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One Way Flights from New York to Quito (NYC to UIO)
Discover alternate routes & cheap one way flights from New York to Quito (NYC to UIO) within a departure date range.
New York (JFK) → Quito (UIO)
Mon Nov 28th 2022 Copa Airlines · CM 812 Depart at 2:42 pm from New York (JFK) Fly for 5h 38m Arrive at 8:20 pm in Panama City (PTY)
Layover in Panama City for 1h 16m
Mon Nov 28th 2022 Copa Airlines · CM 211 Depart at 9:36 pm from Panama City (PTY) Fly for 1h 59m Arrive at 11:35 pm in Quito (UIO)
New York (LGA) → Quito (UIO)
Thu Dec 8th 2022 Frontier Airlines · F9 1335 Depart at 10:45 pm from New York (LGA) Fly for 3h 1m Arrive at 1:46 am in Orlando (MCO)
Layover in Orlando for 7h 12m
Fri Dec 9th 2022 Frontier Airlines · F9 98 Depart at 8:58 am from Orlando (MCO) Fly for 2h 37m Arrive at 12:35 pm in Santo Domingo (SDQ)
Layover in Santo Domingo for 3h 25m
Fri Dec 9th 2022 Maersk · DM 3880 Depart at 4:00 pm from Santo Domingo (SDQ) Fly for 3h 15m Arrive at 6:15 pm in Quito (UIO)
Thu Dec 8th 2022 Delta Air Lines · DL 613 Depart at 8:00 am from New York (JFK) Fly for 4h 50m Arrive at 1:50 pm in Aruba (AUA)
Layover in Aruba for 3h 54m
Thu Dec 8th 2022 Avianca · AV 8381 Depart at 5:44 pm from Aruba (AUA) Fly for 2h 0m Arrive at 6:44 pm in Bogotá (BOG)
Layover in Bogotá for 4h 26m
Thu Dec 8th 2022 Avianca · AV 67 Depart at 11:10 pm from Bogotá (BOG) Fly for 1h 45m Arrive at 12:55 am in Quito (UIO)
Mon Dec 5th 2022 JetBlue Airways · B6 2293 Depart at 6:30 am from New York (JFK) Fly for 3h 11m Arrive at 9:41 am in Miami (MIA)
Layover in Miami for 4h 54m
Mon Dec 5th 2022 LATAM Chile · LA 1453 Depart at 2:35 pm from Miami (MIA) Fly for 4h 20m Arrive at 6:55 pm in Quito (UIO)
Mon Dec 5th 2022 Delta Air Lines · DL 433 Depart at 9:59 am from New York (LGA) Fly for 2h 48m Arrive at 12:47 pm in Atlanta (ATL)
Layover in Atlanta for 2h 8m
Mon Dec 5th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Wed Dec 7th 2022 Delta Air Lines · DL 433 Depart at 9:59 am from New York (LGA) Fly for 2h 48m Arrive at 12:47 pm in Atlanta (ATL)
Wed Dec 7th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Thu Dec 8th 2022 Delta Air Lines · DL 433 Depart at 9:59 am from New York (LGA) Fly for 2h 48m Arrive at 12:47 pm in Atlanta (ATL)
Thu Dec 8th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Tue Dec 6th 2022 Delta Air Lines · DL 433 Depart at 9:59 am from New York (LGA) Fly for 2h 48m Arrive at 12:47 pm in Atlanta (ATL)
Tue Dec 6th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
New York (EWR) → Quito (UIO)
Sun Nov 27th 2022 United Airlines · UA 4425 Depart at 9:10 am from New York (EWR) Fly for 1h 30m Arrive at 10:40 am in Washington, D.C. (DCA)
Layover in Washington, D.C. for 1h 19m
Sun Nov 27th 2022 Delta Air Lines · DL 910 Depart at 11:59 am from Washington, D.C. (DCA) Fly for 1h 56m Arrive at 1:55 pm in Atlanta (ATL)
Sun Nov 27th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Tue Dec 13th 2022 Spirit Airlines · NK 68 Depart at 9:30 am from New York (EWR) Fly for 3h 22m Arrive at 12:52 pm in Miami (MIA)
Tue Dec 13th 2022 Avianca · AV 127 Depart at 6:20 pm from Miami (MIA) Fly for 3h 40m Arrive at 10:00 pm in Bogotá (BOG)
Tue Dec 13th 2022 Avianca · AV 67 Depart at 11:10 pm from Bogotá (BOG) Fly for 1h 45m Arrive at 12:55 am in Quito (UIO)
Tue Dec 6th 2022 Delta Air Lines · DL 253 Depart at 12:00 pm from New York (JFK) Fly for 5h 55m Arrive at 5:55 pm in Bogotá (BOG)
Tue Dec 6th 2022 LATAM Chile · LA 1443 Depart at 9:40 pm from Bogotá (BOG) Fly for 1h 49m Arrive at 11:29 pm in Quito (UIO)
Thu Dec 8th 2022 Spirit Airlines · NK 68 Depart at 10:10 am from New York (EWR) Fly for 3h 22m Arrive at 1:32 pm in Miami (MIA)
Thu Dec 8th 2022 Avianca · AV 127 Depart at 6:20 pm from Miami (MIA) Fly for 3h 40m Arrive at 10:00 pm in Bogotá (BOG)
Mon Dec 12th 2022 Delta Air Lines · DL 613 Depart at 8:00 am from New York (JFK) Fly for 4h 50m Arrive at 1:50 pm in Aruba (AUA)
Mon Dec 12th 2022 Avianca · AV 91 Depart at 5:44 pm from Aruba (AUA) Fly for 2h 0m Arrive at 6:44 pm in Bogotá (BOG)
Mon Dec 12th 2022 Avianca · AV 67 Depart at 11:10 pm from Bogotá (BOG) Fly for 1h 45m Arrive at 12:55 am in Quito (UIO)
Tue Nov 29th
Tue Nov 29th 2022 Delta Air Lines · DL 424 Depart at 6:15 am from New York (JFK) Fly for 2h 41m Arrive at 8:56 am in Atlanta (ATL)
Layover in Atlanta for 5h 59m
Tue Nov 29th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Mon Dec 12th 2022 Spirit Airlines · NK 68 Depart at 10:10 am from New York (EWR) Fly for 3h 22m Arrive at 1:32 pm in Miami (MIA)
Mon Dec 12th 2022 Avianca · AV 127 Depart at 6:20 pm from Miami (MIA) Fly for 3h 40m Arrive at 10:00 pm in Bogotá (BOG)
Mon Dec 5th 2022 JetBlue Airways · B6 9 Depart at 7:30 am from New York (JFK) Fly for 3h 47m Arrive at 12:17 pm in Santo Domingo (SDQ)
Mon Dec 5th 2022 Maersk · DM 3880 Depart at 4:00 pm from Santo Domingo (SDQ) Fly for 3h 15m Arrive at 6:15 pm in Quito (UIO)
Tue Nov 29th 2022 LATAM Chile · LA 6665 Depart at 11:00 am from New York (LGA) Fly for 2h 48m Arrive at 1:48 pm in Atlanta (ATL)
Sun Dec 25th 2022 United Airlines · UA 1309 Depart at 10:00 am from New York (EWR) Fly for 1h 48m Arrive at 11:48 am in Cleveland (CLE)
Layover in Cleveland for 0h 32m
Sun Dec 25th 2022 Delta Air Lines · DL 1355 Depart at 12:20 pm from Cleveland (CLE) Fly for 1h 56m Arrive at 2:16 pm in Atlanta (ATL)
Sun Dec 25th 2022 LATAM Chile · LA 6359 Depart at 3:00 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:15 pm in Quito (UIO)
Fri Dec 9th 2022 United Airlines · UA 3643 Depart at 9:00 am from New York (EWR) Fly for 2h 13m Arrive at 11:13 am in Columbus (CMH)
Layover in Columbus for 1h 0m
Fri Dec 9th 2022 Delta Air Lines · DL 2336 Depart at 12:13 pm from Columbus (CMH) Fly for 1h 44m Arrive at 1:57 pm in Atlanta (ATL)
Fri Dec 9th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Fri Dec 9th 2022 Delta Air Lines · DL 1646 Depart at 8:29 am from New York (EWR) Fly for 2h 32m Arrive at 11:01 am in Atlanta (ATL)
Sat Dec 3rd 2022 Delta Air Lines · DL 2113 Depart at 8:20 am from New York (LGA) Fly for 2h 42m Arrive at 11:02 am in Atlanta (ATL)
Sat Dec 3rd 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Fri Dec 9th 2022 United Airlines · UA 626 Depart at 6:30 am from New York (EWR) Fly for 2h 34m Arrive at 9:04 am in Atlanta (ATL)
Fri Dec 9th 2022 Delta Air Lines · DL 1089 Depart at 11:05 am from Atlanta (ATL) Fly for 1h 54m Arrive at 12:59 pm in Miami (MIA)
Fri Dec 9th 2022 LATAM Chile · LA 1453 Depart at 2:35 pm from Miami (MIA) Fly for 4h 20m Arrive at 6:55 pm in Quito (UIO)
Sun Dec 4th 2022 Delta Air Lines · DL 506 Depart at 7:20 am from New York (LGA) Fly for 2h 40m Arrive at 10:00 am in Atlanta (ATL)
Sun Dec 4th 2022 LATAM Chile · LA 6359 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Sat Dec 24th 2022 United Airlines · UA 1228 Depart at 6:00 am from New York (EWR) Fly for 1h 17m Arrive at 7:17 am in Washington, D.C. (IAD)
Sat Dec 24th 2022 Delta Air Lines · DL 1245 Depart at 10:57 am from Washington, D.C. (IAD) Fly for 1h 57m Arrive at 12:54 pm in Atlanta (ATL)
Sat Dec 24th 2022 LATAM Chile · LA 6359 Depart at 3:00 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:15 pm in Quito (UIO)
Mon Dec 5th 2022 Delta Air Lines · DL 2202 Depart at 7:35 am from New York (JFK) Fly for 3h 18m Arrive at 10:53 am in Miami (MIA)
Wed Dec 7th 2022 Delta Air Lines · DL 2202 Depart at 7:35 am from New York (JFK) Fly for 3h 18m Arrive at 10:53 am in Miami (MIA)
Wed Dec 7th 2022 LATAM Chile · LA 1453 Depart at 2:35 pm from Miami (MIA) Fly for 4h 20m Arrive at 6:55 pm in Quito (UIO)
Sun Dec 25th 2022 Delta Air Lines · DL 4752 Depart at 8:30 am from New York (LGA) Fly for 2h 5m Arrive at 10:35 am in Raleigh (RDU)
Layover in Raleigh for 0h 56m
Sun Dec 25th 2022 Delta Air Lines · DL 2836 Depart at 11:31 am from Raleigh (RDU) Fly for 1h 34m Arrive at 1:05 pm in Atlanta (ATL)
Tue Dec 13th 2022 Delta Air Lines · DL 2776 Depart at 11:33 am from New York (JFK) Fly for 3h 19m Arrive at 2:52 pm in Miami (MIA)
Tue Dec 13th 2022 LATAM Chile · LA 579 Depart at 4:30 pm from Miami (MIA) Fly for 3h 40m Arrive at 8:10 pm in Bogotá (BOG)
Tue Dec 13th 2022 LATAM Chile · LA 1443 Depart at 9:40 pm from Bogotá (BOG) Fly for 1h 49m Arrive at 11:29 pm in Quito (UIO)
Tue Dec 13th 2022 JetBlue Airways · B6 2993 Depart at 1:00 pm from New York (JFK) Fly for 3h 9m Arrive at 4:09 pm in Miami (MIA)
Tue Dec 6th 2022 Delta Air Lines · DL 2177 Depart at 10:59 am from New York (LGA) Fly for 2h 48m Arrive at 1:47 pm in Atlanta (ATL)
Sat Dec 3rd 2022 Delta Air Lines · DL 863 Depart at 8:15 am from New York (JFK) Fly for 2h 47m Arrive at 11:02 am in Atlanta (ATL)
Mon Dec 12th 2022 JetBlue Airways · B6 2993 Depart at 1:00 pm from New York (JFK) Fly for 3h 9m Arrive at 4:09 pm in Miami (MIA)
Tue Nov 29th 2022 LATAM Chile · LA 6635 Depart at 6:15 am from New York (JFK) Fly for 2h 41m Arrive at 8:56 am in Atlanta (ATL)
Mon Dec 12th 2022 Spirit Airlines · NK 58 Depart at 6:00 am from New York (EWR) Fly for 2h 55m Arrive at 8:55 am in Orlando (MCO)
Mon Dec 12th 2022 Spirit Airlines · NK 377 Depart at 9:48 am from Orlando (MCO) Fly for 2h 30m Arrive at 1:18 pm in Santo Domingo (SDQ)
Mon Dec 12th 2022 Maersk · DM 3880 Depart at 4:00 pm from Santo Domingo (SDQ) Fly for 3h 15m Arrive at 6:15 pm in Quito (UIO)
Mon Nov 28th 2022 American Airlines · AA 2819 Depart at 9:00 am from New York (JFK) Fly for 2h 56m Arrive at 10:56 am in Chicago (ORD)
Layover in Chicago for 1h 13m
Mon Nov 28th 2022 American Airlines · AA 1244 Depart at 12:09 pm from Chicago (ORD) Fly for 3h 4m Arrive at 4:13 pm in Miami (MIA)
Layover in Miami for 2h 2m
Mon Nov 28th 2022 American Airlines · AA 2259 Depart at 6:15 pm from Miami (MIA) Fly for 4h 13m Arrive at 10:28 pm in Quito (UIO)
Sun Nov 27th 2022 Delta Air Lines · DL 1646 Depart at 8:19 am from New York (EWR) Fly for 2h 31m Arrive at 10:50 am in Atlanta (ATL)
Sun Nov 27th 2022 Delta Air Lines · DL 633 Depart at 2:55 pm from Atlanta (ATL) Fly for 5h 15m Arrive at 8:10 pm in Quito (UIO)
Mon Dec 5th 2022 American Airlines · AA 419 Depart at 6:00 am from New York (JFK) Fly for 3h 19m Arrive at 9:19 am in Miami (MIA)
Mon Dec 5th 2022 American Airlines · AA 2163 Depart at 10:30 am from Miami (MIA) Fly for 4h 41m Arrive at 3:11 pm in Quito (UIO)
Tue Dec 6th 2022 American Airlines · AA 419 Depart at 6:00 am from New York (JFK) Fly for 3h 19m Arrive at 9:19 am in Miami (MIA)
Tue Dec 6th 2022 American Airlines · AA 2163 Depart at 10:30 am from Miami (MIA) Fly for 4h 41m Arrive at 3:11 pm in Quito (UIO)
Mon Dec 5th 2022 Spirit Airlines · NK 3154 Depart at 8:55 am from New York (LGA) Fly for 3h 9m Arrive at 12:04 pm in Miami (MIA)
Sun Dec 4th 2022 American Airlines · AA 1529 Depart at 1:29 pm from New York (JFK) Fly for 3h 20m Arrive at 4:49 pm in Miami (MIA)
Sun Dec 4th 2022 American Airlines · AA 2259 Depart at 6:15 pm from Miami (MIA) Fly for 4h 14m Arrive at 10:29 pm in Quito (UIO)
Sun Dec 4th 2022 American Airlines · AA 419 Depart at 6:00 am from New York (JFK) Fly for 3h 19m Arrive at 9:19 am in Miami (MIA)
Sun Dec 4th 2022 American Airlines · AA 2163 Depart at 10:30 am from Miami (MIA) Fly for 4h 41m Arrive at 3:11 pm in Quito (UIO)
Sun Dec 4th 2022 American Airlines · AA 2243 Depart at 12:27 pm from New York (JFK) Fly for 3h 20m Arrive at 3:47 pm in Miami (MIA)
Tue Dec 6th 2022 JetBlue Airways · B6 1769 Depart at 1:37 pm from New York (JFK) Fly for 6h 41m Arrive at 8:18 pm in Guayaquil (GYE)
Layover in Guayaquil for 2h 7m
Tue Dec 6th 2022 Avianca · AV 1679 Depart at 10:25 pm from Guayaquil (GYE) Fly for 0h 51m Arrive at 11:16 pm in Quito (UIO)
Sat Dec 3rd 2022 American Airlines · AA 419 Depart at 6:00 am from New York (JFK) Fly for 3h 19m Arrive at 9:19 am in Miami (MIA)
Sat Dec 3rd 2022 American Airlines · AA 2163 Depart at 10:30 am from Miami (MIA) Fly for 4h 41m Arrive at 3:11 pm in Quito (UIO)
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Tips to help you find the cheapest flights from New York to Quito (NYC to UIO).


