Добірка наукової літератури з теми "Physical biases"
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Статті в журналах з теми "Physical biases"
Zhang, Xuefeng, Shaoqing Zhang, Zhengyu Liu, Xinrong Wu, and Guijun Han. "Parameter Optimization in an Intermediate Coupled Climate Model with Biased Physics." Journal of Climate 28, no. 3 (February 1, 2015): 1227–47. http://dx.doi.org/10.1175/jcli-d-14-00348.1.
Повний текст джерелаKishimoto, Reiki, Kyoshiro Sasaki, Akihiko Gobara, Yuko Ojiro, Giyeon Nam, Kayo Miura, and Yuki Yamada. "When a silhouette appears male: Observer’s own physical fitness governs social categorization of sexually ambiguous stimuli." Letters on Evolutionary Behavioral Science 7, no. 1 (April 1, 2016): 17–20. http://dx.doi.org/10.5178/lebs.2016.44.
Повний текст джерелаKeogh, Edmund, Catherine Dillon, George Georgiou, and Caroline Hunt. "Selective attentional biases for physical threat in physical anxiety sensitivity." Journal of Anxiety Disorders 15, no. 4 (July 2001): 299–315. http://dx.doi.org/10.1016/s0887-6185(01)00065-2.
Повний текст джерелаLuhring, Thomas M., Grant M. Connette, and Christopher M. Schalk. "Trap characteristics and species morphology explain size-biased sampling of two salamander species." Amphibia-Reptilia 37, no. 1 (2016): 79–89. http://dx.doi.org/10.1163/15685381-00003034.
Повний текст джерелаBellprat, O., S. Kotlarski, D. Lüthi, and C. Schär. "Physical constraints for temperature biases in climate models." Geophysical Research Letters 40, no. 15 (August 7, 2013): 4042–47. http://dx.doi.org/10.1002/grl.50737.
Повний текст джерелаArenou, Frédéric, and Xavier Luri. "Statistical Effects from Hipparcos Astrometry." Highlights of Astronomy 12 (2002): 661–64. http://dx.doi.org/10.1017/s153929960001460x.
Повний текст джерелаSchwemmer, Carsten, Carly Knight, Emily D. Bello-Pardo, Stan Oklobdzija, Martijn Schoonvelde, and Jeffrey W. Lockhart. "Diagnosing Gender Bias in Image Recognition Systems." Socius: Sociological Research for a Dynamic World 6 (January 2020): 237802312096717. http://dx.doi.org/10.1177/2378023120967171.
Повний текст джерелаCorredera, Alberto, Marta Romero, and Jose M. Moya. "Emotional Decision-Making Biases Prediction in Cyber-Physical Systems." Big Data and Cognitive Computing 3, no. 3 (August 30, 2019): 49. http://dx.doi.org/10.3390/bdcc3030049.
Повний текст джерелаEr, Xinzhong, Junqiang Ge, and Shude Mao. "BIASES IN PHYSICAL PARAMETER ESTIMATES THROUGH DIFFERENTIAL LENSING MAGNIFICATION." Astrophysical Journal 770, no. 2 (May 30, 2013): 110. http://dx.doi.org/10.1088/0004-637x/770/2/110.
Повний текст джерелаNicklin, Jessica M., and Sylvia G. Roch. "Biases Influencing Recommendation Letter Contents: Physical Attractiveness and Gender1." Journal of Applied Social Psychology 38, no. 12 (December 2008): 3053–74. http://dx.doi.org/10.1111/j.1559-1816.2008.00425.x.
Повний текст джерелаДисертації з теми "Physical biases"
Mihajlovits, Bethany. "Probability Elicitation Methods for Avoiding Biases: An Exposition." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/1796.
Повний текст джерелаWilson, Mario N. "The influences of physical attractiveness and sex-based biases on midshipmen performance evaluations at the United States Naval Academy." access online version, 2004. http://theses.nps.navy.mil/04Jun%5FWilson.pdf.
Повний текст джерелаWilson, Mario N. "The influences of physical attractiveness and sex-based biases on midshipman performance evaluations at the United States Naval Academy." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FWilson.pdf.
