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Transport and electrochemical properties of the potential ternary electrolytes MPPyrr-TFSI + (DMSO or γ-butyrolactone, or acetronitile) + Li-TFSI
No full textPublisher Copyright: © 2025 Elsevier B.V.One of the most popular applications of ionic liquids (ILs) is as electrolyte component for electrochemical devices. In this context, and with the aim to complement previous studies, mixtures of 1-methyl-1-propylpyrrolidinium bis(trifluoromethyl sulfonyl)imide (MPPyrr-TFSI) with one of these three different solvents, acetonitrile (AN), γ-butyrolactone (GBL) or dimethyl sulfoxide (DMSO), in a proportion around 50 wt%IL-50 wt%, were prepared. Then, different concentrations of the lithium salt, Li-TFSI, was added to the binary mixtures, obtaining three potential electrolytes based in that IL. Thermal properties of those mixtures and diffusion coefficient of the [Li]+, [MPPyrr]+ and [TFSI]− ions, for selected ternary mixtures, were experimentally measured using the NMR-DOSY technique. To elucidate the [Li]+ cation contribution to charge transport, we have determined its diffusion constant for many different Li salt concentrations in the ternary mixture with DMSO as solvent. By using density, viscosity and electrical conductivity of those same mixtures presented in a previous paper, theoretical values for the molar conductivities, calculated through Nerst-Einstein equation taking into account the obtained data of diffusion coefficients, were calculated. The resulting values are in strong agreement with those measured experimentally. In addition, the electrochemical window of some selected mixtures was measured, and its electrochemical performance in coin cell configuration with LiFePO4 as cathode and Li metal as anode were studied. Results were compared with those obtained with a common carbonate based Li-TFSI electrolyte. As a result, a very promising ternary mixture to be part of future IL based electrolytes for the next battery generation is proposed.Peer reviewe
Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation
No full textPublisher Copyright: © 2025Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during training and validation. However, in clinical settings without manual annotations, assessing segmentation quality becomes challenging, and models lacking reliability indicators face adoption barriers. To address this gap, we propose a novel framework for predicting segmentation quality without requiring ground truth annotations during test time. Our approach introduces two complementary frameworks: one leveraging predicted segmentation and uncertainty maps, and another integrating the original input image, uncertainty maps, and predicted segmentation maps. We present Bayesian adaptations of two benchmark segmentation models—SwinUNet and Feature Pyramid Network with ResNet50—using Monte Carlo Dropout, Ensemble, and Test Time Augmentation to quantify uncertainty. We evaluate four uncertainty estimates—confidence map, entropy, mutual information, and expected pairwise Kullback–Leibler divergence—on 2D skin lesion and 3D liver segmentation datasets, analyzing their correlation with segmentation quality metrics. Our framework achieves an R2 score of 93.25 and Pearson correlation of 96.58 on the HAM10000 dataset, outperforming previous segmentation quality assessment methods. For 3D liver segmentation, Test Time Augmentation with entropy achieves an R2 score of 85.03 and a Pearson correlation of 65.02, demonstrating cross-modality robustness. Additionally, we propose an aggregation strategy that combines multiple uncertainty estimates into a single score per image, offering a more robust and comprehensive assessment of segmentation quality compared to evaluating each measure independently. The proposed uncertainty-aware segmentation quality prediction network is interpreted using gradient-based methods such as Grad-CAM and feature embedding analysis through UMAP. These techniques provide insights into the model's behavior and reliability, helping to assess the impact of incorporating uncertainty into the segmentation quality prediction pipeline. The code is available at: https://github.com/sikha2552/Uncertainty-Aware-Segmentation-Quality-Prediction-Bayesian-Modeling-with-Comprehensive-Evaluation-.Peer reviewe
Diagnosis and Protection of Ground Fault in Electrical Systems: A Comprehensive Analysis
Get PDFPublisher Copyright: © 2013 IEEE.Fault diagnosis in electrical systems is crucial for preventing infrastructure damage, cascading faults, and user injuries. In addition, power converters have become essential due to the rise of renewable energy, electric mobility, HVDC systems, and other emerging technologies. However, they pose a challenge for fault diagnosis and protection due to high-frequency switching noise, different AC and DC stages in the same circuitry, and faster aging of insulation materials. Since ground faults are the most common type of electrical faults, it is of special interest to analyze and understand the complications and limitations of current diagnosis and protection systems. Although several techniques exist for detecting or localizing these types of faults, they require complex processes to carry out the diagnosis. This work provides a comprehensive analysis of the different conventional and advanced methods focused on ground fault diagnosis that are used, not only for distribution and transmission lines, but also for either AC, DC or hybrid systems, which comprise electrical machines, power electronics, drives, HVDC systems, energy storage systems, microgrids, etc. It gives a complete overview of each method, highlighting their advantages and disadvantages. Finally, a discussion is provided studying areas of improvement and the latest emerging trends in the field, where artificial intelligence (AI) and machine learning (ML) techniques are gaining momentum.Peer reviewe
