Robust Hierarchical Model Research Articles

Hierarchical Modeling to Enhance Spectrophotometry Measurements—Overcoming Dynamic Range Limitations for Remote Monitoring of Neptunium

A robust hierarchical model has been demonstrated for monitoring a wide range of neptunium concentrations (0.75–890 mM) and varying temperatures (10–80 °C) using chemometrics and feature selection. The visible–near infrared electronic absorption spectrum (400–1700 nm) of monocharged neptunyl dioxocation (Np(V) = NpO2+) includes many bands, which have molar absorption coefficients that differ by nearly 2 orders of magnitude. The shape, position, and intensity of these bands differ with chemical interactions and changing temperature. These challenges make traditional quantification by univariate methods unfeasible. Measuring Np(V) concentration over several orders of magnitude would typically necessitate cells with varying path length, optical switches, and/or multiple spectrophotometers. Alternatively, the differences in the molar extinction coefficients for multiple absorption bands can be used to quantify Np(V) concentration over 3 orders of magnitude with a single optical path length (1 mm) and a hierarchical multivariate model. In this work, principal component analysis was used to distinguish the concentration regime of the sample, directing it to the relevant partial least squares regression submodels. Each submodel was optimized with unique feature selection filters that were selected by a genetic algorithm to enhance predictions. Through this approach, the percent root mean square error of prediction values were ≤1.05% for Np(V) concentrations and ≤4% for temperatures. This approach may be applied to other nuclear fuel cycle and environmental applications requiring real-time spectroscopic measurements over a wide range of conditions.

Integrated model for the assessment of power generation alternatives through analytic hierarchy process and a fuzzy inference system. Case study of Spain

The main objective of this research was to create a robust hierarchical model to evaluate the power generation technologies in the energy mix of any country, with particular emphasis on its use in the case study of Spain. An exhaustive literature review allowed to identify a balanced number of the most relevant criteria that the model should consider to evaluate the seven alternatives that cover most of the Spanish energy demand. Through expert knowledge, the Analytic Hierarchy Process (AHP) methodology allowed to obtain the local and global weights of the criteria used in the model. Using these weights and the assessments of alternatives for each criterion, their ranking was determined through both AHP and a novel fuzzy inference system (FIS), whose inference rules were automatically constructed based on the weights of the criteria and a distance minimization method. The design of this FIS constitutes the main contribution of the work, firstly, because it avoids a second round of experts consultations and, secondly, because it systematically conceptualises the knowledge base that allows to infer the individual evaluation of any alternative, without the risk of rank reversal that may occur if using only AHP. The results of the case study of Spain showed that environmental was the most important criterion. In terms of the comparative AHP merit order, Photovoltaic (PV) was ranked first and Coal last. In addition, the individual assessment of the technologies through FIS yielded results consistent with the previous AHP ranking. Finally, a sensitivity analysis was performed, which showed good stability in the results obtained.

Population ecology of the spectacled caiman (Caiman crocodilus) in the Apaporis River middle basin

AbstractPopulation ecology studies on spectacled caimans (Caiman crocodilus) in Colombia have been few and far between with many covering short periods and defining population parameters based on relative indices (i.e., individuals/km). This reflects a lack of information on the general effects that environmental variables have on annual cycles of population dynamics, as well as a bias in abundance estimations due to the uncertainty of detection error. Keeping this in mind, we assessed the abundance and demographic structure of the spectacled caiman population inhabiting the Apaporis River middle basin over a year, based on robust hierarchical model that accounts for imperfect detection. We recorded a total of 1156 caiman observations between December 2018 and November 2019, estimating an average predicted value for abundance across all surveys of 29.99 ± 13.17 individuals, slowly increasing as the transect length increases and increasing variation as months passed by. The average detection probability was 0.69 ± 0.25 across all surveys, with no apparent effect as water temperature and relative humidity change across space‐time and slowly decreasing as months go through. The population size estimated based on the top‐performing model was 1763 ± 786 caimans across ~7.1 km2 assessed. We estimate the commonly used relative abundance (encounter rate) index as well as a generalized linear model and discuss how those relate with the values predicted by N‐mixture models. We also discuss the relevance and cautions researchers should have when using N‐mixture models to better understand spectacled caiman ecology.

Bayesian leveraging of historical control data for a clinical trial with time-to-event endpoint.

The recent 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures. It includes the broader application of Bayesian statistics and the use of evidence from clinical expertise. An example of the latter is the use of trial-external (or historical) data, which promises more efficient or ethical trial designs. We propose a Bayesian meta-analytic approach to leverage historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. An ovarian carcinoma trial and a non-small cell cancer trial illustrate methodological and practical aspects of leveraging historical data for the analysis and design of time-to-event trials.

Robust hierarchical model predictive control of graph-based power flow systems

A robust hierarchical model predictive control framework is presented for controlling a linear system of dynamically coupled subsystems. A graph-based modeling framework captures the conservation laws of power flow systems, for which control optimizes the storage and routing of energy to maximize transient and steady-state power throughput. A constructive approach is presented for developing an N-level hierarchical controller, which guarantees satisfaction of state and input constraints in the presence of signal and model uncertainty.