Robust Determination Research Articles

Extraction and purification of glomalin-related soil protein (GRSP) to determine the associated trace metal(loid)s

Glomalin-related soil protein (GRSP) is recalcitrant compound in the soil and sediment and plays a crucial role in metal transportation. However, potential metal speciation changes during GRSP extraction remain unreported. Here, a feasible GRSP extraction and purification procotol is described for robust determination of GRSP-bound metal(loid)s. Several spectrum patterns measured before and after GRSP extraction indicate that the GRSP extraction process does not significantly affect the mineral state of the samples. Potential bias generated by simultaneous metal release during GRSP extraction can be effectively eliminated by applying complete and independent dialysis.•Na signal appeared in the X-ray photoelectron survey spectrum after GRSP extraction, suggesting that Na exchange may be a critical process in releasing metal(loid)s.•Element maps obtained using secondary ion mass spectroscopy exhibited different distribution of C-N and Fe after GRSP extraction, thus suggesting that uncoupling of the Fe-organic framework occurred during GRSP extraction, which could result in the release of organic matter and metal(loid)s.•European Community Bureau of Reference (BCR) subsequent extraction reveals that most of the metal(loid)s were extracted from the acid-exchangeable and residual fraction during GRSP extraction. Remarkable differences in the GRSP-bound metal content before and after dialysis implied that the dialysis could remove most metal(loid)s.

A New Approach for Robust Mean-Variance Portfolio Selection Using Trimmed k-Means Clustering

In this study, we consider the data preprocessing using trimmed k-means clustering for robust mean-variance portfolio selection. The proposed method trims the outliers in the data preprocessing stage. The optimum portfolio is formed by selecting the stock representation for each cluster using the Sharpe ratio. The optimum portfolio formation is accomplished by robust fast minimum covariance determinant (FMCD) and robust S mean-variance (MV) portfolio model. In the empirical experiment, we use fundamental trading data for the year 2017 (to form the clusters) and daily closing price data of LQ45 index stocks from August 2017 to July 2018 taken from the Indonesian Stock Exchange to form the optimum portfolio. As benchmark for portfolio performance formed in this study, we use the performance of the Indonesia Composite Index (ICI). The results reveal that the proposed method can reliably obtain the optimum portfolio and solve the outliers problem. Moreover, the comparison stage shows that the combination of trimmed k-means clustering and robust portfolio model is better in forming the optimum portfolio than the combination of k-means clustering and robust MV portfolio model. Finally, we also find that the combination of trimmed k-means clustering (with α=10%) and robust FMCD MV portfolio model outperforms portfolios produced by other methods.

Forest system hydraulic conductance: partitioning tree and soil components.

Soil-leaf hydraulic conductance determines canopy-atmosphere coupling in vegetation models, but it is typically derived from ex-situ measurements of stem segments and soil samples. Using a novel approach, we derive robust in-situ estimates for whole-tree conductance (ktree ), 'functional' soil conductance (ksoil ), and 'system' conductance (ksystem , water table to canopy), at two climatically different tropical rainforest sites. Hydraulic 'functional rooting depth', determined for each tree using profiles of soil water potential (Ψsoil ) and sap flux data, enabled a robust determination of ktree and ksoil . ktree was compared across species, size classes, seasons, height above nearest drainage (HAND), two field sites, and to alternative representations of ktree ; ksoil was analysed with respect to variations in site, season and HAND. ktree was lower and changed seasonally at the site with higher vapour pressure deficit (VPD) and rainfall; ktree differed little across species but scaled with tree circumference; rsoil (1/ksoil ) ranged from 0 in the wet season to 10× less than rtree (1/ktree ) in the dry season. VPD and not rainfall may influence plot-level k; leaf water potentials and sap flux can be used to determine ktree , ksoil and ksystem ; Ψsoil profiles can provide mechanistic insights into ecosystem-level water fluxes.

