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Claim: 'Hurricane Irma's epic size is being fuelled by global warming'
'The strongest winds are limited to a relatively small area around its centre, but hurricane-force winds of 118 kph or more extend out 85 kilometres from its eye.'
'So why did Irma grow so strong? Most likely because climate change is making Atlantic waters ever warmer.'
By: Marc Morano - Climate DepotSeptember 6, 2017 2:02 PM
By Michael Le Page
It's a monster. As the eye of Hurricane Irma approached the tiny island of Barbuda this morning, wind speeds soared to 250 kph before the instrument broke.
At the time of writing, all contact with the island had been lost and it is unclear how the 1600 inhabitants have fared. But already reports of severe destruction are coming in from other islands in Irma's path.
The destruction could be extreme. Hurricane Irma has the strongest winds of any hurricane to form in the open Atlantic, with sustained wind speeds of 295 kph.
It is also huge. The strongest winds are limited to a relatively small area around its centre, but hurricane-force winds of 118 kph or more extend out 85 kilometres from its eye.
Irma could yet grow stronger and is going to graze or directly hit many densely-populated islands in the Caribbean before possibly making landfall in Florida on Sunday – but there is still a lot of uncertainty about its path and intensity this far ahead.
Warmer waters
So why did Irma grow so strong? Most likely because climate change is making Atlantic waters ever warmer.
Tropical cyclones are fuelled by warm surface waters of around 26°C or more. They draw in moist air from all around them, and as it rises, the water vapour condenses out and releases latent heat, which drives further uplift. Irma's clouds are 20 kilometres high.
However, as tropical cyclones grow stronger they churn up the ocean and bring deeper water to the surface. Usually this deeper water is cooler, and cuts off the energy supply.
The strongest hurricanes, then, can only grow if warm waters extend down to depth of 50 or 100 metres – conditions normally only found in the Gulf or Caribbean.
In 1990, Hurricane Allen reached 305 kph winds, fuelled by these warmer waters. In 2017's warmer world, Irma began growing way out in the Atlantic, thanks to sea surface temperatures that were more than 1°C above average.
Stronger storms
Hurricane intensity depends on many other factors, too, though. For instance, winds high in the atmosphere are often faster than those lower down, blowing away rising air and preventing hurricanes from forming, or growing very strong. Low wind shear helped Irma grow into a perfect storm.
Computer models suggest global warming is likely to increase wind shear over the Atlantic, meaning there could no more or fewer hurricanes overall, but that storms grow stronger when they do form.
While tropical cyclones are currently ranked according to their wind speed, storm surges and flooding from high rainfall typically cause most of the damage, as we saw with Harvey.
The height of a storm surge depends not just on the strength of winds, but on their extent. Hurricane Sandy's winds were not that strong but the size of the storm piled up the huge storm surge that caused most of the damage in New York and elsewhere.
So strong winds don't necessarily mean big damage. The record is held by Hurricane Patricia in the eastern Pacific in 2015, with sustained winds of 345 kph. Fortunately Patricia was small, weakened dramatically before landfall and struck a sparsely populated area.
Irma, ominously, is both big and intense, and could cause big storm surges in highly populated places. Barbuda recorded a storm surge of 2.4 metres.
Filed under: astrology, irma


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Dugway welcomes new Command Sgt. Maj. Kyle Brinkman
By Bonnie A. Robinson, DPGAugust 9, 2018
1 / 5 Show Caption + Hide Caption – Command Sgt. Major Kyle R. Brinkman returns the unit Guidon, August 2, 2018 during a Change of Responsibility ceremony at U.S. Army Dugway Proving Ground. The colors always rest in the care of the current command sergeant major who will now serve as the right arm of the commander. Photo by Bonnie A. Robinson, Dugway Public Affairs (Photo Credit: Bonnie A. Robinson (DPG)) VIEW ORIGINAL
2 / 5 Show Caption + Hide Caption – Col. Brant D. Hoskins, commander, Dugway Proving Ground awards departing Command Sgt. Maj. Joe Bonds the Legion of Merit during a Change of Responsibility ceremony August 2, 2018 for his meritorious conduct, outstanding services and achievements at U.S. Army Dugway Proving Ground, Utah. Photo by Bonnie A. Robinson, Dugway Public Affairs (Photo Credit: Bonnie A. Robinson ( DPG) ) VIEW ORIGINAL
3 / 5 Show Caption + Hide Caption – Command Sgt. Major Kyle R. Brinkman greets attendees after the Change of Responsibility ceremony, August 2, 2018 at U.S. Army Dugway Proving Ground, Utah. Brinkman said he was excited to be here and looking forward to working with everyone on the Chemical and biological test center. Photo by Bonnie A. Robinson, Dugway Public Affairs (Photo Credit: Bonnie A. Robinson (DPG) ) VIEW ORIGINAL
4 / 5 Show Caption + Hide Caption – Command Sgt. Major Kyle R. Brinkman and his wife, Melissa, greet attendees after the Change of Responsibility ceremony, August 2, 2018 at U.S. Dugway Proving Ground, Utah during a Change of Responsibility ceremony at the high school auditorium. Brinkman said he and his wife are excited to be here and looking forward to working with everyone. Photo by Bonnie A. Robinson, Dugway Public Affairs (Photo Credit: Bonnie A. Robinson (DPG) ) VIEW ORIGINAL
5 / 5 Show Caption + Hide Caption – Former Command Sgt. Maj. Joe Bonds departed U.S. Army Dugway Proving Ground shortly after a Change of Responsibility ceremony August 2. 2018 for his new assignment at Fort Campbell, Kentucky. He will serve there as the 101st Airborne Division (Air Assault) CBRNE section sergeant major. Bonds will receive a bit of a homecoming at the 101st having served before at the storied division. Photo by Bonnie A. Robinson, Dugway Public Affairs (Photo Credit: Bonnie A. Robinson (DPG) ) VIEW ORIGINAL
DUGWAY PROVING GROUND, Utah -- It is always a solemn and sentimental moment as Army leaders symbolize the change of responsibility by passing the unit colors as a symbol of the acceptance of new roles.
New Command Sgt. Maj. Kyle R. Brinkman assumed responsibility as the Installation command sergeant major during a Change of Responsibility ceremony August 2 at U.S. Army Dugway Proving Ground, Utah.
Outgoing Command Sgt. Maj. Joe Bonds has accepted a new assignment at Fort Campbell as sergeant major for the CBRNE Section of the 101st Airborne Division. The division is an elite specialized light infantry unit trained for air assault operations.
"I have not been looking forward to this ceremony because we lose Joe Bonds as our command sergeant major," confessed Col. Brant Hoskins, Dugway's commander. "But I know his family is looking forward to having him close by and the Soldiers will definitely benefit from his presence."
Hoskins also praised Bonds for his ability as a team builder and an extremely effective communicator noting his exceptional work in the additional roles as a leader for the Garrison mission and in support of Soldiers and their Families, posted to the Dugway Health Clinic.
"He could have probably gotten by with focusing on the mission side but that is just not Command Sgt. Maj. Bonds. He has used his skills to build partnerships with leaders at the international, national and local levels and advocating for our mission, quality of life and everyone on the installation," Hoskins said.
He also noted Bonds' soft spot for Dugway's children.
"Whether it was the reading program, lunch with the kids or attending their dance and sporting events, he was there, and he was loud." Hoskins said. of his commitment to the community.
Reflecting on his time at Dugway, Bonds spoke of the honor of serving during the test center's 75th Birthday year.
"I am so proud to have served here and add my small contribution to the rich history that has spanned 76 years of supporting the warfighter," he said. "But as I depart, let the record reflect, I have done nothing alone. But I have done many things with many people. Thank you allowing me to serve with you."
Introducing the new Command Sgt. Maj. Hoskins to the community, Hoskins explained the exceptional skill set Brinkman brings to the chemical and biological test capabilities of the test center.
"Command Sgt. Maj. Brinkman comes to us from the Republic of Korea where he has been involved in preparing our forces against the very real danger of weapons of mass destruction on the peninsula," he said.
He assured Brinkman of his continued support.
"Welcome to the team, you have my full faith and confidence. In the months ahead we will continue to train the national mission force in our craft of chemical and biological defense testing."
Brinkman thanked everyone for their tremendous support and pledged to continue the legacy of the test center and a few final words to Bonds.
"I could not have imagined a better brother to follow here," Brinkman said during the ceremony. "You carved out an amazing legacy and inheritance with the community. It's an honor to be here and I look forward to serving at Dugway."
Dugway Proving Ground Command Webpage
Dugway Proving Ground Facebook page
Dugwy Proving Ground YouTube channel
January 21, 2020Empowering acquisition
January 17, 2020IG to Soldiers: 'Every one of us has a responsibility' to honor MLK legacy
January 17, 2020Mason takes reins of Army aviation acquisition
January 17, 2020Garrison welcomes new senior enlisted leader
January 17, 2020USACE fish biologists look forward to 2020 sampling season
January 16, 2020Bidding farewell to Command Sgt. Maj. Garner
January 16, 2020430th Quartermaster Company installs showers and laundry units during emergency in Puerto Rico
January 13, 2020Army Reserve Soldier reflects on accomplishments of first combat tour
January 13, 2020OSHA Written Plans: Are you in compliance?
January 8, 2020Army Reserve Air Traffic Services Company redeploys from USCENTCOM mission
December 19, 2019SOM Chamber Testing at Dugway Proving Ground