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Home BUSINESS BoG endorses merger between Access Bank and Diamond Bank, Nigeria
BoG endorses merger between Access Bank and Diamond Bank, Nigeria
The Bank of Ghana has endorsed a merger between Access Bank and Diamond Bank in Nigeria.
By this, Access Bank has penetrated 7 African countries and three continents including Asia, Europe and Africa.
The two banks received regulatory approval to merge in April this year.
Managing Director of Access Bank, Olumide Olatunji in an interview with Citi Business News said the merger has created a bigger, better and stronger bank to better serve its customers.
"With the new merger, there will be new products we launch in the Ghanaian space as we seek to develop our products here. We want products that are going to be relevant to the Ghanaian market. So, with the Bank of Ghana's approval, we will deploy more exciting products into the market in Ghana," he said.
He made this known at the media unveiling of Access Bank's new brand identity at their head office in Accra.
The unveiling of the new brand signifies the company's global appeal and strategy of becoming Africa's gateway to the world as well as its ambition to be one of the largest retail banks in Ghana.
The new visual identity, which includes a new logo, unique brand colours and refined brand promise, follows the successful merger between our parent company Access Bank Plc and erstwhile Diamond Bank in Nigeria and subsequent approval received from the Bank of Ghana to commence its rebranding exercise.
Providing more insights into the new brand identity, the Managing Director of Access Bank Ghana, Mr. Olumide Olatunji remarked: "These are exciting times for us and thousands of our customers across the country.
Access Bank has grown over time and has built an enviable brand in the wholesale banking segment but increasingly we have always wanted to strengthen our retail franchise to enable us to diversify and serve the whole value chain, right down to the ordinary woman in the village.
With this new identity, we shall be opening more doors that will offer families the opportunity to realise their dreams, providing Ghanaian businesses with access to intra-Africa trade and global markets and giving budding entrepreneurs the tools to build a business," he concluded.
Access Bank now has about 29 million customers and over 27,000 staff in 12 countries across three continents. Customers of Access Bank can expect exciting times ahead with enhanced and more efficient digital platforms for their banking needs, more unique and innovative products and more rewarding benefits from their banking relationship.
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redpajama_set_name: RedPajamaCommonCrawl


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Town Projects
The Town of Bristol has several on-going projects. To learn more about a specific project, please contact Town Hall at 401-253-7000.
Downtown Harbor Boardwalk
The Downtown Harbor Boardwalk will connect Rockwell Park with the State Street dock, completing the missing link in the Bristol Boardwalk. The boardwalk will begin at the Bristol Maritime Center and end at Independence Park.
The Town of Bristol received a grant of $325,000 from Rhode Island Department of Environmental Management to support the project.
The Town of Bristol has awarded the bid, and construction for the project will begin shortly.
Tanyard Brook Culvert Replace Phase II
The Tanyard Brook Culvert Replacement project began in 2002 when the Town commissioned a Watershed Study. Phase 1 of the Culvert replacement was completed and the Town is now in the process of planning for the construction of Phase 2 which is expected to begin in June 2018.
RI Department of Environmental Management approved Phase 2 in February 2017 after reviewing the Water Quality Certification Program Application Engineering Report, prepared by BETA Engineering.
In February 2018, the Town of Bristol received a grant of $1,799,078 from the Community Development Block Grant Disaster Relief Fund to support the project.
Review the Phase II project plans.
Review the Phase I project summary.
Read the 2002 Watershed Study.
Bristol Marina Expansion
In 2016, the residents of Bristol approved a $2 Million bond for the construction of a new marina. In early 2016, the Town of Bristol hired PARE Corporation to survey potential locations and design the final project.
Once completed, the Bristol Marina will add over 70 slips to serve local residents and transient boaters.
The final design for the project is expected to be completed by Fall 2018.
Review the Marina Expansion project plans.
Bristol Maritime Center Renovations
In January 2018, the Town of Bristol received a $150,000 grant from the RI Historic Preservation & Heritage Commission to use toward making the final exterior structural improvement to the Bristol Maritime Center.
The Town of Bristol is actively working with the project engineer for the final design.
Wayfinding Program
Town-wide wayfinding will include signage to direct visitors to points of interest and parking.
The Town of Bristol received a grant of $80,000 from Commerce RI in 2016 to support the Town's efforts.
In February 2018, the Town of Bristol went out to bid for the Wayfinding project.
The Town of Bristol awarded the bid and obtained the necessary permits. Construction is scheduled to begin in June 2018.
Wood Street Streetscape
The Town of Bristol received a grant of $42,855 from Commerce RI to improve the Wood Street Neighborhood.
The streetscape project includes the addition of bike racks, painted lamp post, decorative medallions, trash cans and recycling bins.
The Town of Bristol is working with SteelYard to design and fabricate the custom streetscape pieces.
Legion Square / "Loafers Corner"
The Town of Bristol received a grant of $23,440 from Rhode Island Department of Environmental Management to improve the landscape/streetscape of Loafers Corner.
In 2017, the Town of Bristol went out to bid for the project. Da Ponte's Landscaping Services Inc. was awarded the contract for service.
Work is expected to begin in early summer 2018.
Prudence Island Ferry Dock Repair
The Town of Bristol commissioned PARE Corporation to prepare engineering design plans to repair the north and south wall of the Prudence Island Ferry Dock.
In April 2018, the Town of Bristol secured a federal grant of approximately $1.2 Million from the Federal Transit Administration to repair the dock.
The Town of Bristol will be going out to bid for the project, and work is scheduled to begin in Fall 2018.
On-Site Solar Project
In Fall 2016, the Town of Bristol partnered with the Town of Barrington to request bids for a Public/Private Partnership for On-Site Solar. These projects will provide the Town of Bristol the opportunity to increase energy efficiency and decrease energy costs through the use of renewable energy sources on municipal properties.
The RFP bidding process has concluded, and the Town of Bristol is currently finalizing proposal details.
On-Site Solar Projects


