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  1. 1101
    por Lyons, L.
    Publicado 2016
    “…Accelerators and detectors are expensive, both in terms of money and human effort. It is thus important to invest effort in performing a good statistical anal- ysis of the data, in order to extract the best information from it. …”
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    Practical Statistics
  2. 1102
    por Auer, Michael M., Griffiths, Mark D.
    Publicado 2015
    “…The results showed that online gamblers receiving personalized feedback spent significantly less time and money gambling compared to controls that did not receive personalized feedback. …”
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    The use of personalized behavioral feedback for online gamblers: an empirical study
  3. 1103
    “…A hierarchy of ways to save money on cigarettes included saving the most money by changing to roll your own pipe tobacco, changing to a cheaper brand, cutting down or quitting, changing to cigarillos, and buying online. …”
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    Price-Minimizing Behaviors in a Cohort of Smokers before and after a Cigarette Tax Increase
  4. 1104
    “…The aim of the current study was to examine neural signatures of gaining money for self and charity in adolescence. Participants (N = 160, aged 11–21) underwent functional magnetic resonance imaging-scanning while performing a zero-sum vicarious reward task in which they could either earn money for themselves at the expense of charity, for a self-chosen charity at the expense of themselves, or for both parties. …”
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    Neural reward related-reactions to monetar gains for self and charity are associated with donating behavior in adolescence
  5. 1105
    por Ayodeji, Idowu Oluwasayo
    Publicado 2020
    “…It is widely believed that increase in money supply increases inflation rates in the long run. …”
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    Panel logit regression analysis of the effects of corruption on inflation pattern in the Economic Community of West African states
  6. 1106
    por Sugandi, Eric Alexander
    Publicado 2021
    “…We explore five types of financial markets in Indonesia: (1) the Rupiah (IDR) interbank money market; (2) the US Dollar (USD) interbank money market; (3) government conventional bond (SUN) markets; (4) the stock market; and (5) the USD/IDR spot market. …”
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    Indonesia’s Financial Markets and Monetary Policy Dynamics Amid the COVID-19 Pandemic
  7. 1107
    “…We recruited heavy smokers (Money: 45 subjects, 13 females and 32 males; Nicotine: 21 subjects, 4 females and 17 males) to perform a gambling task with nicotine and monetary reward on separate days. …”
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    Online Artículo Texto
    Neural bases of risky decisions involving nicotine vapor versus monetary reward
  8. 1108
    “…World researchers have studied various phenomena of loans provided through financial institutions, including a loan strategy with Group Loans. Group Loans are money loans made by a group of people to a microfinance institution. …”
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    Methods for studying group loans, join responsibility, and women's empowerment
  9. 1109
    por Ortiz Williams , Samuel Gibran
    Publicado 2023
    “…The mexican peso, like many other currencies in the world, has lost its purchasing power, but this loss has, among other things, to do with the evolution of money. Money has gone through many phases, from cash to it´s merely digital way. …”
