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Agriculture serves as a critical foundation for livelihoods, food security, and sustainable development across the Sahara–Sahelian region. However, this vital sector faces mounting pressures from recurrent armed conflicts that systematically undermine its resilience and long-term sustainability. This study provides a comprehensive analysis of agricultural technical efficiency across 23 African countries in the Sahara–Sahelian region from 2009 to 2021, employing a robust bias-corrected bootstrap Data Envelopment Analysis approach. The findings reveal a concerning regional deterioration, with technical efficiency declining at an average annual rate of 1.7% throughout the study period. Conflict-affected countries demonstrated distinctive vulnerability patterns, exhibiting both higher average efficiency levels (0.875) and greater volatility, with annual declines of 1.8%. Sub-regional analysis highlights the Sahel’s particular fragility, where efficiency decreased by 2.2% yearly, nearly double the decline rate observed in North Africa. The most severe efficiency losses were recorded in countries experiencing intense and protracted conflict, notably Burkina Faso (4.0%) and Mali (3.5%), underscoring the severe association between conflict exposure and the erosion of agricultural productive capacity. These findings underscore the importance of developing integrated strategies that simultaneously address security challenges, climate adaptation, and institutional reform for effective resilience-building. Policy recommendations highlight the importance of enhanced regional connectivity, knowledge transfer, and targeted investments in agricultural capacity building—all aligned with both Sustainable Development Goals and the African Union’s Agenda 2063 objectives for achieving sustainable agricultural transformation in conflict-affected regions. Full article

(This article belongs to the Section Agricultural Systems and Management)

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25 pages, 890 KB

Open AccessReview

High-Performance Interfacial Solar Evaporation for Zero Liquid Discharge Treatment of Coal Chemical Concentrated Brine: Principles, Challenges, and Recent Advances

by Qing Wen, Haoyang Xiong, Chunhui Zhang, Yang Yin, Haocheng Ye and Peidong Su

Nanomaterials 2026, 16(4), 274; https://doi.org/10.3390/nano16040274 - 20 Feb 2026

Abstract

The rapid expansion of the coal chemical industry has led to a growing demand for effective treatment of high salinity wastewater, particularly the concentrated brine streams targeted for zero liquid discharge (ZLD) management. Conventional treatment technologies face significant challenges under such extreme conditions, underscoring the urgency of developing innovative and energy-efficient alternatives. Interfacial solar steam generation (ISSG) has emerged as a promising approach for concentrated brine treatment owing to its rapid evaporation rates, low carbon footprint, and high solar thermal energy utilization. Nevertheless, the long-term stability of solar evaporators remains limited by photothermal material degradation, excessive heat loss, and salt accumulation—all of which constitute major bottlenecks preventing large-scale implementation of ISSG in ZLD systems. This review first outlines the fundamental principles, advantages, and practical constraints of interfacial solar evaporation. It then highlights recent advances in high-performance solar evaporators featuring broadband light absorption, efficient solar thermal conversion, suppressed heat dissipation, robust anti-salt fouling behavior, and sustained operational durability. These emerging designs substantially improve the feasibility of ISSG and provide promising pathways for the clean, efficient, and sustainable treatment of concentrated brine in the coal chemical industry. Full article

(This article belongs to the Section Environmental Nanoscience and Nanotechnology)

25 pages, 101353 KB

Open AccessArticle

A Metaheuristic Optimization Algorithm for Task Clustering in Collaborative Multi-Cluster Systems

by Meixuan Li, Yongping Hao, Hui Zhang and Jiulong Xu

Sensors 2026, 26(4), 1364; https://doi.org/10.3390/s26041364 - 20 Feb 2026

Abstract

To address the task-grouping problem for air–ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm’s exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2–10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies–Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method’s excellent scalability and robustness in multi-constraint, large-scale 3D scenarios. Full article

(This article belongs to the Section Sensors and Robotics)

57 pages, 2619 KB

Open AccessArticle

Reliability-Based Design Optimisation of Bridge Systems Within BIM—Robustness, Redundancy and Safety Metrics

by John Dixon, Van Bac Nguyen and Boris Ceranic

Buildings 2026, 16(4), 854; https://doi.org/10.3390/buildings16040854 - 20 Feb 2026

