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Cough Quotes
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Top Cough Quotes
Browse top 267 famous quotes and sayings about Cough by most favorite authors.
Favorite Cough Quotes
1. "And Desormie dragged the back of his hand back and forth across his mouth twice. And Brodsky coughed fakely to mask his laughter. And there was no more paste in the mouth-corners of Desormie. And Brodsky would not have to stare at paste while they talked in his office. That was nice of me.I went to the gym."
Author: Adam Levin
2. "I gave myself to you sooner than I ever did to any man, I swear to you; and do you know why? Because when you saw me spitting blood you took my hand; because you wept; because you are the only human being who has ever pitied me. I am going to say a mad thing to you: I once had a little dog who looked at me with a sad look when I coughed; that is the only creature I ever loved. When he died I cried more than when my mother died. It is true that for twelve years of her life she used to beat me. Well, I loved you all at once, as much as my dog. If men knew what they can have for a tear, they would be better loved and we should be less ruinous to them."
Author: Alexandre Dumas Fils
3. "This morning I got up to begin this book I coughed. Something was coming out of my throat: it was strangling me. I broke the thread which held it and yanked it out. I went back to bed and said: I have just spat out my heart."
Author: Anaïs Nin
4. "The mouthful of turkey sandwich I'd bitten off caught in my throat when Ren rested his hand on my leg, his fingers exploring the curve of my thigh. I coughed and snatched the bottle of water from his other hand, taking several desperate swallows before swatting his fingers from my leg. "Are you trying to kill me?" I choked the words out. "Keep your hands to yourself."
Author: Andrea Cremer
5. "You with us, Calla?' Lydia asked. 'Trip's over. This is where you get off.'Connor coughed. 'I could help you with that."
6. "I am suddenly comsumed by nostalgia for the little girl who was me, who loved the fields and believed in God, who spent winter days home sick from school reading Nancy Drew and sucking menthol cough drops, who could keep a secret."
Author: Audrey Niffenegger
7. "Starting the DayThe Legacy Letters By Carew PapritzSometimes we make being happy so difficult. And being thankful such a chore. Starting the day like a job we hate. Beginning it like swallowing ten tablespoons of devil-made cough syrup. Because somehow along the way we forget that being alive and healthy and happy are noble goals-or just good ideas. And that the opposite of being alive is being dead. What a choice."
Author: Carew Papritz
8. "What is it, Master Calligrapher, that little girls do in the way that spiders weave?" sleeve asked primly.The Calligrapher coughed, for his room was very dusty, and there was dust even on his eyelashes, and said: "It is right and proper," he said, "for a girl to read as many books as there are bricks in this city, and then, when she is finished, to begin to write new ones which are made out of the old ones, as this city is made of those stones."
Author: Catherynne M. Valente
9. "The borderline Queen experiences what therapists call "oral greediness". The desperate hunger of the borderline Queen is akin to the behavior of an infant who had gone too long between feelings. Starved, frustrated, and beyond the ability to calm of soothe herself, she grabs, flails, and wails until at last the nipple is planted securely and perhaps too deeply in her mouth. She coughs, gags, chokes, and spits, eyeing the elusive breast like a wolf guarding her food. Similarity, the Queen holds on to what is hers, taking more than she could use, in case it might be taken away prematurely."
Author: Christine Ann Lawson
10. "This is some sort of joke, isn't it?" asks Hunt, staring at the flawless blue sky and distant fields.I cough as lightly and briefly as possible into a handkerchief I have made from a towel borrowed from the inn. "Probably," I say. "But then, what isn't?"
Author: Dan Simmons
11. "I sat on the front steps alone, waiting for a sense of ease and peace to settle in the air around me.A woman passing on the street said, ´A decongestant, an antihistamine, a cough suppressant, a pain reliever.´"
Author: Don DeLillo
12. "Enough," he said; "the cough is a mere nothing; it will not kill me. I shall not die of a cough." "True - true," I replied;"
Author: Edgar Allan Poe
13. "Coughs seem very common here, especially among the children, though people look strong and healthy, but in the absence of proper statistics one cannot undertake to say whether the district is a healthy one or not."
Author: Edward Burnett Tylor
14. "She was dry. She was lying on something soft. She was wrapped in quilts. There was a star of light drifting above her, and a smell like a herb garden. Taggle was a long warmth stretched out at one side, his chin in her hand, his tail curled over her neck. She thought they might be in heaven.Taggle farted.Plain Kate coughed and sneezed. And then she really was awake."
Author: Erin Bow
15. "Childhood is a time for pretending and trying on maturity to see if it fits or hangs baggy, tastes good or bitter, smells nice or fills your lungs with smoke that makes you cough. It's sharing licks on the same sucker with your best friend before you discover germs. It's not knowing how much a house cost, and caring less. It's going to bed in the summer with dirty feet on clean sheets. It's thinking anyone over fifteen is 'ancient'. It's absorbing ideas, knowledge, and people like a giant sponge. Childhood is where 'competition' is a baseball game and 'responsibility' is a paper route."
Author: Erma Bombeck
16. "When you spend weeks on end close to another person, so close that you know every hiccough, every smell and every scratch on the skin, you either come out of it hating each other or so deep in each other's gut that you can't find a way out. Klara and I were both. Our little love affair had turned into a Siamese-twin relationship. There wasn't any romance in it. There wasn't room enough between us for romance to occur. And yet I knew every inch of Klara, every pore, and every thought, far better than I'd known my own mother. And in the same way: from the womb out. I was surrounded by Klara"
Author: Frederik Pohl
17. "As a collective exhale hit the airwaves, Qhuinn found himself looking over at Blay. Aw, hell, talk about a suck zone—this was why he avoided the guy like the plague. Just one glance and he was locked on, all kinds of reactions rolling through him, until the room spun a little—For no good reason, Blay's eyes flipped up and met his.It was like getting goosed in the ass with a live wire, his body spasming to the point where he had to hide the reaction by coughing while he glanced away.About as smooth as a crater. Yup. Fantastic."
18. "Wrath walked over to the closet and looked throught the clothes. He took out a black long-sleeved shirt, a pair of leather pants, and --jeez, what was this? Oh, not fucking likely. He was not going to fight in BVDs. He'd go commando before he got cought dead inthose things."
19. "[The Doctor] pulled the thing out of Prince Boris's mouth, waving it around. 'Oh. Blimey. This is not a spatula. What is it?'I [Amy] stared at the stubby thing. It looked like the world's chunkiest novelty gift pen... I coughed. 'That, Doctor, is the sonic screwdriver.''Ah,' Dr Smith boggled. 'Right. Is it? Oh dear.' Another pause. 'What does it do?''Well... it screws things... sonically. On a good day, we fight off monsters with it.''Monsters, eh?' Dr Smith nodded gravely and... pointed it at the doorway like a gun and said, hopefully, 'Pew! Pew! Pew!' He turned back to me. 'Like that?''Other way up,' I said gently."
Author: James Goss
20. "My family suffered. My hair turned up in every corner, every drawer, every meal. Even in the rice puddings Tessie made, covering each little bowl with wax paper before putting it away in the fridge--even into these prophylactically secure desserts my hair found its way! Jet black hairs wound themselves around bars of soap. They lay pressed like flower stems between the pages of books. They turned up in eyeglass cases, birthday cards, once--I swear--inside an egg Tessie had just cracked. The next-door neighbor's cat coughed up a hairball one day and the hair was not the cat's."
Author: Jeffrey Eugenides
21. "My dear doctor, I am surprised to hear you say that I am coughing very badly, as I have been practising all night."
Author: John Philpot Curran
22. "Let me drive," she said, reaching for the reins.He turned to her in disbelief. "This is a phaeton, not a single-horse wagon."Sophie fought the urge to throttle him. His nose was running, his eyes were red, he couldn't stop coughing, and still he found the energy to act like an arrogant peacock. "I assure you," she said slowly, "that I know how to drive a team of horses."
Author: Julia Quinn
23. "And me all the while having to pee—coughing into the mike when my throat was tired and raw—eyes stinging and lips and chin crumpling in grief at his anger. The sweet tinkle of Electra on the bass and Iphy on the treble with Mama's voice counting, "One and two and …" as the twins had their piano lesson inside the trailer. The gurgle and hum of the pumps that filtered my brother Arty's "Aqua Boy" tank. And the dim round moon of baby Fortunato's face peering at me from the dark of the risers above Papa."
Author: Katherine Dunn
24. "Have you ever kissed anybody?" he asked and took a sip.I smirked. "There aren't a whole lot of opportunitiesin the digital world. I did practice on my hand once. It didn't do anything for me".Justin coughed on the water he was swallowing and I slapped my hand over my mouth."Did I just say that aloud?" I mumbled.He was half coughing, half laughing. "Yes, you did", he managed to say."
Author: Katie Macvinsky
25. "Backup?" Tori said. "You mean he didn't need that?""Apparently not," I murmured.Simon looked from her to me, confused, then understanding. "You guys thought...""That if you didn't get your medicine in the next twenty-four hours, you'd be dead?" I said. "Not exactly, but close. You know, the old 'upping the ante with a fatal disease that needs medication' twist. Apparently, it still works.""Kind of a letdown, then, huh?""No kidding. Here we were, expecting to find you minutes from death. Look at you, not even gasping.""All right, then. Emergency medical situation, take two."He leaped to his feet, staggered, keeled over, then lifted his head weakly."Chloe? Is that you?" He coughed. "Do you have my insulin?"I placed it in his outstretched hand."You saved my life," he said. "How can I ever repay you?""Undying servitude sounds good. I like my eggs scrambled."He held up a piece of fruit. "Would you settle for a bruised apple?"
Author: Kelley Armstrong
26. "All right, then. Emergency medical situation, take two." He leaped to his feet, staggered, keeled over, then lifted his head weakly. "Chloe? Is that you?" He coughed. "Do you have my insulin?" I placed it in his outstretched hand. "You saved my life," he said. "How can I ever repay you?""Undying servitude sounds good. I like my eggs scrambled." He held up a piece of fruit. "Would you settle for a bruised apple?" I laughed."
27. "Wooo!' he said, slamming his shot glass down and coughing a bit. 'That's good stuff.'I agreed heartily. 'Shall we do another one?' I asked.'Oh no,' Jesus said quietly, his eyes growing round. 'This is one of those situations where I have to stop and ask myself, what would I do?"
Author: Kevin Hearne
28. "Billy coughed when the door was opened, and when he coughed he shit thin gruel. This was in accordance with the Third Law of Motion according to Sir Isaac Newton. This law tells us that for every action there is a reaction and opposite in direction.This can be useful in rocketry."
Author: Kurt Vonnegut
29. "Kurt Vonnegut speaking to John Irving while Irving was administering the Heimlich maneuver in response to Vonnegut's uncontrollable coughing..."John,stop- I am not choking. I have emphysema."
30. "Klaus, and Sunny had been many, many times to Mr. Poe's office at the bank, where he coughed and talked on the phone and made decisions"
Author: Lemony Snicket
31. "Greetings from sunny Seattle, where women are "gals," people are "folks," a little bit is a "skosh," if you're tired you're "logy," if something is slightly off it's "hinky," you can't sit Indian-style but you can sit "crisscross applesauce," when the sun comes out it's never called "sun" but always "sunshine," boyfriends and girlfriends are "partners," nobody swears but someone occasionally might "drop the f-bomb," you're allowed to cough but only into your elbow, and any request, reasonable or unreasonable, is met with "no worries."Have I mentioned how much I hate it here?"
Author: Maria Semple
32. "It amazes me what humans can do, even when streams are flowing down their faces and they stagger on, coughing and searching, and finding."
Author: Markus Zusak
33. "I peer through the spectral, polluted, nicotine-sodden windows of my sock at these old lollopers in their kiddie gear. Go home, I say. Go home, lie down, and eat lots of potatoes. I had three handjobs yesterday. None was easy. Sometimes you really have to buckle down to it, as you do with all forms of exercise. It's simply a question of willpower. Anyone who's got the balls to stand there and tell me that a handjob isn't exercise just doesn't know what he's talking about. I almost had a heart-attack during number three. I take all kinds of other exercise too. I walk up and down the stairs. I climb into cabs and restaurant booths. I hike to the Butcher's Arms and the London Apprentice. I cough a lot. I throw up pretty frequently, which really takes it out of you. I sneeze, and hit the tub and the can. I get in and out of bed, often several times a day."
