Search Results (212)

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article

Monitoring Peripheral Oxygen Saturation (SpO₂) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO₂-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO₂ without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO₂-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD $\pm 1.96\sigma$ = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation. Full article

Accurate and rapid aquatic organism recognition is a core technology for fisheries automation and aquatic organism statistical research. However, due to absorption and scattering effects, images of aquatic organisms often suffer from poor contrast and color distortion. Additionally, the clustering behavior of aquatic organisms often leads to occlusion, further complicating the identification task. This study proposes a lightweight object detection model, Mobile-YOLO, for the recognition of four representative aquatic organisms, namely holothurian, echinus, scallop, and starfish. Our model first utilizes the Mobile-Nano backbone network we proposed, which enhances feature perception while maintaining a lightweight design. Then, we propose a lightweight detection head, LDtect, which achieves a balance between lightweight structure and high accuracy. Additionally, we introduce Dysample (dynamic sampling) and HWD (Haar wavelet downsampling) modules, aiming to optimize the feature fusion structure and achieve lightweight goals by improving the processes of upsampling and downsampling. These modules also help compensate for the accuracy loss caused by the lightweight design of LDtect. Compared to the baseline model, our model reduces Params (parameters) by 32.2%, FLOPs (floating point operations) by 28.4%, and weights (model storage size) by 30.8%, while improving FPS (frames per second) by 95.2%. The improvement in mAP (mean average precision) can also lead to better accuracy in practical applications, such as marine species monitoring, conservation efforts, and biodiversity assessment. Furthermore, the model’s accuracy is enhanced, with the mAP increased by 1.6%, demonstrating the advanced nature of our approach. Compared with YOLO (You Only Look Once) series (YOLOv5-12), SSD (Single Shot MultiBox Detector), EfficientDet (Efficient Detection), RetinaNet, and RT-DETR (Real-Time Detection Transformer), our model achieves leading comprehensive performance in terms of both accuracy and lightweight design. The results indicate that our research provides technological support for precise and rapid aquatic organism recognition. Full article

Among the craft recipes for artisans collected in the 4th-century Egyptian documents the Leyden and Stockholm papyri, there is one calling for adding animal milk to orchil for wool dyeing. To understand the rationale for this practice, wool yarns were dyed with and without goat milk added to orchil dyebaths, each made using lichens from three different sources. The results showed orchil containing milk dyed yarns a noticeably deeper red hue. The colorants extracted from the dyed yarns were analyzed by liquid chromatography-diode-array-detector-mass spectrometry to assess the relative amounts of nine identifiable orceins. The data showed that the yarns dyed with milk gave extracts exhibiting several fold more α-aminoorcein and α-hydroxyorcein, with only small differences in the other seven. Scanning electron microscopic analysis of a representative pair of dyed yarns showed that milk promoted surface changes in the fiber that may indicate increased cutaneous damage. Hypotheses for the milk’s effects on orchil dyeing were proposed that included the formation of milk–protein complexes with the two enriched orceins that possibly enhanced wool binding and/or better wool uptake of free and/or complexed orceins due to biodegradation of the wool’s surface cuticle caused by microbial growth promoted by the addition of milk. Full article

During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet’s Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. A comprehensive crab dataset is constructed, containing images from diverse farming sites, including variations in species, color, size, angle, and background conditions. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and an mAP50-95 of 88.5%, while maintaining 309 FPS and reducing GFLOPs by 35.3%. Meanwhile, the classification model achieves high accuracy with only 17.4% and 40% of the parameters of VGG16 and AlexNet, respectively. In conclusion, the proposed method achieves an optimal accuracy-speed-complexity trade-off, enabling robust real-time traceability for aquaculture systems. Full article

We developed an optoelectronic sensor capable of quantifying in-plane rotational and linear displacements between two parallel surfaces. The sensor utilizes a photo detector to capture the intensity of red (R), green (G), blue (B), and clear (C, broad visible spectrum) light reflected from a color gradient wheel on the opposing surface. Variations in reflected R, G, B and C light intensities, caused by displacements, were used to predict linear and rotational motion via a polynomial regression algorithm. To train and validate this model, we employed a custom-built positioning stage that produced controlled displacement and rotation while recording corresponding changes in light intensity. The reliability of the predicted linear and rotational displacement results was evaluated using two different color gradient wheels: a wheel with changing color hue, and another wheel with changing color hue and saturation. Benchtop experiments demonstrated high predictive accuracy, with coefficients of determination (R²) exceeding 0.94 for the hue-only wheel and 0.92 for the hue-and-saturation wheel. These results highlight the sensor’s potential for detecting shear displacement and rotation in footwear and wearable medical devices, such as orthotics and prostheses, enabling the detection of slippage, overfitting, or underfitting. This capability is particularly relevant to clinical conditions, including diabetic neuropathy, flat feet, and limb amputations. Full article

