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Data Migrating Issue from Geoserver 2.4.2 to 2.6.2

Data Migrating Issue from Geoserver 2.4.2 to 2.6.2


I am trying to migrate a workspace from Geoserver 2.4.2 to 2.6.6. We started with migrating styles. The style is visible on the Geoserver, howver on opening it is giving the error shown below. Please can anyone help?

org.apache.wicket.WicketRuntimeException: Can't instantiate page using constructor public org.geoserver.wms.web.data.StyleEditPage(org.apache.wicket.PageParameters) and argument name = "[AGA RADAR]" at org.apache.wicket.session.DefaultPageFactory.createPage(DefaultPageFactory.java:212) at org.apache.wicket.session.DefaultPageFactory.newPage(DefaultPageFactory.java:89) at org.apache.wicket.request.target.component.BookmarkablePageRequestTarget.newPage(BookmarkablePageRequestTarget.java:305) at org.apache.wicket.request.target.component.BookmarkablePageRequestTarget.getPage(BookmarkablePageRequestTarget.java:320) at org.apache.wicket.request.target.component.BookmarkablePageRequestTarget.processEvents(BookmarkablePageRequestTarget.java:234) at org.apache.wicket.request.AbstractRequestCycleProcessor.processEvents(AbstractRequestCycleProcessor.java:92) at org.apache.wicket.RequestCycle.processEventsAndRespond(RequestCycle.java:1250) at org.apache.wicket.RequestCycle.step(RequestCycle.java:1329) at org.apache.wicket.RequestCycle.steps(RequestCycle.java:1436) at org.apache.wicket.RequestCycle.request(RequestCycle.java:545) at org.apache.wicket.protocol.http.WicketFilter.doGet(WicketFilter.java:484) at org.apache.wicket.protocol.http.WicketServlet.doGet(WicketServlet.java:138) at javax.servlet.http.HttpServlet.service(HttpServlet.java:620) at javax.servlet.http.HttpServlet.service(HttpServlet.java:727) at org.springframework.web.servlet.mvc.ServletWrappingController.handleRequestInternal(ServletWrappingController.java:159) at org.springframework.web.servlet.mvc.AbstractController.handleRequest(AbstractController.java:153) at org.springframework.web.servlet.mvc.SimpleControllerHandlerAdapter.handle(SimpleControllerHandlerAdapter.java:48) at org.springframework.web.servlet.DispatcherServlet.doDispatch(DispatcherServlet.java:923) at org.springframework.web.servlet.DispatcherServlet.doService(DispatcherServlet.java:852) at org.springframework.web.servlet.FrameworkServlet.processRequest(FrameworkServlet.java:882) at org.springframework.web.servlet.FrameworkServlet.doGet(FrameworkServlet.java:778) at javax.servlet.http.HttpServlet.service(HttpServlet.java:620) at javax.servlet.http.HttpServlet.service(HttpServlet.java:727) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:303) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.apache.tomcat.websocket.server.WsFilter.doFilter(WsFilter.java:52) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.geoserver.filters.ThreadLocalsCleanupFilter.doFilter(ThreadLocalsCleanupFilter.java:28) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.geoserver.filters.SpringDelegatingFilter$Chain.doFilter(SpringDelegatingFilter.java:75) at org.geoserver.wms.animate.AnimatorFilter.doFilter(AnimatorFilter.java:71) at org.geoserver.filters.SpringDelegatingFilter$Chain.doFilter(SpringDelegatingFilter.java:71) at org.geoserver.filters.SpringDelegatingFilter.doFilter(SpringDelegatingFilter.java:46) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.geoserver.platform.AdvancedDispatchFilter.doFilter(AdvancedDispatchFilter.java:50) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.springframework.security.web.FilterChainProxy$VirtualFilterChain.doFilter(FilterChainProxy.java:311) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:69) at org.springframework.security.web.access.intercept.FilterSecurityInterceptor.invoke(FilterSecurityInterceptor.java:116) at org.springframework.security.web.access.intercept.FilterSecurityInterceptor.doFilter(FilterSecurityInterceptor.java:83) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:73) at org.geoserver.security.filter.GeoServerCompositeFilter.doFilter(GeoServerCompositeFilter.java:92) at org.springframework.security.web.FilterChainProxy$VirtualFilterChain.doFilter(FilterChainProxy.java:323) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:69) at org.springframework.security.web.access.ExceptionTranslationFilter.doFilter(ExceptionTranslationFilter.java:113) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:73) at org.geoserver.security.filter.GeoServerCompositeFilter.doFilter(GeoServerCompositeFilter.java:92) at org.springframework.security.web.FilterChainProxy$VirtualFilterChain.doFilter(FilterChainProxy.java:323) at org.geoserver.security.filter.GeoServerAnonymousAuthenticationFilter.doFilter(GeoServerAnonymousAuthenticationFilter.java:54) at org.springframework.security.web.FilterChainProxy$VirtualFilterChain.doFilter(FilterChainProxy.java:323) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:69) at