Relevant bibliographies by topics / Dose prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dose prediction'

Author: Grafiati
Published: 24 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Dose prediction"

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Swartz, Conrad M. "Drug Dose Prediction With Flexible Test Doses." Journal of Clinical Pharmacology 31, no. 7 (1991): 662–67. http://dx.doi.org/10.1002/j.1552-4604.1991.tb03753.x.

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&NA;. "IV aminoglycoside dose prediction." Inpharma Weekly &NA;, no. 995 (1995): 18. http://dx.doi.org/10.2165/00128413-199509950-00043.

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Liu, Y., Z. Chen, Q. Zhou, et al. "A Feasibility Study of Dose Band Prediction in Radiotherapy: Predicting a Dose Spectrum." International Journal of Radiation Oncology*Biology*Physics 117, no. 2 (2023): e691. http://dx.doi.org/10.1016/j.ijrobp.2023.06.2164.

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Gizynska, M., D. Blatkiewicz, B. Czyzew, et al. "EP-1510: Cumulated dose prediction." Radiotherapy and Oncology 115 (April 2015): S822—S823. http://dx.doi.org/10.1016/s0167-8140(15)41502-6.

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Mitchel, R. E. J. "Radiation Risk Prediction and Genetics: The Influence of the TP53 Gene in vivo." Dose-Response 3, no. 4 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.007.

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Risk prediction and dose limits for human radiation exposure are based on the assumption that risk is proportional to total dose. However, there is concern about the appropriateness of those limits for people who may be genetically cancer prone. The TP53 gene product functions in regulatory pathways for DNA repair, cell cycle checkpoints and apoptosis, processes critical in determining ionizing radiation risk for both carcinogenesis and teratogenesis. Mice that are deficient in TP53 function are cancer prone. This review examines the influence of variations in TP53 gene activity on cancer and
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Polizzi, Mitchell, Robert W. Watkins, and William T. Watkins. "Data-Driven Dose-Volume Histogram Prediction." Advances in Radiation Oncology 7, no. 2 (2022): 100841. http://dx.doi.org/10.1016/j.adro.2021.100841.

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Holford, Nick H. G., Shu C. Ma, and Brian J. Anderson. "Prediction of morphine dose in humans." Pediatric Anesthesia 22, no. 3 (2011): 209–22. http://dx.doi.org/10.1111/j.1460-9592.2011.03782.x.

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MIZUTANI, YOSHIKATSU. "Trial of warfarin maintenance dose prediction." Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics 26, no. 1 (1995): 177–78. http://dx.doi.org/10.3999/jscpt.26.177.

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OMORI, Toshiaki, Shinsuke KATO, Minsik KIM, and Shigehiro NUKATSUKA. "RADIATION DOSE PREDICTION FOR DETACHED HOUSES." Journal of Environmental Engineering (Transactions of AIJ) 82, no. 735 (2017): 481–89. http://dx.doi.org/10.3130/aije.82.481.

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Lippi, Giuseppe, Gian Luca Salvagno, and Gian Cesare Guidi. "Genetic Factors for Warfarin Dose Prediction." Clinical Chemistry 53, no. 9 (2007): 1721–22. http://dx.doi.org/10.1373/clinchem.2007.092338.

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Dissertations / Theses on the topic "Dose prediction"

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Eriksson, Niclas. "On the Prediction of Warfarin Dose." Doctoral thesis, Uppsala universitet, Klinisk farmakologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-172864.

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Warfarin is one of the most widely used anticoagulants in the world. Treatment is complicated by a large inter-individual variation in the dose needed to reach adequate levels of anticoagulation i.e. INR 2.0 – 3.0. The objective of this thesis was to evaluate which factors, mainly genetic but also non-genetic, that affect the response to warfarin in terms of required maintenance dose, efficacy and safety with special focus on warfarin dose prediction. Through candidate gene and genome-wide studies, we have shown that the genes CYP2C9 and VKORC1 are the major determinants of warfarin maintenanc
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SKARPMAN, MUNTER JOHANNA. "Dose-Volume Histogram Prediction using KernelDensity Estimation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155893.

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Dose plans developed for stereotactic radiosurgery are assessed by studying so called Dose-Volume Histograms. Since it is hard to compare an individual dose plan with doseplans created for other patients, much experience and knowledge is lost. This thesis therefore investigates a machine learning approach to predicting such Dose-Volume Histograms for a new patient, by learning from previous dose plans.The training set is chosen based on similarity in terms of tumour size. The signed distances between voxels in the considered volume and the tumour boundary decide the probability of receiving a
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Nilsson, Viktor. "Prediction of Dose Probability Distributions Using Mixture Density Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273610.