Abstract

Research shows that structures are often over designed in reliability-based calculations compared to code requirements. To address the knowledge gap in applying Reliability Based Design Optimisation (RBDO) within Building Information Modelling (BIM), this paper presents a novel BIM-integrated RBDO system for highway structures aimed at reducing over design. The approach is aimed at optimising the system reliability index. This value is then applied to the BIM model of the structure as a direct safety metric describing the probability of failure. In addition, minimum robustness and redundancy indices can be derived using this approach to ensure overall compliance with structural design codes, (Eurocodes), yielding key BIM model safety metrics. The system reliability index was optimised by utilising target limit state reliability indices to derive system difference target limits. System element reliability indices were effectively increased or reduced by manipulating element resistance parameters. An optimisation algorithm was employed to ensure compliance with the minimum system difference target limits. A secondary verification was undertaken to ensure minimal element target reliability indices were not compromised. The system reliability-based case studies on one-span bridge structures demonstrated that optimisation resulted in an overall 15% reduction in design resistance compared with the Eurocodes design method. In addition to highlighting element overdesign, the balance between safety and economy is improved by yielding comprehensive structural system safety metrics as a safer approach than direct element reliability-based optimisation. Full article

12 pages, 680 KB

Open AccessArticle

Optimization of Tangential Flow Filtration for High-Yield, Scalable Downstream Processing of Adeno-Associated Virus

by Sara Cardoso, Franziska Bollmann and Alexander Tappe

Membranes 2026, 16(2), 73; https://doi.org/10.3390/membranes16020073 - 20 Feb 2026

Abstract

The demand for effective downstream processing of adeno-associated virus (AAV) is increasing as gene therapies advance toward broader clinical applications. Robust, efficient, and scalable ultrafiltration and diafiltration (UF|DF) operations are essential for generating high-quality AAV preparations, with tangential flow filtration (TFF) serving as a critical unit operation for vector concentration, impurity reduction, and buffer exchange while maintaining viral functionality. Development of TFF processes requires careful consideration of membrane characteristics—including chemistry, pore size or channel architecture—as these parameters directly influence vector retention, fouling behavior, and overall process efficiency. Equally important is the optimization of critical process parameters such as recirculation rate, transmembrane pressure (TMP), and total processing time, all of which govern hydrodynamic performance and product quality. This study assessed two Sartocon^® Hydrosart^® TFF cassette architectures—ECO-Screen and E-Screen—for the ultrafiltration and diafiltration of AAV8 clarified lysate. Through flux characterization and controlled small-scale evaluations, cassette-specific operating regions were defined. Both configurations supported high viral genome retention; however, the E-Screen geometry achieved faster processing and superior removal of host–cell protein and DNA contaminants, whereas the ECO-Screen format allowed for efficient operation under reduced pump rates and, therefore, lower shear conditions. Reproducibility assessments demonstrated minimal run-to-run variability, confirming the robustness of the optimized operating parameters. A 10-fold scale-up further validated the linearity and predictability of the UF|DF process, with consistent impurity-reduction profiles and only modest deviations in viral recovery. Collectively, these findings provide a quantitative basis for rational cassette selection in AAV purification workflows and establish a scalable, scientifically grounded UF|DF framework applicable across development and manufacturing scales. Full article

(This article belongs to the Section Membrane Applications for Other Areas)

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23 pages, 4208 KB

Open AccessArticle

Degradation-Aware Dynamic Kernel Generation Network for Hyperspectral Super-Resolution

