Search Results (4,485)

Noise remains a persistent limitation in coherent imaging systems, degrading image quality and hindering accurate interpretation in critical applications such as remote sensing, medical imaging, and non-destructive testing. This paper presents a physics-informed deep learning framework for effective image denoising under complex noise conditions. The proposed approach integrates nonlinear partial differential equations (PDEs), including the heat equation, diffusion models, MPMC, and the Zhichang Guo (ZG) method, into advanced neural network architectures such as ResUNet, UNet, U2Net, and Res2UNet. By embedding physical constraints directly into the training process, the framework couples data-driven learning with physics-based priors to enhance noise suppression and preserve structural details. Experimental evaluations across multiple datasets demonstrate that the proposed method consistently outperforms conventional denoising techniques, achieving higher PSNR, SSIM, ENL, and CNR values. These results confirm the effectiveness of combining physics-informed neural networks with deep architectures and highlight their potential for advanced image restoration in real-world, high-noise imaging scenarios. Full article

The ultrasonic pulse velocity (UPV) method is widely used for determining the dynamic modulus of elasticity of concrete. Traditionally, this approach requires assuming Poisson’s ratio (arbitrary values ranging from 0.1 to 0.25), regardless of the actual properties of the tested material. Such assumptions can lead to inaccurate estimations of the elastic modulus and limit the reliability of the method. In this study, an experimental methodology is proposed to enhance the accuracy of the estimation of the elastic modulus of concrete by combining digital image correlation (DIC) with UPV testing. The DIC technique is used during axial compression tests to directly measure the Poisson ratio of cubic concrete samples, while the dynamic modulus of elasticity is determined through UPV measurements. Subsequently, conversion models from the literature were applied to estimate the static modulus of elasticity from the dynamic modulus. The obtained values are compared with the experimental measurements of the static modulus, showing strong consistency and validating the proposed approach. The results highlight two key findings: (i) incorporating the actual Poisson ratio of the material significantly improves the precision of modulus predictions obtained via UPV, and (ii) DIC provides a reliable and adaptable tool for measuring Poisson’s ratio in concrete. Overall, the integration of DIC and UPV offers a robust and non-destructive framework for improving the assessment of mechanical properties of concrete. Full article

Thermally modified wood is primarily used in exterior applications due to its enhanced resistance to biotic degradation. However, reduced mechanical performance limits its structural use. This study investigates the structural potential of high-temperature-treated European beech timber (Fagus sylvatica, L.) and evaluates its mechanical properties and grading models for structural design. Timber from 32 beech logs was air-dried and divided into untreated (NoTMW) and thermally modified (TMW) groups. Thermal modification was carried out commercially in an oxidizing atmosphere at 190 °C. All specimens were visually graded according to DIN 4074-5 and assessed using acoustic non-destructive methods before testing in four-point bending following EN 408. Modulus of elasticity (MOE), modulus of rupture (MOR), and density were determined, and characteristic values were calculated according to EN 384. On average, TMW exhibited a 17% reduction in bending strength compared to untreated wood, while both static and dynamic MOE were not significantly affected. The multiple regression model only slightly improved bending strength prediction compared with single linear regression based on global modulus, as the R²-value increased from 17% to 19%. The prediction of stiffness of thermally treated beech timber was greatly improved by combining local and acoustic moduli, explaining 76% of the total variation. Full article

Body fluid identification at crime scenes is the first step in the forensic biology workflow, leading to the identification of the perpetrator and/or, in some cases, the victim. Current methods that are regularly used in forensic criminal evidence analysis utilize well-studied properties of each fluid as the foundation of the protocol. Among these approaches, alternative light sources, chemical reactions, lateral flow immunochromatographic tests, and microscopic detection stand out to identify the main body fluids encountered at crime scenes: blood, semen, and saliva. However, these often come with limits for specificity and sensitivity. There is also difficulty with fluid mixtures, environmental degradation, and destruction of the sample by the method used. Other fluids, like vaginal fluid and fecal matter, lack standardized protocols and require innovative ideas for accurate analysis without compromising the sample. Emerging technologies based on molecular methods have been the focus of body fluid research, with emphasis on topics such as mRNA, microRNA, epigenetics, and microbial analysis. Additional information alongside the determination of fluid origin could be an advantage from new molecular techniques, such as the identification of donors from SNP analysis, if regular STR analysis is not possible. Validation studies and the integration of such research have the potential to expand and enhance the laboratory practices of forensic science. This article will provide an overview of the current methods applied in the crime lab for body fluid identification before exploring active research in this field, pointing out the potential of these techniques for application in forensic cases to overcome present issues and expand the variety of body fluids identified. Full article

