Academic literature on the topic 'Prognostics prediction model'
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Journal articles on the topic "Prognostics prediction model"
Long, Bing, Xiangnan Li, Xiaoyu Gao, and Zhen Liu. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model." Energies 12, no. 17 (August 25, 2019): 3271. http://dx.doi.org/10.3390/en12173271.
Full textLi, Xiaochuan, Xiaoyu Yang, Yingjie Yang, Ian Bennett, and David Mba. "An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data." Structural Health Monitoring 19, no. 5 (October 29, 2019): 1375–90. http://dx.doi.org/10.1177/1475921719884019.
Full textTong, Guoqiang, Xinbo Qian, and Yilai Liu. "Prognostics and Predictive Maintenance Optimization Based on Combination BP-RBF-GRNN Neural Network Model and Proportional Hazard Model." Journal of Sensors 2022 (April 29, 2022): 1–17. http://dx.doi.org/10.1155/2022/8655669.
Full textWon, Dong-Yeon, Hyun Su Sim, and Yong Soo Kim. "Prediction of Remaining Useful Lifetime of Membrane Using Machine Learning." Science of Advanced Materials 12, no. 10 (October 1, 2020): 1485–91. http://dx.doi.org/10.1166/sam.2020.3788.
Full textWang, Yiwei, Christian Gogu, Nicolas Binaud, Christian Bes, Raphael T. Haftka, and Nam-Ho Kim. "Predictive airframe maintenance strategies using model-based prognostics." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (March 1, 2018): 690–709. http://dx.doi.org/10.1177/1748006x18757084.
Full textWang, Xin, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng, and Bo Zheng. "Remaining Useful Life Prediction for Aero-Engines Using a Time-Enhanced Multi-Head Self-Attention Model." Aerospace 10, no. 1 (January 13, 2023): 80. http://dx.doi.org/10.3390/aerospace10010080.
Full textZhiyong, Gao, Li Jiwu, and Wang Rongxi. "Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 1 (January 2, 2021): 154–65. http://dx.doi.org/10.17531/ein.2021.1.16.
Full textChen, Xuefeng, Zhongjie Shen, Zhengjia He, Chuang Sun, and Zhiwen Liu. "Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 12 (January 11, 2013): 2849–60. http://dx.doi.org/10.1177/0954406212474395.
Full textXie, Zhiyuan, Shichang Du, Jun Lv, Yafei Deng, and Shiyao Jia. "A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction." Electronics 10, no. 1 (December 29, 2020): 39. http://dx.doi.org/10.3390/electronics10010039.
Full textMuneer, Amgad, Shakirah Mohd Taib, Sheraz Naseer, Rao Faizan Ali, and Izzatdin Abdul Aziz. "Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis." Electronics 10, no. 20 (October 9, 2021): 2453. http://dx.doi.org/10.3390/electronics10202453.
Full textDissertations / Theses on the topic "Prognostics prediction model"
Mishra, Madhav. "Model-based Prognostics for Prediction of Remaining Useful Life." Licentiate thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17263.
Full textGodkänd; 2015; 20151116 (madmis); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Madhav Mishra Ämne: Drift och underhållsteknik/Operation and Maintenance Engineering Uppsats: Model-based Prognostics for Prediction of Remaining Useful Life Examinator: Professor Uday Kumar Institutionen för samhällsbyggnad och naturresurser Avdelning Drift, underhåll och akustik Luleå tekniska universitet Diskutant: Accos. Professor Jyoti Kumar Sinha University of Manchester, Aerospace and Civil Engineering, Manchester Tid: Torsdag 17 december 2015 kl 10.00 Plats: F1031, Luleå tekniska universitet
Nguyen, Hoang-Phuong. "Model-based and data-driven prediction methods for prognostics." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASC021.