34. "She looks like a jumper to me. Jumpers do that a lot, stand on the edge and stare out. Never kill yourself in a Tube station. Tip number one. You might end up down here forever, staring at the wall."Stephen coughed a little."Just giving advice," Callum said."
Author: Maureen Johnson
35. "Repressive cough"
Author: Nicholas Monsarrat
36. "Oh, don't cough, Ernest. When one is dictating one should speak fluently and not cough. Besides, I don't know how to spell a cough."
37. "Acting is merely the art of keeping a large group of people from coughing."
Author: Ralph Richardson
38. "Keith was just bringing the glass to his lips when Adrian said, "Mmm. O positive, my favorite."Keith sprayed out the wine he'd just drunk and promptly started coughing. I was relieved that none got on me. jill burst into giggles, and Clarence stared at his glass wonderingly."Is it? I thought it was a cabernet sauvignon.""So it is," said Adrian, straight-faced. "My mistake."
Author: Richelle Mead
39. "I think coughing up slugs was quite hard. Ron has a scene where he has to cough up these giant slugs."
Author: Rupert Grint
40. "Why should one think thay are okay mentally? Making a study of ourselves is the best solution to our society.Our minds are so complex and if we don't have an avenue to explore, we just cough it up as it is just who I am...However, it is just half of who you are, we need to find our other half and it is not in the form of another person."-Serena Jade"
Author: Serena Jade
41. "I thought... I thought you might..." "Help you? By my grove, I am helping you. You're not starving anymore, are you? <…> You had a dry night's sleep, too, and you're no longer coughing your liver and lights out. Some might count those as mighty gifts indeed."
Author: Tad Williams
42. "Igor?' said Moist. 'You have an Igor?'Oh, yes,' said Hubert. 'That's how I get this wonderful light. They know the secret of storing lightning in jars! But don't let that worry you, Mr Lipspick. Just because I'm employing an Igor and working in a cellar doesn't mean I'm some sort of madman, ha ha ha!'Ha ha,' agreed Moist.Ha hah hah!,' said Hubert. 'Hahahahahaha!! Ahahahahahahhhhh!!!!!-'Bent slapped him on the back. Hubert coughed.Sorry about that, it's the air down here,' he mumbled."
43. "He found that he had this sudden desperate longing for the fuming, smoky streets of Ankh-Morpork, which was always at its best in the spring, when the gummy sheen on the turbid waters of the Ankh River had a special iridescence and the eaves were full of birdsong, or at least birds coughing rhythmically"
44. "I really shouldn't leave," Zach said, terrified what would happen the second he found himself alone with Nora."Come on, Zach. This party sucks and not in the good way. I've had pap smears more fun than this."Zach covered a laugh with a cough."I must admit you do have a way with words."
Author: Tiffany Reisz
45. "Michael's bed gave me a good night's sleep and a pleasant little cough of settling when I laid on it. I felt like it was familiar already."
Author: Vee Hoffman
46. "Annabelle coughs and glares at Blue. "What did you do? Did I just swallow something?" Blue pulls her into a hug. "You're my best friend in the whole world." "Get off me, leech," she says, smiling."
Author: Victoria Scott
47. "You told some human kid?"I coughed, buying time. "He's Neph, too."Jonathan LaGrey went rigid and his ruddy cheeks paled. I squirmed as his eyes bored into mine."Which one's his father?" he asked through clenched teeth."Richard Rowe. I guess you'd know him as Pharzuph." Oh, boy. He wasn't pale anymore."You came across the country-""Shhh!" I warned him as people looked over. He lowered his voice to a shouted whisper."-with the son of the Duke of Lust?! Son of a-"
Author: Wendy Higgins
48. "Paris is a hard place to leave, even when it rains incessantly and one coughs continually from the dampness."
Author: Willa Cather
49. "True love is the best thing in the world, except for cough drops."
Author: William Goldman
50. "Sophie coughed, and Oliver felt his cheeks becoming warm. "Don't be an ass, Andrew," he mumbled. "That's a little bit difficult to accomplish, y'know?" Andrew retorted. "I mean, unless you'd be so kind so as to paint me with black and white stripes, then I'd be a zebra!"
Author: Zeinab Alayan
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The morality thatgoes with this understanding is, above all, the frank recognition of yourdependence upon enemies, underlings, out-groups, and, indeed, upon allother forms of life whatsoever. Involved as you may be in the conflictsand competitive games of practical life, you will never again be able toindulge in the illusion that the "offensive other" is all in the wrong, andcould or should be wiped out."
Author: Alan Wilson Watts
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Civil engineering science and technical electives
Environmental engineering technical electives
GAANN Ph.D. fellowships in infrastructure resilience
Research topics and advisors
GAANN Fellows
Earthquake and structural engineering
Geotechnical engineering seminar series
Pavement engineering and science
Zong Tian
Department of Civil & Environmental Engineering /
Email: zongt@unr.edu
Building: SEM
Dr. Zong Tian joined the University of Nevada Reno in 2004 and is currently a professor and the founding director of Center for Advanced Transportation Education and Research (CATER).
He obtained his Ph.D. degree from Texas A&M University. He held a position of associate research scientist at the Texas Transportation Institute between 2000 and 2004. He was employed at Kittelson and Associates, Inc. in Portland, Oregon between 1995 and 1999.
Dr. Tian is active in several transportation professional organizations. He served on two two major committees of the Transportation Research Board (TRB): the Highway Capacity and Quality of Service Committee, and the Traffic Signal Systems Committee. He was the paper review chair for the Highway Capacity and Quality of Service Committee of TRB, handling over 60 paper reviews each year. He is the topic area manager for area C: Traffic Management, Operations, and Safety of the World Conference on Transport Research Society (WCTRS). He also chairs the Special Interest Group (SIG-C2) focusing on Urban Transport Operations. He has been serving as a co-organizer for the International Conference on Traffic and Transportation Studies (ICTTS) since 2004 and has been the co-editor of the conference proceedings. He is the associate editor of Case Studies on Transport Policy.
His specialty area is in traffic signal systems and operations, highway capacity, and integrated freeway and arterial operations. Tian received the Young Consultant Award from ITE in 1997.
Ph.D., Civil/Transportation Engineering, Texas A&M University, 2004
M.S., Civil/Transportation Engineering, University of Idaho, 1995
M.S., Railroad Engineering, Northern Jiaotong University, China, 1986
B.S., Railroad Engineering, Northern Jiaotong University, China, 1983
Dr. Tian is currently recruiting students with strong analytical and computational skills from any background.
Dr. Tian's research focuses on various aspects of traffic engineering and intelligent transportation systems, including traffic signal system and operations, freeway ramp metering, integrated freeway/surface street system operations, highway capacity, video image systems, and large-scale transportation network modeling.
Professional certifications and memberships
Registered Professional Engineer in Nevada and Texas
Tian, Z. (2009). Integrated Diamond Interchange Ramp-Metering Traffic Control Systems. VDM Publishing House Ltd. ISBN 978-3-639-17795-4.
Proceedings as an editor
Tian, Z., Nakamura, H., Boltze, M., and Chung, E. (2018). Special Issue on Innovative Intersection Design and Control for Serving Multimodal Transport Users, Transportation Research Part C – Emerging Technologies.
Tian, Z., Romana, M., Troutbeck, R., and Elefteriadou, L. (2014). Innovative Applications of the Highway Capacity Manual 2010, Transportation Research Circular, E-C190, Transportation Research Board.
Referred journal publications
Yang, G., Tian, Z., Wang, Z., and H. Xu (2019). Impact of On-ramp Traffic Flow Arrival Profile on Queue Length at Metered On-Ramps, Journal of Transportation Engineering, Part A: Systems, ASCE, 145(2).
Gholami, A. and Tian, Z. (2018). Model of Transition Delay for Unaccommodated Pedestrian Timing, Journal of Transportation Engineering, Part A: Systems, 144(2), pp. 1-8.
Yang, G., Tian, Z., Wang, D., and Xu, H. (2018). Queue Length Estimation for a Metered on-ramp Using Mesoscopic Simulation, Transportation Letters, pp. 1-10.
Yang, G., Yue, R., Tian, Z., and Xu, H. (2018). Modeling the Impacts of Traffic Flow Arrival Profiles on Ramp Metering Queues, Transportation Research Record, pp. 1-8.
Wang, D., Tian, Z., Gholami, A. and Yang, G. (2018). Using a Virtual Controller Interface Device for Traffic Signal Control Simulations, Transport (in Press).
Wu, J., Xu, H., Zheng, Y., and Tian, Z. (2018). A Novel Method of Vehicle-pedestrian Near-Crash Identification with Roadside LiDAR Data, Accident Analysis & Prevention, Volume 121, pp. 238-249.
Wang, D., Tian, Z., Yang, G. and Gholami, A. (2018). Comparison of Performance between Virtual Controller Interface Device and Controller Interface Device, IET Intelligent Transport Systems, Volume 12, Issue 1, pp. 22-30.
Yang, G., Z. Wang, H. Xu and Z. Tian (2018). Feasibility of Using a Constant Acceleration Rate for Freeway Entrance Ramp Acceleration Lane Length Design. Journal of Transportation Engineering, Part A: System, ASCE, 144(3), pp. 1-8.
Gholami, A., Tian, Z., and Andalibian (2017). Innovative Detector Layout for Automated Traffic Turning Volume Counting: Proposing a New Detector Placement Configuration, Journal of Advanced Transportation, 50(8).
Farivar, S., Andalibian, R. and Tian, Z. (2017). Modeling Right-Turn Blockage and Approach Capacity at Signalized Intersections with Channelized Right-Turn Lanes. Journal of Transportation Engineering, ASCE, Part A, 143(2), pp.
Gholami, A. and Tian, Z. (2017). Increasing the Accuracy of Loop Detector Counts using Adaptive Neural Fuzzy Inference System and Genetic Programming. Transportation Planning and Technology, 40(4), pp. 1-18.
Yang, G., Tian, Z., Xu, H., and Wang, D. (2017). Impacts of Traffic Flow Arrival Pattern on the Necessary Queue Storage Space at Metered On-Ramps. Transportmetrica A: Transport Science, Volume 13, Issue 10, pp. 1-19.
Yang, G., Tian, Z. (2016). Queue Length Estimation for Metered Freeway-to-Freeway Connectors. Journal of Advanced Transportation, 10(1).
Yang, G., Xu, H., Tian, Z. (2016). Vehicle Speed and Acceleration Profile Study for Metered On-Ramps. ASCE Journal of Transportation Engineering, 142(2).
Gholami, A., Tian, Z. (2016). Using Stop Bar Detector Information to Determine Turning Movement Proportions in Shared Lanes. Journal of Advanced Transportation (50), 802-817.
Gholami, A., Tian, Z. (2016). Designing the Required Changes in the Bus Network after Performing Limited Traffic Zone in Mashhad, Iran. Case Studies on Transport Policy, 4(2).
Gholami, A., Tian, Z., Andalibian, R. (2016). Estimating intersection turning volumes from actuated traffic signal information. Journal of Transportation Engineering, ASCE.
Farivar, S., Tian, Z. (2016). Modeling Delay at Signalized Intersections with Channelized Right-turn Lanes Considering the Impact of Blockage, Journal of Advanced Transportation.
Gholami, A., Tian, Z. (in press). Innovative detector layout for automated traffic turning volume counting. Journal of Advanced Transportation.
Wu, J., Liu, P., Tian, Z. (2016). Operational analysis of the contraflow left-turn lane design at signalized intersections in China. Transportation Research Part C, 69, 228-241.
Gholami, A., Tian, Z. A. (2016). A multi-criteria-based guideline for pedestrian timing accommodation into signal coordination. Journal of Transportation Engineering, ASCE.
Zhou, C., Tian, Z. (2016). Right-Turn Traffic Volume Adjustment in Traffic Signal Warrant Analysis. Transportation Research Record, 2562, 28-35.
Lvu, B., Tian, Z. (2016). An offset optimization approach for coordinated signal systems with preferred measure considerations. Advances in Mechanical Engineering, 8(3).
Yang, G., Tian, Z. (2016). Recommendations for Acceleration Lane Length for Metered On-Ramps. Transportation Research Record, 2588, 1-11.
Farivar, S., Tian, Z. (2016). Modeling delay at signalized intersections with channelized right‐turn lanes considering the impact of blockage. Journal of Advanced Transportation.
Gholami, A. and Tian, Z. (2015). Using Stop Bar Detector Information to Determine Turning Movement Proportions in Shared Lanes, Journal of Advanced Transportation.
Tian, Z., Ma, Y., Gao, H. and Su, J. (2015). A Probabilistic Delay Model for Signalized Intersections with Short Right-Turn Lanes, Journal of Transportation Systems Engineering and Information Technology
Yang, G., Xu, H., Tian, Z., Wang, Z. (2015). Acceleration Characteristics at Metered On-Ramps, Transportation Research Record No.2484 (SCI), pp.1-9.
Yang, G., Xu, H., Tian, Z., and Wang, Z. (2015). Vehicle Speed and Acceleration Profile Study for Metered On-Ramps in California, Journal of Transportation Engineering, ASCE, (SCI).
CEE 362 Transportation Engineering
CEE 463/663 Traffic Engineering Analysis and Operations
CEE 761 Traffic Simulation
CEE 762 Math Applications in Transportation
CEE 763 Traffic Safety
CEE 764 Transportation Systems
Department Contact Information
Location: Scrugham Engineering and Mines
Room Number: SEM 105
Email: cee@unr.edu
Directory: Department directory