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Walk the Evil Street
Norman Daniels, real name Norman Danberg, utilized a number of pseudonyms throughout his long and prolific career. His Gothic mysteries were often penned under Angela Gray, Suzanne Somers and even his wife's name, Dorothy Daniels. His Black Bat pulps were written under the name G. Wayman Jones. Daniels' literary work produced well over 15 different pseudonyms, so it was with no surprise that I found him using the name David Wade for a crime-noir titled Walk the Evil Street. The story was originally published by Magazine Productions Inc. in 1952 and reprinted in paperback format by Berkley Diamond in 1960.
Andy Mason is an Army veteran turned hard-nosed reporter. After numerous award-winning articles on crime, drugs and pushers, Mason gains the attention of an older former racketeer named Sam. The famed criminal invites Mason to his mansion with an unusual proposal.
The pitch, and the book's premise, is that Sam is now in his dying days, bound to his bed. He explains to Mason that his son Houleman is gay (frowned upon in 1952) and that his daughter Joyce is now a heroin addict. He's a widow and feels that his life has become a disappointment. Sam asks Mason to take Joyce to his rural Connecticut cabin to crack her habit and make her go clean. In exchange for unhooking her from the horse, Sam will provide Mason his entire diary collection. These books outline his past criminal activities and all of the associates that were involved in his enterprise. Feeling like this could be his career pinnacle, Mason accepts.
Daniels really excels by creating a sexual tension between Mason and Joyce. In addition, the author introduces Sam's daughter-in-law, a sex-starved nympho who was forced to marry the gay guy. However, Walk the Evil Street isn't a Gil Brewer styled sensuous love affair. By taking the job, Mason finds himself in a murder mystery when a body is found that connects Mason, Sam and his son. After fully contemplating the consequences, Mason begins to suspect that there's more to Sam's proposal than he originally thought. With the combination of Joyce's drugs, the nympho's appeal, Sam's dangerous past and a killer on the loose, Norman Daniels has plenty of slack to tie a great story together.
While the plot gets a bit convoluted, I found Walk the Evil Street as a solid crime-noir with a protagonist I really liked. Despite Joyce's drug addiction, I found her to be an exceptional character that the author clearly developed as the narrative tightened. With the plot's many aspects and character motivations, the narrative rarely left me bored or inattentive. I even appreciated the author's brief nods to Sam's pulpy past as a notorious 1930's gangster. It coincided well with Daniels' early pulp-fiction endeavors with the likes of The Black Bat and The Masked Detective.
Walk the Evil Street is another solid effort by Norman Daniels. He was a workhorse but rarely sacrificed quality for quantity. If you are new to the author, there's no reason not to start here. Highly recommended.
Labels: Norman Daniels
The Masked Detective #01 - Alias the Masked Detective
Luther McGavock #01 - Let the Dead Alone
From Carthage Then I Came (aka Eight Against Utopia)
Blood Oath
Barr Breed #01 - The Body in the Bed
Fresh Fiancés for the Devil's Daughter
Paperback Warrior - Episode 83
Virgil Tibbs #01 - In the Heat of the Night
The Hoodlums
Yellowleg (aka The Deadly Companions)
Tiger By the Tail
Brion Brandd #01 - Planet of the Damned
So Fair, So Evil
Exiled!
Johnny Aloha #01 - Dead in Bed
The Black Bat #01 - Brand of the Black Bat
Murder is my Mistress


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Q: Where to find paykey for an order in woocommerce for adaptive payments? I created a sandbox app for my ecommerce website. I placed many orders with paypal payments. But i am not getting paykey associated with the orders, i am only getting transation id. I want to get paykey for a particular order, so if i want to refund to that order i can use that paykey for that. Where have i found that paykey associated with each order. I am using adaptive paypal payments refund api and i need paykey to refund for an order. The code is below in which i need paykey
curl https://svcs.sandbox.paypal.com/AdaptivePayments/Refund \
-s \
--insecure \
-H "X-PAYPAL-SECURITY-USERID: api_username" \
-H "X-PAYPAL-SECURITY-PASSWORD: api_password" \
-H "X-PAYPAL-SECURITY-SIGNATURE: api_signature" \
-H "X-PAYPAL-REQUEST-DATA-FORMAT: NV" \
-H "X-PAYPAL-RESPONSE-DATA-FORMAT: NV" \
-H "X-PAYPAL-APPLICATION-ID: your_app_id "   \
-d requestEnvelope.errorLanguage=en_US \
-d payKey="where to find"

so can anyone tell me why i am not getting paykey for an order ?

A: The pay key comes back in the response to the Pay request sent to PayPal.  Whatever plugin or code you're using for the checkout should be saving that for you in a session, in the database, or somewhere.  You'd need to track that down and make sure it's getting saved in the DB so that you can pull it when you need to.

A: Orders stored in wp_posts table where the post_type = shop_order.
But that only stores the basic info as Order's Title and Date with an "ID".
All the details of that order stored in wp_postmeta table using that post_id of the order. So we can search in wp_postmeta table using order id like post_id='123456' . There a paykey stored in there.

redpajama_set_name: RedPajamaStackExchange


Commencement ceremonies for the University of Illinois Springfield will begin at 2 p.m. on Saturday, May 15, at the Prairie Capital Convention Center, Ninth and Adams streets in downtown Springfield. Of the 1,198 students eligible to graduate, 694 will participate in the ceremony.
The commencement speaker will be Karen Hasara, who graduated from SSU/UIS in 1972 with a bachelor's degree in Psychology and again in 1992 with a master's degree in Legal Studies. The former Springfield mayor was appointed to the U of I Board of Trustees by Governor Pat Quinn in September 2009.
Hasara started her career as an elementary school teacher and became the first woman to be elected Springfield mayor in 1995. Before her time in city government Hasara was elected to the Illinois House of Representatives in 1986 and the Illinois Senate in 1993. She also serves on the Chancellor's Community Advisory Board, Alumni Leadership Roundtable, and is a founding member of the Alumni SAGE Society.
Mary Bohlen, Associate Professor of Communication and Chair of the Communication Department at UIS, has been chosen to serve as grand marshal. The student speaker will be Priyanka Deo, who will receive a bachelor's degree in Political Science and Sociology/Anthropology.
Accountancy: Brian A. Fiddes – B.A. (Peoria), Rhonda R. Neubauer – B.A., Heidi E. Nance – M.A.
Biology: Aubrey L. Calvert – B.S., Sarah S. Popovich – M.S.
Business Administration: Joseph L. Bordewick – B.B.A, Daniel S. Hunt – M.B.A. (Peoria), Kathryn E. Martin – M.B.A.
Chemistry: Stephanie Gorsuch – B.S.
Clinical Laboratory Science: Sarah K. Barrett – B.S.
Communication: Valeree C. Dunn – B.A., James K. Anderson – M.A.
Computer Science: Timothy W. Robert – B.S., Derek N. Murray – M.S.
Criminal Justice: Leslie N. Reutter – B.A.
Economics: Jeremy T. Downin – B.A.
Educational Leadership: Sharon E. Mann – M.A.
English: Amelia K. Maddox – B.A., Michael A. Gammon – M.A.
Environmental Studies: Scott W. Hicks – M.A.
History: Matthew E. Parbs – B.A., Justin A. Blandford – M.A.
Human Development Counseling: Yolanda S. Six – M.A.
Human Services: Rebecca Wendle – M.A.
Legal Studies: Brett A. Walker – B.A., Ryan Charles Reese – M.A.
Liberal Studies: William L. Paulsen – B.A.
Liberal and Integrative Studies: Solomon D. Roberts-Lieb – M.A.
Management Information Systems: Eric William Penegor – M.S.
Mathematical Sciences: Karen Bradley – B.A.
Philosophy: Thomas J. Farmer – B.A.
Political Studies: Priyanka N. Deo – B.A., Joshua A. Blessman – M.A.
Psychology: Amy J. Quarton – B.A.
Public Administration: Nicholas J. Conklin – M.P.A., John Phillips – D.P.A.
Public Affairs Reporting: Kathleen N. Foody – M.A.
Public Health: Ajay A. Myneni – M.P.H.
Social Work: Cassidy E. Crossman – B.S.W.
Sociology/Anthropology: Nina Tangman – B.A.
Teacher Leadership: Nicole Frye – M.A.
A number of events are planned for graduates, their families and guests, beginning with a reception for Peoria-area students on Friday, May 7. Separate brunches for CAP Honors graduates and online degree graduates are scheduled for Saturday morning, May 15.


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redpajama_set_name: RedPajamaGithub


text:
\section{Introduction}
\label{intrd}

The typical grain temperature in cold and dark molecular cloud cores is about 10\,K, which is primarily maintained by the interstellar radiation field \citep[ISRF;][]{Hulst46}. Several processes may be able to temporally heat the grains to substantially higher temperatures of some tens of K. These include the ultraviolet photons of the ISRF \citep[for small grains,][]{Duley73}, grain collisions \citep[e.g.,][]{Oort46,Wickra65}, and, notably, heating of medium and large grains by cosmic rays \citep[CRs;][]{Watson72,Leger85}.

Volatile species adsorbed on interstellar grain surfaces undergo evaporation when the grain temperature exceeds about 30\,K \citep[hereafter KK19]{KK19}. The severing of the physisorption bonds of evaporating molecules robs the grain of thermal energy. This is the process of evaporative cooling, which can be much faster than radiative cooling. Evaporation is possible for temporally heated grains in regions where volatile interstellar ice is present on grain surfaces. This requires a sufficiently low ambient grain temperature and protection from the ISRF, which induces nonthermal desorption. Interstellar cloud cores are an example of such regions.

The most important aspect considered in astrochemistry in which evaporative cooling plays a role is CR-induced whole-grain heating. The grain cools down through evaporation of molecules that escape into the gas phase. This CR-induced desorption (CRD) helps maintain a continuous presence of heavy species in the gas even in cold and dark molecular cloud cores \citep[hereafter HH93]{Hasegawa93}.

Evaporation of solid grains in vacuum has been considered in the literature only to a limited extent. \citet{Watson72} considered grain heating by CRs, followed by evaporation. These authors estimated that evaporation takes about 10\,s and that about 10\,\% of the grain energy is carried away by the evaporating molecules. We were unable to find other published research focusing on this specific topic, although evaporation of liquid droplets in the atmosphere is rather well covered, in connection with aeronautical or weather-related research \citep[e.g.,][]{Hardy47,Du13}. The lack of this knowledge is illustrated by the uncertainties and unknown parameters encountered by \citet{Herbst06}, \citet{Iqbal18}, and \citet{Zhao18} in their studies of CRD.

In astrophysics, \citetalias{Hasegawa93} employed evaporative cooling in their description of CRD. These authors considered a 0.1$\mu$m grain, heated by an impact of a CR iron nucleus to a temperature of 70\,K. They estimated the grain cooling timescale to be similar to the evaporation time of CO ($\sim10^{-5}$\,s), an abundant icy species in the interstellar medium \citep[ISM; e.g.,][]{Lacy84} that is also highly volatile. \citetalias{KK19} considered CR-heated grains with a variety of temperatures. For the cooling of grains, this study employed the same general approach as \citetalias{Hasegawa93}, with the addition of radiative cooling. In this study, radiative cooling was important in cooling of grains with temperatures of 40\,K and below.

Several aspects make grain cooling a much more complex process than considered by \citeauthor{Hasegawa93} and \citeauthor{KK19}. First, the result analysis of the latter study revealed that icy molecules (e.g., N$_2$) other than CO may play an important role in the cooling.

To contribute to evaporative cooling, grain energy loss from thermal desorption for must be higher than or comparable to energy loss from emission of photons. This requirement can be fulfilled by a number of icy species that are potentially abundant on grain surfaces:  N$_2$, O$_2$, CO, CH$_4$, and even CO$_2$. The evaporation of the more refractory species becomes possible only if the numbers of more volatile molecules are insufficient for grain cooling. In a case with high initial temperature $T_0$ (and therefore high thermal energy $E$ of the grain) for a medium or large grain covered with a complex ice layer, this may induce a step-like evaporation sequence: as the volatiles are depleted, cooling timescales increase and refractory molecules become increasingly prone to thermal desorption until radiative cooling ends the evaporation cascade.