Повний текст джерелаThesis advisor(s): Janice H. Laurence, Armando X. Estrada. Includes bibliographical references (p. 93-94). Also available online.
Ossman, Tahani. "Physical-chemical understanding of membrane partitioning and permeation at an atomic resolution : towards in silico pharmacology." Thesis, Limoges, 2016. http://www.theses.fr/2016LIMO0089.
Повний текст джерелаThe mechanism of interaction between drugs or any xenobiotic and membrane is one of thekey factors that affect its biological of action, and so its therapeutic activity. A thoroughrationalization of the relationship between the intrinsic properties of the xenobiotics and theirmechanism of interaction with membranes can now be assessed with atomistic details.Molecular dynamics (MD) is a powerful research tool to study xenobiotics-membraneinteractions, which can access time and space scales that are not simultaneously accessibleby experimental methods. Semi-quantitative molecular and thermodynamic descriptions ofthese interactions can be provided using in silico model of lipid bilayers, often in agreementwith experimental measurements.The main goal of our investigation consisted to get in depth insight into the mechanisms ofinteraction/partitioning/insertion/crossing with/in/into/through membrane and drug deliveryusing MD. In this thesis, we have focused on both drugs used in renal transplantation (e.g.,antivirals, immunosuppressants) and antioxidants, which can also be used to protect organsalong the transplantation processes. We have provided a series of clues showing that MDsimulations can tackle the delicate process of drug passive permeation.Both, unbiased and biased MD (z-constraint) simulations have been used to elucidate thexenobiotics-membrane interactions (i.e., positioning and orientation) and to evaluate crossingenergies, diffusion coefficients, and permeability coefficients. These findings led us to drawqualitative structure-permeability relationships (SPR). We have carefully analyzed how thechemical and physical properties of xenobiotics affect the mechanism of interactions andthus permeability. The robustness of these MD-based methodologies has been determinedto qualitatively predict these pharmacological parameters. Hydrophobic compounds showeda favorable partitioning into the lipid bilayer and relatively low Gibbs energy of crossing thecenter of membrane (ΔGcross). Hydrophilic or charged compounds showed partitioning closeto membrane surface, in interaction with the polar head groups and water molecules; this hasbeen shown to dramatically increase ΔGcross. Amphiphilic compounds are intermediatecompounds in terms of membrane insertion/positioning/crossing. It clearly appears that theyshould be analyzed case by case, an analysis for which MD simulations could be particularlysupportive. Also the influence of size at predicting permeation has been studied (i.e.,relatively large drugs were tested). The molecular size has shown no significant influence onΔGcross whereas diffusion coefficients were significantly affected, depending on themembrane regions
Berlind, Andreas A. "Biased galaxy formation and large scale structure /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486400446371298.
Повний текст джерелаScott, Sarah. "Attentional bias and physical symptom reporting." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/attentional-bias-and-physical-symptom-reporting(3b1382e1-cb80-4986-ba56-51c941d1abb1).html.
Повний текст джерелаTokarek, Nathan. "The Impact of Stand-Biased Desks on After-School Physical Activity Behaviors in Children." Thesis, The University of Wisconsin - Milwaukee, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10620289.
Повний текст джерелаThe purpose of this study was to assess changes in after-school time spent performing sedentary behavior (SB), light intensity physical activity (LPA), and moderate to vigorous-intensity physical activity (MVPA) among elementary school children in response to the introduction of stand-biased desks in the classroom. Thirty-one 6th grade participants randomly assigned by their teacher to a traditional (CON) or stand-biased (INT) desk provided complete accelerometer data. After-school PA and SB were measured on four consecutive weekdays at baseline and 10-weeks. Wilcoxon Rank Sum Tests were used to detect significant differences (p<0.10) in changes in the proportion of after-school wear time performing SB and PA between groups. Results suggested no significant differences in changes in after-school time performing SB (p=0.770), LPA (p=0.740), or MVPA (p=0.470). Significant differences in the change in moderate PA (INT: -1.4%; CON: -0.2%, p=0.093) were detected. Stand-biased desks were not detrimental to children’s after-school PA and SB.