redpajama_set_name: RedPajamaCommonCrawl


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Maxar Technologies Earnings Roar, Stock Falls — Wait, What?
Space company Maxar Technologies (NYSE: MAXR) reported its Q3 2020 earnings late last week, and -- on the surface, at least -- they looked very good indeed.
Heading into earnings day, analysts had forecast that Maxar would earn $1.06 per share for the quarter. In fact, Maxar did better than that, reporting profits of $1.34 per share, outperforming expectations by 26%. Maxar's profits looked even better when compared to how it performed a year ago. Instead of losing money like it did last year, this time around Maxar reported a profit.
Now, you might expect that a huge reversal in fortune like this, and one that crushed consensus earnings expectations to boot, would have netted Maxar Technologies stock price a big gain. That didn't happen, though. To the contrary, Maxar shares collapsed, falling 25% on Friday, and then basically sat out Monday's big stock market rally, rising just 0.5%.
Maybe Maxar's earnings weren't as good as they seemed? Image source: Getty Images.
Why that is
The first reason Maxar stock performed so poorly last week can be summed up in one word: "revenue." Analysts had expected Maxar to reap revenue of $560.3 million in Q3 2020, but the company fell far short of that goal, reporting sales of just $436 million.
The second reason, curiously, can also be summed up in one word: "earnings."
Yes, I know. I just said the earnings Maxar reported looked better than last year's, but here's the thing: All of the $85 million that Maxar reported as its net profit for Q3 came from a single "gain on remeasurement of the previously held equity interest in Vricon," according to the company. Maxar didn't make more money building satellites or selling digital imagery. It just recalculated the value of a subsidiary that it completed acquiring in July 2020.
Despite this, Maxar CFO Biggs Porter argued that "performance in the quarter was solid, with both year-over-year revenue and profit growth on a consolidated basis and positive free cash flow."
And yet, "solid" isn't always the same thing as "good." In fact, Maxar's revenue only grew about 6% year-over-year, with Space Infrastructure sales rising 12%, but Earth Intelligence sales falling 3%. And because Earth Intelligence is by far the more profitable business for Maxar, that tiny decline in Earth Intelligence sales prevented the company from growing operating income at all. (In fact, operating profits declined.)
On a related note, it's worth pointing out that one of the few bright spots in Maxar's earnings report was a note that order backlog had grown to $2.2 billion. Most of the increase, however, came in the form of a $532 million increase in Space Infrastructure orders.
On the one hand, this boost to backlog creates the potential for greater sales growth going forward than the mere 6% growth seen in Q3. On the other hand, though, because the revenue will flow through Maxar's less profitable Space Infrastructure business, we could end up seeing this additional revenue create little in the way of additional profits for the company.
Free cash flow is still negative
One final note: According to Porter, free cash flow was positive in the third quarter of the year. That may be so -- but year to date, things are still looking grim. For the first nine months of this year, Maxar has now generated $132 million in operating cash flow -- but spent $224 million on capital investment. As a result, the company's free cash flow for the year to date is negative $92 million, which is worse than the $57 million in negative free cash flow reported in the equivalent period from a year ago.
Long story short, despite apparently impressive net profits of $343 million year to date, Maxar remains a cash-burning business. Despite its apparently cheap price to trailing earnings ratio of just 3.4, Maxar stock is not as profitable as meets the eye -- and investors are probably right to avoid it.
10 stocks we like better than Maxar Technologies Ltd.
David and Tom just revealed what they believe are the ten best stocks for investors to buy right now... and Maxar Technologies Ltd. wasn't one of them! That's right -- they think these 10 stocks are even better buys.


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The ancient tongues of many BC First Nations support scientific conclusions that state wild salmon returns vary on cycles and have nothing to do with aquaculture or salmon farms.
By Fabian Dawson
SeaWestNews
Some First Nations leaders, influenced by activists, are claiming that the waters in their traditional territories were always full of salmon before the advent of ocean-based net pens in British Columbia.
That is simply not true.
Firstly because there have been record runs of salmon in traditional territories since fish farming began some 35 years ago in the Western seas of Canada.
More importantly, the ancient tongues of many First Nations actually support the voluminous scientific conclusions that salmon stocks vary not only year to year, but also on decades-long time cycles.
Among the first to point this out two years ago is Mamalilikulla Qwe'Qwa' Sot'Em Nation Chief, Harold Sewid.
"If we have a name for it, it means this has been happening for thousands of years and it has nothing to do with salmon farming," Sewid said.
The traditional language of his Broughton-based First Nation refer to the natural phenomenon of low salmon returns in some years as Wayum'gallis or 'the salmon not returning'.
It is a linguistic record, handed down over thousands of years, that is in direct contradiction to the claims that salmon was always abundant before fish farms were set up in BC.
Here are some other ancient First Nation references to fluctuating salmon returns, according to Firstvoices.com, a suite of web-based tools and services designed to support Indigenous people engaged in language archiving, language teaching and culture revitalization;
The Tla'amin Nation (formerly known as Sliammon), whose traditional territory spans along the northern part of B.C.'s Sunshine Coast, extending down both sides of the Strait of Georgia, say hʊkt mot jɛnxʷ k̓ʷʊk̓t (there is no fish at all going up the river to spawn.);
The Nisga'a, original occupants of the Nass River Valley of Northwestern British Columbia, say Wihl ligii dim ḵ'oots'a'ahl hoon k'uuhl tgun (Looks like there will be no fish this year) or ḵ'oots'a'ahl hoon, when salmon is scarce. Their phrase for "a smaller first runs of spring salmon in the upper river" is hangwooyim;
The Vuntut Gwitchin is a First Nation in the northern Yukon in Canada. They say Jùk shin łuk choo kwaa (There were no king salmon this summer);
The Nuučaan̓uɫ (Nuu-chah-nulth) language is spoken by 13 First Nations on the west coast of Vancouver Island. They say huʔinʔaqƛḥ sac̓up when asking "Will the salmon return to the river?"
While the polarising debate over salmon farming shows no sign of letting up, especially because the anti-fish farm activists won't believe any science that is not aligned with their agendas, one thing is clear – the application of indigenous knowledge is vital for the conservation of wild salmon.
The anti-fish farm activists and the First Nations leaders who support them, need to reach into Indigenous knowledge of the past, to better understand what is happening to our salmon in the present, and chart a sustainable future that is both wild and farmed.
(BC Government archive image shows the Le-La-La Dancers a traditional Kwakwaka'wakw dance company, presenting First Nations culture of Northern Vancouver Island)
Tags: aquaculture British Columbia First Nation salmon farms salmon runs
'Transition' does not mean removing ocean salmon farms
The NDP's road to rebuild BC will have to go around activist-MLAs
Broad range of interest for new B.C. salmon fund


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10 Powerful Nelson Mandela Quotes On Poverty, Inequality, and the Environment
by Martina Igini Africa Jul 18th 20222 mins
Mandela Day is celebrated every year on June 18, his birthday. A visionary leader, Mandela became South Africa's first black president after spending 27 years in prison on charges of crimes against the country's former apartheid government. Following his release in 1990, the Nobel Prize winner continued his fight against poverty, inequality, and racism. As the world remembers the incredible work he has done to tear down the oppressive government and install democracy in South Africa as well as his endless commitment to the cause of sustainable development, we prepared a list of the most memorable quotes by Nelson Mandela on the environment.
10 Best Nelson Mandela Quotes
1. "That our government has made significant progress in bringing potable water nearer to so many more people than was previously the case, I rate amongst the most important achievements of democracy in our country" – "No Water, No Future" speech on August 28, 2002.
2. "The very right to be human is denied every day to hundreds of millions of people as a result of poverty, the unavailability of basic necessities such as food, jobs, water and shelter, education, health care and a healthy environment."
3. "I dream of our vast deserts, of our forests, of all our great wildernesses. We must never forget that it is our duty to protect this environment."
4. "As long as poverty, injustice, and gross inequality persist in our world, none of us can truly rest." – in London's Trafalgar Square in 2005.
5. "It is in your hands to create a better world for all who live in it."
6. "The poverty of the people and the devastation of the natural environment painfully strike me."
7. "Like slavery and apartheid, poverty is not natural. It is man-made and it can be overcome and eradicated by the actions of human beings." – London's Trafalgar Square in 2005.
8. "It can be said that there are four basic and primary things that the mass of people in a society wish for: to live in a safe environment, to be able to work and provide for themselves, to have access to good public health and to have sound educational opportunities for their children."
9. "Education is the most powerful weapon which you can use to change the world." – Address by Mandela at launch of Mindset Network in July 2003.
10."Massive poverty and obscene inequality are such terrible scourges of our times — times in which the world boasts breathtaking advances in science, technology, industry, and wealth accumulation — that they have to rank alongside slavery and apartheid as social evils." – Speaking to a crowd at London's Trafalgar Square in 2005 for the "Make Poverty History" campaign.
If you like these Greta Thunberg quotes, you might also like: 15 Inspiring David Attenborough Quotes on Nature, Wildlife and Climate Change
Featured Image: Flickr (CC BY 2.0)
Martina Igini
Martina is the Managing Editor of Earth.Org. She holds two BA degrees, in Translation/Interpreting Studies and Journalism, and a MA in International Development from the University of Vienna. After working at the United Nations Global Communication Department in Vienna, she joined a newspaper in Italy as a reporter before moving to Hong Kong in 2020. Her interests include sustainability and the role of public policy in environmental protection with a focus on developing countries.
martina.igini[at]earth.org
Climate Change Guides
The 21 Best Environmental Films of 2022
Jul 27th 202210 mins
15 Best Environmental Podcasts of 2022
https%3A%2F%2Fearth.org%2Fnelson-mandela-day-quotes%2F