org.springframework.security.web.authentication.AbstractAuthenticationProcessingFilter.doFilter(AbstractAuthenticationProcessingFilter.java:182) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:73) at org.geoserver.security.filter.GeoServerCompositeFilter.doFilter(GeoServerCompositeFilter.java:92) at org.geoserver.security.filter.GeoServerUserNamePasswordAuthenticationFilter.doFilter(GeoServerUserNamePasswordAuthenticationFilter.java:116) at org.springframework.security.web.FilterChainProxy$VirtualFilterChain.doFilter(FilterChainProxy.java:323) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:69) at org.springframework.security.web.authentication.rememberme.RememberMeAuthenticationFilter.doFilter(RememberMeAuthenticationFilter.java:146) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:73) at org.geoserver.security.filter.GeoServerCompositeFilter.doFilter(GeoServerCompositeFilter.java:92) at org.springframework.security.web.FilterChainProxy$VirtualFilterChain.doFilter(FilterChainProxy.java:323) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:69) at org.springframework.security.web.context.SecurityContextPersistenceFilter.doFilter(SecurityContextPersistenceFilter.java:87) at org.geoserver.security.filter.GeoServerSecurityContextPersistenceFilter$1.doFilter(GeoServerSecurityContextPersistenceFilter.java:53) at org.geoserver.security.filter.GeoServerCompositeFilter$NestedFilterChain.doFilter(GeoServerCompositeFilter.java:73) at org.geoserver.security.filter.GeoServerCompositeFilter.doFilter(GeoServerCompositeFilter.java:92) at org.springframework.security.web.FilterChainProxy$VirtualFilterChain.doFilter(FilterChainProxy.java:323) at org.springframework.security.web.FilterChainProxy.doFilter(FilterChainProxy.java:173) at org.geoserver.security.GeoServerSecurityFilterChainProxy.doFilter(GeoServerSecurityFilterChainProxy.java:135) at org.springframework.web.filter.DelegatingFilterProxy.invokeDelegate(DelegatingFilterProxy.java:346) at org.springframework.web.filter.DelegatingFilterProxy.doFilter(DelegatingFilterProxy.java:259) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.geoserver.filters.LoggingFilter.doFilter(LoggingFilter.java:76) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.geoserver.filters.GZIPFilter.doFilter(GZIPFilter.java:42) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.geoserver.filters.SessionDebugFilter.doFilter(SessionDebugFilter.java:48) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.geoserver.filters.FlushSafeFilter.doFilter(FlushSafeFilter.java:44) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.vfny.geoserver.filters.SetCharacterEncodingFilter.doFilter(SetCharacterEncodingFilter.java:109) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:241) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:208) at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:220) at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:122) at org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:504) at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:170) at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:103) at org.apache.catalina.valves.AccessLogValve.invoke(AccessLogValve.java:950) at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:116) at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:421) at org.apache.coyote.http11.AbstractHttp11Processor.process(AbstractHttp11Processor.java:1074) at org.apache.coyote.AbstractProtocol$AbstractConnectionHandler.process(AbstractProtocol.java:611) at org.apache.tomcat.util.net.JIoEndpoint$SocketProcessor.run(JIoEndpoint.java:314) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61) at java.lang.Thread.run(Thread.java:745) Caused by: java.lang.reflect.InvocationTargetException at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) at java.lang.reflect.Constructor.newInstance(Constructor.java:526) at org.apache.wicket.session.DefaultPageFactory.createPage(DefaultPageFactory.java:188)… 106 more Caused by: java.lang.NullPointerException at com.sun.proxy.$Proxy4.equals(Unknown Source) at org.geoserver.catalog.impl.DefaultCatalogFacade.getStyleByName(DefaultCatalogFacade.java:862) at org.geoserver.catalog.impl.CatalogImpl.getStyleByName(CatalogImpl.java:1302) at org.geoserver.catalog.impl.CatalogImpl.getStyleByName(CatalogImpl.java:1283) at org.geoserver.security.SecureCatalogImpl.getStyleByName(SecureCatalogImpl.java:1250) at org.geoserver.catalog.impl.AbstractFilteredCatalog.getStyleByName(AbstractFilteredCatalog.java:680) at org.geoserver.catalog.impl.AbstractCatalogDecorator.getStyleByName(AbstractCatalogDecorator.java:521) at org.geoserver.catalog.impl.LocalWorkspaceCatalog.getStyleByName(LocalWorkspaceCatalog.java:61) at org.geoserver.wms.web.data.StyleEditPage.(StyleEditPage.java:37)… 111 more


