parseTest "Request secondary receivers" secondaryReceiversVal
                   $ DataResponse [ SecondaryReceiverElement (Just $ AppId "12345678910") (Just "David's Composer")
                                  , SecondaryReceiverElement (Just $ AppId "23456789101") (Just "Messenger Rocks")
                                  ]

threadOwnerVal :: Value
threadOwnerVal = $$(decodeFile "test/json/response/thread_owner_response.json")

threadOwnerResponse :: TestTree
threadOwnerResponse = parseTest "Thread owner response" threadOwnerVal
                    $ ThreadOwnerResponse $ AppId "1517776481860111"


text:
Yoenis Cespedes expected to travel to United States within 7-10 days
By D.J. ShortJan 30, 2012, 8:30 PM EST
Yoenis Cespedes' agent Adam Katz has been a pretty popular guy since the Cuban outfielder was granted free agency by MLB last Wednesday.
According to Ken Rosenthal of FOXSports.com, Katz said the market for the 26-year-old has been "meaningful and aggressive" and that he expects his client will be in spring training with an MLB club.
"We are aware of the (spring-training) dates. We expect him to be in camp with one of the clubs."
With that in mind, Jim Bowden of ESPN.com and MLB Network Radio on Sirius XM reports that Cespedes is expected to travel to the United States within the next 7-10 days to meet face-to-face with interested teams.
Bowden also hears that six teams have been the "most serious" in negotiations for Cespedes thus far, including the Marlins, Cubs, White Sox, Orioles, Tigers and a sixth unidentified team. No word on who the sixth team could be, but Cespedes has previously mentioned the Indians as a potential landing spot.
Cespedes recently went 5-for-35 (.143) with one home run and a 10/0 K/BB ratio during a brief stint with Aguilas Cibaenas in the Dominican Winter League playoffs. He's expected to land a contract north of the $30.5 million deal that Cuban left-hander Aroldis Chapman got from the Reds in January of 2010.


text:
Fakaifou – miejscowość w Tuvalu, położona na atolu Funafuti, na wyspie Fongafale.

Osada ma powierzchnię 0,17 km². W 2001 roku zamieszkiwały ją 972 osoby, a w 2012 roku – 1158.

Funafuti
Miasta w Tuvalu

redpajama_set_name: RedPajamaWikipedia


text:
King Eddy to re-open as permanent Calgary live music venue
The revamped King Eddy Hotel in Calgary. It will once again host live music permanently in the city. BRODIE THOMAS / For LiveWire Calgary
By Darren Krause
The King Eddy lives on – permanently!
The historic home of live music in Calgary will once again be wired for sound as a pop-up country music venue and then permanent venue later this summer.
It will all kick off as an East Village honky-tonk as it partners with the Calgary Stampede to bring the Bell Live Series from July 6 to 15.
"We've provided a taste of the resurrected King Eddy over the past two years by piloting successful live music programs like the Bell Live Series, and we're excited to bring it back with another great lineup," said Andrew Mosker, President and CEO of the National Music Centre, in a prepared statement.
"Opening the King Eddy as a permanent live music venue, bar and restaurant will allow us to greatly expand our programming mandate, attract new audiences, and continue to build on the Eddy's storied musical legacy."
The King Edward hotel was built between 1905 and 1910 (phases) and it was one of Calgary's oldest bars when it shut down in 2004. It had a checkered past but was revered for its history as a live blues venue through the 1970s and 80s.
After it was closed down, the National Music Centre and the Calgary Municipal Land Corporation partnered up to rebuild the storied location as part of the overall East Village rejuvenation, and incorporated it into the National Music Centre location.
The Bell Live Series will go daily from 11:30 a.m. to 2 a.m. from July 6 to 15.
Bell Live at the King Eddy headliners include:
Kingston "newgrass" act The Abrams
Edmonton-based country artist Karac Hendriks
Calgary country-folk troubadour Tim Hus
Powerhouse vocalist and Country 105 Rising Star Lauren Mayell
Lethbridge country crooner Trevor Panczak
Edmonton singer-songwriter Ken Stead
The King Eddy will then be opened as a permanent live music venue on July 20. For more information visit kingeddy.ca