Age and structural constraints on the tectonic evolution of the Paleoproterozoic Saimaa orocline in Fennoscandia

The Paleoproterozoic oroclines of Fennoscandia played a vital role in the making of continental equidimensional crust during the Svecofennian orogeny. We propose that the 1.90–1.88 Ga arc rocks in south-central Finland originally formed a single linear arc system that is now represented by the coupled Bothnian oroclines and their southern continuation, the Saimaa orocline, as defined in this study. A regional large-scale approach with robust geochronological age determinations obtained from metasedimentary rocks and granitoids, and field-based structural studies are used to produce a coherent view on the tectonic evolution of the Saimaa orocline.The pre-orocline tectonic evolution started with a NE-SW collision between the Karelia continent (lower plate) and Proto-Svecofennia (upper plate) at ca. 1.91 Ga (D1). This was followed by subduction reversal and arc magmatism at ca. 1.90–1.88 Ga. Collision or flat subduction at 1.885 Ga caused a widespread collisional event (D2). Our results indicate that the presently E-W oriented D2 structures are not, as is widely proposed, products of N-S contraction but instead originated as a N-S to NNW-SSE oriented belt that was subject to E-W contraction. Dorocline, recorded as the first deformation stage in post-D2 sediments, folded the pre-existing structures into their E-W and ENE-WSW orientation. Following orocline formation, the main shortening direction switched back to the NE-SW (D3) resulting in type-1 and type-2 refolded folds, long wavelength regional folds and bending of the hinge zone of the Saimaa orocline. D4-D5 deformation stages are localized intra-continental tectonic events at 1.83–1.79 Ga. The formation of the Saimaa orocline is bracketed at 1875–1865 Ma and formed by margin-parallel shortening. The Bothnian and Saimaa oroclines form continuous buckles, but whereas the Bothnian coupled oroclines are crustal-scale parallel folds due to more rigid crustal core, Keitele microcontinent, the Saimaa orocline is a crustal-scale isoclinal similar fold.

Quantification of Sodium Relaxation Times and Concentrations as Surrogates of Proteoglycan Content of Patellar CARTILAGE at 3T MRI.

Sodium MRI has the potential to depict cartilage health accurately, but synovial fluid can influence the estimation of sodium parameters of cartilage. Therefore, this study aimed to reduce the impact of synovial fluid to render the quantitative compositional analyses of cartilage tissue technically more robust. Two dedicated protocols were applied for determining sodium and relaxation times. For each protocol, data were acquired from 10 healthy volunteers and one patient with patellar cartilage damage. Data recorded with multiple repetition times for measurement and multi-echo data acquired with an additional inversion recovery pulse for measurement were analysed using biexponential models to differentiate longitudinal relaxation components of cartilage () and synovial fluid (), and short () from long () transversal relaxation components. Sodium relaxation times and concentration estimates in patellar cartilage were successfully determined: = 14.5 ± 0.7 ms; = 37.9 ± 2.9 ms; c(-protocol) = 200 ± 48 mmol/L; = 0.4 ± 0.1 ms; = 12.6 ± 0.7 ms; c(-protocol) = 215 ± 44 mmol/L for healthy volunteers. In conclusion, a robust determination of sodium relaxation times is possible at a clinical field strength of 3T to quantify sodium concentrations, which might be a valuable tool to determine cartilage health.

Financialization and income inequality: An extreme bounds analysis

Several theoretical hypotheses predict conflicting effects of financialization on income inequality, and the lack of consensus is reinforced by the mixed findings of related empirical studies. In this paper we address the model uncertainty issue by applying extreme bounds analysis (EBA) to examine the robustness of financialization variables as determinants of income inequality. The results suggest that financialization is a leading driver of income inequality and that the effects are larger when transmitted through financial markets. In addition, the results show that law and order, labour union density, population, globalization, remittances, education, and the agriculture sector share are also robust determinants of income inequality. Policy recommendations are put forward to reduce the power of the financial sector.

Outlier robust identification of dual‐rate Hammerstein models in the presence of unmodeled dynamics

AbstractThe article considers the outlier‐robust recursive least squares algorithm for identification of SISO (single‐input single‐output) dual‐rate Hammerstein model. The output sampling period is an integer multiple of the input sampling period. By using polynomial transformation technique we get a model which is suitable for parameter estimation for dual‐rate measurement data. After that deterministic dual‐rate Hammerstein model is extended with stochastic disturbance (which includes outliers) and unmodeled dynamics. Synthesis of identification algorithm is based on Newton–Raphson method which requires that loss function should be twice differentiable. Huber loss function, relevant for outliers treatment, has just first derivative. In order to overcome problem pseudo‐Huber loss function is introduced. This function behaves similarly to Huber loss function and has derivatives of arbitrary orders. Given recursive algorithm is based on synergy of Huber and pseudo‐Huber loss functions. Then we consider the robustness of algorithm. The main contributions of the article are: (i) determination of dual‐rate Hammerstein model which includes stochastic disturbance with outliers and unmodeled dynamics; (ii) derivation of robust recursive algorithm; (iii) determination of robustness for parameter estimate.

