10784 research outputs found
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High-contrast absorption magnetometry in the visible to near-infrared range with nitrogen-vacancy ensembles
Get PDFPublisher Copyright: © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.Magnetometry with nitrogen-vacancy (NV) centers in diamond has so far been measured via emission of light from NV centers or via absorption at the singlet transition at 1042 nm. Here, we demonstrate a phenomenon of broadband optical absorption by the NV centers starting in the emission wavelength and reaching up to 1000 nm. The measurements are enabled by a high-finesse cavity, which is used for room-temperature continuous wave pump-probe experiments. The red to infrared probe beam shows the typical optically detected magnetic resonance (ODMR) signal of the NV spin with contrasts up to 42 %. This broadband optical absorption has not yet been reported in terms of NV magnetometry. We argue that the lower level of the absorbing transition could be the energetically lower NV singlet state, based on the increased optical absorption for a resonant microwave field and the spectral behavior. Investigations of the photon-shot-noise-limited sensitivity show improvements with increasing probe wavelength, reaching an optimum of 7.5 pT/√Hz. The results show significantly improved ODMR contrast compared to emission-based magnetometry. This opens a new detection wavelength regime with coherent laser signal detection for high-sensitivity NV magnetometry.Peer reviewe
Model predictive control as the cloud control strategy for a battery thermal management system
Get PDFPublisher Copyright: © 2025The fast charges are critical in supporting long-distance travels with battery based electric vehicles and alleviating range anxiety. Nonetheless, the battery heat generation under fast charge damages the battery if the battery thermal management system (BTMS) is not managed correctly. This paper proposes an innovative cloud control strategy for the BTMS that optimizes the electric consumption of the BTMS while managing the battery temperature under extreme events such as fast charges. The optimized control strategy is based on the concepts of model predictive control, which provides the optimized BTMS operation in the next 60 minutes. To do so, firstly, the different components that influence the optimization problem have been modeled. The models are the electro-thermal battery system model, the battery management system model, the energy control unit model and the BTMS model. Secondly, a model predictive control algorithm has been designed to avoid hard constraint violation along with an optimization approach that allows black-box model execution. Finally, the approach has been structured to be integrated as a functional mock-up into a digital twin application to solve current micro-controller computational limitations. The developed cloud control strategy shows a decrease of 1ºC on the maximum achieved temperature at fast charge events.Peer reviewe
Challenges and future research directions in secure multi-party computation for resource-constrained devices and large-scale computations
Get PDFPublisher Copyright: © The Author(s) 2024.In the era of Big Data and the advancement of the Internet of Things, there is an increasing amount of valuable information. It is important to emphasize that this data is usually sensitive or confidential, so security and privacy are two of the highest priorities for organizations when performing Data Mining. Researchers have explored techniques such as secure multi-party computation (SMPC) in the last decades. Nevertheless, there is still a significant gap between the theory of SMPC and its applicability, especially when working with resource-constrained devices or massive data. This work has been conducted with a systematic literature review, and it intends to analyze the open issues of adapting SMPC to those scenarios, by classifying the studies to answer two research questions: (1) how has the use of SMPC attempted to be adapted to constrained devices? and (2) how have traditional techniques fitted with Big Data? At the end of the process, after analyzing a total of 637 studies, 19 papers were selected. Regarding constrained devices, solutions are grouped into three main techniques: secure outsourcing, hardware-based trusted execution, and intermediate representations. As for Big Data, the selected studies use mixed protocols to change over cleartext and ciphertext, combine different types of SMPC protocols, or modify existing protocols through optimizations.Peer reviewe
Polycarbonate Nanofiber Filters with Enhanced Efficiency and Antibacterial Performance
Get PDFPublisher Copyright: © 2025 by the authors.The need for clean and safe air quality is a global priority that extends to diverse environments, including households, industrial spaces, and areas requiring respiratory personal protection. In this study, polycarbonate (PC) nanofiber filters coated with a coating containing a silver salt were prepared by the electrospinning process and a subsequent dipping–extraction method. These nanofiber filters presented the enhancement of air filtration efficiency and reinforcement of antibacterial properties. The research includes diverse PC filter structures, assessing beaded and non-beaded structures and varying areal weights. The study evaluated filtration efficiency across NaCl particle sizes (50–400 nm) and pressure drop outcomes. In addition, the antibacterial activity of the coated filters against E. coli and other coliforms was investigated by the filtration membrane method. Repetitive testing consistently yields high efficiencies, reaching 100% in thicker filters, and minimal air resistance in beaded filters, presenting an advantage over the current systems. Furthermore, the new properties of the filters will enhance environmental safety, and their time of use will be increased since they prevented the growth of bacteria, and no significant colonies were seen. Considering all these factors, these filters presented promising application in environments with harmful microorganisms, for the development of advanced industrial filtering systems or even hygienic masks.Peer reviewe