by Huadong Liu, Haifeng Liang and Qian Wang

Sensors 2026, 26(4), 1362; https://doi.org/10.3390/s26041362 - 20 Feb 2026

Abstract

Addressing the problems of the difficulty in reconstructing high-resolution hyperspectral images caused by dynamic degradation characteristics, the poor adaptability of traditional static degradation models, and the oversimplified noise modeling, this paper proposes a degradation-aware dynamic Fourier network (DADFN) for hyperspectral super-resolution. This method employs a dual-channel split module to decouple and encode spectral and spatial degradation information, realizes the independent mapping of spectral and spatial features via a multi-layer perceptron module, and integrates a spectral–spatial dynamic cross-attention fusion module to generate 3D dynamic blur kernels tailored to different bands and spatial positions. The proposed method designs a multi-scale spectral–spatial collaborative constraint (MSSCC) loss function to ensure the coordinated optimization of modeling rationality, spectral continuity, and spatial detail fidelity. Experiments on the CAVE and Harvard benchmark datasets demonstrate that the DADFN algorithm outperforms the baseline methods in all evaluation metrics, which proves the proposed method’s strong robustness in real-world complex degradation scenarios. This method provides a novel solution balancing physical interpretability and performance superiority for hyperspectral image super-resolution tasks and holds significant value for advancing its applications in remote sensing monitoring, precision agriculture, and other related fields. Full article

(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning for Sensing Applications—3rd Edition)

26 pages, 1580 KB

Open AccessArticle

Spatiotemporal Dynamics and Regional Disparities of Urban Resilience in China’s Mining Cities

by Hua Wei, Qipeng Liao, Jie Yang, Xinsheng Hu and Daojun Zhang

Land 2026, 15(2), 348; https://doi.org/10.3390/land15020348 - 20 Feb 2026

Abstract

Building safe and resilient cities is a key objective of China’s urbanisation and a prerequisite for high-quality development. This study assesses urban resilience in 73 mining cities from 2014 to 2023 using a composite index system (30 indicators) structured around robustness, resistance, and recovery. We integrate ARIMA-based forecasting, kernel density estimation, and Dagum Gini decomposition to characterise spatiotemporal dynamics and quantify regional inequality. Urban resilience increases steadily over the study period and can be characterised by three sequential stages, with further gains forecast for 2024–2030. Spatially, high-resilience cities shift from a dispersed pattern to belt-like and clustered agglomerations, consistent with an increasingly stratified centre–periphery structure. Inequality is driven primarily by between-region disparities: the East performs best, followed by the Central region, whereas the West and Northeast lag behind, revealing a pronounced gap between the Northeast and the East, alongside relatively convergent Central–West trajectories. These patterns are associated with interacting differences in location and market development, fiscal capacity and transition pathways, infrastructure endowment and ecological constraints, and institutional and demographic dynamics. The findings underscore the need for place-based regional coordination and targeted investments to strengthen recovery-related capacities. Full article

(This article belongs to the Special Issue Resilient Urban Regeneration: Adaptive Strategies for Future Metropolis)

33 pages, 12030 KB

Open AccessArticle

An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images

by Riyadh M. Al-Tam, Aymen M. Al-Hejri, Fatma A. Hashim, Sachin M. Narangale, Mugahed A. Al-Antari and Sarah A. Alzakari

Diagnostics 2026, 16(4), 622; https://doi.org/10.3390/diagnostics16040622 - 20 Feb 2026

Abstract

Background/Objectives: Early and accurate detection of breast cancer is essential for reducing mortality and improving patient outcomes. However, the manual interpretation of breast ultrasound images is challenging due to image variability, noise, and inter-observer subjectivity. This study aims to address these limitations by developing an automated and interpretable computer-aided diagnosis (CAD) system. Methods: We propose an automated and interpretable computer-aided diagnosis (CAD) system that integrates ensemble transfer learning with Vision Transformer architectures. The system combines the Data-Efficient Image Transformer (Deit) and Vision Transformer (ViT) through concatenation-based feature fusion to exploit their complementary representations. Preprocessing, normalization, and targeted data augmentation enhance robustness, while Gradient-weighted Class Activation Mapping (Grad-CAM) provides visual explanations to support clinical interpretability. The proposed model is benchmarked against state-of-the-art CNNs (VGG16, ResNet50, DenseNet201) and Transformer models (ViT, DeiT, Swin, Beit) using the Breast Ultrasound Images (BUSI) dataset. Results: The ensemble achieved 96.92% accuracy and 97.10% AUC for binary classification, and 94.27% accuracy with 94.81% AUC for three-class classification. External validation on independent datasets demonstrated strong generalizability, with 87.76%/88.07% accuracy/AUC on BrEaST, 86.77%/85.90% on BUS-BRA, and 86.99%/86.99% on BUSI_WHU. Performance decreased for fine-grained BI-RADS classification—76.68%/84.59% accuracy/AUC on BUS-BRA and 68.75%/81.10% on BrEaST—reflecting the inherent complexity and subjectivity of clinical subclassification. Conclusions: The proposed Vision Transformer-based ensemble demonstrates high diagnostic accuracy, strong cross-dataset generalization, and clinically meaningful explainability. These findings highlight its potential as a reliable second-opinion CAD tool for breast cancer diagnosis, particularly in resource-limited clinical environments. Full article

