Uncertainty Region Research Articles

System Identification for Robust Control of an Electrode Positioning System of an Industrial Electric Arc Melting Furnace

Through system identification for robust control methods and utilizing real-time experimental field data, a comprehensive mathematical model is derived that represents the dynamic performance of a single electrode positioning system (EPS) in an industrial electric arc melting furnace (EAF). This EPS is characterized by large, time-varying dynamic parameters, which fluctuate based on operating conditions, specifically as the electrode weight changes within its operational range. The system identification methodology for robust control is developed in four main steps, progressing from experimental design to model validation. This approach yields a nominal model of the actual system and provides a trustworthy estimate of the region of uncertainty of the model, bounded by models of the real system under maximum and minimum electrode weight conditions (limit operating models). The methodology generates three fourth-order time-delay models using an ARMAX structure. The results are promising, as system identification for robust control enables the derivation of mathematical models specifically tailored for designing robust controllers. These controllers significantly enhance the EPS control system’s performance and substantially reduce energy consumption and environmental emissions.

Swift-XRT follow-up analysis of unidentified hard X-ray sources. Searching for soft X-ray counterparts of unidentified hard X-ray sources

It has been established that the sources contributing to the cosmic X-ray background (CXB) emission are mainly nearby active galactic nuclei (AGNs), in particular those that are obscured. Thus, it is important to fully identify the hard X-ray sky source population to accurately characterize the individual contribution of different AGNs to the overall CXB emission. We present a follow-up analysis of all 218 sources marked as unidentified in our previous revision of the third release of the Palermo Swift -BAT hard X-ray catalog (3PBC) based on our multifrequency classification scheme. These 218 sources were classified as unidentified in our previous analyses because they lacked an assigned low-energy counterpart. We searched for soft X-ray counterparts of these 218 3PBC sources in archival Swift -XRT observations obtained between January 1, 2005, and August 1, 2018. In particular, we found 1213 archival Swift -XRT observations for 192 of the 218 unidentified sources. We find 93 possible Swift -XRT counterparts within the Swift -BAT positional uncertainty regions. They correspond to 73 3PBC sources, 60 of which have only a single Swift -XRT detection; the rest have multiple detections. We present all the detected possible counterparts of the as-of-yet-unidentified hard X-ray sources to the community as a catalog for future spectroscopic follow-up targets, together with a short catalog of our classification of the ten sources for which there were available spectra.

PKS 2254+074: A Blazar in Likely Association with the Neutrino Event IceCube-190619A

We report our study of the field of the ≃0.2 PeV neutrino event IC-190619A. This neutrino belongs to Gold events, which more likely have an astrophysical origin. Among the two γ-ray sources within the neutrino’s positional uncertainty region, we find that one of them, the BL Lac–type blazar PKS 2254+074, had a γ-ray flare at the arrival time of the neutrino. The flare is determined to have lasted ∼2.5 yr in a 180 day binned light curve, constructed from the data collected with the Large Area Telescope on board the Fermi Gamma-ray Space Telescope. Accompanying the flare, optical and mid-infrared brightening is also seen. In addition, ≥10 GeV high-energy photons from the source have been detected, suggesting a hardening of the emission during the flare. Given both the positional and temporal coincidence of PKS 2254+074 with IC-190619A, we suggest that this blazar is likely another member of a few recently identified (candidate) neutrino-emitting blazars.

Employing a Generalization of Open Sets Defined by Ideals to Initiate Novel Rough Approximation Spaces with a Chemical Application

A close similarity and analogy between rough set theory and topology is attributed to the corresponding behavior of lower and upper rough approximations with interior and closure topological operators, respectively. This relation motivates joint studies between topology and this theory. We endeavor by rough set theory to enlarge the knowledge we obtain from the information systems, for this reason, we apply the abstract concept of ideal structures to build new generalized approximation spaces with less vagueness. In the present work, we employ a novel type of nearly open sets in topology so-called ``${\mathcal{L}}$-${\theta\beta}_{\lambda}$-open" with an ideal structure to introduce novel approximation spaces satisfying the desired properties concerning shrinking the boundary region of uncertainty and expanding the domain of confirmed information. We set up the fundamentals of the proposed rough paradigms and demonstrate their superiority over the preceding paradigms induced by some nearly open sets. Two algorithms are furnished to illustrate the way of specifying the family of ${\mathcal{L}}$-${\theta\beta}_{\lambda}$-open sets and exploring whether a subset is ${\mathcal{L}}$-${\theta\beta}_{\lambda}$-definable or ${\mathcal{L}}$-${\theta\beta}_{\lambda}$-rough. Then, we put forward the concepts of rough membership relations and functions and uncover their core characterizations. Finally, we examine the proposed models to model a real situation in the Chemistry field and clarify how our models improve the outcomes of generalized approximation spaces over the previous models.