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Raynham town meeting seeks extension of dog racing
Sharon Holliday
After quickly approving a $30 million budget on Monday, voters approved articles that could represent a first step toward possible K-8 deregionalization and a two-year extension of dog racing at Raynham-Taunton Greyhound Park.
More than 200 residents at Raynham's annual town meeting on Monday voted to accept a $30 million budget — an 11 percent increase over this fiscal year — that includes dwindling revenue from Raynham-Taunton Park, which is scheduled to close in January.
The budget for fiscal 2010 included only $292,193 as the town's local share of racing taxes, compared to $413,000 received by the town this fiscal year.
Five kennel owners have already left the park since a ballot question banning dog racing in the state was approved by voters in November. Live racing at the park has also been reduced to four days a week since November.
But Raynham voters on Monday, with little discussion and a unanimous vote, approved a citizens petition requesting an extension of dog racing at the park — the second-largest employer in the town, after Johnson & Johnson — from Jan. 1, 2010 to Jan. 1, 2012.
Voters on Monday also authorized members of the Board of Selectmen to meet with Bridgewater selectmen to discuss the possibility of a grade K-8 superintendency union in the towns of Bridgewater and Raynham.
Selectmen Chairman Donald McKinnon said the article authorized only a discussion and any decision on a superintendency union would be made by the voters at town meetings in Bridgewater and Raynham this fall.
A superintendency union would involve a K-8 break from the regional school district on the part of Bridgewater and Raynham and the formation of two, separate school districts under one central office and one superintendent.
The articleincluded a deadline of Sept. 15 for Bridgewater to reach a decision. If no decision is reached by Bridgewater by Sept. 15, the article states that Raynham would then consider a total, preK-8 break.
Also approved was a request to reduce the number of registered voters required for a quorum at town meetings from 75 to 50.


On Friday, the team traveled to California University of Pennsylvania, a match in which the Scots would fall 3-0. In the first set Cal would get the win 25-16. The Fighting Scots would come back in the second set, but fall with a score 25-21. Cal would take the sweep in the third set with a 25-20 score. The Vulcans record moved to 11-6 overall.
Underclassmen Cierah Jackson (12 kills), Alicia Eldredge (29 assists and 8 digs) and Sally Sterkel (4 kills and 1 ace) all put points on the board this match. Senior Megan Screptock (6 kills, one ace, 3 assists and 12 digs) also put marks on the board in the match. The Fighting Scots moved on to their match on Saturday with their record sitting at 8-7 overall and 1-4 in the PSAC.
Edinboro continued their travels to Seton Hill to face the Griffins at 1 p.m. on Saturday afternoon. Going into the match, the Griffins sat at 9-6 overall and 3-2 in the PSAC.
Seton Hill would win the first set of the match 25-19. Edinboro would come back but fall short in the second set 25-21 and again in the third where the score ended 27-25.
Eldredge recorded a total of 24 assists, 6 kills and 8 digs in this match, while Jackson would have 10 kills and 4 digs. Hannah Cantrill had 7 kills and 2 blocks in this loss.
After this match, Edinboro would move on to 8-8 overall and 1-5 in conference play. On Tuesday, the team will again travel to Slippery Rock, but the team will return home for matches this weekend. On Friday they will face Clarion University at 6 p.m. and on Saturday they will face Indiana University of Pennsylvania at 1 p.m.


As with all my other camping soups, this one requires only one pot over one burner, though you may want to roast your peppers over that burner first. Otherwise, they may be roasted at home in the oven (500 degree broiler, spray with olive oil, turn them every couple of minutes until charred), or over a flame (using tongs, roast them until bubbly and charred, turning regularly). When you're finished, it's best to peel and seed them right away, as soon as you can safely touch them -- waiting too long makes peeling more difficult. HOWEVER, if you're going to make this entire soup at your campsite, skip the peeling and just pull out the seeds.
It's best to make this at home, so you can use an immersion blender to create the smooth texture we love. If you'd rather do this at your campsite, the texture will be chunky, but the taste is still quite wonderful. Take along a few extra roasted peppers, as they're excellent dipped in the soup and eaten on their own.
This is a vegan dish to boot -- but feel free to substitute chicken or turkey stock for the vegetable stock, if keeping the recipe vegan is not important to you.
Of all the soups I've posted so far this month, this one is definitely kid #3's favorite. In fact, to be honest, no one else in the family even got to eat this soup today -- he scarfed it all. ALL. THE WHOLE THING.
This is a terrific take on your standard (but by no means boring) Indian dal recipe, in soup form. I overcook the lentils on purpose, giving the soup a very luscious, smooth texture. If you prefer your lentil soup a bit chunkier, just hold on another week 'til I post my French Lentil Soup, which is hearty, chewy, and full of texture.
And the greens in the photo? Those are baby cilantro sprigs from our greenhouse, NOT fennel! Aren't they adorable?! We've never been able to grow cilantro well here, but this year it's absolutely going bonkers in the greenhouse and the taste is amazing (ok, disclosure time: although I love to say, "We have a greenhouse," [hear it in a highbrow British accent] in reality it's a tiny little metal and plastic thing that one person can barely stand in...but it does the job, and this fall I'm loving all the herbs coming out of it. And the best part? So far I haven't killed anything, so that's a step in the right direction).
If making this for a camping trip, the entire recipe may be done on-site; however, I find it easier to cook the lentils and vegetables at home to save on stove fuel. Seal cooked and pureed vegetables, freeze, and finish the rest of the soup at your campsite.
"Ficelle" is french for "string," and it's possibly the very best method for cooking some ridiculously impressive gourmet food in the most rustic way possible.
My kids love this method for four reasons: 1, it's a heck of a lot of fun to fling meat around and not get into trouble (you'll understand what I'm talking about in a minute); 2, it looks really cool; 3, there is basically no clean up involved; and 4, it's seriously some of the very best camping food we've ever had -- and that's saying a lot, considering how we eat when we're in the backcountry.


The Web3D Consortium is pleased to announce a competition for the best browser-based 3D rendering of a city. We seek software solutions to stream a large 3D city model including textured buildings, terrain, sensor, and model data. As a minimum requirement, the implementation has to use some kind of a Web Service and be browser-based. On client side it has to be possible to select individual features (a building) and display further information about the selected feature. The Web Service and the streaming protocol have to be documented and open. For the streaming, an existing data format such as X3DOM, XML3D, KML might be used, but other solutions are possible as well. However, the streaming should not be a "black box" but should enable interoperability.
Maps and other features can be added on top of the terrain. These can be integrated as OGC Services online.
The winner will receive financial support to attend the 20th International Conference on 3D Web Technology in Heraklian, Crete, Greece.
Submission deadline is 15 April 2015.
Please sign up for the GeoWeb3DContest mailling list to keep up to date with information on the competition.
3D Portrayal Interoperability Experiment (3DPIE), which was designed to test and demonstrate different approaches for service-based 3D visualization using the candidate Open Geospatial Consortiumm (OGC) standards for 3D portrayal: the OGC Web 3D Service (W3DS) and Web View Service (WVS) Interface standards.