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    Online Artículo
    EL DERECHO A LA MONEDA ESTABLE EN MÉXICO (THE RIGHT TO A STABLE CURRENCY IN MEXICO)
  10. 1110
    “…RESULTS: Prosocial behavior on the new trial types added in this revised game correlated strongly with prosocial behavior on the old trial type (i.e., trials where the participant will gain money and the charity will lose money; r = 0.71; p-value<0.001; n = 485). …”
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    A revised prosocial behavior game: Testing associations with psychopathic traits and the effects of moral elevation using a randomized clinical trial
  11. 1111
    “…RESULTS: The probability of spending money on cigarettes was higher among lower SES households. …”
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    Socioeconomic status and cigarette expenditure among US households: results from 2010 to 2015 Consumer Expenditure Survey
  12. 1112
    “…CONCLUSIONS: In selecting research projects for funding, Australian research organisations focus largely on research appropriateness, significance and feasibility; however, value for money is most often overlooked. Research funding decisions should include an assessment of value for money in order to maximise return on research investment.…”
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    Directing research funds to the right research projects: a review of criteria used by research organisations in Australia in prioritising health research projects for funding
  13. 1113
    por Venezuelan, A., Ausman, James
    Publicado 2019
    “…(“Rent-seeking” is simply getting money from the government for the oil it sells and giving little or nothing back to the government in return. …”
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    The devastating Venezuelan crisis
  14. 1114
    “…Although no effect was demonstrated on time spent gambling, inducements increased money wagered, gambling-related expectancies and perceived loss of control. …”
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    Online Artículo Texto
    Impact of Wagering Inducements on the Gambling Behaviors, Cognitions, and Emotions of Online Gamblers: A Randomized Controlled Study
  15. 1115
    “…We measured salivary oxytocin concentration, relating it to the amount of money transferred among participants (a proxy for trust) and the autism-spectrum quotient (AQ). …”
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    Online Artículo Texto
    Oxytocin-Trust Link in Oxytocin-Sensitive Participants and Those Without Autistic Traits
  16. 1116
    “…Lorcaserin significantly decreased VAS ratings of “feel irritable,” “feel hungry,” and “I am craving.” For the cocaine vs. money choice procedure, there was a significant interaction between choice (cocaine vs. money) and lorcaserin. …”
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    Safety and Preliminary Efficacy of Lorcaserin for Cocaine Use Disorder: A Phase I Randomized Clinical Trial
  17. 1117
    “…INTRODUCTION: Financial domination involves the payment of cash or gifts from a wish list by a money slave to a money mistress, financial dominatrix, or findomme. …”
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    Online Artículo Texto
    Findommes, Cybermediated Sex Work, and Rinsing
  18. 1118
    “…On a 5‐point Likert scale, participants were most likely to agree that sufficient money, health and finishing secondary school were important for preterm babies to have a good life (mean (SD): money 4.43 (0.81); health 4.39 (0.72); education 4.37 (0.81); normal weight 4.10 (0.81); intelligence 4.03 (0.94); longevity 4.01 (1.07), P < 0.0001). …”