The correct procedure for upgrading an instance of GeoServer is:

  1. Stop old GeoServer
  2. make backup of existing data directory
  3. Start new GeoServer pointing it to data directory.

If you find that you've fallen more than one version behind you may need to make two steps to update i.e. 2.4 -> 2.5 and 2.5 -> 2.6 but I'm fairly sure that there were no major changes between those two.

Any other changes to the data directory will probably result in unrecoverable errors.


Health-Related Workplace Absenteeism Among Full-Time Workers — United States, 2017–18 Influenza Season

Surveillance using mortality, health care encounters, and laboratory data does not reflect the full extent of influenza morbidity. CDC&rsquos National Institute for Occupational Safety and Health conducts monthly monitoring of health-related workplace absenteeism.

What is added by this report?

During the 2017&ndash18 influenza season, absenteeism increased sharply in November and peaked in January, at a level significantly higher than the average during the previous five seasons. Workers who were male, aged 45&ndash64 years, and working in certain U.S. Census regions and occupations were more affected than were other subgroups.

What are the implications for public health practice?

Workplace absenteeism is an important supplementary measure of influenza&rsquos impact on the working population that can inform prevention messaging and pandemic preparedness planning.

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During an influenza pandemic and during seasonal epidemics, more persons have symptomatic illness without seeking medical care than seek treatment at doctor&rsquos offices, clinics, and hospitals (1). Consequently, surveillance based on mortality, health care encounters, and laboratory data does not reflect the full extent of influenza morbidity. CDC uses a mathematical model to estimate the total number of influenza illnesses in the United States (1). In addition, syndromic methods for monitoring illness outside health care settings, such as tracking absenteeism trends in schools and workplaces, are important adjuncts to conventional disease reporting (2). Every month, CDC&rsquos National Institute for Occupational Safety and Health (NIOSH) monitors the prevalence of health-related workplace absenteeism among full-time workers in the United States using data from the Current Population Survey (CPS) (3). This report describes the results of workplace absenteeism surveillance analyses conducted during the high-severity 2017&ndash18 influenza season (October 2017&ndashSeptember 2018) (4). Absenteeism increased sharply in November, peaked in January and, at its peak, was significantly higher than the average during the previous five seasons. Persons especially affected included male workers, workers aged 45&ndash64 years, workers living in U.S. Department of Health and Human Services (HHS) Region 6* and Region 9, &dagger and those working in management, business, and financial installation, maintenance, and repair and production and related occupations. Public health authorities and employers might consider results from relevant absenteeism surveillance analyses when developing prevention messages and in pandemic preparedness planning. The most effective ways to prevent influenza transmission in the workplace include vaccination and nonpharmaceutical interventions, such as staying home when sick, covering coughs and sneezes, washing hands frequently, and routinely cleaning frequently touched surfaces (5).

CPS is a monthly national survey of approximately 60,000 households conducted by the U.S. Census Bureau for the Bureau of Labor Statistics. The survey collects information on employment, demographics, and other characteristics of the civilian, noninstitutionalized population aged &ge16 years CPS is the nation&rsquos primary source of labor force statistics. Data on all sample household members are collected from a single respondent by trained interviewers using a standardized questionnaire during in-person or telephone interviews (3). During July 2016&ndashJune 2018, the response rates ranged from 84% to 88%. §

A full-time worker is defined as an employed person who reports usually working &ge35 hours per week. Health-related workplace absenteeism is defined as working <35 hours during the reference week because of the worker&rsquos own illness, injury, or other medical issue. Because CPS questions refer to 1 week of each month, absenteeism during the other weeks is not measured. These 1-week measures are intended to be representative of all weeks of the month during which they occur.

Each month, NIOSH updates an influenza season&ndashbased time series of the prevalence of health-related workplace absenteeism among full-time workers with the previous month&rsquos estimate (i.e., with a 1-month lag). Point estimates and 95% confidence intervals (CIs) are calculated and compared with an epidemic threshold defined as the 95% upper confidence limit of a baseline established using data from the previous five seasons, aggregated by month (6). Estimates with lower 95% confidence limits that exceed the epidemic threshold are considered significantly elevated. Estimates by sex, age group, geographic region (HHS Regions ¶ ), and specific occupational group** are also calculated.

Using these data, health-related workplace absenteeism prevalence during the high-severity 2017&ndash18 influenza season (October 2017&ndashSeptember 2018) was analyzed. All analyses were weighted using the CPS composite weight, and estimates of all standard errors were adjusted to account for the complex design of the CPS sample. Analyses were performed using SAS software (version 9.4 SAS Institute).

The prevalence of health-related workplace absenteeism among full-time workers was 1.7% (95% CI = 1.6%&ndash1.8%) in October 2017, increased sharply beginning in November, peaked in January 2018 at 3.0% (95% CI = 2.8%&ndash3.2%), and declined steadily thereafter to a low of 1.4% (95% CI = 1.3%&ndash1.5%) in July before gradually increasing again in August and September ( Table). The January absenteeism peak significantly exceeded the epidemic threshold ( Figure 1). Absenteeism remained elevated in February, but not significantly. Peak absenteeism in the 2017&ndash18 influenza season exceeded that of any of the five previous seasons except the 2012&ndash13 season ( Figure 2).

The epidemic threshold was significantly exceeded for the following subgroups: male workers in January and February workers aged 45&ndash64 years in January and February workers in HHS Region 6 in January and February and in Region 9 in December and March and workers in management, business, and financial occupations and installation, maintenance, and repair occupations in January and in production and related occupations in February (Table) Regional absenteeism peaks corresponded to concurrent peaks in influenza-like illness (ILI) activity in those regions. &dagger&dagger


Immigrants Now and Historically

Definitions

"Foreign born" and "immigrant" are used interchangeably and refer to persons with no U.S. citizenship at birth. This population includes naturalized citizens, lawful permanent residents, refugees and asylees, persons on certain temporary visas, and unauthorized immigrants.

Geographical regions: MPI follows the definition of Latin America as put forth by the United Nations and the U.S. Census Bureau, which spans Central America (including Mexico), the Caribbean, and South America. For more information about geographical regions, see the U.S. Census Bureau and United Nations Statistics Division.

How many immigrants reside in the United States?