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RESEARCH ARTICLE| 23 October 2019
Scaling of swimming performance in baleen whales
In collection:
Comparative biomechanics of movement
William T. Gough
William T. Gough *
Marine Station
*Author for correspondence (wgough@stanford.edu)
Paolo S. Segre
K. C. Bierlich,
K. C. Bierlich
Nicholas School of the Environment, Duke University
Beaufort, NC 28516
David E. Cade
Jean Potvin
St Louis, MO 633103
Frank E. Fish
Julian Dale,
Julian Dale
Jacopo di Clemente
Accademia del Leviatano
le dell'Astronomia, 00144 Rome
Ari S. Friedlaender
0000-0002-2822-233X
Institute of Marine Sciences, University of California Santa Cruz
David W. Johnston,
David W. Johnston
Shirel R. Kahane-Rapport
John H. Long
Departments of Biology and Cognitive Science
Machiel Oudejans,
Machiel Oudejans
Kelp Marine Research
1624 CJ Hoorn
Gwenith Penry
Institute for Coastal and Marine Research, Nelson Mandela University
Port Elizabeth, 6019
Matthew S. Savoca
Malene Simon,
Malene Simon
Greenland Climate Research Centre, Greenland Institute of Natural Resources
Kivioq 2, 3900 Nuuk
Simone K. A. Videsen
Zoophysiology, Department of Bioscience, Faculty of Science and Technology
Aarhus 8000
Fleur Visser
Institute for Biodiversity and Ecosystem Dynamics – Freshwater and Marine Ecology, University of Amsterdam
1090 GE Amsterdam
Royal Netherlands Institute for Sea Research
1790 AB Den Burg, Texel
David N. Wiley,
David N. Wiley
US National Oceanic and Atmospheric Administration, Office of National Marine Sanctuaries, Stellwagen Bank National Marine Sanctuary
Jeremy A. Goldbogen
The authors declare no competing or financial interests.
Received: 29 Mar 2019
Accepted: 24 Sep 2019
National Science Foundation (IOS-1257762)
Office of Naval Research (N00014-08-1-0984)
PADI Foundation (NO AWARD)
Stanford University (NO AWARD)
http://www.biologists.com/user-licence-1-1/
J Exp Biol (2019) 222 (20): jeb204172.
A related article has been published: Whales no faster than other ocean inhabitants despite size
PDF LinkPDF File PDFPDF+SI
Article Versions Icon Versions
Version of Record 23 October 2019
Accepted Manuscript 01 January 2019
William T. Gough, Paolo S. Segre, K. C. Bierlich, David E. Cade, Jean Potvin, Frank E. Fish, Julian Dale, Jacopo di Clemente, Ari S. Friedlaender, David W. Johnston, Shirel R. Kahane-Rapport, John Kennedy, John H. Long, Machiel Oudejans, Gwenith Penry, Matthew S. Savoca, Malene Simon, Simone K. A. Videsen, Fleur Visser, David N. Wiley, Jeremy A. Goldbogen; Scaling of swimming performance in baleen whales. J Exp Biol 15 October 2019; 222 (20): jeb204172. doi: https://doi.org/10.1242/jeb.204172
The scale dependence of locomotor factors has long been studied in comparative biomechanics, but remains poorly understood for animals at the upper extremes of body size. Rorqual baleen whales include the largest animals, but we lack basic kinematic data about their movements and behavior below the ocean surface. Here, we combined morphometrics from aerial drone photogrammetry, whale-borne inertial sensing tag data and hydrodynamic modeling to study the locomotion of five rorqual species. We quantified changes in tail oscillatory frequency and cruising speed for individual whales spanning a threefold variation in body length, corresponding to an order of magnitude variation in estimated body mass. Our results showed that oscillatory frequency decreases with body length (∝length−0.53) while cruising speed remains roughly invariant (∝length0.08) at 2 m s−1. We compared these measured results for oscillatory frequency against simplified models of an oscillating cantilever beam (∝length−1) and an optimized oscillating Strouhal vortex generator (∝length−1). The difference between our length-scaling exponent and the simplified models suggests that animals are often swimming non-optimally in order to feed or perform other routine behaviors. Cruising speed aligned more closely with an estimate of the optimal speed required to minimize the energetic cost of swimming (∝length0.07). Our results are among the first to elucidate the relationships between both oscillatory frequency and cruising speed and body size for free-swimming animals at the largest scale.
Morphometrics, Hydrodynamic modeling, Unoccupied aerial systems, Locomotion, Frequency, Speed
For most animals, locomotion and maneuverability factor into critical life functions such as prey capture, predator avoidance and migratory success, and therefore can influence the margin between life and death (Dakin et al., 2018; Fish et al., 2003; Goldbogen et al., 2012; Hein et al., 2012; Williams et al., 2014). These locomotor functions manifest as complex maneuvers or behaviors that can be deconstructed into a series of simple elemental changes in body conformation, rotation or displacement (Full et al., 2002; Pennycuick, 1975; Segre et al., 2016, 2018; Webb, 1997). Many of these elements have evolved similarly among different taxa with distinct body plans as a result of common physical constraints or functional requirements (Donley et al., 2004). As a result, animals have evolved a wide range of convergent locomotor and maneuvering strategies across varying body sizes (Gleiss et al., 2011, 2017). Nowhere are the effects of scale more interesting than in the ocean, where release from gravitational constraints allows species with similar body plans and locomotory styles to range across several orders of magnitude in body mass, from the smallest fishes up to the blue whale (Balaenoptera musculus) (Motani, 2002; Pennycuick, 1992). Both toothed whales (Odontoceti) and baleen whales (Mysticeti) evolved gigantism after the evolutionary appearance of specialized foraging strategies (i.e. echolocation and filter feeding) that required the coupling of locomotion and feeding (Goldbogen and Madsen, 2018; Higham et al., 2016; Pyenson, 2017; Slater et al., 2017). However, what remains poorly understood is the influence of gigantism on the locomotor performance of whales and the ways in which it has enabled their foraging strategies and overall success as predators in a wide range of ocean ecosystems.
The body plans for cetaceans (whales and dolphins) typically consist of a fusiform body shape with appendages modified into streamlined flippers, fins and tails that oscillate or undulate to generate thrust, adjust attitude and help overcome drag (Fish and Lauder, 2006; Webb and De Buffrénil, 1990). At the largest scales, many large aquatic vertebrates (sharks, tuna, cetaceans) have evolved high aspect ratio lunate tails that are specialized for efficient locomotion and maneuvering (Bose and Lien, 1989; Dewar and Graham, 1994; Donley et al., 2004; Fish, 1998; Fish et al., 2014; Graham et al., 1990; Webb and De Buffrénil, 1990; Woodward et al., 2006). Paired, anteriorly placed flippers or fins are generally used as control surfaces to stabilize body trim or generate asymmetrical forces during turns, rolls or pitching motions (Fish and Lauder, 2017; Fish and Shannahan, 2000; Fish et al., 2006; Segre et al., 2016, 2018). Cetaceans (whales, dolphins and porpoises) include the largest animals that have ever lived (McClain et al., 2015), making them a unique and interesting organismal model to study the extremes of swimming performance.
Previous research on cetaceans has focused primarily on small, highly maneuverable toothed whales that can be studied in captivity (Fish, 1993, 1998; Fish and Rohr, 1999; Fish et al., 2014, 2018; Rohr and Fish, 2004). In contrast, the logistical difficulties of working with larger baleen whales has made it more difficult to study the biomechanics and locomotion of these animals in the wild. As a result, much of the progress in this area has come about recently with the advent of archival, motion-sensing tags, which can be attached to wild animals for hours to days (Gleiss et al., 2011; Goldbogen et al., 2017; Martín López et al., 2016; Sato et al., 2007).
In considering the effects of scale on locomotion, previous studies have predicted that the oscillatory frequency of propulsive structures decreases in proportion to increasing body length (Hill, 1950; Pennycuick, 1992; Sato et al., 2007). Using simple muscle models, Hill (1950) predicted that the contraction speed of geometrically similar but differently sized muscles should scale inversely with their physical dimensions in order to balance the production of useful work and wasteful kinetic energy at a given movement speed. Accordingly, steady-state cruising by most vertebrates has been shown to occur in the 1–2 m s−1 range regardless of body size (Fish and Rohr, 1999; Sato et al., 2007; Watanabe et al., 2011). In contrast, dependence on body size has been shown to occur in other swimming performance measures such as maximum speed (Meyer-Vernet and Rospars, 2016; Shadwick and Gemballa, 2005; Wardle, 1975). Thus, the choice of movement speed for routine swimming may not reflect maximally achievable performance but instead relate to economy of transport (or the minimum cost of transport, COTmin) (Williams and Noren, 2009; Williams et al., 1993). However, recent research on free-ranging largemouth bass showed that swimmers exhibited slower speeds than their predicted COTmin, perhaps in order to balance competing functional demands such as prey capture (Han et al., 2017), suggesting that swimming performance is highly context dependent.
The hypothesis that stroke frequency is inversely correlated with body length is also supported by analyses of the dimensionless Strouhal number St, a ratio of the oscillatory frequency f and tailbeat amplitude A over swimming speed U [St=(f·A)/U]. High propulsive efficiency occurs at St≈0.25–0.35 (Anderson et al., 1998; Rohr and Fish, 2004; Taylor et al., 2003; Triantafyllou et al., 1991). If swimming speed is constant and tailbeat amplitude scales isometrically with body length, then the oscillatory frequency must scale inversely with body length (∼L−1.0) in order to achieve optimal vortex generation (Fish, 1998; Pennycuick, 1992).
These size-dependent factors have played a major role in shaping the functional ecological theory of gigantic marine predators, but, until now, we have lacked the data for a comprehensive study on the kinematics of their oscillatory swimming. Specifically, it remains to be seen how swimming speed and oscillatory frequency scale with body length, particularly at the largest scale. To answer this question, we used tags with inertial sensors paired with unoccupied aerial system (UAS, or drones) imagery to quantify how key locomotor kinematics scale across a body length range of an order of magnitude, from morphologically similar Antarctic minke whales (∼9 m; Balaenoptera bonaerensis, Burmeister 1867) to blue whales (∼30 m) (Fig. 1). We used these data to test the hypothesis that stroke frequency decreases in inverse proportion with body length but instead found it scales to the approximate power of −0.5, while cruising speed remains near 2 m s−1 in order to minimize energy expenditure and thus is expected to be largely invariant with size (Sato et al., 2007; Watanabe et al., 2011, 2015).
Representative overhead unoccupied aerial system (UAS) photographs and scale bars for each study species. (A) Minke, (B) humpback, (C) Bryde's, (D) fin and (E) blue whale.
All procedures in the USA were conducted under approval of the National Marine Fisheries Service (Permits 781-1824, 16163, 14809, 16111, 19116, 15271, 20430), Canada DFO SARA/MML 2010-01/SARA-106B, National Marine Sanctuaries (MULTI-2017-007) and institutional IACUC committees. South Africa operations were conducted under approval from the Department of Environmental Affairs (Permit RES 2018/63) and Nelson Mandela University (Animal Ethics Approval A18-SCI-ICMR_001).
Tag data collection
We used multi-sensor suction-cup tags (Customized Animal Tracking Solutions, CATS; www.cats.is) to collect high sample rate kinematic and behavioral data from humpback whales (Megaptera novaeangliae, Borowski 1781), blue whales [Balaenoptera musculus (Linnaeus 1758)], fin whales [Balaenoptera physalus (Linnaeus 1758)], Bryde's whales (Balaenoptera brydei, Olsen 1913) and Antarctic minke whales (Balaenoptera bonaerensis, Burmeister 1867). Each tag included tri-axial accelerometers, magnetometers and gyroscopes sampling at 40–400 Hz, and pressure sensors sampling at 10 Hz. Pressure and high sample rate accelerometer data recorded by the tag allowed estimation of swimming speed using the root-mean-square (RMS) amplitude of tag vibrations (Cade et al., 2018). Tag sensors were rotated into whale-body frame orientation, and animal pitch, roll and heading were derived (Cade et al., 2016; Johnson and Tyack, 2003). Cameras collected video at 30 frames s−1 and were integrated into the tags in different orientations: (1) forward along the mid-line axis of the tag, (2) offset at ∼45 deg from the mid-line axis, and (3) forward and backward along the mid-line axis. Additional information about tag design and deployment can be found in Cade et al. (2016). Tag deployments occurred on whales from July 2014 to October 2018 in a wide range of geographic locations including the Antarctic Peninsula for B. bonaerensis, the Azores, Portugal, for B. physalus and B. musculus, Eastern Greenland for B. physalus and M. novaeangliae, Monterey Bay, USA, for B. physalus, B. musculus and M. novaeangliae, Plettenburg Bay, South Africa, for B. brydei, Santa Barbara Channel, USA, for B. musculus and M. novaeangliae, and Stellwagen Bank, USA, for M. novaeangliae (Table S1).
Whale photogrammetry and morphometrics
We collected high-resolution aerial images via UAS between 2017 and 2018 (Fig. 1). Nadir images of whales were collected during a subset of tag deployments for B. bonaerensis (Antarctic Peninsula), M. novaeangliae (Antarctic Peninsula, Eastern Greenland, Monterey Bay and Santa Barbara Channel), B. brydei (Plettenburg Bay), B. physalus (Azores, Eastern Greenland, Monterey Bay) and B. musculus (Monterey Bay, Santa Barbara Channel, Azores). Specifically, we used a DJI Phantom 3 Professional quadcopter, a DJI Phantom 4 Pro quadcopter, and two types of hexacopters, the FreeFly Alta 6 and a custom Mikrokopter-based LemHex-44. The Phantom 3 Professional quadcopter was fitted with a Sony EXMOR 1/2.3 inch camera, 4000×3000 pixel resolution and a 94 deg field of view. The Phantom 4 Pro quadcopter was fitted with a 1 inch CMOS camera sensor, 5472×3078 pixel resolution and an 84 deg field of view. Both hexacopters were fitted with a Lightware SF11/C laser altimeter and a Sony Alpha A5100 camera with an APS-C (23.5×15.6 mm) sensor, 6000×4000 pixel resolution and either a Sony SEL50 50 mm or SEL35 35 mm focal length low distortion lens. The laser altimeter and cameras were co-located on a 2-axis gimbal with pitch angle controlled via remote control to aid in positioning and ensure image collection at the nadir. Measurement error for both quadcopters was calculated as <1.10% by Putch (2017). Measurement error for both hexacopters was calculated by measuring an object of known size placed in the water (i.e. a paddle) at various altitudes. The Alta 6 had a measurement error of <0.30%, while the LemHex-44 measurement error was <1.50%. We used similar methods for hand launch and recovery from small boats to those described in Durban et al. (2015), with the addition of a first-person view (FPV) screen attached to each flight controller, giving the pilot a live feed from the photogrammetry camera. The LemHex-44 required a single operator where the pilot manually controlled the gimbal and camera's shutter, whereas the Alta 6 required two operators, a pilot and camera operator. We collected images in bursts (LemHex-44 and Alta 6) or on a 2 s timer (Phantom 3, Phantom 4) with the whale in full frame lengthwise with a high shutter rate of 6 frames s−1 as the animal surfaced or was just below the surface (Fig. 1).
We manually selected UAS images for measuring the total length and maximum diameter of individual whales if the lower jaw, fluke notch and sides were clearly visible and if the animal appeared straight with minimum curvature at the surface or just below. We measured total length from the tip of the lower jaw to the fluke notch and manually selected maximum diameter as the widest width of the whale posterior of the axilla. We performed all measurements using ImageJ 1.5i (Schindelin et al., 2012). We used the segmented or straight-line tool to draw a line from the tip of the lower jaw to the fluke notch, or from each side at the widest width, to measure the distance in number of pixels. We calculated total length and maximum diameter using an approach similar to Fearnbach et al. (2012), where the number of pixels was multiplied by the ground sampling distance (GSD):
where L is the total length of the animal (m), npixels is the number of pixels, a is altitude (m), lfocal is focal length (mm), wS is the width of the sensor (mm) and wp is the width of the image resolution (pixels). The width was used for the sensor size and image resolution because the whales were captured full frame widthwise.
Tailbeat measurement
We chose the transverse axis of the gyroscope signal to quantify rotational body movements along the pitch axis of the animal. This improved upon previous studies (Martin López et al., 2015, 2016) which relied on the longitudinal axis of the accelerometer because it allowed us to disregard the orientation of the body and it was highly effective during high pitch angle segments that would otherwise be prone to gimbal lock. For 5 out of 143 deployments, we relied on the longitudinal axis accelerometer method with a high-pass filter because of malfunctioning gyroscope signals. We compared gyroscope and accelerometer signals from the same deployment to quantify uncertainties and ensure that slight differences would not affect our results. For each deployment, we calculated the average value for the oscillatory signal, then used a customized Matlab script to identify full tailbeats, here defined as one consecutive upstroke and downstroke, using a zero-crossing method. We set a consistent and conservative series of thresholds aimed at ignoring incomplete or asymmetrical tailbeats in the data sets. To be included in our analyses, a tailbeat had to have a period measured at less than 10 s, feature exactly two peaks (one upstroke and one downstroke), with a magnitude towards either the upstroke or downstroke being less than 15 times the magnitude in the other direction. We visually inspected the resultant tailbeats to confirm that our thresholds were performing correctly. We removed deployments from subsequent analyses if they identified fewer than 200 full tailbeats (Table 1). We calculated multiple time-synchronized kinematic and behavioral parameters for the duration of each full tailbeat (Fig. 2) including the oscillatory frequency, f, as 1/period, the average swimming speed (Cade et al., 2018), the average depth and the modal behavioral state (described below).
Metadata related to each included species
Section of CATS tag data showing time-synced data streams. The video stills are faced caudally from a common reference point and illustrate a single tailbeat cycle. These stills are correlated in time with the vertical red bar intersecting each data stream. The green horizontal lines at the zero line of the first data stream (Gyroscope) correspond to symmetrical tailbeats that fit our detection thresholds and were included in our analyses. The three lines in the third data stream (Orientation) correspond to pitch (right y-axis; orange), roll (left y-axis; pink) and heading (left y-axis; blue) changes.
Measurement of speed
Measurement of the swim speed relied on turbulent flow to vibrate the tag and, as a result, could not resolve speeds below ∼1 m s−1 (Cade et al., 2018). For each deployment, we regressed the oscillatory frequency of each individual tailbeat period against the average speed during that stroke. To ensure that our regressions were accurately capturing the relationship between oscillatory frequency and swimming speed, we only included tailbeat periods with an average speed higher than one standard deviation above the lowest recorded speed. So as not to bias results with artificially high minimum speeds as a result of the method's low-speed threshold, we used median swimming speed values for comparative analyses among species.
Modeling optimal speed
We combined measurements of body total length and maximum diameter with a simple model of non-feeding transport to predict the swimming speed associated with the minimum energetic cost of transport (Weihs, 1973). The modeling starts with estimating the (metabolic) energy expended (ε; J) to travel a given distance (d) as follows:
