In the framework of risk assessment, computer codes are increasingly used to understand, model and predict physical phenomena. As these codes can be very time-consuming to run, which severely limit ...the number of possible simulations, a widely accepted approach consists in approximating the CPU-time expensive computer model by a so-called “surrogate model”. In this context, the Gaussian Process regression is one of the most popular technique. It offers the advantage of providing a predictive distribution for all new evaluation points. An uncertainty associated with any quantity of interest (e.g. a probability of failure in reliability studies) to be estimated can thus be deduced and adaptive strategies for choosing new points to run with respect to this quantity can be developed. This paper focuses on the estimation of the Gaussian process covariance parameters by reviewing recent works on the analysis of the advantages and disadvantages of usual estimation methods, the most relevant validation criteria (for detecting poor estimation) and recent robust and corrective methods.
•Different estimation methods of hyperparameters of the Gaussian process metamodel are reviewed.•Most of estimation methods lead to good predictivity, but with poor quality prediction intervals.•Several adequate metrics are described for Gaussian process predictive distribution validation.•Bayesian estimation approaches are theoretically very attractive, but may be intractable.•Approaches relying on ad-hoc corrections to have reliable prediction intervals may be irrelevant.
In reliability engineering studies, computer codes are increasingly used to model physical phenomena which, in many cases, can be very time-consuming to run. A widely accepted approach consists in ...approximating the CPU-time expensive computer model by a surrogate model. One of the most popular surrogate model is the Gaussian Process regression, as it provides, additionally to a prediction at an unobserved point, an uncertainty around this prediction (a predictive distribution). However, in practice, the quality of this metamodel depends on several choices, as the estimation and validation algorithms. The present work aims at proposing a new algorithm, based on constrained optimization multi-objective techniques, to estimate the Gaussian process hyperparameters in order to ensure robust and accurate (i.e. reliable) predictive distribution of the Gaussian process. An intensive numerical benchmark on various analytical functions, with different input dimensions and learning sample sizes, shows its good performance in comparison with standard estimation algorithms. The new algorithm is also applied to a real test case modeling an aquatic ecosystem. It is compared with a recent robust and sophisticated Bayesian method; it proves to be as efficient while being less sensitive to the specification of the Gaussian process model.
•New algorithm for obtaining robust estimation of the Gaussian process metamodel hyperparameters.•It jointly maximizes the likelihood and empirical coverage function of GP prediction intervals.•It is validated on a several analytical test functions of variable dimension (1 to 20).•The application relevance of this algorithm is shown on a real aquatic ecosystem model.•It competes with Bayesian methods while being less sensitive to certain tuning choices.
Modal parameter estimation requires a lot of user interaction, especially when parametric system identification methods are used and the modes are selected in a stabilization diagram. In this paper, ...a fully automated, generally applicable three-stage clustering approach is developed for interpreting such a diagram. It does not require any user-specified parameter or threshold value, and it can be used in an experimental, operational, and combined vibration testing context and with any parametric system identification algorithm. The three stages of the algorithm correspond to the three stages in a manual analysis: setting stabilization thresholds for clearing out the diagram, detecting columns of stable modes, and selecting a representative mode from each column. An extensive validation study illustrates the accuracy and robustness of this automation strategy.
► A fully automated, three-stage clustering approach is developed for interpreting stabilization diagrams. ► It follows the course of a manual analysis but it does not contain parameters that need to be specified or tuned by the user. ► It only needs a single data record and it can be used with any parametric identification algorithm. ► An extensive validation study confirms its robustness and accuracy.
Inter-laboratory reproducibility of biomethane potential (BMP) is dismal, with differences in BMP values for the same sample exceeding a factor of two in some cases. A large group of BMP researchers ...directly addressed this problem during a workshop held in Leysin, Switzerland, in June 2015. The workshop resulted in a new set of guidelines for BMP tests published in 2016, which is the subject of the present commentary. The work has continued with two international inter-laboratory studies and one additional workshop held in Freising, Germany, in 2018. The dataset generated by the two inter-laboratory studies were used to refine the validation criteria for BMP tests. Based on these new results an update to the original guidelines is proposed here.
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory ...roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.
Background
Resistance to antidepressant drug treatment remains a major health problem. Animal models of depression are efficient in detecting effective treatments but have done little to increase the ...reach of antidepressant drugs. This may be because most animal models of depression target the reversal of stress-induced behavioural change, whereas treatment-resistant depression is typically associated with risk factors that predispose to the precipitation of depressive episodes by relatively low levels of stress. Therefore, the search for treatments for resistant depression may require models that incorporate predisposing factors leading to heightened stress responsiveness.
Method
Using a diathesis-stress framework, we review developmental, genetic and genomic models against four criteria: (i) increased sensitivity to stress precipitation of a depressive behavioural phenotype, (ii) resistance to chronic treatment with conventional antidepressants, (iii) a good response to novel modes of antidepressant treatment (e.g. ketamine; deep brain stimulation) that are reported to be effective in treatment-resistant depression and (iv) a parallel to a known clinical risk factor.