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In recent years, machine learning has become utilized in external radiation therapy treatment planning. This involves automatic generation of treatment plans based on CT-scans and other spatial information such as the location of tumors and organs. The utility lies in relieving clinical staff from the labor of manually or semi-manually creating such plans. Rather than predicting a deterministic plan, there is great value in modeling it stochastically, i.e. predicting a probability distribution of dose from CT-scans and delineated biological structures. The stochasticity inherent in the RT trea
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Harris, Shelley A. "The development and validation of a pesticide dose prediction model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0002/NQ41170.pdf.

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Irving, Benjamin. "Radiation dose measurement and prediction for linear slit scanning radiography." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/3251.

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Includes abstract.<br>Includes bibliographical references (leaves 112-117).<br>This study describes dose measurements made for linear slit scanning radiography (LSSR) and a dose prediction model that was developed for LSSR. The measurement and calculation methods used for determining entrance dose and effective dose (E) in conventional X-ray imaging systems were verified for use with LSSR. Entrance dose and E were obtained for LSSR and compared to dose measurements on conventional radiography units. Entrance dose measurements were made using an ionisation chamber and dosemeter; E was calculate
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Patel, Raj B., and Raj B. Patel. "Prediction of Human Intestinal Absorption." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624487.

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The proposed human intestinal absorption prediction model is applied to over 900 pharmaceuticals and has about 82.5% true prediction power. This study will provide a screening tool that can differentiate well absorbed and poorly absorbed drugs in the early stage of drug discovery and development. This model is based on fundamental physicochemical properties and can be applied to virtual compounds. The maximum well-absorbed dose (i.e., the maximum dose that will be more than 50 percent absorbed) calculated using this model can be utilized as a guideline for drug design, synthesis, and pre-clini
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Eriksson, Ivar. "Image Distance Learning for Probabilistic Dose–Volume Histogram and Spatial Dose Prediction in Radiation Therapy Treatment Planning." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273608.

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Construction of radiotherapy treatments for cancer is a laborious and time consuming task. At the same time, when presented with a treatment plan, an oncologist can quickly judge whether or not it is suitable. This means that the problem of constructing these treatment plans is well suited for automation. This thesis investigates a novel way of automatic treatment planning. The treatment planning system this pipeline is constructed for provides dose mimicking functionality with probability density functions of dose–volume histograms (DVHs) and spatial dose as inputs. Therefore this will be the
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Eriksson, Oskar. "Scenario dose prediction for robust automated treatment planning in radiation therapy." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302568.

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Cancer is a group of diseases that are characterized by abnormal cell growth and is considered a leading cause of death globally. There are a number of different cancer treatment modalities, one of which is radiation therapy. In radiation therapy treatment planning, it is important to make sure that enough radiation is delivered to the tumor and that healthy organs are spared, while also making sure to account for uncertainties such as misalignment of the patient during treatment. To reduce the workload on clinics, data-driven automated treatment planning can be used to generate treatment plan
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Schuler, Paul Joseph. "Polymer dose prediction for sludge dewatering with a belt filter press." Thesis, Virginia Tech, 1990. http://hdl.handle.net/10919/42227.

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This study was undertaken to examine the polymer mixing requirements for sludge dewatering with a belt filter press. This involved correlating full-scale field studies to small scale laboratory testing. Bench testing involved the use of a high-speed mixer and two sludge dewatering response tests: the capillary suction time test and the time-to filter test. Full-scale testing measured the belt press response to belt speed, sludge throughput, and polymer dose. Data indicated that the conditioning and dewatering scheme of the three belt filter presses was a low shear, low total mixing energy op
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Podda, G. "PREDICTION OF OPTIMAL WARFARIN MAINTENANCE DOSE USING ADVANCED ARTIFICIAL NEURAL NETWORKS." Doctoral thesis, Università degli Studi di Milano, 2013. http://hdl.handle.net/2434/219087.

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Introduction. The individual response to vitamin K antagonists (VKA) is highly variable, being influenced by clinical factors and genetic variants of enzymes that are involved in the metabolism of VKA (CYP2C)) and vitamin K (VKORC1). Currently, the dose of VKA is adjusted based on measurements of the prothrombin time. In the last years, mathematical algorithms were developed for estimating the appropriate VKA dose, based on different mathematical approaches working on clinical and genetic data. Artificial Neural Networks (ANN) are computerized algorithms resembling interactive processes of th
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Books on the topic "Dose prediction"

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Reid, J. A. Keith. The effects of age-dependent dose conversion factors from ICRP-72 on biosphere model dose predictions. AECL, Whiteshell Laboratories, Environmental Science Branch, 1997.