The second term corresponds to the energy expended by the locomotor musculature to compensate for the drag produced (as propulsive drag equals drag in this instance). Here, d is the distance traveled (set to 1 body length), U is the swimming speed (m s−1), ρ is the density of seawater (=1025 kg m−3), S is the wetted surface area (m2) of the animal calculated using coefficients from computational fluid dynamics modeling or literature sources for each included species (Bose and Lien, 1989; Kermack, 1948; Parry, 1949), Cd is the dimensionless drag coefficient of the body in a rigid configuration (∼0.002–0.005) (Kooyman, 1989), λ is the drag correction due to the heaving body and caudal tail (∼2–3) (Fish, 1993, 1998), ηm is the (dimensionless) metabolic efficiency and ηp is the (dimensionless) propulsive efficiency. Both of the efficiency terms are considered to be constant across body size ranges (Fish, 1996, 2001; Fish and Rohr, 1999). The first term in Eqn 3 accounts for the energy expenditure by the organ and tissue in the rest of the body, which here is estimated by the Kleiber (1975) basal metabolic rate (BMR) correlation, corrected by a factor of 1.6, that is based upon previous estimates of swimming or dive recovery metabolic rates (Costa and Williams, 1999; Lockyer, 1981; Scholander, 1940) and accounts for a higher level of metabolic activity during active swimming over long durations:
where m is body mass (kg). The model yields an optimal swimming speed (Uopt) for which a minimum of energy stored in the body is used to cover distance d (Weihs, 1973):
Tail-attached tag measurements
As for fish swimming in the wild (Han et al., 2017; Watanabe et al., 2019), stroke amplitude is difficult to measure in tagged cetaceans (van der Hoop et al., 2017). However, during a single humpback whale deployment in 2017, the tag slipped from its original placement on the back of the animal (between the dorsal fin and blowhole) and attached to the dorsal aspect of the tail near the right fluke-blade midline. As shown in Fig. 3, the tag was aimed anteriorly towards the body and remained so for 68 min. Such a fortuitous event allowed us to measure the tailbeat amplitude as a local, short-duration change in depth from the tag's pressure transducer. We included sequences with at least three tailbeats that had an overall depth change near zero (n=14). For each sequence, we calculated the midline – from the midpoint of the first tailbeat to the midpoint of the final tailbeat – and then averaged the distance from that midpoint to the top of each upstroke and to the bottom of each downstroke (Fig. 3 inset). For these same tailbeat sequences, we measured the oscillatory frequency and swimming speed, and estimated the Strouhal number for each sequence.
Tailbeat amplitude measurement. A video still from the deployment of interest. (A) Rostrum, (B) dorsal fin, (C) left flipper, (D) right flipper, (E) peduncle, (F) left fluke blade and (G) right fluke blade. The inset is a schematic diagram of our peak-to-peak amplitude measurement method.
Comparison with two simple oscillatory models
We used a derivation of the Strouhal number equation to estimate the oscillatory frequency of an optimally oscillating vortex generator:
with U set at 2 (m s−1) based upon the average speeds in our observed dataset, St set at 0.3 to simulate optimality and A set at one-fifth of body length (Bainbridge, 1958; Fish and Rohr, 1999; Rohr and Fish, 2004). We applied this model over a 10–20 m range of total body lengths to facilitate a direct comparison with our observed dataset.
An alternative to Strouhal number scaling is the idea that, to sustain a form of low-cost swimming, the oscillatory frequency is set to the resonant frequency of the caudal tail, here seen as a driven and damped oscillator (Ahlborn et al., 2009; Hamilton et al., 2004; Pabst, 1996, 2000). In this model, drag provides the damping, the tail musculature provides the driving force, and the sub-dermal sheath and blubber surrounding the tail stock acts as the spring-like oscillatory restoring force. Swimming near the 'optimal' oscillatory frequency means that the driving force will be set at the oscillatory frequency that generates the greatest tailbeat amplitude. In low-damping conditions, this oscillatory frequency will coincide with the so-called 'natural' frequency of the tail tissue and bone under the sole influence of the spring-like restoring force provided by the sub-dermal sheath (Kreyszig, 2016). With the sub-dermal sheath forces acting like a cantilever beam oscillating in seawater, the oscillatory frequency would thus be modeled as:
where E parameterizes the effective Young's modulus of cetacean tissue (approximated as 2 MPa from Long et al., 2002), I is the second moment of area (m4) and me is the effective mass (kg) of the beam (Denny, 1988); we doubled that effective mass to account for the added mass of the body accelerating in a fluid (Lighthill, 1970; Webb, 1975). Here, the model assumed that the cranial 40% of body length was stationary during oscillation, with the caudal 60% of body length oscillating (Los). We applied this model over the same 10–20 m range of total body lengths as our Strouhal model.
The datasets supporting this article are available from the corresponding author on request. We performed all statistical analyses using R (version 3.4.1, package nlme) with a significance level of 0.05. For each analysis, continuous variables (total length, oscillatory frequency, median cruising speed, coefficient of variation) were log10 transformed before inclusion as predictors or response variables to normalize our data and conform to the basic model of scaling as a power function. For the subset of deployments with morphometric measurements (n=55), we created a linear mixed-effects model with body length as the predictor, the mean oscillatory frequency of a deployment as the response and species as a random effect. The results of this model were used to determine the regression line and standard error of the mean for our comparison of body length and oscillatory frequency. Next, we created a linear mixed-effects model with body length as the predictor, the coefficient of variation for the oscillatory frequency of a deployment as the response and species as a random effect. Finally, we created a linear mixed-effects model with body length as the predictor, median swimming speed as the response and species as a random effect.
We used our complete dataset to create a linear mixed model comparing the oscillatory frequency between dive descent and dive ascent stages across all species. The model included species and individuals within species as random effects. We also performed identical analyses on a species-specific basis for B. bonaerensis, M. novaeangliae and B. musculus. These models included individuals within species as a random effect. Our final models included swimming phase as the predictor, coefficient of variation for the oscillatory frequency of a deployment as the response and species as a random effect.
Variation in oscillatory kinematics
Metadata and summary statistics for tagged whales (n=143 total, n=60 with UAS-measured morphometrics) are shown in Table 1. The number of tailbeats in a deployment ranged from 260 to 9458 with a mean (±s.d.) of 1927±1784. Swim speeds ranged up to 8.28 m s−1 (lower bound was unresolved) with a median of 1.89±0.038 (median±standard error) and oscillatory frequencies ranged from 0.10 to 1.00 Hz with a mean (±s.e.m.) of 0.23±0.004 Hz (Fig. 4). Swim speed increased with an increase in oscillatory frequency in all species (Fig. 5). For all species combined, we calculated a single regression equation of swim speed on oscillatory frequency ( ; R2=0.24). For the lone humpback whale tail-attached deployment, we found the mean (±s.d.) tailbeat amplitude (2.63±0.79 m), oscillatory frequency (0.25±0.06 m), swimming speed (2.61±0.25 m) and Strouhal number (0.24±0.04).
Density distributions of the oscillatory frequency calculated for each included tailbeat for a single whale. Distributions have been grouped by species. The box plots show the mean, 25% and 75% percentiles, and whiskers moving out to the farthest outliers.
Regression between the oscillatory frequency (Hz) and average speed (m s−1) of each whale. Individuals of each species are represented by the colored regression lines. The black regression lines are the averages for each species. In the 'All' plot (bottom right), each of the species-averaged regression lines is matched to the initial line color chosen for that species. The number of individuals (N) and the mean (±s.d.) number of tailbeats is shown in each panel. Additional information (R2 and slope) is given in the form of density distributions grouped by species.
Effect of body length on oscillatory frequency and swimming speed
Fig. 6 compares the tag-based measurements of the heaving frequency with predictions from optimal Strouhal scaling (Eqn 6) and oscillating beam models (Eqn 7). The same figure also shows a comparison between measurements of steady-state swimming speed and the predicted optimal speed as per Eqn 5. There was a clear decrease in oscillatory frequency (fobs) with increasing total length in both an intraspecific and interspecific context, but no relationship between cruising speed and total length (Table 1). The regression for observed speed (Uobs) corresponded very closely with the regression for optimal speed (Uopt) (Table 2). Overall, the measured swimming speed followed the insensitivity to body length exhibited by the model. However, the scaling of our oscillatory frequency data f≈L−0.5 does not agree with the f≈L−1.0 trend suggested by both the St and oscillating cantilever models.
Relationships between body length and both oscillatory frequency and cruising speed. (A) Interspecific log-transformed mean oscillatory frequency (Hz) for all species. (B) Intraspecific humpback mean oscillatory frequency. (C) Interspecific log-transformed median cruising speed (m s−1) for all species. (D) Intraspecific humpback median cruising speed. Solid lines denote the observed regressions and gray shading represents the standard error. We calculated the regression for A using our linear mixed model with species as a random effect. The dot-dash and dotted lines represent the regressions for our cantilever beam and Strouhal models, respectively. The open circles shown in C represent the modeled Uopt values for each whale. For each point in C and D, a single vertical line extends from the 25th percentile (bottom of the line) to the 75th percentile (top of the line), with the point in the middle denoting the median.
Equations and R2 values calculated for each regression shown in Fig. 6
More specifically, we found a significant dependence on total body length in the mean oscillatory frequency for an individual whale of any species (d.f.=54; t=−6.69; P<0.001). The effect of total length on the coefficient of variation of the oscillatory frequency for an individual whale was not significant (d.f.=54; t=−0.62; P=0.54) and, similarly, the effect of total length on the median cruising speed for an individual whale was not significant (d.f.=54; t=0.90; P=0.37).
The scale dependence of locomotor performance
Our measurements from baleen whales of varying body lengths indicate that while oscillatory frequency decreases with body length to the power of −0.53, cruising speed is largely invariant with body length (to the power of 0.08). These results are broadly consistent with previous studies for other swimming animals (Sato et al., 2007), particularly endotherms (Watanabe et al., 2015). The empirically derived oscillatory frequencies and cruising speeds found in our investigation can inform studies attempting to model these kinematic parameters across wide body length ranges (Alexander, 2005; Vogel, 2008). Many studies have included inexact measurements for large swimming animals or excluded them completely from the modeling (Bejan and Marden, 2006; Gazzola et al., 2014). The model put forth by Gazzola et al. (2014) includes an average blue whale cruising speed of 6 m s−1 and a resulting oscillatory frequency of 0.36 Hz. These values are roughly triple what we found in the present study using empirical data for the same species performing natural behaviors.
Our observed scaling exponent of −0.53, derived by least-squares regression of f onto L, differs from that predicted by the two simple scaling models. The cantilevered beam model (Eqn 7) – taking as inputs constant values of E and I, and variable values of L and M – predicts a scaling exponent of −1, nearly double what we observed. We obtained the same exponent, −1, in the Strouhal model, which takes as inputs a constant speed (2 m s−1), constant Strouhal number (0.3) and the variable L. The fact that the observed exponent differs from the predicted suggests that these whales are not operating as simple cantilevered beams or as optimally oscillating vortex generators. The cantilevered beam model lacks damping forces that would reduce the predicted exponent. The Strouhal model assumes optimal fluking based on efficient vortex shedding and interaction. The apparent failure of these models points us toward appropriate ways to complicate subsequent models through added damping or relaxation of optimality constraints.
Our results corroborate previous controlled studies and lend support for a set of hydrodynamic principles governing the locomotion of free-swimming animals down to the level of an individual stroke (Bainbridge, 1958; Fish, 1998; Kohannim and Iwasaki, 2014). The difficulty of obtaining stroke amplitude measures for our study animals is a limitation that precludes the type of precise hydrodynamic analysis found in previous studies, but the stroke amplitude determined from the tail-attached deployment corresponds well with previous estimates of stroke amplitude as one-fifth of body length (Bainbridge, 1958; Fish and Rohr, 1999; Rohr and Fish, 2004). Although we lacked UAS-based measures of the tail-attached animal, a calculation for our average amplitude measure of 2.37 m suggests a total body length of 11.85 m. For comparison, the mean (±s.d.) body length for humpback whales within our dataset was 11.09±1.81 m. Previous studies have shown amplitude to be invariant with changes in forward speed, so the lack of amplitude measures for each stroke does not considerably lessen our understanding of the animal's overall hydrodynamic scope (Bainbridge, 1958; Fish, 1998; Kohannim and Iwasaki, 2014). Using the tail-attached deployment, we also obtained the first empirical measurements of Strouhal number for a large cetacean. These values fall near the predicted range of St≈0.25–0.35 that has been shown to correspond to high hydrodynamic efficiency (Anderson et al., 1998; Rohr and Fish, 2004; Taylor et al., 2003; Triantafyllou et al., 1991). Throughout the duration of this deployment, the animal transited (moved steadily from one location to another) and performed a single feeding lunge. Of all the behaviors performed by animals in our dataset, transiting is most similar to the steady-state swimming performed by animals in controlled, laboratory and aquarium studies (Bainbridge, 1958; Fish and Rohr, 1999; Fish et al., 2014). This could explain why the Strouhal number for this individual fell so close to the theoretical optimum.
One of the common methods used to quantify the efficiency of locomotor modes is calculating the cost of transport over a set distance, either as a mass-specific or as an objective estimation (Alexander, 1999; Schmidt-Nielsen, 1972; Watanabe et al., 2011; Williams, 1999; Williams et al., 2014). Calculation of the cost of transport typically requires an estimate of metabolic rate using oxygen consumption or some other metric of metabolism (Schmidt-Nielsen, 1972; Sumich, 1983; Williams, 1999; Williams et al., 1993). Here, from first principles we predicted the forward speed at which the energetic cost of transport is minimized. This is a common optimization estimate and can be useful as a comparison against measured speed (Alexander, 1999; Watanabe et al., 2011). The results of our modeled optimum speed agree with previous research showing that the preferred speed of many endothermic swimmers is 1–2 m s−1 (Sato et al., 2007; Watanabe et al., 2011, 2015). The slight increase in observed swimming speed above our modeled values could be due to increased behavioral variation throughout the course of the deployment. Our optimum swimming speed equation assumes that an animal is in a steady-state, non-feeding locomotor mode. Conversely, our observed values include maneuvers and non-steady swimming related to feeding or other ecologically relevant functions, many of which are being performed above typical cruising speeds (Cade et al., 2016; Goldbogen et al., 2006, 2011, 2012; Simon et al., 2012). Han et al. (2017) performed a similar comparison between optimal and observed swimming speeds in largemouth bass (Micropterus salmoides) and found the opposite result. Bass routinely swam slower than their predicted optimum for cost of transport. It was suggested by Han et al. (2017) that other considerations may be prioritized in the wild for largemouth bass, such as the need for slower speeds in order to efficiently detect and capture food.
Our study is the first to quantify the kinematics of free-swimming baleen whales over an order of magnitude in body length. Our results for oscillatory frequency and cruising speed are more accurate than previous models and show that, while oscillatory frequency decreases with increasing body length, cruising speed remains largely invariant. A model predicting the optimal swimming speed aligned closely with the cruising speeds observed in our study animals. Models predicated on Strouhal number and an oscillating cantilever beam could not predict the full variation in oscillatory frequency within our dataset, but a small number of empirical measurements of the Strouhal number fell within the range for high hydrodynamic efficiency. Major deviations from theory may reflect the competing demands for different locomotor functions during common behaviors such as foraging.
Our fieldwork efforts would not have been successful without our boat crew and logistics personnel. Dr Nikolai Liebsch and Dr Peter Kraft from Customized Animal Tracking Solutions have been instrumental in the design and maintenance of our inertial sensing tags. Xitlaali Castellanos performed the Strouhal measurements for the humpback tail-attached deployment. Dr Robin Elahi assisted with the design and implementation of our statistical analyses.
Conceptualization: W.T.G., P.S.S., D.E.C., J.P., F.E.F., A.S.F., J.H.L., M.S.S., J.A.G.; Methodology: W.T.G., P.S.S., K.C.B., D.E.C., J.P., F.E.F., A.S.F., J.K., J.H.L., M.S.S., J.A.G.; Software: W.T.G., D.E.C., J.K.; Formal analysis: W.T.G., K.C.B., J.P., J.K.; Investigation: W.T.G., P.S.S., K.C.B., D.E.C., J.P., F.E.F., J.D., J.d.C., A.S.F., D.W.J., S.R.K.-R., M.O., G.P., M.S.S., M.S., S.K.V., F.V., D.N.W., J.A.G.; Resources: K.C.B., D.E.C., J.P., J.D., J.d.C., A.S.F., D.W.J., J.K., M.O., G.P., M.S., S.K.A.V., F.V., D.N.W., J.A.G.; Data curation: W.T.G., P.S.S., K.C.B., D.E.C., J.D., A.S.F., D.W.J., S.R.K.-R., J.K., M.O., G.P., M.S.S., M.S., S.K.A.V., F.V., D.N.W.; Writing - original draft: W.T.G.; Writing - review & editing: W.T.G., P.S.S., K.C.B., D.E.C., J.P., F.E.F., J.D., J.d.C., A.S.F., D.W.J., S.R.K.-R., J.K., J.H.L., M.O., G.P., M.S.S., M.S., S.K.A.V., F.V., D.N.W., J.A.G.; Visualization: W.T.G., M.S.S., J.A.G.; Supervision: J.P., F.E.F., A.S.F., J.H.L., M.S.S., J.A.G.; Project administration: A.S.F., J.A.G.; Funding acquisition: J.P., F.E.F., A.S.F., J.A.G.
This research was funded in part by grants from the National Science Foundation (IOS-1656691, IOS-1656676, IOS-1656656; OPP-1644209), the Office of Naval Research (N000141612477), a Terman Fellowship from Stanford University, and a National Science Foundation INSPIRE Special Projects grant (1344227). Additional Monterey Bay CATS tag deployments were funded by grants from the American Cetacean Society Monterey and San Francisco Bay chapters, and by the Meyers Trust. Bryde's whale tagging operations in South Africa were funded by the Percy Sladen Memorial Fund, PADI Foundation, Torben and Alice Frimodts Fund, and Society for Marine Mammology.
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Underwater acrobatics by the world's largest predator: 360° rolling manoeuvres by lunge-feeding blue whales
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Field swimming behavior in largemouth bass deviates from predictions based on economy and propulsive efficiency
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Energetic and biomechanical constraints on animal migration distance
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Hydrodynamic properties of fin whale flippers predict maximum rolling performance
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Supplementary information- pdf file