redpajama_set_name: RedPajamaCommonCrawl


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Maryland football gets four four-star recruits on National Signing Day
Alexander Dacy
Maryland football head coach Mike Locksley prior to the Terps' 16-34 loss to Minnesota on Oct. 23, 2021. (Julia Nikhinson/The Diamondback)
Mike Locksley's recruiting prowess paid off for Maryland football once again.
The head coach inked 20 recruits on early signing day Wednesday, highlighted by flipping four-star linebacker Jaishawn Barham from South Carolina. Barham is ranked as a top 100 prospect in the class of 2022 by ESPN and Rivals and fills a position of need after last year's top two linebacker recruits departed College Park.
Barham's commitment, along with the flip of three-star offensive lineman Andre Roye Jr. from Penn State, gives the Terps a recruiting foothold at Baltimore powerhouse St. Frances Academy, which had been a difficult spot for Maryland to secure players in past years.
"They both made decisions early on, and I think as they got closer to signing day, they started to see the value of what staying at home looks like," Locksley said. "I think us being able to show that as a program, we did take a significant step with our program by becoming a bowl eligible team … really gave us a lot of firepower in terms of being able to continue to press those guys and sell the vision I have for Maryland football."
[Maryland football tight end Chig Okonkwo declares for NFL Draft]
Outside of St. Frances, Locksley continued to keep local talent in the state. The Terps secured four of the top 10 players in Maryland — Barham, Roye, wide receiver Octavian Smith Jr. and tight end Preston Howard — along with quarterback Jayden Sauray, the state's highest rated signal caller.
Smith was a dynamic and versatile quarterback at nearby Paint Branch High School, earning First Team All-Met honors from The Washington Post. He's expected to slot into a deep receiving room, though the four-star athlete's flexibility could see him land at other positions if needed.
Like Smith, Howard was a utility player in high school and is expected to assume a role with a young tight end corps. Sauray provides valuable depth and a dual-threat presence behind quarterback Taulia Tagovailoa and could find himself on the field once the redshirt sophomore departs.
"The goal is to keep the right local talent here," Locksley said. "I feel like we did just that as a staff."
This strong class, however, did not appear certain even 48 hours before Wednesday's signing day. At that point, Locksley had just one four-star recruit signed — wide receiver Shaleak Knotts — and the Terps' recruiting class was middle-of-the-pack nationally.
Now, Locksley has four four-stars — Knotts, Barham, Smith and running back Ramon Brown, who flipped from Virginia Tech Tuesday evening. That has vaulted Maryland's recruiting class up to 28th in the country and eighth in the Big Ten, per 247Sports.
Nine signees, including Barham, will enroll early and participate in spring practice.
"I really do see this class being kind of the DNA of the player-driven culture that we're working to create here," Locksley said.
andre roye jr.
jaishawn barham
jayden sauray
mike locksley
octavian smith jr.
preston howard
ramon brown
shaleak knotts
taulia tagovailoa
Season in Review: Maryland football took an important step forward in 2021
The Diamondback's best sports stories of 2021
Diamondback Staff
Photo essay: Maryland football celebrates first bowl win since 2010
Diamondback Photo Staff


