Relevant bibliographies by topics / Meta Models

Contents

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

Academic literature on the topic 'Meta Models'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Meta Models"

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Wheeler, Gregory. "Models, Models, and Models." Metaphilosophy 44, no. 3 (April 2013): 293–300. http://dx.doi.org/10.1111/meta.12036.

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Ehrig, Karsten, Jochen Malte Küster, and Gabriele Taentzer. "Generating instance models from meta models." Software & Systems Modeling 8, no. 4 (July 1, 2008): 479–500. http://dx.doi.org/10.1007/s10270-008-0095-y.

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Shenton, Andrew K., and Naomi V. Hay‐Gibson. "Information behaviour meta‐models." Library Review 61, no. 2 (February 24, 2012): 92–109. http://dx.doi.org/10.1108/00242531211220735.

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Elfaham, Haitham, and Ulrich Epple. "Meta models for intralogistics." at - Automatisierungstechnik 68, no. 3 (March 26, 2020): 208–21. http://dx.doi.org/10.1515/auto-2019-0083.

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AbstractIn industrial automation, adaptability is a key feature that can enhance the degree of autonomy in a plant sparing engineering months and costs. With the introduction of the service oriented architecture in control automation, a topology model is required to identify the vicinity relationships between the devices. Nowadays, due to the absence of the coupling between logistic aspects and control logic, the topology models are manually constructed which consequently affects the autonomy of the procedures generation. In this contribution, we introduce a concept to couple the logistics model with the devices abilities to generate product flow paths and procedures considering adaptable conditions.
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Conn, Vicki S. "Meta-Analysis for Models." Western Journal of Nursing Research 37, no. 4 (February 25, 2015): 423–24. http://dx.doi.org/10.1177/0193945914563305.

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Babenyshev, S., and V. Rybakov. "Logics of Kripke meta-models." Logic Journal of IGPL 18, no. 6 (September 3, 2009): 823–36. http://dx.doi.org/10.1093/jigpal/jzp047.

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Ma, Han, Baoyu Fan, Benjamin K. Ng, and Chan-Tong Lam. "VL-Meta: Vision-Language Models for Multimodal Meta-Learning." Mathematics 12, no. 2 (January 16, 2024): 286. http://dx.doi.org/10.3390/math12020286.

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Multimodal learning is a promising area in artificial intelligence (AI) that can make the model understand different kinds of data. Existing works are trying to re-train a new model based on pre-trained models that requires much data, computation power, and time. However, it is difficult to achieve in low-resource or small-sample situations. Therefore, we propose VL-Meta, Vision Language Models for Multimodal Meta Learning. It (1) presents the vision-language mapper and multimodal fusion mapper, which are light model structures, to use the existing pre-trained models to make models understand images to language feature space and save training data, computation power, and time; (2) constructs the meta-task pool that can only use a small amount of data to construct enough training data and improve the generalization of the model to learn the data knowledge and task knowledge; (3) proposes the token-level training that can align inputs with the outputs during training to improve the model performance; and (4) adopts the multi-task fusion loss to learn the different abilities for the models. It achieves a good performance on the Visual Question Answering (VQA) task, which shows the feasibility and effectiveness of the model. This solution can help blind or visually impaired individuals obtain visual information.
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Patterson, Eann. "On the credibility of engineering models and meta-models." Journal of Strain Analysis for Engineering Design 50, no. 4 (April 14, 2015): 218–20. http://dx.doi.org/10.1177/0309324715577930.

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Sheu, Ching-Fan, and Sawako Suzuki. "Meta-analysis using linear mixed models." Behavior Research Methods, Instruments, & Computers 33, no. 2 (May 2001): 102–7. http://dx.doi.org/10.3758/bf03195354.

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Bruns, Stephan B. "Meta-Regression Models and Observational Research." Oxford Bulletin of Economics and Statistics 79, no. 5 (March 22, 2017): 637–53. http://dx.doi.org/10.1111/obes.12172.

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Dissertations / Theses on the topic "Meta Models"

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Bäckström, Fredrik, and Anders Ivarsson. "Meta-Model Guided Error Correction for UML Models." Thesis, Linköping University, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8746.