You can expect a lot from the 2018 Hyundai Elantra. This 4 door, 5 passenger sedan still has less than 10,000 miles! It features a front-wheel-drive platform, an automatic transmission, and a 2 liter 4 cylinder engine. Top features include front bucket seats, 1-touch window functionality, air conditioning, power door mirrors, power windows, remote keyless entry, cruise control, and much more. Premium sound drives 6 speakers, providing you and your passengers a sensational audio experience. Hyundai also prioritized safety and security by including: head curtain airbags, front side impact airbags, traction control, brake assist, a panic alarm, and ABS brakes. Electronic stability control ensures solid grip atop the road surface, no matter how challenging the driving conditions. This vehicle has achieved Certified Pre-Owned status, by passing Hyundai's comprehensive certification process, including a rigorous 150 point inspection! Our aim is to provide our customers with the best prices and service at all times. Please don't hesitate to give us a call.


Yes yes, my birthday was on April 3rd, so this is a superbly overdue post! But such gorgeous food do not deserve to remain hidden in my hard disk forever, so here it is! Dinner was at La Cantina tucked far away at Changi Hotel and we made the effort to go alll the way there despite staying rather far away. That's how much I am willing to go for good food. This is a food-only photo post, so if you are feeling hungry, or have craving for pasta/steaks/delicious food, stay far far away. You read the disclaimer!
For starters, mushroom soup! We ordered one to share, and the 1/2 portion was actually just nice! Creamy and made with real mushrooms, mmm.
The warm grainy bread served with vinegar and olive oil (?).
Calamari! We had no idea they are so huge. The most disappointing among the line up, really. Too oily!
and now... for the SUPERSTAR of the night, Crabmeat Linguine!
As you can see it is a full plate of al dente spaghetti with tomato cream and crabmeat CHUNKS. When i say chunks, they are really big pieces of crab and not slivers of them. Definitely worth it. But it can get quite 'gelat' after a while for a small-tummied person so sharing would be ideal!
Looking at it is making me want to eat it again.
My dear friend had a wild mushroom steak.. i think. In any case it was DELISH. the meat was tender and well flavoured and served with a generous helping of potatoes and mushrooms.
The plate made the food look like its colours are "running". haha!
Here's the crabmeat linguine up close. mmm.
and finally for dessert after a tough time stuffing all that good food down -- a pineapple sorbet served IN the pineapple! it was uber refreshing and rather exotic i must say. and a grand dessert to top off a grand meal.
The emergency hotline for pasta craving is 6546 9190, which is La Cantina in Venezia's number. Hope you enjoyed looking through the food as much as I did!