Basalt Fiber Reinforced Polymer (BFRP) bolts offer a high mechanical performance, yet their non-destructive evaluation in anchorage systems remains scarcely investigated. This work examines guided wave propagation in BFRP bolt anchorage structures through a combined experimental and numerical analysis. Optimal excitation within 35–100 kHz was determined experimentally, revealing 40 kHz as the most stable mode, with a pronounced bottom reflection and a peak-to-peak amplitude of 0.31 V. Numerical simulations explored the influence of anchorage medium properties, bolt characteristics, and de-bonding defect locations and lengths on dispersion, attenuation, velocity, radial energy distribution, and echo response. The results indicate that denser anchorage media reduce velocity and attenuation but enhance radial nonuniformity, whereas a higher elastic modulus decreases amplitude and increases attenuation; a larger Poisson’s ratio elevates both velocity and attenuation. For the bolt, a higher density lowers velocity and attenuation, while a greater modulus amplifies both; Poisson’s ratio exerts a minor positive effect. Defect echo time varies linearly with defect position, and increasing the defect length elevates velocity yet diminishes amplitude. These findings elucidate the interplay between material parameters, defect geometry, and guided wave behavior, offering a basis for the optimized non-destructive testing (NDT) of BFRP bolts and facilitating their deployment in engineering applications. Full article

Refill Friction Stir Spot Welding (RFSSW) provides significant advantages over competing spot joining technologies, but detecting RFSSW’s often small and subtle defects remains challenging. In this study, kinematic feedback data from a RFSSW machine’s factory-installed sensors was used to successfully predict defect presence with 96% accuracy (F1 = 0.92) and preliminary multi-class defect diagnosis with 84% accuracy (F1 = 0.82). Thirty adverse treatments (e.g., contaminated coupons, worn tools, and incorrect material thickness) were carried out to create 300 potentially defective welds, plus control welds, which were then evaluated using profilometry, computed tomography (CT) scanning, cutting and polishing, and tensile testing. Various machine learning (ML) models were trained and compared on statistical features, with support vector machine (SVM) achieving top performance on final quality prediction (binary), random forest outperforming other models in classifying welds into six diagnosis categories (plus a control category) based on the adverse treatments. Key predictors linking process signals to defect formation were identified, such as minimum spindle torque during the plunge phase. In conclusion a framework is proposed to integrate these models into a manufacturing setting for low-cost, full-coverage evaluation of RFSSWs. Full article

Compact antennas with ultra-wideband operation and stable radiation are essential for portable and airborne ground-penetrating radar (GPR), yet miniaturization in the sub 3 GHz region is strongly constrained by the wavelength-driven aperture requirement and often leads to impedance discontinuity and radiation instability. This paper presents a compact aperture-slot antipodal Vivaldi antenna (AS-AVA) designed under a radiation stability-driven co-design strategy, where the miniaturization features are organized along the energy propagation path from the feed to the flared aperture. The proposed structure combines (i) aperture-slot current-path engineering with controlled meandering to extend the low-frequency edge, (ii) four tilted rectangular slots near the aperture to restrain excessive edge currents and suppress sidelobes, and (iii) back-loaded parasitic patches for coupling-based impedance refinement to eliminate residual mismatch pockets. A fabricated prototype on FR-4 (thickness 1.93 mm) occupies $111.15\times 156.82$ mm² and achieves a measured $S_{11}$ below $-10$ dB from 0.63 to 2.03 GHz (fractional bandwidth 105.26%). The measured realized gain increases from 2.1 to 7.5 dBi across the operating band, with stable far-field radiation patterns; the group delay measured over 0.6–2.1 GHz remains within 4–8 ns, indicating good time-domain fidelity for stepped-frequency continuous-wave (SFCW) operation. Finally, the antenna pair is integrated into an SFCW-GPR testbed and validated in sandbox and outdoor experiments, where buried metallic targets and a subgrade void produce clear B-scan signatures after standard processing. These results confirm that the proposed AS-AVA provides a practical trade-off among miniaturization, broadband matching, and radiation robustness for compact sub 3 GHz GPR platforms. Full article