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Modeling is a complex process which is quite hard to do in a structured and controlled way. Many companies provide a set of guidelines for model structure, naming conventions and other modeling rules. Using meta-models to describe these guidelines makes it possible to check whether an UML model follows the guidelines or not. Providing this error checking of UML models is only one step on the way to making modeling software an even more valuable and powerful tool.

Moreover, by providing correction suggestions and automatic correction of these errors, we try to give the modeler as much help as possible in creating correct UML models. Since the area of model correction based on meta-models has not been researched earlier, we have taken an explorative approach.

The aim of the project is to create an extension of the program MetaModelAgent, by Objektfabriken, which is a meta-modeling plug-in for IBM Rational Software Architect. The thesis shows that error correction of UML models based on meta-models is a possible way to provide automatic checking of modeling guidelines. The developed prototype is able to give correction suggestions and automatic correction for many types of errors that can occur in a model.

The results imply that meta-model guided error correction techniques should be further researched and developed to enhance the functionality of existing modeling software.

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Trefan, Laszlo. "Development of empirical models for pork quality." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5761.

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Pork quality is an important issue for the whole meat chain, from producers, abattoirs, retailers through to costumers and is affected by a web of multi-factorial actions that occur throughout the pork production chain. A vast amount of information is available on how these diverse factors influence different pork quality traits. However, results derived from individual studies often vary and are in some cases even contradictory due to different experimental designs or different pork quality assessment techniques or protocols. Also, individual influencing factors are often studied in isolation, ignoring interacting effects. A suitable method is therefore required to account for a range of interacting factors, to combine the results from different experiments and to derive generic response-laws. The aim of this thesis was to use meta-analyses to produce quantitative, predictive models that describe how diverse factors affect pork quality over a range of experimental conditions.
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Liu, Yang. "Automatic calibration of numerical models using meta-models." Thesis, University of Exeter, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430566.

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Bashar, Hasanain. "Meta-modelling of intensive computational models." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13667/.

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Engineering process design for applications that use computationally intensive nonlinear dynamical systems can be expensive in time and resources. The presented work reviews the concept of a meta-model as a way to improve the efficiency of this process. The proposed meta-model will have a computational advantage in implementation over the computationally intensive model therefore reducing the time and resources required to design an engineering process. This work proposes to meta-model a computationally intensive nonlinear dynamical system using reduced-order linear parameter varying system modelling approach with local linear models in velocity based linearization form. The parameters of the linear time-varying meta-model are blended using Gaussian Processes regression models. The meta-model structure is transparent and relates directly to the dynamics of the computationally intensive model while the velocity-based local linear models faithfully reproduce the original system dynamics anywhere in the operating space of the system. The non-parametric blending of the meta-model local linear models by Gaussian Processes regression models is ideal to deal with data sparsity and will provide uncertainty information about the meta-model predictions. The proposed meta-model structure has been applied to second-order nonlinear dynamical systems, a small sized nonlinear transmission line model, medium sized fluid dynamics problem and the computationally intensive nonlinear transmission line model of order 5000.
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Kajero, Olumayowa T. "Meta-model assisted calibration of computational fluid dynamics simulation models." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/813857/.