Conjugated polymers (CPs) offer many attractive features for photodiodes and photovoltaics, including solution processability, ease of scale-up, light weight, low cost, and mechanical flexibility. CPs have a wide range of energy gaps; thus, the choice of the specific polymer determines the optimum operational wavelength range. However, there are relatively few CPs with a strong absorption in the blue region of the spectrum where the human eye is most sensitive (440 to 470 nm) and none with an energy gap at 2.75 eV (450 nm), which corresponds to the peak of the CIE-1931 z(λ) color-matching function and the dominant blue light emission wavelength in computer and smartphone displays. Blue-light detectors in this wavelength range are important for light hazard control, sky polarization studies, and for blue-light information devices, where 450 nm corresponds to the principal emission of GaN-based light sources. We report on a new CP called Atums Green (AG), which shows promising characteristics as a blue-light photodetection polymer optimized for exactly this range of wavelengths centered around 450 nm. We built and measured a simple photodetector made from spin-coated films of AG and showed that its photosensitivity can be improved by the addition of asphaltene, a low-cost carbonaceous waste product. Full article

Thangka image detection poses unique challenges due to complex iconography, densely packed small-scale elements, and stylized color–texture compositions. Existing detectors often struggle to capture both global structures and local details and rarely leverage domain-specific visual priors. To address this, we propose a frequency- and prior-enhanced detection framework based on YOLOv11, specifically tailored for Thangka images. We introduce a Learnable Lifting Wavelet Block (LLWB) to decompose features into low- and high-frequency components, while LLWB_Down and LLWB_Up enable frequency-guided multi-scale fusion. To incorporate chromatic and directional cues, we design a Color-Gabor Block (CGBlock), a dual-branch attention module based on HSV histograms and Gabor responses, and embed it via the Color-Gabor Cross Gate (C2CG) residual fusion module. Furthermore, we redesign all detection heads with decoupled branches and introduce center-ness prediction, alongside an additional shallow detection head to improve recall for ultra-small targets. Extensive experiments on a curated Thangka dataset demonstrate that our model achieves 89.5% mAP@0.5, 59.4% mAP@[0.5:0.95], and 84.7% recall, surpassing all baseline detectors while maintaining a compact size of 20.9 M parameters. Ablation studies validate the individual and synergistic contributions of each proposed component. Our method provides a robust and interpretable solution for fine-grained object detection in complex heritage images. Full article

Mangroves serve as critical transitional ecosystems between land and sea. However, their large-scale possible impacts on coastal water quality have not been investigated. This study systematically examined the possible impacts of mangrove dynamics on coastal water quality in China over a 20-year period (1998–2018). Theil–Sen trend analysis and Mann-Kendall tests were employed to assess long-term trends of mangrove area and four water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), particulate attenuation coefficient at 660 nm (Cp660), and seawater transparency (Secchi disk depth, SDD). Partial correlation analysis and convergent cross-mapping (CCM) techniques were applied to evaluate the relationships between mangroves and water quality parameters, while a factor detector was used to quantify the specific contribution of mangroves to water quality improvement. The results revealed the following: (1) a significant nationwide expansion of mangroves, particularly after 2005, accompanied by accelerated recovery rates; (2) notable variations in water quality indicators, with SDD and CDOM experiencing degradation, while Chl-a and Cp660 showed varying degrees of improvement; (3) statistical evidence indicating that mangrove expansion was negatively partially correlated with Chl-a concentrations, and had moderate effects on CDOM, Cp660, and SDD. These findings highlight the measurable role of mangroves in improving coastal water quality at a national scale, provide a robust scientific basis for integrated coastal zone management, and underscore the need for further investigation into the underlying mechanisms, with comprehensive consideration of the dynamic impacts of climate change and anthropogenic activities. Full article

Colorimetric gas sensors are based on a color changing reaction of a sensor dye upon exposure to an analyte. For most sensor applications, the sensor dye must be immobilized in a sensor matrix. The choice of matrix significantly influences the dye’s response due to different physical and chemical effects. Ideal matrix materials should be transparent, stable, compatible with the sensor dye, and processable. Polymers are often applied as matrix materials, as they can be easily applied to sensor structures. In this study, we present a method to examine the impact of polymers of different structures and functionalities on sensor dyes. Therefore, 18 polymers are studied in combination with the pH indicator bromocresol green regarding their sensitivity to ammonia. The measurement setup is based on a camera as a detector of the color changing reaction of the sensor materials and allows for the simultaneous measurement of the sensor materials. Furthermore, the response and regeneration time, the stability, and the influence of the environmental parameters humidity and temperature on the colorimetric reaction are investigated. The study demonstrates that polymers as sensor matrices have an influence on the response of sensor dyes, due to their different properties, such as polarity. This has to be considered when choosing a suitable sensor matrix. Full article