Reducing annotation effort in agricultural data: simple and fast unsupervised coreset selection with DINOv2 and K-means
Get PDFPublisher Copyright: Copyright © 2025 Gómez-Zamanillo, Portilla, Picón, Egusquiza, Navarra-Mestre, Elola and Bereciartua-Perez.The need for large amounts of annotated data is a major obstacle to adopting deep learning in agricultural applications, where annotation is typically time-consuming and requires expert knowledge. To address this issue, methods have been developed to select data for manual annotation that represents the existing variability in the dataset, thereby avoiding redundant information. Coreset selection methods aim to choose a small subset of data samples that best represents the entire dataset. These methods can therefore be used to select a reduced set of samples for annotation, optimizing the training of a deep learning model for the best possible performance. In this work, we propose a simple yet effective coreset selection method that combines the recent foundation model DINOv2 as a powerful feature selector with the well-known K-Means clustering method. Samples are selected from each calculated cluster to form the final coreset. The proposed method is validated by comparing the performance metrics of a multiclass classification model trained on datasets reduced randomly and using the proposed method. This validation is conducted on two different datasets, and in both cases, the proposed method achieves better results, with improvements of up to 0.15 in the F1 score for significant reductions in the training datasets. Additionally, the importance of using DINOv2 as a feature extractor to achieve these good results is studied.Peer reviewe
Study of the hydrogen decrepitation process of Nd-Fe-B alloys with different Nd content and the addition of Nb and Ga as doping metals
No full textPublisher Copyright: © 2025 The AuthorsThe objective of this work is to analyze the effect of Nd concentration and additions of Nb and Ga on the hydrogen decrepitation of gas-atomized powders after grain growth annealing at 1150 °C for 5 h. Ga and Nb additions reduced the grain size upon atomization. Thermodynamic calculations have demonstrated that, in general, the amount of liquid at 1150 °C increases with Nd concentration and with the addition of Nb or Nb-Ga. Grain growth during annealing is accelerated when the Nd content is raised or with the addition of Ga, which can be explained by the larger amount of liquid in the alloy. On the other hand, Nb forms precipitates that delay grain growth, especially if the amount of liquid is low. Comparing the grain size distribution of the annealed samples with the particle size distribution of hydrogen decrepitated powders allowed identifying the dominant fracture mechanism. Some transgranular cracking occurred in all compositions, increasing the fraction of fine irregular particles. When the Nd concentration is raised, the particle size is reduced. In general, the dominant mechanism of fracture is intergranular crack propagation. However, the transgranular fracture was the dominant mechanism in Ga-containing alloys. Particle shape was not sensitive to the compositional changes evaluated in this work.Peer reviewe
Ethool, a tool to enable legal decision-making in research involving humans using new technologies in Europe: design and usability validation
Get PDFPublisher Copyright: © 2023, The Author(s).Human participation in technological research projects has become more frequent in recent years. However, most of the technological researchers who organize such experimentation are unaware about the various ethical and legal implications involved. Further, when it is a medical device prototype that is being evaluated, the ethical and legal implications may be even more complex. A review of the laws, standards and recommendations in Europe has been drawn up regarding human participation in new technology co-design, development and evaluation, focusing on technological research for assistive and medical devices. An easy-to-use tool, called Ethool, which acts as a guideline for European technological researchers has been designed and developed. In this manuscript, the iterative usability evaluation of Ethool is explained as well as the improvements made, following participants’ feedback. Ethool was rated as acceptable and usable by participants obtaining a SUS score of 93.0 in its last evaluation. The tool is currently available to be used by any interested parties with the aim of gathering additional feedback.This work was supported by the Ministry of Economy and Competitiveness of the Spanish Government and by the European Regional Development Fund under Grant TIN2017-85409-P; Department of Education, Universities and Research of the Basque Government under Grant IT1437-22; European Union’s Horizon 2020 research and innovation programme under Grant 825003 (DIH-HERO project).Peer reviewe
HMR-Based Optical Gas Detection With CuO and ZnO Coatings
No full textPublisher Copyright: © 2017 IEEE.This work presents the fabrication of hyperbolic mode resonance-based optical sensors by means of sputtered copper oxide (CuO) and zinc oxide (ZnO), and the study of their performance for gas sensing purposes. Two sensors were fabricated in a planar waveguide configuration with an intermediate gold thin film, and resonances were observed in the visible region of the electromagnetic spectrum. Both materials were analyzed with X-ray diffraction techniques, and their response was characterized by different concentrations of a group of gases comprised of nitric oxide, acetylene (C2H2), ethanol, carbon dioxide, and relative humidity. The best performance corresponds to the CuO sensor for C2H2 gas, presenting a sensitivity of 1.11 nm/parts per million (ppm) and a limit of detection of 12.6 ppb, with response and recovery times of 70 and 68 s, respectively. ZnO-based sensors allowed for a comprehensive study of ethanol in a range of thousands of ppm, while CuO-based sensors showed exceptional sensitivity for most gases in the range of a few ppm. All measurements were performed at room temperature.Peer reviewe