The growing prevalence of multidrug-resistant (MDR) Candida spp. necessitates the development of new antifungal strategies. Photodynamic therapy (PDT), already widely used in the treatment of various oral infections, is based on the synergistic interaction of three key elements: a photosensitizer capable of selectively binding to microbial cells, a light source with the appropriate wavelength, and the presence of molecular oxygen. This interaction results in the production of singlet oxygen and reactive oxygen species, responsible for the selective destruction of microorganisms. In recent years, numerous natural compounds have been explored as potential photosensitizers. Olive oil, a cornerstone of the Mediterranean diet, was recently recognized by the U.S. Food and Drug Administration as a medicinal substance thanks to its soothing, immunomodulatory, and antimicrobial properties, which have also been documented in regard to oral administration. Materials and Methods: The aim of this in vitro study was to evaluate the efficacy of activated olive oil as a novel photosensitizer in PDT against Candida species. Oral MDR clinical isolates of C. albicans, C. krusei, and C. glabrata were analyzed using the Kirby–Bauer method according to EUCAST protocols. Six different experimental conditions were considered for each strain: (i) 100 μL of extra-virgin olive oil (EVOO); (ii) 100 μL of EVOO pre-activated with 3% H₂O₂ (EVOO-H); (iii) 100 μL of EVOO irradiated for 5 min with polarized light (480–3400 nm, 25 W); (iv) 100 μL of EVOO-H subjected to the same polarized light; (v) 100 μL of EVOO irradiated for 5 min with a 660 nm diode laser (100 mW); and (vi) 100 μL of EVOO-H irradiated with the same laser. All plates were incubated at 37 °C for 48 h. Results: The results showed a variable response among the different Candida species. C. glabrata showed sensitivity to all experimental conditions, with a 50% increase in the diameter of the inhibition zone in the presence of polarized light. C. krusei showed no sensitivity under any of the conditions tested. C. albicans showed antifungal activity exclusively when EVOO-H was activated by light. In particular, activation of EVOO and EVOO-H with polarized light resulted in the largest inhibition zones. Conclusions: In conclusion, olive oil, both alone and pre-activated with hydrogen peroxide, can be considered an effective photosensitizer against drug-resistant Candida spp., especially when combined with polarized light. Full article

Ophiocordyceps sinensis is a precious medicinal material with significant pharmacological and economic value. However, the visual similarity between its wild and cultivated forms poses a challenge for authentication. This study investigates the influence of specimen orientation on the accuracy of hyperspectral identification. Hyperspectral data were systematically acquired from four standard specimen orientations (left lateral, right lateral, dorsal, and ventral) for each sample. Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Fully Connected Neural Network (FCNN) models were trained and evaluated using both single-orientation and multi-orientation fused data. Results indicate that the LR model achieved superior and stable performance, with an average identification accuracy exceeding 98%. Crucially, for all tested models, no statistically significant difference in identification accuracy was observed across the different specimen orientations. This finding demonstrates that specimen orientation does not significantly influence identification accuracy. The conclusion was further corroborated in experiments using randomly orientation-fused datasets, in which model performance remained consistent and reliable. It is therefore concluded that precise specimen orientation control is unnecessary for the hyperspectral identification of Ophiocordyceps sinensis. This insight substantially simplifies the hardware design of dedicated identification devices by eliminating the need for complex orientation-fixing mechanisms and facilitating the standardization of operational protocols. The study provides a practical theoretical foundation for developing cost-effective, user-friendly, and widely applicable identification instruments for Ophiocordyceps sinensis and offers a reference for similar non-destructive testing applications involving anisotropic medicinal materials. Full article

