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That other pandemic: 2022 was worst year for bird flu this century – The Mendocino Voice
The Mendocino Voice | Mendocino County, CA (https://mendovoice.com/2023/01/that-other-pandemic-2022-was-worst-year-for-bird-flu-this-century/)
Frank Hartzell/The Mendoicno Voice
MENDOCINO Co., 1/3/22 — When one of Renne's Clark's ducks died this December, the Albion resident dutifully had it tested through her veterinarian, Village Vet in Mendocino. Then she got unexpected news — her duck, sent to UC Davis, tested positive for H5N1, or avian flu. She quarantined her flock and observed them with no further deaths. But she took the precaution of warning other poultry keepers on Facebook's Fort Bragg-Mendocino chicken group.
Clark explained in a recent interview that while she knew there were reasons not to use her name, she is more interested in issuing a call to arms to other poultry lovers.
"We're taking the necessary steps to deal with it, but wanted to let the community know it's here on the coast. It's great that Davis offers free necropsies for dead poultry — I've used it several times over the years," the Albion resident wrote on Facebook.
At the same time as Clark told this reporter about her case, the California Department of Food and Agriculture announced that the first case of the current strain of avian flu in a domestic flock in Mendocino County was confirmed. Although the timing is right, state agricultural officials could not confirm whether that first case was Clark's, or even where in the county the confirmation occurred. There is no proof that the two reports are the same.
Such confusion exemplifies the information fog around avian flu, ranging from public disinterest to the spin put on the subject by the commercial poultry industry. This avian flu epidemic was the worst ever among birds and many other species, and it only continues to rage in 2023. Outbreaks in Czechia and South Korea were reported on New Year's Day, but the flu is not infecting many humans — yet. It can get any mammal that is likely to touch sick or dead birds- including housecats.
Researchers like virologist Rob Wallace say factory farming of chickens and pigs poses a clear and present danger to humanity, one likely to increase with time. Wallace, credited with predicting the coronavirus pandemic, soon became a pariah in the scientific community where he had flourished for many years. Early research in HIV/AIDS and influenza led him to be a consultant for the United Nations' Food and Agriculture Organization and the Centers for Disease Control and Prevention. His downfall came when he was tracking bird flu origins in China, asking questions that he says got him shut out from some industry-funded university programs. The Unemployed Epidemiologist Who Predicted the Pandemic | The Nation
Wallace says money, not science, determines where and what research is done, thus directing what courses are considered to save humanity from our unsustainable appetites and practices. Unfortunately, he says, the wrong choices are being made. He and many others, including people inside some of the largest industrial chicken and meat companies, have ideas that might create a less terrifying and more sustainable future. But the available public narrative places the blame for the spread of avian flu on wild birds and backyard flocks, ignoring the fact that both lived in harmony for millennia before the advent of Confined Animal Feeding Operations (CAFOs). The fact that this avian flu pandemic hitting birds is the worst of the century and is worrying for the future, has gone mostly unnoticed due to pandemic news fatigue. Also lacking is credible information about what keeps causing avian flu, apart from books like Dr. Michael Greger's Bird Flu: A Virus of Our Own Hatching or Wallace's Big Farms Make Big Flu.
Meanwhile the chicken industry is not seriously considering changing its husbandry practices but instead is utilizing decades of research into gene editing to breed transgenic birds that can resist bird flu.
Scientists Are Close to Creating Genetically Edited Chickens That are Resistant to Bird Flu and Could Prevent Egg Shortages
The current outbreak of H5N1 is impacting a much wider array of species than past outbreaks. Even more ominous, it is killing wild birds and domestic ducks that normally survive it.
While H5N1 has infected just one human being so far in the USA — a prisoner in Colorado assigned to cull chickens — scientists are worried that a strain of avian flu could someday become a respiratory disease and cause a pandemic along the lines of Covid-19. The California Department of Food and Agriculture says the biggest concern is that people with human flu strains could be exposed to an infected bird, with the two diseases commingling to produce a dangerous new strain that spreads through the air in the way Covid and conventional flu strains are spread.
In 2014 H5N1 mostly disappeared, though it was replaced by H5N6 and H5N8 subtypes from 2014 through 2020. The epidemic of those two varieties wiped out 50 million birds in the Midwest, where factory farms are closer together, especially in Wisconsin and Iowa. California and the West were virtually untouched by those outbreaks.
However, in 2021 H5N1 emerged again in both Western Europe and East Asia. And in the meantime, it had evolved into something more deadly, likely inside Asian factory farms and outdoor markets, a true monster that attacks any mammal or bird that comes in direct contact with the corpses of the wild birds it kills. The virus was brought into factory farms by wild birds, then apparently escaped back out having now evolved to kill its hosts. Historically, avian flus did not kill or even sicken wild birds, and backyard chickens largely survived outbreaks that whirled around the globe.
A wide variety of mammals have been exposed to these dead birds and caught avian flu, which some scientists had thought was not possible. The following, provided to the Voice by the state Department of Food and Agriculture, lists mammal species infected by the current avian flu through December: American black bear, Amur leopard, Bobcat, bottlenose dolphin, Dixie striped skunk and virginia opossum, coyote, fisher, gray seal, harbor seal, raccoon, red fox, all animals believed to have gotten the avian flu directly from wild bird carcasses.
Hon Ip, who runs one of the best sources of information, the Facebook group "Avian Influenza, a.k.a bird flu," provided US year-end stats for the bizarre worldwide interspecies phenomenon. "There were 98 highly pathogenic avian influenza detections in wild mammals in 2022. These came from 15 states, with Maine and Wisconsin tied at 17 detections each. The 98 detections came from 13 different species of mammals, with red fox making up 54 percent of the total."
There have been only four human confirmed cases of this outbreak of H1N5 worldwide, two serious with one death and two less serious. To some, this means the disease has less potential human harm than other manifestations of bird flu, including earlier more fatal H151 rounds. To others, it means people have become smarter about handling dead birds. The mortality rate from human avian flu cases from 21st century varieties has been a horrifying 60 percent, according to the World Health Organization.
As of December 22, there have been 192 confirmed detections in wild birds in 37 California counties, including one bird in Mendocino County, since the first confirmed detection on July 13, according to the California Department of Food and Agriculture. People are warned to stay away from the carcasses of birds and leave handling to professionals wearing PPE.
In an interview, Laura Bradley, public information officer for California Department of Food & Agriculture Animal Health Branch, said there had not been any flock-to-flock transmission observed as of Dec. 22, only domestic poultry likely getting the disease from wild birds. Clark had not seen wild ducks with her birds, but Bradley said that is often the case, as the disease can be transmitted from the droppings of waterfowl flying over a backyard flock. A list of wild bird species carrying and often being killed by the disease can be found on the USDA APHIS website: https://www.aphis.usda.gov/animal_health/animal_diseases/avian/images/hpai-wild-birds-map.png
Another worrisome factor about the current avian flu pandemic is that it has been around for two years, while past manifestations lasted a single flu season. By December the global bird flu outbreak had killed hundreds of millions of chickens and turkeys worldwide. (Bradley pointed out that everything about avian flu is measured by its impact on chickens.)
That Clark's duck was apparently the first recorded case in Mendocino County and just one case has been confirmed here in a wild bird (unknown area of the county), highlights the fact that California has been largely left out of the global H151 pandemic (at least among chickens).
Bird flu travels by the migratory routes of waterfowl, along the Pacific and Central flyways. The Mississippi Flyway, which includes the Midwestern states where chickens and eggs are largely raised, has seen chicken populations hit harder by the avian flu, as has much of Europe and Asia. But the number of cases in wild birds is similar along the nation's flyways in 2022. California's stronger regulatory structure and greater biosecurity measures by the chicken industry may have helped keep avian flu from killing chickens in the numbers that the Midwest has seen. Avian flu has been found in US commercial and backyard poultry in 44 states and in wild birds in 46 states since early 2022.
This is a huge jump over the last time avian flu created major problems for the poultry industry. The number of chickens, turkeys and other fowl that were killed or died from the bird flu in the USA became the highest ever this December at more than 58 million, surpassing 2015's total chicken deaths.
This number, relatively low when wild and domestic outbreaks are compared over time, is considered a victory for US reporting and observation practices by government agencies like CDFA, USDA and CDFA, and measures taken by the poultry industry to make factory farms more biosecure. Yet the industry continues to blame backyard flocks and wild birds in its science and media releases. A solution now proposed by industrial interests is mandatory worldwide vaccinations of chickens. This is seen by critics as impractical and possibly obfuscating the real issue; they say a more likely solution is reforming free trade away from pure profit to more sustainable farming practices.
Wallace says in Big Farms, Big Flu that blaming wild birds is a distraction that can no longer be tolerated. Wild birds bring bird flu in "low pathogenic" form to backyard flocks, which can acquire some immunity, as they are often older birds exposed to more diverse conditions. Industrial flocks, slaughtered after 6-8 weeks for meat chickens to 1-2 years for egg chickens, have very low genetic diversity or variety, which makes it easier for pathogens to get at their immune systems. When the jump is made to the CAFOs, the low pathogenic becomes the deadly "highly pathogenic." That has been the story in the past, confirmed by research in the Midwest, but this current deadlier virus does not seem to have a low pathogenic component. Bradley said she has only seen cases of highly pathogenic H5N1 in this cycle. Backyard flocks like Clark's are also both more healthy and better spaced than the industrial model, making their experience with disease different. One duck died, with the rest of Clark's flock not even showing signs of illness.
In a speech on YouTube, Dr. Michael Greger, author of Bird Flu: a VIrus of Our Own Hatching, said, "The emergence of H5N1 has been widely blamed on free-ranging flocks and wild birds. This is somehow the fault of people… keeping chickens in the backyard for thousands of years. Birds have been migrating for millions of years. Bird flu has been accompanying them. What suddenly turned bird flu into a killer? Now we put millions of chickens into a chicken factory next door to a pig factory," he continued. "These chicken factories make billions and billions of these mutations continuously. … The big shift in the ecology of avian influenza has been this intensification of the global poultry sector."
Greger was praised for his viewpoint by many researchers, including an editorial in Virology journal. Others replay the official line that bird flu comes from mutations in the breeding grounds of migratory waterfowl. Wallace points out that money determines what is studied in labs and universities and what boundaries are pushed. He says most research money goes into vaccines that benefit big Pharma, not into the problem's source or sustainable solutions.
Chickens in CAFOs are not exposed to the outside and its natural strengthening of the immune system.They have closely matching genetics and they were not bred for the health of the birds. These conditions help evolve much more virulent viruses. Viruses are less virulent in nature because they cannot kill the host or they die themselves.
"In nature, there's kind of a limit to how virulent these viruses can get," said Greger. "Or at least there was, until now. Enter intensive poultry production when the next host is just inches away. There may be no limit to how nasty these viruses can get. Evolutionary biologists refer to this as the key to the emergence of hypervirulent so-called predator-type viruses, like HIV."
Greger continued, "When you have a situation where the healthy cannot escape the disease, where the virus can just knock you flat and still transmit to someone else just because it's so crowded, then there may be no stopping rapidly mutating viruses from becoming truly ferocious."
The worst avian flu pandemic and worst overall pandemic in human history was in 1918-20 when a strain of probable avian flu killed 50 million people. Of course, there was no such thing as a CAFO in 1918. The deadly mutations are believed to have come from the packed conditions of troops fighting World War I. The virus spread in barracks and battlefields. This reporter's great uncle, Floyd Sturm, died from the misnomered "Spanish Flu" in 1918 on his way to war.
"Just like the chickens when this harmless virus found itself in these packed conditions, it mutated and became more deadly," Greger said. "Millions of soldiers were forced together in these stressful unhygienic conditions with no escaping a sick car. The same trench warfare conditions exist today in every industrial egg operation."
While it's not 100 percent sure that Spanish Flu was an avian flu variety that jumped into humans, the consensus among scientists now is that is exactly what happened. Here is a link to a study about the topic in the prestigious science journal Nature. The CDC definitively lists the 1918 pandemic as being an avian originated flu.
Study revives bird origin for 1918 flu pandemic | Nature
History of 1918 Flu Pandemic | Pandemic Influenza (Flu) | CDC
Omnivores all, chickens, pigs and humans, have some similarities in their respiratory and digestive systems on a molecular level. As these viruses rip through packed chickens, they create something much more virulent, and that virulence could jump to humans. One study, conducted by universities in the USA and China, was suppressed as the information on how avian flu could become a respiratory disease was deemed too dangerous to be released. Engineering H5N1 avian influenza viruses to study human adaptation | Nature
These are the kind of alarm bells about the potential of avian flu that Dr. Greger and Wallace warn are being ignored at great potential peril to humanity's future. Each book offers ways to change meat production and reduce meat consumption. But chicken farming took off in an even bigger way during the pandemic, and CAFO chicken farming is spreading in south Asia with increasing demand for meat. The chicken is also the creature on which genetic engineering is most commonly done. It is unknown the effect transgenic chickens might have on bird flu or other diseases that threaten humans, such as salmonella strains that have become highly virulent and antibiotic-resistant due to CAFO practices. Some in the industry are advocating the creation of new transgenic chicken varieties to resist the bird flu, without changing the conditions that cause it.
Creating Disease Resistant Chickens: A Viable Solution to Avian Influenza? – PMC
Almost all flu varieties that infect humans originate in China and other packed parts of Asia, creating mutations that require a new flu vaccine every year. Flu mutates so quickly that it is impossible to be sure that vaccines (or genetically altered chickens) will work against fast-changing strains.
CAFOS were actually stopped cold by a virus shortly after they were invented in Delaware in the 1920s. Large chicken operations suffered total losses of their birds from a virus called Marek's disease. Researchers believe that Marek's disease only began to evolve when factory farms were created. A vaccine for Marek's saved the industry. However, Marek's disease has been spread to every corner of the world by the chicken industry, especially industrial hatcheries. Now no one can raise chickens without fear of Marek's disease. Although Marek's seems to pose no risk to humans, the New York Times did an investigative story into the frightening things that has happened in the evolution of Marek's that scientists thought impossible for any virus. The Merck veterinary manual says the following about Marek's disease, wholly created by the chicken CAFOs and now spawning super deadly variants inside chicken factories. "Marek's Disease is identified in chicken flocks worldwide. Every flock, except for those maintained under strict pathogen-free conditions, is presumed to be infected."
This chicken vaccine makes its virus more dangerous | PBS NewsHour
Large chicken farms have also created massive numbers of new strains of human-sickening bacteria like salmonella, E. coli and Campylobacter. Overuse of antibiotics made these bacteria evolve into strains that are now untreatable by medicine. Although antibiotic use has been greatly curtailed in the USA and Europe, the CAFO industry has been growing exponentially in China, Thailand, Indonesia and Vietnam, where many controls are not in place.
Free trade rules are written by the global meat industry. This reporter, when he raised chickens and eggs for the farmers' market, found the industry had made it illegal to sell to grocery stores and restaurants unless I chose to drive chickens more than 200 miles to an industrial inspection facility, where they might get their first exposure to industrial pathogens. Chicken raisers in countries like South Africa that signed neo-liberal free trade agreements soon found it impossible to compete with giant industrial concerns. Others, in places like Gambia, can raise chickens in traditional ways and sell to anyone, because their nation did not sign free trade treaties that kill the small farm. Many of these nations have never had large outbreaks of H151. (there was an outbreak in Senegal).
The Gambia bans importation of live birds from Senegal amid bird flu outbreak | Photos
In March 2009, the first case of a novel H1N1 influenza virus infection was reported in the Mexican state of Veracruz. The virus quickly spread through Mexico and the United States, and in June 2009 the World Health Organization officially declared it a pandemic. Within a year, the Centers for Disease Control and Prevention (CDC) estimates, it had killed about 575,000 people worldwide.
Early reports suggested that the source of the outbreak lay in the factory-style pig farms in the area around its epicenter in Veracruz. In-depth research traced the genetic lineage of the virus to a strain that had emerged in a supersized industrial hog farm in Newton Grove, North Carolina, in the late 1990s, where it had circulated and evolved among pigs before crossing to humans.
Materials considered by the creators of the Paris climate accords were written by the meat industry, with claims that industrial meat chickens are better for the climate. The chickens were described as better because they moved less and ate less and thus created less carbon, an outright lie easily disproved by the tremendous consumption of food by the genetically engineered meat chicken, available on any chart used by industry itself. The notion that grass-fed cattle, pigs allowed to forage and pasture, and integrated farms could actually help with climate change was discarded due to meat industry pressures. This reporter obtained the language about agriculture that was considered in the secret meetings held that led to the Paris accords. The following published journal article documents the secrecy and the compromises that led to a Paris agreement that is not enforceable and does not confront issues like factory farming.
The Paris Agreement on Climate Change: Behind Closed Doors | Global Environmental Politics | MIT Press
The actual Paris accords missed what may have been a key opportunity to take a stance on improving agricultural methods and punted on the entire subject, with agriculture not even appearing in the actual agreement. It did commit to reducing meat-eating overall but initiatives led by industrial agriculture and the Gates foundation have pushed for large scale, industrial monocrops and the destruction of the movement toward local farming. Now, it's agriculture that is said to be the biggest threat to the Paris agreements. With nothing in the agreements, many nations in Southeast Asia, Africa and South America have greatly increased the size and number of their CAFOS.
Intensive farming worldwide threatens Paris climate accord, report says
"Waterfowl species are the natural host of avian influenza viruses, so we tend to see most infections among species of waterfowl," said Krysta Rogers, senior environmental scientist with the California Department of Fish and Wildlife. "Other species that share habitat with waterfowl may also be susceptible, such as other water birds like American white pelicans, herons, egrets, and cormorants. Also, species that prey or scavenge on sick birds such as bald eagles, hawks, falcons, owls, turkey vultures, and ravens." Rogers emphasized that all statistics rely on people reporting dead birds, which often doesn't happen in the wide open spaces of the North Coast area.
"Testing of wild birds is generally biased towards areas with higher human populations. Areas with more people means the bird is more likely to be detected and submitted for testing versus an area with fewer people. As of December 22, the number of confirmed detections in wild birds for Mendocino County is 1, Del Norte County is 0, Humboldt County is 6, Lake County is 0, and Sonoma County is 8," she said.
Mapping studies indicate this particular strain may have come from both Asia and Europe to the USA."The strain of highly pathogenic avian influenza H5N1 currently in circulation has not been previously detected in North America. Prior to its detection along the Atlantic Coast in December 2021 (Canada) and January 2022 (U.S.), detections of this strain of highly pathogenic H5N1 had been on the rise across parts of Europe," she said.
"This current outbreak is unprecedented in terms of the geographic range, diversity of wild birds potentially impacted, and number of wild birds that may die from infection," she said. "Prior to this outbreak, highly pathogenic avian influenza viruses were considered more of a disease of domestic poultry with occasional spill-over into wild birds which may or may not have caused mortality."
Operations in Europe have greatly increased biosecurity and worked toward easing the misery and ill health conditions that meat and egg chickens are raised in. This has also happened in California but has been resisted in the farm belt of the USA. During the Trump Administration, measures to regulate the chicken industry were discarded or slowed in favor of making the USA more competitive in the worldwide chicken trade.
While industry seeks pharmaceutical and bioengineering solutions to specific issues such as transitioning away from antibiotics or bird flu, this does not solve the causal problems with CAFOs. A current bill sponsored by Bay Area Democratic Congressman Ro Khanna and New Jersey Democratic Senator Cory Booker would force existing USA CAFOs to cease operating in that fashion by 2040. But is the problem consumers or CAFOs? As a farmer's market chicken seller, I got frowns for charging $6 per pound for meat from chickens that lived happy lives outdoors with real nutrition.
During that period, I interviewed a chicken company president who said I could use his name if the article blamed not the industry, but on the consumer demand for $2 per pound chicken. He was right. The enemy, as Pogo said, is us.
The president was part of an industry group trying to interest consumers in better-tasting chicken. The effort largely failed. US chicken companies were sued and prosecuted after Walmart complained they were working together to raise prices. Meanwhile, European chicken producers are downsizing chicken operations but the cost of the chicken they sell must be government-subsidized to be affordable. And free trade brings in super cheap chicken from the world's largest and worst CAFOs in Vietnam and Indonesia.
Industry leaders say that free trade agreements must be rewritten to force these countries, which are using the USA CAFO model and often working for USA corporations, to create smaller and healthier poultry factories. This crashes headlong into conservative forces that don't believe in regulation. Even more powerful opposition comes from neoliberal forces, led by Bill Gates and his foundation, that believe bigger is better and work against the continuation of traditional farming practices in favor of genetic engineering, processed fake meat and other fake foods made from genetically modified soybeans. Their plans call for the massive planting of monocrops in the global South, decimating Amazon rainforests and remaining African savannas and jungles.
To some, Gates and his technology will save the world from hunger and global warming in much the way the chemical "green revolution" did with monocrops and sprays in the 1970s. To others, Gates is just creating another type of too big, badly scaled, overly processed and unhealthy food. Critics like Wallace suggest that global agriculture should be forced to pay its real costs including sewage, insect and air pollution. Operations creating weak, sick animals should be regularly tested for the creation of new pathogens and taxed for the cost of those diseases. Authorities already track new strains of the likes of salmonella, and tie them directly to the source, which is usually an industrial hatchery.
In the journal Global Jurist, Federico Regaldo cites CAFOs for pandemics and epidemics they create. Who is Going to Pay for Causing Pandemics?
Throughout human history, animal diseases like smallpox, measles and bubonic plague have been among the worst humans faced. But in the last 50 years, the situation has dramatically worsened.
According to a July 2020 report from the United Nations, three out of four of all "new and emerging human infectious diseases" are zoonotic in origin, and a study in the journal Nature found that agriculture was associated with half of all the zoonotic pathogens that emerged in humans. In Wallace's view, this increase is "concurrent" with the livestock revolution, the expansion and consolidation of the meat sector that began in the 1970s in the southeastern United States and then spread around the world. Wallace asks how profitable it is to create low-priced food that can kill a billion people. It's a question we all need to face.
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Wild birds are a priceless treasure in "our" world (whose world is it, anyway?). That factory farming of chickens and pigs is endangering our precious wild bird friends is very sad. Not only for the pathetic animals whose only purpose is to feed humans, but for humans, whose world will be reduced beyond repair should such a catastrophe as the decimation of our wild birds come to pass. This isn't even mentioning the human pandemic that could occur because of this terrible practice of crowding as many animals as possible into greedy money-making butchery. A terrible example of our corporations being given the green light to influence policy that is stupid, destructive and incredibly inhumane.
The Mendocino Voice is a local, worker-owned, news service — based in, and covering Mendocino County, California. We believe in useful news for all of Mendocino.
© Copyright 2023, The Mendocino Voice, Mendocino News Media LLC