Background: Dynamic changes in histone acetylation play crucial roles during cellular differentiation and disease development, but their detection in living cells is still a challenging task. Objectives: Here, we developed a Bimolecular Anchor Detector (BiAD) sensor for the detection of locus-specific changes in histone acetylation in living cells by fluorescence microscopy. Methods: We used the BRD9 bromodomain cloned as tandem double domain (2xBRD9-BD) as a reader of histone acetylation. It was integrated into a dual-color BiAD chassis that was previously described by us. Results: We identified the gene body of TTC34 as a potential target for our sensor, because it contains dense histone acetylation and 392 local sequence repeats. Using a binding-deficient mutant of 2xBRD9-BD as a negative control, we established a successful readout of histone acetylation at the TTC34 locus. A single-domain reader did not function, indicating the requirement for the double reader to enhance the affinity and specificity of the chromatin interaction via avidity effects. With this sensor, we could detect dynamic increases in histone acetylation at the TTC34 locus after the treatment of cells with the histone deacetylase inhibitor Trichostatin A for 6 h indicating the applicability of the sensor for single-cell epigenome studies. Conclusions: Our data demonstrate that active chromatin modifications can be detected by BiAD sensors using 2xBRD9-BD as a reader. This complements the toolkit of the available BiAD sensors and documents the modularity of BiAD sensors. Full article

Recently, the only UNESCO river in Nigeria has become polluted, with its color turning dark brown. Osun River serves not only domestic purposes in the city of Osogbo, but also spiritual purposes during the annual Osun Osogbo Festival (OOF). This study examines the physicochemical properties and presence of heavy metals in Osun River, and the air quality at the heritage site before, during, and after the festival. Water samples from Osun River at the UNESCO site were collected before, during, and after the 2024 festival. The water was analyzed at the Department of Environmental Health Sciences of Osun State University, Nigeria, to determine the quantity of heavy metals present in the river. Additionally, an air quality detector was used to assess the quantity of pollutants (CO₂, CO, PM_2.5, PM₁₀, TVOC, and HCHO) in the air before, during, and after the festival. In Osun River, the quantities of arsenic and copper were within the permissible levels set by the World Health Organization (WHO) for drinking water, while those of lead, chromium, and cadmium were far above the safety standards set by the WHO. The pollution rate of the river was in the order of festival day > before the festival > after the festival. The air quality on the festival day was hazardous to human health, as particulate matter (PM_2.5 and PM₁₀) and carbon dioxide were found to be far above the permissible levels set by the WHO. The implications of the findings of this study are discussed, and measures to ensure the future sustainability of this important UNESCO site in the city of Osogbo are recommended. Full article

Deltamethrin (Del), a widely utilized pyrethroid pesticide, exhibits significant risks to human health due to its persistent environmental residues. This study aims to develop an efficient sensing detector for rapid Del detection through aptamer-based recognition. A modified Capture-SELEX strategy successfully identified Del-1, a high-affinity DNA aptamer demonstrating specific binding to Del with a dissociation constant (Kd) of 82.90 ± 6.272 nM. Molecular docking analysis revealed strong intermolecular interactions between Del-1 and Del, exhibiting a favorable binding energy of −7.35 kcal·mol⁻¹. Leveraging these findings, we constructed a colorimetric detector using gold nanoparticles (AuNPs) and poly dimethyl diallyl ammonium chloride (PDDA)-mediated aggregation modulation. The sensing detector employed dual detection parameters: (1) a characteristic color transition from red to blue and (2) a quantitative ∆A650/A520 ratio measurement. This optimized system achieved a detection limit of 54.57 ng·mL⁻¹ with exceptional specificity against other competitive pesticides. Practical validation using spiked fruit samples (apples and pears) yielded satisfactory recoveries of 74–118%, demonstrating the sensor’s reliability in real-sample analysis. The developed methodology presents a promising approach for the on-site monitoring of pyrethroid contaminants in agricultural products. Full article

Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the system complexity. In addition, real applications of CSIs require advanced reconstruction algorithms. This paper proposes a low-cost color-coded compressive snapshot spectral imaging method to reduce the system complexity and improve the HSI reconstruction performance. The combination of a color-coded aperture and an RGB detector is exploited to achieve higher degrees of freedom in the spatio-spectral modulations, which also renders a low-cost miniaturization scheme to implement the system. In addition, a deep learning method named Focus-based Mask-guided Spectral-wise Transformer (F-MST) network is developed to further improve the reconstruction efficiency and accuracy of HSIs. The simulations and real experiments demonstrate that the proposed F-MST algorithm achieves superior image quality over commonly used iterative reconstruction algorithms and deep learning algorithms. Full article

The durability of the materials is often limited as they fade under the influence of external factors, particularly light. The present research aimed to study the photodegradation of commercial inkjet inks in an aqueous solution. The results were compared with their stability on prints in order to establish the connection between the kinetics of photodegradation of dye in the solution and the durability of the final print. Thin-layer chromatography (TLC), chromatography with a mass selective detector (GC/MS), and spectrophotometric measurements were used to study the effect of light, including near UV. The results clearly show that the catalytic effect between different dyes cannot be avoided, as the inks for inkjet printing are usually a mixture of different colorants. A comparison of the results of photodegradation of the dye in solution and on the final prints does not show a direct connection due to the different influences of external factors. Consequently, it was established that it is not possible to predict the photodegradation of prints solely based on a single dye’s analysis in solution. The paper as a substrate must be included in the analysis, as it significantly influences the photodegradation of the print. Full article