(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Signal Processing)

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15 pages, 644 KB

Open AccessArticle

Bootstrap-Augmented Analysis of Non-Linear Associations Between Glucose, hsCRP, and First Myocardial Infarction in a Cardiovascular Population

by Joanna Kostanek, Kamil Karolczak, Wiktor Kuliczkowski and Cezary Watala

Int. J. Mol. Sci. 2026, 27(4), 2025; https://doi.org/10.3390/ijms27042025 - 20 Feb 2026

Abstract

Myocardial infarction (MI) remains one of the most severe acute cardiac events, despite significant progress in diagnostics and therapy. Early identification of patients at risk within the broader cardiovascular disease (CVD) population is crucial for prevention and management. This study aimed to characterize the nonlinear distributions of glucose and high-sensitivity C-reactive protein (hsCRP) in patients experiencing their first MI compared with individuals hospitalized for other CVD conditions, using a bootstrap-augmented analytical approach. This retrospective study included 743 adults with confirmed CVD. Biochemical variables, including lipid profile, glucose, hsCRP, and estimated glomerular filtration rate (eGFR), were analyzed in relation to the occurrence of MI. Statistical analyses were supported by bootstrap-based validation to ensure the robustness of findings. Among the examined variables, serum glucose and hsCRP levels showed the strongest ability in discriminating MI(+) and MI(–) groups. Both variables exhibited complex, non-linear associations with the occurrence of MI, with the most pronounced differences observed in the lower and intermediate quartiles. Bootstrap-supported analyses confirmed the stability of these effects. In CVD patients, both blood glucose and hsCRP levels display non-linear relationships with the first occurrence of MI. The strongest distinctions between MI(+) and MI(–) groups were found at moderate concentrations of these variables, emphasizing the need for cautious interpretation and highlighting their role in characterizing biochemical patterns in MI(+) and MI(–) patients. Full article

(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)

36 pages, 851 KB

Open AccessArticle

Carbon Risk Without a Stable Premium: Nonlinear and State-Dependent Evidence from European ESG Leaders

by Eleonora Salzmann

Risks 2026, 14(2), 41; https://doi.org/10.3390/risks14020041 - 20 Feb 2026

Abstract

Despite the economic relevance of climate-transition risk, firm-level carbon exposure often fails to appear as a robustly priced factor when ESG measures and sustainability shocks are conflated. This study examines whether carbon exposure is conditionally priced in European equity returns using a strongly balanced quarterly panel of 238 firms from the MSCI Europe ESG Leaders universe (2018–2024). Total greenhouse gas emissions act as a proxy for carbon exposure, mapped to within-year percentiles and standardized by sector-year. Regressions control for ESG scores and controversies and include firm and quarter fixed effects with firm-clustered, dependence-robust standard errors. The linear carbon coefficient is small and statistically indistinguishable from zero, indicating no stable return premium from within-firm changes in carbon exposure. Functional-form tests reject linearity: quadratic and quintile specifications reveal curvature and a non-monotonic pattern, with return differences concentrated in the middle of the carbon distribution. Conditioning on macro-financial stress, measured by the ECB Composite Indicator of Systemic Stress, yields limited evidence of a uniform carbon penalty. However, high-controversy states are associated with lower returns, while ESG scores show negative associations under dependence-robust inference. Overall, carbon-related pricing appears to be nonlinear and state-dependent, whereas controversy risk is the most robust sustainability predictor of returns. Full article