We all know that moment when you're doing your lab and for learning/troubleshooting purposes you wish you had an option to capture the traffic on all networks in your lab in a convenient way. As of this writing, UNetLab latest release version is 1.0.0-10 and so far UNetLab developers haven't released an official feature that allows the users to capture traffic on all network interfaces simultaneously. If you need to capture traffic using UNetLab GUI, you have the option to select only one network, which will result in firing up one Wireshark instance. So, if you need to capture traffic on all network simultaneously and pipe directly to Wireshark you're out of luck and instead, you'll have to fire up several instances of Wireshark, which isn't practical because of the number of windows you'll have to deal with. To remedy this situation, I'm going to show you how you can setup rpcap on UNetLab so you can specify from you client machine what networks you need to capture simultaneously in only one Wireshark window.
Download rpcapd-linux from Github and build its dependencies.
Lastly, we'll need to understand how UNetLab maps bridge networks on Linux interfaces and then fire up Wireshark to capture on all network we're interested in capturing simultaneously.
warning, got bogus unix line.
As far as I understand, whenever you create a bridge network on your lab on UNetLab, this network segment will be mapped on Linux as an interface named vnet0_<ID>, where ID is the network bridge identifier that is automatically assigned when you're building your topology on UNetLab GUI. For instance, let's analyze this topology in Figure 1.
To help you visualize this, I put these IDs as labels in Figure 1. For example, the network segment named PE1_TO_PE2 has ID = 1. Note that although the IDs are expected to be numbered sequentially, if you delete network segments you'll have a gap of IDs. For example, because I deleted some segments when I was doing this lab the other day, both IDs 6 and 4 aren't being used anymore. Anyway though, since my entire topology is made of 8 network segments I have 8 IDs as expected: 1, 2, 3, 5, 7, 8, 9, 10. With that being said, if you need to capture traffic on all segments or a subset of these segments, these are the IDs that will have to select on Wireshark.
Now that the UNetLab virtual machine is running rpcap and we understand how the underlying Linux network interfaces are mapped to our lab network interfaces, we can start Wireshark on the client PC. The first step is to fire up Wireshark, click on Capture Options as depicted in Figure 3. Next, you'll have to click on Manage Interfaces, which can be found on the input tab.
As soon as you open up the Manage Interfaces, you'll have the option to specify your rpcap server, in the remote interfaces tab by pressing the button named Add remote host, which is the IP address of the UNetLab virtual machine. Remember that the authentication was set as null and the port rpcapd is listening is 2002.
If you have L3 reachability to UNetLab virtual machine and the rpcap service is enabled you should be able to see all the Linux network interfaces as illustrated in Figure 4.
Interface column to sort them. Finally, hit the start button to start the capture on the vnet0 interfaces you're interested and enjoy! As you can see in Figure 5, I selected all the 8 IDs as we saw in Figure 1 and 2 to capture traffic on all interfaces of this topology.
Good! You're ready to capture whatever traffic you want in these set of interfaces and enjoy your lab on UNetLab. Plus, here one of the coolest feature that I find about rpcap, it always tags the interface name where the packet was captured. For instance, in Figure 6, the ARP packet was captured on "Interface 5", which actually is rpcap://unl.vm:2002/vnet0_2, so you know that this packet was captured on vnet0_2. This information is really handy when you need to narrow down your filters on wireshark because you're dealing with an all interfaces capture. Of course, you could use other filters like VLAN IDs, MAC addresses and so on, but this option is quite helpful and sometimes more practical to use.
That's it! Before we finish, I'd like to give a massive thank you to Andrea Dainese and his team for developing this amazing networking tool. I really wish in the upcoming releases we had this feature already builtin on UNetLab GUI, so we could easily specify if we'd like to capture traffic on more than one interface simultaneously in just one Wireshark window. Meanwhile, I'll continue to use rpcap. I hope this information is valuable to you guys!