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    Prioritising long‐term outcomes for preterm babies: A survey of consumers and clinicians
  19. 1119
    “…Therefore, researchers may include money as an incentive to improve the response rate while conducting surveys.…”
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    Does usage of monetary incentive impact the involvement in surveys? A systematic review and meta-analysis of 46 randomized controlled trials
  20. 1120
    por Unwin, Harry J. A., Kopczynska, Maja, Pugh, Richard, Tan, Laura J. P., Subbe, Christian P., Ellis, Gemma, Morgan, Paul, Havalda, Peter, Sharif, Ben, Burke, John, Szakmany, Tamas, Hobrok, Maria, Thomas, Moriah, Burden, Annie, Youssef, Nadia, Carnegie, Katherine, Colling-Sylvester, Helena, Logier, Natasha, Alsaeed, Meshari, Williams, Hannah, Ayob, Arfa, Farzana, Nor, Parida, Sweta, Lawson, David, Evans, Emily, Davis, Laura Jane, Atkins, Billie, Davies, Llywela Wyn, Sanders-Crook, Lee, Seal, Steffan Treharne, Cains, Alice, Pugh, Richard, Crisp, Katy, Venning, Sarah, Sykes, Ella, Narine, Stephanie, Parry, Georgia, Dillon, Emily Angela, Siah, Qi Zhuang, Yang, Ting, Jones, Tyler, Thara, Parvathi, Wood, Emma, Wirt, Lara, Pier, Georgina St, Betts, Richard, Mitsaki, Kyriaki, Tachweed Pierce, Mari, Davies, Sioned, Hafouda, Yakeen, Ifan, Erin, Lacey, Grace, Mitchell, Francesca, Lynch, John, Mazur, Michal, D’Souza, Lezia, Ponting, Bethan, Lau, Terrance, Kerrigan, Ruairidh, Morgan, Lucy, Vindla, Roshan, Zeicu, Claudia, James, Becky, Ariff, Amirah Amin, Wan Azzlan, Wan Binti, Collins, Charlotte, Wickens, Elizabeth, Norbee, Alisa, Zulkefli, Aliya, Haddock, Thomas, Thomas, Megan, Lee, Matthew, Cynan, Miriam, Nik Azis, Nik-Syakirah, Hay, Imogen, Russell, Catherine, Vreugdenhil, Margriet, Abdimalik, Mustafa, Davies, Joseph, Havalda, Peter, Evans, Angharad, Robertson, Kate, Gitau, Grace, Gruber, Mei-yin, Telford, Thomas, Qarout, Anas, Nandra, Naomi, Garrard, Hannah, Cutler, James, Tammy Jones, Rhiannon, Prideaux, Amy, Spence, Timothy, Hardie, Sarah, Seymour, Harriet, Willis, Sam, Warlow, Matthew, Thanthilla, Shanali, Downs, Thomas, Foley, Nina, McKeown, Chad, Dandawate, Akshita, Shayan-Arani, Holleh, Taylor, Ellie, Kyriakides, Oliver, Price, Rachel, Haf Mackey, Ffion, Haines, Emily, Chun, Samuel, Vignarajah, Nilarnti, Chamberlain, Tessa, Zhao, Dongying, Tantirige, Nayanatara Nadeesha T, Dennehey, Naomi, Evans, Georgina, Watts, John, Battle, Ceri, Jones, Ryan, Jones, Selina, James, Charlotte, O’Hanlon, James, Bridges, Isabella, Hughes, Bethany, Polchar, Leo, Bisson, Elise, Mykura, Charlotte, Money, Lara, McKenna, Joshua, Kinsman, Sarah, Hanna, Demiana, Baker, Emily, Sprague, Harrison, Sharma, Liam, Pontin, Tom, Shore, Emma, Hughes, Tamara, Nightingale, Sam, Baby, Philby, Shield, Matthew, Cross, Alice, Boss, Jenna, Ross, Olivia, Ashton, George, Pandit, Kimaya, Davies, Daniel, Garbutt, Cameron, Johnston, Charlotte, Cox, Marcus, Roberts, Chantal, Waller, Alessia, Heekin, Laura, Wang, Kathy, Church, Rhianna, Patel, Shrina, Broderick, Marianne, Whillis, Hannah, Hathaway, Daniel Craig, Yildirim, Emel, Atkins, Caitlin, Walters, Elin, Durie, Carys, Hamilton Sinnerton, Robert James, Tanner, Benjamin, Abreu, Julimar, Bashir, Kiran, Hamlyn, Vincent, Tee, Amelia, Hinchcliffe, Zoe Ann, Otto, Rita, Covell, Georgie, Stone, Megan, Maidman, Victoria, Godfray, Katherine, Caradine, Rhidian, Beetham, Hannah, Anomneze-Collins, Adanna