Search Results (589)

Tea Anthracnose (TA), caused by fungi of the genus Colletotrichum, is one of the major threats to global tea production. UAV remote sensing has been explored for non-destructive and high-efficiency monitoring of diseases in tea plantations. However, variations in illumination, background, and meteorological factors undermine the stability of cross-temporal data. Data processing and modeling complexity further limits model generalizability and practical application. This study introduced a cross-temporal, generalizable disease monitoring approach based on UAV multimodal data coupled with relative-difference standardization. In an experimental tea garden, we collected multispectral, thermal infrared, and RGB images and extracted four classes of features: spectral (Sp), thermal (Th), texture (Te), and color (Co). The Normalized Difference Vegetation Index (NDVI) was used to identify reference areas and standardize features, which significantly reduced the relative differences in cross-temporal features. Additionally, we developed a vegetation–soil relative temperature (VSRT) index, which exhibits higher temporal-phase consistency than the conventional normalized relative canopy temperature (NRCT). A multimodal optimal feature set was constructed through sensitivity analysis based on the four feature categories. For different modality combinations (single and fused), three machine learning algorithms, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP), were selected to evaluate disease classification performance due to their low computational burden and ease of deployment. Results indicate that the “Sp + Th” combination achieved the highest accuracy (95.51%), with KNN (95.51%) outperforming SVM (94.23%) and MLP (92.95%). Moreover, under the optimal feature combination and KNN algorithm, the model achieved high generalizability (86.41%) on independent temporal data. This study demonstrates that fusing spectral and thermal features with temporal standardization, combined with the simple and effective KNN algorithm, achieves accurate and robust tea anthracnose monitoring, providing a practical solution for efficient and generalizable disease management in tea plantations. Full article

Malignant pleural effusion, commonly referred to as MPE, is a prevalent complication associated with individuals diagnosed with neoplastic disorders. The data acquired by pleural fluid cytology is beneficial for diagnostic objectives. Consequently, the initial step in the diagnostic procedure for lung cancer is the analysis of pleural effusion fluid. This research aims to provide a cutting-edge model for analyzing PE cytology images. This model utilizes a computer-aided diagnosis (CAD) system that integrates hyperspectral imaging (HSI) technology for the classification of spectral variations. Giemsa, which is one of the most popular microscopic stains, is employed to stain the samples, after which a sensitive CCD mounted on a microscope captures the images. Subsequently, the HSI model is tasked with obtaining the image spectra. Principal Component Analysis (PCA) constitutes the concluding phase in the classification procedure of various cell types. We expect that the suggested technique will enable medical professionals to stage lung cancer more rapidly. In the future, we aspire to develop an extensive data system that utilizes deep learning techniques to facilitate the automatic classification of cells, thereby ensuring the most precise diagnosis. Furthermore, enhancing accuracy and minimizing data dimensions are important priorities to accelerate diagnostics, conserve resources, and reduce computing time. Full article

Effective thermal management is essential in metal additive manufacturing to ensure process stability and desirable material properties. Directed energy deposition using a laser beam and wire (DED-LB/w) enables the production of large, high-performance components but remains sensitive to adverse thermal effects during multi-layer deposition due to heat accumulation. While prior studies have investigated interlayer temperature control and substrate preheating in DED modalities, including laser-powder and arc-based systems, the influence of substrate preheating in DED-LB/w has not been thoroughly examined. This study employs substrate preheating to simulate heat accumulation and assess its effects on melt pool geometry, wire–melt pool interaction, and the microstructural evolution of Alloy 718. Experimental results demonstrate that increased substrate temperatures lead to a gradual expansion of the melt pool, with a notable transition occurring beyond 400 °C. Microstructural analysis reveals that elevated preheat temperatures promote coarser secondary dendrite arm spacing and the development of wider columnar grains. Moreover, Nb-rich secondary phases, including the Laves phase, exhibit increased size but relatively unchanged area fractions. Observations from electrical conductance measurements and coaxial visual imaging show that preheat temperature significantly affects the process dynamics and microstructural evolution, providing a basis for advanced process control strategies. Full article