EU Support - BE REEL!
What is a LIFE-integrated project?
Integrated projects were created to implement the environmental and climate legislation and objectives on a larger scale and to increase the impact of the LIFE programme in the EU. They act as a catalyst for the implementation of plans, programmes and strategies developed on a regional, multiregional or national scale as a result of EU legislation. Central to the integrated project are the involvement of stakeholders, the effects in the long term and the coordination with complementary actions and projects that contribute to the realisation of the same strategic plan.


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Are there study guides for the exam I am taking?


text:
Q: I can't write data on a new line in a file Whatever I did, I did not write a new data on a new line in the file.
How can I fix it?
For example mary's score is 100 and smith's score is 150, but in the txt file it is
 mary 100smith 150

I wanna smith 150 in a new line
public class HighScores {
public HighScores(){

    String txt = "";
    Scanner sc = null;
    PrintWriter pw = null;
    File Checker = null;
    try{
        Checker = new File("highScores.txt");
        if(!Checker.exists()){
            Checker.createNewFile();
        }

sc = new Scanner(new File("highScores.txt"));
        while(sc.hasNextLine()){
            txt = txt.concat(sc.nextLine()+"\n");
        }

        String score=String.valueOf(Game3.score);
        String name = NewPlayer.name;

        txt = txt.concat(name + " "+ score +"\n");
        pw = new PrintWriter(Checker);// writing the checker
        pw.write(txt +"\r\n");

pw.println()  gives the same problem too.
    }catch(FileNotFoundException fnfe){

        fnfe.printStackTrace();
    }catch(IOException ioe){
        ioe.printStackTrace();
    }finally{
        sc.close();
        pw.close();
    }

}

}

A: The code is dodgy, but should actually do what you expect of it.
You are probably viewing the resulting file with a Windows notepad app. It expects "\r\n" as a newline separator, as do most other Windows apps.