Sequential binary classification of lithofacies from well-log data and their uncertainty quantification

Understanding and identifying the composition of various lithofacies in the subsurface is essential for successful reservoir characterization in hydrocarbon exploration. However, conventional methods such as core sampling and manual well-log interpretation are labor-intensive. As a result, many scientists are conducting research to use machine learning to study lithofacies more effectively and efficiently. However, as researchers are becoming more dependent on machine learning, an uncertainty analysis of machine-learning models is crucial to determine the reliability of the prediction results. Machine-learning algorithms that use ensemble methods provide an easy method for an uncertainty analysis but algorithms that do not use ensemble methods have difficulty in quantifying the level of uncertainty. In our research, we introduce a method known as sequential binary classification (SBC), which helps not only classify lithofacies but also to quantify and visualize the regions of uncertainty of the machine-learning models. SBC provides a method for using any classification algorithm of the user’s choice to construct an ensemble, which allows the user to quantify uncertainty readily. Our research uses the SBC algorithm to classify and quantify the uncertainty from the well-log data obtained from the North Sea near Norway. The results show that most of the lithofacies that exist in the region of interest share similar characteristics, which results in high uncertainty among the various lithofacies, and SBC helps to visualize these high uncertainties. We additionally demonstrate how SBC alleviates the class imbalance issue among the various lithofacies in the area, which is a very common problem in well-log data analytics.

Support Vector Machines With Uncertainty Option and Incremental Sampling for Kriging

ABSTRACTThis paper presents a novel approach to pollution assessment by investigating support vector machines (SVM) with an uncertainty option to overcome the limitations of traditional kriging. While kriging is a major tool for geostatistical modelling, allowing to estimate the distribution of contaminants in a region from a small set of samples, it does not allow to extract also the uncertainty map. An uncertainty map is of great interest, as it allows to identify regions of high uncertainty where one should sample in order to reduce high level of uncertainties. In this paper, we propose two variants of the SVM with an uncertainty option, each using a different hinge loss to improve the accuracy and efficiency. These losses allow to estimate different levels of contaminations, as well as uncertainty, such as the three levels: positive, uncertain and negative, namely for pollution estimation: high‐pollution, uncertain and low‐pollution. In addition to the exploration of SVM variants, we propose an innovative active sample selection strategy based on the uncertainty criterion. This strategy is designed to systematically reduce uncertainties in pollution assessment, thus providing adaptability to dynamic environmental changes. An incremental SVM with an uncertainty option is introduced to further optimise the sample selection process. Furthermore, the decision‐making process is refined through the introduction of a novel three‐hinge loss. The corresponding optimization problem and its resolution allow for a more nuanced contamination assessment with multiple levels of estimation, providing a valuable tool for characterising contamination levels with increased granularity. Extensive experiments on synthetic and real data validate the proposed methodology. Synthetic data simulations assess the quality of the approach, while real data from a two‐dimensional porosity measurement demonstrate practical applicability. This research contributes to the advancement of pollution assessment methodologies, providing an adaptable solution for environmental monitoring.

Assessing the value of combined use of distributed acoustic sensing (DAS) and multicomponent vertical seismic profiling (VSP) data with network-based full waveform inversion and uncertainty quantification: A case study in Alberta, Canada

Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for non-disruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. Full waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks, or RNNs) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in models selected through FWI. In particular, we seek to understand whether either DAS data or conventional geophone data alone are optimal for FWI model selection in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP dataset, which includes multi-offset and multi-azimuth broadband sources illuminating both fiberoptic cable and densely sampled accelerometer phones in the borehole, was acquired by our group to directly address these questions. In this study, we quantify uncertainty (UQ) by sampling the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this UQ context, the complementary effect of combining accelerometer and DAS data is evident in the Vp and Vs models. Our confidence in the approach is bolstered by its independent prediction of a region of known high uncertainty. In the overall pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.