This study determined the elastic properties of “green” concrete with cement partially replaced by oil shale ash (OSA) and reinforced with short basalt integral fibers (BIFs). Commercially available Deutsche Basalt Faser (DBF) GmbH Turbobuild Integral basalt fibers were used. There is currently a high demand both for strengthening concrete and applying ecological approaches with respect to circular economy. Oil shale ash is the byproduct of oil shale combustion. Basalt fiber is produced by melting basalt rock. Both BIF and OSA are used as additives in concrete. Cement replacement by OSA non-linearly changes the concrete’s strength properties, and the addition of BIF improves them. An experimental investigation was conducted using four-point bending tests and cube sample compression tests. Theoretical methods such as Voigt and Reuss boundaries, the Halpin–Tsai method, and the Mori–Tanaka method were used to predict the elastic properties of the fabricated samples. The theoretical models can provide useful information, although they may not fully capture the real properties observed experimentally. The results show that BIFs protect against instant brittle destruction. The experiments demonstrated an optimal OSA concentration for a fixed amount of BIF, resulting in the highest load-bearing capacity of the concrete. The addition of either 15% OSA only or 20% OSA and CBF can increase the stiffness of the concrete. This article provides guidance to the construction sector on using OSA and CBF together. Full article

Drilling resistance (DR) measurement is a promising non-destructive technique for evaluating the mechanical properties of concrete. However, the reliability and repeatability of DR measurements are still limited by an insufficient understanding of how drill rotational speed influences the recorded drilling response. In addition, a systematic investigation of the influence of rotational speed on multiple drilling response parameters simultaneously is still lacking. This study investigates the relationship between imposed rotational speed and DR parameters—namely, rotational speed reduction, drilling force, and electrical power consumption—measured during controlled drilling tests in C30 and C50 concretes. A laboratory-developed DR testing methodology with constant feed rate and synchronized RPM, force, and power measurements was applied. Five nominal drilling speeds (in the range of 1400–2200 RPM) were examined. The results show clear, speed-dependent trends across all measurements. Strong correlations between nominal and in-hole rotational speeds were observed, while drilling force exhibited a nonlinear dependence on rotational speed. This study reveals distinct drilling behavioral signatures that differentiate concrete strength classes and clarify the mechanical origin of drilling-induced RPM reduction. The findings confirm that DR parameters, when analyzed collectively rather than individually, provide valuable diagnostic information and have strong potential for application in the non-destructive evaluation of concrete structures. Full article

Fused deposition modeling (FDM) suffers from unpredictable mechanical properties in nominally identical prints. Current quality assurance relies on destructive testing or expensive post-process inspection, while existing machine learning approaches focus primarily on printing parameters rather than real-time thermal environments. The objective of this proof-of-concept study is to develop a low-cost, non-destructive framework for predicting tensile strength during FDM printing by directly measuring convective thermal gradients surrounding the print. To accomplish this, we introduce thermal fingerprinting: a novel non-destructive technique that combines Background-Oriented Schlieren (BOS) imaging with machine learning to predict tensile strength during printing. We captured thermal gradient fields surrounding PLA specimens (n = 30) under six controlled cooling conditions using consumer-grade equipment (Nikon D750 camera, household hairdryers) to demonstrate low-cost implementation feasibility. BOS imaging was performed at nine critical layers during printing, generating thermal gradient data that was processed into features for analysis. Our initial dual-model ensemble system successfully classified cooling conditions (100%) and showed promising correlations with tensile strength (initial 80/20 train–test validation: R² = 0.808, MAE = 0.279 MPa). However, more rigorous cross-validation revealed the need for larger datasets to achieve robust generalization (five-fold cross-validation R² = 0.301, MAE = 0.509 MPa), highlighting typical challenges in small-sample machine learning applications. This work represents the first successful application of Schlieren imaging to polymer additive manufacturing and establishes a methodological framework for real-time quality prediction. The demonstrated framework is directly applicable to real-time, non-contact quality assurance in FDM systems, enabling on-the-fly identification of mechanically unreliable prints in laboratory, industrial, and distributed manufacturing environments without interrupting production. Full article