Gauthier, Michel. "Modelling a highly biased random walk: Application to gel electrophoresis." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26334.
Повний текст джерелаSeu, Keoki A. "Static and ultrafast MOKE studies of exchange -biased cobalt systems." W&M ScholarWorks, 2006. https://scholarworks.wm.edu/etd/1539623503.
Повний текст джерелаPistenon, Nicolas. "Découvrir la loi de comportement de matériaux viscoélastiques non linéaires par réseaux de neurones à base physique et données expérimentales." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLM044.
Повний текст джерелаThe application of machine learning techniques based on neural networks provides novel insights into the modelling of the mechanical behaviour of materials. These networks are capable of capturing a wide variety of complex behaviours due to their ability to act as universal function approximators. However, the deployment of these techniques requires large datasets, which are often difficult to obtain experimentally. This manuscript introduces various physical biases that enable the modelling of mechanical behaviour, specifically non-linear viscoelastic behaviour, using limited experimental data, thereby addressing this limitation.The two fundamental principles of thermodynamics provide a robust framework for constraining the formulation of constitutive laws. This approach reduces the quantity of data required for model training, while simultaneously improving the models' resilience to measurement errors.Recurrent neural networks, on the other hand, are particularly well-suited for modelling behaviour that depends on the loading history. Their hidden memories mirror the internal variables introduced in mechanics by the local state principle. However, these networks present challenges in terms of training and generalisation. To overcome these difficulties, a neural network model with mechanical encoding is proposed. This model employs the internal variables of a linear viscoelasticity model to encode the material's history, which proves to be sufficient for modelling its non-linear mechanical behaviour.One of the most significant challenges in three-dimensional modelling from experimental data is the incorporation of material symmetries in order to avoid the need for superfluous testing. For isotropic materials, a method of increasing the data set by randomly rotating the tests, combined with lateral transfer learning, enables the development of a three-dimensional constitutive law using only two types of uniaxial test. A thermodynamically consistent formulation that inherently preserves the material's isotropy is proposed; however, challenges related to training remain to be addressed in order to optimise this approach
Книги з теми "Physical biases"
Elmessiri, Abdelwahab M. Bias: Epistemological bias in the physical and social sciences. Herndon, VA: International Institute of Islamic Thought, 2006.
Знайти повний текст джерелаRich, Emma, Lee F. Monaghan, and Lucy Aphramor. Debating obesity: Critical perspectives. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2011.
Знайти повний текст джерелаBridges, Dwan M. Cultural bias in sport play. Reston, VA: Ethnic Minorities Council of the American Association for Active Life Styles and Fitness, 1999.
Знайти повний текст джерелаBerry, Bonnie. Beauty bias: Discrimination and social power. Westport, Conn: Praeger Publishers, 2007.
Знайти повний текст джерелаBerry, Bonnie. Beauty bias: Discrimination and social power. Westport, CT: Praeger, 2008.
Знайти повний текст джерелаĀparāda, Āsā. Darda jo sahā mainne... Nayī Dillī: Rājakamala Prakāśana, 2013.
Знайти повний текст джерелаRogers, Amy Keating. Guide to being a hero. New York: Golden Books Pub. Co, 2001.
Знайти повний текст джерелаDelagrave, Anne-Marie. Le contrôle de l'apparence physique du salarié. Cowansville, Québec: Éditions Y. Blais, 2010.
Знайти повний текст джерелаKim, Chi-yang. Mom kwa ot: 2017-2020, 89-myŏng yŏsŏng ŭi mom kwa ot e taehan kirok. Sŏul-si: 66100 Press, 2021.
Знайти повний текст джерелаЧастини книг з теми "Physical biases"
Miesen, Floreana, and Marjolein Gevers. "9. Inclusive practices in fieldwork." In Critical Physical Geography: Interdisciplinary Approaches to Nature, Power and Politics, 153–70. Cambridge, UK: Open Book Publishers, 2025. https://doi.org/10.11647/obp.0418.09.
Повний текст джерелаWattie, Nick, and Joseph Baker. "An Uneven Playing Field: Talent Identification Systems and the Perpetuation of Participation Biases in High Performance Sport." In Sport and Physical Activity across the Lifespan, 117–33. London: Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-48562-5_6.
