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Статті в журналах з теми "Environmental risk scores"
Zeng, Jingjing, Meiquan Jiang, and Meng Yuan. "Environmental Risk Perception, Risk Culture, and Pro-Environmental Behavior." International Journal of Environmental Research and Public Health 17, no. 5 (March 7, 2020): 1750. http://dx.doi.org/10.3390/ijerph17051750.
Повний текст джерелаDudbridge, Frank, Nora Pashayan, and Jian Yang. "Predictive accuracy of combined genetic and environmental risk scores." Genetic Epidemiology 42, no. 1 (November 26, 2017): 4–19. http://dx.doi.org/10.1002/gepi.22092.
Повний текст джерелаGuan, Zhong, Janhavi R. Raut, Korbinian Weigl, Ben Schöttker, Bernd Holleczek, Yan Zhang, and Hermann Brenner. "Individual and joint performance of DNA methylation profiles, genetic risk score and environmental risk scores for predicting breast cancer risk." Molecular Oncology 14, no. 1 (November 19, 2019): 42–53. http://dx.doi.org/10.1002/1878-0261.12594.
Повний текст джерелаCicek, S., and F. Karadag. "The Potential Relationship Between The Environmental Risk Factors And Social Cognition in Psychosis." European Psychiatry 65, S1 (June 2022): S284. http://dx.doi.org/10.1192/j.eurpsy.2022.727.
Повний текст джерелаAraújo, Daniel S., and Heather E. Wheeler. "Genetic and environmental variation impact transferability of polygenic risk scores." Cell Reports Medicine 3, no. 7 (July 2022): 100687. http://dx.doi.org/10.1016/j.xcrm.2022.100687.
Повний текст джерелаLewis, Anna C. F., Robert C. Green, and Jason L. Vassy. "Polygenic risk scores in the clinic: Translating risk into action." Human Genetics and Genomics Advances 2, no. 4 (October 2021): 100047. http://dx.doi.org/10.1016/j.xhgg.2021.100047.
Повний текст джерелаSameroff, Arnold J., Ronald Seifer, Ralph Barocas, Melvin Zax, and Stanley Greenspan. "Intelligence Quotient Scores of 4-Year-Old Children: Social-Environmental Risk Factors." Pediatrics 79, no. 3 (March 1, 1987): 343–50. http://dx.doi.org/10.1542/peds.79.3.343.
Повний текст джерелаPusker, Stephanie M., Kelly A. DeBie, Maggie L. Clark, Andreas M. Neophytou, Kayleigh P. Keller, Margaret J. Gutilla, and David Rojas-Rueda. "Environmental Justice and Sustainable Development: Cumulative Environmental Exposures and All-Cause Mortality in Colorado Counties." Sustainability 16, no. 21 (October 22, 2024): 9147. http://dx.doi.org/10.3390/su16219147.
Повний текст джерелаKarlsson Linnér, Richard, and Philipp D. Koellinger. "Genetic risk scores in life insurance underwriting." Journal of Health Economics 81 (January 2022): 102556. http://dx.doi.org/10.1016/j.jhealeco.2021.102556.
Повний текст джерелаShin, Jiyoung, Harris Hyun-soo Kim, Eun Mee Kim, Yookyung Choi, and Eunhee Ha. "Impact of an Educational Program on Behavioral Changes toward Environmental Health among Laotian Students." International Journal of Environmental Research and Public Health 17, no. 14 (July 14, 2020): 5055. http://dx.doi.org/10.3390/ijerph17145055.
Повний текст джерелаДисертації з теми "Environmental risk scores"
Guimbaud, Jean-Baptiste. "Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10188.
