Search Results (5,895)

Aerosol sprayed from the wheels of vehicles driving on wet roads is a significant source of pollution in the vicinity of roads. If it contains residues of chemical de-icing agents, it can contribute to the faster degradation of objects and structures within its reach. The aim of this research was to determine how the direction of the wind and the intensity of traffic affect the dispersion of the aerosol particles. Using a numerical model of turbulent flow incorporating discrete phase modeling, seven variants of wind direction and two traffic intensities represented by the passing of one or two vehicles were simulated. The results showed that when the wind blew from the location where the particle amount was measured, particle deposition was highly concentrated near the road—peaking at 6.5% of the injected amount at a distance of 5 m—followed by a steep decline to negligible levels at 9 m. Conversely, in the opposite wind direction, deposition was lower (<1%) but exhibited a flat profile, maintaining stable particle concentrations even at the most distant sampling plane (13 m). The passage of two vehicles led to a higher number of particles being detected (reaching up to 8.1%) and induced a vertical dispersion plume reaching up to 13 m above the road surface, compared to a maximum of approximately 7 m observed for a single vehicle. A comparison of the simulated data with long-term in situ experimental measurements confirmed a decrease in aerosol particle deposition with distance from the road. The simulations revealed that the aerosol dispersion is influenced not only by the wind or traffic intensity, but also by specific flow conditions resulting from the terrain configuration. In conclusion, the study shows that while increased traffic intensity mainly extends the vertical reach of the aerosol, wind direction determines its spatial distribution. Since the particle cloud is uneven, measuring devices in a single line perpendicular to the road axis may not accurately capture the highest concentrations. Therefore, to reliably capture aerosol dispersion, it is recommended to also place measuring devices in a direction that is parallel to the road, with a spacing of approximately 9 m. Full article

The development of high-performance optical fiber sensors based on surface plasmon resonance (SPR) represents a significant advancement in precision detection technology, particularly for biomedical and environmental monitoring applications requiring real-time response and minimal sample consumption. This research conducts a systematic numerical investigation of a D-shaped fiber SPR sensor incorporating an optimized silver-hematite (Ag-α-Fe₂O₃) composite grating structure. Through comprehensive finite element simulations and parameter analysis, we demonstrate that controlling the silver layer thickness at 45 nm while maintaining the α-Fe₂O₃ thickness at 12 nm achieves optimal electric field confinement. The grating gap width optimization at 30 nm enables maximum sensitivity through enhanced localized surface plasmon resonance effects, while the residual cladding thickness of 0.5 μm provides the ideal balance between detection accuracy and sensitivity. The research establishes fundamental design principles for high-performance SPR sensors by elucidating the critical relationships between geometric parameters and sensing characteristics, providing valuable insights for developing next-generation sensors with enhanced performance for advanced sensing applications in environmental monitoring and medical diagnostics. Full article

Unmanned aerial vehicle (UAV)-based multispectral monitoring has become an increasingly important tool for assessing crop vigor and stress under commercial agricultural conditions. However, most UAV-based studies using the normalized difference vegetation index (NDVI) in citrus systems have focused on yield estimation, disease detection, or canopy characterization during active growth phases, while the immediate post-harvest recovery period remains poorly documented. In this study, UAV-derived NDVI products were used to evaluate the canopy response in a commercial ‘Washington Navel’ orange orchard located in La Yarada Los Palos district (Tacna, Peru) following harvest. The study specifically assessed the effect of an on-farm, residue-based organic biostimulant produced from local organic wastes within a circular economy framework. The results indicate that treated plots exhibited a faster and more pronounced recovery of canopy vigor compared to untreated controls during the early post-harvest period. By integrating high-resolution UAV-based multispectral monitoring with a residue-derived biostimulant strategy, this work advances current NDVI-based applications in citrus by shifting the analytical focus from productive stages to post-harvest physiological recovery. The proposed approach provides a scalable and non-invasive framework for evaluating post-harvest canopy dynamics under water-limited, hyper-arid conditions and highlights the potential of locally sourced biostimulants as complementary management tools in precision agriculture systems. Full article