The winners of the Exeter University's Professional Service Recognition Awards will be unveiled at a glittering award ceremony to take place on Friday July 11th.
The awards, which aim to recognise the level of commitment offered by university staff, are sponsored by Picturehouse Apartments, who are proud to play a part in honouring those who have made an outstanding contribution to university life.
There are six categories for this year's awards, including the David Allen Hero/ Heroine of the Year award, which has now been shortlisted, with final voting opening on June 30th. If you're still unsure who to vote for, you can view the nominees videos here and the full list of shortlisted candiates can be viewed here.
Picturehouse Apartments, managed by Collegiate AC, deliver student living with a difference in Exeter. Our accommodation is high-spec and superbly designed with students at the heart of everything we do. Equipped with fast broadband, private gym, in-house cinema, reading rooms and more, our accommodation offers great value and peace of mind.
Visit http://www.exeter.ac.uk/staff/benefits/excellence/ to find out more about the awards.


text:
Q: Cannot trigger build via push to another repository in a YML Pipeline I have the following repositories:

*

*my-app-repo - Contains the code for the app

*pipeline-repo - A collection of pipelines that build my-app-repo as well as others

I'm trying to build a yml pipeline within pipeline-repo that will be triggered on a commit to my-app-repo.
According to the official yml documentation, it sounds possible but I'm not able to get it working.
Here's what I've tried:
pipeline-repo/my-app-repo-build.yml
resources:
  repositories:
  - repository: target_repo
    type: git
    name: my-project/my-app_repo
    trigger:
      branches:
        include:
        - master

jobs:
  - job:
    steps:
      - script: echo "Should be triggered from a push to my-app-repo!"