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Computational fluid dynamics (CFD) is a computer-based analysis of the dynamics of fluid flow, and it is widely used in chemical and process engineering applications. However, computation usually becomes a herculean task when calibration of the CFD models with experimental data or sensitivity analysis of the output relative to the inputs is required. This is due to the simulation process being highly computationally intensive, often requiring a large number of simulation runs, with a single simulation run taking hours or days to be completed. Hence, in this research project, the kriging meta-modelling method was coupled with expected improvement (EI) global optimisation approach to address the CFD model calibration challenge. In addition, a kriging meta-model based sensitivity analysis technique was implemented to study the model parameter input-output relationship. A novel EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution, where existing normal distribution-based EI measures are not applicable. This novel EI measure suggested the values of CFD model parameters to simulate with, hence minimising SSE and improving the match between simulation and experiments. To test the proposed methodology, a non-CFD numerical simulation case of the semi-batch reactor was considered as a case study which confirmed a saving in computational time, and an improvement of the simulation model with the actual plant data. The usefulness of the developed method has been subsequently demonstrated through a CFD case study of a single-phase flow in both a straight type and convergent-divergent type annular jet pump, where both a single turbulent model parameter, C_μ and two turbulent model parameters, C_μ and C_2ε where considered for calibration. Sensitivity analysis was subsequently based on C_μ as the input parameter. In calibration using both single and two model parameters, a significant improvement in the agreement with experimental data was obtained. The novel method gave a significant reduction in simulation computational time as compared to traditional CFD. A new correlation was proposed relating C_μ to the flow ratio, which could serve as a guide for future simulations. The meta-model based calibration aids exploration of different parameter combinations which would have been computationally challenging using CFD. In addition, computational time was significantly reduced with kriging-assisted sensitivity analysis studies which explored effect of different C_μ values on the output, the pressure coefficient. The numerical simulation case of the semi-batch reactor was also used as a basis of comparison between the previous EI measure and the newly proposed EI measure, which overall revealed that the latter gave a significant improvement at fewer number of simulation runs as compared to the former. The research studies carried out has hence been able to propose and successfully demonstrate the use of a novel methodology for faster calibration and sensitivity analysis studies of computational fluid dynamics simulations. This is essential in the design, analysis and optimisation of chemical and process engineering systems.
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Popaiz, Alessandro <1987&gt. "Business Meta Model: A Structured Framework for Business Models Representation." Master's Degree Thesis, Università Ca' Foscari Venezia, 2013. http://hdl.handle.net/10579/2390.

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Given the central role of Business Models in company characterization, it is not surprising that a great deal of effort has been spent in studying suitable representations for them. Most of the proposed models, however, pursue a semi-formal human-readable graphical paradigm that is mainly meant to be discussed among stakeholders, rather than to be easily handled by information systems. In this paper we introduce a formal meta model that aspires to be general enough to capture the expressiveness of most currently adopted paradigms. At the same time, each produced Business Model instance is regular and structured enough to be processed through automated algorithms. Specifically, data are organized as a structured graph, allowing for the adoption of well-known graph-based mining techniques. The ability of the framework to deal with real-world scenarios is assessed by modelling several actual companies. Further, some examples of data processing are given, specifically with the aim of spotting common patterns within a data base of Business Models.
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Barrowman, Nicholas J. "Nonlinear mixed effects models for meta-analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ57342.pdf.

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Salimi-Khorshidi, Gholamreza. "Statistical models for neuroimaging meta-analytic inference." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:40a10327-7f36-42e7-8120-ae04bd8be1d4.

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A statistical meta-analysis combines the results of several studies that address a set of related research hypotheses, thus increasing the power and reliability of the inference. Meta-analytic methods are over 50 years old and play an important role in science; pooling evidence from many trials to provide answers that any one trial would have insufficient samples to address. On the other hand, the number of neuroimaging studies is growing dramatically, with many of these publications containing conflicting results, or being based on only a small number of subjects. Hence there has been increasing interest in using meta-analysis methods to find consistent results for a specific functional task, or for predicting the results of a study that has not been performed directly. Current state of neuroimaging meta-analysis is limited to coordinate-based meta-analysis (CBMA), i.e., using only the coordinates of activation peaks that are reported by a group of studies, in order to "localize" the brain regions that respond to a certain type of stimulus. This class of meta-analysis suffers from a series of problems and hence cannot result in as accurate results as desired. In this research, we describe the problems that existing CBMA methods are suffering from and introduce a hierarchical mixed-effects image-based metaanalysis (IBMA) solution that incorporates the sufficient statistics (i.e., voxel-wise effect size and its associated uncertainty) from each study. In order to improve the statistical-inference stage of our proposed IBMA method, we introduce a nonparametric technique that is capable of adjusting such an inference for spatial nonstationarity. Given that in common practice, neuroimaging studies rarely provide the full image data, in an attempt to improve the existing CBMA techniques we introduce a fully automatic model-based approach that employs Gaussian-process regression (GPR) for estimating the meta-analytic statistic image from its corresponding sparse and noisy observations (i.e., the collected foci). To conclude, we introduce a new way to approach neuroimaging meta-analysis that enables the analysis to result in information such as “functional connectivity” and networks of the brain regions’ interactions, rather than just localizing the functions.
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Ko, Hung-Tse. "Distribution system meta-models in an electronic commerce environment." Ohio : Ohio University, 2001. http://www.ohiolink.edu/etd/view.cgi?ohiou1173977323.

