Search Results (114)

A digital colorimetric method (ACETimage), which utilizes aldol condensation, crotonization, and resinification, was developed and validated to quantify acetaldehyde in the head fraction of Pisco distillation. The optimal conditions for the reaction were as follows: the head Pisco samples were placed in headspace vials, 20% w/w NaOH was added, and the mixture was boiled in water for 2 min. The Color Grab app was used to capture and analyze images of the reactions, with a screen brightness intensity of 0.5, within a maximum post-reaction time of 10 min. The Euclidean distance (ED-RGB) was the color parameter most sensitive to changes, showing a linear correlation with the square of acetaldehyde concentration, with R² values ranging from 0.9926 to 0.9976. The limit of detection (LOD) and limit of quantification (LOQ) for the ACETimage method were determined to be 30 and 95.3 mg/L, respectively. A significant correlation was observed between the acetaldehyde content measured using ACETimage and gas chromatography (Spearman’s r = 0.9373). Bland–Altman analysis indicated that the differences between the two methods were within the 95% limits of agreement. ACETimage offers a rapid, cost-effective, and user-friendly solution for monitoring acetaldehyde levels during Pisco distillation, enabling easy implementation in production environments, both artisanal and industrial, with minimal sample preparation and limited personnel training. Full article

The Laser Interferometer Space Antenna (LISA) is an ESA mission designed to detect gravitational waves from space. To initiate the science phase, six test masses (TMs) are precisely handled and released into near-perfect free fall by dedicated mechanisms known as the Grabbing, Positioning, and Release Mechanisms (GPRMs). The stringent requirements on the noise level affecting the TMs’ release acceleration are extremely ambitious, motivating the need to experimentally verify the feasibility of achieving such performance. To this end, a dedicated precursor mission, LISA Pathfinder (LPF), flew from 2015 to 2017 to test key technologies. However, during the LPF mission, most release tests exhibited anomalous release velocities, often exceeding the requirements. In addition, the TM repositioning tests also revealed a bi-stable behavior in the TM rotations, which depend on the repositioning direction. This effect is produced by an unexpected non-rectilinear motion of the GPRM end effector, characterized by a micrometric side motion at the reversal of its axial motion. The bi-stable behavior also contributes to a TM-GPRM end effector misalignment, producing unwanted contacts and increasing the probability of a non-compliant TM release. Previous analyses identified asymmetric friction forces in the side-guiding system of the GPRM end effector as the primary cause of this behavior. Starting from the LPF flight experience, the GPRM delta development project in view of LISA led to a redesign of the mechanism architecture, supported by numerical analyses and multi-body models. Since the rectilinearity of the end-effector motion has been identified as critical for flight operation, alternative side-guiding concepts are developed, analyzed, and tested experimentally to evaluate their impact on the overall mechanism performance. The correlation of the models with ground and flight experimental data strengthens the understanding of the guiding system behavior, providing pivotal insights for selecting the GPRM design baseline for LISA. Full article

Pesticide contamination poses significant threats to both humans and the environment, with residues frequently detected in surface waters worldwide. This study compares the effectiveness of passive samplers (POCIS and Chemcatcher) and grab sampling coupled with Stir-Bar Sorptive Extraction (SBSE) and Solid-Phase Extraction (SPE) for monitoring pesticides in surface waters. The comparative study was conducted at three sites in Victoria, Australia, representing different land uses. A total of 230 pesticides were screened, with 79 different pesticides detected overall. SBSE extracted the highest number of pesticides from grab samples, followed by SPE and passive samplers. The study highlights the complementarity of different sampling and extraction techniques in detecting a wide range of pesticides. The study also explores the suitability of these techniques for citizen science applications, emphasizing the importance of selecting appropriate methods based on specific research objectives and available resources. The findings underscore the need for a tiered approach, combining passive samplers for initial screening and grab sampling for quantitative analysis, to develop a robust monitoring strategy for protecting water quality. Full article