More than 44.9 million immigrants lived in the United States in 2019, the historical numeric high since census records have been kept. Immigrants’ share of the overall U.S. population has increased significantly from the record low of 4.7 percent in 1970. In 2019, immigrants comprised 13.7 percent of the total U.S. population, a figure that remains short of the record high of 14.8 percent in 1890.

The foreign-born population remained largely flat between 2018 and 2019, with an increase of 204,000 people, or growth of less than 0.5 percent. This is consistent with the 203,000 increase from 2017 to 2018 and much lower than the approximately 787,000 increase—or 2 percent growth—seen between 2016 and 2017. The slowing growth of the immigrant population over the past few years is mirrored by the slowing growth of the total U.S. population since 2015.

How have the number and share of immigrants changed over time?

In 1850, the first year the United States began collecting nativity data through the census, the country had 2.2 million immigrants, representing nearly 10 percent of the total population.

Between 1860 and 1920, the immigrant share of the population fluctuated between 13 percent and almost 15 percent, peaking at 14.8 percent in 1890, largely due to high levels of immigration from Europe. Restrictive immigration laws in 1921 and 1924 kept permanent immigration open almost exclusively to northern and western Europeans. Combined with the Great Depression and World War II, this led to a sharp drop in new arrivals from the Eastern Hemisphere. The foreign-born share steadily declined, hitting a record low of 4.7 percent (or 9.6 million immigrants) in 1970 (see Figure 1).

Figure 1. Size and Share of the Foreign-Born Population in the United States, 1850-2019

Source: Migration Policy Institute (MPI) tabulation of data from U.S. Census Bureau, 2010-19 American Community Surveys (ACS), and 1970, 1990, and 2000 decennial census. All other data are from Campbell J. Gibson and Emily Lennon, "Historical Census Statistics on the Foreign-Born Population of the United States: 1850 to 1990" (Working Paper no. 29, U.S. Census Bureau, Washington, DC, 1999).

Since 1970, the share and number of immigrants have increased rapidly, mainly because of large-scale immigration from Latin America and Asia. The vast diversification of immigration flows was ushered in by important shifts in U.S. immigration law (including the Immigration and Nationality Act of 1965 which abolished national-origin admission quotas the creation of a formal refugee resettlement program with the Refugee Act of 1980 and the Cold War-era grant of preferential treatment to Cuban immigrants) the United States’ growing economic and military presence in Asia and Latin America economic ties, social linkages, and deep migration history between the United States and its southern neighbors and major economic transformations and political instability in countries around the world.

  • To see the changing regional makeup of immigration to the United States, use the Regions of Birth for Immigrants in the United States, 1960-Present data tool.
  • Read about historical U.S. immigration trends and policies in Immigration in the United States: New Economic, Social, Political Landscapes with Legislative Reform on the Horizon.
  • Learn about the impact of the 1965 law in Fifty Years On, the 1965 Immigration and Nationality Act Continues to Reshape the United States.
  • Read more about the end of national-origin quotas in The Geopolitical Origins of the U.S. Immigration Act of 1965.

How do today’s top countries of origin compare to those 50 years ago?

In 2019, Mexicans comprised 24 percent of all immigrants in the United States a decline from 30 percent in 2000. Immigrants from India and China (including those born in Hong Kong and Macao but not Taiwan) were the next two largest immigrant groups, each making up about 6 percent of the foreign-born population. Other top countries of origin include the Philippines (5 percent) El Salvador, Vietnam, Cuba, and the Dominican Republic (each accounting for 3 percent) and Guatemala and Korea (each 2 percent). Together, these ten countries accounted for 57 percent of all immigrants in the United States in 2019.

The predominance of immigration from Latin America and Asia in the late 20th and early 21st centuries starkly contrasts with the trend in the mid-1900s, when immigrants were largely European. In the 1960s no single country accounted for more than 15 percent of the U.S. immigrant population. Italians were the top origin group, making up 13 percent of the foreign born in 1960, followed by Germans and Canadians (about 10 percent each).

How long have immigrants lived in the United States, and what are the leading sending countries?

Fifty percent of all immigrants in the United States in 2019 had entered the country prior to 2000 (29 percent entered before 1990 and 21 percent between 1990 and 1999), while 25 percent entered between 2000 and 2009 and the remaining 25 percent in 2010 or later.

While immigrants from Mexico have dominated the flows post-1970, the composition of new arrivals has changed since 2010. Recently arrived immigrants are more likely to come from Asia, with India and China leading the way. In fact, in 2013, India and China overtook Mexico as the top origin countries for new arrivals, displacing its longstanding position.

The number of immigrants from the Dominican Republic, the Philippines, Cuba, Venezuela, Guatemala, and El Salvador also increased between 2010 and 2019. By contrast, the number of Mexican immigrants in the United States declined by more than 779,000 during the same period, representing the biggest absolute decline of all immigrant groups.

Among the origin countries with at least 100,000 immigrants in the United States in 2019, the top five that experienced the fastest growth between 2010 and 2019 were Venezuela (an increase of 153 percent), Afghanistan (143 percent), Nepal (140 percent), Myanmar (also known as Burma 84 percent), and Nigeria (79 percent).