A: Use the following code
public class HighScores {
public HighScores() {
    String txt = "";
    Scanner sc = null;
    PrintWriter pw = null;
    File Checker = null;
    try {
        Checker = new File("highScores.txt");
        if (!Checker.exists()) {
            Checker.createNewFile();
        }

        sc = new Scanner(new File("highScores.txt"));
        while (sc.hasNextLine()) {
            txt = txt.concat(sc.nextLine() + "\n");
        }

        String score = String.valueOf(Game3.score);
        String name = NewPlayer.name;

        txt = txt + name + " " + score;
        pw = new PrintWriter(Checker);// writing the checker

        pw.println(txt);
    } catch (FileNotFoundException fnfe) {

        fnfe.printStackTrace();
    } catch (IOException ioe) {
        ioe.printStackTrace();
    } finally {
        sc.close();
        pw.close();
    }
}

}

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}

\IEEEPARstart{A}{t} the Paul Scherrer Institut (PSI) in Switzerland we routinely use plastic scintillators read out by silicon photomultipliers (SiPMs) to build detectors for various experiments \cite{Stoykov12a,Cattaneo14,Amato17,Rostomyan21}.
The basis for estimating the time resolution of such detectors was put in the research works \cite{Stoykov12b,Sedlak12}. It was shown both experimentally and in simulations, that in case of using a constant fraction discriminator (CFD), the time resolution $\sigma$ is inversely proportional to the square root of the energy $E$ deposited in the scintillator. Here, we represent such dependence in the following form:

\begin{equation}
\sigma = \frac{\sigma_{\rm 1MeV}}{\sqrt{k E}}\ ,
\label{Eq:sigma=f(E)}
\end{equation}
where $k$ is the fraction of emitted scintillation light collected to the photosensor (single SiPM or group of SiPMs); $k E = E_{\rm det}$ is the energy effectively reaching the photosensor (detected energy); $\sigma_{\rm 1MeV}$, expressed in ${\rm ps} \times \rm{MeV}^{0.5}$, is the time resolution per 1\,MeV detected energy.

The best value of $\sigma_{\rm 1MeV}$, reported in \cite{Stoykov12b}, is 18\,ps$\times$MeV$^{0.5}$, assuming $k = 1$. At that time this was the first proof that with a SiPM based detector one can have as good time resolution as with using a PMT. Taking a more appropriate estimate $k \approx 0.75$  (see below) for the light collection under the given conditions, we get $\sigma_{\rm 1MeV} \approx 15\,{\rm ps} \times {\rm MeV}^{0.5}$. None of the detectors in \cite{Stoykov12a,Cattaneo14,Amato17,Rostomyan21} exceeded this performance limit.

The minimum achievable time resolution $\sigma_{\rm 1MeV,min}$ as a function of scintillator and SiPM parameters can be expressed as \cite{Gundacker20}:

\begin{equation}
\sigma_{\rm 1MeV,min} =
\sqrt{ \frac{\tau_{\rm d} \times (1.57\,\tau_{\rm r} + 1.13\,\sigma_{\rm ts})}
{{\rm LY} \times {\rm PDE}} }\  ,
\label{Eq:sigma1MeVmin}
\end{equation}
where $\tau_{\rm r}$ and $\tau_{\rm d}$ are the rise time and the effective decay time of the scintillation emission; LY is the scintillation light yield per unit energy; PDE is the photon detection efficiency of the SiPM;
$\sigma_{\rm ts} = \sqrt{\sigma_{\rm PTS}^2 + \sigma_{\rm SPTR}^2}$ is the standard deviation characterizing cumulative time spread in the light collection and SiPM response formation processes, with abbreviations PTS and SPTR standing for the photon transfer time spread and single photon time resolution, respectively.

Taking the datasheet values for BC422 \cite{Bicron} (equivalent EJ-232 \cite{Eljen}) scintillator
as $\tau_{\rm r} = 350$\,ps, $\tau_{\rm d} = 1600$\,ps, and ${\rm LY} = 8400$\,photons/MeV,
a reasonably high ${\rm PDE} = 0.5$ and $\sigma_{\rm ts} = 0$\,ps,
using (\ref{Eq:sigma1MeVmin}) we get:
$\sigma_{\rm 1MeV,min} \approx 14.5\,{\rm ps} \times {\rm MeV}^{0.5}$.
Compared with the result \cite{Stoykov12b}, this estimate seems to indicate that practically no further improvement of the time resolution with this scintillator is possible.

However, in \cite{Gundacker20} the authors pointed out, that the rise time for the scintillation emission of BC422 is actually a factor of 10 lower, compared to its datasheet value. From this they naturally concluded that the time resolution of BC422 coupled to a SiPM is not limited by the scintillator emission properties and should be more influenced by the SPTR value of the SiPM. Their detector consisted of a small ($3 \times 3 \times 3$)\,mm$^3$ cube scintillator wrapped in Teflon tape attached to one SiPM (estimated light collection $k \approx 0.75$). The coincidence time resolution (CTR) between two such detectors for 511\,keV annihilation photons from a $^{22}$Na radioactive source was measured for a narrow energy window set around the Compton edge with the mean value of 340\,keV. A low-noise signal processing at 1.5\,GHz bandwidth with a low-threshold leading edge (LE) timing was used. The CTR value of 35\,ps (FWHM) or 15\,ps (sigma) was reported, which gives $\sigma_{\rm 1MeV} = 5.3\,{\rm ps} \times {\rm MeV}^{0.5}$.

This result shows a factor of three improvement compared to that of \cite{Stoykov12b}, indicating
that our data on $\sigma_{\rm 1MeV}$ need updating. In this work, we perform measurements with BC422 scintillator and different SiPMs. We decided against repeating the signal-processing scheme of \cite{Gundacker20} and utilized our standard approach with signal bandwidth of $\sim 600$\,MHz and a CFD.

\section{Experimental Conditions}

\subsection{Scintillators and SiPMs}

Most of the measurements in this work are done with ($3 \times 3 \times 3$)\,mm$^3$ cubes of BC422 or EJ-232 plastic scintillator (see Fig.\,\ref{Fig:scint}). Initially, all scintillator faces are polished with a diamond tool. Two SiPMs of the same type are attached to opposite faces of the cube using either optical cement EJ-500 or optical grease EJ-550. Three types of such detectors are used:

\begin{itemize}
\item{
(cube, Teflon, cement): scintillator faces being attached to SiPMs are rough grinded, coupling to SiPMs is done via optical cement, afterwards  the detector is wrapped in a Teflon tape;}
\item{
(cube, black, cement): all faces of the scintillator are rough grinded, the SiPMs are attached with optical cement, afterwards the ``free`` faces are painted with black paint EJ-510BL;}
\item{
(cube, black, grease): scintillator faces being attached to SiPMs are left polished, the ``free`` faces are rough grinded and painted with black paint, coupling to SiPMs is done via optical grease.}
\end{itemize}

The small cube detectors are used to obtain the best possible $\sigma_{\rm 1MeV}$ value for each SiPM type. However, one can ask up to which extent such results reflect the situation with larger detectors, used in practice. To answer this question, we also perform measurements with a relatively large detector built as a stripe ($100 \times 14 \times 4$)\,mm$^3$ of BC422 scintillator with 3 SiPMs coupled to each ($14 \times 4$)\,mm$^2$ face and electrically connected together in series
(Fig.\,\ref{Fig:scint}). All scintillator faces are diamond polished, the coupling to SiPMs is done via optical grease.

\begin{figure}[!h]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure1.eps}
\caption{
Scintillation detectors used in this work.
}
\label{Fig:scint}
\end{figure}

The tested SiPMs are listed in Table~\ref{Table:SiPMs}. Two SiPMs with lowest SPTR, reported in \cite{Gundacker20}, are non-commercial devices and were not available for the current measurements.

\begin{table}[!htb]
\centering
\renewcommand{\arraystretch}{1.1}
\caption{
List of SiPMs used in this work. $U_0$  is the typical breakdown voltage and $\Delta U_1$  is the chosen operation overvoltage per 1 SiPM, also in case of their series connection. Hamamatsu S10931-050PX is an old device similar to the one used in \cite{Stoykov12b}. Active area of Broadcom SiPM is ($3.72 \times 3.72$)\,mm$^2$; for all others is ($3.0 \times 3.0$)\,mm$^2$
}
\begin{tabular}{clcc}
\hline
\lower 5pt \hbox{Producer}
  & \lower 5pt \hbox{SiPM}
  & \lower 5pt \hbox{$U_0$,\,V}
  & \lower 5pt \hbox{$\Delta U_1$,\,V} \\[10pt]
\hline
\phantom{00}Broadcom	& AFBR-S4N44C013 	& 27 		& \phantom{1}6.7 \\
\phantom{00}Advansid	& ASD NUV3S-P       	& 26 		& \phantom{1}3.5 \\
\phantom{00000}Ketek	& PM3325-WB-D0	 	& 25 		& \phantom{1}5.7 \\
\phantom{}Hamamatsu	& S10931-050PX		& 70		& \phantom{1}1.8 \\
\phantom{}Hamamatsu	& S12572-025P			& 65		& \phantom{1}4.2 \\
\phantom{}Hamamatsu	& S13360-3050PE		& 53		& \phantom{1}7.4 \\
\phantom{}Hamamatsu	& S14160-3015PS		& 38		& 10.0 \\
\phantom{}Hamamatsu	& S14160-3050HS		& 38		& \phantom{1}3.5 \\
\hline
\end{tabular}
\label{Table:SiPMs}
\end{table}

\subsection{Measurement Setup}

Figures\ 2 and 3 show two setups used to perform the measurements. The scheme of the experiment is the same in both cases and comprises a $^{90}$Sr radioactive source, a collimator, a detector under test D2, and a reference detector D1.  The collimator is a 1\,mm thick brass plate with $\varnothing 1.2$\,mm hole. The detector under test D2 is the one for which the construction (SiPMs) and the signal processing (amplifier and CFD settings) will be varied to find their impact on $\sigma_{\rm 1MeV}$. The construction of the reference detector D1 is chosen to ensure the best timing performance: BC422 cube wrapped in Teflon with Broadcom SiPMs coupled via optical cement.  After calibration, in which the time resolution of this detector is established, the corresponding signal processing parameters are fixed throughout the measurements.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure2.eps}
\caption{
Measurement setup 1. Two SiPMs in each D1 and D2 detectors are connected in series and perform as a single photosensor. No compensation for the particle hit position is possible; some degradation of the time resolutions is to be expected.
}
\label{Fig:setup1}
\end{figure}