A second aspect that complicates the cooling through evaporation is that all surface molecules are not immediately available for evaporation. Grain mantles consist of multiple layers \citep[e.g.,][]{Garrod13b} containing rather refractory molecules, such as water and ammonia \citep{Merrill76}, that do not evaporate under typical conditions of grain heating. The evaporation rate is therefore affected by the successive revelation of ice monolayers (MLs) below the initial surface. During evaporation, the proportion of surface refractory species grows as the volatile molecules escape. The situation is made even more complex with the ability of subsurface species to become mobile at elevated temperatures \citep{Oberg09}, and to diffuse out to the surface before they evaporate.

We aim to describe the physical process of evaporative cooling of a grain covered with an interstellar ice mantle and clarify if the existing approximation of this cooling needs to be improved. For this purpose, calculations were performed considering all of the above issues (Section~\ref{clcl}). In addition to satisfying curiosity on how the evaporation unfolds (Section~\ref{rslt}), we pay particular attention to implications on the CRD process (Section~\ref{cncl}). Because basic phenomena had to be investigated, only a single variety of grains was considered.

\section{Calculations}
\label{clcl}

\subsection{Evaporation of icy molecules}
\label{clc-evap}

We consider a spherical grain covered by an icy mantle with constant thickness. The mantle consists of ice molecules arranged in MLs. MLs near the icy surface encompass a larger sphere and thus have a larger area. Grain cooling was assumed to start at point when the grain has an elevated temperature $T_0$, achieved by some event just prior to the simulation.

\subsubsection{Basic equations}
\label{clc-eq}

For a chemical species undergoing desorption from a limited surface, $N$ is the initial amount of molecules, which changes to $N_{\Delta t}$ after a time $\Delta t$. The number of evaporated molecules is
   \begin{equation}
   \label{bas1}
N_{\rm evap} = N - N_{\Delta t} \,.
   \end{equation}

For a single time-step $\Delta t$ for a grain with temperature $T$, $N_{\Delta t}$ for species undergoing thermal desorption was calculated as for a first-order decay,
   \begin{equation}
   \label{bas2}
         N_{\Delta t} = N \times {\rm exp}(-\Delta t/t_{\rm evap}) \,,
   \end{equation}
where $t_{\rm evap}$ is the evaporation timescale,
   \begin{equation}
   \label{bas3}
         t_{\rm evap} = \frac{{\rm exp}(E_D/k_BT)}{\nu_0} \,.
   \end{equation}
Here, $E_D$ is the desorption energy of the volatile species under consideration, $k_B$ is the Boltzmann constant, and $T$ is the grain temperature. $\nu_0$ is the characteristic vibration frequency of molecules of species $j$,
   \begin{equation}
   \label{bas4}
         \nu_{0,j} = \sqrt{ \frac{2 n_S E_D}{\pi^2 m} } \,,
   \end{equation}
where $m$ is the molecule mass and $n_S$ is the number density of adsorption sites \citep[cm$^{-2}$;][]{Hasegawa92}. For the purposes of this model, we calculated the $n_S$ and ice thickness assuming that the average molecule occupies a volume equal to a cube with edge length $3.5\times10^{-8}$\,cm. This corresponds to a watery ice that includes tens of percent other molecules with higher molecular masses. The average density is similar to 1\,g\,cm$^{-3}$, in line with the adopted composition of the ice (Section~\ref{icprop}).

The evaporating molecules rob the grain of energy, prompting recalculation of $T$ and initiation of the next calculation step (Section~\ref{clc-t}). The total number of molecules that is desorbed during evaporative cooling from an initial temperature $T_0$ to the final temperature $T_2$ is
   \begin{equation}
   \label{bas5}
      N_{\rm ev.tot} = \int_{T_0}^{T_2} N_{\rm evap}(T,\Delta t) {\rm d}T\,.
   \end{equation}

\subsubsection{Evaporation from the surface}
\label{clc-rig}

The icy mantles on interstellar grains consist of a variety of species, some of which are volatile and some of which are refractory for the temperatures and timescales of interest. The molecules were arranged in a series of MLs. For dark cloud cores with a complete freeze-out, grains with a typical size of 0.1\,$\mu$m carry about 100\,MLs of ice.

Below we outline the sequence for calculating the number of molecules $j$ that evaporate from the $n$-th layer during the $m$-th time step. The ice contains several volatile evaporating species ($i$ to $k$, including $j$).

As a result of desorption, deeper ice layers become increasingly exposed (become part of the surface). For the $m$-th step, the part $X_{n,m}$ of all molecules in layer $n$ that have become exposed to evaporation depends on the amount of molecules evaporated from the layer above in the previous step,
   \begin{equation}
   \label{rig1}
   X_{n,m} = \frac{\sum_i^k N_{{\rm evap},j,n-1,m-1}}{N_{n-1}} \,.
   \end{equation}
Here,  $N_{n-1}$ is the number of adsorption sites in the ice ML above the $n$-th layer and $\sum_i^k N_{{\rm evap},j,n-1,m-1}$ is the total number of molecules of all evaporated species (i.e., vacated adsorption sites) in the previous time step $m-1$. There are a few special cases for $X_{n,m}$. For the first layer (outer surface ML), $X_{1,m}$ is always unity. In the first time step, when evaporation has not yet started and all MLs are complete, $X_{n,1}$ is zero for all MLs, except for the first ML.

The number of molecules $j$ in a layer $n$ exposed to evaporation during the current time step $m$ was calculated by modifying the respective value from the previous time step $N_{X,j,n,m-1}$ by adding the number of exposed molecules and subtracting evaporated molecules from the previous time step $m-1$,
   \begin{equation}
   \label{rig2}
   N_{X,j,n,m} = N_{X,j,n,m-1} + X_{n,m} N_{j,n} - N_{{\rm evap},j,n,m-1} \,.
   \end{equation}
$N_{j,n}$ is the initial number of $j$ molecules in the $n$-th layer, equal to the number of adsorption sites in that layer; this is a constant. For the first time step, the number of evaporated molecules is zero. By paralleling Equations (\ref{bas1}) and (\ref{bas2}), the number of evaporated molecules from $N_{X,j,n,m}$ during the current step $m$ can be calculated,
   \begin{equation}
   \label{rig3}
         N_{{\rm evap},j,n,m} = N_{X,j,n,m} -   N_{X,j,n,m} \times {\rm exp}(-t_m/t_{{\rm evap},j}) \,,
   \end{equation}
where $t_m$ is the length of the $m$-th time step ($\Delta t$ in seconds) and $t_{\rm evap,j}$ is the characteristic evaporation timescale for $j$ at the current temperature in the $m$-th step, following Equation~(\ref{bas3}). At this point, we start the next time step by returning to Equation~(\ref{rig1}),
   \begin{equation}
   \label{rig4}
   X_{n,m+1} = \frac{\sum_i^k N_{{\rm evap},j,n-1,m}}{N_{n-1}} \,.
   \end{equation}

\subsubsection{Diffusive evaporation}
\label{clc-dff}

Experiments with interstellar ice analogs have shown that volatile molecules in bulk ice can escape from the H$_2$O matrix at elevated temperatures \citep[e.g.,][]{Collings04}. This means that the molecules undergo subsurface diffusion. This makes the calculation of evaporation rate more complex because buried volatile molecules can now also undergo evaporation.

In this research, we are interested only in a molecular diffusion that eventually results in thermal desorption, that is, diffusion from bulk ice MLs to the surface, followed by evaporation. The exchange of molecules between bulk-ice layers was ignored for the sake of simplicity and to allow us to focus on processes that result in evaporation. The diffusion rate was calculated according to \citetalias{KK19}, Equation~(1). The time of diffusion to the surface layer of a molecule ($n-1$) MLs below the surface is thus
   \begin{equation}
   \label{dff1}
   t_{{\rm diff},j,n} = t_{{\rm hop},j} \times 6 (n-1)^2 \,,
   \end{equation}
where the hopping time between the MLs was calculated in analogy with the evaporation time of Equation~(\ref{bas3}),
   \begin{equation}
   \label{dff2}
         t_{{\rm hop},j} = \frac{{\rm exp}(E_{b,j,n}/k_BT)}{\nu_{1,j}} \,.
   \end{equation}
Here, $E_{b,j,n}$ is the binding energy for species $j$ in the $n$-th layer, discussed in Section~\ref{icprop}. This barrier has to be overcome by bulk-ice molecules to change their location to an adjacent absorption site (molecule-sized cell), discussed in Section~\ref{icprop}. $\nu_1$ was calculated similarly to $\nu_0$ in Equation~(\ref{bas4}), but by assuming that bulk-ice molecules have desorption energies equal to $2E_D$, following \citet{Garrod13a}.

In the diffusive mantle, surface evaporation and evaporation of diffused bulk-ice molecules was treated separately. Both mechanisms affect each other's rate by changing the number of molecules in ice MLs. Surface evaporation depends on $N_{X,j,n,m}$, the number of exposed molecules $j$ in layer $n$ at time step $m$ (Equation~(\ref{rig3})), while diffusive evaporation occurs for molecules that are still covered by other MLs above. The number of these molecules is abbreviated $N_{C,j,n,m}$. Again, by paralleling Equation (\ref{bas1}), the number of molecules $j$ that evaporated through diffusion is
   \begin{equation}
   \label{dff3}
         N_{{\rm ev-diff},j,n,m} = N_{C,j,n,m} -        N_{C,j,n,m} \times {\rm exp}\left(\frac{-t_m}{t_{{\rm evap},j}+t_{{\rm diff},j,n}}\right) \,.
   \end{equation}
The number of the covered molecules is reduced in each step according to
   \begin{equation}
   \label{dff4}
   N_{C,j,n,m} = N_{C,j,n,m-1} - N_{{\rm ev-diff},j,n,m} - X_{n,m} (N_{j,n}-N_{{\rm ev-diff},j,n}) \,,
   \end{equation}
where the number of $j$ molecules diffused out so far (until the $m$-th step) is
   \begin{equation}
   \label{dff5}
   N_{{\rm ev-diff},j,n} = \sum_1^m N_{{\rm ev-diff},j,n,m} \,.
   \end{equation}
The quantity $X_{n,m}(N_{j,n}-N_{{\rm ev-diff},j,n})$ is the number of $j$ molecules exposed to the outer surface during the current time step $m$ in the $n$-th ML. The number of molecules exposed to the surface for possible evaporation changes according to
   \begin{equation}
   \label{dff6}
   N_{X,j,n,m} = N_{X,j,n,m-1} + X_{n,m} N_{C,j,n,m} - N_{{\rm evap},j,n,m-1} \,.
   \end{equation}
In the case of a diffusive mantle, evaporation from the surface and the bulk-ice occurs concurrently, and the above equation replaces Equation~(\ref{rig2}), which does no consider diffusion. The number of molecules that evaporated from the surface $N_{{\rm evap},j,n,m}$ and the proportion of the $n$-th ML that is exposed to the surface $X_{n,m}$ are still calculated with Equations (\ref{rig3}) and (\ref{rig4}).