24 pages, 1622 KB

Open AccessArticle

Real-Time Wire Rope Inclination Detection Using YOLOv9-Based Camera–LiDAR Fusion for Overhead Cranes

by Anh-Hung Pham, Ga-Eun Jung, Xuan-Kien Mai, Byeong-Soo Go and Seok-Ju Lee

J. Mar. Sci. Eng. 2026, 14(4), 393; https://doi.org/10.3390/jmse14040393 - 20 Feb 2026

Abstract

Safe and efficient cargo handling is essential in modern port logistics, where overhead cranes are widely used to move containers, bulk materials, and heavy equipment. Accurate real-time measurement of wire rope inclination is critical for preventing collisions, reducing load sway, and enabling autonomous crane operation under challenging maritime conditions. This paper presents a You Only Look Once v9 (YOLOv9)-based camera–LiDAR fusion system for real-time estimation of the trolley–hook rope inclination angle in overhead cranes. A monocular industrial camera and a YOLOv9 detector provide semantic region-of-interest (ROI) masks for the trolley and hook, while a 3D LiDAR sensor, rigidly mounted and extrinsically calibrated to the camera, provides depth information. LiDAR points projected onto the image and filtered by YOLOv9 bounding boxes allow efficient extraction of safety-critical 3D geometry and reconstruction of the rope vector. Experimental results on an overhead crane testbed show that the proposed fusion estimator achieves an angle RMSE below 1 degree in dynamic swing and low-illumination scenarios, significantly outperforming a camera-only baseline (RMSE ≈ 2.11). These metrically validated results indicate that the proposed detection pipeline offers a robust foundation for intelligent crane monitoring and automation in maritime logistics and smart port operations. Full article

(This article belongs to the Section Ocean Engineering)

20 pages, 1019 KB

Open AccessArticle

An Adaptive Fault-Tolerant Federated Kalman Filter for a Multi-Sensor Integrated Navigation System

by Guangle Gao, Guoqing Li, Yingmin Yi and Yongmin Zhong

Sensors 2026, 26(4), 1360; https://doi.org/10.3390/s26041360 (registering DOI) - 20 Feb 2026

Abstract

To achieve autonomous and reliable all-weather cross-domain aerospace navigation, this study proposes an adaptive fault-tolerant federated Kalman filter (AFTFKF) for an INS/SRNS/CNS integrated navigation system to enhance system robustness against measurement outliers. First, a noise estimator based on maximum likelihood estimation (MLE) and aided by a sequential probability ratio test (SPRT) is introduced to handle slowly growing outliers. Second, a double residual-based Chi-square test (DCST) information factor is designed to mitigate the impact of inaccurate local state estimation in subsystems under abruptly changed outliers. Finally, the SPRT-MLE-based noise estimator and the DCST-based information factor are integrated into the federated Kalman filter framework to construct the complete AFTFKF. Simulation results demonstrate that the proposed method achieves superior accuracy and strong stability for SINS/SRNS/CNS integrated navigation in the presence of outliers. Full article

(This article belongs to the Special Issue New Challenges and Sensor Techniques in Robot Positioning)

33 pages, 23602 KB

Open AccessArticle

SLC-Domain SAR RFI Suppression via Sliding-Window Local Tensorization and Energy-Guided CUR Projection