text:
by Jim L. Sumner, 2006
North Carolina's mild climate and varied landscape have helped make golf an extremely popular and profitable sport in North Carolina. Early forms of golf were played in colonial North Carolina, but the modern game dates from the 1890s. It was during that decade that a number of golf courses were constructed in the state, including the Asheville Country Club, Fayetteville's Highland Country Club, and the Hot Springs Country Club in Madison County. There is little doubt, however, that the most important golf course construction in the state took place in the Sandhills community of Pinehurst, where, beginning in 1895, James Walker Tufts, a retired Massachusetts businessman, created one of America's premier golfing resorts. Pinehurst's famous Number Two course is the best-known course in the state and is regarded as one of the world's finest. It and several other North Carolina courses were designed by Donald J. Ross, a native Scotsman who settled in Pinehurst around 1898.
Golf courses outside of Pinehurst that have received national acclaim include the Grandfather Mountain Golf Club in Linville, the Quail Hollow course in Charlotte, Tanglewood Golf Park in Clemmons, and Forest Oaks Country Club in Greensboro. Golf initially was an upper-class pastime, but this began to change after World War II, when leisure time and recreational opportunities expanded. President Dwight D. Eisenhower's highly publicized golf outings gave the sport a boost in the 1950s, as did the charisma of dynamic champion and Wake Forest graduate Arnold Palmer. The sport has become wildly popular with a broad cross section of the sporting public.
North Carolina has hosted many professional and amateur tournaments. The Greater Greensboro Open was first held in 1938, making it one of the oldest tournaments on the Professional Golfers Association (PGA) tour. Over the years Pinehurst, Charlotte, Durham, Asheville, Raleigh, Winston-Salem, and Wilmington have all hosted PGA tournaments. The North and South Amateur has been held at Pinehurst since 1900. Golf was one of the first sports in which women competed seriously, and women's tournaments have been held in the state since the turn of the century. Raleigh, High Point, Greensboro, and other cities have all hosted tournaments of the Ladies' Professional Golfers Association tour.
Perhaps the most successful North Carolinian on the PGA tour has been Fayetteville native Raymond Floyd. His 62 professional victories include the 1986 U.S. Open, the 1976 Masters, and the 1969 and 1982 PGA titles. Badin's Johnny Palmer, Charlotte's Clayton Heafner, and Durham's Stewart "Skip" Alexander enjoyed success on the PGA tour in the 1940s and 1950s. More recent standouts include Chip Beck (Fayetteville), Joe Inman (Greensboro), and Scott Hoch (Raleigh). Charlotte native Charlie Sifford overcame years of racial prejudice to become the first African American to win on the PGA tour. Tarboro's Harvie Ward, High Point's Dale Morey, and Morganton's Billy Joe Patton were outstanding amateurs in the 1950s. Chapel Hill's Estelle Lawson Page was a standout amateur player in the 1930s and 1940s. Greensboro's Marge Burns succeeded her as North Carolina's top female amateur, and High Point's Kathy Johnston was a standout at UNC before becoming a professional in the early 1990s.
Most North Carolina colleges and many high schools have golf teams. Wake Forest University has been extremely successful in men's college golf. Besides being Arnold Palmer's alma mater, the school, under coach Jesse Haddock, won the 1974, 1975, and 1986 National Collegiate Athletic Association (NCAA) titles. Haddock's program included such notable future PGAstandouts as Curtis Strange and Lanny Wadkins. Duke University's Art Wall and Mike Souchak in the 1950s and the University of North Carolina's Davis Love III in the 1990s all have had considerable success on the PGA tour.
Beginning in the early 1990s, North Carolina underwent a golfing boom that pushed the number of courses in the state to well over the 400 mark. Many residential developments and resorts have been built around golf courses, and the sport has become a major source of tourist income.
William Price Fox, Golfing in the Carolinas (1990).
O. B. Keeler, Golf in North Carolina (1938).
Charlie Sifford with James Gullo, Just Let Me Play: The Story of Charlie Sifford (1992).
Richard S. Tufts, The Scottish Invasion, Being a Brief Review of American Golf in Relation to Pinehurst and the Sixty-second National Amateur (1962).
"Pinehurst No. 2," Our State, January 2012. http://digital.ncdcr.gov/cdm/ref/collection/p16062coll18/id/104712
Usher, Jess. "'The Golfers': African American Golfers of the North Carolina Piedmont and the Struggle for Access." The North Carolina Historical Review 87, no. 2 (2010): 158–93. http://www.jstor.org/stable/23523892.
100 North Carolina Icons
Sumner, Jim L.
1 January 2006 | Sumner, Jim L.


text:
House Democrats open flood gate of subpoenas
The House Judiciary Committee is moving to authorize a "tidal wave" of subpoenas for people tied to special counsel Robert Mueller's report, including President Donald Trump's son in law, Jared Kushner, and former Attorney General Jeff Sessions.
The resolution coming up for a vote Thursday would authorize subpoenas for documents and testimony from 12 people in all, also including former national security adviser Michael Flynn, former White House chief of staff John Kelly and former Deputy Attorney General Rod Rosenstein.
Sponsored: Harvard Doctor: "There is no maximum human lifespan"
Lawmakers will also be voting on subpoenas for documents and testimony on the Trump administration's handling of immigration, including the abandoned "zero tolerance" approach of separating migrant families at the southern border. The resolution authorizes an investigation into whether there have been any discussions of Trump offering pardons to Homeland Security officials who work on immigration issues.
Judiciary Chairman Jerrold Nadler, D-N.Y., will make the final decision about issuing the subpoenas. He said they will only be avoided if the information is provided voluntarily.
The Judiciary panel is investigating whether Trump obstructed justice after Mueller's report detailed several episodes in which Trump tried to influence his investigation. Mueller said he could not indict Trump on obstruction and said there was no evidence of collusion. Despite asking to be left alone, Mueller is being compelled by Democrats to testify to Congress next week.
Democrats have had little luck so far finding anything beyond what Mueller detailed in his exhaustive, 2-year, 448-page report. The White House has blocked several witnesses from answering questions. It's unclear if they would have better luck with the latest group of witnesses, as most worked for the White House or the administration.
Republicans have fought the Democratic efforts to investigate obstruction, saying the issue should be laid to rest after the Mueller report.
Clinically Studied Pain Relief Formula Used by Surgeons, Now Available to the Public [sponsored]
"Today's latest effort to relitigate the special counsel's investigation remains unimpressive and unproductive," said the top Republican on the Judiciary panel, Georgia Rep. Doug Collins. "Mr. Mueller's team issued more than 2,800 subpoenas before concluding that no Americans conspired with Russia."
"Even if Chairman Nadler still believes subpoenas are conversation starters, it's hard to imagine this handful of subpoenas will do anything but reinforce the principal conclusions we've been able to read about for months," Collins said.
The Associated Press contributed to this article


Muscular Dystrophy is a progressive disease where a group of mutated genes interferes with protein production. Our bodies need protein to help form healthy muscle tissue. Muscular Dystrophy is either caused by a genetic mutation, or a spontaneous mutation and the outcome is a loss of muscle mass. In most cases symptoms begin in childhood, when the muscles are still forming, however, there have also been some cases of the disease surfacing in adulthood. Primarily found in boys, the most common thread of the disease is called Duchenne muscular dystrophy. Symptoms of this variation of the disease are falling over frequently and having trouble getting up from sitting or lying, difficulty running or jumping, muscle pain and stiffness. Other types of the disease have similar symptoms but are milder forms of the condition and specific to an area, limbs or organs; these are myotonic, facioscapulohumeral, congenital, and limb-girdle. In most cases of this progressive muscle weakness, the patient will lose the ability to walk, have a shortening of muscles and tendons, curved spine heart trouble, and have trouble breathing and swallowing. There is no cure for muscular dystrophy, but there are treatments and medications to slow and manage the disease.


How can we detect the topological order in 1+1D topological superconductor numerically?
I read some material in this forum and realize that entanglement entropy does not correspond to long range entanglement. Then what quantity can be used to characterize the topological order in 1+1D topological superconductor that can be obtained numerically?
For simple systems, when a simple Bogoliubov-deGennes Hamiltonian is sufficient, you can calculate the band structure with periodic boundary condition. Then you calculate the band structure imposing open boundary conditions. The topological aspects usually show themselves as zero energy band crossing.
The previous method is particularly efficient when you do not need to consider the self-consistency condition for superconductivity, and/or without impurities. Adding these two effects... well I do not know other numerical method than the previous one, sorry.
I'm a bit under rush. Please ask for further precisions if you need some.
For bosonic systems, there is no topological order in 1+1D. For fermionic systems, the only topological order in 1+1D is the p-wave state, that has Majorana zero mode at the chain end.
Thank you for your reply. I know that in Kitaev model there is Majorana zero model at the chain end, However, in this model, U(1) symmetry is explicitly broken. I am trying to search for models beyond mean field theory level that show topological order in one dimension by density-matrix renormalization group method, for example, Hubbard-like model with various extra terms. I am wondering what kind of quantities, which are numerically accessible, can be used to characterize topological order in 1+1D.
If you bosonize the 1+1D fermion model, the fermion parity conservation becomes a $Z_2$ symmetry in the bosonize model. If the bosonize model spontaneously break such a $Z_2$ symmetry, then the 1+1D fermion model is in the topologically ordered phase.


text:
#pragma once
#include <aws/sagemaker/SageMaker_EXPORTS.h>
#include <aws/sagemaker/SageMakerRequest.h>
#include <aws/core/utils/memory/stl/AWSString.h>
#include <aws/core/utils/memory/stl/AWSMap.h>
#include <aws/sagemaker/model/AlgorithmSpecification.h>
#include <aws/core/utils/memory/stl/AWSVector.h>
#include <aws/sagemaker/model/OutputDataConfig.h>
#include <aws/sagemaker/model/ResourceConfig.h>
#include <aws/sagemaker/model/VpcConfig.h>
#include <aws/sagemaker/model/StoppingCondition.h>
#include <aws/sagemaker/model/Channel.h>
#include <aws/sagemaker/model/Tag.h>
#include <utility>

namespace Aws
{
namespace SageMaker
{
namespace Model
{

  /**
   */
  class AWS_SAGEMAKER_API CreateTrainingJobRequest : public SageMakerRequest
  {
  public:
    CreateTrainingJobRequest();

    // Service request name is the Operation name which will send this request out,
    // each operation should has unique request name, so that we can get operation's name from this request.
    // Note: this is not true for response, multiple operations may have the same response name,
    // so we can not get operation's name from response.
    inline virtual const char* GetServiceRequestName() const override { return "CreateTrainingJob"; }

    Aws::String SerializePayload() const override;

    Aws::Http::HeaderValueCollection GetRequestSpecificHeaders() const override;

    /**
     *
The name of the training job. The name must be unique within an AWS Region in
     * an AWS account.