With the development of extreme ultraviolet (EUV) lithography technology to higher numerical aperture (NA), it provides higher resolution imaging quality, which may be more sensitive to the phase defect in EUV mask. Therefore, it is necessary to comprehensively understand the effect of phase defect on the imaging quality depending on the NA. We simulated aerial images of patterned EUV masks for the EUV lithography exposure tool of NA = 0.55 and NA = 0.33 using the rigorous coupled-wave analysis (RCWA) method. The results shows that higher NA enhances the contrast of aerial images, which, in turn, provides greater tolerance for phase defect. This indicates that high NA can mitigate the negative impact of phase defect on imaging quality to some extent. Furthermore, it is found that both the defect signal and the intensity loss ratio of the aerial image first increase and then decrease as the width of the phase defect increases, due to the height/width ratio of the phase defect. Meanwhile, the defect width corresponding to the maximum phase defect signal tends to become smaller as the NA becomes larger. It is also worth noting that when NA = 0.33, variations in the position of the phase defect led to fluctuations in the CD error due to the shadow effect of the absorber, while it diminishes at NA = 0.55. This is because a higher NA of 0.55 provides a stronger background field, which suppresses the shadow effect of the absorber more effectively than it does at NA = 0.33. Full article

Objective: Axillary lymph node (ALN) status in breast cancer is pivotal for guiding treatment and determining prognosis. The study aimed to explore the feasibility and efficacy of a radiomics model using voxel-wise dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-intensity-curve (TIC) profile maps to predict ALN metastasis in breast cancer. Methods: A total of 615 breast cancer patients who underwent preoperative DCE-MRI from October 2018 to February 2024 were retrospectively enrolled and randomly allocated into training (n = 430) and testing (n = 185) sets (7:3 ratio). Based on wash-in rate, wash-out enhancement, and wash-out stability, each voxel within manually segmented 3D lesions that were categorized into 1 of 19 TIC subtypes from the DCE-MRI images. Three feature sets were derived: composition ratio (type-19), radiomics features of TIC subtypes (type-19-radiomics), and radiomics features of third-phase DCE-MRI (phase-3-radiomics). Student’s t-test and the least absolute shrinkage and selection operator (LASSO) was used to select features. Four models (type-19, type-19-radiomics, type-19-combined, and phase-3-radiomics) were constructed by a support vector machine (SVM) to predict ALN status. Model performance was assessed using sensitivity, specificity, accuracy, F1 score, and area under the curve (AUC). Results: The type-19-combined model significantly outperformed the phase-3-radiomics model (AUC = 0.779 vs. 0.698, p < 0.001; 0.674 vs. 0.559) and the type-19 model (AUC = 0.779 vs. 0.541, p < 0.001; 0.674 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. The type-19-radiomics showed significantly better performance than the phase-3-radiomics model (AUC = 0.764 vs. 0.698, p = 0.002; 0.657 vs. 0.559, p = 0.037) and type-19 model (AUC = 0. 764 vs. 0.541, p < 0.001; 0.657 vs. 0.435, p < 0.001) in cross-validation and independent testing sets. Among four models, the type-19-combined model achieved the highest AUC (0.779, 0.674) in cross-validation and testing sets. Conclusions: Radiomics analysis of voxel-wise DCE-MRI TIC profile maps, simultaneously quantifying temporal and spatial hemodynamic heterogeneity, provides an effective, noninvasive method for predicting ALN metastasis in breast cancer. Full article

The precise analysis of protein interactions in their native cellular context and the sensitive quantification of protein abundance in biological fluids are both fundamental to understanding health and disease. Traditional methods for cellular imaging and biochemical quantification often face limitations in specificity, sensitivity, or the preservation of spatial information. The proximity ligation assay (PLA) is a versatile technological platform developed to overcome these challenges by converting protein recognition events into amplifiable DNA signals, thereby achieving exceptional sensitivity. This foundational principle has given rise to two major formats: in situ PLA (isPLA) and solution-phase PLA. In basic research, isPLA provides high-resolution visualization of protein–protein interactions (PPIs), post-translational modifications (PTMs), and subcellular architecture directly within fixed cells and tissues. In translational and clinical applications, solution-phase PLA enables the highly sensitive quantification of low-abundance biomarkers in liquid samples, which is critical for diagnostics and prognostics in fields such as oncology, neuroscience, and infectious diseases. This review discusses the foundational principles, development, and diverse applications of PLA platforms. We also highlight significant technological advancements, including the development of high-throughput formats, integration with advanced readouts, and the use of alternative affinity reagents. These innovations continue to transform PLA from a targeted validation method into a powerful and multifaceted platform for both fundamental systems biology and clinical diagnostics. Full article