Accurate segmentation of adhered (sticky) corn kernels and reliable damage detection are critical for quality control in corn processing and kernel selection. Traditional watershed algorithms suffer from over-segmentation, whereas deep learning methods require large annotated datasets that are impractical in most industrial settings. This study proposes W&C-SVM, a hybrid computer vision method that integrates an improved watershed algorithm (Sobel gradient and Euclidean distance transform), convex hull defect detection and an SVM classifier trained on only 50 images. On an independent test set, W&C-SVM achieved the highest damage detection accuracy of 94.3%, significantly outperforming traditional watershed SVM (TW + SVM) (74.6%), GrabCut (84.5%) and U-Net trained on the same 50 images (85.7%). The method effectively separates severely adhered kernels and identifies mechanical damage, supporting the selection of intact kernels for quality control. W&C-SVM offers a low-cost, small-sample solution ideally suited for small-to-medium food enterprises and breeding laboratories. Full article

This study presents the first evaluation of plastic particle contamination along a complete drinking water supply chain within the distribution system of Milan, Northern Italy. Fourteen grab water samples were collected from various points, including groundwater extraction, post-treatment stages, a public fountain, and ten household taps. Plastic particles were identified using µFTIR spectroscopy and characterized by polymer type, shape, size, and color. Overall, low concentrations of plastic particles were detected, ranging from 0.3 ± 0.5 particles/L in the accumulation tank to an average of 1.9 ± 1.4 particles/L in household tap water, with no significant increase observed along the supply chain. The entire data set was dominated by cellulose particles (76%), as plastics accounted for only 8%. Microplastics (1 µm–1 mm) were the most commonly detected (90%), while the remaining 10% were large microplastics (1–5 mm). Qualitatively, polyester fibers were the most prevalent particles identified. However, greater variability and higher concentrations were found in private residence samples, suggesting that internal plumbing systems may be a primary source of contamination. Estimated human exposure through this supply system, based on a current theoretical model, was minimal compared to global benchmarks. These findings highlight the necessity of integrating private distribution infrastructure into future regulatory frameworks to assist stakeholders in making informed decisions to mitigate potential plastic contamination. Full article

Per- and polyfluoroalkyl substances (PFASs) represent a group of persistent environmental contaminants with known adverse health effects. This study assessed the presence and concentrations of PFASs in surface water across various locations along the Gunpowder River Watershed in Maryland, United States. Gunpowder RIVERKEEPER^® a 501(c)(3) nonprofit collected eleven surface water grab samples from the Gunpowder River Watershed for the study, including both drinking water sources and non-drinking tributaries. Of the 55 PFASs analyzed, multiple compounds, including PFOS, PFOA, PFBS, PFHxA, PFPeA, and PFHpA, were detected above reporting limits at all sampled locations. Total PFAS concentrations varied substantially across the watershed, ranging from 2.1 to 21.3 ng/L in drinking water source tributaries and 6.6–18.4 ng/L in non-drinking tributaries. Several sites exhibited PFOS and PFOA concentrations exceeding the 2022 U.S. EPA interim lifetime health advisory levels, indicating potential risk to downstream communities relying on these water sources. Short-chain PFASs (C ≤ 7) were more abundant than long-chain PFASs, reflecting their greater mobility and persistence in surface waters. These findings demonstrate watershed-wide PFAS contamination and highlight the potential for trophic transfer and bioaccumulation in fish species in these tributaries and subsequent human exposure. Continued monitoring, regulation, and remediation efforts are required to mitigate PFAS contamination and safeguard public health in vulnerable ecosystems and populations. Further research is needed to better understand the extent of PFAS exposure, associated health risks, and effective strategies for prevention and management. Full article

Norovirus is the leading cause of acute gastroenteritis worldwide, responsible for up to 90% of viral gastroenteritis outbreaks and an estimated 10.6 billion USD in annual economic losses in the U.S. Despite its well-documented seasonality, wastewater surveillance in the Eastern Upper Peninsula of Michigan reveals persistent GI norovirus detection year-round, diverging from national clinical trends that consistently show far greater GII prevalence. To characterize norovirus dynamics in this region, 250 mL wastewater influent grab samples were collected once per week across 14 sites, concentrated using a PEG-based method, and analyzed via digital droplet PCR (ddPCR) for GI and GII concentrations. Across the study period, the rate of positive sites per month ranged from 57 to 100% for GI and 74 to 97% for GII, with mean positivity rates of 85.4% (GI) and 88.7% (GII), indicating that both genogroups were detected frequently at comparable levels. GI was more prevalent in winter and spring (December–May), whereas GII was more prevalent during spring and summer (March–August). Mean GI gene copies per 100 mL ranged from 12,898 (October) to 532,792 (February), while mean GII concentrations ranged from 29,806 (December) to 1,100,215 (May). These patterns contrast with national clinical data, where GI contributes to a small minority of reported norovirus cases. This study explores potential environmental and behavioral factors contributing to this regional pattern. GI norovirus demonstrates greater resistance to wastewater treatment and environmental stability, which may facilitate its persistence in the region. Additionally, congregate living settings, such as college campuses and correctional facilities, may contribute to sustained GI prevalence through foodborne transmission and asymptomatic viral shedding. Overall, these findings suggest that environmental and social factors influence norovirus seasonality and genogroup distribution in this region, underscoring the need for improved monitoring and expanded multi-site wastewater and epidemiological research to better understand norovirus persistence in similar communities. Full article