Redshifts of candidate host galaxies of four fast X-ray transients using VLT/MUSE

Context. Fast X-ray transients (FXTs) are X-ray flares that last from minutes to hours. Multi-wavelength counterparts to these FXTs have proven hard to find. As a result, distance measurements are made through indirect methods such as a host galaxy identification. Of the three main models proposed for FXTs, that is, supernova shock breakout emission (SN SBO), binary neutron star (BNS) mergers, and tidal dirsuption events (TDEs) of an intermediate-mass black hole (IMBH) disrupting a white dwarf (WD), the SN SBO predicts a much lower maximum peak X-ray luminosity (LX, peak). If the distance to FXTs were to be obtained, it would be a powerful probe for investigating the nature of these FXTs. Aims. We aim to obtain distance measurements to four FXTs by identifying candidate host galaxies. Through a redshift measurement of the candidate host galaxies, we derive LX, peak and the projected offset between the candidate host galaxy and the FXT. Methods. We obtained Very Large Telescope (VLT)/Multi Unit Spectroscopic Explorer (MUSE) observations of a sample of FXTs. We report the redshift of between 13 and 22 galaxies per FXT. We used these redshifts to calculate the distance, LX, peak and the projected offsets between the FXT position and the position of the sources. Additionally, we computed the chance alignment probabilities for these sources with the FXT postitions. Results. We find LX, peak > 1044 erg s−1 when we assume that any of the sources with a redshift measurement is the true host galaxy of the corresponding FXT. For XRT 100831, we find a very faint galaxy (mR, AB = 26.5 ± 0.3, z ∼ 1.22, LX, peak ∼ 8 × 1045 erg s−1 if the FXT is at this distance) within the 1σ uncertainty region with a chance alignment probability of 0.04. For XRT 060207, we find a candidate host galaxy at z = 0.939 with a low chance alignment probability within the 1σ uncertainty region. However, we also report the detection of a late-type star within the 3σ uncertainty region with a similar chance alignment probability. For the remaining FXTs (XRT 030511 and XRT 070618), we find no sources within their 3σ uncertainty regions. The projected offsets between the galaxies and the FXT positions is > 33 kpc at 1σ uncertainty. Therefore, if one of these candidate host galaxies turns out to be the true host galaxy, it would imply that the FXT progenitor originated from a system that received a significant kick velocity at formation. Conclusions. We rule out an SN SBO nature for all FXTs based on LX, peak and the projected offsets between the FXT position and the sources, assuming any of the candidate host galaxies with a redshift determination is the true host galaxy to the FXT. For XRT 100831, we conclude that the detected galaxy within the 1σ uncertainty position is likely to be the host galaxy of this FXT based on the chance alignment probability. From the available information, we are not able to determine whether XRT 060207 originated from the galaxy found within 1σ of the FXT position or was due to a flare from the late-type star detected within the 3σ uncertainty region. Based on LX, peak and the offsets within our sample, we are not able to distinguish between the BNS merger and the IMBD-WD TDE progenitor model. However, for the candidate host galaxies with an offset ≳30 kpc, we can conclude that the IMBH-WD TDE is unlikely due to the large offset.

Cross Observatory Coordination with tilepy: A Novel Tool for Observations of Multimessenger Transient Events

Time-domain astrophysics has leaped forward with the direct discovery of gravitational waves and the emergence of new generation instruments for multimessenger studies. The capacity of the multimessenger multiwavelength community to effectively pursue follow-up observations is hindered by the suboptimal localization of numerous transient events and the escalating volume of alerts. Thus, we have developed an effective tool to overcome the observational and technical hurdles inherent in the emerging field of multimessenger astrophysics. We present tilepy, a Python package for the automatic scheduling of follow-up observations of poorly localized transient events. It is ideally suited to tackle the challenge of complex follow-up in mid- and small-field-of-view telescope campaigns, with or without human intervention. We demonstrate the capabilities of tilepy in the realm of multiobservatory, multiwavelength campaigns, to cover the localization uncertainty region of various events ultimately aiming at pinpointing the source of the multimessenger emission. The tilepy code is publicly available on GitHub and is sufficiently flexible to be employed either automatically or in a customized manner, tailored to collaboration and individual requirements. tilepy is also accessible via a public API and through the Astro-COLIBRI platform.