Full textDegradation is an unavoidable phenomenon that affects engineering components and systems, and which may lead to their failures with potentially catastrophic consequences depending on the application. The motivation of this Thesis is trying to model, analyze and predict failures with prognostic methods that can enable a predictive management of asset maintenance. This would allow decision makers to improve maintenance planning, thus increasing system availability and safety by minimizing unexpected shutdowns. To this aim, research during the Thesis has been devoted to the tailoring and use of both model-based and data-driven approaches to treat the degradation processes that can lead to different failure modes in industrial components, making use of different information and data sources for performing predictions on the degradation evolution and estimating the Remaining Useful Life (RUL).The Ph.D. work has addressed two specific prognostic applications: model-based prognostics for fatigue crack growth prediction and data-driven prognostics for multi-step ahead predictions of time series data of Nuclear Power Plant (NPP) components.Model-based prognostics relies on the choice of the adopted Physics-of-Failure (PoF) models. However, each degradation model is appropriate only to certain degradation process under certain operating conditions, which are often not precisely known. To generalize this, ensembles of multiple degradation models have been embedded in the model-based prognostic method in order to take advantage of the different accuracies of the models specific to different degradations and conditions. The main contributions of the proposed ensemble of models-based prognostic approaches are the integration of filtering approaches, including recursive Bayesian filtering and Particle Filtering (PF), and novel weighted ensemble strategies considering the accuracies of the individual models in the ensemble at the previous time steps of prediction. The proposed methods have been validated by case studies of fatigue crack growth simulated with time-varying operating conditions.As for multi-step ahead prediction, it remains a difficult task of Prognostics and Health Management (PHM) because prediction uncertainty tends to increase with the time horizon of the prediction. Large prediction uncertainty has limited the development of multi-step ahead prognostics in applications. To address the problem, novel multi-step ahead prediction models based on Long Short- Term Memory (LSTM), a deep neural network developed for dealing with the long-term dependencies in the time series data have been developed in this Thesis. For realistic practical applications, the proposed methods also address the additional issues of anomaly detection, automatic hyperparameter optimization and prediction uncertainty quantification. Practical case studies have been considered, concerning time series data collected from Steam Generators (SGs) and Reactor Coolant Pumps (RCPs) of NPPs
Alrabady, Linda Antoun Yousef. "An online-integrated condition monitoring and prognostics framework for rotating equipment." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9204.
Full textGorjian, Nima. "Asset health prediction using the explicit hazard model." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/57314/1/Nima_Gorjian_Jolfaei_Thesis.pdf.
Full textTamssaouet, Ferhat. "Towards system-level prognostics : modeling, uncertainty propagation and system remaining useful life prediction." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0079.
Full textPrognostics is the process of predicting the remaining useful life (RUL) of components, subsystems, or systems. However, until now, the prognostics has often been approached from a component view without considering interactions between components and effects of the environment, leading to a misprediction of the complex systems failure time. In this work, a prognostics approach to system-level is proposed. This approach is based on a new modeling framework: the inoperability input-output model (IIM), which allows tackling the issue related to the interactions between components and the mission profile effects and can be applied for heterogeneous systems. Then, a new methodology for online joint system RUL (SRUL) prediction and model parameter estimation is developed based on particle filtering (PF) and gradient descent (GD). In detail, the state of health of system components is estimated and predicted in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, the proposed estimation method is used to correct and to adapt the IIM parameters. Finally, the developed methodology is verified on a realistic industrial system: The Tennessee Eastman Process. The obtained results highlighted its effectiveness in predicting the SRUL in reasonable computing time
Sánchez, Sardi Héctor Eloy. "Prognostics and health aware model predictive control of wind turbines." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/463321.
Full textEls components dels aerogeneradors estan sotmesos a considerable estrès i fatiga, degut a les condicions ambientals extremes a les quals estan exposats, especialment els localitzats en alta mar. Per aquest motiu, al comunitat científica durant els últims anys ha investigat les averies més comunes presents en els aerogeneradors, fet que ha portat a proposar un cas d'estudi de diagnosi i control tolerant de fallades que inclou un conjunt de fallades que afecten a diversos components dels aerogeneradors. Aquesta tesi presenta algunes contribucions en els camps de la diagnosi de fallades, el control tolerant de fallades i la prognosi, així com la seva integració amb el control d'aerogeneradors, fet que ha portat a proposar una tècnica de control anomenada control predictiu basada en models conscients de la salut del sistema (HAMPC). Concretament les aportacions es poden resumir en: - Diagnosi de fallades basada en models: per a la detecció s'utilitzen observadors intervalars i l'aïllament de la fallada es fa en base el conjunt d'ARRs obtinguts de l'anàlisi estructural i de la matriu de signatures de fallades que relaciona les ARRs amb les fallades. - Control tolerant de fallades: es proposa un esquema de control tolerant a fallades que integra la detecció de fallades i algoritme d'acomodació de fallades, i té per objectiu evitar l'augment de càrregues en la pala i la torre quan es produeix una fallada en el sensor azimuth quan es fa un control individual de la inclinació de les pales (IPC). - Prognosi de la fatiga i la degradació de les pales: la fatiga s'avalua amb un algorisme denominat "rainflow counting" amb el qual es fa estimació del dany acumulat i per a la degradació es fa servir un model de degradació de la rigidesa del material amb el qual es fan prediccions de la vida útil restant (RUL). - Control de la salut d'aerogeneradors: s'ha integrat la gestió de la salut del sistema basat en danys per fatiga o prediccions de RUL amb control predictiu basat en models (MPC) donant lloc al control que anomenem HAMPC. Les contribucions presentades en aquesta tesi han sigut validades en un cas d'estudi d'aerogeneradors basat en un aerogenerador de referència de 5MW de potència implementat en el simulador d'aerogeneradors d'alta fidelitat conegut amb el nom de FAST.