The total number of molecules $j$ that evaporated during the $m$-th time step is equal to
   \begin{equation}
   \label{dff7}
N_{{\rm ev.tot,}j,m} = \sum_1^{n_{\rm tot}} (N_{{\rm evap},j,n,m} + N_{{\rm ev-diff},j,n,m})\,,
   \end{equation}
where $n_{\rm tot}$ is the number of ice MLs. By adding $N_{{\rm ev.tot,}j,m}$ step by step, we obtained the total number $N_{{\rm ev.tot,}j}$ of molecules $j$ that evaporated throughout the entire simulation. This quantity corresponds to $N_{\rm ev.tot}$ in Equation~(\ref{bas5}). The number of icy molecules remaining in the MLs during a given step is the aggregate of exposed and covered $j$ populations,
   \begin{equation}
   \label{dff8}
N_{j,n,m} = N_{X,j,n,m} + N_{C,j,n,m}\,.
   \end{equation}

\subsection{Adopted properties of ice}
\label{icprop}
   \begin{figure}
    \vspace{-2cm}
    \hspace{-2cm}
    \includegraphics[width=24cm]{att-poly.eps}
    \vspace{-26cm}
   \caption{Example of source data for the CO molecule for obtaining ice molecule abundances as a function of ice depth. The $y$-axis indicates the part of an ice layer that consists of CO, while the $x$-axis gives the mantle depth from the surface in decimal parts (in Figure~\ref{att-ini} it is multiplied by 100, the number of MLs). The dotted line is the average relative abundance of CO in ice, and the shaded area represents the interval of possible values, while the solid line is the CO abundance function used in the model, Equation~(\ref{ic3}); this is a trend line of the average values.}
              \label{att-poly}
    \end{figure}
   \begin{figure*}
   \centering
    \includegraphics[width=17cm]{att-ini.eps}
    \vspace{-15cm}
   \caption{Initial ice composition per ML, as used in the model.}
              \label{att-ini}
    \end{figure*}
\begin{table}
\caption{Desorption energies and the overall abundance of the icy species relative to water.}
\label{tab-ini}
\centering
\begin{tabular}{l c r}
\hline\hline
 & & Abundance, \\
Species & $E_D$, K & \% of water \\
\hline
N$_2$ & 1000 & 13.9 \\
O$_2$ & 1000 & 1.4 \\
CO & 1150 & 56.7 \\
CH$_4$ & 1300 & 0.4 \\
CO$_2$ & 2580 & 22.2 \\
H$_2$O & 5700 & 100.0 \\
\hline
\end{tabular}
\end{table}
We considered a mantle consisting of compact nonporous ice, which is expected to be similar to the case of interstellar ice \citep{Jenniskens95}. When the grain is heated, evaporation arises, including diffusive evaporation from below the surface. We assumed that the mobility of molecules that ensures diffusion and desorption also allows the remaining ice molecules to occupy energetically favorable adsorption and absorption sites. This keeps the ice compact, while the grain always remains spherical.

To properly model processes in interstellar ice, we need a reliable estimate on the composition of the ice. Our aim is to model cooling for grains that are relevant to the cores of dark molecular clouds. Observations of interstellar ice in these cores indicate polar and nonpolar ice \citep{Tielens91} and give information on the proportions of different icy species \citep[e.g., see the references in Table~A1 of][]{K18mn}. However, these data are insufficient for deriving the composition of a typical icy mantle with a resolution of one ML.

Detailed information on the composition of different ice MLs can fortunately be derived from astrochemical models. For this task, we employed the results of our previous modeling studies \citep[\citetalias{KK19}]{K15apj1,K15apj2,K18iau,K18mn,K17} considering the chemistry in starless and prestellar cores. These models follow the evolution of a cloud core that undergoes delayed gravitational collapse up to a certain maximum gas density in the range $10^5...10^7$\,cm$^{-3}$. Some simulations continue, either considering the heating of a protostellar envelope or steady-state chemical evolution of a stable starless core. The calculated ice composition for each simulation was sampled at the point when maximum density is reached for the first time. This point typically coincides with the freeze-out of heavy molecules. For simulations extending beyond this point, the chemical abundances of ice were sampled for the second time at integration times 2.0\,Myr for \citet{K15apj2}, 0.1\,Myr for \citet{K17}, and 2.5\,Myr for \citetalias{KK19}. These particular times were chosen so that they ensured that the sampled relative abundances of ice components remained in line with observational constraints and were relevant for cloud cores that do not contain stars.

The average abundances of five major and potentially volatile icy species,  N$_2$, O$_2$, CO, CH$_4$ , and CO$_2$ , were obtained for data from each of the above-mentioned papers. In turn, these values were averaged again, obtaining a single value for each `sublayer'. All these models describe the icy mantles as consisting of four sublayers: the surface, and three bulk-ice layers, with averaged thickness proportions 2:31:33:34, respectively. A trend line of these data was then derived to obtain the species' proportion $X$ in ice as a function of ice depth $n$, measured in MLs, as illustrated in Figure~\ref{att-poly}. Minimum and maximum values of $X_n$ were also obtained to ensure that the trend line did not exceed the limits provided by the calculations in these published studies. The abundance functions were expressed with the help of polynomials:
   \begin{equation}
   \label{ic1}
   X_{{\rm N_2},n} = 0.1335x^2 - 0.1869E-1x + 0.1164 \,,
   \end{equation}
   \begin{equation}
   \label{ic2}
   X_{{\rm O_2},n} = 0.03292x^2 - 0.05287x + 0.02123 \,,
   \end{equation}
   \begin{equation}
   \label{ic3}
   X_{{\rm CO},n} = 1.722x^4 - 2.145x^3 + 0.2029x^2 - 0.2340x + 0.4999 \,,
   \end{equation}
   \begin{equation}
   \label{ic4}
   X_{{\rm CH_4},n} = 0.1340x^4 - 0.3104x^3 + 0.2463x^2 - 0.07486x + 0.008230 \,,
   \end{equation}
   \begin{equation}
   \label{ic5}
   X_{{\rm CO_2},n} = 0.1195x^2 + 0.1176x + 0.02771 \,,
   \end{equation}
   \begin{equation}
   \label{ic6}
   X_{{\rm H_2O},n} = 1 - X_{{\rm N_2},n} - X_{{\rm O_2},n} - X_{{\rm CO},n} - X_{{\rm CH_4},n} - X_{{\rm CO_2},n} \,.
   \end{equation}

Here, $x=n/n_{\rm tot}$, that is, $x$ is depth of the n-th ML expressed as part of the mantle thickness.  Equation~(\ref{ic6}) means that the remainder of the ice was assumed to consist of refractory species, primarily water. Figure~\ref{att-ini} shows the ice composition per ML according to Equations (\ref{ic1}-\ref{ic6}), while Table~\ref{tab-ini} lists the overall relative abundance of the icy species, as well as their desorption energies.

Temperature-programmed desorption experiments with interstellar ice analogs (icy mixtures) show that volatile molecules may escape the water-ice matrix. Some molecules diffuse to the surface and evaporate, and some remain trapped in bulk ice. This desorption stops when the surface becomes saturated with water ice or other refractory species \citep{Fayolle11,Martin14}. Molecules buried deeper in the ice are less likely to escape, but small and nonpolar species with low $E_D$ can escape from deeper layers \citep{Oberg09}.

In calculating the number of molecules that is removed from ice through diffusive evaporation, we employed the following approach on energy barriers. The binding energy $E_{b,n}$ (for Equation~(\ref{dff2})) that has to be overcome for a bulk-ice molecule residing in the second ML (immediately below the surface) for diffusion to the surface was taken to be 0.55 times its desorption energy ($2E_D$, see Equation~(\ref{dff2})), i.e. $E_{b,2}=1.1E_D$. The multiplier 0.55 was adopted from the diffusion-to-desorption energy ratio for surface species, as determined by \citet{Minissale16}. This $E_b$ value was then linearly increased with depth, layer by layer. After an assumed number $c$ of layers, $E_b$ (of any molecule) reaches a maximum of 5700\,K, the $E_D$ of water, meaning that the molecule has become immobile, unless the water-ice lattice itself becomes unstable. Thus, the binding energy of bulk ice for icy species $j$, residing in the $n$-th ML, was calculated as
   \begin{equation}
   \label{ic7}
   \begin{array}{l}
	E_{b,j,n} = 1.1E_{D,j} \frac{c-(n-2)}{c} + E_c \frac{(n-2)}{c} \\
	E_{b,j,n} \leq E_{D,{\rm H_2O}} \,.
   \end{array}
   \end{equation}
The parameter $E_c$ is a constant, here taken to be equal to the water absorption energy, $2E_{D,{\rm H_2O}}$. $E_b$ was never allowed to be higher than $E_{D,{\rm H_2O}}$ for any species, except for water, whose $E_{b,{\rm H_2O}}$ was taken to be $1.1E_{D,{\rm H_2O}}$ in all bulk-ice MLs. Molecules in the first ML (surface) do not undergo diffusive evaporation, and their binding energy $E_{b,j,1}$ is irrelevant in this model. Volatile species with smaller $E_D$ reach this value in deeper layers and thus are able to diffuse out from greater depths. Higher grain temperature also encourages escape from greater depth. Our implemented approach takes into account that the ice thickness may decrease as a result of evaporation.

\citet{Fayolle11} found diffusion depths of 20.5 and 80\,MLs, respectively, for CO$_2$ and CO. At laboratory timescales and 100\,K temperature, the diffusion can be estimated to be possible with energy barriers up to about 3200\,K (from Equations (\ref{dff1}) and (\ref{dff2}); 100\,K is the maximum grain temperature considered in this study). The value of the parameter $c$ was taken to be 450\,ML; this caused CO and CO$_2$ diffusion to occur from MLs down to approximately the values indicated by \citeauthor{Fayolle11}. Our model therefore describes evaporation after icy molecule diffusion and trapping of volatiles below a layer of refractory molecules, with a resolution of 1\,ML.

As the molecules leave, the size of the grain decreases, as does the surface area $\pi r^2$, where $r$ is the total radius (size of the olivine core $a$ plus ice thickness) of the grain. This was taken into account by matching the total number of icy molecules with the number of adsorption and absorption sites available in an icy mantle of a certain thickness. With this model, the ice thickness was reduced by up to a maximum of 13\,MLs for $T_0=100$\,K. The resolution of the grain size is 1\,ML, that is, no partial layers were considered in the model.

\subsection{Change in grain temperature}
\label{clc-t}

Each molecule $j$ robs the grain of a certain amount of energy $E_{{\rm evap},j}$. For the special case with low $T$, $E_D\gg kT$, and considering a large number of molecules, the energy lost by the grain can be estimated as their $E_D$ plus the average remaining energy of the departing molecules,
   \begin{equation}
   \label{t2}
	E_{{\rm evap},j} = \frac{\int_{E_{D,j}}^{\infty} E_j {\rm exp}(E_j/kT) {\rm d}E_j}
	{\int^{\infty}_{E_{D,j}}{\rm exp}(E_j/kT){\rm d}E_j} = E_{D,j} + kT \,,
   \end{equation}
where $E_j$ is the energy of molecule $j$ residing on grain surface. The result means that the average surplus energy with which the molecule leaves the grain is equal to $kT$, while energy equal to $E_{D,j}$ is consumed in the severing of the physisorption bond and is thus also lost from the grain. The simple and interesting finding of evaporating molecules having energy $kT$ can be interpreted as follows. The average energy of surface molecules is $kT$. At a given time instant, a small proportion of molecules exceed (or receive a kick) and traverse the energy barrier $E_D$ while keeping a tail of surplus energy, which on average still corresponds to the temperature of the grain at the evaporation time of the molecule.