Burning mouth syndrome is a persistent, often painful sensation, or soreness. The main part, or back of your mouth can be affected, along with your tongue, gums, the inside of your cheeks, or throat.
Your mouth can also feel dry, or might hold a bitter, or metallic taste. Symptoms could go beyond the mouth if they continue for a sustained period, including sleeplessness, irritability, or depression.
The condition can arise in men, or women and at a variety of ages. Women are still more likely to be affected, often during or after the menopause.
There is no absolute, single cause for burning mouth syndrome. A condition which can be related to diabetes, nutritional deficiencies, hormonal changes, infections, physical, or psychological causes.
This is the reason you should see a maxillofacial specialist and why they examine a wide range of possibilities. Along with talking to you, discussing your medical history, how you feel and how life is affected.
Straighforward causes can be brought to light, from medication, to infection, to dietary factors, or dental hygiene products. In a fair number of cases, further investigation could be needed.
Identifying allergies is a route to consider. Blood tests are likely to be helpful, to detect causes mentioned above, along with others such as abnormal thyroid function.
Nobody should ever give you a guarantee that burning mouth syndrome will be cured. The condition can have both psychological cause and effect, or be related to parts of the central nervous system we don't fully understand.
Maxillofacial surgeons are however experienced in treating the hard and soft tissues of your face, mouth, or jaws. Their knowledge of your individual symptoms can bring diagnosis and treatment.
Even when missed previously, straightforward causes such as a wrongly fitting denture, unsuitable dental work, a traceable infection, even the wrong mouthwash can be found.
The same applies to a range of known, underlying conditions, which when treated relieve the further problem they are causing. This could be an unrecognised serious issue, or simply a vitamin, or mineral deficiency.
As with other neurological pain conditions, long term, low dosage of anti depressant medication can help. Moderate dose anti convulsant medicines may suit in a few cases.
Whilst there should be no guarantee, our London clinic will ensure that you see a leading maxillofacial consultant, who holds specific knowledge of burning mouth syndrome.
In some cases, diagnosis and intervention can be rapid, in others, process of elimination can take time. Whichever the case, our staff will work with your case on an individual basis and support you.
Burning mouth syndrome is a condition we wish to see nobody live with. We understand the issues this may bring and our team will be pleased to talk through any way they can help.


[lindner-01] - 24 users already solved this challenge,	8 are working on it.
Your challenge is, to find a shortest nonzero lattice vector for a given lattice basis.
[esslinger-09] - 94 users already solved this challenge,	6 are working on it.
A friend sends you a cipher designed by him and asks you to discover the used key, using a given plaintext and the corresponding ciphertext.
[esslinger-10] - 45 users already solved this challenge,	3 are working on it.
Your friend calls you again and wants you to "test" his cipher (see Kaskade-S/T Part 1) with a longer key. He sends you a plaintext and the corresponding ciphertext and asks, if you could discover the key used for encryption.
[esslinger-11] - 29 users already solved this challenge,	2 are working on it.
This time your friend only sends you a ciphertext, which has been encrypted using his cipher (see Kaskade-S/T Part 1 and Part 2). He wants you to figure out the title of the decrypted text.
[ramaekers-01] - 39 users already solved this challenge,	1 is working on it.
The Fully Homomorphic Encryption Scheme by Gentry and Halevi turns out to be vulnerable to so called lunchtime attacks. In this challenge you have to perform such an attack.