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Preston, Carrol Lesley. "Statistical models of publication basis in meta-analysis." Thesis, Queen Mary, University of London, 2000. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1624.

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Objectives: To review, apply and compare existing publication bias methodology. To extend the selection model methods that adjust combined estimates and to develop models to adjust for publication bias and heterogeneity simultaneously. ' Methods: Methodologies that test for the existence of publication bias, estimate the number of missing studies, and adjust combined estimates for publication bias are reviewed. Parametric weighted distribution methodology is developed further. The existing family of distributions is extended to include a logistic function. Weight functions previously limited to modelling selection based on two-tailed p-values have been restructured for one-tailed p-values. The selection mechanism model has been developed to incorporate both p-values and precision. The model for effect size has been developed to incorporate linear predictors, so heterogeneity and publication bias can be modelled simultaneously. Data: Two systematic reviews taken from the Cochrane Library and simulation studies. Results: Methods that test for the existence of publication bias or estimate the number of missing studies are limited by the strength of their assumptions and low power. Weighted distributions offer the only way to directly assess the impact of publication bias. In data sets in which there is heterogeneity or the true treatment effect is null, modelling the selection mechanism on p-values only can lead to over-adjusted estimates and considerable variability between estimates with wide confidence intervals. Extending the selection model to include precision reduces this. It is then possible to include other covariates such as study quality or type. The effect-size model can be extended in a similar way to include linear predictors. Combination of these two models allows simultaneous consideration of the influence of publication bias and heterogeneity. Conclusions: Weighted distributions offer a flexible approach to modelling publication bias. Inclusion of precision in the selection model reduces sensitivity of the model to the shape of the selection model improving consistency of results. No selection model should be used on its own but in conjunction with others to allow a sensitivity approach
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Books on the topic "Meta Models"

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Michael, Jennings, ed. Universal meta data models. Indianapolis, Ind: Wiley, 2004.

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Marco, David. Universal Meta Data Models. New York: John Wiley & Sons, Ltd., 2004.

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Konstantopoulos, Spyros. Fixed and mixed effects models in meta-analysis. Bonn, Germany: IZA, 2006.

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R, Lehmann Donald, ed. Meta-analysis in marketing: Generalization of response models. Lexington, Mass: Lexington Books, 1986.

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Mohsen S.M.S.A.S Mostafa. Meta bolic alterations in models of diabetes mellitus. Manchester: University of Manchester, 1996.

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Cheung, T. W. Editing facilities for meta-models and application schema modelling. Manchester: UMIST, 1994.

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George, Bailey, and Wenzek Hagen, eds. Irresistible!: Markets, models, and meta-value in consumer electronics. Indianapolis: Prentice Hall Professional Technical Reference, 2006.

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Meta-Analysis. Muenchen: Hogrefe & Huber Publishers, 2006.

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Meta-communication for reflective online conversations: Models for distance education. Hershey, PA: Information Science Reference, 2012.

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Schüll, Anke. Ein Meta-Modell-Konzept zur Analyse von Geschäftsprozessen. Lohmar: J. Eul, 1999.

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Book chapters on the topic "Meta Models"

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Denker, Marcus, Mathieu Suen, and Stéphane Ducasse. "The Meta in Meta-object Architectures." In Objects, Components, Models and Patterns, 218–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69824-1_13.

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Lehmann, Grzegorz, Marco Blumendorf, Frank Trollmann, and Sahin Albayrak. "Meta-modeling Runtime Models." In Models in Software Engineering, 209–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21210-9_21.

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Ehrig, Karsten, Jochen M. Küster, Gabriele Taentzer, and Jessica Winkelmann. "Generating Instance Models from Meta Models." In Lecture Notes in Computer Science, 156–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11768869_13.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Meta-analysis with General Loglinear Models." In Modern Meta-Analysis, 177–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55895-0_15.