The energy carried away by all molecules $j$ is $N_{{\rm ev.tot},j}E_{{\rm evap},j}$. The total energy carried away by all volatiles $i$ through $j$ to $k$ is
   \begin{equation}
   \label{t6}
	E_{\rm evap} =  \sum^k_i{N_{{\rm evap},j}E_{{\rm evap},j}}\,.
   \end{equation}

Alongside evaporation, radiative cooling takes place. The energy lost through radiative cooling is $E_{\rm rad}$. It was calculated for each time step $m$ by solving Equation (21) of \citet{Cuppen06},
   \begin{equation}
   \label{t7}
	\frac{E_{\rm rad}}{t_m} =  4\pi^2 r^2 \int_{\rm91.2\,nm}^{\rm1\,cm} Q_{\rm em}(\lambda) B_\lambda(T) {\rm d}\lambda \,,
   \end{equation}
where $Q_{\rm em}$ is the emission coefficient and $B$ is the Planck function. Here we did not distinguish the icy mantle from the olivine grain core. While we considered the emission of photons in the wavelength range of 91.2\,nm to 1\,cm, only photons with $\lambda>5\,\mu$m contribute to grain cooling with $T\leq100$\,K. Radiative cooling becomes more effective than surface evaporation of N$_2$ at temperatures of 31\,K and below. This can be compared to the 34\,K threshold obtained with the simpler radiative cooling approach of \citet{Duley73}, which we used in our previous study \citepalias{KK19}.

The thermal energy lost by the grain during the cooling process is
   \begin{equation}
   \label{t8}
	E_{\rm cool} = E_{\rm evap} + E_{\rm rad}\,.
   \end{equation}
The thermal energy changes for the grain were followed by obtaining the $E_{\rm evap}$ and $E_{\rm rad}$ for time steps of length $t_m$ with a fixed current temperature $T$. The loss of the thermal energy $E_{\rm cool}$ of the grain during the $m$-th time step was used to calculate a new temperature according to
   \begin{equation}
   \label{t9}
	T_{m+1} = T_m - E_{\rm cool}/C(T_m) \,.
   \end{equation}
$C(T_m)$ is the heat capacity of the grain at a temperature $T_m$. $C(T_m)$ was calculated separately for the icy mantle (with current thickness $n_{tot,m}$) and the 0.1\,$\mu$m olivine grain core, according to \citet{K16}.

\subsection{Model resolution and limitations}
\label{inststp}

The length $t_m$ (or $\Delta t$) of the time steps for the simulations was calculated as a function of the simple radiative cooling rate by \citet{Duley73}. At low temperatures, simple proportionality is sufficient. At high temperatures, where evaporation dominates, a steeper function has to be used. Moreover, regardless of $T_0$ (except for $T_0<30$\,K), rapid evaporation occurs directly from the surface during the first steps of each simulation, and the temperature also rapidly falls. This requires shorter $t_m$ at the beginning of each simulation.

The number of molecules that evaporated during the initial stages subsequently affects the cooling at lower temperatures, when evaporation is regulated by the gradual revelation of subsurface layers and diffusion. These different cooling patterns required unique $t_m$ functions for each simulation with differing $T_0$.  Typically, 100 to 10000 molecules of a single species evaporate during a single time step. Only small decreases in the grain temperature ($\Delta T$ similar to or smaller than 0.01\,K) were tolerated. Test calculations with one hundred times shorter time steps changed the resulting cooling times only by slightly more than 0.1\,\%, therefore we considered the accuracy of the simulations to be satisfactory.

The minimum length of $\Delta t$ is limited by the physics included in the model. For timescales shorter than about $10^{-10}$\,s, heat transfer within the grain has to be considered. Additionally, molecules departing from the grain with a thermal velocity cover a distance that is equal to the grain radius within about $10^{-9}$\,s. Thus, models of high-temperature grains undergoing massive evaporation at these timescales also need to consider the pressure of the evaporated gas. Finally, the calculation of the grain heat capacity has an upper application limit of 150\,K.

\section{Results}
\label{rslt}

We implemented this sequence of calculations in a \textsl{Fortran} program that we called ``TCOOL''. Because we did not find previous studies that would have considered evaporation of grains in any detail, this research focuses on explaining the main qualitative aspects related to the evaporation process. Two complementary viewpoints have to be considered: first, the evolution of the grain temperature after a heating event, and second, the chemical consequences resulting from evaporation. The latter aspect considers changes in the composition of the icy mantle and the number of different molecules desorbed, which in interstellar clouds affects the chemistry of the gas phase.

We considered only `standard' grains with an olivine core of size of 0.1\,$\mu$m for consistency and ice mantle thickness of 100\,MLs. These parameters are relevant for grains in dark cloud cores. The ice composition was described with Equations (\ref{ic1}-\ref{ic5}). Numerous combinations for grain size, mantle thickness, and ice composition are possible, and the exploration of these is reserved for a study in the near future. Impacts of heavy CR ions can heat these standard grains to a temperature of about 90\,K \citep{K16}.

Unlike the case of radiative cooling, evaporative cooling occurs in an unique way for different initial temperatures. This is because previous evaporation affects the number of molecules that is prone to evaporation during a given time step with a given temperature. Therefore, it makes sense to explore cooling from different temperatures. Similarly to \citetalias{KK19}, we employed a set of initial temperatures $T_0$, where each value differed by 10 degrees, in the range 20--100\,K (i.e., 100, 90, 80, 70, 60, 50, 40, 30, and 20\,K). The final temperature was always assumed to be $T_2=10$\,K, a standard value for grains in dark clouds.

\subsection{Evolution of the grain temperature}
\label{res-T}
   \begin{figure*}
   \centering
    \includegraphics[width=20cm]{att-T.eps}
		\vspace{-20.5cm}
   \caption{Calculated temperature for icy grains with different initial temperatures $T_0$ (K). The inset shows the last few degrees of cooling (before the stop at 10\,K), which takes most of the entire cooling time.}
              \label{att-T}
    \end{figure*}
   \begin{figure*}
   \centering
    \includegraphics[width=18cm]{att-E.eps}
		\vspace{-14cm}
   \caption{Energy released during cooling from different initial grain temperatures $T_0$. The vertical axis shows the amount of energy that is lost by the grain during cooling, and the horizontal axis shows the evolving grain temperature. The solid line represents the energy lost through evaporation, and the double line plots the radiated energy.}
              \label{att-E}
    \end{figure*}
\begin{table*}
\caption{Indicative results on grain cooling from different initial grain temperatures $T_0$ down to the ambient grain temperature of 10\,K.}
\label{tab-E}
\centering
\begin{tabular}{r l r l r c}
\hline\hline
$T_0$ & $E_{\rm cool}$, MeV & \% $E_{\rm rad}$ & $T_{\rm switch}$, K & $t_{\rm switch}$, s  & Final MLs \\
\hline
100\,K & 2.91 & 14.4 & 54.0 & 14.7 & 87 \\
90\,K & 2.07 & 14.2 & 48.8 & 19.1 & 90 \\
80\,K & 1.42 & 15.8 & 44.8 & 22.9 & 94 \\
70\,K & 0.933 & 17.8 & 40.9 & 28.1 & 96 \\
60\,K & 0.572 & 21.2 & 37.3 & 29.5 & 98 \\
50\,K & 0.316 & 29.0 & 34.1 & 46.1 & 99 \\
40\,K & 0.151 & 57.2 & 33.6 & 45.5 & 100 \\
30\,K & 0.0584 & 94.1 & 30.0\tablefootmark{a} & 0.0 & 100 \\
20\,K & 0.0147 & 100.0 & 20.0\tablefootmark{a} & 0.0 & 100 \\
\hline
\end{tabular}
\tablefoottext{a}{Cooling dominated by radiation from the start.}
\tablefoot{$E_{\rm cool}$ is the total released thermal energy of the grain, with the proportion of energy radiated away as photons $E_{\rm rad}$ given as the percentage of $E_{\rm cool}$. $T_{\rm switch}$ and $t_{\rm switch}$ are the temperature and time for switching from evaporative to radiative cooling. The last column gives the final ice thickness after the grain has cooled.}
\end{table*}
Figure~\ref{att-T} shows the evolution of the grain temperature during cooling. The wavy structure for some of the curves is an artifact that arises because the model only considers full MLs of icy molecules (no partially filled layers). Each time the number of MLs is reduced by one (due to evaporation), some molecules in the bulk-ice layers suddenly become more susceptible to diffusion and subsequent evaporation, according to Equations (\ref{dff1}) and (\ref{ic7}). This results in irregularities in the momentary cooling rate.

Comparing the temperature curves for different initial grain temperatures $T_0$, we find that cooling from higher $T_0$ takes much longer (see the inset of Figure~\ref{att-T}). This can be  attributed to any objects undergoing evaporative cooling with a limited pool of volatiles. In the modeled case of icy interstellar grains, any easily evaporating molecules are rapidly removed at high temperatures. This means that a grain cooling from $T_0=100$\,K will have fewer available volatile molecules at 70\,K than a grain that has been heated to just $T_0=70$\,K. Instead, the continued cooling of the grain with $T_0=100$\,K has to rely on molecules that have higher energy barriers and longer evaporation timescales. These are volatiles that reside deeper below the icy surface of the grain, or species that otherwise could be regarded as refractory, such as CO$_2$.

The cooling curve is regulated by evaporation until the grain reaches a point when photons carry away more energy than molecules. This transition occurs gradually because the surface volatiles have long been depleted, and for volatiles in bulk ice, it takes increasingly longer to diffuse to the surface at the current temperature $T_m$ because molecules in shallower layers diffuse out first, while $T_m$ continues to decrease. The transition or switching from a cooling process controlled by evaporation to radiation-dominated cooling can be characterized by the temperature $T_{\rm switch}$ and time $t_{\rm switch}$, when $E_{\rm rad}$ for the current step becomes greater than $E_{\rm evap}$. $T_{\rm switch}$ and $t_{\rm switch}$ are unique for each $T_0$ regime, and are listed in Table~\ref{tab-E}.

The transition to radiation is significant because radiative cooling takes longer and the grain spends more time in an elevated temperature.  Figure~\ref{att-E} shows that $E_{\rm rad}$ starts to grow significantly only shortly before $T_{\rm switch}$. At 30\,K or below, the timescale for even direct surface evaporation of $N_2$ is longer than the radiative cooling timescale, and grain cooling is dominated by radiation.

\subsection{Evaporated molecules}
\label{res-evap}
   \begin{figure*}
   \centering
    \includegraphics[width=20cm]{att-tot.eps}
		\vspace{-16cm}
   \caption{Comparison of the total numbers of evaporated molecules in simulations with different initial grain temperatures $T_0$.}
              \label{att-tot}
    \end{figure*}
   \begin{figure*}
   \centering
    \includegraphics{att-evap.eps}
		\vspace{-17cm}
   \caption{Evolution of the numbers of icy molecules that evaporated during cooling from different initial grain temperatures $T_0$. The temperature curve is shown for context. $T_0$ can be read as the maximum value of the secondary vertical axis on the right-hand side of each plot. The $T_0=20$\,K regime has no evaporated molecules.}
              \label{att-evap}
    \end{figure*}
Figure~\ref{att-tot} shows that an appreciable number of volatiles are evaporated by all $T_0$ regimes, except for that with $T_0=20$\,K. Even the $T_0=40$\,K and $T_0=30$\,K regimes release tens of thousands of molecules, even though the majority of grain energy in these cases is lost through radiation.