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure3.eps}
\caption{
Measurement setup 2. Two SiPMs in each detector are readout independently which allows, by averaging,
a compensation for the particle hit position.
}
\label{Fig:setup2}
\end{figure}

In setup 1 (Fig.\,\ref{Fig:setup1}) the two SiPMs in D1 and D2 are connected together acting as a single photosensor. We use a series connection of SiPMs both for DC and AC current. The signals D1 and D2 are first sent via 0.5\,m long 50\,Ohm cables to a voltage amplifier. After the amplifier each signal is split using a passive 50\,Ohm splitter between a trigger logic and a digital oscilloscope (DSO, model LeCroy WaveRunner 640Zi \cite{LeCroy}). The trigger logic is realized in NIM modules. Its function is to set a detection threshold for each channel and generate a trigger signal for the DSO. In calibration mode the trigger is generated as tr\ =\ (D1)D2, i.e. coincidence D1 and D2 taken on the leading edge of D1; in measurement mode tr\,=\,D1. The DSO is operated at the sampling rate of 40\,GS/s. The bandwidth for each channel is adjusted by tuning the ERes (enhanced resolution) filter of the DSO. This is a sort of a moving average filter trading bandwidth for vertical resolution, which can be enhanced up to 3\ bits in steps of 0.5\ bit. In all, except one, measurements the ERes bandwidth is set to $\sim 600$\,MHz. The DSO measures charge spectra of signals D1 and D2, and perform analog CFD transformation on them, generating signals F1 and F2. The detection times $t_1$ and $t_2$ are taken at the zero-crossing of F1 and F2. The collected histograms $t_2 - t_1$ are used in analysis to extract the CTR values. The electronic noise contribution (mainly DSO) to the time jitter of F1 and F2 at their zero-crossing is also measured.

In setup 2 (Fig.\,\ref{Fig:setup2}) the two SiPMs in D1 and D2 are readout independently.
The corresponding signals C1$\ldots$C4 are processed in a similar way as before;
four CFD signals F1$\ldots$F4 are generated in DSO. The trigger signals are tr\ =\ (C1)C2C3C4\ =\ (D1)D2 and tr\ =\ (C1)C2\ =\ D1 for calibration and measurements, respectively. The sampling rate of the DSO is 20\,GS/s. The detection times are calculated as
$t_1 = (t_{\rm F1} + t_{\rm F2})/2$ and $t_2 = (t_{\rm F3} + t_{\rm F4})/2$. Such averaging allows compensating for the particle hit position and improving the time resolution of D1 and D2 compared to the setup 1. The use of setup 1 is justified, on the other hand, by a possibility to have the DSO sampling rate of 40\,GS/s and by a more simple and faster re-tuning of the whole signal processing chain, when the detector D2 is changed.

\subsection{Voltage Amplifier}

Here, as also in other applications \cite{Stoykov12a,Cattaneo14,Amato17,Rostomyan21}, we use a common approach for taking the signals from detectors. In this approach a single SiPM or their group is connected to a bias power supply and to an amplifier via a single 50\,Ohm cable. A typical scheme of the biasing and the amplifier circuits is shown in Fig.\,\ref{Fig:amp-scheme}. A two-stage non-inverting voltage amplifier is built around MAR6SM+ monolithic amplifier chips from Mini-Circuits. It has 50\,Ohm input impedance, adjustable gain and signal pulse shape.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure4.eps}
\caption{
Biasing and the amplifier circuits. The tunable parameters to adjust the gain and the signal pulse-shape are: pi-att, C1, Rz, C2.
}
\label{Fig:amp-scheme}
\end{figure}

\subsection{Constant Fraction Discriminator}

As the pulse-height distributions of D1 and D2 signals are rather broad, we use a constant fraction discriminator (CFD) technique \cite{Leo} to correct for the signal walk.  In a CFD the trigger time corresponds to a given fraction of the signal and is independent of its amplitude as long as the signal shape is preserved. The constant fraction algorithm is realized in the DSO software and is explained in Fig.\,\ref{Fig:CFD}. The input signal (we take it to be positive and starting at time zero) is split in two signals: main and attenuated. The main signal is the unchanged input one. The attenuated signal is the main multiplied by a factor $att$ and shifted backward by introducing a negative time offset, referred to as $delay$. The crossing of the main and attenuated signals occurs at their levels $fraction$ and $Latt$, respectively.
The corresponding time $tF$ is the CFD trigger time. In practice $tF$ is found by zero-crossing of a CFD signal, calculated as main minus attenuated. The input parameters used for tuning the CFD are $fraction$ and $Latt$, i.e. the levels of main and attenuated signals, at which the trigger will occur. In the classical description of the CFD algorithm \cite{Leo} it is required the crossing of the main and attenuated signals to occur at the maximum of the attenuated signal (the case of $Latt = 1$). In this work we set $Latt = 0.9$.
By using the averaged signal waveform we find the trigger time $tF$, corresponding to the level $fraction$, and the time $tL$, corresponding to the level $Latt$ taken on the main signal. The CFD settings $att$ and $delay$ to ensure triggering at the given levels are: $att = fraction/Latt$, $delay = tL - tF$.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure5.eps}
\caption{
Explanation of the CFD algorithm. The main signal is positive and starts at time-zero. The attenuated signal is the main multiplied by $att$ and shifted backward by $delay$. The CFD signal is calculated as main minus attenuated. The zero-line crossing of the CFD signal gives the trigger time $tF$. The levels of the main and attenuated signals at this time are $fraction$ and $Latt$, respectively. To ensure triggering at the given levels, the CFD settings $att$ and $delay$ are calculated as: $att = fraction/Latt$,  $delay = tL – tF$.
The main signal, taken here as an example, is a reverted averaged waveform obtained with a BC422 black scintillator cube read out by two Broadcom SiPMs connected in series.
The amplifier settings are:
pi-att\ =\ 6\,dB, C1\ =\ 5.6\,pF, Rz\ =\ 470\,Ohm, C2\ =\ 22\,pF.
The signal rise time is 0.75\,ns.
}
\label{Fig:CFD}
\end{figure}

\section{Measurements}

The following measurements are aimed at obtaining the time resolution of the detector under test D2 at varying experimental conditions, including its construction (scintillators, SiPMs) and the signal processing. This is achieved by measuring the coincidence time resolution between D2 and the reference detector D1 and subtracting the time resolution of D1, found in calibration measurements.

\subsection{Calibration}

For calibration measurements the detector under test D2 is chosen to be identical with the reference detector D1: BC422 cube wrapped in Teflon with Broadcom SiPMs coupled via optical cement.
Figure~\ref{Fig:cal-energy} shows charge spectra of D1 and D2 signals taken with setup 2 under the trigger condition tr\,=\,(D1)D2, i.e. by setting a certain detection threshold for each channel. The charge is obtained by integrating only the negative part of the signal waveform (positive part for the inverted signal shown in Fig.\,\ref{Fig:CFD}).
The threshold value for D2 is chosen just high enough to cut the low-energy tail of the spectrum. The threshold for D1 is increased aiming at the relative width of D1 spectrum (ratio of the standard deviation to the mean) to approach that of D2. The threshold increase for D1 is, however, constrained by the requirement of having the coincidence count rate with the given radioactive source above 10\,cps. The full spectrum of D2 signals taken with tr\,=\,D1 is also measured and shown in Fig.\,\ref{Fig:cal-energy}.
The mean charge of 6.58\,pC in this spectrum corresponds to the mean deposited energy of 570\,keV
in the scintillator. This energy estimate comes from simulations \cite{Cattaneo14,Stoykov12b}, where for similar detector setups with 2\,mm and 5\,mm thick plastic scintillators the mean deposited energies were obtained as 380\,keV and 950\,keV, respectively.
The established correspondence between the measured charge and the energy allows finding the mean deposited energies $E_1$ and $E_2$ at tr\,=\,(D1)D2.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure6.eps}
\caption{
Energy calibration in setup 2. The detectors D1 and D2 are identical: BC422 cubes wrapped in Teflon with Broadcom SiPMs coupled via optical cement. Shown are charge spectra of D1 and D2 signals at chosen detection thresholds with tr\,=\,(D1)D2.
The full spectrum of D2 taken with tr\,=\,D1 is also shown. The mean signal charge of 6.58\,pC in this spectrum corresponds to the mean deposited energy $E_2 = 570$\,keV (see text). The established correspondence between the measured charge and the energy allows finding the mean deposited energies $E_1$ and $E_2$ at tr\,=\,(D1)D2.
}
\label{Fig:cal-energy}
\end{figure}

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure7.eps}
\caption{
Time calibration in setup 2. Time-difference spectra between D2 and D1 are taken with tr\,=\,(D1)D2 and the detection thresholds same as for Fig.\,\ref{Fig:cal-energy}. The Gaussian fit to the data (dashed line) gives the coincidence time resolution $\sigma_{12} = 12.6$\,ps. Calculated from this value using (\ref{Eq:cal-sigma1}), the time resolution of the reference detector is $\sigma_1 = 8.3$\,ps.
}
\label{Fig:cal-time}
\end{figure}