     */
    inline const Aws::String& GetTrainingJobName() const{ return m_trainingJobName; }

    /**
     *
The name of the training job. The name must be unique within an AWS Region in
     * an AWS account.

     */
    inline void SetTrainingJobName(const Aws::String& value) { m_trainingJobNameHasBeenSet = true; m_trainingJobName = value; }

    /**
     *
The name of the training job. The name must be unique within an AWS Region in
     * an AWS account.

     */
    inline void SetTrainingJobName(Aws::String&& value) { m_trainingJobNameHasBeenSet = true; m_trainingJobName = std::move(value); }

    /**
     *
The name of the training job. The name must be unique within an AWS Region in
     * an AWS account.

     */
    inline void SetTrainingJobName(const char* value) { m_trainingJobNameHasBeenSet = true; m_trainingJobName.assign(value); }

    /**
     *
The name of the training job. The name must be unique within an AWS Region in
     * an AWS account.

     */
    inline CreateTrainingJobRequest& WithTrainingJobName(const Aws::String& value) { SetTrainingJobName(value); return *this;}

    /**
     *
The name of the training job. The name must be unique within an AWS Region in
     * an AWS account.

     */
    inline CreateTrainingJobRequest& WithTrainingJobName(Aws::String&& value) { SetTrainingJobName(std::move(value)); return *this;}

    /**
     *
The name of the training job. The name must be unique within an AWS Region in
     * an AWS account.

     */
    inline CreateTrainingJobRequest& WithTrainingJobName(const char* value) { SetTrainingJobName(value); return *this;}

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline const Aws::Map<Aws::String, Aws::String>& GetHyperParameters() const{ return m_hyperParameters; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline void SetHyperParameters(const Aws::Map<Aws::String, Aws::String>& value) { m_hyperParametersHasBeenSet = true; m_hyperParameters = value; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline void SetHyperParameters(Aws::Map<Aws::String, Aws::String>&& value) { m_hyperParametersHasBeenSet = true; m_hyperParameters = std::move(value); }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& WithHyperParameters(const Aws::Map<Aws::String, Aws::String>& value) { SetHyperParameters(value); return *this;}

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& WithHyperParameters(Aws::Map<Aws::String, Aws::String>&& value) { SetHyperParameters(std::move(value)); return *this;}

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& AddHyperParameters(const Aws::String& key, const Aws::String& value) { m_hyperParametersHasBeenSet = true; m_hyperParameters.emplace(key, value); return *this; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& AddHyperParameters(Aws::String&& key, const Aws::String& value) { m_hyperParametersHasBeenSet = true; m_hyperParameters.emplace(std::move(key), value); return *this; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& AddHyperParameters(const Aws::String& key, Aws::String&& value) { m_hyperParametersHasBeenSet = true; m_hyperParameters.emplace(key, std::move(value)); return *this; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& AddHyperParameters(Aws::String&& key, Aws::String&& value) { m_hyperParametersHasBeenSet = true; m_hyperParameters.emplace(std::move(key), std::move(value)); return *this; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& AddHyperParameters(const char* key, Aws::String&& value) { m_hyperParametersHasBeenSet = true; m_hyperParameters.emplace(key, std::move(value)); return *this; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& AddHyperParameters(Aws::String&& key, const char* value) { m_hyperParametersHasBeenSet = true; m_hyperParameters.emplace(std::move(key), value); return *this; }

    /**
     *
Algorithm-specific parameters that influence the quality of the model. You
     * set hyperparameters before you start the learning process. For a list of
     * hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
     *

You can specify a maximum of 100 hyperparameters. Each hyperparameter is
     * a key-value pair. Each key and value is limited to 256 characters, as specified
     * by the Length Constraint.

     */
    inline CreateTrainingJobRequest& AddHyperParameters(const char* key, const char* value) { m_hyperParametersHasBeenSet = true; m_hyperParameters.emplace(key, value); return *this; }

    /**
     *
The registry path of the Docker image that contains the training algorithm
     * and algorithm-specific metadata, including the input mode. For more information
     * about algorithms provided by Amazon SageMaker, see Algorithms.
     * For information about providing your own algorithms, see Using
     * Your Own Algorithms with Amazon SageMaker.

     */
    inline const AlgorithmSpecification& GetAlgorithmSpecification() const{ return m_algorithmSpecification; }

    /**
     *
The registry path of the Docker image that contains the training algorithm
     * and algorithm-specific metadata, including the input mode. For more information
     * about algorithms provided by Amazon SageMaker, see Algorithms.
     * For information about providing your own algorithms, see Using
     * Your Own Algorithms with Amazon SageMaker.

     */
    inline void SetAlgorithmSpecification(const AlgorithmSpecification& value) { m_algorithmSpecificationHasBeenSet = true; m_algorithmSpecification = value; }

    /**
     *
The registry path of the Docker image that contains the training algorithm
     * and algorithm-specific metadata, including the input mode. For more information
     * about algorithms provided by Amazon SageMaker, see Algorithms.
     * For information about providing your own algorithms, see Using
     * Your Own Algorithms with Amazon SageMaker.

     */
    inline void SetAlgorithmSpecification(AlgorithmSpecification&& value) { m_algorithmSpecificationHasBeenSet = true; m_algorithmSpecification = std::move(value); }

    /**
     *
The registry path of the Docker image that contains the training algorithm
     * and algorithm-specific metadata, including the input mode. For more information
     * about algorithms provided by Amazon SageMaker, see Algorithms.
     * For information about providing your own algorithms, see Using
     * Your Own Algorithms with Amazon SageMaker.

     */
    inline CreateTrainingJobRequest& WithAlgorithmSpecification(const AlgorithmSpecification& value) { SetAlgorithmSpecification(value); return *this;}

    /**
     *
The registry path of the Docker image that contains the training algorithm
     * and algorithm-specific metadata, including the input mode. For more information
     * about algorithms provided by Amazon SageMaker, see Algorithms.
     * For information about providing your own algorithms, see Using
     * Your Own Algorithms with Amazon SageMaker.

     */
    inline CreateTrainingJobRequest& WithAlgorithmSpecification(AlgorithmSpecification&& value) { SetAlgorithmSpecification(std::move(value)); return *this;}

    /**
     *
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
     * assume to perform tasks on your behalf.

During model training, Amazon
     * SageMaker needs your permission to read input data from an S3 bucket, download a
     * Docker image that contains training code, write model artifacts to an S3 bucket,
     * write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch.
     * You grant permissions for all of these tasks to an IAM role. For more
     * information, see Amazon
     * SageMaker Roles.
 <note>
To be able to pass this role to Amazon
     * SageMaker, the caller of this API must have the iam:PassRole
     * permission.
 </note>
     */
    inline const Aws::String& GetRoleArn() const{ return m_roleArn; }

    /**
     *
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
     * assume to perform tasks on your behalf.

During model training, Amazon
     * SageMaker needs your permission to read input data from an S3 bucket, download a
     * Docker image that contains training code, write model artifacts to an S3 bucket,
     * write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch.
     * You grant permissions for all of these tasks to an IAM role. For more
     * information, see Amazon
     * SageMaker Roles.
 <note>
To be able to pass this role to Amazon
     * SageMaker, the caller of this API must have the iam:PassRole
     * permission.
 </note>
     */
    inline void SetRoleArn(const Aws::String& value) { m_roleArnHasBeenSet = true; m_roleArn = value; }

    /**
     *
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
     * assume to perform tasks on your behalf.

During model training, Amazon
     * SageMaker needs your permission to read input data from an S3 bucket, download a
     * Docker image that contains training code, write model artifacts to an S3 bucket,
     * write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch.
     * You grant permissions for all of these tasks to an IAM role. For more
     * information, see Amazon
     * SageMaker Roles.
 <note>
To be able to pass this role to Amazon
     * SageMaker, the caller of this API must have the iam:PassRole
     * permission.
 </note>
     */
    inline void SetRoleArn(Aws::String&& value) { m_roleArnHasBeenSet = true; m_roleArn = std::move(value); }

    /**
     *
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
     * assume to perform tasks on your behalf.

During model training, Amazon
     * SageMaker needs your permission to read input data from an S3 bucket, download a
     * Docker image that contains training code, write model artifacts to an S3 bucket,
     * write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch.
     * You grant permissions for all of these tasks to an IAM role. For more
     * information, see Amazon
     * SageMaker Roles.
 <note>
To be able to pass this role to Amazon
     * SageMaker, the caller of this API must have the iam:PassRole
     * permission.
 </note>
     */
    inline void SetRoleArn(const char* value) { m_roleArnHasBeenSet = true; m_roleArn.assign(value); }

    /**
     *
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
     * assume to perform tasks on your behalf.

During model training, Amazon
     * SageMaker needs your permission to read input data from an S3 bucket, download a
     * Docker image that contains training code, write model artifacts to an S3 bucket,
     * write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch.
     * You grant permissions for all of these tasks to an IAM role. For more
     * information, see Amazon
     * SageMaker Roles.
 <note>
To be able to pass this role to Amazon
     * SageMaker, the caller of this API must have the iam:PassRole
     * permission.
 </note>
     */
    inline CreateTrainingJobRequest& WithRoleArn(const Aws::String& value) { SetRoleArn(value); return *this;}

    /**
     *
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
     * assume to perform tasks on your behalf.

During model training, Amazon
     * SageMaker needs your permission to read input data from an S3 bucket, download a
     * Docker image that contains training code, write model artifacts to an S3 bucket,
     * write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch.
     * You grant permissions for all of these tasks to an IAM role. For more
     * information, see Amazon
     * SageMaker Roles.
 <note>
To be able to pass this role to Amazon
     * SageMaker, the caller of this API must have the iam:PassRole
     * permission.
 </note>
     */
    inline CreateTrainingJobRequest& WithRoleArn(Aws::String&& value) { SetRoleArn(std::move(value)); return *this;}

    /**
     *
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can
     * assume to perform tasks on your behalf.