Повний текст джерелаPea, Roy D., Paulina Biernacki, Maxwell Bigman, Kelly Boles, Raquel Coelho, Victoria Docherty, Jorge Garcia, et al. "Four Surveillance Technologies Creating Challenges for Education." In AI in Learning: Designing the Future, 317–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09687-7_19.
Повний текст джерелаFriedman, Eli A. "Physician Bias." In Legal and Ethical Concerns in Treating Kidney Failure, 79–84. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4355-4_7.
Повний текст джерелаOakley, Justin. "The Virtuous Physician and Antimicrobial Prescribing Policy and Practice." In Ethics and Drug Resistance: Collective Responsibility for Global Public Health, 125–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27874-8_8.
Повний текст джерелаTsatiris, Dimitrios. "Bias in Medicine." In Physician Burnout, 43–50. New York: Routledge, 2025. https://doi.org/10.4324/9781003473923-7.
Повний текст джерелаMahapatra, Souvik, Narendra Parihar, Subhadeep Mukhopadhyay, and Nilesh Goel. "Physical Mechanism of NBTI Parametric Drift." In Recent Advances in PMOS Negative Bias Temperature Instability, 37–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6120-4_3.
Повний текст джерелаThougaard, Simon, and Bruce McMillin. "Distributed Bias Detection in Cyber-Physical Systems." In IFIP Advances in Information and Communication Technology, 245–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62840-6_12.
Повний текст джерелаFazzini, P. F., P. G. Merli, G. Pozzi, and F. Ubaldi. "Interference electron microscopy of reverse-biased p-n junctions." In Springer Proceedings in Physics, 217–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-31915-8_43.
Повний текст джерелаSlater, Gary W., and Jaan Noolandi. "The Biased Reptation Model of DNA Gel Electrophoresis." In New Trends in Physics and Physucal Chemistry of Polymers, 547–600. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0543-9_41.
Повний текст джерелаТези доповідей конференцій з теми "Physical biases"
Misiorowski, Matthew, Farhan Gandhi, and Assad Oberai. "Computational Analysis and Flow Physics of a Ducted Rotor in Edgewise Flight." In Vertical Flight Society 73rd Annual Forum & Technology Display, 1–18. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12004.
Повний текст джерелаHu, Haoyang, Siyu Huang, Wenbin Hu, Xiaomeng Li, Mingzhong Xiao, and Qinghua Chen. "Citation Bias in the Physical Sciences at the National Scale." In 2024 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC), 837–43. IEEE, 2024. https://doi.org/10.1109/csis-iac63491.2024.10919324.
Повний текст джерелаArif, Farrukh, Waleed Ahmed Khan, and Asad-ur-Rehman Khan. "LiDAR-UAV Integrated Digitization of Civil Infrastructure for Visualization of Physical Condition." In Technology Enabled Civil Infrastructure Engineering & Management Conference, 11–18. Switzerland: Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-6boxwp.
Повний текст джерелаHubbard, William A., and B. C. Regan. "Imaging Nanoscale Electronic Changes in a Biased GaN HEMT." In ISTFA 2024, 317–19. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.istfa2024p0317.
Повний текст джерелаGan, Yong Yang, Bee Chin Loke, Aik Leng Tan, May Shin Chang, and Nirbhaya Pathak. "Failure analysis of ASIC controller integrated in embedded flash memory package under biased-HAST reliability failure." In 2024 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), 01–05. IEEE, 2024. http://dx.doi.org/10.1109/ipfa61654.2024.10691167.
Повний текст джерелаWu, Hao, Fan Xu, Chong Chen, Xian-Sheng Hua, Xiao Luo, and Haixin Wang. "PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction." In MM '24: The 32nd ACM International Conference on Multimedia, 2917–26. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3664647.3681489.
Повний текст джерелаSilva, Mariana O., Luiza de Melo-Gomes, and Mirella M. Moro. "Gender Representation in Literature: Analysis of Characters' Physical Descriptions." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/kdmile.2023.232571.