Large field-of-view (FOV) infrared imaging, widely utilized in applications including target detection and remote sensing, generates massive datasets that pose significant challenges for transmission and storage. To address this issue, we propose an efficient lossless compression method for large FOV infrared video. Our approach employs a hybrid prediction strategy within the transform domain. The video frames are first decomposed into low- and high-frequency components via the discrete wavelet transform. For the low-frequency subbands, an improved low-latency Multi-view High-Efficiency Video Coding (MV-HEVC) encoder is adopted, where the background reference frames are treated as one view to enable more accurate inter-frame prediction. For high-frequency components, pixel-wise clustered edge prediction is applied. Furthermore, the prediction residuals are reduced by optimal direction prediction, according to the principle of minimizing residual energy. Experimental results demonstrate that our method significantly outperforms mainstream video compression techniques. While maintaining compression performance comparable to MV-HEVC, the proposed method exhibits a 19.3-fold improvement in computational efficiency. Full article

The biosynthesis of zinc oxide nanoparticles (ZnO NPs) using plant extracts offers several important advantages, including low residue generation, reduced costs, and potentially faster production as compared to traditional chemical methods. In this study, for the first time, ZnO NPs were biosynthesized using an annatto plant (Bixa orellana) leaf extract and characterized using a range of analytical techniques, including scanning electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, ultraviolet–visible and Fourier transform infrared spectroscopies, thermogravimetric analysis, and point of zero charge measurements, thus ensuring a comprehensive elucidation of their physicochemical properties. Subsequently, photodegradation of methylene blue (MB) dye using the biosynthesized ZnO NPs was successfully demonstrated. The photodegradation studies showed that the ZnO NPs were capable of decomposing over 95% of MB after 110 min of UV irradiation. In addition, the potential application of ZnO NPs for water disinfection was evaluated by assessing their ability to eliminate microbial pathogens. Furthermore, cell-free singlet oxygen and intracellular ROS detection assays were performed to investigate the NP antibacterial molecular mechanisms. Overall, our results reveal that the ZnO NPs exhibit excellent potential for photodegradation applications and may contribute to the development of more effective and sustainable solutions for water treatment and quality control. Full article

Recently, deep learning methods have seen major advancements and are preferred for medical image analysis. Clinically, deep learning techniques for cancer image analysis are among the main applications for early diagnosis, detection, and treatment. Consequently, segmentation of breast histology images is a key step towards diagnosing breast cancer. However, the use of deep learning methods for image analysis is constrained by challenging features in the histology images. These challenges include poor image quality, complex microscopic tissue structures, topological intricacies, and boundary/edge inhomogeneity. Furthermore, this leads to a limited number of images required for analysis. The U-Net model was introduced and gained significant traction for its ability to produce high-accuracy results with very few input images. Many modifications of the U-Net architecture exist. Therefore, this study proposes the watershed encoder–decoder neural network (WEDN) to segment cancerous lesions in supervised breast histology images. Pre-processing of supervised breast histology images via augmentation is introduced to increase the dataset size. The augmented dataset is further enhanced and segmented into the region of interest. Data enhancement methods such as thresholding, opening, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted parts from the image. Consequently, further segmentation via the connected component analysis method is used to combine image pixel components with similar intensity values and assign them their respective labeled binary masks. The watershed filling method is then applied to these labeled binary mask components to separate and identify the edges/boundaries of the regions of interest (cancerous lesions). This resultant image information is sent to the WEDN model network for feature extraction and learning via training and testing. Residual convolutional block layers of the WEDN model are the learnable layers that extract the region of interest (ROI), which is the cancerous lesion. The method was evaluated on 3000 images–watershed masks, an augmented dataset. The model was trained on 2400 training set images and tested on 600 testing set images. This proposed method produced significant results of $98.53\%$ validation accuracy, $96.98\%$ validation dice coefficient, and $97.84\%$ validation intersection over unit (IoU) metric scores. Full article