During model training, Amazon
     * SageMaker needs your permission to read input data from an S3 bucket, download a
     * Docker image that contains training code, write model artifacts to an S3 bucket,
     * write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch.
     * You grant permissions for all of these tasks to an IAM role. For more
     * information, see Amazon
     * SageMaker Roles.
 <note>
To be able to pass this role to Amazon
     * SageMaker, the caller of this API must have the iam:PassRole
     * permission.
 </note>
     */
    inline CreateTrainingJobRequest& WithRoleArn(const char* value) { SetRoleArn(value); return *this;}

    /**
     *
An array of Channel objects. Each channel is a named input
     * source. InputDataConfig describes the input data and its location.
     *

Algorithms can accept input data from one or more channels. For example,
     * an algorithm might have two channels of input data, training_data
     * and validation_data. The configuration for each channel provides
     * the S3 location where the input data is stored. It also provides information
     * about the stored data: the MIME type, compression method, and whether the data
     * is wrapped in RecordIO format.

Depending on the input mode that the
     * algorithm supports, Amazon SageMaker either copies input data files from an S3
     * bucket to a local directory in the Docker container, or makes it available as
     * input streams.

     */
    inline const Aws::Vector<Channel>& GetInputDataConfig() const{ return m_inputDataConfig; }

    /**
     *
An array of Channel objects. Each channel is a named input
     * source. InputDataConfig describes the input data and its location.
     *

Algorithms can accept input data from one or more channels. For example,
     * an algorithm might have two channels of input data, training_data
     * and validation_data. The configuration for each channel provides
     * the S3 location where the input data is stored. It also provides information
     * about the stored data: the MIME type, compression method, and whether the data
     * is wrapped in RecordIO format.

Depending on the input mode that the
     * algorithm supports, Amazon SageMaker either copies input data files from an S3
     * bucket to a local directory in the Docker container, or makes it available as
     * input streams.

     */
    inline void SetInputDataConfig(const Aws::Vector<Channel>& value) { m_inputDataConfigHasBeenSet = true; m_inputDataConfig = value; }

    /**
     *
An array of Channel objects. Each channel is a named input
     * source. InputDataConfig describes the input data and its location.
     *

Algorithms can accept input data from one or more channels. For example,
     * an algorithm might have two channels of input data, training_data
     * and validation_data. The configuration for each channel provides
     * the S3 location where the input data is stored. It also provides information
     * about the stored data: the MIME type, compression method, and whether the data
     * is wrapped in RecordIO format.

Depending on the input mode that the
     * algorithm supports, Amazon SageMaker either copies input data files from an S3
     * bucket to a local directory in the Docker container, or makes it available as
     * input streams.

     */
    inline void SetInputDataConfig(Aws::Vector<Channel>&& value) { m_inputDataConfigHasBeenSet = true; m_inputDataConfig = std::move(value); }

    /**
     *
An array of Channel objects. Each channel is a named input
     * source. InputDataConfig describes the input data and its location.
     *

Algorithms can accept input data from one or more channels. For example,
     * an algorithm might have two channels of input data, training_data
     * and validation_data. The configuration for each channel provides
     * the S3 location where the input data is stored. It also provides information
     * about the stored data: the MIME type, compression method, and whether the data
     * is wrapped in RecordIO format.

Depending on the input mode that the
     * algorithm supports, Amazon SageMaker either copies input data files from an S3
     * bucket to a local directory in the Docker container, or makes it available as
     * input streams.

     */
    inline CreateTrainingJobRequest& WithInputDataConfig(const Aws::Vector<Channel>& value) { SetInputDataConfig(value); return *this;}

    /**
     *
An array of Channel objects. Each channel is a named input
     * source. InputDataConfig describes the input data and its location.
     *

Algorithms can accept input data from one or more channels. For example,
     * an algorithm might have two channels of input data, training_data
     * and validation_data. The configuration for each channel provides
     * the S3 location where the input data is stored. It also provides information
     * about the stored data: the MIME type, compression method, and whether the data
     * is wrapped in RecordIO format.

Depending on the input mode that the
     * algorithm supports, Amazon SageMaker either copies input data files from an S3
     * bucket to a local directory in the Docker container, or makes it available as
     * input streams.

     */
    inline CreateTrainingJobRequest& WithInputDataConfig(Aws::Vector<Channel>&& value) { SetInputDataConfig(std::move(value)); return *this;}

    /**
     *
An array of Channel objects. Each channel is a named input
     * source. InputDataConfig describes the input data and its location.
     *

Algorithms can accept input data from one or more channels. For example,
     * an algorithm might have two channels of input data, training_data
     * and validation_data. The configuration for each channel provides
     * the S3 location where the input data is stored. It also provides information
     * about the stored data: the MIME type, compression method, and whether the data
     * is wrapped in RecordIO format.

Depending on the input mode that the
     * algorithm supports, Amazon SageMaker either copies input data files from an S3
     * bucket to a local directory in the Docker container, or makes it available as
     * input streams.

     */
    inline CreateTrainingJobRequest& AddInputDataConfig(const Channel& value) { m_inputDataConfigHasBeenSet = true; m_inputDataConfig.push_back(value); return *this; }

    /**
     *
An array of Channel objects. Each channel is a named input
     * source. InputDataConfig describes the input data and its location.
     *

Algorithms can accept input data from one or more channels. For example,
     * an algorithm might have two channels of input data, training_data
     * and validation_data. The configuration for each channel provides
     * the S3 location where the input data is stored. It also provides information
     * about the stored data: the MIME type, compression method, and whether the data
     * is wrapped in RecordIO format.

Depending on the input mode that the
     * algorithm supports, Amazon SageMaker either copies input data files from an S3
     * bucket to a local directory in the Docker container, or makes it available as
     * input streams.

     */
    inline CreateTrainingJobRequest& AddInputDataConfig(Channel&& value) { m_inputDataConfigHasBeenSet = true; m_inputDataConfig.push_back(std::move(value)); return *this; }

    /**
     *
Specifies the path to the S3 bucket where you want to store model artifacts.
     * Amazon SageMaker creates subfolders for the artifacts.

     */
    inline const OutputDataConfig& GetOutputDataConfig() const{ return m_outputDataConfig; }

    /**
     *
Specifies the path to the S3 bucket where you want to store model artifacts.
     * Amazon SageMaker creates subfolders for the artifacts.

     */
    inline void SetOutputDataConfig(const OutputDataConfig& value) { m_outputDataConfigHasBeenSet = true; m_outputDataConfig = value; }

    /**
     *
Specifies the path to the S3 bucket where you want to store model artifacts.
     * Amazon SageMaker creates subfolders for the artifacts.

     */
    inline void SetOutputDataConfig(OutputDataConfig&& value) { m_outputDataConfigHasBeenSet = true; m_outputDataConfig = std::move(value); }

    /**
     *
Specifies the path to the S3 bucket where you want to store model artifacts.
     * Amazon SageMaker creates subfolders for the artifacts.

     */
    inline CreateTrainingJobRequest& WithOutputDataConfig(const OutputDataConfig& value) { SetOutputDataConfig(value); return *this;}

    /**
     *
Specifies the path to the S3 bucket where you want to store model artifacts.
     * Amazon SageMaker creates subfolders for the artifacts.

     */
    inline CreateTrainingJobRequest& WithOutputDataConfig(OutputDataConfig&& value) { SetOutputDataConfig(std::move(value)); return *this;}

    /**
     *
The resources, including the ML compute instances and ML storage volumes, to
     * use for model training.

ML storage volumes store model artifacts and
     * incremental states. Training algorithms might also use ML storage volumes for
     * scratch space. If you want Amazon SageMaker to use the ML storage volume to
     * store the training data, choose File as the
     * TrainingInputMode in the algorithm specification. For distributed
     * training algorithms, specify an instance count greater than 1.

     */
    inline const ResourceConfig& GetResourceConfig() const{ return m_resourceConfig; }

    /**
     *
The resources, including the ML compute instances and ML storage volumes, to
     * use for model training.

ML storage volumes store model artifacts and
     * incremental states. Training algorithms might also use ML storage volumes for
     * scratch space. If you want Amazon SageMaker to use the ML storage volume to
     * store the training data, choose File as the
     * TrainingInputMode in the algorithm specification. For distributed
     * training algorithms, specify an instance count greater than 1.

     */
    inline void SetResourceConfig(const ResourceConfig& value) { m_resourceConfigHasBeenSet = true; m_resourceConfig = value; }

    /**
     *
The resources, including the ML compute instances and ML storage volumes, to
     * use for model training.

ML storage volumes store model artifacts and
     * incremental states. Training algorithms might also use ML storage volumes for
     * scratch space. If you want Amazon SageMaker to use the ML storage volume to
     * store the training data, choose File as the
     * TrainingInputMode in the algorithm specification. For distributed
     * training algorithms, specify an instance count greater than 1.

     */
    inline void SetResourceConfig(ResourceConfig&& value) { m_resourceConfigHasBeenSet = true; m_resourceConfig = std::move(value); }

    /**
     *
The resources, including the ML compute instances and ML storage volumes, to
     * use for model training.

ML storage volumes store model artifacts and
     * incremental states. Training algorithms might also use ML storage volumes for
     * scratch space. If you want Amazon SageMaker to use the ML storage volume to
     * store the training data, choose File as the
     * TrainingInputMode in the algorithm specification. For distributed
     * training algorithms, specify an instance count greater than 1.

     */
    inline CreateTrainingJobRequest& WithResourceConfig(const ResourceConfig& value) { SetResourceConfig(value); return *this;}

    /**
     *
The resources, including the ML compute instances and ML storage volumes, to
     * use for model training.