  • Read more about Immigrants fromNew Origin Countries in the United States.
  • Check out Largest U.S. Immigrant Groups over Time, 1960-Present, an interactive tool showing the top ten source countries by decade.
  • To learn more about key immigrant populations, check out the Migration Information Source’s Spotlights archive, which includes data profiles of individual immigrant groups in the United States, including Mexicans, Indians, Chinese, Vietnamese, Koreans, South and Central Americans, Europeans, sub-Saharan Africans, and those from the Middle East-North Africa region.

How many U.S. residents are of immigrant origin?

Immigrants and their U.S.-born children number approximately 85.7 million people, or 26 percent of the U.S. population, according to the 2020 Current Population Survey (CPS), a slight decline from 2019. The Pew Research Center has projected that the immigrant-origin share of the population will rise to about 36 percent by 2065.


A. The Department of Environmental Quality, after consultation with the Virginia Department of Wildlife Resources and the U.S. Fish and Wildlife Service, has determined that the majority of Virginia freshwaters are likely to contain, or have contained in the past, freshwater mussel species in the family Unionidae and contain early life stages of fish during most times of the year. Therefore, the ammonia criteria presented in subsections B and C of this section are designed to provide protection to these species and life stages. In an instance where it can be adequately demonstrated that either freshwater mussels or early life stages of fish are not present in a specific waterbody, potential options for alternate, site-specific criteria are presented in subsection D of this section. Acute criteria are a one-hour average concentration not to be exceeded more than once every three years 1 on the average, and chronic criteria are 30-day average concentrations not to be exceeded more than once every three years on the average. 2 In addition, the four-day average concentration of total ammonia nitrogen (in mg N/L) shall not exceed 2.5 times the chronic criterion within a 30-day period more than once every three years on the average.

1 The default design flow for calculating steady state wasteload allocations for the acute ammonia criterion for freshwater is the 1Q10 (see 9VAC25-260-140 B footnote 6) unless statistically valid methods are employed that demonstrate compliance with the duration and return frequency of the water quality criteria.

2 The default design flow for calculating steady state wasteload allocations for the chronic ammonia criterion for freshwater is the 30Q10 (see 9VAC25-260-140 B footnote 6) unless statistically valid methods are employed which demonstrate compliance with the duration and return frequency of the water quality criteria.

B. The acute criteria for total ammonia (in mg N/L) for freshwaters with trout absent or present are in the following tables:

Acute Ammonia Freshwater Criteria
Total Ammonia Nitrogen (mg N/L)

Acute Ammonia Freshwater Criteria
Total Ammonia Nitrogen (mg N/L)

The acute criteria for trout present shall apply to all Class V-Stockable Trout Waters and Class VI-Natural Trout Waters as listed in 9VAC25-260-390 through 9VAC25-260-540. The acute criteria for trout absent apply to all other fresh waters.

To calculate total ammonia nitrogen acute criteria values in freshwater at different pH values than those listed in this subsection, use the following equations and round the result to two significant digits:

Acute Criterion Concentration (mg N/L) =

Where MIN = 51.93 or 23.12 X 10 0.036 X (20 – T) , whichever is less

Or where trout are present, whichever of the following calculation results is less:

Acute Criterion Concentration (mg N/L) =

C. The chronic criteria for total ammonia nitrogen (in mg N/L) where freshwater mussels and early life stages of fish are present in freshwater are in the following table:

Chronic Ammonia Freshwater Criteria
Mussels and Early Life Stages of Fish Present

Total Ammonia Nitrogen (mg N/L)

To calculate total ammonia nitrogen chronic criteria values in freshwater when freshwater mussels and early life stages of fish are present at different pH and temperature values than those listed in this subsection, use the following equation and round the result to two significant digits:

Chronic Criteria Concentration =

) X (2.126 X 10 0.028 X (20 - MAX(T,7)) )

Where MAX = 7 or temperature in degrees Celsius, whichever is greater

D. Site-specific considerations and alternate criteria. If it can be adequately demonstrated that freshwater mussels or early life stages of fish are not present at a site, then alternate site-specific criteria can be considered using the information provided in this subsection. Recalculated site-specific criteria shall provide for the attainment and maintenance of the water quality standards of downstream waters.

1. Site-specific modifications to the ambient water quality criteria for ammonia to account for the absence of freshwater mussels or early life stages of fish shall be conducted in accordance with the procedures contained in this subdivision. Because the department presumes that most state waterbodies have freshwater mussels and early life stages of fish present during most times of the year, the criteria shall be calculated assuming freshwater mussels and early life stages of fish are present using subsections B and C of this section unless the following demonstration that freshwater mussels or early life stages of fish are absent is successfully completed. Determination of the absence of freshwater mussels requires special field survey methods. This determination must be made after an adequate survey of the waterbody is conducted by an individual certified by the Virginia Department of Wildlife Resources for freshwater mussel identification and surveys. Determination of absence of freshwater mussels will be done in consultation with the Department of Wildlife Resources. Early life stages of fish are defined in subdivision 2 of this subsection. Modifications to the ambient water quality criteria for ammonia based on the presence or absence of early life stages of fish shall only apply at temperatures below 15°C.

a. During the review of any new or existing activity that has a potential to discharge ammonia in amounts that may cause or contribute to a violation of the ammonia criteria contained in subsection B of this section, the department may examine data from the following approved sources in subdivisions 1 a (1) through (5) of this subsection or may require the gathering of data in accordance with subdivisions 1 a (1) through (5) on the presence or absence of early life stages of fish in the affected waterbody.