Sugar beet (Beta vulgaris L.) is very sensitive to fluctuations in micronutrient availability, and either an excess or a shortage of boron (B) may reduce the plant’s development and its ability to withstand stress. B is essential for photosynthesis and cell wall integrity, but the physiological requirements for an optimal supply during early development remain unclear. The photosynthetic efficiency and oxidative stress reactions of sugar beet seedlings were tested under five different B concentrations: 0, 50, 500, 1000, and 2000 µM H₃BO₃. Integrating non-invasive methods like SPAD, delayed fluorescence (DF), and maximum quantum efficiency of PSII (Fv/Fm) with red–green–blue (RGB) imaging enabled the detailed processing of both the initial and decay phases of DF. According to the results, SPAD and Fv/Fm were not sensitive indicators of early B stress; however, DF decay slopes and red–green–blue pixel distribution distinguished between optimum (500 µM), inadequate (0 µM), and hazardous (2000 µM) treatments. Moreover, lipid oxidation-related biochemical analyses were used to evaluate the ferric reducing antioxidant capacity (FRAP) and malondialdehyde (MDA) concentration. At the extremes of insufficiency and toxicity, MDA levels demonstrated enhanced lipid peroxidation, while FRAP increased with B concentration. The outcome of the research revealed optimum (500 µM) and toxicity-inducing (2000 µM) concentrations at early stages of sugar beet development. The study highlights that the combined use of DF kinetics and RGB analysis provides valuable, non-invasive markers for the early identification of B-stress, which is also confirmed by biochemical indicators, thereby promoting more efficient micronutrient management in sugar beet cultivation. Full article

For large-scale measurement of microbubble parameters on the ocean surface beneath breaking waves, a buoy-type bubble sensor (BBS) is proposed. This sensor integrates a panoramic bubble imaging sub-sensor with a circular array fiber-optic sub-sensor. The sensitive unit of the latter sub-sensor is designed via theoretical modeling and experimental validation. Theoretical calculations indicate that the optimal cone angle for a quartz fiber-optic-based sensitive unit ranges from 45.2° to 92°. A prototype array element with a cone angle of 90° was fabricated and used as the core component for feasibility experiments in static and dynamic two-phase (gas and liquid) identification. During static identification, the reflected optical power differs by an order of magnitude between the two phases. For dynamic sensing of multiple microbubble positions, the reflected optical power varies from 13.4 nW to 29.3 nW, which is within the operating range of the array element’s photodetector. In theory, assembling conical quartz fiber-based sensitive units into fiber-optic probes and configuring them as arrays could overcome the resolution limitations of the panoramic bubble imaging sub-sensor. Further discussion of this approach will be presented in a subsequent paper. Full article

Introduction: Various classifications of radial head fractures have been reported in the literature, most of them are based solely on conventional radiographic criteria. The Mason–Johnston classification, currently the most widely used system worldwide, is affected by the limitations of conventional radiographs. The aim of our study is to confirm or refute the low reliability and reproducibility of the Mason–Johnston classification. Materials and Methods: The study collected elbow X-rays showing radial head fractures from 2011 to 2021. Images were evaluated by eight orthopedic surgeons and one radiologist consultant from different hospitals for classification. The first phase assessed inter-observer agreement, comparing classifications among participants. After four months, the same images were randomly reordered and then reclassified to evaluate intra-observer agreement. A total of 90 elbow X-rays from 50 women and 40 men were analyzed. Inter- and intra-observer agreement was assessed using Fleiss’ kappa, Krippendorff alpha, and Cohen’s kappa. Results: Overall inter-observer agreement by unweighted Fleiss’ κ was moderate in both sessions (κ = 0.49 and κ = 0.50), with overall pairwise percent agreement 63% and prevalence- and bias-adjusted κ (PABAK, k = 4) ≈ 0.50. As an ordinal sensitivity analysis, Krippendorff’s α (ordinal) was 0.726 and 0.744, indicating substantial agreement. Type-specific reliability was moderate for Types II–III and higher for Type IV. Unweighted Cohen’s kappa coefficients were calculated to assess intra-observer agreement, demonstrating moderate to substantial levels of concordance. Conclusions: The Mason–Johnston classification shows moderate inter-observer reliability, especially for Types II–III, and moderate to substantial intra-observer agreement. Full article