Moskowitz, Chaya S. "Quantifying and comparing the predictive accuracy of prognostic factors /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/9610.
Full textIwakami, Naotsugu. "Optimal Sampling in Derivation Studies was Associated with Improved Discrimination in External Validation for Heart Failure Prognostic Models." Kyoto University, 2020. http://hdl.handle.net/2433/259731.
Full textGwilliam, Bridget. "The development of prognostic models for predicting survival in patients with advanced cancer." Thesis, St George's, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546796.
Full textMarques, Maria João Pereira Vicente Dias. "Análise retrospetiva de 92 casos de cólica em equinos admitidos em segunda opinião para tratamento hospitalar." Master's thesis, Universidade de Lisboa, Faculdade de Medicina Veterinária, 2018. http://hdl.handle.net/10400.5/15833.
Full textA cólica é uma patologia de importância preeminente em equinos, cuja identificação da causa nem sempre é fácil, fazendo com que a determinação precoce de um prognóstico seja essencial. Assim, foi realizado um estudo retrospetivo em 92 casos de cólica recebidos pelo Serviço de Cirurgia e Urgência em Equinos da FMV-ULisboa. Os objetivos do presente estudo foram: 1) caracterizar os casos de cólica referenciados para o SCUE FMV-ULisboa, avaliando o tipo de intervenção clínica, a causa de cólica e a taxa de alta hospitalar; 2) avaliar o valor prognóstico de cada um dos indicadores recolhidos na admissão; 3) comparar o valor destes indicadores entre os dois tipos de intervenção clínica, médica e cirúrgica; e 4) elaborar um modelo multivariado de predição de prognóstico. Estimou-se que 82% dos animais submetidos a intervenção cirúrgica e 75% dos animais tratados medicamente tiveram alta hospitalar, e que 25% dos animais submetidos a laparotomia sofreram íleo pós-cirúrgico. Foram recolhidos na admissão os seguintes dados: idade, tempo entre sinalização e admissão hospitalar, refluxo gastrointestinal, frequência cardíaca, hematócrito, proteínas totais séricas, proteínas totais do líquido peritoneal, lactato peritoneal e lactato sanguíneo. Nas cólicas médicas, os indicadores hematócrito, frequência cardíaca e lactato peritoneal foram considerados estatisticamente significativos (p<0,05), o lactato sanguíneo marginalmente significativo (p=0,053) e as proteínas do líquido peritoneal tendencialmente significativas (p<0,10). Foram elaborados dois modelos de predição multivariável. O modelo de 3 preditores (lactato sanguíneo, frequência cardíaca e hematócrito) com especificidade de 42,9% e sensibilidade de 96,0%. O modelo de 5 preditores (lactato sanguíneo, frequência cardíaca, hematócrito, idade e proteínas totais séricas) com especificidade de 71,4% e sensibilidade de 95,7%. Nas cólicas cirúrgicas, não foi possível determinar preditores significativos nem elaborar modelos de predição. Foi, ainda, criada uma aplicação informática de cálculo de probabilidade de alta hospitalar com base nos modelos descritos. Finalmente, conclui-se que a recolha de líquido peritoneal deverá ser feita com mais frequência pois os seus indicadores parecem transmitir informação valiosa. O modelo de 3 preditores, apesar de ter uma especificidade menor para a amostra em estudo, será provavelmente mais fiável do ponto de vista clínico, para utilização futura. Para além disso, é espectado que com o aumento da dimensão da amostra, estes modelos se tornem mais robustos.