Nicole, Tan, Jeanette, Abdelrazik, Yasmina, Khan, Azizah, Malik, Nabihah, Clack, Aidan, Oliva, Lewis, Thomas, Tyler, Mounce, Adam George, Ramjeeawon, Anoopama, Mtunzi, Ndaba, Soppitt, Duncan, Hale, Jay, Wellington, Jack, Buchanan Ross, Robert, Lis, Danielle, Parsonson, Rebecca, Joseph-Gubra, Jude, Arunthavarajah, Ajitha, Nicholas, Jessica, Harris, Aaron, Atkinson, Henry, Webster, Jessica, Burnett, Tim, Raffan Gowar, Josephine, DeFriend, Sam, Whitaker, Jasmine, Beasant, Elizabeth, Macchiavello, Luis, Usman, Danyal, Mahdi, Abdullah, Tze Shan, Tiffany Ye, Savill, Nick, Gee, Jennifer, Hodges, Lizzie, Desai, Ami, Rossiter, Hannah, Taylor, Matthew, Pinto, Kevin, Hartley, Eleanor, Emanuel, Oscar, Long, Rhiannon, Selby, Megan, Wardle, Elilis, Urquhart, Alexandra, Barrington, Jack, Ashman, Matthew, Adcock, Elizabeth, Dickinson, Amelia, Jordache, Rebecca, El Alaoui, Rym Chafai, Stovold, Sophie, Vickery, Sam, Jones, Nia, O’Donnell, Alice, Cuthbert, Monty, Eghosa, Osa, Karim, Muhammad, Williams, Lowri, Tucker, Louise, Downs, Tom, Walford, Rebecca, Hook, Annabelle, Mounce, Adam, Eccles, Emily, Edwards, Ross, Ramesh, Kirtika, Hall, Charlie, Lazarou, Maria, Jones, Rhidian, McGillian, Katy, Bhachoo, Hari Singh, The, Zoe, Inpahas, Vithusha, Desai, Ruchi, Cheema, Yusuf, Hughes, Andrew, Cranage, Olivia, Bee, Felicity, Osman, Khalid, Khan, Humza, Pitt, Jennifer, Pickwick, Charlotte, Carter, Jorge, Andrew, Fiona, Seedat, Naseera, Patel, Roshni, Walker, Megan, Boam, Alicia, Randall, Jessica, Bowyer, Beth, Edwards, Josh, Jones, Natasha, Walker, Emma, MacNaught, Ailsa, Balachandran, Swagath, Shipley, Abbie, Louise Kent, Jennifer, Tilley, Samuel, Davies, Bethany, Withers, Emma, Parmar, Krishna, Webber, Lucie, Sharma, Angelica, Handley, Amy, Gordon, Alexandra, Allen, Lucy, Paddock, Rebecca, Penney, Harriet, Banerjee, Lopa, Vanderpump, Chloe Victoria, Harding, Kate, Burke, John, Minik, Orsolya, Jarrett, Nia, Rowe, Ellie, Anomneze-Collins, Adanna, Griffiths, Harry, Pengelly, Sarah, Bennett, Ffion, Bilal, Ahmed, El-badawey, Abdullah, Ellis, Bethan, Cook, Luke, Elizabeth Valentine Maine, Harriet, Armstrong, Kiri, Beresford, Hannah, Raven-Gregg, Timia, Liddell-Lowe, Tom, Ong, Caitlin, Reed, Harriet, Alice St John, Frederika, Julia Kozuch, Weronika, Ray, Isabelle, Anuprabha Silva, Irukshi, Natalie Cheng, Sin Ting, Ali, Umme-Laila, Syed, Noreena, Murphy, Luke, Grother, Thomas, Smith, Harry, Watson, Rachel, Marei, Omar, Kirby, Emma, Gilfedder, Anna, Maw, Lydia, O’Connor, Sarah, Maden, Charlotte, Jones, Helena, Preston, Hazel, Binti Maliki, Nur Amirah, Zimmerman, Mark, Webber, Jessica, Jones, Llewelyn, Phillips, Rebecca, McCarthy, Lauren, Hubbard, Emily, Duffy, Leo, Guerrier Sadler, Abigail, Szakmany, Tamas, Richards, Owen, King, Charles, Killick, Charlotte, Chema, Yusuf, Shergill, Kavita, Huen, Yi, Lau, Lillian, Mustafa Ali, Hannah, Wilcock, Lucas, Timlin, Molly, Rela, Ayeesha, Smith, Daniel, Ireland, Sarah, Evans, Jennifer, Poobalan, Nayanatara, Pearce, Jessica, Vadiveloo, Thivya V, Black, Zoe, Samuel, Daniel Elis, Hussain, Humaira, Hawkins, Joanna, Atiyah, Zeid, Creamer, Rebecca, Zafar, Maham, Almazeedi, Ahmad, Brunnock, Hannah, Nasser, Zain, Jeyanthi, Mekha, Moghbel, Poorya, Kwan, Katie, Sutherland, Isobel, Davis, Frank, Rogers, Abigail, Tan, Zhao Xuan, Chantrill, Clare, Robertson, Amal, Foulkes, Jonathan, Khanam, Rahana, John, Jomcy, Meehan, Sarah Hannah, Metezai, Huria, Dawson, Hannah, Vadivale, Navrhinaa, Lee, Camilla, Dhadda, Amrit, Cleaver, Sian, Logue, Genna, Inns, Joy, Jones, Isabel, Howcroft, Robyn, Gilbert, Carys, Bradley, Matthew, Pike, Louise, Keeling, Rachel, Banks, Charldré, Cochrane, Eleanor, McFadyen, James, Mo, Matthew, Ireland, Emily, Brittain, Esme, Laid, Ihssen, Green, Charlotte, Mcforrester, Adriel, Michelle