Повний текст джерелаFrom conception onward, environmental factors such as air quality or dietary habits can significantly impact the risk of developing various chronic diseases. Within the epidemiological literature, indicators known as Environmental Risk Scores (ERSs) are used not only to identify individuals at risk but also to study the relationships between environmental factors and health. A limit of most ERSs is that they are expressed as linear combinations of a limited number of factors. This doctoral thesis aims to develop ERS indicators able to investigate nonlinear relationships and interactions across a broad range of exposures while discovering actionable factors to guide preventive measures and interventions, both in adults and children. To achieve this aim, we leverage the predictive abilities of non-parametric machine learning methods, combined with recent Explainable AI tools and existing domain knowledge. In the first part of this thesis, we compute machine learning-based environmental risk scores for mental, cardiometabolic, and respiratory general health for children. On top of identifying nonlinear relationships and exposure-exposure interactions, we identified new predictors of disease in childhood. The scores could explain a significant proportion of variance and their performances were stable across different cohorts. In the second part, we propose SEANN, a new approach integrating expert knowledge in the form of Pooled Effect Sizes (PESs) into the training of deep neural networks for the computation of extit{informed environmental risk scores}. SEANN aims to compute more robust ERSs, generalizable to a broader population, and able to capture exposure relationships that are closer to evidence known from the literature. We experimentally illustrate the approach's benefits using synthetic data, showing improved prediction generalizability in noisy contexts (i.e., observational settings) and improved reliability of interpretation using Explainable Artificial Intelligence (XAI) methods compared to an agnostic neural network. In the last part of this thesis, we propose a concrete application for SEANN using data from a cohort of Spanish adults. Compared to an agnostic neural network-based ERS, the score obtained with SEANN effectively captures relationships more in line with the literature-based associations without deteriorating the predictive performances. Moreover, exposures with poor literature coverage significantly differ from those obtained with the agnostic baseline method with more plausible directions of associations.In conclusion, our risk scores demonstrate substantial potential for the data-driven discovery of unknown nonlinear environmental health relationships by leveraging existing knowledge about well-known relationships. Beyond their utility in epidemiological research, our risk indicators are able to capture holistic individual-level non-hereditary risk associations that can inform practitioners about actionable factors in high-risk individuals. As in the post-genetic era, personalized medicine prevention will focus more and more on modifiable factors, we believe that such approaches will be instrumental in shaping future healthcare paradigms
Huang, Xiufeng. "Immunogenetics of acute anterior uveitis and comparison to ankylosing spondylitis." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213839/1/Xiufeng_Huang_Thesis.pdf.
Повний текст джерелаFallon, Aidan P. "A critical assessment and application of the groundwater ubiquity score as a pesticide risk assessment tool for the soil/freshwater environment in Northern Ireland." Thesis, University of Ulster, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322423.
Повний текст джерелаAndersson, Pontus, and John Eskilson. "Hållbara fonders avkastning : En kvantitativ studie om en jämförelse av riskjusterad avkastning för svenska fonder baserat på ESG-score." Thesis, Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177754.
Повний текст джерелаARATA, LINDA. "Il Ruolo dei Programmi Agro-ambientali: un'analisi attraverso il Propensity Score Matching e la Programmazione Matematica Positiva con il Rischio." Doctoral thesis, Università Cattolica del Sacro Cuore, 2014. http://hdl.handle.net/10280/2469.
Повний текст джерелаThe increasing attention to the relationship between agriculture and the environment and the rise in price volatility on agricultural markets has led to a new emphasis on agri-environmental policies as well as to a search for new risk management strategies for the farmer. The research objective of this PhD thesis is in line with this challenging context, since it provides an analysis of the EU agri-environmental schemes (AESs) from two viewpoints. First, an ex-post analysis aims at investigating the AESs for their traditional role as measures which encourage sustainable farming while compensating the farmer for the income foregone in five EU Member States. The effects of AESs participation on farmer’s production plans and economic performances differs widely across Member States and in some of them the environmental payment is not enough to compensate the income foregone of participants. This study has been performed by applying a semi-parametric technique which combines a Difference-in-Differences estimator with a Propensity Score Matching estimator. The second piece of research develops a new methodological proposal to incorporate risk into a farm level Positive Mathematical Programming (PMP) model. The model presents some innovations with respect to the previous literature and estimates simultaneously the resource shadow prices, the farm non-linear cost function and a farm-specific coefficient of absolute risk aversion. The proposed model has been applied to three farm samples and the estimation results confirm the calibration ability of the model and show values for risk aversion coefficients consistent with the literature. Finally different scenarios have been simulated to test the potential role of an AES as risk management tool under different scenarios of crop price volatility.