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Seltzer, Stephen M. Technical progress report on predictions of dose from electrons in space ... National Aeronautics and Space Administration, 1992.

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Sparrow, Paul R. Does national culture really matter?: Predicting HRM preferences of Taiwanese employers. Sheffield University Management School, 1997.

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Ward, Peter L. The Loma Prieta earthquake of October 17, 1989: A brief geologic view of what caused the Loma Prieta earthquake and implications for future California earthquakes: what happened ... what is expected ... what can be done. U.S. Geological Survey, 1990.

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Kay, Helen. Does the validity of the selection system depend more on the criteria than the predictor? UMIST, 1995.

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Barnoski, Robert P. Sex offender sentencing in Washington State: Does the prison treatment program reduce recidivism? Washington State Institute for Public Policy, 2006.

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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY. AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62061e70cc4ed1.46611016.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of&#x0D; children with extremely low body weight, is to provide a wide range of doctors with basic information&#x0D; about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria&#x0D; of postnatal pathology, based on their own research. The specific features of the immunological&#x0D; reactivity of premature infants of various gestational ages who have developed bronchopulmonary&#x0D; dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after
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Harris, Shelley Anne. The development and validation of a pesticide dose prediction model. 1999.

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Andrzej, Wojcik, and Colin J. Martin. Biological effects of ionizing radiation. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199655212.003.0003.

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Biological effects of radiation have been interpreted based on the assumption that DNA is the primary target, but recent research has shown that non-targeted mechanisms may affect cells that are not directly exposed. The most important effect in humans from low doses of radiation is the induction of cancer, but risks of other effects such as cataract and cardiac or circulatory disease are becoming apparent. Epidemiological studies of Japanese survivors of atomic bombs demonstrate a clear linear relationship between solid cancer incidence and organ dose. This is supported by other epidemiologic
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Trainor, Laurel J., and Robert J. Zatorre. The neurobiological basis of musical expectations. Edited by Susan Hallam, Ian Cross, and Michael Thaut. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199298457.013.0016.

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This article explores how the auditory system processes incoming information and generates perceptual representations that allow it to make predictions about future sound events from past context, and how music appears to make use of this general processing mechanism. It focuses on expectation formation in auditory cortex because this is where the most research has been done, but there is also evidence for prediction mechanisms at subcortical levels and at levels beyond sensory areas. The article presents a framework for thinking about the neurological basis of expectation and prediction in mu
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Book chapters on the topic "Dose prediction"

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Nguyen, Dan. "Imaged-Based Dose Planning Prediction." In Medical Image Synthesis. CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-8.

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Chauhan, Akash, Ayush Dubey, Md Aftab Alam, Rishabha Malviya, and Mohammad Javed Naim. "Dose Prediction in Oncology using Big Data." In Big Data in Oncology: Impact, Challenges, and Risk Assessment. River Publishers, 2023. http://dx.doi.org/10.1201/9781003442639-9.

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Jove, Esteban, Jose M. Gonzalez-Cava, José-Luis Casteleiro-Roca, et al. "Remifentanil Dose Prediction for Patients During General Anesthesia." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92639-1_45.

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Feng, Zhenghao, Lu Wen, Peng Wang, et al. "DiffDP: Radiotherapy Dose Prediction via a Diffusion Model." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_19.

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Ma, Jianhui, Ti Bai, Dan Nguyen, et al. "Individualized 3D Dose Distribution Prediction Using Deep Learning." In Artificial Intelligence in Radiation Therapy. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32486-5_14.

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Kang, Jiayin, Yaozong Gao, Yao Wu, et al. "Prediction of Standard-Dose PET Image by Low-Dose PET and MRI Images." In Machine Learning in Medical Imaging. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10581-9_35.

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Berndt, J., M. Misslbeck, and P. Kneschaurek. "Dose QA Using EPID and a Dose Prediction Algorithm Independent of the Planning System." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03474-9_128.

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Sunag, Bhagya, and Shrinivas Desai. "Low-Dose Imaging: Prediction of Projections in Sinogram Space." In Computational Vision and Bio-Inspired Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6862-0_43.