Ly, Xuong, Nalbanti, Mariana, Joseph, Raven, Tagg, Jack, Purchase, David, Myat, Pan, Niina, Ayako, Joshua Jones, Tyler, Hughes Thomas, Lowri, Hoyle, Natalie, Benc, Patrick, Davies, Ellen, Wu, Meng-Chieh, Fellows, David, Tilley, Sam, Baxendale, Eloise, Khan, Karishma, Forrester, Andrew, Moore, Oliver, Lim, Hse Juinn, Owen, Aimee, Hussain, Faris, Allybocus, Nima-banu, Sethi, Maneha, Waring, Harry, Khan, Adeel, Smith, Claire, Doyle, Nicholas, Yahya Amjad, Mohammad, Galloway, Luke, Morgan, Paul, Ellis, Gemma, Lundin, Robert, Al Hassan, Haamed, Markall, Bethan, Kaur, Namratha, Onyango, Emmanuel, Beard, Heather, Field, Elliot, Nelson-Rowe, Ellen, Adcock, Lizzie, Stoddart, Amelia, St John, Frederika, Sivananthan, Mathoorika, Jones, Rhys, Yeon Kwak, Sung, Farakish, Lily, Rhys-Ellis, Holly, Moss, Kate, Ray, Tallulah, David, Tessa, Roberts, Talea, Quy, Annie, Paranjape, Aniket, Bee, Felicity, Poolworaluk, Nutchanun, Keast, Mary, Yao, Si Liang, Manning, Dion, Irwin, Isobel, Asim, Umair, Boggon, Emelia, Alkurd, Ibrahim, Lawerece, Genevieve, Brown, Jade, Murphy, Emily, Lambert, Evie, Guilford, Jeremy, Payne, Beth, Almulaifi, Mariam, Poacher, Arwel, Ganesananthan, Sashiananthan, Cunningham-Walker, Berenice, Spooner, Chloe, Kiran, Akanksha, Nadeem, Nabeegh, Unadkat, Vidhi, Sparey, Esme, Li, David, Smith, Jessica, Corrin, India, Kurani, Amit, McNulty, Paul, Brown, Ceri, Groblewski, Wojciech, Szoke, Szilvia, Redman, Amelia, McKeag, Esther, Donnir, Anastasia, Ravishangar, Gaautham, Howard, Emanuela, Salmon, Charlotte, Tanatova, Sara, Kew, Jasmine, Clark, Megan Eilis, Hannay, Ellen, Godsafe, Olesya, Houghton, Christina, Lavric, Francesca, Mallinson, Rachel, Littler, Chris, Reddy, Harsha, Campbell, Andrew, Soo, Benedict, Evans, Rachel, Donowho, Georgina, Cawthra, Alexandra, Davies, Maddison, Lawrence Ashman, Matthew, Scriven, Jamie, Vautrey, James, Seet, Shannon, Britton, Imogen, Hodgson, Abigail, Twohey, Emma, Robbins, Joseph, Yung Ling, Vanessa Yeo, Asjadi, Kimiya, Yee Shean, Carven Chin, McCarroll, Zoe, Amatotsero, Oritseweyimi, Priscilla Chan, Hei Man, Cho Hui, John Ng, Ashaye, Antonia, Acheampong, Josephine, Adeleye, Ayowade, Ahmed, Saber, Chrysostomou, Alexandra, Unwin, Harry, Ang, Eshen, McSwiney, Niamh, Lim, Yin Yin, Lee, Zong Xuan, Kulikouskaya, Svetlana, Zulkifili, Nur, Lim, Sheryl, Xin, Lim, Urazbayeva, Adiya, Ahmad Hanif, Nur Haslina, Ying, Yau Ke, Coleclough, Alice, Higgins, Eilis, Spencer, Naomi, Ng, Tze Gee, Booth, Sam, Wai Yee Ng, Stephanie, Subbe, Christian P, Patterson, Isabella, Chia, Wen Li, Mukit, Abdullah, Vivian Pak, Hei Yi, Lock, Felicity, Nalmpanti, Mariana, Thomas, Shôn Alun, Burgher, Tanisha, Zhen Yeo, Alfred Wei, Powell Jones, Siwan, Miles, Charlie, Perry, Millicent, Burton, Holly, Powell, Katharine, Nessa, Luthfun, Fadlalla, Aalaa, Morgan, Rhian, Hodges, Elizabeth, Heal, Amelia, Scott, Chloe, Tayler, Alice, Chandy, Thomas, Taufik, Abduahad, Cochrane, James, Willis, Samuel, Heng, Sieh Yen, Cooper, Alex, von der Pahlen, Henrik Graf, Talbot, Isabella, Roberts, Robin Gwyn, Smith, Jessica Sharma, Sweeney, Aisling, Roberts, Cerian, Bausor, Laura, Chania, Thomas, Daniah, Puw, Elen Wyn, Lyons, Ronan A, Hall, Judith E
    Publicado 2021
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    The Use of Different Sepsis Risk Stratification Tools on the Wards and in Emergency Departments Uncovers Different Mortality Risks: Results of the Three Welsh National Multicenter Point-Prevalence Studies
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