This study presents a comprehensive evaluation and environmental risk assessment (ERA) of pharmaceutical residues in the Cerrón Grande Reservoir, one of the most important surface water bodies in El Salvador. Sampling campaigns were conducted over a one-year period, covering both the dry (January 2024) and rainy (July 2024) seasons. A total of 76 pharmaceutical compounds were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), of which only five were not detected. During the dry season, the highest environmental concentrations were observed for mecamylamine (1710–6913 µg L⁻¹), 1,7-dimethylxanthine (379–2829 µg L⁻¹), chloroquine (2.29–362.7 µg L⁻¹), and hydroxychloroquine (5.02–315.4 µg L⁻¹). Concentrations generally decreased in the rainy season, with mecamylamine (1526–2198 µg L⁻¹), 1,7-dimethylxanthine (0.018–0.55 µg L⁻¹), and caffeine (0.2–0.474 µg L⁻¹) remaining the most prevalent. Compounds exceeding 1 µg L⁻¹ were assessed using predicted no-effect concentrations (PNEC) to calculate risk quotients (RQ). Chloroquine (RQ = 3346.3), mecamylamine (RQ = 1437.8), hydroxychloroquine (RQ = 1027.2), and manidipine (RQ = 271.0) posed the highest risks during the dry season, while only mecamylamine (RQ = 502.0) exceeded this threshold in the rainy season. To our knowledge, this represents the first in-depth study of pharmaceutical residues in Salvadoran surface waters, providing a foundational reference for future research and environmental policy in the region. Full article

Circulating tumor cells (CTCs) are shed from the primary tumor into the bloodstream and represent dynamic molecular biomarkers for monitoring the progression of cancer. While profiling tumor tissues with over expression of cell surface markers, such as PD-L1 or HER2, is standard in guiding therapy, tissue samples are often inaccessible and inadequate, especially post-surgery or in cases of recurrence. Emerging clinical evidence indicates that CTC counts and biomarker surface expression can predict prognosis and therapeutic resistance more accurately than imaging or tissue-based approaches. Recent advancements in the CTC detection methods, based on physical properties or surface markers (e.g., EpCAM), coupled with next-generation sequencing (NGS) have enabled the isolation of these rare cells and their molecular characterization. Consequently, CTCs provide a real-time alternative, enabling repeated, longitudinal assessment of tumor phenotype and therapeutic response. This review emphasizes the translational potential of surface protein biomarkers on CTCs for profiling, namely PD-L1, HER2, and EGFR, as a clinically actionable approach to stratify patients, guide immunotherapy decisions, and monitor minimal residual disease (MRD), especially when longitudinal tissue biopsies are not feasible. Full article

The significance of gut epithelial cell autoantibodies (GECAs), human leukocyte antigen (HLA) alleles, and other scientifically relevant factors has been largely overlooked, despite their potential importance in the medical management of HIV-infected individuals, in understanding the pathogenesis of AIDS, and in improving epidemiological and diagnostic approaches. This review may be considered as a hypothesis-driven narrative paper mostly considering GECAs and some easily detectable genetic markers. Thus, the aim is to highlight these neglected medical and scientific issues. Addressing them may contribute to a deeper understanding of HIV pathology at both the individual and population levels. Autoantibodies against enterocytes (GECAs) are present in the majority of HIV-positive patients. These intestinal epithelial cells are crucial for nutrient absorption and because of their role as antigen-presenting cells (APCs) within the immune system. Furthermore, the number of CD4-positive lymphocytes depends largely on daily antigenic stimulation rather than on thymic function, which becomes residual or inactive after puberty. The fall of CD4+ lymphocyte counts observed in HIV-infected patients may therefore be exacerbated by enterocyte dysfunction/damage, as indicated by the presence of GECAs. These autoantibodies either cause or reflect damage to these important antigen-presenting cells, which may impair intestinal antigen presentation by their surface HLA proteins to the clonotypic T-cell receptor of lymphocytes. Additionally, the association between specific HLA alleles and a CCR5 variant affects HIV disease progression or transmission and should be considered in both adults and mother–infant pairs. In particular, HLA-B35 and HLA-B57 allelic groups have been implicated in influencing both the transmission and progression of HIV infection. Moreover, several aspects of the natural history of HIV infection remain unresolved and controversial, and these issues warrant urgent clarification. For instance, diagnostic tests are not yet standardised globally, and viral abundance in HIV-infected individuals or AIDS patients’ cells may be relatively low. In summary, the neglected facets of HIV infection demand renewed investigation, particularly now that an HIV diagnosis is no longer the devastating prognosis it once was. The objective of this work is to emphasise additional factors that may influence the course of AIDS, such as enterocyte injury reflected by presence of GECAs. Ultimately, we propose that GECAs may impair enterocytes’ HLA (MHC II)-mediated antigen presentation by enterocytes to CD4+ T lymphocytes (through T-cell receptors), thereby diminishing T-cell proliferation, reducing CD4+ cell numbers, and impairing immune function. Full article