text:
Last Updated: Dec-14-2006
African-American Experience
Cross-Cultural Issues
For months, Junior Brown, an obese adolescent, has been meeting secretly with his friend Buddy, a street-wise homeless boy who lives in an abandoned building, and a former teacher, now janitor, Mr. Pool. In the basement of the school building, Mr. Pool has rigged up a model of the solar system that rotates, illuminated in the dark. He and the boys discuss astronomy, math, and a vision of worlds to come while the boys skip classes and take refuge in their basement hideaway.
Junior is mentally disturbed; both Buddy and Mr. Pool know this and take care of him as they can. Junior's fiercely controlling mother exacerbates his obesity by serving him excessive helpings of food and feeding his fantasy that his father will return. She herself has asthma, which ties Junior to her as intermittent caretaker.
Junior has a musical gift, but his mother has removed the strings from the piano, so he practices on keys that produce no sound. Fridays he finds his way to the home of a demented old piano teacher who won't allow him to play her piano because of her delusion that a dangerous relative is hiding in her apartment. Ultimately the boys and Mr. Pool are caught in their marginal existence below the school.
They retreat to Buddy's urban hideaway where he cares for two other boys, teaching them how to survive. Buddy is convinced he can help Junior survive as well, with Mr. Pool's help. He knows that if he allows Junior to be retrieved by his mother or school officials, he'll be locked up in an institution where no one will recognize his gifts or his worth.
Babylon Boyz
Mowry, Jess
Asian Experience
Father-Son Relationship
Pook, Dante, and Wyatt inhabit the social margins of an inner-city school in Oakland. Pook's family has disintegrated from drug trade, Dante needs a heart operation he can't afford as a result of his now-dead mother's addiction to crack cocaine, Wyatt, slowed and ostracized by obesity, provides a frequent refuge for the other two at his mother's rundown dockside café. The three of them are no strangers to the violence of drug-infested neighborhoods, Wyatt manages to smuggle a gun into the schoolyard despite metal detectors, but none of the boys is eager to use weapons. They are "homies," committed to each other's survival, and intensely loyal.
Radgi, a younger, smaller homeless kid, follows them for occasional handouts and eventually is taken into Dante's apartment where his father, a dock worker, is frequently absent. All are threatened repeatedly by "Air Touch," a leader in the local drug trade who deals with smugglers and rich white patrons. Another occasional friend is Kelly, a Korean boy whose father runs a convenience store in the "hood."
The plot follows the fortunes of the boys after they witness the police beating Air Torch, see him toss his gun and briefcase away before being apprehended, and pick up both as they run for home. In the briefcase is a load of cocaine ready for sale. They have to decide whether to sell it to get the money for Dante's operation or pour it down the toilet. They sell the gun with the help of Kelly who, discovered by Air Torch, is killed, along with his father.
Eventually, after some hair-raising close calls, the boys get rid of the drugs, assemble in Dante's apartment, and discover that the petite Radgi, who they thought was bloated from starvation, is a girl, about to have a baby as a result of rape. Pook, who longs to be a doctor and has read a medical book sequestered among his few possessions, helps deliver the child, a "little brutha."
Park's Quest
Park's full name is Parkington Waddell Broughton V. He knows he has ancestors who have distinguished themselves and the name he shares with four generations of them. But his father died in Vietnam and he has never met his father's family. Though he is nearly twelve, his mother still avoids answering any questions about his father. Finally, to satisfy his curiosity, Park gets on a bus for the short ride from his home to the Vietnam Memorial in Washington DC. There he finds his father's name. There he also resolves to get some of his questions answered.
After a painful conversation, his mother puts him on a bus for south-western Virginia where his grandfather and uncle maintain the farm on which his father grew up. His grandfather has had a stroke and is now inarticulate, able to communicate in only the most rudimentary ways. His uncle has a Vietnamese wife, and shares his home with a Vietnamese girl about Park's age whose origin and status is not clear to Park until he discovers, after a number of uncomfortable encounters, that she is his half-sister, and that because of his father's infidelity, his mother divorced him before his second, and fatal, term in Vietnam. Park, whose fantasies about his father's past and his own future have been highly romanticized, does some important growing up in the short visit that puts him in touch with a more complex idea of family, grief, forgiveness, and acceptance than he has ever before had to develop.
Trapped! Cages of Mind and Body
Duncan, L., ed.
Genre: Anthology ( Short Stories for Young Adults)
Father-Daughter Relationship
The editor solicited this collection of thirteen stories on the theme of entrapment from experienced young adult fiction writers. They represent a variety of kinds of entrapment: in a relationship too serious too early; in an abusive relationship; in a body distorted through the psychological lens of anorexia; in a dream world; in a canyon fire; in a web of secrets woven in an abused childhood; in a maze with a minotaur; in a habit of perfectionism; in the sites of urban violence; in dementia induced by post-traumatic stress (long remembered by a Viet Nam vet); in an unsought relationship with a lost and disturbed brother; in poverty. In each of the stories an adolescent protagonist encounters some challenge either to find his or her way out of a trap, or to understand others' entrapments. The stories vary widely in setting and style, but held together by this theme, they serve to enlarge understanding of the ways in which any of us may find ourselves entrapped, and how "liberation" may require both imagination and compassion.
Death of the Good Doctor: Lessons from the Heart of the AIDS Epidemic
Scannell, Kate
Poirier, Suzanne
Lesbian/Gay/Bisexual/Transgender Issues
The title of Scannell's book refers to an episode in her work with AIDS patients when she realizes that the "good doctor" she'd been taught to be--scientifically precise, medically focused and aggressive--was not what many of her patients wanted or needed. From that point on, she strove to understand the nature of her patients' suffering and how they might be cherished and morally supported during the last weeks and days of their lives. In a series of essays she offers haunting portraits of the men and women she served--and of herself, as she learns to recognize and grapple with her own anger, grief, comfort, and joy.