ARATA, LINDA. "Il Ruolo dei Programmi Agro-ambientali: un'analisi attraverso il Propensity Score Matching e la Programmazione Matematica Positiva con il Rischio." Doctoral thesis, Università Cattolica del Sacro Cuore, 2014. http://hdl.handle.net/10280/2469.
Повний текст джерелаThe increasing attention to the relationship between agriculture and the environment and the rise in price volatility on agricultural markets has led to a new emphasis on agri-environmental policies as well as to a search for new risk management strategies for the farmer. The research objective of this PhD thesis is in line with this challenging context, since it provides an analysis of the EU agri-environmental schemes (AESs) from two viewpoints. First, an ex-post analysis aims at investigating the AESs for their traditional role as measures which encourage sustainable farming while compensating the farmer for the income foregone in five EU Member States. The effects of AESs participation on farmer’s production plans and economic performances differs widely across Member States and in some of them the environmental payment is not enough to compensate the income foregone of participants. This study has been performed by applying a semi-parametric technique which combines a Difference-in-Differences estimator with a Propensity Score Matching estimator. The second piece of research develops a new methodological proposal to incorporate risk into a farm level Positive Mathematical Programming (PMP) model. The model presents some innovations with respect to the previous literature and estimates simultaneously the resource shadow prices, the farm non-linear cost function and a farm-specific coefficient of absolute risk aversion. The proposed model has been applied to three farm samples and the estimation results confirm the calibration ability of the model and show values for risk aversion coefficients consistent with the literature. Finally different scenarios have been simulated to test the potential role of an AES as risk management tool under different scenarios of crop price volatility.
Delgado, João André Ferreira Costa. "The effects of environmental, social and governance (ESG) factors on the default risk of a fixed income portfolio." Master's thesis, 2020. http://hdl.handle.net/10362/105976.
Повний текст джерела(8905460), Aura Ankita Mishra. "The Association between Childhood Maltreatment, Substance Use Frequency, and Physical Intimate Partner Violence: A Gene-Environment Study." Thesis, 2020.
Знайти повний текст джерелаThis dissertation evaluated the complex inter-relatedness between co-occurring childhood maltreatment exposures, physical intimate partner violence (perpetration and victimization), substance use frequency, and molecular genetics for substance use, utilizing appropriate developmental models and theoretical approaches. Three studies were proposed within this dissertation. Data for the three studies come from a national longitudinal panel study: The National Longitudinal Study of Adolescent to Adult Health (Add Health; Harris, 2013). Across studies, latent profile analysis was used to evaluate co-occurring childhood maltreatment exposures based on type and severity of exposures, which resulted in three homogenous sub-groups. The first sub-group was composed of individuals that had high levels of physical abuse exposure and moderate levels of childhood neglect and emotional abuse exposures (high physical abuse sub-group). The second sub-group (high sexual abuse sub-group) included individuals with high severity of sexual abuse exposure and moderate severity of all other childhood maltreatment types (i.e., physical abuse, emotional abuse, and neglect). This second sub-group was, therefore, the most vulnerable in terms of their childhood maltreatment exposure. A final normative sub-group was also found that included a majority of individuals with low severity of childhood maltreatment exposure across types. Additionally, across all three studies, a probabilistic multifaceted genetic risk score (i.e., polygenic risk score) was created to evaluate substance use related genetic risk. The first study evaluated the role of co-occurring childhood maltreatment exposure on substance use development from adolescence to young adulthood while evaluating substance use related genetic moderation. Generalized estimating equations were used to test the proposed model in study 1. Findings suggest that the high physical abuse sub-group was more susceptible to genetic risk and had increases in substance use frequency only at high levels of genetic risk. In contrast, for the high sexual abuse sub-group, childhood maltreatment and environmental exposures were more ubiquitous for substance use development from adolescence to young adulthood. To elaborate, the high sexual abuse sub-group demonstrated increases in substance use from adolescence to young adulthood irrespective of genetic risk. In study 2, substance use frequency in young adulthood was tested as a mechanism between childhood maltreatment sub-groups and subsequent physical intimate partner violence perpetration in adulthood. Once again, genetic moderation for the direct association between childhood maltreatment sub-groups and substance use frequency in young adulthood was tested within the larger mediation model. In study 3, physical partner violence victimization in young adulthood was tested as a mediator of the association between childhood maltreatment sub-groups and substance use frequency in adulthood. In study 3, in addition to the above-mentioned genetic risk score, an additional substance use related dopamine polygenic risk score was also tested. Specifically, in study 3, genetic moderation by both genetic risk scores was tested on 1) the direct pathway from childhood maltreatment sub-groups to substance use frequency in adulthood, and 2) the direct pathway from physical intimate partner violence victimization in young adulthood to substance use frequency in adulthood. In both studies 2 and 3, product of co-efficient method was used to estimate mediation hypothesis, and moderated-mediation models were used to test for genetic moderation within the mediation model. Research aims for studies 2 and 3 were largely not supported. However, supplementary models indicate that substance use frequency may not be a causal mechanism but may be a contextual factor exacerbating the association between childhood maltreatment exposures and physical intimate partner violence perpetration. Implications for findings are discussed in detail.