ML storage volumes store model artifacts and
     * incremental states. Training algorithms might also use ML storage volumes for
     * scratch space. If you want Amazon SageMaker to use the ML storage volume to
     * store the training data, choose File as the
     * TrainingInputMode in the algorithm specification. For distributed
     * training algorithms, specify an instance count greater than 1.

     */
    inline CreateTrainingJobRequest& WithResourceConfig(ResourceConfig&& value) { SetResourceConfig(std::move(value)); return *this;}

    /**
     *
A VpcConfig object that specifies the VPC that you want your training
     * job to connect to. Control access to and from your training container by
     * configuring the VPC. For more information, see Protect
     * Training Jobs by Using an Amazon Virtual Private Cloud.

     */
    inline const VpcConfig& GetVpcConfig() const{ return m_vpcConfig; }

    /**
     *
A VpcConfig object that specifies the VPC that you want your training
     * job to connect to. Control access to and from your training container by
     * configuring the VPC. For more information, see Protect
     * Training Jobs by Using an Amazon Virtual Private Cloud.

     */
    inline void SetVpcConfig(const VpcConfig& value) { m_vpcConfigHasBeenSet = true; m_vpcConfig = value; }

    /**
     *
A VpcConfig object that specifies the VPC that you want your training
     * job to connect to. Control access to and from your training container by
     * configuring the VPC. For more information, see Protect
     * Training Jobs by Using an Amazon Virtual Private Cloud.

     */
    inline void SetVpcConfig(VpcConfig&& value) { m_vpcConfigHasBeenSet = true; m_vpcConfig = std::move(value); }

    /**
     *
A VpcConfig object that specifies the VPC that you want your training
     * job to connect to. Control access to and from your training container by
     * configuring the VPC. For more information, see Protect
     * Training Jobs by Using an Amazon Virtual Private Cloud.

     */
    inline CreateTrainingJobRequest& WithVpcConfig(const VpcConfig& value) { SetVpcConfig(value); return *this;}

    /**
     *
A VpcConfig object that specifies the VPC that you want your training
     * job to connect to. Control access to and from your training container by
     * configuring the VPC. For more information, see Protect
     * Training Jobs by Using an Amazon Virtual Private Cloud.

     */
    inline CreateTrainingJobRequest& WithVpcConfig(VpcConfig&& value) { SetVpcConfig(std::move(value)); return *this;}

    /**
     *
Sets a duration for training. Use this parameter to cap model training costs.
     * To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
     * signal, which delays job termination for 120 seconds. Algorithms might use this
     * 120-second window to save the model artifacts.

When Amazon SageMaker
     * terminates a job because the stopping condition has been met, training
     * algorithms provided by Amazon SageMaker save the intermediate results of the
     * job. This intermediate data is a valid model artifact. You can use it to create
     * a model using the CreateModel API.

     */
    inline const StoppingCondition& GetStoppingCondition() const{ return m_stoppingCondition; }

    /**
     *
Sets a duration for training. Use this parameter to cap model training costs.
     * To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
     * signal, which delays job termination for 120 seconds. Algorithms might use this
     * 120-second window to save the model artifacts.

When Amazon SageMaker
     * terminates a job because the stopping condition has been met, training
     * algorithms provided by Amazon SageMaker save the intermediate results of the
     * job. This intermediate data is a valid model artifact. You can use it to create
     * a model using the CreateModel API.

     */
    inline void SetStoppingCondition(const StoppingCondition& value) { m_stoppingConditionHasBeenSet = true; m_stoppingCondition = value; }

    /**
     *
Sets a duration for training. Use this parameter to cap model training costs.
     * To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
     * signal, which delays job termination for 120 seconds. Algorithms might use this
     * 120-second window to save the model artifacts.

When Amazon SageMaker
     * terminates a job because the stopping condition has been met, training
     * algorithms provided by Amazon SageMaker save the intermediate results of the
     * job. This intermediate data is a valid model artifact. You can use it to create
     * a model using the CreateModel API.

     */
    inline void SetStoppingCondition(StoppingCondition&& value) { m_stoppingConditionHasBeenSet = true; m_stoppingCondition = std::move(value); }

    /**
     *
Sets a duration for training. Use this parameter to cap model training costs.
     * To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
     * signal, which delays job termination for 120 seconds. Algorithms might use this
     * 120-second window to save the model artifacts.

When Amazon SageMaker
     * terminates a job because the stopping condition has been met, training
     * algorithms provided by Amazon SageMaker save the intermediate results of the
     * job. This intermediate data is a valid model artifact. You can use it to create
     * a model using the CreateModel API.

     */
    inline CreateTrainingJobRequest& WithStoppingCondition(const StoppingCondition& value) { SetStoppingCondition(value); return *this;}

    /**
     *
Sets a duration for training. Use this parameter to cap model training costs.
     * To stop a job, Amazon SageMaker sends the algorithm the SIGTERM
     * signal, which delays job termination for 120 seconds. Algorithms might use this
     * 120-second window to save the model artifacts.

When Amazon SageMaker
     * terminates a job because the stopping condition has been met, training
     * algorithms provided by Amazon SageMaker save the intermediate results of the
     * job. This intermediate data is a valid model artifact. You can use it to create
     * a model using the CreateModel API.

     */
    inline CreateTrainingJobRequest& WithStoppingCondition(StoppingCondition&& value) { SetStoppingCondition(std::move(value)); return *this;}

    /**
     *
An array of key-value pairs. For more information, see Using
     * Cost Allocation Tags in the AWS Billing and Cost Management User
     * Guide.

     */
    inline const Aws::Vector<Tag>& GetTags() const{ return m_tags; }

    /**
     *
An array of key-value pairs. For more information, see Using
     * Cost Allocation Tags in the AWS Billing and Cost Management User
     * Guide.

     */
    inline void SetTags(const Aws::Vector<Tag>& value) { m_tagsHasBeenSet = true; m_tags = value; }

    /**
     *
An array of key-value pairs. For more information, see Using
     * Cost Allocation Tags in the AWS Billing and Cost Management User
     * Guide.

     */
    inline void SetTags(Aws::Vector<Tag>&& value) { m_tagsHasBeenSet = true; m_tags = std::move(value); }

    /**
     *
An array of key-value pairs. For more information, see Using
     * Cost Allocation Tags in the AWS Billing and Cost Management User
     * Guide.

     */
    inline CreateTrainingJobRequest& WithTags(const Aws::Vector<Tag>& value) { SetTags(value); return *this;}

    /**
     *
An array of key-value pairs. For more information, see Using
     * Cost Allocation Tags in the AWS Billing and Cost Management User
     * Guide.

     */
    inline CreateTrainingJobRequest& WithTags(Aws::Vector<Tag>&& value) { SetTags(std::move(value)); return *this;}

    /**
     *
An array of key-value pairs. For more information, see Using
     * Cost Allocation Tags in the AWS Billing and Cost Management User
     * Guide.

     */
    inline CreateTrainingJobRequest& AddTags(const Tag& value) { m_tagsHasBeenSet = true; m_tags.push_back(value); return *this; }

    /**
     *
An array of key-value pairs. For more information, see Using
     * Cost Allocation Tags in the AWS Billing and Cost Management User
     * Guide.

     */
    inline CreateTrainingJobRequest& AddTags(Tag&& value) { m_tagsHasBeenSet = true; m_tags.push_back(std::move(value)); return *this; }

  private:

    Aws::String m_trainingJobName;
    bool m_trainingJobNameHasBeenSet;

    Aws::Map<Aws::String, Aws::String> m_hyperParameters;
    bool m_hyperParametersHasBeenSet;

    AlgorithmSpecification m_algorithmSpecification;
    bool m_algorithmSpecificationHasBeenSet;

    Aws::String m_roleArn;
    bool m_roleArnHasBeenSet;

    Aws::Vector<Channel> m_inputDataConfig;
    bool m_inputDataConfigHasBeenSet;

    OutputDataConfig m_outputDataConfig;
    bool m_outputDataConfigHasBeenSet;

    ResourceConfig m_resourceConfig;
    bool m_resourceConfigHasBeenSet;

    VpcConfig m_vpcConfig;
    bool m_vpcConfigHasBeenSet;

    StoppingCondition m_stoppingCondition;
    bool m_stoppingConditionHasBeenSet;

    Aws::Vector<Tag> m_tags;
    bool m_tagsHasBeenSet;
  };

} // namespace Model
} // namespace SageMaker
} // namespace Aws

redpajama_set_name: RedPajamaGithub


DR. JENNIFER C. FRANKLIN is a licensed psychologist (NC #4137; CA #PSY20709) by trade and anthropologist by nature. Genuine curiosity drives the passion I have for my work. My interest in becoming a psychologist was inspired by my own personal experience of healing from Irritable Bowel Syndrome. My aim is to understand who each client is as a unique whole human being in order to individualize my approach and maximize the effectiveness of the therapeutic experience.
I work in private practice primarily with adult individuals and couples.
I've also had training in and exposure to a wide range of other affective, cognitive, somatic, spiritual, relationship, and trauma healing modalities including but not limited to Gestalt, Authentic movement, dream work, creative expression, group process, and Imago. If it is important to you to work with a therapist with an understanding of a particular framework or modality not mentioned above, please let me know. Perhaps I've had exposure to and/or training in it as well.
My approach to therapy has been shaped by my work with some prominent thinkers and leaders in their respective fields. Early in my career, I was supervised by Humanistic Psychologist Thomas C. Greening, PhD, former Editor of the Journal of Humanistic Psychology (JHP), and became an associate editor for the JHP while working as a psychotherapist prior to licensure. Upon relocating to North Carolina I worked with Douglas Drossman, MD, a pioneer for the treatment of functional gastrointestinal disorders from a biopsychosocial perspective at the world-renowned UNC Center for Functional GI & Motility Disorders in Chapel Hill, NC, where I worked psychotherapeutically with in-patients and out-patients suffering from severe functional gastrointestinal disorders.
I have a PhD in Transpersonal Psychology and a Master's in Counseling Psychology, both from the Institute of Transpersonal Psychology (Sofia University) in Palo Alto, California. There I received a mindfulness-based, experiential education in psychology. I also have a Bachelor of Science in Accounting from the University of Southern California in Los Angeles, California.
I earned my doctorate upon completion of my dissertation entitled Breaking a Leg Without Breaking the Spirit. This was a study examining the psychology, spirituality, and self-actualizing tendencies of 116 professional working actors. It involved years of research on creativity, the psychology of acting, multiple intelligences, creativity, fame, consciousness, and the range of spiritual experiences common among actors and other creative artists. I was given the rare opportunity to go behind the scenes in the life of the average working Screen Actors Guild (SAG) actor.
I was personally diagnosed with Irritable Bowel Syndrome as a teenager and suffered greatly for several years. Through my personal journey, I became symptom-free and was inspired to offer others the kind of guidance that supports not only symptom management and reduction but also the kind of genuine healing that leads to a fully functioning life.
My healing process began when I was referred by my gastroenterologist to a psychologist for weekly therapy. Though I resisted, I became so desperate for relief that I finally yielded to the idea that perhaps it would help. And it did. After just one year of weekly therapy with a psychologist, my symptoms ameliorated significantly. Later, while training to become a psychologist, my exposure to a variety of approaches, modalities, and experiences led me to the realization that though I was better, I still hadn't fully healed. I was still tolerating IBS symptoms and living in constant fear. By addressing these residual symptoms, I transitioned into a life free of symptoms and fear. My life didn't just go back to the way things were before I developed IBS; it became even better than it ever had been!