Sensitivity analysis of turbine stage aerothermal characteristics and blade cooling performance considering combustor swirl and hot spot

This paper investigates the global sensitivity of high-pressure-turbine (HPT) first stage cascades aerothermal characteristics and blade cooling performance under the influence of combustor swirl and hot spot. The combustor outflow conditions impact on turbine working performance and robustness. This paper is focused on the influence of uncertain combustor swirling flow and hot spot, rather than deterministic working conditions. A simplified combustor outflow simulation method is firstly proposed, and the effects of combustor outflow are quantified by combining chaos polynomial expansion with Sobol’ decomposition method. The results show that different rotating directions of the swirl lead to restricted influences on the averaged aerothermal performance of the turbine stage, but significantly affects its robustness. The positive swirl increases the total pressure loss generated by the lower passage vortex of the stator cascade, and the high uncertainty region of the stator cascade total pressure loss profile is the combined products of the residual swirl and the interaction between the passage vortex and mainstream. The total pressure loss of the rotor cascade due to leakage vortex has low sensitivity to the hot spot and swirl strength, while the total pressure loss generated by the passage vortex is affected by hot spot temperature. Temperature distribution affected by the passage vortex is sensitive to swirl strength. Generally, uncertainty variance of stage aerothermal parameters with positive swirl is about 30% larger than that of the negative case, since the positive swirl has stronger interactions with the blades, leading to more significant uncertainty. However, the averaged heat transfer coefficients with positive swirl are 37% less sensitive, compared to the negative swirl case. The results give better understanding on the influence mechanisms of turbine inflow conditions, emphasize the meaning of investigating uncertain characteristics of the turbine cascades. Investigations of this article provide reference for robust turbine blade cooling design.

Probabilistic Method to Fuse Artificial Intelligence-Generated Underground Utility Mapping.

Utility as-built plans, which typically provide information about underground utilities' position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies and mitigate the high rate of accidental underground utility strikes during excavation activities. Adapting data fusion into utility engineering and investigation practices has been shown to be effective in generating information with improved accuracy. However, the complexities in data interpretation and associated prohibitive costs, especially for large-scale projects, are limiting factors. This paper addresses the problem of data interpretation, costs, and large-scale utility mapping with a novel framework that generates probabilistic inferences by fusing data from an automatically generated initial map with as-built data. The probabilistic inferences expose regions of high uncertainty, highlighting them as prime targets for further investigations. The proposed model is a collection of three main processes. First, the automatic initial map creation is a novel contribution supporting rapid utility mapping by subjecting identified utility appurtenances to utility inference rules. The second and third processes encompass the fusion of the created initial utility map with available knowledge from utility as-builts or historical satellite imagery data and then evaluating the uncertainties using confidence value estimators. The proposed framework transcends the point estimation of buried utility locations in previous works by producing a final probabilistic utility map, revealing a confidence level attributed to each segment linking aboveground features. In this approach, the utility infrastructure is rapidly mapped at a low cost, limiting the extent of more detailed utility investigations to low-confidence regions. In resisting obsolescence, another unique advantage of this framework is the dynamic nature of the mapping to automatically update information upon the arrival of new knowledge. This ultimately minimizes the problem of utility as-built accuracies dwindling over time.

Uncertainty quantification in multi-class segmentation: Comparison between Bayesian and non-Bayesian approaches in a clinical perspective.

Automatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra- and inter-operator variability. However, the adoption of these approaches in clinical practice is slowed down by some factors, such as the difficulty in providing an accurate quantification of their uncertainty. This work aims to evaluate the uncertainty quantification provided by two Bayesian and two non-Bayesian approaches for a multi-class segmentation problem, and to compare the risk propensity among these approaches, considering CT images of patients affected by renal cancer (RC). Four uncertainty quantification approaches were implemented in this work, based on a benchmark CNN currently employed in medical image segmentation: two Bayesian CNNs with different regularizations (Dropout and DropConnect), named BDR and BDC, an ensemble method (Ens) and a test-time augmentation (TTA) method. They were compared in terms of segmentation accuracy, using the Dice score, uncertainty quantification, using the ratio of correct-certain pixels (RCC) and incorrect-uncertain pixels (RIU), and with respect to inter-observer variability in manual segmentation. They were trained with the Kidney and Kidney Tumor Segmentation Challenge launched in 2021 (Kits21), for which multi-class segmentations of kidney, RC, and cyst on 300 CT volumes are available. Moreover, they were tested considering this and other two public renal CT datasets. Accuracy results achieved large differences across the structures of interest for all approaches, with an average Dice score of 0.92, 0.58, and 0.21 for kidney, tumor, and cyst, respectively. In terms of uncertainties, TTA provided the highest uncertainty, followed by Ens and BDC, whereas BDR provided the lowest, and minimized the number of incorrect certain pixels worse than the other approaches. Again, large differences were seen across the three structures in terms of RCC and RIU. These metrics were associated with different risk propensity, as BDR was the most risk-taking approach, able to provide higher accuracy in its prediction, but failing to assign uncertainty on incorrect segmentation in every case. The other three approaches were more conservative, providing large uncertainty regions, with the drawback of giving alert also on correct areas. Finally, the analysis of the inter-observer segmentation variability showed a significant variation among the four approaches on the external dataset, with BDR reporting the lowest agreement (Dice = 0.82), and TTA obtaining the highest score (Dice = 0.94). Our outcomes highlight the importance of quantifying the segmentation uncertainty and that decision-makers can choose the approach most in line with the risk propensity degree required by the application and theirpolicy.