To reduce reliability testing time, experiments are often terminated at a predetermined time, producing right-censored lifetime data. Alternatively, when test samples are inspected at fixed intervals, failures are only observed within these intervals, resulting in interval-censored lifetime data. Although quality control methods for right-censored data are well established, relatively little attention has been given to interval-censored observations. Motivated by the success of residual control charts based on convolutional neural network (CNN) for right-censored data, this study extends the chart for monitoring normally distributed interval-censored lifetime data. Simulation results based on average run length (ARL) indicate that the proposed method outperforms the traditional exponentially weighted moving average (EWMA) chart in detecting decreases in mean lifetime. The findings also highlight the practical benefits of employing high- or low-order autoregressive CNN models depending on the magnitude of process shifts. Full article

In the diagnostics of plasmas heated by neutral beam injection (NBI), which serves as a fundamental heating technique, critical core parameters such as ion temperatures and rotational velocities can be measured through NBI’s associated diagnostic methods. However, conventional spectral analysis methods applied in NBI-based Beam Emission Spectroscopy diagnostics face a significant limitation: a relatively high false detection rate during characteristic peak detection and boundary determination. This issue stems from three primary factors: persistent noise interference, overlapping spectral peaks, and dynamic broadening effects. To address this critical issue, we propose a spectral feature extraction method based on morphological segmentation and optimized clustering, with three key innovations that work synergistically: (1) an adaptive chunking algorithm driven by gradient, Laplacian, and curvature features to dynamically partition spectral regions, laying a foundation for localized analysis; (2) a hierarchical residual iteration mechanism combining dynamic thresholding and Gaussian template subtraction to enhance weak peak signals; (3) optimized DBSCAN clustering integrated with morphological closure to refine peak boundaries accurately. Among them, the adaptive chunking technique is distinct from general adaptive methods: its chunking granularity can be dynamically adjusted according to peak structures and can accurately adapt to low signal-to-noise ratio (SNR) scenarios. Experimental results based on measured data from the EAST device demonstrate that the adaptive chunking strategy maintains a missed detection rate of 0–20% across the full signal-to-noise ratio (SNR) range, with false positive rates limited to 16.67–50.00%. Notably, it achieves effective peak detection even under extremely low SNR conditions. Full article

This study investigates the technological and chemical causes of early zinc-coating degradation on cold-formed steel sections produced from DX51D+Z140 galvanized coils. Commercially manufactured products exhibiting early corrosion symptoms were used in this study. The entire processing route, which included strip preparation, cold rolling, hot-dip galvanizing, passivation, multi-roll forming, storage, and transportation to customers, was analyzed with respect to the residual surface chemistry and process-related deviations that affect the coating integrity. Thirty-three specimens were examined using electromagnetic measurements of coating thickness. Statistical analysis based on the Cochran’s and Fisher’s criteria confirmed that the increased variability in zinc coating thickness is associated with a higher susceptibility to localized corrosion. Surface and chemical analysis revealed chloride contamination on the outer surface, absence of detectable Cr(VI) residues indicative of insufficient passivation, iron oxide inclusions beneath the zinc coating originating from the strip preparation, traces of organic emulsion residues impairing wetting and adhesion, and micro-defects related to deformation during roll forming. Early zinc coating degradation was shown to result from the cumulative action of multiple technological (surface damage during rolling, variation in the coating thickness) and environmental (moisture during storage and transportation) parameters. On the basis of the obtained results, a methodology was proposed to prevent steel product corrosion in industrial conditions. Full article