The integration of a pixelated Fabry–Pérot filter array onto the image sensor enables on-chip snapshot multispectral imaging, significantly reducing the size and weight of conventional spectral imaging equipment. However, a traditional Fabry–Pérot cavity, based on metallic or dielectric layers, exhibits a narrow bandwidth, which restricts their utility in broader applications. In this work, we propose novel Fabry–Pérot filter structures that employ dielectric thin films for phase modulation, enabling single-peak filtering across a broad operational wavelength range from 400 nm to 1100 nm. The proposed structures are easy to fabricate and compatible with complementary metal-oxide-semiconductor (CMOS) image sensors. Moreover, the structures show low sensitivity to oblique incident angles of up to 30° with minimal wavelength shifts. This advanced Fabry–Pérot filter design provides a promising pathway for expanding the operational wavelength of snapshot spectral imaging systems, thereby potentially extending their application across numerous related fields. Full article

The crystal structure, texture, martensitic transformation, and magnetic properties of magnetic shape-memory Heusler alloys of Ni_51−xMn_33.4In_15.6V_x (x = 0; 0.1; 0.3; 0.5; 1) were investigated. Experimental studies of the magnetic properties and meta-magnetostructural transition (martensitic transition—MT) confirm the main sensitivity of the martensitic transition temperature to vanadium doping and to an applied magnetic field. This makes this family of shape-memory alloys promising for use in numerous applications, such as magnetocaloric cooling and MEMS technology. Diffuse electron scattering was analyzed, and the structures of the austenite and martensite were determined, including the use of TEM in situ experiments during heating and cooling for an alloy with a 0.3 at.% concentration of V. In the austenitic state, the alloys are characterized by a high-temperature-ordered phase of the L2₁ type. The images show nanodomain structures in the form of tweed contrast and contrast from antiphase domains and antiphase boundaries. The alloy microstructure in the temperature range from the martensitic finish to 113 K consists of a six-layer modulated martensite, with 10 M and 14 M modulation observed in local zones. The morphology of the double structure of the modulated martensite structure inherits the morphology of the nanodomain structure in the parent phase. This suggests that it is possible to control the structure of the high-temperature austenite phase and the temperature of the martensitic transition by alloying and/or rapidly quenching from the high-temperature phase. In addition, attention is paid to maintaining fine interface structures. High-resolution transmission electron microscopy showed good coherence along the austenite–martensite boundary. Full article