Everything That Rises Must Converge
O'Connor, Flannery
Aull, Felice
Genre: Short Story
Narrative as Method
The story is told from the perspective of Julian, a recent college graduate who appears to be waiting for employment commensurate with his education; he lives at home with his solicitous widowed mother. The setting is the recently integrated South of the 1960's. Events unfold during a ride on an integrated bus, in which all of the story's complex relationships are played out: the vindictive, self-deluding dependency of Julian on his mother; the insightless yet well-intentioned doting of his mother, who is tied to the societal conventions in which she was raised; the condescension of "enlightened" whites toward blacks; the resentment of blacks toward well-meaning whites- all depicted with great skill and humor.
The crisis occurs in a confrontation between Julian's mother and a black woman wearing the same hat, when the mother tries to give a penny to her counterpart's child. In the incident, Julian's mother suffers a stroke to which Julian is at first oblivious, being so consumed with fury at his mother's (to him inappropriate) gesture to the child. When he realizes how disabled his mother is, Julian is overwhelmed with grief and fear; the extent of his self-deception is fully confirmed.
Black Boy
This is American writer Richard Wright's story of his life as a black child in the American South (Mississippi, Tennessee, and Arkansas) in the early decades of the 20th century. Black Boy opens with the disaster in which Richard at the age of four accidentally burns down his grandparents' house and is beaten nearly to death by his mother as punishment. The book ends with Richard's hopeful escape north to Chicago at the age of eighteen.
In between are years of heart-stopping survival stories, as Richard, an intelligent and willful child who tries to resist many of the demands of his strongly segregated environment, runs head-on into the hatred of racists and the deep poverty, hunger, and oppression that so often were the lot of the system's victims. (On the subject of hunger, one of the book's working titles was American Hunger, and Wright was chronically hungry all these years. He gets so used to extreme hunger that at one point late in the book after a short interlude with regular meals he is surprised to discover that he can suddenly read faster!)
Black Boy, originally published by Harper & Bros. in 1945, is only the first half of Wright's original manuscript. After production had begun on the complete manuscript, Wright accepted an offer from the Book-of -the-Month Club to make his book one of their selections if only the first half were published. The second half was first published in its entirety by Harper & Row as American Hunger in 1977. The 1993 edition titled Black Boy (American Hunger) brings both halves together for the first time. The second volume describes Wright's experiences in Chicago from 1926 to 1936, including his frustrating attempt to work with the Communist Party as a way of supporting unemployed workers during the Great Depression.
Albert Schweitzer. The Enigma
Bentley, James
Last Updated: Nov-30-2006
Prayer as Medicine
This book sketches the development of Schweitzer's ideas and accomplishments in theology, philosophy, musicology, and medicine. The author tends to pick up a theme at one time and then follow further developments on that theme at later points in Schweitzer's life. Thus, the book is not a comprehensive biography and it often departs from a strict chronological approach.
While there is some discussion of Schweitzer's "tortured" childhood and his later world-renown as the "jungle doctor," of Gabon, Bentley focuses on four intellectual and spiritual developments in Schweitzer's life. The first is his theological career, which led to the groundbreaking Quest for the Historical Jesus (1906) and subsequent theological books such as The Mysticism of Paul the Apostle (1930).
The second is his philosophy of "reverence for life, "which was first fully articulated in Civilization and Ethics (1923). The third is Schweitzer's career as a musician, musicologist, and organ designer. Finally, Bentley traces the development of Schweitzer's ministry as a medical missionary in Central Africa.
Ash on a Young Man's Sleeve
Abse, Dannie
This autobiographical novel tells the story of Michael, a young Jewish boy growing up in South Wales in the 1930's. Michael and Keith, his best friend, make their way along the difficult interface between the worlds of childhood and adulthood during the years in which Europe is preparing for war. The structure of the book is a series of tales told years later: "Cariad, clean heart, listen to me, this is my beginning. Let me start again." (The identity of "Cariad, clean heart" is left to the reader to imagine.)
The tales are humorous (e.g. the boys' first aborted attempt to purchase condoms) and touching (e.g. the death of Keith's mother). The novel ends with childhood's end: Keith is killed in a German air raid. Michael, now a medical student, stands by helplessly as the body of his friend is carried away.
The Doubleman
Koch, Christopher
The novel begins in Hobart, Tasmania, in the 1950's. Richard Miller, a student at the Christian Brothers' school, spends his adolescence recovering from the effects of polio. His one friend is Brian Brady, who is expelled from the school after a tiff with a sadistic Brother. Brian finds work and studies the guitar with Clive Broderick, the "doubleman" of the novel's title, an enigmatic occultist who believes in separate worlds of good (invisible) and evil (visible) reality.
Years later, in the freewheeling '60's, Richard has become a television producer in Sydney. He once again encounters Brian and his friend Darcy Burr, who have been eking out a living as a folk singing duo. Darcy, who has inherited Broderick's Gnosticism, is convinced that his group, the Rymers (which soon grows to include Richard's wife Katrin as lead singer), will achieve fame and fortune.
Richard produces a series of television shows, which, in fact, turn the Rymers into a very hot commodity. They are on the verge of a triumphant tour to England when the threads of their complex story begin to unravel.
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text:
import unittest
from core.yahoo_client import FindPlaceClient, GetWeatherClient, Place, Weather
from tests import logger, mock_data