Thermochemical reduction modeling in a high-temperature moving-bed reactor for energy storage: 1D model

The design of robust and efficient high-temperature thermochemical reactors and determination of operating conditions are critical steps toward enabling high-efficiency long-duration solar energy storage. This work presents a computational model for the thermal reduction of a metal oxide material (Mg-Mn-O) up to 1450 °C and the coupled complex transport phenomena in a novel tubular thermal reactor design that features the capability for a high extent-of-reduction (high energy storage density) and inherent heat recuperation. A one-dimensional model coupling counter-current gas–solid flow, two-phase heat transfer, thermochemical redox reactions, and species transport in a moving-bed reactor is developed. Simplified versions of the model are validated with published results in the literature for packed beds with both inert and reactive particles; the fully coupled model is also validated with experimental measurements of a moving-bed reactor in terms of local temperatures and oxygen release at the exit. Detailed comparisons on the effects of different boundary conditions in the reaction zone (prescribed wall temperature vs. heat flux conditions) and formulations based on a simple uniform flow assumption vs. plug flow using Ergun equation for gas flow are investigated. The results are compared with experimental measurements, and for all cases, the energy flow components in the reactor system and the thermal to chemical conversion efficiency and overall system efficiency are computed. The predicted high thermal-to-chemical efficiency ∼95% and system efficiency ∼30% agree with experimental measurements.

Time Series Clustering for Robust Mean-Variance Portfolio Selection: Comparison of Several Dissimilarity Measures

This paper shows how to create a robust portfolio selection with time series clustering by using some dissimilarity measure. Based on such dissimilarity measures, stocks are initially sorted into multiple clusters using the Partitioning Around Medoids (PAM) time series clustering approach. Following clustering, a portfolio is constructed by selecting one stock from each cluster. Stocks having the greatest Sharpe ratio are selected from each cluster. The optimum portfolio is then constructed using the robust Fast Minimum Covariance Determinant (FMCD) and robust S MV portfolio model. When there are a big number of stocks accessible for the portfolio formation process, we can use this approach to quickly generate the optimum portfolio. This approach is also resistant to the presence of any outliers in the data. The Sharpe ratio was used to evaluate the performance of the portfolios that were created. The daily closing price of stocks listed on the Indonesia Stock Exchange, which are included in the LQ-45 indexed from August 2017 to July 2018, was utilized as a case study. Empirical study revealed that portfolios constructed using PAM time series clustering with autocorrelation dissimilarity and a robust FMCD MV portfolio model outperformed portfolios created using other approaches.

Rapid and Accurate Antibiotic Susceptibility Determination of tet(X)-Positive E. coli Using RNA Biomarkers.

ABSTRACTThe emergence and prevalence of novel plasmid-mediated tigecycline resistance genes, namely, tet(X) and their variants, pose a serious threat to public health worldwide. Rapid and accurate antibiotic susceptibility testing (AST) that can simultaneously detect the genotype and phenotype of tet(X)-positive bacteria may contribute to the deployment of an effective antibiotic arsenal, mortality reduction, and a decrease in the use of broad-spectrum antimicrobial agents. However, current bacterial growth-based AST methods, such as broth microdilution, are time consuming and delay the prompt treatment of infectious diseases. Here, we developed a rapid RNA-based AST (RBAST) assay to effectively distinguish tet(X)-positive and -negative strains. RBAST works by detecting specific mRNA expression signatures in bacteria after short-term tigecycline exposure. As a proof of concept, a panel of clinical isolates was characterized successfully by using the RBAST method, with a 3-h assay time and 87.9% accuracy (95% confidence interval [CI], 71.8% to 96.6%). Altogether, our findings suggest that RNA signatures upon antibiotic exposure are promising biomarkers for the development of rapid AST, which could inform early antibiotic choices.IMPORTANCE Infections caused by multidrug-resistant (MDR) Gram-negative pathogens are an increasing threat to global health. Tigecycline is one of the last-resort antibiotics for the treatment of these complicated infections; however, the emergence of plasmid-encoded tigecycline resistance genes, namely, tet(X), severely diminishes its clinical efficacy. Currently, there is a lack of rapid and accurate antibiotic susceptibility testing (AST) for the detection of tet(X)-positive bacteria. In this study, we developed a rapid and robust RNA-based antibiotic susceptibility determination (RBAST) assay to effectively distinguish tet(X)-negative and -positive strains using specific RNA biomarkers in bacteria after tigecycline exposure. Using this RBAST method, we successfully characterized a set of clinical strains in 3 h. Our data indicate that the RBAST assay is useful for identifying tet(X)-positive Escherichia coli.