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Wang, Bin, Lin Teng, Lanzhuju Mei, et al. "Deep Learning-Based Head and Neck Radiotherapy Planning Dose Prediction via Beam-Wise Dose Decomposition." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16449-1_55.

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Geburtig, Anja, Volker Wachtendorf, Peter Trubiroha, et al. "Polypropylene Numerical Photoageing Simulation by Dose–Response Functions with Respect to Irradiation and Temperature: ViPQuali Project." In Service Life Prediction of Exterior Plastics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06034-7_14.

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Conference papers on the topic "Dose prediction"

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Feng, Zhenghao, Lu Wen, Yuanyuan Xu, Binyu Yan, Jiliu Zhou, and Yan Wang. "Content-Aware Adversarial Network with Gradient-Enhanced Dose Rectification for Radiotherapy Dose Prediction." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635667.

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Fu, Linjie, Xia Li, Xiuding Cai, Xueyao Wang, Yali Shen, and Yu Yao. "MD-Dose: A diffusion model based on the Mamba for radiation dose prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822581.

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Wen, Lu, Qihun Zhang, Zhenghao Feng, et al. "Triplet-Constraint Transformer with Multi-Scale Refinement for Dose Prediction in Radiotherapy." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635240.

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Cui, Jiaqi, Yuanyuan Xu, Jianghong Xiao, et al. "Dose Prediction Driven Radiotherapy Parameters Regression via Intra- and Inter-Relation Modeling." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635537.

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Chen, Jingyun, Martin King, and Yading Yuan. "FedKBP: federated dose prediction framework for knowledge-based planning in radiation therapy." In Image-Guided Procedures, Robotic Interventions, and Modeling, edited by Maryam E. Rettmann and Jeffrey H. Siewerdsen. SPIE, 2025. https://doi.org/10.1117/12.3044379.

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Ahmed, Abrar, Minhazul Islam Mahi, M. Akhtaruzzaman, et al. "Towards Designing a Medication Assistive Intelligent System with Automated Insulin-dose Prediction for Elderlies." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11012957.

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Wen, Lu, Wenxia Yin, Zhenghao Feng, Xi Wu, Deng Xiong, and Yan Wang. "ARANet: Attention-based Residual Adversarial Network with Deep Supervision for Radiotherapy Dose Prediction of Cervical Cancer." In 2024 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2024. http://dx.doi.org/10.1109/cis-ram61939.2024.10672659.

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Zhang, J., A. Bousse, Y. Li, et al. "Pre-therapy dose prediction in targeted radionuclide therapy using semi-supervised learning: An in-silico preliminary study." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655434.

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Henique, Gautier, Chulmin Bang, Daniel Markel, et al. "Dose Aware Toxicity Prediction in Head and Neck Cancer Patients Using a Deformable 3D CNN on Daily CBCT Acquisitions." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635238.

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Orji, Martina P., Kyle Williams, Jonathan Troville, Swetadri Vasan Setlur Nagesh, Stephen Rudin, and Daniel R. Bednarek. "Investigation of the effect of training set parameters on deep neural network prediction accuracy of fluoroscopic procedure-room scatter dose distributions." In Physics of Medical Imaging, edited by John M. Sabol, Shiva Abbaszadeh, and Ke Li. SPIE, 2025. https://doi.org/10.1117/12.3046512.

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Reports on the topic "Dose prediction"

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Committee on Toxicology. New Approach Methodologies (NAMs) In Regulatory Risk Assessment Workshop Report 2020- Exploring Dose Response. Food Standards Agency, 2024. http://dx.doi.org/10.46756/sci.fsa.cha679.

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The UK Food Standards Agency (FSA) and the Committee on Toxicity of Chemicals in Food, Consumer Products and the Environment (COT) held an “Exploring Dose Response” workshop in a multidisciplinary setting inviting regulatory agencies, government bodies, academia and industry. The workshop provided a platform from which to address and enable expert discussions on the latest in silico prediction models, new approach methodologies (NAMs), physiologically based pharmacokinetics (PBPK), future methodologies, integrated approaches to testing and assessment (IATA) as well as methodology validation. U
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Ahmed, Kareem. Multitude Characterization and Prediction of DOE Advanced Biofuels Properties. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1807468.

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Wilkowski. PR-276-04503-R02 Multi-Scale Mechanics and Welding Process Simulation in Weld Integrity Assessment. Pipeline Research Council International, Inc. (PRCI), 2014. http://dx.doi.org/10.55274/r0010845.

