Search Results (37)

The increasing application of underwater robotic systems in deep-sea exploration, inspection, and resource extraction has created a strong demand for reliable visual perception under challenging conditions. However, image quality is severely degraded in low-light underwater environments due to the combined effects of light absorption and scattering, resulting in color imbalance, low contrast, and illumination instability. These factors limit the effectiveness of visual-based autonomous operations. We propose ATS-UGAN, a Dual-domain Adaptive Synergy Generative Adversarial Network for low-light underwater image enhancement to confront the above issues. The network integrates Multi-scale Hybrid Attention (MHA) that synergizes spatial and frequency domain representations to capture key image features adaptively. An Adaptive Parameterized Convolution (AP-Conv) module is introduced to handle non-uniform scattering by dynamically adjusting convolution kernels through a multi-branch design. In addition, a Dynamic Content-aware Markovian Discriminator (DCMD) is employed to perceive the dual-domain information synergistically, enhancing image texture realism and improving color correction. Extensive experiments on benchmark underwater datasets demonstrate that ATS-UGAN surpasses state-of-the-art approaches, achieving 28.7/0.92 PSNR/SSIM on EUVP and 28.2/0.91 on UFO-120. Additional reference and no-reference metrics further confirm the improved visual quality and realism of the enhanced images. Full article

This study explores the feasibility of an automated sensor system for precise plant protection product application in plum orchards, aiming to address issues related to inefficient spraying practices, environmental pollution, and reduced crop quality associated with traditional training systems. The research focuses on detecting tree canopy presence, evaluating electromagnetic valve actuation in different plum training systems, and optimizing plant protection product usage. Sensor-based spraying demonstrates its potential to improve operational efficiency, reduce product losses, and foster environmentally responsible agricultural practices, contributing to the broader field of precision agriculture. For the selected scene, the results show the possibility of a substantial savings of 71.37%, 47.17%, 58.59%, and 55.06% for the One-axis, Bi-axis, UFO, and Combine systems, respectively. Implementing this technology can potentially lead to significant improvements in plum orchard operations while minimizing the industry’s ecological impact on the environment. Full article

The exponential growth of digital news sources presents a critical challenge in efficiently processing and analyzing vast datasets to derive actionable insights. This paper introduces a GPT-based news analytics system that addresses this issue using advanced mathematical modeling and AI techniques. Over a 405-day period, the system processed 1,033,864 news articles, categorizing 90.67% into 202 subcategories across 11 main categories. The system achieved an average precision of 0.924, recall of 0.920, and F1-score of 0.921 in event correlation analysis and demonstrated a fast average execution time of 21.38 s per query, enabling near-real time analysis. The system critically analyzes semantic relationships between events, allowing for robust event correlation analysis, with precision and recall reaching up to 1.000 for specific pairs such as “UFO” and “Cyber”. Using dimensional augmentation, probabilistic feature extraction, and a semantic knowledge graph, the system provides robust event relationships for modeling unstructured news reports. Additionally, the integration of spectral residual and convolutional neural networks helps to identify anomalies in time-series news data with 85% sensitivity. Unlike existing solutions reported in the literature, the proposed system introduces a unified mathematical framework for large-scale news analytics, seamlessly integrating advanced methods such as large language models, knowledge graphs, anomaly detection, and event correlation to deliver fast and efficient performance. This scientifically novel and scalable framework offers a transformative approach to solving the pressing problem of news analytics, offering significant value to researchers, policymakers, and media analysts. Full article

To prevent frost damage in fruit trees, growers employ passive and active methods, and one of these second methods is the use of biostimulant compounds against abiotic stress. In this study, two trials were conducted to evaluate the effectiveness of a multi-attribute approach biostimulant—containing α-tocopherol, boron, and glycols, in peach (‘UFO-4’ cultivar) and almond (‘Vairo’ cultivar) trees. In a first trial, one-year-old shoots with flowers were collected after 24 h, 48 h, and 96 h of the biostimulant applications. Two different application rates of the product (1000 and 2000 cc ha⁻¹) were tested and compared to an untreated control. In a second trial, one-year-old shoots with fruitlets were collected after 24 h of the biostimulant applications. In this case, only an application rate (2000 cc ha⁻¹) was tested. In the two trials, the collected one-year-old shoots were subjected to different frost temperatures using a controlled environment chamber. The damage level was assessed by a morphological analysis of the flowers and fruitlets 96 h after each frost cycle simulation. The lethal temperatures (LT₁₀, LT₅₀, and LT₉₀) of each treatment were calculated by probit analysis. The product applied 24 h and 48 h before the frost simulations significantly decreased the LT₁₀ and LT₅₀ in 1.5 °C in peach flowers, and 2.5 °C in almond flowers (a temperature reduction of 50% and 75%, respectively). These results were more consistent when the application volume was 2000 cc ha⁻¹, instead of 1000 cc ha⁻¹. Significant differences between treated and non-treated fruitlets were observed only in almond fruitlets, with LT₁₀ and LT₅₀ being 0.5 °C lower in treated fruitlets (20% reduction). In conclusion, the multi-attribute approach biostimulant applied 24 or 48 h before the frost reduced the mortality of peach and almond flowers, but its effectiveness to protect fruitlets after bloom was inconsistent. Full article