Digitalization of the Workflow for Drone-Assisted Inspection and Automated Assessment of Industrial Buildings for Effective Maintenance Management
Get PDFPublisher Copyright: © 2025 by the authors.Industrial buildings are a key element in the industrial fabric, and their maintenance is essential to ensure their proper functioning and avoid disruptions and costly economic losses. Continuous maintenance based on an accurate diagnosis makes it possible to meet the challenges of aging infrastructures, which demands a reliable data-based assessment for maintenance management implementing corrective and preventive actions, according to the damage criticality. This paper researches an innovative digitalized process for the inspection and diagnosis of industrial buildings, which leads to categorizing and prioritizing maintenance actions in an objective and cost-effective way from the inspection data. The process integrates some technical developments carried out in this work, aimed to automate the workflow: the drone-based inspection, the building condition assessment from the definition of a standardized construction pathology library, and a visual analysis of pathology evolution based on photogrammetry. The use of drones for digitalized inspection involves some challenges related to the positioning of the drone for damage localization, which has been herein overcome by developing a geo-annotation system for image acquisition. This system has also enabled the capture of geo-located images intended to generate 3D photogrammetric models for quantifying the pathological process evolution. Moreover, the assessment procedure outlined through multi-criteria decision-making methodology MIVES establishes a single criterion to automatically weight the relative importance of the damage defined in the library. As a result, this procedure yields the so-called Intervention Urgency Index (IUI), which allows prioritizing the maintenance actions associated with the damage while also considering economic criteria. In such a way, the overall process aims to increase reliability and consistency in the results of inspection and diagnosis needed for the effective maintenance management of industrial buildings.Peer reviewe
Model-predictive control with admittance matrix estimation for the optimal power sharing in isolated DC microgrids
Get PDFPublisher Copyright: © 2024 Elsevier B.V.Direct current (DC) microgrids are relevant in modern energy systems due to their high efficiency, simplified architecture, and capability of direct integration of distributed resources (DERs). A proportional power-sharing is essential in these grids to balance the power injections according to the capability of each DER. However, the intermittency of DERs, load variations, and the non-linear nature of the model are major challenges. Therefore, this paper proposes a non-linear model predictive control (MPC) alongside an estimator for the nodal admittance matrix. By using MPC, it is possible to achieve optimal operation, considering voltage and power constraints. Moreover, the estimator enables the consideration of load variations with a reduced number of measurements. The robustness of the proposed control strategy is evaluated both in simulations and through a Power-Hardware-in-the-loop (PHIL) implementation. Radial and meshed microgrids were tested with different numbers of nodes. These results validate the practical feasibility and performance of the proposed approach.Peer reviewe
When synthetic plants get sick: Disease graded image datasets by novel regression-conditional diffusion models
Get PDFPublisher Copyright: © 2024This paper introduces DiffusionPix2Pix, an innovative extension of diffusion models (DMs) that revolutionizes synthetic image generation by seamlessly integrating image priors, surpassing existing state-of-the-art models. Key contributions include regression (graded) conditioning and an arbitrary binary mask, enabling regression-conditional image-to-image translation. DiffusionPix2Pix is compared with Pix2Pix-G and Pix2Pix-GD, two alternative models that rely on image-conditioned GANs adapted for an additional regression conditional task. The model is applied to generate a graded plant disease dataset focusing on Puccinia striiformis symptoms, using disease degree as an additional conditioning input to control the level of disease in generated images. Experiments demonstrate that DiffusionPix2Pix outperforms GAN-based approaches in both sample fidelity and diversity, achieving an Improved Precision (fidelity) of 0.81 (versus 0.45 and 0.47) and an Improved Recall (diversity) of 0.58 (versus 0.31 and 0.31). Furthermore, DiffusionPix2Pix obtained the best Fréchet Inception Distance (FID), with a score of 31.61 compared to 57.38 and 54.34 for GAN-based models. Additionally, perception-based tests with field technicians showed 71.3% of images generated by DiffusionPix2Pix were classified as authentic, significantly outperforming the 20.19% and 22.22% rates for GAN-based models. These findings substantiate the performance of the proposed DiffusionPix2Pix model, both quantitatively and through subjective assessments by domain experts, highlighting its potential in applications requiring precise regression conditioning.Peer reviewe
In-depth 3D exploration of autosomal dominant polycystic kidney disease through light sheet fluorescence microscopy