An enhanced method called FreeViBe+ for moving target segmentation is proposed in this paper, addressing limitations in the ViBe algorithm such as ghosting, shadows, and holes. To eliminate ghosts, multi-frame background modeling is introduced. Shadows are detected and removed based on their characteristics in the HSV color space, while holes are filled by merging GrabCut segmentation results with the ViBe extraction output. Furthermore, the Structure-measure is tuned to optimize image fusion, enabling improved foreground–background separation. Comprehensive experiments on the UCF101 and Weizmann datasets demonstrate the effectiveness of FreeViBe+ in comparison with Finite Difference, Gaussian Mixture Model, and ViBe methods. Ablation studies confirm the individual contributions of multi-frame modeling, shadow removal, and GrabCut refinement, while sensitivity analysis verifies the robustness of key parameters. Quantitative evaluations show that FreeViBe+ achieves superior performance in precision, recall, and F-measure compared with existing approaches. Full article

In the Northern Hemisphere, annual waves of influenza disease with varying degrees of spread and severity are observed each winter. With wastewater-based surveillance (WBS), including both centralized (one wastewater treatment plant, WWTP) and decentralized (three sewers) sampling, we aimed to detect differences in influenza viral copy numbers in wastewater over time, to investigate (sub)-community transmission within a city. A total of 313 grab/spot and composite samples were collected in Munich, Germany, during three consecutive influenza seasons (2022/23, 2023/24, and 2024/25) and were analyzed for influenza A virus (IAV) and influenza B virus (IBV) nucleic acids using digital droplet PCR (ddPCR). IAV and IBV wastewater copy numbers and citywide reported influenza cases showed strong correlations in both sampling approaches, suggesting the decentralized approach to be a reliable indicator of infection trends across the city. The three influenza seasons analyzed differed significantly in terms of their seasonal distribution, for example, exhibiting a strong co-circulation of IAV and IBV only in the 2024/25 season. Only with wastewater analysis, we reveal a reporting delay of influenza A cases at the beginning of the 2023/24 season. Higher influenza copy numbers were detected in sewer samples compared to the WWTP influent, likely due to viral decay. The study underscores the potential of influenza WBS to enable detection of seasonal onset early, identify local transmission patterns, and reveal underreporting in routine surveillance systems. Full article

Aiming at the low qualification rate and high damage caused by the lack of identification, localization, and posture estimation of tea buds in the mechanical harvesting process of famous tea, a framework of lightweight detection + PCA-skeleton fusion posture estimation was proposed. Based on the YOLOv8n model, the StarNet backbone network was introduced to enable lightweight detection, and the ASF-YOLO multi-scale attention module was embedded to improve the feature fusion ability. Based on the detection frame, the GrabCut-Watershed fusion segmentation was employed to obtain the bud mask. Combined with PCA and skeleton extraction algorithms, the main direction deviations of bent buds and clasped leaves were solved by Bézier curve fitting, and the morphology–posture dual-factor scoring model was thereby constructed to realize the picking ranking. Compared with the original YOLOv8n model, the results showed that the detection accuracy and mAP50 of the Improved model decreased to 85.6% and 90.5%, respectively, and the recall rate increased to 81.7%. Meanwhile, the calculation load of the improved model was reduced by 23.6%, reaching 6.8 GFLOPs, indicating a significant improvement in lightweight. The morphology–posture dual-factor scoring model achieved a score of 0.88 for a single bud in vertical direction (θ ≈ 90°), a score of approximately 0.66–0.71 for buds with partially unfolded leaves and slightly bent buds, and a score of 0.48–0.53 for severely bent and overlapped buds. The results of this study have the potential to guide the picking robotic arms to preferentially pick tea buds with high adaptability and provide a reliable visual solution for low-loss and high-efficiency mechanized harvesting of famous tea in complex tea gardens. Full article