__author__ = 'lahim'

class YahooRealClientTest(unittest.TestCase):
    def setUp(self):
        self.find_place_client = FindPlaceClient()
        self.get_weather_client = GetWeatherClient()

    def test_find_place(self):
        place = self.find_place_client.find_place(53.430261, 14.550909)
        self.assertIsNotNone(place)
        logger.debug('place: %s', place)

    def test_get_weather(self):
        place = self.find_place_client.find_place(53.430261, 14.550909)
        self.assertIsNotNone(place)
        logger.debug('place: %s', place)

        weather = self.get_weather_client.get_weather_by_woeid(place.woeid)
        self.assertIsNotNone(weather)
        logger.debug('weather: %s', weather)

class YahooClientObjectDeserializationTest(unittest.TestCase):
    def setUp(self):
        self.find_place_json = mock_data.find_place['query']['results']['Result']
        self.get_weather_json = mock_data.get_weather['query']['results']['channel']

    def test_find_place(self):
        place = Place.deserialized(self.find_place_json)
        self.assertIsNotNone(place)

        logger.debug('place: %s', place)

        self.assertEqual(place.neighborhood, 'Szczecin')
        self.assertEqual(place.county, 'Szczecin')
        self.assertEqual(place.street, 'Ulica Przyjaciol Ronda')
        self.assertEqual(place.city, 'Szczecin')
        self.assertEqual(place.country, 'Poland')
        self.assertEqual(place.state, 'Woj. Zachodniopomorskie')
        self.assertEqual(place.country_code, 'PL')
        self.assertEqual(place.postal, '71-685')
        self.assertEqual(place.woeid, '22714523')

    def test_get_weather(self):
        weather = Weather.deserialized(self.get_weather_json)
        self.assertIsNotNone(weather)

        logger.debug('weather: %s', weather)

        self.assertEqual(weather.pressure, '982.05')
        self.assertEqual(weather.rising, '0')
        self.assertEqual(weather.visibility, '9.99')
        self.assertEqual(weather.humidity, '82')
        self.assertEqual(weather.condition_date, 'Wed, 25 Sep 2013 8:29 pm CEST')
        self.assertEqual(weather.condition_text, 'Fair')
        self.assertEqual(weather.condition_code, '33')
        self.assertEqual(weather.condition_temp, '10')
        self.assertEqual(weather.unit_distance, 'km')
        self.assertEqual(weather.unit_speed, 'km/h')
        self.assertEqual(weather.unit_temp, 'C')

        self.assertIsNotNone(weather.forecast)
        self.assertEqual(len(weather.forecast), 5)

        self.assertIsNotNone(weather.forecast[0])
        self.assertEqual(weather.forecast[0].code, '11')
        self.assertEqual(weather.forecast[0].text, 'Light Rain')
        self.assertEqual(weather.forecast[0].high_temp, '14')
        self.assertEqual(weather.forecast[0].low_temp, '6')
        self.assertEqual(weather.forecast[0].date, '25 Sep 2013')
        self.assertEqual(weather.forecast[0].day, 'Wed')