A search algorithm for the compact uncertainty region of system elements with interval and affine constraint properties

This study proposes a search algorithm for the compact uncertainty region of system elements with affine constraints and interval information. While existing studies have considered only the interval information of uncertainty, the proposed algorithm reflects both the interval and affine constraints of uncertain elements to reduce conservatism due to uncertainties. First, the affine constraints of uncertainty are plotted in an N-dimensional space, where N is the number of elements that should be considered. Due to the properties of the affine constraints, the affine constraints are expressed as an (N-1) dimensional structure. The interval information is then expressed as an N-dimensional structure. The algorithm iteratively checks the edges of the N-dimensional structure between two geometrical structures to determine the intersection region. The algorithm then collects the vertices of the convex polytope that overlap the two structures. To show the effectiveness of proposed algorithm, this study proposes a consensus criterion for a multi-agent time-delayed system with uncertain switching topologies as an application of a system with uncertainties in system elements.

Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems.

Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image, resulting in an exaggerated volume of uncertainty. In this paper, we propose PUQ (Principal Uncertainty Quantification) - a novel definition and corresponding analysis of uncertainty regions that takes into account spatial relationships within the image, thus providing reduced volume regions. Using recent advancements in generative models, we derive uncertainty intervals around principal components of the empirical posterior distribution, forming an ambiguity region that guarantees the inclusion of true unseen values with a user-defined confidence probability. To improve computational efficiency and interpretability, we also guarantee the recovery of true unseen values using only a few principal directions, resulting in more informative uncertainty regions. Our approach is verified through experiments on image colorization, super-resolution, and inpainting; its effectiveness is shown through comparison to baseline methods, demonstrating significantly tighter uncertainty regions.

The pointing uncertainty Region's analysis of inter-satellite laser alignment systems under multiple uncertainties

Laser inter-satellite links (LISLs) are crucial for enabling rapid information exchange within low earth orbit (LEO) constellations. Previous research on laser link establishment often relied on the idealized probability density function (PDF) of the pointing uncertainty regions (PURs) when designing scan-and-capture methods, neglecting the impact of various practical uncertainties. To address the above issues, there is an urgent need to analyze the practical uncertainties to enhance the stability and reliability of LISL. This paper first provides a comprehensive introduction to the alignment process in LISL and mathematically formulates typical sources of uncertainty errors. Subsequently, a generalized system model (GSM) for laser link establishment was established at three stages: Orbit, Attitude, and Laser terminal. Finally, we integrate uncertainty models into the GSM and propose a PUR generating method to obtain precise PDFs. The research method proposed in this study can consider the influence of multiple uncertainties and generate the PDF for the PURs. Compared with traditional research, the PDF obtained by fitting the oscillation range considering dynamics is more detailed. By analyzing the PDF generated by typical uncertainty errors, it is observed that the orbit initial uncertainty may be the predominant factor influencing the PUR.

The Main Factors Controlling Probabilistic Seismic Hazard Variability Due to Epistemic Uncertainty in Greece: Results from a One-Factor-at-a-Time Analysis

Abstract We present a one-factor-at-a-time (OFAT) sensitivity study for the Probabilistic Seismic Hazard Assessment (PSHA) in Greece. The analysis examines several PSHA controlling factors, such as seismic source model (geometry, etc.), active tectonics (faulting type, etc.), as well as the employed ground-motion prediction equations (GMPEs). We have considered only peak ground acceleration (PGA) and peak ground velocity (PGV) for a typical PSHA scenario (10% probability of exceedance in 50 yr). OFAT results are presented using Tornado diagrams for 42 major urban centers of Greece that cover the study area revealing regions of different PSHA uncertainty. In general, the most critical factors controlling PSHA uncertainties are the Gutenberg–Richter (G-R) parameters (a and b), as well as the employed GMPE. However, significant spatial variability of these factors is observed with large differences between high- and low-seismicity regions, as well as between PGA and PGV. The results not only provide an objective assessment of the variability of PSHA uncertainties but also verify the need for additional work to minimize epistemic PSHA uncertainties, focusing on the most critical factors that control PSHA.