Eye-tracking technology has gained traction in the field of medical rehabilitation due to its non-invasive and intuitive nature. However, current eye-tracking methods based on object detection technology suffer from insufficient accuracy in detecting the eye socket and iris, as well as inaccuracies in determining eye movement direction. To address this, this study improved the YOLOv11 model using the EFFM and ORC modules, resulting in a 1.7% and 9.9% increase in recognition accuracy for the eye socket and iris, respectively, and a 5.5% and 44% increase in recall rate, respectively. A method combining frame voting mechanisms with eye movement area discrimination was proposed for eye movement direction discrimination, achieving average accuracy rates of 95.3%, 92.8%, and 94.8% for iris fixation, left, and right directions, respectively. The discrimination results of multiple eye movement images were mapped to a binary value, and eye movement encoding was used to obtain control commands that align with the robotic arm. The average matching degree of eye movement encoding ranged from 93.4% to 96.8%. An experimental platform was established, and the average completion rates for three object-grabbing tasks controlled by eye movements were 98%, 78%, and 96%, respectively. Full article

Traditional GrabCut algorithms are limited by their reliance on manual intervention, often resulting in segmentation errors and missed detections, particularly against complex backgrounds. This study addresses these limitations by introducing the Auto Trademark Cut (AT-Cut), an advanced automated trademark image-segmentation method built upon an enhanced GrabCut framework. The proposed approach achieves superior performance through three key innovations: Firstly, histogram equalization is applied to the entire input image to mitigate noise induced by illumination variations and other environmental factors. Secondly, state-of-the-art object detection techniques are utilized to precisely identify and extract the foreground target, with dynamic region definition based on detection outcomes to ensure heightened segmentation accuracy. Thirdly, morphological erosion and dilation operations are employed to refine the contours of the segmented target, leading to significantly improved edge segmentation quality. Experimental results indicate that AT-Cut enables efficient, fully automated trademark segmentation while minimizing the necessity for labor-intensive manual labeling. Evaluation on the public Real-world Logos dataset demonstrates that the proposed method surpasses conventional GrabCut algorithms in both boundary localization accuracy and overall segmentation quality, achieving a mean accuracy of 90.5%. Full article

The determination of contaminants of emerging concern (CECs) in the marine environment is performed through many different sampling approaches. Therefore, the main objective of this study was to compare different existing sampling strategies: biofilm mesocosms, considering micro- and macrofouling; passive sampling; and grab marine water. The sampling of grab water was performed considering spatial and time-line variations. The spatial analysis of CECs showed that three sun agents and caffeine represent the biggest proportion of CECs in the Malaga Mediterranean coastal area, ranging from 0.391 to 0.495 ng/L. The time-line analysis did not show any upward or downward trend in CEC concentration. The mesocosm study comprised a separate evaluation of micro- and macrofouling that showed similar profiles, in which the sun agents presented the highest concentrations. While certain compounds were detected at comparable levels in both fouling types, such as clotrimazole around 0.001 ng/L, others exhibited significant differences in concentration, like caffeine. The passive sampling was also performed, obtaining similar results to those observed in the biofilm mesocosm macrofouling. Finally, all the obtained results from the different samplings were statistically compared, showing that passive sampling presented greater similarities with macrofouling and that there are significant differences between the sampling approach employed. Full article

Microplastic (MP) contamination in riverine systems poses a growing environmental challenge, and their spatial and temporal variability complicates proper assessments. This study investigated MP concentrations (≥10 µm) across three German rivers using fluorescent staining-based detection. The results reveal highly heterogeneous distributions ranging from 4 to 1761 MP/L. The Rehbach displayed the highest mean MP concentration (540 ± 476 MP/L), whereas the Alb had the lowest (98 ± 54 MP/L). Long-term monitoring underscored pronounced temporal fluctuations linked to changing inputs, weather events, and hydrodynamics. To capture these fluctuations, monitoring campaigns must consider an appropriate temporal sampling framework. Further, to address detection challenges, the study compared 0.5 L grab sampling with 100 L pump sampling (PSU) and observed that the PSU yielded 4.7 times higher MP concentrations with improved reproducibility (27 ± 25% vs. 49 ± 33%). These results highlight the critical need for standardized protocols and scalable, cost-effective methods for reliable MP quantification and hotspot identification in freshwater environments. Full article

Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture. Full article