Figure~\ref{Fig:cal-time} shows the time-difference spectra between D2 and D1 taken with tr\,=\,(D1)D2 and the detection thresholds same as for Fig.\,\ref{Fig:cal-energy}. By fitting the Gaussian function to this distribution we get the coincidence time resolution between the two detectors $\sigma_{12}$. From this value and the mean deposited energies $E_1$ and $E_2$ the time resolution $\sigma_1$ of the reference detector is calculated as:

\begin{equation}
\sigma_1 = \sqrt{(\sigma_{12}^2 - \sigma_{\rm noise,1}^2 - \sigma_{\rm noise,2}^2)
\,\frac{E_2}{E_1 + E_2} + \sigma_{\rm noise,1}^2}\ ,
\label{Eq:cal-sigma1}
\end{equation}
where $\sigma_{\rm noise,1}$  and $\sigma_{\rm noise,2}$ are the electronic noise contributions to the time resolution of D1 and D2, obtained from the measured time jitter $\sigma_{\rm noise,F}$  of the CFD signals at their zero-crossing. For setup 1: $\sigma_{\rm noise,1} = \sigma_{\rm noise,F1}$ and
$\sigma_{\rm noise,2} = \sigma_{\rm noise,F2}$.
For setup 2: $\sigma_{\rm noise,1} = \frac{1}{\sqrt{2}} \sigma_{\rm noise,F1}$  and
$\sigma_{\rm noise,2} = \frac{1}{\sqrt{2}} \sigma_{\rm noise,F3}$. In case of setup 2 the noise in channel F2 is equal to that in F1, and in channel F4 equal to F3: accordingly only the time jitters $\sigma_{\rm noise,F1}$  and
$\sigma_{\rm noise,F3}$  are measured in this setup.
The values $\sigma_{12}$  (CTR) and $\sigma_1$  (time resolution of the reference detector), obtained in calibration measurements, are listed in Table~\ref{Table:calibration}.

\begin{table}[!htb]
\centering
\renewcommand{\arraystretch}{1.1}
\caption{
Coincidence time resolution $\sigma_{12}$ and the time resolution of the reference detector $\sigma_1$ obtained in calibration measurements with setup 1 and setup 2
}
\begin{tabular}{ccc}
\hline
\lower 5pt \hbox{}
  & \lower 5pt \hbox{$\sigma_{12}$\,ps}
  & \lower 5pt \hbox{$\sigma_1$,\,ps} \\[10pt]
\hline
setup 1	& 14.3 & 9.8 \\
setup 2	& 12.6 & 8.3 \\
\hline
\end{tabular}
\label{Table:calibration}
\end{table}

\subsection{BC422 cubes}

In the following we measure the time resolution of the detector D2 under different conditions (SiPM, signal processing) and calculate the time resolution per 1\,MeV detected energy as:

\begin{equation}
\sigma = \sqrt{\sigma_{12}^2 - \sigma_1^2} \ ,
\label{Eq:meas-sigma2}
\end{equation}

\begin{equation}
\sigma_{\rm 1MeV} = \sqrt{k E (\sigma^2 - \sigma_{\rm noise}^2)} \ ,
\label{Eq:meas-sigma1MeV}
\end{equation}
where $k$, $E$, and $\sigma$ are the light collection, the mean deposited energy, and the measured time resolution of D2, respectively; $\sigma_{\rm noise}$ is the electronic noise contribution to $\sigma$.
In contrast to calibration, no detection threshold is applied to the signals of D2 during the measurements:
the mean deposited energy in D2 is $E = 570$\,keV.
Note, that here and further on we omit the sub-indexes 2 under the parameters referring solely to D2. Also, as all the changes from now on are done only to the detector D2, all references to the detector characteristics and to the parameters of the signal processing imply only this detector.

Figure~\ref{Fig:contributions} shows contributions to the measured time resolution as a function of the trigger fraction obtained with setup\ 1 and Broadcom SiPMs.
The corresponding dependencies of $\sigma_{\rm 1MeV}$ with and without noise subtraction are shown in Fig.\,\ref{Fig:noise-subtraction}. The noise contribution to $\sigma_{\rm 1MeV}$ increases with decreasing the trigger fraction thus limiting its minimum accessible value to about 0.07. Further on all the data on $\sigma_{\rm 1MeV}$, unless explicitly mentioned, are given with the noise contribution subtracted.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure8.eps}
\caption{
Contributions to the measured time resolution as a function of the trigger fraction. Sub-indexes under the parameters referring to D2 are omitted. Experimental conditions: setup 1, Broadcom, BC422 (cube, black, cement), signal rise time 0.75\,ns, bandwidth 580\,MHz, sampling rate 40\,GS/s.
}
\label{Fig:contributions}
\end{figure}

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure9.eps}
\caption{
Dependencies of $\sigma_{\rm 1MeV}$ on the trigger fraction extracted using (\ref{Eq:meas-sigma1MeV}) from the data in Fig.\,\ref{Fig:contributions}. The dashed line is a fit to the data using a combination of a constant line and a linear function with the fit results given in the plot.
}
\label{Fig:noise-subtraction}
\end{figure}

The light collection $k$ in a painted black ($3 \times 3 \times 3$)\,mm$^3$ cube scintillator is assumed being equal to 1/3. Accordingly, the detected energy $E_{\rm det} = k E$ in such detectors is:
$E_{\rm det} = 1⁄3 \times 570\,{\rm keV} \approx 190\,{\rm keV}$. The light collection in a Teflon wrapped scintillator, measured relative to the black one, is found to be 0.88. This measurement is done with Broadcom SiPMs at a low overvoltage to avoid a systematic shift due to the optical crosstalk. Even though we do not use this value in our analysis, it serves as a cross-check for the validity of the above estimate for the black cubes. As the light collection can`t exceed 1, the maximum possible negative systematic shift in estimating the detected energy is 12\,\% and, accordingly, 6\,\% in $\sigma_{\rm 1MeV}$.
Also remember, that for a ($3 \times 3 \times 3$)\,mm$^3$ BC422 scintillator wrapped in Teflon with a SiPM coupled at one face, the light collection is estimated in \cite{Gundacker20} as 0.75.

As in practical applications one often needs to take compromises between the detector performance on the one hand, and its technical complexity and the cost on the other, the sampling rate as high as 40\,GS/s, used in this work, might not be possible. Figure~\ref{Fig:s-rate} shows degradation of $\sigma_{\rm 1MeV}$ at lower sampling rates. This measurement, as most of the others in this work, is done with Broadcom SiPMs, which provide the best timing performance. Down to 5\,GS/s the value of $\sigma_{\rm 1MeV}$ increases by about 8\,\%. Also notice an increased noise contribution with decreasing the sampling rate. This comes from the fact that at decreasing the sampling rate we, to keep the bandwidth constant, also decrease the strength (number of bits) of the ERes filter, which makes the noise suppression less efficient.

\begin{figure}[!htb]
\centering
\includegraphics[width=0.95\columnwidth,clip]{stoykov-figure10.eps}
\caption{
Time resolution $\sigma_{\rm 1MeV}$ as a function of DSO sampling rate. The dashed line is drawn to guide an eye. Experimental conditions: setup 1, Broadcom, BC422 (cube, black, cement), rise time 0.75\,ns,
bandwidth $\approx 600$\,MHz.
}
\label{Fig:s-rate}
\end{figure}

\begin{figure}[!htb]
\centering
\includegraphics[width=0.95\columnwidth,clip]{stoykov-figure11.eps}
\caption{
Time resolution $\sigma_{\rm 1MeV}$ as a function of overvoltage measured with a detector using Broadcom SiPMs. The dashed line is drawn to guide an eye. The chosen operation point is at $\Delta U_1 = 6.7$\,V. Experimental conditions: setup 1, Broadcom, BC422 (cube, black, cement), signal rise time 0.75\,ns, bandwidth 580\,MHz, sampling rate 40\,GS/s.
}
\label{Fig:dU}
\end{figure}

An overvoltage dependence of $\sigma_{\rm 1MeV}$ is measured only with Broadcom SiPMs
(see Fig.\,\ref{Fig:dU}).
It shows initially some decrease with increasing $\Delta U_1$ and saturates at larger $\Delta U_1$ values (both PDE and SPTR saturate \cite{Gundacker20}). With other SiPMs we rely on the chosen overvoltages being sufficiently high to ensure a close-to-optimum performance.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure12.eps}
\caption{
Time resolution $\sigma_{\rm 1MeV}$  as a function of trigger fraction at different settings of the amplifier, resulting in different signal rise times. The dashed lines are fits to the data using either a linear function or a combination of a linear function applied above certain fraction value and a constant line below this value.
The DSO bandwidth (MHz), the signal rise time (ns),
the minimum time resolution $\sigma_{\rm 1MeV}(0)$ (ps$\times$MeV$^{0.5}$)
and, characterizing the quality of the fit, reduced chi-square value are indicated on the plot. The measurement with DSO bandwidth 1160\,MHz is done only once in this work. Experimental conditions: setup 1, Broadcom, BC422 (cube, black, cement), sampling rate 40\,GS/s.
}
\label{Fig:sigma1MeV=f(amp)}
\end{figure}

Figure~\ref{Fig:sigma1MeV=f(amp)} shows dependencies of $\sigma_{\rm 1MeV}$ on the trigger fraction at different settings of the amplifier providing different signal rise times.
For a given fraction, the value of $\sigma_{\rm 1MeV}$ is smaller the shorter is the signal rise time.
Extrapolated to zero fraction $\sigma_{\rm 1MeV}(0)$ seems to be independent on the rise time.