Reproducible Determination of High-Density Lipoprotein Proteotypes.

High-density lipoprotein (HDL) is a heterogeneous mixture of blood-circulating multimolecular particles containing many different proteins, lipids, and RNAs. Recent advancements in mass spectrometry-based proteotype analysis show promise for the analysis of proteoforms across large patient cohorts. In order to create the required spectral libraries enabling these data-independent acquisition (DIA) strategies, HDL was isolated from the plasma of more than 300 patients with a multiplicity of physiological HDL states. HDL proteome spectral libraries consisting of 296 protein groups and more than 786 peptidoforms were established, and the performance of the DIA strategy was benchmarked for the detection of HDL proteotype differences between healthy individuals and a cohort of patients suffering from diabetes mellitus type 2 and/or coronary heart disease. Bioinformatic interrogation of the data using the generated spectral libraries showed that the DIA approach enabled robust HDL proteotype determination. HDL peptidoform analysis enabled by using spectral libraries allowed for the identification of post-translational modifications, such as in APOA1, which could affect HDL functionality. From a technical point of view, data analysis further shows that protein and peptide quantities are currently more discriminative between different HDL proteotypes than peptidoforms without further enrichment. Together, DIA-based HDL proteotyping enables the robust digitization of HDL proteotypes as a basis for the analysis of larger clinical cohorts.

Microseismic source location using a 3D velocity model: From the ray tracing method to waveform inversion

Microseismic (MS) source location is important in MS monitoring, providing the basis for determining fracture zones and calculating seismic source parameters (e.g., event magnitude and focal mechanisms). To date, homogenous, 1D, and simple 3D velocity models have been adopted in MS source location. However, a mine is usually characterized by a strong velocity heterogeneity due to engineering geology, 3D geostress, and excavation. In this work, we adopted a travel-time tomography-based high-resolution 3D velocity model and then applied 3D ray tracing based on the shooting method, 3D Gaussian beam-based reverse-time method, and waveform inversion method to MS source location. A semiautomatic waveform-cut method based on the cross-correlation (WCC) technique was developed for a quick, robust, and precise determination of the direct P-phase relative delay times. Additionally, the spectral element method for wavefield modeling, multiscale grid (coarse grid + fine grid) 3D waveform inversion, and L-BGFS iterative method were applied in the waveform inversion. Our results show that the commonly used ray tracing method may be affected by multiray path effects and waveform focusing/defocusing during wavefield propagation, whereas the Gaussian beam method has a frequency-dependent width, and the waveform inversion method has a broader frequency width, which can effectively overcome the issues of ray tracing. The average location errors of eight blasting events obtained using the 3D ray tracing, Gaussian beam, and waveform inversion methods are 26.2, 17.0, and 17.6 m, respectively, which are smaller than those of previous researches obtained using the homogenous-velocity model (average location error > 40 m). In conclusion, the high resolution of the location methods based on 3D velocity models can provide an effective approach to improve the MS source location accuracy and exhibit broad application potential.

Initial orbit determination methods for track-to-track association

The detection and identification of Resident Space Objects (RSOs) from survey tracks requires robust and efficient orbit determination methods for the association of observations of the same RSO. Both Initial Orbit Determination (IOD) and Orbit Determination (OD) methods perform the orbital estimation in which the association of tracks relies. The choice of proper IOD and OD methods is essential for the whole data association, since they are in charge of providing the estimation required to evaluate the figure of merit of the association. In this paper, we review the state of the art and propose a novel method that does not require initialisation, accounts for measurement noise and provides a full estimation (i.e., state vector and covariance) from an arbitrary number of optical observations. To do so, a boundary value problem is formulated to find a pair of ranges leading to a minimum residuals of the observations. The proposed methods are compared against classical alternatives simulated in scenarios representative of the current space debris environment.