Fire Safety of Curtain Walling: Evidence-Based Critical Review and New Test Configuration Proposal for EN 1364-4
Get PDFPublisher Copyright: © 2025 by the authors.This article focuses on the fire safety risks associated with conventional glass–aluminum façades—with a particular focus on stick and unitized curtain walling systems—providing an overview of possible fire spread mechanisms, considering the role of the curtain wall in maintaining compartmentation at the spandrel zone. First, it analyzes some of the relevant requirements of different European building regulations. Then, it provides a test evidence-based critical analysis of the gaps and loopholes in the relevant fire resistance standard for partial curtain wall configurations (EN 1364-4), where the evaluation of the propagation within the façade system is not necessarily considered in the fire-resistant spandrel zone. Finally, it presents a proposal for addressing these gaps in the form of a theoretical concept for a new test configuration and additional assessment criteria. This is followed by an initial experimental analysis of the concept. The standard testing campaign showed that temperature rise in mullions can exceed 180 °C after 30 min if limiting measures are not considered in the façade design. However, this can be only detected if framing is in the non-exposed area of the sample, being part of the evaluation surface. Meanwhile, differences are detected between the results from standard and new assessment criteria in the new configuration proposed, including a more rapid temperature rise for framing elements (207 K in a second level mullion at minute 90) than for the common non-exposed assessment surface of the sample (172 K at the same time) in cases where cavities are not protected. Accordingly, the proposed configuration successfully detected vertical temperature transfer within mullions, which can remain undetected in standard EN 1364-4 tests, highlighting the potential for fire spread even in EI120-rated assemblies.Peer reviewe
AI-driven predictive modeling of homogeneous bead geometry for WAAM processes
No full textPublisher Copyright: © The Author(s) 2025.With the increasing number of applications employing additive manufacturing solutions, these deposition processes must become more autonomous, which can be helped by the application of machine learning monitoring. This study presents a fully online, low-cost framework for real-time quality control in Invar wire-arc additive manufacturing (WAAM). Synchronized current and voltage signals are transformed into spatial heatmaps and temporal Markov transition images, which are processed by an optimized ResNet-18 to classify the quality of each layer on-the-fly. Validation using cross-validation on an internal Invar dataset yields an accuracy of up to 94% under clean conditions, with inference times below 20 ms per layer, enabling deployment during natural cooling between layers. These results demonstrate the feasibility of non-intrusive signal-based anomaly detection, enabling rapid identification of weld spalls and useful for scalable and automated WAAM monitoring in industrial environments.Peer reviewe
Development of an Explainable-AI Enabled Decision Support System for Improved Risk Assessment of Atrial Fibrillation in Cardiac Patients during Hospital Stay
No full textCardiovascular disease (CVD) is the primary cause of hospitalization and mortality worldwide, implying a critical burden on the healthcare system. Enhancing CVD risk assessment requires the integration of heterogeneous data sources to provide accurate, robust, and explainable predictions. This study focuses on developing an explainable artificial intelligence decision support system to predict the risk of in-hospital postoperative atrial fibrillation (AF). The use case was selected through extensive discussions and strong collaboration with healthcare professionals from different centers to be aligned with clinical needs and to provide practical applicability, AF being the most common complication after a cardiac surgery. The proposed pipeline includes data preprocessing, feature extraction, feature selection, model training, and explainability analysis, ensuring that methods are transferable from research to practice. A retrospective Italian dataset of 2,445 patients admitted to hospital following an acute myocardial infarction (AMI) was analyzed, incorporating clinical and ECG-derived features. Explainable AI (XAI) techniques such as SHAP and MDI were employed to provide interpretable insights, which are visualized through a user-friendly software framework tailored to support clinical decision-making. The performance of these models will be cross-validated with Finnish data as well as prospective Italian data. The system's implementation balances performance and accessibility, aiming to facilitate wide applicability across diverse populations and healthcare settings. Moreover, Ethical Legal and Societal Aspects (ELSA) interviews have been conducted to ensure patient and clinician acceptance of AI-driven CVD risk assessment.Clinical Relevance- This study presents an AI-driven decision support system, addressing a well-defined clinical use case, that integrates multi-modal data and explainability techniques to enhance personalized CVD risk assessment and bridge the gap between research and clinical practice, while also taking into account Ethical, Legal and Societal aspect.Peer reviewe