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This report is on a jointly funded project by DOE and PRCI. The DOE version of this report was separately published in 2008. This project dealt with various aspects of prediction of the stress-based and strain-based girthweld flaw tolerance of linepipe steels, as well as some supplementary investigations.
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Kohnert, Aaron Anthony, G. van Couvering, G. S. Was, and Brian D. Wirth. Models Predicting Void Swelling Incubation Dose as a function of Irradiation Conditions. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1524349.

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Konsam, Manis Kumar, Amanda Thounajam, Prasad Vaidya, Gopikrishna A, Uthej Dalavai, and Yashima Jain. Machine Learning-Enhanced Control System for Optimized Ceiling Fan and Air Conditioner Operation for Thermal Comfort. Indian Institute for Human Settlements, 2024. http://dx.doi.org/10.24943/mlcsocfacotc6.2023.

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This paper proposes and tests the implementation of a sustainable cooling approach that uses a machine learning model to predict operative temperatures, and an automated control sequence that prioritises ceiling fans over air conditioners. The robustness of the machine learning model (MLM) is tested by comparing its prediction with that of a straight-line model (SLM) using the metrics of Mean Bias Error (MBE) and Root Mean Squared Error (RMSE). This comparison is done across several rooms to see how each prediction method performs when the conditions are different from those of the original ro
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Iselin, Columbus O'Donnell. Preliminary report on the prediction of "Afternoon Effect". Woods Hole Oceanographic Institution, 2022. http://dx.doi.org/10.1575/1912/29562.

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With moderate or light winds and a clear sky the diurnal heating which occurs near the sea surface can cause a serious reduction in the range of submarine detection, especially on shallow targets. This has usually been called the “afternoon effect", although as will be noticed below the ranges often remain short long after sun down. The heating of surface waters which causes such sharp downward refraction can of course be noted on a bathythermograph record, provided pen vibration does not confuse the upper part of the trace. Unfortunately it is the upper 20 or 30 feet of a bathythermograph cur
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Verma, Monika, Thomas Hertel, and Paul Preckel. Predicting Within Country Household Food Expenditure Variation Using International Cross-Section Estimates. GTAP Working Paper, 2009. http://dx.doi.org/10.21642/gtap.wp57.

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There is a long and distinguished literature involving demand analysis using international cross-section data. Such models are widely used for predicting national per capita consumption. However, there is nothing in this literature testing the performance of estimated models in predicting demands across the income spectrum within a single country. This paper fills the gap. We estimate an AIDADS model using cross-section international per capita data, and find that it does well in predicting food demand across the income distribution within Bangladesh. This suggests that there may be considerab
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Eilerts. L52026 Improved Prediction of Burnthrough for In-Service Welding. Pipeline Research Council International, Inc. (PRCI), 2002. http://dx.doi.org/10.55274/r0011153.

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Experiments were conducted to evaluate the effect of pressure on burnthrough risk.� The results indicate that hoop stress has a significant effect for thin-wall pipe.� The experimental data was used to develop and evaluate an alternative burnthrough prediction approach that accounts for pressure in the pipe.� The approach that was developed assumes that the volume of heated metal under the arc behaves similar to an area of metal loss caused by corrosion pitting.� An equivalent pit size is determined from the pipe diameter and wall thickness and the calculated weld penetration.� The predicted b
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9

Shiva, B. G. GMC-93-T03 Regenerative Heat Transfer in Reciprocating Compressors. Pipeline Research Council International, Inc. (PRCI), 1993. http://dx.doi.org/10.55274/r0011944.

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Abstract:
Evaluates the impact of heat transfer on reciprocating compressor performance, especially with respect to flow capacity. This paper gives results of the experimental measurements done to determine the contribution of regenerative heat transfer to suction gas heating and its comparison with earlier empirical models. It forms part of ongoing research on estimating the effects of heat transfer on compressor performance with a view to modeling such effects for improved prediction of compressor performance.
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10

Paolinelli, Luciano, and Srdjan Nesic. PR646-173609-Z01 Water Wetting Prediction Tool for Pipeline Integrity. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012111.

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Abstract:
Current approaches for pipeline integrity management, as related to internal corrosion, largely depend on Internal Corrosion Direct Assessment (ICDA) type approach. An essential part of these assessments is to predict if corrosive water phase is in direct contact with the internal pipe wall, a phenomenon commonly called "water wetting". In general, water wetting prediction models currently used by pipe integrity engineers are lagging behind the current level understanding. The Institute for Corrosion and Multiphase Technology (ICMT) at Ohio University has developed and validated a mechanistic
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