On the low ortho-to-para H2 ratio in star-forming filaments

The formation of stars and planetary systems is a complex phenomenon that relies on the interplay of multiple physical processes. Nonetheless, it represents a crucial stage for our understanding of the Universe, and in particular of the conditions leading to the formation of key molecules (e.g. water) on comets and planets. Herschel observations demonstrated that stars form in gaseous filamentary structures in which the main constituent is molecular hydrogen (H2). Depending on its nuclear spin H2 can be found in two forms: ‘ortho’ with parallel spins and ‘para’ where the spins are anti-parallel. The relative ratio among these isomers, the ortho-to-para ratio (OPR), plays a crucial role in a variety of processes related to the thermodynamics of star-forming gas and to the fundamental chemistry affecting the deuteration of water in molecular clouds, commonly used to determine the origin of water in Solar System bodies. Here, for the first time, we assess the evolution of the OPR starting from the warm neutral medium by means of state-of-the-art 3D magnetohydrodynamic simulations of turbulent molecular clouds. Our results show that star-forming clouds exhibit a low OPR (≪0.1) already at moderate densities (∼1000 cm−3). We also constrain the cosmic-ray ionisation rate, finding that 10−16 s−1 is the lower limit required to explain the observations of diffuse clouds. Our results represent a step forward in the understanding of the star and planet formation processes providing a robust determination of the chemical initial conditions for both theoretical and observational studies.

Discovery of a recurrent spectral evolutionary cycle in the ultra-luminous X-ray sources Holmberg II X–1 and NGC 5204 X–1

Context. Most ultra-luminous X-ray sources (ULXs) are now thought to be powered by stellar-mass compact objects accreting at super-Eddington rates. While the discovery of evolutionary cycles have marked a breakthrough in our understanding of the accretion flow changes in the sub-Eddington regime in Galactic black hole binaries, their evidence in the super-Eddington regime has so far remained elusive. However, recent circumstantial evidence hinted at the presence of a recurrent evolutionary cycle in two archetypal ULXs: Holmberg II X–1 and NGC 5204 X–1. Aims. We aim to build on our previous work and exploit the long-term high-cadence monitoring of Swift-XRT in order to provide robust evidence of the evolutionary cycle in these two sources and investigate the main physical parameters inducing their spectral transitions. Methods. We studied the long-term evolution of both sources using hardness-intensity diagrams (HID) and by means of Lomb–Scargle periodograms and Gaussian process modelling to look for periodic variability. We also applied a physically motivated model to the combined Chandra, XMM-Newton, NuSTAR, and Swift-XRT data of each of the source spectral states. Results. We robustly show that both sources follow a clear and recurrent evolutionary pattern in the HID that can be characterised by the hard ultra-luminous (HUL) and soft ultra-luminous (SUL) spectral regimes, and a third state with characteristics similar to the super-soft ultra-luminous (SSUL) state. The transitions between the soft states seem consistent with aperiodic variability, as revealed by a timing analysis of the light curve of Holmberg II X–1; albeit, further investigation is warranted. The light curve of NGC 5204 X–1 shows a stable periodicity on a longer baseline of ∼200 days, possibly associated with the duration of the evolutionary cycle. Conclusions. The similarities between both sources provide strong evidence of both systems hosting the same type of accretor and/or accretion flow geometry. We support a scenario in which the spectral changes from HUL to SUL are due to a periodic increase of the mass-transfer rate and subsequent narrowing of the opening angle of the super-critical funnel. The narrower funnel, combined with stochastic variability imprinted by the wind, might explain the rapid and aperiodic variability responsible for the SUL–SSUL spectral changes. The nature of the longer periodicity of NGC 5204 X–1 remains unclear, and robust determination of the orbital period of these sources could shed light on the nature of the periodic modulation found. Based on the similarities between the two sources, a long periodicity should be detectable in Holmberg II X–1 with future monitoring.

Photobleaching step analysis for robust determination of protein complex stoichiometries.