The build is not triggering when I push to my-app-repo. It only kicks off for commits to the source repo (pipeline-repo) which I cannot change since that holds the yml definition.
Am I missing something easy?

A: Edit:
I see you've added an issue to the github repository you mentioned. I agree that it seems to be broken.
While you aren't exactly trying to provide template functionality to your pipelines, you might use it as a work around until the issue you created is addressed.
I've tested this in my playground and it seems to work.
In the app repository
resources:
  repositories:
    - repository: templates
      type: git
      name: Pipeline-Templates

trigger:
  branches:
    include:
      - master

jobs:
- template: azure-pipelines-template.yml@templates

In the pipelines repository
jobs:
- job: Get_Last_10_Commits
  pool:
    vmImage: 'vs2017-win2016'
  steps:
  - task: PowerShell@2
    inputs:
      targetType: 'inline'
      script: |
        cd $(Build.SourcesDirectory)
        Write-Host "Show git log (last 10):"
        git log --oneline -10

One "advantage" of this work around is that you no longer need to specify that the repository to checkout is the resource repository and not the self (your template-repository) as the self repository is the app-repo.
This would allow you to restrict changes to the pipeline core by having it in a separate repo like you want, but would still trigger on the app-repo master.
Not ideal b/c you now have 2x .yml files for each build you need, but that's basically the definition of work around: not ideal.

It looks like your include syntax is wrong. Have you tried to use the simple syntax?
All of the examples (and my experience) show that you should use the wildcard syntax when your trigger has an include or exclude specification.
resources:
  repositories:
  - repository: myPHPApp
    type: GitHub
    connection: myGitHubConnection
    source: ashokirla/phpApp
    trigger:
      branches:
        include:
        - features/*
        exclude:
        - features/experimental/*
      paths:
        exclude:
        - README.md

redpajama_set_name: RedPajamaStackExchange


text:
Q: Which HTML5 forms javascript polyfill to has the broadest feature and browser reach? The new forms attributes and tags in HTML5 are great.  Unfortunately, there is only limited support for these in Chrome and Firefox, and essentially no support of these in IE9 and below.
I have investigated using Modernizr and HTML5 polyfills to polyfill this functionality in browsers lacking native support.   However, there seems to be a large number of polyfills for HTML5 forms functionality, such as webshims lib, webforms2 and h5f (see for example the listing at https://github.com/Modernizr/Modernizr/wiki/HTML5-Cross-Browser-Polyfills) and I am not sure which one or ones I should invest the time in learning.
Which one of these HTML5 forms polyfills would give me the broadest coverage in terms of HTML5 features and browser coverage?

A: I have used Webshims Lib and like it because:

*

*It runs 'more lightweight' if a browser supports more HTML5 form features natively (which, of course, should do any good polyfill).

*You can simply use the declarative markup (attributes) of HTML5 forms in all cases.

*The project seems to be more 'alive' than others.

By the way, the Webshims creator answered another question on stackoverflow quite convincingly: html5 forms with polyfills - is it worth it?


Deadpool is being saturated into corny over at Marvel. He's even the front man to sell a ton of variant covers. I And now he's actually getting his own corps. WTF? NSFW! Everything looks final and we also got previews of the covers after the break if you're still interested.
Deadpool is a great character and a funny one at that. He's the psycho hero amidst all the psychos out in the Marvel universe. I just hope they don't over use Deadpool too much like what they did with Wolverine. Here's a look at the upcoming covers of issues 1 - 5 of Deadpool Corps illustrated by Dave Johnson.


text:
package main

import (
	"fmt"
	"io"
	"io/ioutil"
	"math/rand"
	"os"
	"os/exec"
	"text/template"
)

// RI generator that uses GLPK to find satisfying matrix RI.
type GlpkRIGenerator struct {
	DontAcceptRIGeneratorParams
}

func makeGlpkRIGenerator() *GlpkRIGenerator {
	return &GlpkRIGenerator{}
}

func (_ GlpkRIGenerator) String() string {
	return "glpk"
}

const model = `
param nodes, integer, >= 1;
param slaves, integer, >= 0;

param tags_count, integer, >= 1;
param tags{0..nodes-1}, integer, >= 0, < tags_count;

var RI{i in 0..nodes-1, j in 0..nodes-1}, binary;

subject to zeros{i in 0..nodes-1, j in 0..nodes-1: tags[i] == tags[j]}: RI[i,j] = 0;
subject to active_balance{i in 0..nodes-1}: sum{j in 0..nodes-1} RI[i,j] = slaves;
subject to replica_balance{j in 0..nodes-1}: sum{i in 0..nodes-1} RI[i,j] = slaves;

var tag_max;
subject to tags_balance{i in 0..nodes-1, t in 0..tags_count-1}:
sum{j in 0..nodes-1} RI[i,j] * (if tags[j] == t then 1 else 0) <= tag_max;

minimize obj: tag_max;

solve;

for {i in 0..nodes-1}
{
  for {j in 0..nodes-1}
  {
    printf "%d\n", RI[i,j];
  }
}

end;
`

const dataTemplate = `
data;

param nodes := {{ .NumNodes }};
param slaves := {{ .NumSlaves }};
param tags_count := {{ .Tags.TagsCount }};
param tags := {{ range $node, $tag := .Tags }}{{ $node }} {{ $tag }} {{ end }};

end;
`

type GlpkResult uint

func genDataFile(file io.Writer, params VbmapParams) error {
	tmpl := template.Must(template.New("data").Parse(dataTemplate))
	return tmpl.Execute(file, params)
}

func readSolution(params VbmapParams, outPath string) (ri RI, err error) {
	output, err := os.Open(outPath)
	if err != nil {
		return
	}
	defer output.Close()

	var values []int = make([]int, params.NumNodes*params.NumNodes)

	for i := range values {
		_, err = fmt.Fscan(output, &values[i])
		if err == io.EOF && i == 0 {
			err = ErrorNoSolution
			return
		}

		if err != nil {
			err = fmt.Errorf("Invalid GLPK output (%s)", err.Error())
			return
		}
	}

	ri.TagAwarenessRank = StrictlyTagAware
	ri.Matrix = make([][]bool, params.NumNodes)
	for i := range ri.Matrix {
		for _, v := range values[i*params.NumNodes : (i+1)*params.NumNodes] {
			value := (v != 0)
			ri.Matrix[i] = append(ri.Matrix[i], value)
		}
	}

	return ri, nil
}

func (_ GlpkRIGenerator) Generate(params VbmapParams, _ SearchParams) (ri RI, err error) {
	dataFile, err := ioutil.TempFile("", "vbmap_glpk_data")
	if err != nil {
		err = fmt.Errorf("Couldn't create data file: %s", err.Error())
		return
	}
	defer func() {
		dataFile.Close()
		os.Remove(dataFile.Name())
	}()

	if err = genDataFile(dataFile, params); err != nil {
		err = fmt.Errorf("Couldn't generate data file %s: %s",
			dataFile.Name(), err.Error())
		return
	}

	outputFile, err := ioutil.TempFile("", "vbmap_glpk_output")
	if err != nil {
		err = fmt.Errorf("Couldn't create output file: %s", err.Error())
		return
	}
	outputFile.Close()
	defer os.Remove(outputFile.Name())

	modelFile, err := ioutil.TempFile("", "vbmap_glpk_model")
	if err != nil {
		err = fmt.Errorf("Couldn't create model file: %s", err.Error())
		return
	}
	modelFile.Close()
	defer os.Remove(modelFile.Name())

	err = ioutil.WriteFile(modelFile.Name(), []byte(model), os.FileMode(0644))
	if err != nil {
		err = fmt.Errorf("Couldn't populate model file %s: %s",
			modelFile.Name(), err.Error())
		return
	}

	cmd := exec.Command("glpsol",
		"--model", modelFile.Name(),
		"--tmlim", "10",
		"--data", dataFile.Name(),
		"--display", outputFile.Name(),
		"--seed", fmt.Sprint(rand.Int31()),
		"--mipgap", "0.5")

	terminal, err := cmd.CombinedOutput()

	diag.Printf("=======================GLPK output=======================")
	diag.Printf("%s", string(terminal))
	diag.Printf("=========================================================")

	if err != nil {
		return
	}

	return readSolution(params, outputFile.Name())
}

redpajama_set_name: RedPajamaGithub


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