The widespread adoption of lithium-ion battery-powered electric vehicles has raised increasing concerns regarding battery safety under mechanical abuse conditions. However, mechanical abuse scenarios, such as battery extrusion, are highly diverse, making it impractical to conduct extensive destructive experiments and independent modeling for each specific scenario. In this work, a cross-scenario mechanical safety modeling framework for lithium-ion batteries is proposed based on transfer learning. Three quasi-static mechanical abuse tests, including flat-plate, rigid-rod, and hemispherical compression, are conducted on 18650 lithium-ion batteries. An equivalent mechanical model with a spring–damper parallel structure is employed to characterize the mechanical response and generate simulation data. Based on data from a single mechanical abuse scenario, a backpropagation neural network (BPNN)-based safety model is established to predict the maximum stress in the battery. The learned knowledge is then transferred to other mechanical abuse scenarios using a transfer learning strategy. The results demonstrate that, under limited target-domain data, the transferred models achieve stable prediction performance, with the average relative error controlled within 3.6%, outperforming models trained from scratch under the same conditions. Compared with existing studies that focus on single-scenario modeling, this work explicitly investigates cross-scenario transferability and demonstrates the effectiveness of transfer learning in reducing experimental and modeling effort for battery mechanical safety analysis. Full article

To address the prediction of fatigue damage for 3D-printed Carbon Fiber Reinforced Polymer (CFRP), this study used 3D-printing technology to fabricate CFRP specimens. Through multi-stage fatigue testing, samples with varying porosity levels were obtained. Based on porosity test results and ultrasonic attenuation coefficient measurements of specimens under different fatigue cycle counts, a quantitative relationship model was established between the porosity and ultrasonic attenuation coefficient of 3D-printed CFRP. According to the porosity and fatigue-loading cycles obtained from tests, the Time-series Generative Adversarial Network (TimeGAN) algorithm was employed for data augmentation to meet the requirements for neural-network training. Subsequently, the Long Short-Term Memory (LSTM) neural network was utilized to predict the fatigue damage evolution of 3D-printed CFRP specimens. Research findings indicate that by integrating the established relationship between porosity and ultrasonic attenuation coefficient, non-destructive testing of material fatigue damage evolution based on ultrasonic attenuation coefficient can be achieved. Full article

Reliable models for predicting the uniaxial compressive strength (UCS) of rocks are crucial for mining operations and rock engineering design. Empirical methods, including statistical methods, are often faced with many limitations when generalizing in a wide range of lithological types. To address this limitation, this study investigates the capability of grey wolf optimization (GWO)-optimized ensemble machine learning models, including decision tree (DT), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) for predicting UCS using a small dataset of easily measurable and non-destructive rock index properties. The study’s objective is to evaluate whether metaheuristic-based hyperparameter optimization can enhance model robustness and generalization performance under small-sample conditions. A unified experimental framework incorporating GWO-based optimization, three-fold cross-validation, sensitivity analysis, and multiple statistical performance indicators was implemented. The findings of this study confirm that although the GWO-XGBoost model achieves the highest training accuracy, it exhibits signs of mild overfitting. In contrast, the GWO-AdaBoost model outpaced with significant improvement in terms of coefficient of determination (R²) = 0.993, root mean square error (RMSE) = 2.2830, mean absolute error (MAE) = 1.6853, and mean absolute percentage error (MAPE) = 4.6974. Therefore, the GWO-AdaBoost has proven to be the most effective in terms of its prediction potential of UCS, with significant potential for adaptation due to its effectively learned parameters. From a theoretical perspective, this study highlights the non-equivalence between training accuracy and predictive reliability in UCS modeling. Practically, the findings support the use of GWO-AdaBoost as a reliable decision-support tool for preliminary rock strength assessment in mining and geotechnical engineering, particularly when comprehensive laboratory testing is not feasible. Full article