(1) Species and distribution data contained in the Virginia Department of Wildlife Resources Wildlife Information System database.

(2) Species and distribution data contained in Freshwater Fishes of Virginia, 1994.

(3) Data and fish species distribution maps contained in Handbook for Fishery Biology, Volume 3, 1997.

(4) Field data collected in accordance with U.S. EPA's Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers, Second Edition, EPA 841-B-99-002. Field data must comply with all quality assurance and quality control criteria.

(5) The American Society for Testing and Materials (ASTM) Standard E-1241-88, Standard Guide for Conducting Early Life-Stage Toxicity Tests with Fishes.

b. If data or information from sources other than subdivisions 1 a (1) through (5) of this subsection are considered, then any resulting site-specific criteria modifications shall be reviewed and adopted in accordance with the site-specific criteria provisions in 9VAC25-260-140 D and submitted to EPA for review and approval.

c. If the department determines that the data and information obtained from subdivisions 1 a (1) through (5) of this subsection demonstrate that there are periods of each year when no early life stages are expected to be present for any species of fish that occur at the site, the department shall issue a notice to the public and make available for public comment the supporting data and analysis along with the department's preliminary decision to authorize the site-specific modification to the ammonia criteria. Such information shall include, at a minimum:

(1) Sources of data and information.

(2) List of fish species that occur at the site as defined in subdivision 3 of this subsection.

(3) Definition of the site. Definition of a "site" can vary in geographic size from a stream segment to a watershed to an entire eco-region.

(4) Duration of early life stage for each species in subdivision 1 c (2) of this subsection.

(5) Dates when early life stages of fish are expected to be present for each species in subdivision 1 c (2) of this subsection.

(6) Based on subdivision 1 c (5) of this subsection, identify the dates (beginning date, ending date), if any, where no early life stages are expected to be present for any of the species identified in subdivision 1 c (2) of this subsection.

d. If, after reviewing the public comments received in subdivision 1 c of this subsection and supporting data and information, the department determines that there are times of the year when no early life stages are expected to be present for any fish species that occur at the site, then the applicable ambient water quality criteria for ammonia for those time periods shall be calculated using the table in this subsection, or the formula for calculating the chronic criterion concentration for ammonia when early life stages of fish are absent.

e. The department shall maintain a comprehensive list of all sites where the department has determined that early life stages of fish are absent. For each site the list will identify the waterbodies affected and the corresponding times of the year that early life stages of fish are absent. This list is available either upon request from the Office of Water Quality Programs at 1111 East Main Street, Suite 1400 Richmond, VA 23219, or from the department website at http://www.deq.virginia.gov/programs/water/waterqualityinformationtmdls/waterqualitystandards.aspx.

2. The duration of the "early life stages" extends from the beginning of spawning through the end of the early life stages. The early life stages include the prehatch embryonic period, the post-hatch free embryo or yolk-sac fry, and the larval period, during which the organism feeds. Juvenile fish, which are anatomically similar to adults, are not considered an early life stage. The duration of early life stages can vary according to fish species. The department considers the sources of information in subdivisions 1 a (1) through (5) of this subsection to be the only acceptable sources of information for determining the duration of early life stages of fish under this procedure.

3. "Occur at the site" includes the species, genera, families, orders, classes, and phyla that are usually present at the site are present at the site only seasonally due to migration are present intermittently because they periodically return to or extend their ranges into the site or were present at the site in the past or are present in nearby bodies of water, but are not currently present at the site due to degraded conditions, and are expected to return to the site when conditions improve. "Occur at the site" does not include taxa that were once present at the site but cannot exist at the site now due to permanent physical alteration of the habitat at the site.

4. Any modifications to ambient water quality criteria for ammonia in subdivision 1 of this subsection shall not likely jeopardize the continued existence of any federal or state listed, threatened, or endangered species or result in the destruction or adverse modification of such species' critical habitats.

5. Site-specific modifications to the ambient water quality criteria for ammonia to account for the absence of freshwater mussels shall be conducted in accordance with the procedures contained in this subsection. Because the department presumes that most state waterbodies have freshwater mussel species, the criteria shall be calculated assuming mussels are present using subsections B and C of this section unless the demonstration that freshwater mussels are absent is successfully completed and accepted by DEQ and the Department of Wildlife Resources.

6. Equations for calculating ammonia criteria for four different site-specific scenarios are provided in subdivisions 6 a through d of this subsection as follows: (i) acute criteria when mussels are absent but trout are present, (ii) acute criteria when mussels and trout are absent, (iii) chronic criteria when mussels are absent and early life stages of fish are present, and (iv) chronic criteria when mussels and early life stages of fish are absent. Additional information regarding site-specific criteria can be reviewed in appendix N (pages 225�) of the EPA Aquatic Life Ambient Water Quality Criteria to Ammonia--Freshwater 2013 (EPA 822-R-13-001).

a. Acute criteria: freshwater mussels absent and trout present. To calculate total ammonia nitrogen acute criteria values (in mg N/L) in freshwater with freshwater mussels absent (procedures for making this determination are in subdivisions 1 through 5 of this subsection) and trout present, use the following equations. The acute criterion is the lesser of the following calculation results. Round the result to two significant digits.