ABSTRACT - A RETROSPECTIVE REVIEW OF 92 EQUINE COLIC CASES REFERRED FOR HOSPITAL TREATMENT - Colic is a really important syndrome in the equine species. To identify a diagnosis can be a true challenge, so the early determination of a prognosis is essential. Therefore, a retrospective study was performed in 92 colic cases admitted at the “Equine Surgery and Emergency Services” (Lisbon University). The objectives of this study were: 1) describe the colic cases and evaluate the clinical approach (medical or surgical), the origin of the problem and rate of survival; 2) estimate the prognostic value of each one of the collected predictors at the admission process; 3) compare the predictors according to the clinical approach; and 4) elaborate a multivariable prognostic prediction model. In this study, the survival rate was 82% for the horses submitted to surgical intervention and 75% for the horses treated medically; and, 25% of the horses in which laparotomy was performed developed post-operative ileus. The following data were collected during admission at the hospital: age, time between the onset of clinical signs and referral, gastrointestinal reflux, cardiac frequency, haematocrit, blood total protein, peritoneal fluid total protein, peritoneal fluid lactate, blood lactate. In medical colics, haematocrit, cardiac frequency and peritoneal fluid lactate were statistically significant (p<0,05), blood lactate was marginally significant (p=0,053) and peritoneal fluid total protein was tendentially significant (p<0,10). Two multivariable prognostic prediction models were elaborated. The three predictors model (blood lactate, cardiac frequency and haematocrit) had a specificity of 42,9% and a sensibility of 96,0%. The five predictors model (blood lactate, cardiac frequency, haematocrit, blood total protein and age) had a specificity of 71,4% and a sensibility of 95,7%. In surgical colics, it wasn’t possible to determine statistically significant predictors neither to elaborate prediction models. Based on the previously descript models, a computerized application to calculate the survival probability was created. It was concluded that peritoneal fluid should be collected more often, since peritoneal lactate and peritoneal fluid total protein seem to be providers of valuable information. Even though, the three predictors model has a reduced specificity for the study sample, it will be probably more reliable from the clinical point of view for further applications. Furthermore, it’s expected that with the increasing of the sample size, these models will get more robust.
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Books on the topic "Prognostics prediction model"
Len'kov, Roman. Social forecasting and planning. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058988.
Full textWagner, Carolin. Process-Centric View on Predictive Maintenance and Fleet Prognostics: Development of a Process Reference Model and a Development Method for Fleet Prognostics to Guide Predictive Maintenance Projects. Logos Verlag Berlin, 2022.
Find full textSteinhauser, Karen E., and James A. Tulsky. Defining a ‘good’ death. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656097.003.0008.
Full textRiley, Richard D., Danielle van der Windt, Peter Croft, and Karel G. M. Moons, eds. Prognosis Research in Health Care. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198796619.001.0001.
Full textRubia, Katya. ADHD brain function. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0007.
Full textBook chapters on the topic "Prognostics prediction model"
Tinga, Tiedo, and Richard Loendersloot. "Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance." In Predictive Maintenance in Dynamic Systems, 313–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_11.
Full textHarrell, Frank E., Kerry L. Lee, and Daniel B. Mark. "Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors." In Tutorials in Biostatistics, 223–49. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470023678.ch2b(i).
Full textStaibano, Stefania. "Molecular Markers for Patient Selection and Stratification: Personalized Prognostic Predictive Models." In Prostate Cancer: Shifting from Morphology to Biology, 213–19. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7149-9_13.
Full textPrathan, Sorada, and Siew Hock Ow. "A Model for Predicting and Determining the Best-Fit Programmers Using Prognostic Attributes." In Lecture Notes in Electrical Engineering, 294–301. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8276-4_28.
Full textArunKumar, K., and S. Vasundra. "Prognostic Outcome Prediction on Patient Treatment Trajectory Data Using PSO Optimization on LTSM-RNN Model." In Advances in Intelligent Systems and Computing, 1045–61. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7330-6_78.
Full textWang, Dong, and Kwok-Leung Tsui. "State Space Models Based Prognostic Methods for Remaining Useful Life Prediction of Rechargeable Batteries." In Statistical Modeling for Degradation Data, 307–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5194-4_16.