Results
We identify 18 models that may have some potential. All require further validation. Currently, the most promising are the Wistar-Kyoto (WKY) and congenital learned helplessness (cLH) rat strains, the high anxiety behaviour (HAB) mouse strain and the CB1 receptor knockout and OCT2 null mutant mouse strains.
Conclusion
Further development is needed to validate models of antidepressant resistance that are fit for purpose. The criteria used in this review may provide a helpful framework to guide research in this area.
AbstractAutomated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming increasingly important for continuous structural health-monitoring applications. This paper ...describes an experimental study aimed at the identification of modal parameters of a full-scale cable-stayed bridge from the collected output-only vibration data without the need for any user interactions. The work focuses on the development of an automated and robust operational modal analysis (OMA) algorithm, using a multistage clustering approach. The main contribution of the work is to discuss a comprehensive case study to demonstrate the reliability of a novel criterion aimed at defining the hierarchical clustering threshold to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is demonstrated to work with any parametric system identification algorithm that uses the system order n as the sole parameter. In particular, the results from the covariance-driven stochastic subspace identification (SSI-Cov) methods are presented.
Revisiones sistemáticas en educación? Rodríguez, Alixon David Reyes
Revista de ciencias sociales (Maracaibo, Venezuela),
12/2023, Letnik:
29, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Las revisiones sistemáticas se han posicionado como una metodología que sistematiza la mejor evidencia científica disponible en relación con un tema y preguntas de investigación. Originadas a partir ...de las ciencias de la salud, han ampliado su rango y aplicación hacia otros campos, con adaptaciones, protocolos y formas de abordaje según los criterios de validación de conocimiento de dichos campos. Sin embargo, existen voces detractoras por la implementación de la revisión sistemática en educación aduciendo discrepancias irreconciliables a nivel ontológico, epistemológico y metodológico. En tal sentido, el objetivo de este trabajo es analizar las circunstancias bajo las cuales se cuestiona la aplicabilidad de las revisiones sistemáticas en el campo de la educación, al tiempo que se ofrecen argumentos desde ese análisis crítico para validar la revisión sistemática como posibilidad de investigación en educación, habida cuenta la consolidación de esta metodología en el campo y la solidez de la evidencia científica que entrega en sus resultados.
Biochemical methane potential (BMP) tests used to determine the ultimate methane yield of organic substrates are not sufficiently standardized to ensure reproducibility among laboratories. In this ...contribution, a standardized BMP protocol was tested in a large inter-laboratory project, and results were used to quantify sources of variability and to refine validation criteria designed to improve BMP reproducibility. Three sets of BMP tests were carried out by more than thirty laboratories from fourteen countries, using multiple measurement methods, resulting in more than 400 BMP values. Four complex but homogenous substrates were tested, and additionally, microcrystalline cellulose was used as a positive control. Inter-laboratory variability in reported BMP values was moderate. Relative standard deviation among laboratories (RSDR) was 7.5 to 24%, but relative range (RR) was 31 to 130%. Systematic biases were associated with both laboratories and tests within laboratories. Substrate volatile solids (VS) measurement and inoculum origin did not make major contributions to variability, but errors in data processing or data entry were important. There was evidence of negative biases in manual manometric and manual volumetric measurement methods. Still, much of the observed variation in BMP values was not clearly related to any of these factors and is probably the result of particular practices that vary among laboratories or even technicians. Based on analysis of calculated BMP values, a set of recommendations was developed, considering measurement, data processing, validation, and reporting. Recommended validation criteria are: (i) test duration at least 1% net 3 d, (ii) relative standard deviation for cellulose BMP not higher than 6%, and (iii) mean cellulose BMP between 340 and 395 NmLCH4 gVS−1. Evidence from this large dataset shows that following the recommendations—in particular, application of validation criteria—can substantially improve reproducibility, with RSDR < 8% and RR < 25% for all substrates. The cellulose BMP criterion was particularly important. Results show that is possible to measure very similar BMP values with different measurement methods, but to meet the recommended validation criteria, some laboratories must make changes to their BMP methods. To help improve the practice of BMP measurement, a new website with detailed, up-to-date guidance on BMP measurement and data processing was established.
When clustering produces more than one candidate to partition a finite set of objects O , there are two approaches to validation (i.e., selection of a "best" partition, and implicitly, a best value ...for c , which is the number of clusters in O ). First, we may use an internal index, which evaluates each partition separately. Second, we may compare pairs of candidates with each other, or with a reference partition that purports to represent the "true" cluster structure in the objects. This paper generalizes many of the classical indices that have been used with outputs of crisp clustering algorithms so that they are applicable for candidate partitions of any type (i.e., crisp or soft, with soft comprising the fuzzy, probabilistic, and possibilistic cases). Space prevents inclusion of all of the possible generalizations that can be realized this way. Here, we concentrate on the Rand index and its modifications. We compare our fuzzy-Rand index with those of Campello, Hullermeier and Rifqi, and Brouwer, and show that our extension of the Rand index is O(n), while the other three are all O(n 2 ). Numerical examples are given to illustrate various facets of the new indices. In particular, we show that our indices can be used, even when the partitions are probabilistic or possibilistic, and that our method of generalization is valid for any index that depends only on the entries of the classical (i.e., four-pair types) contingency table for this problem.
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