Ocular melanomas, comprising uveal melanoma (UM) and conjunctival melanoma (CoM), represent the most common primary intraocular and ocular surface malignancies in adults. Although rare compared with cutaneous melanoma, they exhibit unique molecular landscapes that provide critical opportunities for biomarker-driven precision medicine. In UM, recurrent mutations in GNAQ and GNA11, together with alterations in BAP1, SF3B1, and EIF1AX, have emerged as key prognostic biomarkers that stratify metastatic risk and guide surveillance strategies. Conversely, in CoM, the mutational spectrum overlaps with cutaneous melanoma, with frequent alterations in BRAF, NRAS, NF1, and KIT, offering actionable targets for personalised treatment. Beyond genomics, epigenetic signatures, microRNAs, and protein-based markers provide further insights into tumour progression, microenvironmental remodelling, and immune evasion. In parallel, liquid biopsy has emerged as a minimally invasive approach for real-time disease monitoring. Analyses of circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), and exosome-derived microRNAs demonstrate increasing potential for early detection of minimal residual disease, prognostic assessment, and evaluation of treatment response. However, the clinical integration of these biomarkers remains limited by tumour heterogeneity, technical variability, and the lack of unified translational frameworks. This review synthesises current knowledge of molecular and liquid biopsy biomarkers in ocular melanoma, highlighting their relevance for diagnosis, prognosis, and treatment personalisation. The integration of established tissue-based molecular markers with novel liquid biopsy technologies will enable a unique framework for biomarker-guided precision oncology and risk-adapted surveillance in uveal and conjunctival melanoma, offering insight into strategies for early detection, therapeutic monitoring, and personalised clinical management. Full article

This study proposes an analytical redundancy method that combines empirical models with a Kalman filter to ensure the reliability of measurements from axial deformation sensors in a turbine head-cover bolt-monitoring system. This integration enables the development of predictive models that optimally estimate the dynamic deformation of each bolt during turbine operation at full and partial load. The test results of the models under conditions of outliers, measurement noise, and changes in turbine operating mode, evaluated using accuracy and sensitivity metrics, confirmed their high accuracy (Acc ≈ 0.146 µm) and robustness (S_A < 0.001). The evaluation of the models’ responses to simulated sensor faults (offset, drift, precision degradation, stuck-at) revealed characteristic residual patterns for faults with magnitudes > 5 µm. These findings establish the foundation for developing a fault detection and isolation algorithm for continuous monitoring of these sensors’ operational health. For practical implementation, the models require validation across all operational modes, and maximum admissible deformation thresholds must be defined. Full article

Predictive modeling of extrusion processes through machine learning (ML) offers significant improvements over classical response surface methodology (RSM) when addressing nonlinear and multivariable systems. This study evaluated hemp residues (Cannabis sativa) as a non-conventional ingredient in ruminant diets and compared the performance of polynomial regression models against several ML algorithms, including artificial neural networks (ANNs), random forest (RF), K-Nearest neighbors (KNN), and XGBoost. Three experimental datasets from previous extrusion studies were concatenated with new laboratory experiments, creating a unified database in excel. Input variables included extrusion parameters (temperature, screw speed, and moisture) and formulation components, while output variables comprised expansion index, BD, penetration force, water absorption index and water solubility index. Data preprocessing involved robust z-score detection of outliers (MAD criterion) with intra-group winsorization, followed by normalization to a [−1, +1] range. Hyperparameter optimization of ANN models was performed with Optuna, and all algorithms were evaluated through 5-fold cross-validation and independent external validation sets. Results demonstrated that ML models consistently outperformed quadratic regression, with ANNs achieving R² > 0.80 for BD and water solubility index, and RF excelling in predicting solubility. These findings establish machine learning as a robust predictive framework for extrusion processes and highlight hemp residues as a sustainable feed ingredient with potential to improve ruminant nutrition and reduce environmental impacts. Full article