Full textAria, Massimo, Corrado Cuccurullo, and Agostino Gnasso. "Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests." In Proceedings e report, 179–84. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.34.
Full textMarano, Giuseppe, Patrizia Boracchi, and Elia M. Biganzoli. "Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 183–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24462-4_16.
Full textDi Maso, Matteo, Monica Ferraroni, Pasquale Ferrante, Serena Delbue, and Federico Ambrogi. "Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models." In Proceedings e report, 191–96. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.36.
Full textD'Agostino, Ralph B., Albert J. Belanger, Elizabeth W. Markson, Maggie Kelly-Hayes, and Philip A. Wolf. "Prognostic/Clinical Prediction Models: Development of Health Risk Appraisal Functions in the Presence of Multiple Indicators: The Framingham Study Nursing Home Institutionalization Model." In Tutorials in Biostatistics, 209–22. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470023678.ch2b.
Full textConference papers on the topic "Prognostics prediction model"
Li, Zhixiong, Dazhong Wu, Chao Hu, Janis Terpenny, and Sheng Shen. "Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68315.
Full textYuan, Yuchen, and Wanqing Song. "Degradation Prediction Of Tool Based On Fractional Levy Prediction Model." In 2022 Global Reliability and Prognostics and Health Management (PHM-Yantai). IEEE, 2022. http://dx.doi.org/10.1109/phm-yantai55411.2022.9942217.
Full textXi, Zhimin, and Pingfeng Wang. "A Copula Based Sampling Method for Residual Life Prediction of Engineering Systems Under Uncertainty." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71105.
Full textIslam, Mohammad Rubyet, and Peter Sandborn. "Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-70508.
Full textPang, Zhenan, Changhua Hu, Xiaosheng Si, Jianxun Zhang, and Hong Pei. "Life Prediction Approach by Integrating Nonlinear Accelerated Degradation Model and Hazard Rate Model." In 2018 Prognostics and System Health Management Conference (PHM-Chongqing). IEEE, 2018. http://dx.doi.org/10.1109/phm-chongqing.2018.00073.
Full textWang, Wenbin, and Matthew Carr. "An adapted Brownion motion model for plant residual life prediction." In 2010 Prognostics and System Health Management Conference (PHM). IEEE, 2010. http://dx.doi.org/10.1109/phm.2010.5413487.
Full textXiao, Bei, Peng-Cheng Luo, Zhi-Jun Cheng, Xiao-Nan Zhang, and Xin-Wu Hu. "Dam Deformation Prediction Model Based on Combined Gaussian Process." In 2019 Prognostics and System Health Management Conference (PHM-Qingdao). IEEE, 2019. http://dx.doi.org/10.1109/phm-qingdao46334.2019.8942944.
Full textTang, Liang, Jonathan DeCastro, Greg Kacprzynski, Kai Goebel, and George Vachtsevanos. "Filtering and prediction techniques for model-based prognosis and uncertainty management." In 2010 Prognostics and System Health Management Conference (PHM). IEEE, 2010. http://dx.doi.org/10.1109/phm.2010.5413490.
Full textWenjia Xu and Wenbin Wang. "An adaptive gamma process based model for residual useful life prediction." In 2012 Prognostics and System Health Management Conference (PHM). IEEE, 2012. http://dx.doi.org/10.1109/phm.2012.6228785.
Full textLall, Pradeep, Junchao Wei, and Peter Sakalaukus. "Bayesian probabilistic model for life prediction and fault mode classification of solid state luminaires." In 2014 IEEE Conference on Prognostics and Health Management (PHM). IEEE, 2014. http://dx.doi.org/10.1109/icphm.2014.7036401.
Full textReports on the topic "Prognostics prediction model"
Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.
Full textHuilai, Zhang. Prognostic factors or prediction models for POD24 in patients with newly diagnosed FL:a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2021. http://dx.doi.org/10.37766/inplasy2021.2.0034.
Full textShadurdyyev, G. Analysis of sets of factors affecting the variable flow of the Amu Darya River to create a seasonal prognostic model. Kazakh-German University, December 2022. http://dx.doi.org/10.29258/dkucrswp/2022/53-72.eng.
Full textNeodo, Anna, Fiona Augsburger, Jan Waskowski, Joerg C. Schefold, and Thibaud Spinetti. Monocytic HLA-DR expression and clinical outcomes in adult ICU patients with sepsis – a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0119.
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