Повний текст джерелаMohd Razak, Syamil, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, and Behnam Jafarpour. "Dynamic Physics-Guided Deep Learning for Production Forecasting in Unconventional Reservoirs." In SPE Western Regional Meeting. SPE, 2023. http://dx.doi.org/10.2118/212962-ms.
Повний текст джерелаValcheva, Rilka, Ivan Popov, and Nikola Gerganov. "A SENSITIVITY STUDY OF THE NON-HYDROSTATIC REGIONAL CLIMATE MODEL REGCM-4.7.1 TO PHYSICAL PARAMETRIZATION SCHEMES OVER THE BALKAN PENINSULA AND BULGARIA." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022v/4.2/s19.20.
Повний текст джерелаCorso, Greg, and Molly Hunker. "The Commoning of Architectural Representation." In 111th ACSA Annual Meeting Proceedings. ACSA Press, 2023. http://dx.doi.org/10.35483/acsa.am.111.15.
Повний текст джерелаЗвіти організацій з теми "Physical biases"
Hinrichs, Claudia, and Judith Hauck. Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. OceanNets, June 2022. http://dx.doi.org/10.3289/oceannets_d4.4.
Повний текст джерелаVickers, Jr, Hodgdon Ross R., Beckett James A., and Marcie B. Physical Ability-Task Performance Models: Assessing the Risk of Omitted Variable Bias. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada515128.
Повний текст джерелаVergani, Matteo, Angelique Stefanopoulos, Alexandra Lee, Haily Tran, Imogen Richards, Dan Goodhardt, and Greg Barton. Defining and identifying hate motives: bias indicators for the Australian context. Centre for Resilient and Inclusive Societies, November 2022. http://dx.doi.org/10.56311/pozs1016.
Повний текст джерелаEli, Shari, Trevon Logan, and Boriana Miloucheva. Physician Bias and Racial Disparities in Health: Evidence from Veterans' Pensions. Cambridge, MA: National Bureau of Economic Research, May 2019. http://dx.doi.org/10.3386/w25846.
Повний текст джерелаSelph, Shelly S., Andrea C. Skelly, Ngoc Wasson, Joseph R. Dettori, Erika D. Brodt, Erik Ensrud, Diane Elliot, et al. Physical Activity and the Health of Wheelchair Users: A Systematic Review in Multiple Sclerosis, Cerebral Palsy, and Spinal Cord Injury. Agency for Healthcare Research and Quality (AHRQ), October 2021. http://dx.doi.org/10.23970/ahrqepccer241.
Повний текст джерелаAgüero, Jorge M., and Verónica Frisancho. Misreporting in Sensitive Health Behaviors and Its Impact on Treatment Effects: An Application to Intimate Partner Violence. Inter-American Development Bank, December 2017. http://dx.doi.org/10.18235/0011808.
Повний текст джерелаBray, Jonathan, Ross Boulanger, Misko Cubrinovski, Kohji Tokimatsu, Steven Kramer, Thomas O'Rourke, Ellen Rathje, Russell Green, Peter Robertson, and Christine Beyzaei. U.S.—New Zealand— Japan International Workshop, Liquefaction-Induced Ground Movement Effects, University of California, Berkeley, California, 2-4 November 2016. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, March 2017. http://dx.doi.org/10.55461/gzzx9906.
Повний текст джерелаE, Flemyng, and Mitchell D. Increased versus stable doses of inhaled steroids for exacerbations of chronic asthma in adults and children: Protocol. Epistemonikos Interactive Evidence Synthesis, January 2022. http://dx.doi.org/10.30846/ies.b984bf9656.v3.
Повний текст джерелаE, Flemyng, and Mitchell D. Increased versus stable doses of inhaled steroids for exacerbations of chronic asthma in adults and children: Protocol. Epistemonikos Interactive Evidence Synthesis, January 2022. http://dx.doi.org/10.30846/ies.b984bf9699.v2.
Повний текст джерелаE, Flemyng, and Mitchell D. Increased versus stable doses of inhaled steroids for exacerbations of chronic asthma in adults and children: Update. Epistemonikos Interactive Evidence Synthesis, January 2022. http://dx.doi.org/10.30846/ies.b984bf9639.v2.
Повний текст джерела