        self.assertIsNotNone(weather.forecast[1])
        self.assertEqual(weather.forecast[1].code, '30')
        self.assertEqual(weather.forecast[1].text, 'Partly Cloudy')
        self.assertEqual(weather.forecast[1].high_temp, '13')
        self.assertEqual(weather.forecast[1].low_temp, '6')
        self.assertEqual(weather.forecast[1].date, '26 Sep 2013')
        self.assertEqual(weather.forecast[1].day, 'Th')

        self.assertIsNotNone(weather.forecast[2])
        self.assertEqual(weather.forecast[2].code, '30')
        self.assertEqual(weather.forecast[2].text, 'Partly Cloudy')
        self.assertEqual(weather.forecast[2].high_temp, '14')
        self.assertEqual(weather.forecast[2].low_temp, '6')
        self.assertEqual(weather.forecast[2].date, '27 Sep 2013')
        self.assertEqual(weather.forecast[2].day, 'Fri')

        self.assertIsNotNone(weather.forecast[3])
        self.assertEqual(weather.forecast[3].code, '28')
        self.assertEqual(weather.forecast[3].text, 'Mostly Cloudy')
        self.assertEqual(weather.forecast[3].high_temp, '14')
        self.assertEqual(weather.forecast[3].low_temp, '3')
        self.assertEqual(weather.forecast[3].date, '28 Sep 2013')
        self.assertEqual(weather.forecast[3].day, 'Sat')

        self.assertIsNotNone(weather.forecast[4])
        self.assertEqual(weather.forecast[4].code, '30')
        self.assertEqual(weather.forecast[4].text, 'Partly Cloudy')
        self.assertEqual(weather.forecast[4].high_temp, '16')
        self.assertEqual(weather.forecast[4].low_temp, '5')
        self.assertEqual(weather.forecast[4].date, '29 Sep 2013')
        self.assertEqual(weather.forecast[4].day, 'Sun')


The Fairfield Home for Babies was opened by the Waifs and Strays Society in 1943 as a wartime evacuation home for the children from the Seagry Manor at Chippenham. It was located at Fairfield House, 2 Kelston Road, Bath. The property was loaned to the Society by Haile Selassie, the Emperor of Abyssinia.
The home housed up to 35 children aged from two weeks to two years.
The home closed in 1946 and the children were transferred to the Saville House Nursery in Bath.
In more recent times, the property was used as a care home until 1993. Since then, it has been used as a day centre by various local groups.


SD or HD, H.264/AVC Video and Audio Decoder for Receiving Live Streams with Most Any Protocol IP In.
from Remote Sites, Content Delivery Networks (CDNs), or IPTV Compatible Appliances or Players. Tested to Work Over Public Internet, WiFi and Other RF Backbones.
incoming Flash wrapped streams to HD and SD/SDI.
decode the streams with the D-Streamer IP/DIG.
and multi level security is built in.
works for both multicast and unicast traffic.
Also supports any custom resolution not listed here.


text:
Millie Bobby Brown says she is not starring in The Eternalshttps://indianexpress.com/article/entertainment/hollywood/millie-bobby-brown-not-starring-in-the-eternals-marvel-5827660/
Millie Bobby Brown says she is not starring in The Eternals
Millie Bobby Brown took to Instagram to shoot down the rumours that she will be starring in the The Eternals, which will be directed by Chloe Zhao of The Rider fame.
By PTI |Los Angeles | Published: July 13, 2019 1:53:14 pm
Millie Bobby Brown denied that she has been offered a role in Marvel Studios' The Eternals. (Source: Millie Bobby Brown/Instagram)
Stranger Things star Millie Bobby Brown has denied that she has been offered a role in Marvel Studios' upcoming feature The Eternals.
The 15-year-old actor took to Instagram to shoot down the rumours that she will be starring in the film, which will be directed by Chloe Zhao of The Rider fame.
"OK, guys, this is the problem. Everybody thinks that I'm going to be in a Marvel movie. Not that I know of. My family and I have no idea. So, I just want to let everybody know that I'm not… as of right now," Brown said in a video she posted on her Instagram story.
Marvel is reportedly lining up a star-studded cast for the film.
Salma Hayek, Angelina Jolie, Richard Madden, as well as Keanu Reeves, are all in negotiations with the studio to join the film.
Created by Jack Kirby in 1976, the story of The Eternals is set millions of years ago when the cosmic beings known as the Celestials genetically experimented on humans to create super-powered individuals known as the Eternals along with the villainous Deviants.
Zhao is directing from a script by Matthew and Ryan Firpo. Marvel head Kevin Feige is producing.
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