Книги з теми "Environmental risk scores"
Duvic-Paoli, Leslie-Anne. Prevention in International Environmental Law and the Anticipation of Risk(s). Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198795896.003.0008.
Повний текст джерелаGuidotti, Tee L. The Praeger Handbook of Occupational and Environmental Medicine. Praeger, 2010. http://dx.doi.org/10.5040/9798216190530.
Повний текст джерелаWestfall, Nils C., and Charles B. Nemeroff. Child Abuse and Neglect as Risk Factors for Post-Traumatic Stress Disorder. Edited by Charles B. Nemeroff and Charles R. Marmar. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190259440.003.0025.
Повний текст джерелаKrieger, Heike, Anne Peters, and Leonhard Kreuzer, eds. Due Diligence in the International Legal Order. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198869900.001.0001.
Повний текст джерелаGhebrehewet, Sam, Alex G. Stewart, and Ian Rufus. Key principles and practice of health protection. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198745471.003.0003.
Повний текст джерелаOlawuyi, Damilola S., José Juan González, Hanri Mostert, Milton Fernando Montoya, and Catherine Banet, eds. Net Zero and Natural Resources Law. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/9780198925033.001.0001.
Повний текст джерелаJakobsson, Jan. Anaesthesia for day-stay surgery. Edited by Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0068.
Повний текст джерелаClark, Gordon L., and Ashby H. B. Monk. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793212.003.0001.
Повний текст джерелаRoberts, Michael. Evangelicals and Science. Greenwood, 2008. http://dx.doi.org/10.5040/9798400648267.
Повний текст джерелаDowdall, George W. College Drinking. Greenwood Publishing Group, Inc., 2008. http://dx.doi.org/10.5040/9798400628450.
Повний текст джерелаЧастини книг з теми "Environmental risk scores"
Charlier, Dorothée, and Bérangère Legendre. "Energy Poverty and Health Pathologies: An Empirical Study on the French Case." In Studies in Energy, Resource and Environmental Economics, 59–87. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35684-1_4.
Повний текст джерелаRady, Ahmed El-Said, Ashraf A. Zahran, Mokhtar S. Beheary, and Mossad El-Metwally. "Developing an Innovative Regression Model to Predict Industrial Facilities’ Risk Scores by Leveraging Environmental Compliance Assessments Data." In Proceedings of The First International Conference on Green Sciences, 125–37. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62672-2_9.
Повний текст джерелаDianatfar, Morteza, Saeid Heshmatisafa, Jyrki Latokartano, and Minna Lanz. "Feasibility Analysis of Safety Training in Human-Robot Collaboration Scenario: Virtual Reality Use Case." In Lecture Notes in Mechanical Engineering, 246–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_25.
Повний текст джерелаPoonia, Ramesh Chandra, Kamal Upreti, Bosco Paul Alapatt, and Samreen Jafri. "Real-Time Cyber-Physical Risk Management Leveraging Advanced Security Technologies." In Lecture Notes in Networks and Systems, 339–50. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4581-4_25.