Figure~\ref{Fig:sigma1MeV=f(SiPM)} shows dependencies of $\sigma_{\rm 1MeV}$  on the trigger fraction obtained with different SiPMs. Table~\ref{Table:summary-cube} summarizes the data on $\sigma_{\rm 1MeV}(0)$ for all tested SiPMs. Among SiPMs available for this study, the one from Broadcom is by far the best for timing applications using BC422 scintillator with
$\sigma_{\rm 1MeV}(0) = 6.4$\,ps$\times$MeV$^{0.5}$. The oldest Hamamatsu SiPM S10931-050PX shows the worst time resolution saturating at the level of 9.8\,ps$\times$MeV$^{0.5}$. This result, however, is substantially better compared to that obtained in \cite{Stoykov12b} with the same type device. As a consistency check we repeated the measurement with this SiPM under the conditions of \cite{Stoykov12b} (signal rise time 1.3\,ns,
$\Delta U_1 = 1.0$\,V, $Latt = 1.0$, $fraction = 0.2$) and obtained $\sigma_{\rm 1MeV}$ values in the range ($14.8 - 15.8$)\,ps$\times$MeV$^{0.5}$, consistent with \cite{Stoykov12b}.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure13.eps}
\caption{
Time resolution $\sigma_{\rm 1MeV}$ as a function of trigger fraction obtained with different SiPMs. For clarity, not all tested SiPMs are shown. The dashed lines are fits to the data explained in
Fig.\,\ref{Fig:sigma1MeV=f(amp)}. The SiPM type, signal rise time (ns), $\sigma_{\rm 1MeV}(0)$ (ps$\times$MeV$^{0.5}$), and reduced chi-square value, are indicated on the plot. Experimental conditions: setup 1, BC422 (cube, black, cement), bandwidth 580\,MHz,
sampling rate 40\,GS/s.
}
\label{Fig:sigma1MeV=f(SiPM)}
\end{figure}

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure14.eps}
\caption{
Time resolution $\sigma_{\rm 1MeV}$ as a function of trigger fraction obtained with Broadcom SiPMs using setup 1 and setup 2. The dashed lines are fits to the data explained in Fig.\,\ref{Fig:sigma1MeV=f(amp)}.
Obtained in the fit $\sigma_{\rm 1MeV}(0)$ in ps$\times$MeV$^{0.5}$ and the reduced chi-square value are indicated on the plot. Experimental conditions setup 1: Broadcom, BC422 (cube, black, cement), rise time 0.75\,ns, bandwidth 580\,MHz, sampling rate 40\,GS/s. Experimental conditions setup 2: Broadcom, BC422 (cube, black, grease), rise time 0.77\,ns, bandwidth 580\,MHz, sampling rate 20\,GS/s.
}
\label{Fig:sigma1MeV=f(setup)}
\end{figure}

\begin{table}[!htb]
\centering
\renewcommand{\arraystretch}{1.1}
\caption{
Summary on the minimum time resolution $\sigma_{\rm 1MeV}(0)$ for all SiPMs available for the current study measured with setup 1
}
\begin{tabular}{cc}
\hline
\lower 5pt \hbox{SiPM} & \lower 5pt \hbox{$\sigma_{\rm 1MeV}(0)$,\,ps$\times$MeV$^{0.5}$} \\[10pt]
\hline
\phantom{0}Ham S10931-050PX	& $9.80 \pm 0.05$ \\
\phantom{00}Ham S12572-025P		& $9.46 \pm	0.05$ \\
\phantom{}Ham S14160-3015PS		& $9.06 \pm 0.06$ \\
\phantom{0000000009}Advansid	& $8.84 \pm	0.04$ \\
\phantom{0000000000000}Ketek	& $8.33 \pm	0.07$ \\
\phantom{}Ham S13360-3050PE		& $8.16 \pm	0.04$ \\
\phantom{}Ham S14160-3050HS	& $7.83 \pm	0.07$ \\
\phantom{0000000000}Broadcom	& $6.40 \pm	0.02$ \\
\hline
\end{tabular}
\label{Table:summary-cube}
\end{table}

As in the setup 1, used up to now, there is no compensation for the particle hit position, some potential for improving the time resolution is still present. To check this we repeat the measurements with Broadcom SiPMs using setup 2 and keeping all the relevant settings the same as before.
No saturation of $\sigma_{\rm 1MeV}$ and its improvement by another 10\,\% to
$\sigma_{\rm 1MeV}(0) = 5.7$\,ps$\times$MeV$^{0.5}$ is observed
(see Fig.\,\ref{Fig:sigma1MeV=f(setup)}). This result approaches that of \cite{Gundacker20} obtained, however, with a SiPM with presumably better timing performance.

\subsection{BC422 stripe}
To the cumulative time spread $\sigma_{\rm ts}$ in (\ref{Eq:sigma1MeVmin}) contribute both the light collection and the SiPM response formation processes.
This means that in practice one might face a situation when all advantages of using a fast scintillator read out by a low SPTR value SiPM are gone at changing from a tiny test detector to a real size one, having substantially larger dimensions and, accordingly, a larger PTS. To clarify this question, we perform measurements with a BC422 scintillator stripe of dimensions ($100 \times 14 \times 4$)\,mm$^3$ read out by 3 Broadcom SiPMs attached to each ($14 \times 4$)\,mm$^2$ face (see Fig. 1).
The SiPMs are connected in series; the coupling to the scintillator is done using optical grease. In terms of maximum dimension of 100\,mm we consider this detector to adequately represent our typical detectors \cite{Stoykov12a,Cattaneo14,Rostomyan21,Amato17}. The measurement is performed with setup 2; the collimated $^{90}$Sr source is positioned at the center of a ($100 \times 14$)\,mm$^2$ face of the scintillator. To obtain the detected energy we subsequently connect to the output only one SiPM out of 3 in each group (the other 2 are short cut) and thus measure the charge spectra. Mean values of the charge, obtained in three such measurements, are summed and compared with the charge measured under the same conditions with BC422 ($3 \times 3 \times 3$)\,mm$^3$ painted black cube, where the mean detected energy equals to 190\,keV. Accordingly, for the stripe we get $E_{\rm det} = 121$\,keV. Taking $E = 760$\,keV for the expected mean deposited energy in 4\,mm thick scintillator \cite{Cattaneo14,Stoykov12b}, for the light collection we get: $k = 0.16$. Often it is convenient, especially for large detectors, to separate different contributions to the light collection as: $k = k_1 k_2$, where $k_1$ is the fraction of the scintillation light transported from the particle interaction point to the scintillator surface, to which the photosensor is attached (light transport), and $k_2$ is the fraction of the transported light actually seen by the photosensor (sensor acceptance). Here we assume a perfect optical contact between the scintillator and the SiPMs, ensuring no reflection on this interface, and take for the sensor acceptance its geometrical value, calculated as the ratio of the total active area of all attached SiPMs to that of the corresponding scintillator surfaces: $k_2 = 0.74$. Accordingly, for the light transport we get:
$k_1 \approx 0.22$.

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure15.eps}
\caption{
Time resolution $\sigma$ as a function of trigger fraction measured with BC422 stripe depicted
in Fig.\,\ref{Fig:scint}. The collimated $^{90}$Sr source is positioned at the center of a ($100 \times 14$)\,mm$^2$ face of the scintillator. The dashed line is a linear fit to the data; the results of the fit are given on the plot. Experimental conditions: setup 2, Broadcom (3x, series), BC422 (stripe, grease), rise time 0.83\,ns, bandwidth 580\,MHz,
sampling rate 20\,GS/s.
}
\label{Fig:stripe-sigma}
\end{figure}

\begin{figure}[!htb]
\centering
\includegraphics[width=1.0\columnwidth,clip]{stoykov-figure16.eps}
\caption{
Time resolution $\sigma_{\rm 1MeV}$ as a function of trigger fraction for BC422 stripe compared to that for BC422 black painted cube measured under similar experimental conditions. The dashed lines are linear fits to the data. The $\sigma_{\rm 1MeV}(0)$ (ps$\times$MeV$^{0.5}$) and the reduced chi-square value are indicated on the plot.
Experimental conditions stripe: setup 2, Broadcom (3x, series), BC422 (stripe, grease), rise time 0.83\,ns, bandwidth 580\,MHz, sampling rate 20\,GS/s.
Experimental conditions cube: setup 2, Broadcom, BC422 (cube, black, grease), rise time 0.77\,ns,
bandwidth 580\,MHz, sampling rate 20\,GS/s.
}
\label{Fig:stripe-sigma1MeV}
\end{figure}

Figure~\ref{Fig:stripe-sigma} shows the measured time resolution of the stripe detector as a function of the trigger fraction. The obtained value $\sigma \approx 20$\,ps is at least a factor of 2 below any result we ever measured in practice before. Time resolution per 1\,MeV detected energy extracted from the above data, is compared in Fig.\,\ref{Fig:stripe-sigma1MeV} with that measured with a BC422 painted black scintillator cube under the same experimental conditions. The observed degradation of $\sim 15$\,\% comes from an increased PTS contribution. In Table~\ref{Table:check-model} we use (\ref{Eq:sigma1MeVmin}) to estimate
$\sigma_{\rm PTS}$ of the stripe detector from the current data on $\sigma_{\rm 1MeV}(0)$ and that of \cite{Gundacker20} on $\sigma_{\rm SPTR}$.
We assume $\sigma_{\rm 1MeV}(0) = \sigma_{\rm 1MeV,min}$.
First, we take the case of setup~2 with Broadcom SiPMs and painted black cube scintillator,
where contribution of $\sigma_{\rm PTS}$ to $\sigma_{\rm ts}$ can be considered as negligible, and calculate the PDE of this SiPM with $\sigma_{\rm PTS}=0$\,ps. Second, we use the obtained PDE value and estimate
$\sigma_{\rm PTS}$ of the stripe detector as $\sim 52$\,ps.