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Kumar, Anoop. "Models." In Meta-analysis in Clinical Research: Principles and Procedures, 33–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2370-0_5.

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Hahn, Christian, Cristián Madrigal-Mora, Klaus Fischer, Brian Elvesæter, Arne-Jørgen Berre, and Ingo Zinnikus. "Meta-models, Models, and Model Transformations: Towards Interoperable Agents." In Multiagent System Technologies, 123–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11872283_11.

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Steyerberg, Ewout, Daan Nieboer, Thomas Debray, and Hans van Houwelingen. "Meta-Analysis of Prediction Models." In Handbook of Meta-Analysis, 503–22. First edition. | Boca Raton : Taylor and Francis, [2020] | Series: Chapman & Hall/CRC handbooks of modern statistical methods: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315119403-22.

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White, Barbara Y., Allan Collins, and John R. Frederiksen. "The Nature of Scientific Meta-Knowledge." In Models and Modeling, 41–76. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0449-7_3.

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de Lara, Juan, and Esther Guerra. "Deep Meta-modelling with MetaDepth." In Objects, Models, Components, Patterns, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13953-6_1.

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Alberts, Berend T., Lucas O. Meertens, Maria-Eugenia Iacob, and Lambert J. M. Nieuwenhuis. "A Meta-Model Perspective on Business Models." In Lecture Notes in Business Information Processing, 64–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37478-4_4.

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Conference papers on the topic "Meta Models"

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Hartmann, Thomas, Assaad Moawad, Cedric Schockaert, Francois Fouquet, and Yves Le Traon. "Meta-Modelling Meta-Learning." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00014.

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Saidi, Imed Eddine, Mahmoud El Hamlaoui, Taoufiq Dkaki, Nacer Eddine Zarour, and Pierre-Jean Charrel. "Translation of Heterogeneous Requirements Meta-Models Through a Pivot Meta-Model." In 13th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006789903760382.

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Skene, James, and Wolfgang Emmerich. "Specifications, not meta-models." In the 2006 international workshop. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1138304.1138315.

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Rivas-Posada, Eduardo, and Mario I. Chacon-Murguia. "General meta-learning paradigm based on prior-models, meta-model, meta-algorithm, and few-shot-base-model." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533374.

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Muctadir, Hossain Muhammad, Lars König, Thomas Weber, Moussa Amrani, and Loek Cleophas. "Co-Evolving Meta-Models and View Types in View-Based Development." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00150.

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Hartong, Mark, Rajni Goel, and Duminda Wijesekera. "Meta-models for misuse cases." In the 5th Annual Workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1558607.1558645.

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Thiry, Laurent, Frederic Fondement, and Pierre-Alain Muller. "Categorical Reasoning about Meta-models." In 2012 Sixth International Symposium on Theoretical Aspects of Software Engineering (TASE). IEEE, 2012. http://dx.doi.org/10.1109/tase.2012.23.

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Johnson, Dean, Cara Wrigley, Karla Straker, and Sam Bucolo. "Designing innovative business models: Five emerging meta-models." In 2013 IEEE Tsinghua International Design Management Symposium (TIDMS). IEEE, 2013. http://dx.doi.org/10.1109/tidms.2013.6981218.

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Jimenez, Maxime, Darko Durisic, and Miroslaw Staron. "Measuring the Evolution of Meta-models - A Case Study of Modelica and UML Meta-models." In 5th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006218204960502.

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Martínez-Lasaca, Francisco, Pablo Díez, Esther Guerra, and Juan de Lara. "Model Sensemaking Strategies: Exploiting Meta-Model Patterns to Understand Large Models." In 2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2023. http://dx.doi.org/10.1109/models58315.2023.00023.

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Reports on the topic "Meta Models"

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Moeyaert, Mariola. Advanced Meta-Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/ttn9i9ntp8uvj469.

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This seminar will introduce you to advanced meta-analytic methods. Commonly encountered meta-analytic topics and issues will be covered, including meta-regression models, methods for handling multiple effect sizes per study (i.e., dependent effect sizes), missing data, publication bias, meta-analysis SEM, and single-case experiments meta-analysis. During this seminar, participants will learn how to use RStudio to model these commonly encountered complexities. Hands-on exercises will be incorporated throughout the seminar. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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Moeyaert, Mariola. Advanced Meta-Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/k4me5g0k92l56469.