Повний текст джерелаZaidi, Syed Farhan Alam, Rahat Hussain, Muhammad Sibtain Abbas, Jaehun Yang, Doyeop Lee, and Chansik Park. "iSafe Welding System: Computer Vision-Based Monitoring System for Safe Welding Work." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 669–75. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.66.
Повний текст джерелаZaidi, Syed Farhan Alam, Rahat Hussain, Muhammad Sibtain Abbas, Jaehun Yang, Doyeop Lee, and Chansik Park. "iSafe Welding System: Computer Vision-Based Monitoring System for Safe Welding Work." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 669–75. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.66.
Повний текст джерелаMangili, Silvia, Tianzhi Sun, and Alexander Achille Johnson. "Nursing Homes During COVID-19 Pandemic—A Systematic Literature Review for COVID-19 Proof Architecture Design Strategies." In The Urban Book Series, 981–90. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29515-7_87.
Повний текст джерелаDu, Mulong, Junyi Xin, Silu Chen, and Meilin Wang. "Environmental Pollutant-Based Polygenic Risk Score: A Genetic Surrogate for Environmental Pollutants Assessment." In Genetic Variations - Unraveling Causalities [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1007519.
Повний текст джерелаCafri, Reyhan, and Pınar Kaya Samut. "A Panel Asymmetric Causality Between Health and Climate Change." In Environmental Exposures and Human Health Challenges, 223–47. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7635-8.ch011.
Повний текст джерелаÇilek, Arif, and Onur Şeyranlıoğlu. "Do ESG Risk Ratings Affect Financial Performance? Evidence from Selected BIST Banking Sector Companies with LODECI and CRADIS Methods." In Finans Alanında Güncel Uygulamalı Çalışmalar. Özgür Yayınları, 2024. http://dx.doi.org/10.58830/ozgur.pub488.c2033.
Повний текст джерелаТези доповідей конференцій з теми "Environmental risk scores"
Kleinstern, Geffen, Dennis Robinson, Tim G. Call, Mark Liebow, Silvia de Sanjosé, Yolanda Benavente, James R. Cerhan, and Susan L. Slager. "Abstract A06: Association of environmental risk factors, family history, and polygenic risk scores with chronic lymphocytic leukemia." In Abstracts: AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; November 16-19, 2016; Orlando, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.carisk16-a06.
Повний текст джерелаKleinstern, Geffen, Dennis Robinson, Tim G. Call, Mark Liebow, Silvia de Sanjosé, Yolanda Benavente, James R. Cerhan, and Susan L. Slager. "Abstract PR03: Association of environmental risk factors, family history, and polygenic risk scores with chronic lymphocytic leukemia." In Abstracts: AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; November 16-19, 2016; Orlando, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.carisk16-pr03.
Повний текст джерелаDo, Quan T., Alexa K. Doig, and Tran C. Son. "Deep Q-learning for Predicting Asthma Attack with Considering Personalized Environmental Triggers’ Risk Scores." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857172.
Повний текст джерелаAntaki, George A., Thomas M. Monahon, and Ralph W. Cansler. "Risk-Based Inspection (RBI) of Steam Systems." In ASME 2005 Pressure Vessels and Piping Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pvp2005-71678.
Повний текст джерелаFeldman, Jolene, Gregory Costedoat, Lynne Martin, and Vimmy Gujral. "Usability of pre-flight planning interfaces for Supplemental Data Service Provider tools to support Uncrewed Aircraft System Traffic Management." In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004333.
Повний текст джерелаMurray, Jake. "Transitioning a Relative Risk Model to Absolute." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64700.
Повний текст джерелаMorton, Mark, and Janaka Jayamaha. "Technical Justification and Choice for Remediation Methods." In ASME 2011 14th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2011. http://dx.doi.org/10.1115/icem2011-59398.