Only the regime with $T_0=100$\,K is able to evaporate any number of CO$_2$ molecules. Only tens of these molecules are evaporated, largely because CO$_2$ primarily resides deep below the surface of ice in our model (Figure~\ref{att-ini}). Side calculations with the model revealed that this situation changes for grains with only few volatiles and available surface CO$_2$, where the latter can serve as a major cooling agent.

Table~\ref{tab-E} shows that the majority of thermal energy for high-$T_0$ grains is lost through evaporation. The total number of evaporated molecules therefore primarily depends on the heat content of a grain, not on its cooling time, as was assumed by \citetalias{Hasegawa93}. The other important parameter is the number of accessible molecules. Grains with higher $T_0$ release more molecules \textit{\textup{and}} lose more energy through radiation. Table~\ref{tab-E} shows that the proportion of $E_{\rm rad}$ grows with $T_0$ until 90\,, when there is a sufficient number of volatile molecules for evaporation. The proportion of $E_{\rm rad}$ for the $T_0=100$\,K regime is higher than that for $T_0=90$\,K because the lack of volatiles has taken effect.

Figure~\ref{att-evap} shows details of the evaporation progress. The number of evaporated molecules grows most rapidly at the beginning of simulations, and the evaporation rate decreases exponentially until evaporation becomes insignificant at approximately $t_{\rm switch}$. CO and N$_2$ are the primary coolants, while O$_2$ and CH$_4$ are of secondary importance. Because methane has the highest $E_D$, its evaporation numbers vary more strongly with temperature. Except for the $T_0=100$\,K simulation, in all cases N$_2$ evaporation is fastest; CO takes over when N$_2$ has been depleted.

\subsection{Ice composition}
\label{rslt-ic}
   \begin{figure*}
   \centering
    \includegraphics{att-lay.eps}
		\vspace{-8cm}
   \caption{Composition of ice for simulations with different $T_0$, given at several temperatures (multiples of 10\,K) that occur during cooling. Low temperatures are not shown because the ice composition changes little after $T_{\rm switch}$. The results for each $T_0$ are depicted in a pair of plots, with the respective temperatures indicated for only one of the species, but the temperature is also relevant for other species for the given $T_0$. For clarity, the respective curves for each species are indicated only in every other plot. Water and carbon dioxide are not included because their numbers remain unchanged. The curve marked with the value of $T_0$ is always the initial ice composition, fully depicted in Figure~\ref{att-ini}.}
              \label{att-lay}
    \end{figure*}
Figure~\ref{att-lay} shows the depletion of icy volatiles at several points for each simulation. We note that molecules remain associated with their parent ML for the entire duration of the simulation, but the properties of molecule populations in each ML (e.g., the number of the above MLs in Equation~(\ref{dff1})) were calculated correctly by evaluating the actual ice thickness in each integration step. Table~\ref{tab-E} lists the final ice thickness for each simulation after the entire heating-cooling event, when the grain has returned to its ambient temperature of 10\,K.

Of all the molecules, the most volatile ones, nitrogen and oxygen, are depleted most. Their evaporation also reduces the grain temperature, making the removal of other species less efficient. For the simulation with highest $T_0=100$\,K, N$_2$ and O$_2$ were completely removed down to the 22nd\,ML, CO to the 18th\,ML, CH$_4$ to the 16th\,ML, with the overall loss of volatiles amounting to 13\,MLs, which is a considerable part of the nonpolar CO-dominated ice layer. Ten percent or more of N$_2$ molecules are removed down to the 30th\,ML, while >10\,\% CO molecules are removed down to the 25th\,ML.

For simulations with lower $T_0$, these molecule removal depths are increasingly lower. Substantial changes in the composition of the surface (e.g., complete removal of a species) occur for $T_0$ down to 40\,K. For $T_0$ values of 50\,K and above, a complete change in the properties of the surface occurs as it changes from nonpolar CO-dominated to polar H$_2$O-dominated.

\section{Conclusions on cosmic-ray induced desorption}
\label{cncl}

\begin{table}
\caption{Comparison of molecular CRD yields at $T_0=70$\,K after 10$^{-5}$\,s \citepalias[the approach of][]{Hasegawa93} and the total yield, after complete cooling of the grain from $T_0$ to 10\,K.}
\label{tab-crd}
\centering
\begin{tabular}{c l l}
\hline\hline
 & $Y_{\rm HH93}$ & $Y_{70}$ \\
Molecule & after 10$^{-5}$ s & total \\
\hline
N$_2$ & 4.52E+5 & 1.77E+6 \\
O$_2$ & 8.03E+4 & 3.07E+5 \\
CO & 7.11E+5 & 5.48E+6 \\
CH$_4$ & 2.06E+3 & 5.94E+4 \\
\hline
\end{tabular}
\end{table}
   \begin{figure}
		\hspace{-1cm}
    \includegraphics[width=18cm]{att-co.eps}
		\vspace{-15cm}
   \caption{Comparison of evaporation (CRD) yields for the CO molecule from simulations with different $T_0$ values at the time $T_{{\rm evap,CO,}T_0}$ $Y_t$ (empty columns) and total yield $Y$ from the full-length simulations (filled columns). The simulation with $T_0=20$\,K does not produce evaporated CO.}
              \label{att-co}
    \end{figure}
   \begin{figure}
		\vspace{-2cm}
		\hspace{-1cm}
    \includegraphics[width=20cm]{att-Rco.eps}
		\vspace{-20cm}
   \caption{Time-averaged CRD rates of CO molecules for several $T_0$ regimes of CR-induced whole grain heating at an $A_V$ of 10\,mag.}
              \label{att-Rco}
    \end{figure}
The evaporative cooling process has the overall chemical consequence of removing volatile molecules from ice. When the grains have been heated by CRs, the number of molecules that evaporated is the desorption yield $Y_{T_0}$. Figure~\ref{att-co} compares our obtained yields with those obtained by the approach in the original research of \citetalias{Hasegawa93}, $Y_{HH93}$. These authors considered grains heated by iron CR nuclei to 70\,K and assumed that the heated grain cools (and the majority of volatile molecules depart) in a time similar to the evaporation timescale of the CO molecule, which they assumed to be 10$^{-5}$\,s (in our model, the precise $t_{\rm evap, CO}$ is $1.81\times10^{-5}$\,s). A requirement for this approach is that there always must be a sufficient number of molecules available for immediate evaporation. Our study shows that this requirement is not met, primarily because cooling from 70\,K requires removal of several layers of volatiles. Table~\ref{tab-crd} shows that in our model with $T_0=70$\,K, only about one-fourth of N$_2$ and one-eigth of CO molecules have evaporated until 10$^{-5}$\,s, compared to the total yield $Y_{70}$. Grain temperature in the former case drops to only 66.9\,K.

Moreover, the study shows that it is inherently incorrect to relate the CRD yield to the characteristic evaporation timescale of CO (or another coolant species). When it is attributed to different temperature regimes, as was done by \citetalias{KK19}, this approach results in short cooling times at high temperatures and long cooling times at low temperatures. In other words, the assumed timescale is a constant relative to the evaporation rate of the coolant. As a result, the numbers of evaporated molecules are similar for all temperatures, which is obviously incorrect.

Figure~\ref{att-co} demonstrates that the discrepancy of the total CO molecule desorption yield $Y_{70}$ with the yield $Y_t$ resulting from grain cooling until $t_{\rm evap, CO}$ grows with $T_0$. The values of $Y_t$ can be expected to be equal for all $T_0$ values in an ideal case of unlimited evaporation of surface CO. The obtained values are not constant because the model is complex; it considers several coolants, arranged in layers, and radiative cooling. However, for the $T_0$ range of 70-100\,K, $Y_t$ remains fairly unchanged at $(7...10)\times10^{5}$, in line with the discussion above.

These findings make it tempting to reevaluate the results of \citetalias{KK19}. These authors determined that CRD is most effective for grains that are moderately heated by CRs to 40-60\,K. Here, the CRD effectiveness for the important CO molecule (i.e., the average rate of CO desorption, $R_{\rm CRD, CO}$) can be reestimated by multiplying the frequency of the CR-induced grain heating events $f_{T_0}$ \citepalias[from][]{KK19} with the total desorption yield $Y_{T_0}$ for the CO molecule obtained in this work. However, these data are rather incompatible because $Y_{T_0}$ obtained from our study is relevant only for the assumed average ice composition per layer, while $f_{T_0}$ does not have a single value; it is a function of the column density that is traversed by CRs, proportional to the interstellar extinction $A_V$. Nevertheless, we made such an indicative comparison for $A_V=10$\,mag; Figure~\ref{att-Rco} shows that the grain heating to moderate temperatures is still more effective at evaporating CO than low- and high-temperature regimes.

The obtained CRD yields are relevant only to the presented grain model, with its given ice composition, grain size, and ice thickness. These parameters conform to starless cloud core ice that is abundant with volatile molecules. In case of thin ice or ice that is poor in volatiles, the obtained desorption yields overestimate the CRD efficiency for volatile species. On the other hand, the desorption rate of CO$_2$ is likely underestimated for photoprocessed ice that is rich in CO$_2$ and poor in CO. This is because CO$_2$ and other species with similar $E_D$ can become major coolants in the absence of more volatile ice compounds. Moreover, vastly different results can be obtained  when the ice composition is similar but its chemical structure\textup{} is different. We here considered an icy mantle with volatiles that were concentrated mostly in the outer layers. If volatiles lie below tens of layers that are dominated by water, evaporation for them can be expected to be slow and limited.

Massive evaporation at the initial stages of cooling with $T\approx T_0$ depletes the reservoir of available volatile molecules. This reduces the cooling rate and prolongs the time the grain spends in moderate but still elevated temperature. This leaves more time for molecules to overcome barriers in processes other than desorption, such as diffusion or chemical reactions. This can result in an additional chemical processing of icy mantles on grains suffering from CR impacts that heat grains to high initial temperatures \citep{K14,K15aa,Reboussin14}.

\begin{acknowledgements}
JK has been funded by ERDF postdoctoral grant No. 1.1.1.2/VIAA/I/16/194 'Chemical effects of cosmic ray induced heating of interstellar dust grains'. The work of JRK has been funded by ERDF project 'Physical and chemical processes in the interstellar medium', No 1.1.1.1/16/A/213. Both projects are being implemented in Ventspils University of Applied Sciences. We also thank Ventspils City Council for its persistent support.
\end{acknowledgements}

   \bibliographystyle{aa}

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The IELTS and TOEFL essay's success is determined by its introduction. In this writing lesson we will look at how to construct an introduction paragraph that will not only make reading easier for the grader, but will also keep you focused on what you need to say.
It really is as simple as 1, 2, 3, 4.


text:
Warrior is een hopvariëteit, gebruikt voor het brouwen van bier.

Deze hopvariëteit is een "dubbeldoelhop", bij het bierbrouwen gebruikt zowel voor zijn aromatische als zijn bittereigenschappen. Deze Amerikaanse variëteit werd gekweekt in de Yakima Chief Ranches.