Hardware and Network

The following table lists minimum recommended specifications for hardware servers intended to support Greenplum Database on Linux systems in a production environment. All host servers in your Greenplum Database system must have the same hardware and software configuration. Greenplum also provides hardware build guides for its certified hardware platforms. It is recommended that you work with a Greenplum Systems Engineer to review your anticipated environment to ensure an appropriate hardware configuration for Greenplum Database.

  • 150MB per host for Greenplum installation
  • Approximately 300MB per segment instance for meta data
  • Appropriate free space for data with disks at no more than 70% capacity

NIC bonding is recommended when multiple interfaces are present

Pivotal Greenplum can use either IPV4 or IPV6 protocols.


Hardware and Network

The following table lists minimum recommended specifications for hardware servers intended to support Greenplum Database on Linux systems in a production environment. All host servers in your Greenplum Database system must have the same hardware and software configuration. Greenplum also provides hardware build guides for its certified hardware platforms. It is recommended that you work with a Greenplum Systems Engineer to review your anticipated environment to ensure an appropriate hardware configuration for Greenplum Database.

  • 150MB per host for Greenplum installation
  • Approximately 300MB per segment instance for meta data
  • Appropriate free space for data with disks at no more than 70% capacity

NIC bonding is recommended when multiple interfaces are present

Greenplum Database can use either IPV4 or IPV6 protocols.

Tanzu Greenplum on DCA Systems

Tanzu Greenplum version 6.9 and later is supported on Dell EMC DCA systems with software version 4.2.0.0 and later.


Acknowledgements

The authors wish to thank the veterinary staff of the US Navy Marine Mammal Program, in particular, S. Cassle, N. Daugenbaugh, E. Jensen, S. Johnson, B. Lutmerding, C. Smith, E. Alford, D. Smith, K. Carlin and V. Cendejas. The project would not have been possible without the support of animal training and husbandry staff from Science Applications International Corporation. The following people were especially helpful in the daily operations and logistics of the study: R. Dear, L. Green, L. Lewis, J. Orr, R. Sadowsky, B. Swenberg,J. Bridger, M. Todd, M. Beeler, E. Bauer, R. Jauck, S. Price and many dedicated interns.


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Students in the USG must declare one home institution at a time. Students who transfer from one institution to another automatically change their home institution.

Students must meet the USG-specified minimum number of hours in each Area A–E.

Students successfully completing a course in one institution’s Areas A–E will receive full credit in Areas A–E for the course upon transfer to another USG institution as long as the following conditions are met:

  • The course is within the Area hours limitations of either the sending institution or the receiving institution and
  • The student does not change from a non-science major to a science major

An Example to Illustrate Cross-Area Transfer Credit

Decatur State Winder State Moultrie State
Area A1 6 hours 6 hours 6 hours
Area A2 3 hours 3 hours 3 hours
Area B 3 hours 3 hours 3 hours
Area C 12 hours 9 hours 9 hours
Area D 9 hours 12 hours 9 hours
Area E 9 hours 9 hours 12 hours
Total 42 hours 42 hours 42 hours

A student transferring from Decatur State to Winder State having completed the Decatur State core must be given credit in Area D (Natural Science) for the 3 excess hours of work done in Area C (Humanities, Fine Arts, and Ethics). If a student took 12 hours of Area E (Social Science) courses at Decatur State, only nine of those hours would transfer to Winder State but all 12 would transfer to Moultrie State.

Students successfully completing a course in one institution’s Area F will receive full credit for the course upon transferring to another USG institution as long as the student retains the same major.

Receiving institutions may require transfer students to complete the requirements as specified for native students. However, the total number of hours required of transfer students for the degree must not exceed the number of hours required of native students for the same major.

Students who wish to take Area A–F courses (including distance learning courses) from a USG institution other than the home institution, either concurrently or intermittently, may receive transient permission to take and receive credit for Areas A–F courses satisfying home institution Area A–F requirements.

Provided that native and transfer students are treated equally, institutions may impose additional reasonable expectations, such as a grade of “C” in Area A–F courses.

Chief Transfer Officer
Each institution will designate a Chief Transfer Officer (CTO) to facilitate the transfer of students within the USG. The CTO must have senior administrative and/or faculty status. The CTO is the contact person for students, faculty, advisors, records and admissions personnel, and academic administrators when problems related to transfer of Area A–F course work across USG institutions occur. However, CTOs should also be proactive and work to develop institutional procedures that minimize transfer problems.

Students with questions or concerns about the transfer of credit between USG institutions should contact the CTO at the receiving institution.


Crime and Victimization

As Chapter 7 “Alcohol and Other Drugs” discusses, poor (and near poor) people account for the bulk of our street crime (homicide, robbery, burglary, etc.), and they also account for the bulk of victims of street crime. That chapter will outline several reasons for this dual connection between poverty and street crime, but they include the deep frustration and stress of living in poverty and the fact that many poor people live in high-crime neighborhoods. In such neighborhoods, children are more likely to grow up under the influence of older peers who are already in gangs or otherwise committing crime, and people of any age are more likely to become crime victims. Moreover, because poor and near-poor people are more likely to commit street crime, they also comprise most of the people arrested for street crimes, convicted of street crime, and imprisoned for street crime. Most of the more than 2 million people now in the nation’s prisons and jails come from poor or near-poor backgrounds. Criminal behavior and criminal victimization, then, are other major consequences of poverty.