Повний текст джерелаWickham, S. M., D. A. Galson, T. W. Hicks, R. V. Kemp, and M. J. White. "Towards a Holistic Risk Management Strategy or Long-Term Interim Storage." In ASME 2003 9th International Conference on Radioactive Waste Management and Environmental Remediation. ASMEDC, 2003. http://dx.doi.org/10.1115/icem2003-4973.
Повний текст джерелаTaştan, Buket, and Kenan Terzioğlu. "Environmental Degradation: Monetary Transmission Mechanism and CO2 Emission." In International Conference on Eurasian Economies. Eurasian Economists Association, 2021. http://dx.doi.org/10.36880/c13.02552.
Повний текст джерелаЖигульский, В., V. Zhigul'skiy, Д. Жигульская, D. Zhigul'skaya, В. Шуйский, V. Shuyskiy, Е. Максимова, et al. "Comparative Score/Ranking Multi-Criteria Assessment of Hazardous Industrial Facilities Environmental Risk." In XXVII International Shore Conference "Arctic Coast: The Path to Sustainability". Academus Publishing, 2019. http://dx.doi.org/10.31519/conferencearticle_5cebbc16c31657.63918856.
Повний текст джерелаЗвіти організацій з теми "Environmental risk scores"
Wraight, Sarah, Julia Hofmann, Justine Allpress, and Brooks Depro. Environmental justice concerns and the proposed Atlantic Coast Pipeline route in North Carolina. RTI Press, March 2018. http://dx.doi.org/10.3768/rtipress.2018.mr.0037.1803.
Повний текст джерелаRRS/Schirmer. PR-339-093708-R01 Survey for Control Room Operators and Operations Specialists Usability Study. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2010. http://dx.doi.org/10.55274/r0010714.
Повний текст джерелаChefetz, Benny, Baoshan Xing, and Yona Chen. Interactions of engineered nanoparticles with dissolved organic matter (DOM) and organic contaminants in water. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7699863.bard.
Повний текст джерелаO'Donnell, Kevin, and Anne Greene. A Risk Management Solution Designed to Facilitate Risk-Based Qualification, Validation, and Change Control Activities within GMP and Pharmaceutical Regulatory Compliance Environments in the EU—Part I. Institute of Validation Technology, July 2006. http://dx.doi.org/10.1080/21506590.wp7132006agko-rmsdfrbq.
Повний текст джерелаRycroft, Taylor, Sabrina Larkin, Alexander Ganin, Treye Thomas, Joanna Matheson, Tessa Van Grack, Xinrong Chen, Kenton Plourde, Alan Kennedy, and Igor Linkov. A framework and pilot tool for the risk-based prioritization and grouping of nano-enabled consumer products. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41721.
Повний текст джерелаO'Donnell, Kevin, and Anne Greene. A Risk Management Solution Designed to Facilitate Risk-Based Qualification, Validation, and Change Control Activities within GMP and Pharmaceutical Regulatory Compliance Environments in the EU—Part II. Institute of Validation Technology, July 2006. http://dx.doi.org/10.1080/21506590.wp7142006agko-rmsdii.
Повний текст джерелаCornick, Jorge. The Organization of Public-Private Cooperation for Productive Development Policies. Inter-American Development Bank, August 2013. http://dx.doi.org/10.18235/0011484.
Повний текст джерелаHernández, Juan, and Daniel Wills. Fighting for the Best, Losing with the Rest: The Perils of Competition in Entrepreneurial Finance. Inter-American Development Bank, January 2024. http://dx.doi.org/10.18235/0005506.
Повний текст джерелаBelkin, Shimshon, Sylvia Daunert, and Mona Wells. Whole-Cell Biosensor Panel for Agricultural Endocrine Disruptors. United States Department of Agriculture, December 2010. http://dx.doi.org/10.32747/2010.7696542.bard.
Повний текст джерелаAnderson, Donald M., Lorraine C. Backer, Keith Bouma-Gregson, Holly A. Bowers, V. Monica Bricelj, Lesley D’Anglada, Jonathan Deeds, et al. Harmful Algal Research & Response: A National Environmental Science Strategy (HARRNESS), 2024-2034. Woods Hole Oceanographic Institution, July 2024. http://dx.doi.org/10.1575/1912/69773.
Повний текст джерела