Kenmerken
 Alfazuur: 15 – 18%
 Bètazuur: 4,5 - 5,5%
 Eigenschappen: zachte aangename bitterheid

Zie ook
 Lijst van hopvariëteiten

Hopvariëteit

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text:
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Contact Office of Gift Planning at plannedgiving@dominican.edu or (415) 257-1396 to learn more about the renewed and expanded tax incentives for 2021.
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ANAAL NATHRAKH—A night at the movies with Dave Hunt.
November 21, 2011 Jonathan Horsley
It was the other week at Candlefest in London that reminded the Deciblog that Dave Hunt, vocalist and master of ceremonies for Birmingham/Orange County(!) duo Anaal Nathrakh, had dutifully filled an optional extra on an email interview. It was right around the time when Hunt was mooting the idea that the Deciblog had imbibed amphetamines—which…
Featured, Interviews anaal nathrakh, Benediction, black metal, Candlelight Records, Dave Hunt, extreme metal, movies, Passion.
STREAMING: Anaal Nathrakh "Paragon Pariah"
March 21, 2011 Chris Dick
Obliterating everyone in attendance at this year's Scion Rock festival in Pomona, California, British black metallers Anaal Nathrakh appear poised for extreme metal dominance. Though the group's history takes them back to 1998 (debut album, The Codex Necro, landed in 2001), it's the recent output, namely 2009's In the Constellation of the Black Widow, that's…
Featured anaal nathrakh, Candlelight.
Scion Rock Fest 2011 – Unannounced Bands, uh Announced!
January 20, 2011 Chris Dick
Scion A/V announced the partial line-up for the 2011 Scion Rock Fest. It was ridiculous! But it just got even more ridiculous! So much so, Scion A/V proposed to fly Decibot from his digital hovel to Pomona, CA to witness the awesomeness, but Decibot declined because the new Rev Theory album is imminent. OK, so…
News Agalloch, anaal nathrakh, Atheist, Bastard Noise, Black Breath, Bonded By Blood, Christian Mistress, Cough, Crom, Dark Castle, Death Angel, Dispirit, Floor, Fuck The Facts, immolation, Integrity, Kvelertak, Morbid Angel, Municipal Waste, Nails, Necrite, Obituary, Primate, The Body, Woe, Wormrot.
I always like to refer to Type O Negative as "Flock of Assholes." But we have somewhat better haircuts.
- Peter Steele, Type O Negative (Issue #69)


Waterfront acre plus on Mystic Lake with a floating dock! Newly renovated and refreshed custom built Ranch. The living room has a wood burning fireplace, built in bookshelves, sliders out to a balcony, overlooking the trees and water views of the lake. The screened porch is a great place to relax enjoying the privacy. The two bathrooms have been remodeled with new tile flooring, vanities and toilets. The master bedroom is en suite, the two other bedrooms have knotty pine floors which have been redone, just gorgeous. The downstairs family room has new flooring and there also is another bonus room downstairs, full cellar on the other side for the woodworking enthusiast. Two car garage w/ direct access to the kitchen, the kitchen is a large eat in with lots of cherry cabinets, full walk in pantry, desk space. We have left the kitchen renovation for the new homeowners to make their dream kitchen or take the wall down between the kitchen and the living room opening up the large floor plan more.


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Home / Events / Monophonics, It's Only Us Tour w/ Paul & the Tall Trees
Monophonics, It's Only Us Tour w/ Paul & the Tall Trees
8:00pm, March 21, 2021 | WAVE
Monophonics are just hitting their stride as one of the premier soul bands in the country. The Bay Area band delivers cinematic songs with timeless hooks anchored by Kelly Finnigan's soulful organ and powerhouse vocals. Their sound is inspired equally by classic soul, heavy funk, psychedelic rock, and classic American songwriting.
Monophonics' meticulous, yet raw recordings start just like their raucous and soulful live shows, "in the same room, cutting live as a rhythm section together," Finnigan explains. Ryan Scott and Mike Rinta's horn and string arrangements add accents and sonic colors to the tracks, completing the soul symphony. In the tradition of Stax, Muscle Shoals, Daptone and Dunham, Monophonics' sonic approach is equal parts classic analog gear, (everything's recorded to an old Tascam eight-track 1/4" tape machine), old-fashioned woodshedding, and Ian McDonald and Kelly Finnigan's late night overdubs and studio wizardry. "We're from the same school as the producers from the studios we love. We use the tools that we have to make the best records we can."
W/ PAUL AND THE TALL TREES
Paul Schalda is a poet, perhaps a bit of a tortured soul, a hopeless romantic, and a guy from Staten Island. That last bit might not make sense to people who aren't from New York. Let's say that it puts the blue in his collar and sticks his feet to the ground. Paul's music seems to embody the unexpected overlap of The Band's Americana, Ian MacKaye's unhinged emotion, Otis Redding's raw soul, and the doo-wop melodies his father, Bill Schalda Sr. (a member of Brooklyn vocal group, The Montereys) raised him on. His sophomore album is produced by long time collaborator and old friend Tommy Brenneck and Big Crown's own Leon Michels. For the people who are already fans of Paul's debut Our Love In Light, the growth and maturity will be impossible to miss.
*This event was originally scheduled for 5/16/20 and rescheduled for 10/15 before being rescheduled again for 3/21/21*
2021-03-21 2021-03-21 20:00:00 Monophonics, It's Only Us Tour w/ Paul & the Tall Trees
Monophonics are just hitting their stride as one of the premier soul bands in the country. The Bay Area band delivers cinematic songs with timeless hooks anchored by Kelly Finnigan's soulful organ and powerhouse vocals. Their sound is inspired equally by classic soul, heavy funk, psychedelic rock, and classic American songwriting.

Monophonics' meticulous, yet raw recordings start just like their raucous and soulful live shows, "in the same room, cutting live as a rhythm section together," Finnigan explains. Ryan Scott and Mike Rinta's horn and string arrangements add accents and sonic colors to the tracks, completing the soul symphony. In the tradition of Stax, Muscle Shoals, Daptone and Dunham, Monophonics' sonic approach is equal parts classic analog gear, (everything's recorded to an old Tascam eight-track 1/4" tape machine), old-fashioned woodshedding, and Ian McDonald and Kelly Finnigan's late night overdubs and studio wizardry. "We're from the same school as the producers from the studios we love. We use the tools that we have to make the best records we can."

W/ PAUL AND THE TALL TREES

Paul Schalda is a poet, perhaps a bit of a tortured soul, a hopeless romantic, and a guy from Staten Island. That last bit might not make sense to people who aren't from New York. Let's say that it puts the blue in his collar and sticks his feet to the ground. Paul's music seems to embody the unexpected overlap of The Band's Americana, Ian MacKaye's unhinged emotion, Otis Redding's raw soul, and the doo-wop melodies his father, Bill Schalda Sr. (a member of Brooklyn vocal group, The Montereys) raised him on. His sophomore album is produced by long time collaborator and old friend Tommy Brenneck and Big Crown's own Leon Michels. For the people who are already fans of Paul's debut Our Love In Light, the growth and maturity will be impossible to miss.

Get Tickets

*This event was originally scheduled for 5/16/20 and rescheduled for 10/15 before being rescheduled again for 3/21/21*
 650 E. 2nd St. N. Wichita, KS America/Chicago
650 E. 2nd St. N.


Progressive Web Applications introduced in the year 2015, is now one of the major features you must have in your web development kit. It gives your client a similar experience on phones as on desks. You must have observed your computer browser displaying an application-like page, and progressive web applications do the same for the user. It enables the user to have the same experience on their mobiles.
A research report says over 85% of client communications will happen through a machine on the business end by the year 2020. Chat box are the triumph of robots taking over humans. It helps you retain the client for long and solve their questions instantly.
Working in the field of web development needs the dedication to keep updated to the new trends. It's time to make a change to your website before it's too late. Need any assistance regarding the emerging trends in the web development field? Connect to us, and our experts will help you with the best viable solution.


text:
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How prevalent is HPV in vaccinated adolescents?
©Evan_Lorne/shutterstock.com
Judith M. Orvos, ELS
In adolescents, detection of a cervical human papillomavirus (HPV) infection may signal an increased risk of detection of the virus in the oral cavity, but incidence declines after vaccination. That is one of the key findings of new research on sexually active female adolescents who received the quadrivalent vaccine.
Published in JAMA Network Open, the conclusion is from a longitudinal cohort study of repeated collection of oral rinse specimens. Researchers took them from sexually active female adolescents from October 19, 2007 to March 9, 2017 at a large adolescent health center in New York, New York that provides free health care, including HPV vaccination.
The authors aimed to identify risk factors for oral HPV in sexually active female adolescents receiving the quadrivalent vaccine and the main outcome was prevalence of HPV in the oral cavity. Median age of the 1259 participants was 18. Of them, 50.7% were African American, 45.2% were Hispanic, 3.4% reported another race/ethnicity, and race/ethnicity was unspecified in 0.7%).
The median age at first sexual activity in the cohort was 14.8 years and 92.2% reported having had oral sex. They were followed up every 6 months until age 25 with collection of oral, cervical, and anal samples for HPV testing.
Of the oral rinse samples taken at baseline, 6.2% contained HPV DNA. Detection of oral HPV decreased significantly with time since first sexual activity, independent of age and concurrent detection of cervical HPV. Comparing 4 or more years with 1 year or less, the odds ratio (OR) was 0.45. Of the oral cases of HPV detection, 88% cleared within 12 months. The authors noted, however, that two participants had persistence of oral HPV for more than 1 year, one with HPV-6/11 and the other with HPV-16. Neither had oral or cervical disease.
Detection of HPV-6, HPV-11, HPV-16, and HPV-18 was significantly lower in participants who had received at least one dose of the quadrivalent HPV vaccine at time of enrollment than in those who were unvaccinated (OR 0.20; 95% CI, 0.04-0.998).
"Although our study population had a greater median number of sexual partners and a younger age at sexual initiation compared with previous studies," the authors concluded, "we observed a significant vaccination benefit reflected by the lower prevalence of 4vHPV vaccine types. Our findings suggest that concurrent detection of an HPV infection in the cervix may be a risk factor for detection of HPV in the oral cavity."
IUD Provider Toolkit
Contemporary OB/GYN 2018 Maternal Mortality coverage
Technology Matters: Next-Generation RNA-Based Diagnostics Improve Disease Detection
The Role of Hysteroscopy in Reproductive Health


text:
Brought to you by [Blacklight](http://www.blacklight.co.za).

Deployment role for standard apps. Deployment can be done via Git, SVN, Mercurial, and Rsync. Tries to imitate a similar
structure to what you would see with other deployment tools such as [Capistrano](http://capistranorb.com/). The final
directory structure will look something like this:

```
project
    releases
        // All releases will be going here
    current // This will be a symlink to the latest release
```

The role also has error checking in place. If any of the steps fail the role will delete the newly created release folder
and stop execution. If the deploy was successful the role will remove old releases.

Other Deployment Roles
---------------------

Symfony: [symfony-deploy](https://galaxy.ansible.com/list#/roles/2111)

Laravel5: [laravel5-deploy](https://galaxy.ansible.com/list#/roles/2145)

Requirements
------------

The only requirement is Ansbile >= 1.2.

Installation
------------

```
ansible-galaxy install blacklight.standard-deploy
```

Role Variables
--------------

### deploy_root_dir (Required)

The root directory of the project.

### deploy_repo (Required)

The URL to the repo containing the application code.

### deploy_branch (Defaults: master)

The branch that you would like to deploy.

### deploy_strategy (Defaults: git)

The deployment strategy to use. Available options: git, svb, mercurial, rsync

### deploy_local_root (Defaults: /)

This option is only used when deploying via Rsync. It defines the path to the local folder to upload to the server.

*Important Note!* The path is relative to your playbook file.

### deploy_releases (Defaults: 5)

The amount of releases to keep in the releases directory.

Example Playbook
----------------

```yml
---
- hosts: web
  vars:
    deploy_root_dir: /var/www/example
    deploy_repo: git@github.com/example/example-project.git
    ansible_ssh_user: root

  roles:
    - blacklight.standard-deploy
```

License
-------

MIT