A mixed uncertain structural reliability analysis method considering random and convex set variables with correlation

AbstractIn this paper, a mixed model reliability analysis method is put forward for the problem of assessing the reliability of complex engineering structures containing both random and convex aggregate variables. By integrating the ellipsoidal model with correlation and the interval model, the uncertainty region characterized by the ellipsoidal model with correlation is optimized with full consideration of the limited amount of engineering structure sample data, and the risk region represented by the reliability model is redefined, a new mixed reliability assessment criterion is established, and the minimum safe nonprobability reliability index of the structure is built. A key reference is offered by the safety limit diagram of constant reliability indexes set for the first time for the optimal design of engineering structure reliability. The proposed mixed reliability model is compared and analyzed with three classical models. The sensitivity of nonprobability reliability indexes, influenced by random variable parameters and correlation coefficients, is analyzed. This verification confirms that the new reliability model not only provides an accurate assessment of engineering structures' reliability but also lowers the computational demands of engineering design. In this paper, aero‐engine blades and wind turbine blades are taken as examples to expound the validity of the reliability model built using this method and its importance to the structural safety analysis of actual engineering.

Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation

This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: “How is it done?”. The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities.

Firmamento: A Multimessenger Astronomy Tool for Citizen and Professional Scientists

Firmamento (https://firmamento.hosting.nyu.edu) is a new-concept, web-based, and mobile-friendly data analysis tool dedicated to multifrequency/multimessenger emitters, as exemplified by blazars. Although initially intended to support a citizen researcher project at New York University–Abu Dhabi, Firmamento has evolved to be a valuable tool for professional researchers due to its broad accessibility to classical and contemporary multifrequency open data sets. From this perspective Firmamento facilitates the identification of new blazars and other multifrequency emitters in the localization uncertainty regions of sources detected by current and planned observatories such as Fermi-LAT, Swift, eROSITA, CTA, ASTRI Mini-Array, LHAASO, IceCube, KM3Net, SWGO, etc. The multiepoch and multiwavelength data that Firmamento retrieves from over 90 remote and local catalogs and databases can be used to characterize the spectral energy distribution and the variability properties of cosmic sources as well as to constrain physical models. Firmamento distinguishes itself from other online platforms due to its high specialization, the use of machine learning and other methodologies to characterize the data, and for its commitment to inclusivity. From this particular perspective, its objective is to assist both researchers and citizens interested in science, strengthening a trend that is bound to gain momentum in the coming years as data retrieval facilities improve in power and machine-learning/artificial-intelligence tools become more widely available.

Enhancing the Design of Experiments on the Fatigue Life Characterisation of Fibre-Reinforced Plastics by Incorporating Artificial Neural Networks.

Fatigue life testing is a complex and costly matter, especially in the case of fibre-reinforced thermoplastics, where other parameters in addition to force alone must be taken into account. The number of tests required therefore increases significantly, especially if the influence of different fibre orientations is to be taken into account. It is therefore important to gain the greatest possible amount of knowledge from the limited number of available tests. In order to achieve this, this study aims to utilise adaptive sampling, which is used in numerous areas of computational engineering, for the design of experiments on fatigue life testing. Artificial neural networks (ANNs) are therefore trained on data for the short-fibre-reinforced material PBT GF30, and their areas of greatest model uncertainty are queried. This was undertaken with ANNs from various numbers of hidden layers, which were analysed for their performance. The ideal case turned out to be four hidden layers, for which a squared error as small as 1 × 10-3 was recorded. Locally resolved, the ANN was used to identify the region of greatest uncertainty for samples of vertical orientation and small numbers of cycles. With information such as this, additional data can be obtained in such uncertain regions in order to improve the model prediction-almost halving the recorded error to only 0.55 × 10-3. In this way, a model of comparable value can be found with less experimental effort, or a model of better quality can be set up with the same experimental effort.