by Qiang Guo, Yuhang Tian, Shuai Huang, Liangang Qi and Sergiy Shulga

Remote Sens. 2026, 18(4), 652; https://doi.org/10.3390/rs18040652 - 20 Feb 2026

Abstract

Synthetic aperture radar (SAR) imaging is highly vulnerable to radio-frequency interference (RFI) in complex electromagnetic environments, which can introduce structured artifacts and obscure targets in single-look complex (SLC) products. Most existing suppression methods rely on separability along a single dimension or require interference-specific parameter tuning, limiting robustness under multidimensional coupling and strong scatterers. We propose a range-domain sliding-window local tensorization that rearranges SLC data into localized range–azimuth–block-index tensors to better expose multi-mode correlations. On this representation, an energy-guided tensor CUR low-rank projector is embedded into an alternating-projection scheme that alternates complex-valued soft-thresholding for the sparse scene-plus-noise term and CUR-based projection for the structured RFI term. The cleaned SLC image is obtained by de-tensorizing the estimated RFI component and subtracting it from the input SLC. Experiments on semi-synthetic data, where controlled RFI is superimposed on real SLC scenes, and on real Sentinel-1 SLC data containing RFI demonstrate improved Pearson correlation coefficient (PCC) and perceptual image quality while preserving target signatures and scene textures, particularly under strong interference and strong coupling. The proposed approach provides a practical SLC-domain RFI mitigation tool for post-focusing SAR products without requiring explicit interference parameterization. Full article

(This article belongs to the Section Remote Sensing Image Processing)

23 pages, 6187 KB

Open AccessArticle

Design and Optimization of Thermosensitive Hydrogels Combined with Lipid Nanotechnology for Topical Curcumin Application

by Daniela Vergara, Benjamín Vega, Claudia Sanhueza, Mariela Bustamante, Francisca Acevedo and Olga López

Gels 2026, 12(2), 181; https://doi.org/10.3390/gels12020181 - 20 Feb 2026

Abstract

A novel co-encapsulation platform based on curcumin-loaded liposomes (Cur-Lip) incorporated into thermosensitive hydrogels (TSH) was developed to address the physicochemical and biological limitations of topical curcumin (Cur) delivery. Response Surface Methodology (RSM) was used to optimize Pluronic^® F-127, glycerol, and alginate concentrations with respect to gelation time and viscosity. The optimized formulation (22% Pluronic^® F-127, 5% glycerol, and 0.5% alginate) exhibited rapid time sol–gel transition (~86 s), suitable viscosity (~377 mPa·s), excellent model fitting (R² = 0.99) and prediction accuracy. Three formulations (TSH, Cur-TSH, and Cur-Lip-TSH) were subsequently prepared and displayed appropriate thermoresponsive behavior. The Cur-Lip system showed high encapsulation efficiency (~78%). Upon incorporation into the TSH, Cur-Lip-TSH displayed increased viscosity and mechanical strength at physiological temperature. In vitro studies confirmed its cytocompatibility toward human keratinocytes, significant antibacterial activity against Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa, and no irritation potential as assessed by the Hen’s Egg Test on the Chorioallantoic Membrane assay (HET-CAM). Overall, Cur-Lip-TSH represents a safe and robust thermosensitive platform that provides a foundation for future studies on controlled curcumin release and topical performance. Full article

(This article belongs to the Special Issue Liposomal and Ethosomal Gels: From Design to Application (2nd Edition))

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22 pages, 4772 KB

Open AccessArticle

Beyond the Page: Solar Loading Thermographic Imaging and Predictive Modeling for Ancient Book Diagnostics—Preliminary Results

by Elena Marini, Gilda Russo, Hai Zhang and Stefano Sfarra

Sensors 2026, 26(4), 1358; https://doi.org/10.3390/s26041358 - 20 Feb 2026

Abstract

This study investigates the application of NDTs for the detection of sub-surface defects in an ancient book, with the aim of improving conservation methods in the field of cultural heritage. A sequence of thermographic images in a solar loading thermography (SLT) scenario was acquired during a diagnostic campaign in Harbin, China, to identify four distinct fabricated dowels made of Wool, Rubber, Teflon^®, and Synthetic material. The images were processed in two ways: the first combined advanced image-processing methods: pre-processing via MdFIF, post-processing, PCT and RPCT, applied both to the original sequence and to the MdFIF-filtered thermograms. The second approach employed numerical simulations in COMSOL Multiphysics^® to develop a predictive thermal model. The comparison of localized thermal anomalies obtained from the two approaches demonstrated the capability of NDTs to reliably reveal artificial defects, confirming their suitability for diagnostic conservation. Overall, the integration of advanced image processing with numerical simulation enhances diagnostic accuracy, particularly for subtle or low-contrast anomalies, thereby enabling more informed condition assessment and supporting rapid, targeted, and preventive conservation strategies. Full article

(This article belongs to the Section Physical Sensors)

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