We're highlighting Meatless Mondays to kick off the New Year, courtesy of dietitian Brittyn Coleman. Check out the previous installments of the Sesame Buddha Bowl and Lentil Soup + Quinoa & Veggies, then check out this week's recipe for plant-based meal inspo: Veggie Tempeh Bowl (or Nachos!).
For spiciness, add 1 teaspoon red pepper flakes, ½ tsp cayenne, a splash of hot sauce, or jalepeños, according to your taste!
Rinse cashews thoroughly with cold water and place in a bowl. If soaking cashews overnight, cover cashews with enough cool water to cover. If quick soaking, boil water and pour over cashews to cover. Place a lid or plate on top of the bowl to retain heat.
While cashews are soaking, start to prepare tempeh taco meat. Lightly spray a medium pan with avocado or vegetable oil. Use your hands to crumble tempeh into the pan over medium heat. In a small bowl, pour out ½ packet of taco seasoning and mix with 2-3 tbsp water (should be a thin paste). Set aside.
Heat tempeh thoroughly and then pour in the taco seasoning sauce and mix thoroughly. Cook for 1-2 more minutes and set aside. Heat black beans over the stove in a small pot or in the microwave.
Drain cashews and place in blender with the rest of the cashew queso ingredients and blend on high for 1-2 minutes. If using a Vitamix, you can place it on the "hot soup" setting to get it piping hot!
Drizzle cashew queso over your prepared plate and top with pineapple salsa and avocado. Enjoy!


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text:
Finding the Right Fit for Veterans – Like Justin MacGregor – at Consumers Energy
Posted by Force4Michigan | Nov 24, 2021 | Powering Michigan | 0 |
By: Matt Hendrian and Austin Bauer
When Justin MacGregor earned his associate degree, he was at a crossroads.
The education he was pursuing wasn't what he loved to do, and he had questions about his future. Working in construction at the time, he leaned on his coworkers, many of whom were veterans, and they asked if he had considered joining the armed services.
A year after researching whether this was the right choice for his career, Justin found himself standing on a set of yellow footprints at Marine Corps Basic Training – or boot camp. "A lot is going through your head when you arrive," he recalled about his experience, "It's the most chaotic place I have ever been. In the back of your mind, you're wondering if you did the right thing".
Reinforcing Resilience
Through all the challenges an enlistment in the military can bring, Justin discovered that something was happening within him. Not a change, but an affirmation that the traits he had relied on his entire life weren't just valuable – they were essential. "It's eye-opening how much you can learn in such a short time. It makes you grow up quick, they give you a lot of responsibility at a young age."
Justin was no stranger to a determined mindset, even before he completed boot camp, the difference that separated his time in construction from his time at Consumers Energy was the reinforcement of the values that guided him there in the first place. "Linework has always been in the back of my mind and after a successful career fixing helicopters in the Marine Corps, I knew I could do it" A formative experience he credits for setting the stage for the next chapter of his life, "I don't know if it changed my career path, but it set in stone what direction I wanted to take it."
With valuable experience from the military now a part of him and a clear idea of what he wanted his future to be, he was ready to embrace a new challenge.
Finding His Fit
When reflecting on his time serving in the Marine Corps, Justin drew on some lessons that he brings with him every day, "Quitting or giving up isn't an option, you have to find a way to get it done. Make it work, whatever you must do. Even with limited resources or time, it reinforced finding a way to overcome that."
As Justin begins his new career as an electric line worker, he is confident that his path was right for him, "I am happy I did it; it was a good experience," he recalled of his decision.
He can also feel assured that the same qualities that were valued in the armed services are valued by the company and his coworkers. In a year fraught with the challenges of powerful storms, his mentality and approach towards problem-solving are traits that make him an integral part of our team and a coworker we are proud to have.
Veterans Electric Lines Boot Camp
Justin is among the many veterans who now call Consumers Energy home.
We are always committed to serving our rich community of veterans through programs like the one Justin got his start in, The Electric Lines Bootcamp.
This year, we launched the first ever Veterans Electric Lines Boot Camp. We are proud to have partnered with the Michigan State Utility Workers Council (MSUWC), Power for America, and the Electric Joint Apprentice Committee to hire Veterans as Electric Lineman Apprentices. In order to properly train these veterans, the team created a Veterans Electric Lines Bootcamp, which gives the Veterans a solid foundation to begin their apprenticeships.
The boot camp was successful thanks to the ongoing partnership of the Veterans Advisory Panel, an internal employee resource group, the MSUWC, Power for America, Electric Join Apprentice Committee and our training department Learning & Development.
Amber Fogarty, Manager of Strategic Talent Pipeline Programs who manages the program said, "Of all the projects I've worked on at Consumers Energy, this has, by far, been the most rewarding, passion-filled program I've ever been a part of. As a veteran myself, being able to connect my military service with my civilian work has been an incredible experience."
Shortly after sending out a notice to recruit for the program, we received over 200 applicants. The applications were reviewed by a diverse panel that found 90 Veterans who met the requirements for the program. These 90 applicants were interviewed over the course of a few weeks which resulted in 40 Veterans being selected for the Veterans Electric Lines Bootcamp.
The Veterans Electric Lines Boot Camp started earlier this year on March 15, followed by a second on March 29. The ten-day training period exposed the candidates to safety, proper climbing technique, and several basic linework related skills. They had to complete the linework orientation at the end to be offered a paid linework apprenticeship.
'"The Michigan State Utility Workers Council is extremely proud to continue our partnership with Consumers Energy, as well as the Power for America Training Trust to allow for career opportunities in the Utility Industry for our Veterans," said Craig Wright, President of the MSUWC. "On behalf of the MSUWC, we thank all that were involved in building this training program from the bottom up and we look forward to continued success with our Veterans training programs."
The program is currently recruiting for 2022. To learn more, visit P4A Electric Lines Boot Camp.
As we celebrate Justin beginning his journey with us through the first chapter of his story, we reflect on the achievement, sacrifice, and bravery of all our veterans here at Consumers Energy and across the country. Thank you, Justin and veterans everywhere for your service.
2021 Best For Vets: Employers
Recently, we were ranked the number one company in Michigan in the 2021 Best For Vets: Employers by The Military Times. This survey is the most comprehensive annual ranking of the country's best employers and organizations with military-connected employment, programs, benefits and support efforts. We were ranked 20th overall and 3rd out of 7 utilities.
We would love for you to join our team. Find open positions on our website.
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Home Entertainment Jamie Lynn Spears says "hurt" for not telling Britney about her pregnancy...
Jamie Lynn Spears says "hurt" for not telling Britney about her pregnancy "lingers"
Of Jamie Samhan.
Jamie Lynn Spears is taking her time to share her life with fans in her next memoir Things I should have said.
The "Zoey 101" alumni wants to use the book to tell her "the truth the right way", including getting pregnant at 16 from her daughter Maddie.
In an excerpt shared with People, Spears recalled the aftermath of the revelation of her pregnancy and the "anger" of her father, Jamie Spears.
"The entire Spears team was already involved in my sister's PR difficulties and everyone around me just wanted to make this 'problem' go away," he wrote. "My family and leaders pulled me out of school until they figured out what to do next. They took away my smartphone, fearing the news would come out, and insisted that no one share any information with anyone, especially the press. My father and I stopped talking and the tension was terrible. "
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The young star then spent most of the time in her room.
"One person after another – and there were many – came into my room trying to convince me that having a baby at this point in my life was a bad idea," Spears recalled.
According to her, there was only one person on her "team to show any support for my desire to keep my baby".
"The next option was for me to go to Mercy Ministries, a home for single mothers in Tennessee, where I could eventually give my baby up for adoption. Dad and I had a fight, throwing words and insults. He grabbed me by the shoulders and held on tight in hopes of yielding to his will. I got in his face and yelled, 'NO! I will not go.' I couldn't face any of them. I ran away from them, panting with rage, "she continued.
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She later claimed she wasn't even allowed to tell her sister, Britney Spears.
"They went so far as to hide my pregnancy from my sister, saying, 'It's too risky to tell Britney about the baby.' I needed her more than ever and she was unable to help me in my most vulnerable moment, "he said." Britney's condition was escalating into something more troubling. They were worried that her instability at the time made her unreliable. . I followed what my team told me to do because I was a minor and didn't want to cause any more problems. Britney learned about the pregnancy when the article was published. To this day, the pain of not being able to tell. to my sister it remains ".
"I was banned and practically hidden. The misery and loneliness persisted ".
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