Lessons from Other Societies

Poverty and Poverty Policy in Other Western Democracies

To compare international poverty rates, scholars commonly use a measure of the percentage of households in a nation that receive less than half of the nation’s median household income after taxes and cash transfers from the government. In data from the late 2000s, 17.3 percent of US households lived in poverty as defined by this measure. By comparison, other Western democracies had the rates depicted in the figure that follows. The average poverty rate of the nations in the figure excluding the United States is 9.5 percent. The US rate is thus almost twice as high as the average for all the other democracies.

This graph illustrates the poverty rates in western democracies (i.e., the percentage of persons living with less than half of the median household income) as of the late 2000s

Why is there so much more poverty in the United States than in its Western counterparts? Several differences between the United States and the other nations stand out (Brady, 2009 Russell, 2011). First, other Western nations have higher minimum wages and stronger labor unions than the United States has, and these lead to incomes that help push people above poverty. Second, these other nations spend a much greater proportion of their gross domestic product on social expenditures (income support and social services such as child-care subsidies and housing allowances) than does the United States. As sociologist John Iceland (Iceland, 2006) notes, “Such countries often invest heavily in both universal benefits, such as maternity leave, child care, and medical care, and in promoting work among [poor] families…The United States, in comparison with other advanced nations, lacks national health insurance, provides less publicly supported housing, and spends less on job training and job creation.” Block and colleagues agree: “These other countries all take a more comprehensive government approach to combating poverty, and they assume that it is caused by economic and structural factors rather than bad behavior” (Block et, al., 2006).

The experience of the United Kingdom provides a striking contrast between the effectiveness of the expansive approach used in other wealthy democracies and the inadequacy of the American approach. In 1994, about 30 percent of British children lived in poverty by 2009, that figure had fallen by more than half to 12 percent. Meanwhile, the US 2009 child poverty rate, was almost 21 percent.

Britain used three strategies to reduce its child poverty rate and to help poor children and their families in other ways. First, it induced more poor parents to work through a series of new measures, including a national minimum wage higher than its US counterpart and various tax savings for low-income workers. Because of these measures, the percentage of single parents who worked rose from 45 percent in 1997 to 57 percent in 2008. Second, Britain increased child welfare benefits regardless of whether a parent worked. Third, it increased paid maternity leave from four months to nine months, implemented two weeks of paid paternity leave, established universal preschool (which both helps children’s cognitive abilities and makes it easier for parents to afford to work), increased child-care aid, and made it possible for parents of young children to adjust their working hours to their parental responsibilities (Waldfogel, 2010). While the British child poverty rate fell dramatically because of these strategies, the US child poverty rate stagnated.

In short, the United States has so much more poverty than other democracies in part because it spends so much less than they do on helping the poor. The United States certainly has the wealth to follow their example, but it has chosen not to do so, and a high poverty rate is the unfortunate result. As the Nobel laureate economist Paul Krugman (2006, p. A25) summarizes this lesson, “Government truly can be a force for good. Decades of propaganda have conditioned many Americans to assume that government is always incompetent…But the [British experience has] shown that a government that seriously tries to reduce poverty can achieve a lot.”

Key Takeaways

  • Poor people are more likely to have several kinds of family problems, including divorce and family conflict.
  • Poor people are more likely to have several kinds of health problems.
  • Children growing up in poverty are less likely to graduate high school or go to college, and they are more likely to commit street crime.

For Your Review

  1. Write a brief essay that summarizes the consequences of poverty.
  2. Why do you think poor children are more likely to develop health problems?

Issue No. 10. Amid Fears of Provoking Backlash, Governments Move Slowly and Softly on Global Compact for Migration Implementation

The January 2019 Women's March in Vancouver, Canada featured some demonstrators opposed to the Global Compact for Migration. (Photo: William Chen)

Coming on the heels of a contentious, but ultimately broad, endorsement by UN Member States in December 2018 of the Global Compact for Safe, Orderly, and Regular Migration, 2019 was meant to be the year in which national governments began to demonstrate tangible commitments to advancing goals under the most substantive international agreement yet on international migration. Progress, however, has been slow, and trepidation among some states to ascribe new developments in migration and refugee policy to the compact suggests a lingering fear of reigniting backlash.

Still, 2019 witnessed action on some of the nonbinding compact’s 23 objectives—which include addressing drivers of migration, improving the quality of data gathering on migrants and migration trends, and providing basic services for immigrants. Among them: In Chile, a platform to monitor the compact’s progress was established and the African Union in September unveiled efforts to strengthen data gathering. More quietly, some governments undertook actions, albeit without tying them to the compact.

One significant aspect remains in neutral: a lack of donor commitments to a start-up fund to help countries implement compact-related initiatives. With the first of the quadrennial Regional Migration Review Fora coming up in 2020, offering a showcase for governments to indicate the steps taken towards compact implementation, it will be interesting to see if countries become more visible about discussing a topic that for some this year verged on the taboo.