The counting of discrete photobleaching steps in fluorescence microscopy is ideally suited to study protein complex stoichiometry in situ. The counting range of photobleaching step analysis has been significantly improved with more-sophisticated algorithms for step detection, albeit at an increasing computational cost and with the necessity for high-quality data. Here, we address concerns regarding robustness, automation, and experimental validation, optimizing both data acquisition and analysis. To make full use of the potential of photobleaching step analysis, we evaluate various labeling strategies with respect to their molecular brightness, photostability, and photoblinking. The developed analysis algorithm focuses on automation and computational efficiency. Moreover, we validate the developed methods with experimental data acquired on DNA origami labeled with defined fluorophore numbers, demonstrating counting of up to 35 fluorophores. Finally, we show the power of the combination of optimized trace acquisition and automated data analysis by counting labeled nucleoporin 107 in nuclear pore complexes of intact U2OS cells. The successful in situ application promotes this framework as a new resource enabling cell biologists to robustly determine the stoichiometries of molecular assemblies at the single-molecule level in an automated manner.

General guidelines for a successful joint determination of the diffusion and the partition coefficients in polymeric food contact materials based on optimal experimental design

AbstractThree experimental methodologies that aim at giving general guidelines for a robust joint determination of the diffusion and the partition coefficients on polymeric food contact materials are presented and compared: One based on optimal experimental design, one based on taking samples at equally spaced concentrations, and one based on taking samples at equally spaced times. The three methodologies are tested for a wide range of mass transfer speeds (D between 0.0005 and 5 10−13 m2 s−1) and equilibrium conditions (K between 1 and 100), that mimic the behavior of high‐ and low‐barrier polymers, under constraints that affect the sampling technique (global or local measuring), maximum sampling number (3–5), minimum spacing between samples and maximum experiment time. Results show that even with slow kinetics (D ≈ 5∙10−15 m2 s−1), the joint determination is feasible under certain conditions, within a reasonable experiment running time and a lower uncertainty than the current recommendations given by the EU directive.

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

Chemical versus infrared spectroscopic measurements of quality attributes of sun or oven dried fruit leathers from apple, plum and apple-plum mixture

This study tested the effect of convective and sun drying on quality parameters of apple, plum and a blend of apple and plum (50:50, w/w) leathers. Moreover, physical, sensory and chemical attributes determined by chemical methods were compared with an infrared spectroscopic method. All products had optimum values of L* (brightness), huge angle and chroma within naturally occurrence range. Convective drying significantly (P < 0.05) caused reduced L* and huge angle and increased chroma, only in plum leather. All products have high general acceptability, while the texture of apple leather was highly preferable. Convective drying lead to significantly decreased dry matter, brix, pH and titratable acidity (TA) and increased aw, total phenolic compound (TPC) and antioxidant capacity (AOC), particularly for plum leather. An overall increase of 10–25% in AOC is found valuable. Peak absorbance values at a band of 1800 to 800 cm−1 in infrared spectra were significantly correlated with TA, pH, TPC and AOC contents. Having compared with chemical methods, infrared spectroscopic method with higher coefficient of determinant, precision and ratio of prediction determinant values as well as lower bias and RMSEP values is recommended for a robust and fast determination of physico-chemical attributes in fruit leather production.

How Effective is Macroeconomic Imbalance Procedure (MIP) in Predicting Negative Macroeconomic Phenomena?

Purpose: The evaluation of the predictive power of Macroeconomic Imbalance Procedure (MIP) indicators is crucial for coordinating the economic policies of the EU countries. MIP is one of the pillars of the economic prevention procedure. Design/Methodology/Approach: Using the Bayesian model averaging (BMA) framework, we compare different models where lagged MIP indicators try to explain several macroeconomic variables associated with crises. Findings: The results show that the importance of MIP indicators between 2001 and 2017 was diversified. In the case of annual real GDP growth, including a 1-year lagged house price index, nominal unit labor cost, real effective exchange rate (1-year change), and export market share in the model improves the model's explanatory power most. For explaining inflation rate, export market share (again), and house price index is valid. Practical Implications: The construction of the MIP procedure should be simplified, as not all indicators have a fundamental capability of predicting excessive imbalances which result in events. Indicators are relevant to the current economic priorities of the EU, which do not have a significant capacity to anticipate phenomena should be excluded from the Alert Mechanism. Originality/Value: We use the Bayesian model averaging (BMA) framework BMA that directly deals with heterogeneity by finding a combination of regressors that account for it to the greatest extent within a conditioning set of information. Consequently, BMA appears to be ideally suited for finding robust determinants of crisis variables.