\begin{table}[!htb]
\centering
\renewcommand{\arraystretch}{1.1}
\caption{
Estimation of $\sigma_{\rm PTS}$ of the stripe detector from the experimental data on
$\sigma_{\rm 1MeV}(0)$ in Fig.\,\ref{Fig:stripe-sigma1MeV} using (\ref{Eq:sigma1MeVmin}).
The parameters
$\tau_{\rm r} = 32$\,ps, $\tau_{\rm d} = 1510$\,ps, and ${\rm LY} = 10100$\,photons/MeV for BC422 scintillator and $\sigma_{\rm SPTR} = 37$\,ps for Broadcom SiPM are taken from \cite{Gundacker20}
}
\begin{tabular}{cccc}
\hline
\lower 5pt \hbox{scint} &
\lower 5pt \hbox{$\sigma_{\rm 1MeV}(0)$,\,ps$\times$MeV$^{0.5}$} &
 \lower 5pt \hbox{PDE} &
\lower 5pt \hbox{$\sigma_{\rm PTS}$,\,ps} \\[10pt]
\hline
\phantom{}	cube	& 5.67 	&	0.43	& 0   \\
\phantom{}	stripe	& 6.52 	&	0.43	& 52 \\
\hline
\end{tabular}
\label{Table:check-model}
\end{table}

\section{Conclusion}

In \cite{Gundacker20} an exceptional time resolution of 5.3\,ps (sigma) per 1\,MeV detected energy was demonstrated with the BC422 scintillator. The result has been made possible through the use of low SPTR value SiPMs and a dedicated low-noise signal processing at 1.5\,GHz bandwidth. Also the rise time of BC422 scintillation was revisited and shown not to be a limiting factor for the time resolution of such detectors.

In this work we use our standard technique (signal processing bandwidth $\sim$\,600\,MHz, constant fraction discriminator) to measure the time resolution of BC422 read out by different SiPMs.
The best results ${\rm CTR} = 12.5$\,ps (sigma) and $\sigma_{\rm 1MeV} = 5.9$\,ps$\times$MeV$^{0.5}$ obtained with a small ($3 \times 3 \times 3$)\,mm$^3$ scintillator are comparable with those reported in \cite{Gundacker20}.

Therefore we confirm that the time resolution with BC422 scintillator can be substantially improved by using low SPTR values SiPMs.

\section{Acknowledgment}

We thank Florian Barchetti for cutting and polishing the scintillators, Andreas Hofer for his contribution in preparing the detector setup, Urs Greuter and Mario Liechti for the help in preparation of SiPMs in TSV packages for measurements. Discussions on several topics with Urs Greuter were very fruitful and are very much appreciated.

redpajama_set_name: RedPajamaArXiv


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Acerca de John Jellinek founded Jelco Ventures of La Jolla, California, in 1971. He currently still resides and operates his businesses in the San Diego area today. With over 40 years of industry experience, John Jellinek has personally been involved with over 50 private equity transitions during his career. Some of his most notable projects have included partnerships with Bell Sports Corporation, IMNET Systems Inc, Apogent Technologies, Tapsgotts, Inc., Labor Source LLC, Pelstar LLC, and CourtLink. John Jellinek has also served as Shareholder and Operating Director of the mid-market investment bank, BPC Group. Outside of work, John is heavily involved in philanthropy.


A place for fans of the country group Gloriana to come and chat!
For those of you who haven't downloaded The World Is Ours Tonight and want to hear the full song, here is a link for a video on youtube someone posted. Enjoy!
I typed up the lyrics for anyone that wants them.
i love the song, thank you!!!
Ahhh! Im so excited..Im about it listin in a second!
I tryed uploading it..but my computer didnt work after it..like it's the slowest computer if I download anything! So i had to delete it before I even got a chance to listin to the song..Thanks for posting this!!
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Thanks Kristen for posting the lyrics for us!!


text:
Uber suspends pool rides in US and Canada to prevent spread of coronavirus
Posted: Mar 17, 2020 12:35 PM EST
By Alicia Lee, CNN
(CNN) -- Starting today, Uber is suspending the pooled ride component of its service in the US and Canada to prevent the spread of the novel coronavirus.
The UberPool option, which offers riders a lower price by allowing them to share the car with other passengers heading in the same direction, is now unavailable when users open the app.
"Our goal is to help flatten the curve of community spread in the cities we serve," Senior Vice President of Uber Rides and Platform Andrew Macdonald said in a statement. "We remain in close contact with local leaders and will continue to work with them to discourage non-essential travel."
The company said it will continue to monitor developments in other cities outside of the US and Canada and determine if similar measures are necessary.
Other ride options will remain available, the company said. But riders in the US and Canada will see a message in the app notifying them to "travel only when necessary."
Uber's move comes as the number of coronavirus cases in the US near 4,500 with 87 deaths. Cities across the nation have closed down businesses, schools, bars and restaurants, and are encouraging people to self-isolate at home.
As of Tuesday, shared rides on Lyft are still available in the US. Lyft did not respond to CNN's immediate request for comment.

redpajama_set_name: RedPajamaCommonCrawl


text:
Hares & Hyenas
Venue Hire & Usage
Privacy, Security and Ordering Policies
Lesbian & Bi Crime / Romance
Maxine Wore Black
Author(s): Nora Olsen
Maxine is the girl of Jayla's dreams: she's charming, magnetic, and loves Jayla for her transgender self. There's only one problem with Maxine--she already has a girlfriend, perfect Becky. Jayla quickly falls under Maxine's spell, and she's willing to do anything to win her. But when Becky turns up dead, Jayla is pulled into a tangle of deceit, lies, and murder. Now Jayla is forced to choose between love and the truth. Jayla will need all the strength she has to escape the darkness that threatens to take her very life.
Nora Olsen was born and raised in New York City. She received a B.A. from Brown University. Although her mother, a prize-winning author, warned her not to become a writer, Nora didn't listen. Nora's debut novel The End: Five Queer Kids Save The World was published in 2010. Swans and Klons is her second YA novel. Her short fiction has appeared in Collective Fallout and the anthology Heiresses of Russ 2011: The Year's Best Lesbian Speculative Fiction. Nora's goal is to write thrilling stories and novels that LGBTQ teens can see themselves reflected in. Nora lives in New York's Hudson Valley with her girlfriend, writer Aine Ni Cheallaigh, and their two adorable cats. When not writing, Nora works as a babysitter. She also enjoys volunteering for Room to Write, an organization of publishing professionals and writers who visit NYC classrooms to teach creative writing. The highlight of Nora's year is volunteering at Camp Jabberwocky, a summer camp for children and adults with disabilities.
Publisher : Bold Strokes Books
Imprint : Bold Strokes Books
Author : Nora Olsen
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41. How Everton and Tottenham's pioneering tour gave Walter Tull his chance
(Stories)
... just CD Everton but other, amateur namesakes in Argentina and Uruguay. Everton are not alone in this – Argentina's top flight has an Arsenal, Uruguay's a Liverpool and Chile's a Rangers, though only Arsenal ...
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... in England was former Queens Park Rangers and Tottenham striker Clive Allen, who played for the London Monarchs in the 1990s. Yet the most successful was Austrian forward Toni Fritsch, who scored a memorabl ...
43. It's Not All About The Old Firm: Defying the odds in Scottish football
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by Scott Burns Pitch Publishing, £12.99 Reviewed by Gordon Cairns From WSC 373, March 2018 Buy the book Eight clubs from outside Glasgow have won a senior domestic competitive trophy since Rangers ...
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... but presented their own problems. Queens Park Rangers were promoted to the First Division in 1983 with a surface that their goalkeeper Peter Hucker described as "basically a bit of carpet over two fee ...
45. How the Texaco Cup briefly gripped parts of Scotland and the West Midlands
... the quarter-finals. Great in theory, but all four Scottish clubs lost – including Rangers, who were soundly beaten by Southampton at Ibrox in front of over 40,000. Newcastle won their second Texaco Cup ...
46. The best and worst moments of 2017 ~ part one
... es. Lincoln became the first non-League club to reach the quarter-finals since Queens Park Rangers in 1914, a year when boys wore detachable collars, a three-bedroomed house cost under £1,000 and Everton ...
47. Dalglish & Rush, Summerbee & Lee, Warboys & Bannister – where have the great duos gone?
... more in the bars and gossip columns of Turin than on the pitch, obviously). When Sunderland signed Jim Baxter from Rangers they also bought his cousin George Kinnell to stop the Slim One getting homesick. ...
48. Fans across UK donating tonnes of food and clothes to help local communities
Embed from Getty Images From Celtic, Rangers and St Mirren to Newcastle and the two Merseyside clubs, supporters' groups have been coming together to assist people in their time of need 7 December ...
49. Sean Geddes on FA Cup fame: "I watched myself in my local on Match of the Day"
... goal and FA Cup exploits, the wages offered were less than Geddes could make through work and the semi-professional game. He moved between Sutton Coldfield Town and Stafford Rangers last season, but ...
Created on 30 November 2017
50. Bobby Brown: A life in football, from goals to the dugout by Jack Davidson
(Players)
... a Devonshire haystack. He marries the Wren. This sort of Ealing Studios idyll contributed to Rangers' liquidation; the desperation to recreate everything embodied by the club's most dashing goalkeeper, ...
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REPORTER: Thank you, Mr. President. What would your biggest mistake be, would you say, and what lessons have you learned from it?
BUSH: Hmm. I wish you would have given me this written question ahead of time, so I could plan for it.
BUSH: With all the pressure of trying to come up with an answer, but it hadn't yet.
BUSH: I, uh, hope I -- I don't want to sound like I've made no mistakes. I'm confident I have.
BUSH: I just haven't -- you just put me under the spot here, and maybe I'm not as quick on my feet as I should be in coming up with one.
www.livingroomcandidate.org/commercials/2004/mistakes-were-made-web (accessed April 22, 2019).