Background/Objectives: Accurately distinguishing malignancy in thyroid micronodules (≤10 mm) is crucial for clinical management, yet it is challenging due to the limitations of conventional ultrasonography-guided biopsy. This study aims to evaluate the predictive value of dual-layer spectral computed tomography (DSCT)-derived arterial enhancement fraction (AEF) in diagnosing thyroid microcarcinomas. Methods: In the study, 321 pathologically confirmed thyroid micronodules (benign = 131, malignant = 190) from Chongqing General Hospital underwent preoperative DSCT. Quantitative parameters of DSCT, including the normalized iodine concentration (NIC), normalized effective atomic number (NZ_eff), and slope of the spectral Hounsfield unit curve (λHU_(40–100)), were assessed. Both single-energy CT (SECT)-derived AEF (AEF_S) and DSCT-derived AEF (AEF_D) were calculated. Conventional image features included microcalcifications and enhancement blurring. Correlation between AEF_D and AEF_S was determined using Spearman’s correlation coefficient. Diagnostic performance was evaluated by calculating the area under the curve (AUC) using receiver operating characteristic (ROC) analysis. Results: Malignant micronodules exhibited significantly lower AEF_D (0.958 vs. 1.259, p < 0.001) and AEF_S (0.964 vs. 1.436, p < 0.001) versus benign nodules. Arterial phase parameters—APλHU_(40–100), APNIC, APNZ_eff—differed significantly between groups (all p < 0.001), whereas venous phase parameters (VPλHU_(40–100), VPNIC, VPNZ_eff) showed no differences (all p > 0.05). Multivariate analysis revealed that λHU_(40–100) as an independent predictor of malignancy, with an odds ratio (OR) of 0.600 (95% confidence interval (CI): 0.437–0.823; p = 0.002) and an AUC of 0.752 (95% CI: 0.698–0.806). A significant positive correlation was identified between AEF_D and AEF_S (r = 0.710; p < 0.001). For diagnosing malignancy, AEF_D demonstrated superior overall performance (AUC: 0.794; sensitivity: 70.5%; specificity: 81.7%; accuracy: 75.1%) to AEF_S (0.753; 71.1%; 74.0%; 72.3%), APλHU_(40–100) (0.752; 68.9%; 75.6%; 71.7%), and calcification (0.573; 21.6%; 92.4%; 50.5%). Clinically, AEF_D reduced the unnecessary biopsy rate to 18.3%, preventing 107 procedures in our cohort. Conclusions: AEF_D and AEF_S demonstrated strong correlation and comparable diagnostic performance in the evaluation of thyroid micronodules. Furthermore, AEF_D showed favorable diagnostic efficacy compared to both spectral parameters and conventional imaging feature. More importantly, the application of AEF_D significantly reduced unnecessary biopsy rates, highlighting its clinical value in optimizing patient management. Full article

Small satellites or CubeSats orbiting in low Earth orbit (LEO) have become increasingly popular in Earth Observation missions, where high-resolution imaging is essential. Due to the lower mass of these spacecrafts, they are more sensitive to vibrations, and image quality can be particularly negatively affected by micro-vibrations. These vibrations originate from on-board subsystems, such as the Attitude Determination and Control System (ADCS), which uses reaction wheels to change the orientation of the satellite. The main goal of our research was to analyze these micro-vibrations so that the acquired data could be used for post-correction of camera images. Obuda University, as a participant in a research project, was tasked with designing and building a micro-vibration measuring device for the LEO CubeSat called WREN-1. In the first phase of the project, the satellite was launched into orbit, and test data were collected and analyzed. The results are presented in this article. Based on the data obtained in this way, the next step will be to analyze the images taken at the same time as the vibration measurements and to search for a correlation between the image quality and the vibrations. Based on the results of the entire project, it could be possible to improve the image quality of the onboard cameras of microsatellites. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

Recent progress in computer vision and embedded systems has facilitated real-time monitoring of bioprocesses; however, lightweight and scalable solutions for resource-constrained settings remain limited. This work presents a modular framework for monitoring Chlorella vulgaris growth by integrating RGB image processing with multimodal sensor fusion. The system incorporates a Logitech C920 camera and low-cost pH and temperature sensors within a compact photobioreactor. It extracts RGB channel statistics, luminance, and environmental data to generate a 10-dimensional feature vector. A feedforward artificial neural network (ANN) with ReLU activations, dropout layers, and SMOTE-based data balancing was trained to classify growth phases: lag, exponential, and stationary. The optimized model, quantized to 8 bits, was deployed on an ESP32 microcontroller, achieving 98.62% accuracy with 4.8 ms inference time and a 13.48 kB memory footprint. Robustness analysis confirmed tolerance to geometric transformations, though variable lighting reduced performance. Principal component analysis (PCA) retained 95% variance, supporting the discriminative power of the features. The proposed system outperformed previous vision-only methods, demonstrating the advantages of multimodal fusion for early detection. Limitations include sensitivity to lighting and validation limited to a single species. Future directions include incorporating active lighting control and extending the model to multi-species classification for broader applicability. Full article