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This seminar will introduce you to advanced meta-analytic methods. Commonly encountered meta-analytic topics and issues will be covered, including meta-regression models, methods for handling multiple effect sizes per study (i.e., dependent effect sizes), missing data, publication bias, meta-analysis SEM, and single-case experiments meta-analysis. During this seminar, participants will learn how to use RStudio to model these commonly encountered complexities. Hands-on exercises will be incorporated throughout the seminar. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, each seminar offers 2 ECTS Equivalent points.
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Deng, Yingjun, ShengJing Liu, Ming Zhao, Feng Zhao, Jun Guo, and Bin Yan. Diet-induced male infertility in mice models: a systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0116.

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Review question / Objective: In order to compare the different high energy diet such as high-fat diet and high sugar diet how to damage the male mice model in metabolize and fertility,and explore a reliable mice model method in the study of obesity with male infertility. P：obesity mice model with male infertility. I: High energy diet such as High-fat or High-sugar diet. C:High-fat diet,High-sugar diet, compared with normal diet in mice model. O:High energy diet induce male mice obesity model and damage their fertility. S: Use network meta-analysis. Condition being studied: The relationship between obesity and male infertility attacth more and more attention at present.So many animal expriments are carried out on this problem,there are enough exprimental article to support this meta analysis.
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van der Mensbrugghe, Dominique. The META 21 Integrated Assessment Model in GAMS and LHS Sampling. GTAP Working Paper, May 2023. http://dx.doi.org/10.21642/gtap.wp95.

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The META 21 Integrated Assessment Model (Dietz et al., 2021) represents a fairly comprehensive climate change simulation model incorporating a number of features: (a) a recent simple climate model; (b) down-scaling of temperature change to the country level; (c) integration of a number of bio-physical tipping points linked to rising temperatures; and (d) economic damages linked to rising sea levels and temperature using recent country-level estimates from the literature. The original implementation of META 21 was in Excel and linked to the @Risk Excel add-in for performing Monte Carlo-type analysis. This paper reflects a translation of META 21 into GAMS. One of the key purposes is to allow for ready incorporation of many of the features of META 21 into other models—notably CGE-based IAMs. To replace the features provided by the @Risk Excel add-in, this paper also introduces a software package that produces random deviates—and, similar to @Risk, uses the Latin Hypercube Sampling (LHS) approach, which is a stratified sampling technique intended to cover the entire sampling space efficiently and, in addition, orders the sample to reflect a desired correlation matrix for the sampled random variables.
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Huang, Jiapeng, Chunlan Yang, Kehong Zhao, Ziqi Zhao, Yin Chen, Tingting Wang, and Yun Qu. Transcutaneous Electrical Nerve Stimulation in Rodent Models of Neuropathic Pain: A Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0104.

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Liu, Yafeng, and Fan Zhang. A meta-analysis of different hralth education models for intestinal preparation before colonoscopy. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2022. http://dx.doi.org/10.37766/inplasy2022.3.0155.

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Li, Guangyao, Yuling Shi, Chuanghui Yang, Jinghu Li, Xueqin Hong, and Min Li. A Bayesian network meta-analysis of acupuncture to treat vascular dementia in animal models. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0036.

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van Gestel, Natasja, Kees Jan van Groenigen, Craig Osenberg, Jeffrey Dukes, and Paul Dijkstra. Biogeochemical Responses and Feedbacks to Climate Change: Synthetic Meta-Analyses Relevant to Earth System Models. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1429337.

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Hu, Xu, and Yiwu Zhou. Prediction models for prognosis of vespidae sting: A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, March 2021. http://dx.doi.org/10.37766/inplasy2021.3.0076.

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DING, YACONG, WANG YANSONG, TANG MO, ZHAI JIAWEI, LI RUI, and YANG YUFEI. Performance of Prediction Models of Non-Metastatic Colorectal Cancer : 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/inplasy2022.11.0130.

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