No full textPublisher Copyright: © The Author(s) 2025.Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent genetic kidney disorder. Animal preclinical studies are one of the main tools to study this disease, often through either 2D histology imaging for high-resolution analysis or CT or MRI for full kidney segmentation. As an alternative to these modalities, we propose the use of Light Sheet Fluorescence Microscopy (LSFM) for high-resolution 3D imaging of healthy and ADPKD-induced mouse kidneys, enabling a detailed volumetric morphological analysis of the disease’s effects. In a mouse ADPKD model, ex vivo imaging of the kidneys was performed through LSFM, after which a combination of machine learning and other processing techniques allowed us to perform an in-depth image analysis. This includes the segmentation of key structures, such as the full kidney volume and, within it, its internal cavities, cortex, glomeruli, and cysts, complemented by texture analysis of tubular structures in the cortical area. Pathological kidneys exhibited significant volume enlargement and increased internal cavities due to cystogenesis. While glomerular count remained stable, their spatial distribution was altered, showing increased interglomerular distances and showcasing the deformations produced by the disease. The texture analysis of tubules from the cortex region identified Local Binary Pattern (LBP) uniformity and porosity as key biomarkers of tissue deformation, which could be used as markers to further evaluate the development of the disease. These findings underscore the potential of LSFM imaging as a powerful tool for detailed ADPKD characterization and treatment assessment.Peer reviewe
CRTSC: Channel-Wise Recalibration and Texture-Structural Consistency Constraint for Anomaly Detection in Medical Chest Images
Get PDFPublisher Copyright: © 2025 by the authors.Unsupervised medical image anomaly detection, which does not need any labels, holds a pivotal role in early disease detection for advancing human intelligent health, and it is among the prominent research endeavors in the realm of biomedical image analysis. Existing deep model-based methods mainly focus on feature selection and interaction, ignoring the relative position and shape uncertainty of the anomalies themselves, which play an important guiding role in disease diagnosis, hampering performance. To address this issue, our study introduces a novel and effective framework, termed CRTSC, which integrates a channel-wise recalibration module (CRM) along with the texture–structural consistency constraint (TSCC) for anomaly detection in medical chest images acquired from different sensors. Specifically, the CRM adjusts the weight of different medical image feature channels, which are used to establish spatial relationships among anomalous patterns, enhancing the network’s representation and generalization capabilities. The texture–structural consistency constraint is devoted to enhancing the anomaly’s structural (shape) definiteness via evaluating the loss function of similarity between two images and optimizing the model. The two collaborate in an end-to-end fashion to optimize and train the entire framework, thereby enabling anomaly detection in medical chest images. Extensive experiments conducted on the public ZhangLab and CheXpert datasets demonstrate that our method achieves a significant performance improvement compared with the state-of-the-art methods, offering a robust and generalizable solution for sensor-based medical imaging applications.Peer reviewe
The SAbyNA platform: a guidance tool to support industry in the implementation of safe- and sustainable-by-design concepts for nanomaterials, processes and nano-enabled products
Get PDFPublisher Copyright: © 2025 The Royal Society of Chemistry.Simple, cost-effective and reliable methods are needed for pragmatic, flexible, safe and sustainable evaluations at early stages of product (chemical/material) development. This is especially true for nanoforms and nano-enabled products, for which guidance on the application of validated methods and tools for the assessment and management of safety and sustainability is still lacking. The SAbyNA guidance platform fills these gaps in the following ways: by integrating i) informative modules covering the needs of all stakeholder profiles (i.e., industry, consultants, RTOs, and regulatory bodies) by guiding them in the choice of methods, models and tools for exposure and hazard assessment, as well as in the selection of specific safe-by-design interventions; and ii) assessment modules for a screening-level evaluation of environmental sustainability and costs and for a screening and detailed safety assessment of nanoforms and nano-enabled products along their life cycle. The potential of this digital tool to support different stakeholders towards safer and more sustainable developments is demonstrated in a real case study: a nano-enabled 3D-printed vacuum cleaner plastic component composed of single-walled carbon nanotube-polycarbonate composites with antistatic properties. This study shows how a user inputs data to perform a screening assessment on an additive manufacturing case study, and the digital platform provides the user with some safe-by-design recommendations, such as reducing the fiber length or rigidity or changing process parameters to reduce emissions. Hazard, exposure, costs, sustainability and functionality case study data were added in the detailed assessment module of the platform to check whether the implemented safe-by-design intervention was able to improve the safety profile of this nano-enabled product without affecting sustainability and functionality performances. This study also demonstrated the added value of using the SAbyNA guidance platform at the early stage of the nano-enabled product development for the quantification and visualization of safety, sustainability, cost and functionality aspects of nano-enabled products and processes.Peer reviewe