Melon pest management relies on the excessive application of pesticides. Reducing pesticide spraying has become a global issue for environmental sustainability and human health. Therefore, developing a new cropping system that is sustainable and eco-friendly is important. This study found that melon seedlings irrigated with ultrafine water containing H₂ and O₂ (UFW) produced more root hairs, increased shoot height, and produced more flowers than the control irrigated with reverse osmosis (RO) water. Surprisingly, we also discovered that UFW irrigation significantly reduced aphid infestation in melons. Based on cryo-scanning electron microscope (cryo-SEM) observations, UFW treatment enhanced trichome development and prevented aphid infestation. To investigate whether it was H₂ or O₂ that helped to deter insect infestation, we prepared UF water enrichment of H₂ (UF+H₂) and O₂ (UF+O₂) separately and irrigated melons. Cryo-SEM results indicated that both UF+H₂ and UF+O₂ can increase the density of trichomes in melon leaves and petioles. RT-qPCR showed that UF+H₂ significantly increased the gene expression level of the trichome-related gene GLABRA2 (GL2). We planted melons in a plastic greenhouse and irrigated them with ultrafine water enrichment of hydrogen (UF+H₂) and oxygen (UF+O₂). The SPAD value, photosynthetic parameters, root weight, fruit weight, and fruit sweetness were all better than the control without ultrafine water irrigation. UFW significantly increased trichome development, enhanced insect resistance, and improved fruit traits. This system thus provides useful water management for pest control and sustainable agricultural production. Full article

There has been a growing policy interest in establishing connections between heritage and education. Nevertheless, there seems to be very little evaluation or critical reflection on what actually happens in practice, and it remains unclear how heritage education is employed in countries throughout Europe. The aim of this paper is to assess the current status of heritage education in Flanders (the Dutch-speaking northern part of Belgium) via a literature review and an exploratory case study. The perceptions and opinions of pre-service teachers (n = 17) were investigated using three instruments: a questionnaire, document analysis, and the think aloud method. The results show that the pre-service teachers had a traditional interpretation of heritage, mainly relying on well-known and monumental examples and following a rather historical–artistic conception. However, it was found that their opinions were positive towards the use of heritage as an educational resource, and their evaluations of educational materials from the heritage and museum sector provided detailed information concerning teachers’ desires and needs in this regard. The implications of this study should encourage initial teacher training in Flanders to further consider the epistemological and methodological challenges in the emerging field of heritage education. Full article

Santiago, Chile is a very segregated city, with higher childhood obesity rates observed in vulnerable areas. We compared the counts and proximity of unhealthy food outlets (UFOs) around a 400 m buffer of 443 public schools (municipal and subsidized) located in socioeconomically diverse neighborhoods in 14 municipalities of Santiago. This was a cross-sectional study in which the socioeconomic status (SES) of the population living inside the buffer was classified as middle-high, middle, and low. We used the Kruskal–Wallis test for comparisons of density and proximity between type of school, SES, and population density. We used a negative binomial model (unadjusted and adjusted by population density) to determine the expected change in counts of UFOs by SES, which was compared to the reference (middle-high). Low SES neighborhoods had significantly more counts of UFOs, and these were located much closer to schools. Low and middle SES neighborhoods had an 88% and 48% higher relative risk of having UFOs compared to middle-high SES areas; (IRR = 1.88; 95% CI 1.59–2.23) and (IRR = 1.48; 95% CI 1.20–1.82), respectively. A socio-spatial segregation of UFOs associated with childhood obesity across public schools was observed in Santiago. Full article

Background: Severe coronavirus disease 2019 (COVID-19) disease courses are characterized by immuno-inflammatory, thrombotic, and parenchymal alterations. Prediction of individual COVID-19 disease courses to guide targeted prevention remains challenging. We hypothesized that a distinct serologic signature precedes surges of IL-6/D-dimers in severely affected COVID-19 patients. Methods: We performed longitudinal plasma profiling, including proteome, metabolome, and routine biochemistry, on seven seropositive, well-phenotyped patients with severe COVID-19 referred to the Intensive Care Unit at the German Heart Center. Patient characteristics were: 65 ± 8 years, 29% female, median CRP 285 ± 127 mg/dL, IL-6 367 ± 231 ng/L, D-dimers 7 ± 10 mg/L, and NT-proBNP 2616 ± 3465 ng/L. Results: Based on time-series analyses of patient sera, a prediction model employing feature selection and dimensionality reduction through least absolute shrinkage and selection operator (LASSO) revealed a number of candidate proteins preceding hyperinflammatory immune response (denoted ΔIL-6) and COVID-19 coagulopathy (denoted ΔD-dimers) by 24–48 h. These candidates are involved in biological pathways such as oxidative stress/inflammation (e.g., IL-1alpha, IL-13, MMP9, C-C motif chemokine 23), coagulation/thrombosis/immunoadhesion (e.g., P- and E-selectin), tissue repair (e.g., hepatocyte growth factor), and growth factor response/regulatory pathways (e.g., tyrosine-protein kinase receptor UFO and low-density lipoprotein receptor (LDLR)). The latter are host- or co-receptors that promote SARS-CoV-2 entry into cells in the absence of ACE2. Conclusions: Our novel prediction model identified biological and regulatory candidate networks preceding hyperinflammation and coagulopathy, with the most promising group being the proteins that explain changes in D-dimers. These biomarkers need validation. If causal, our work may help predict disease courses and guide personalized treatment for COVID-19. Full article

The underwater images acquired by marine detectors inevitably suffer from quality degradation due to color distortion and the haze effect. Traditional methods are ineffective in removing haze, resulting in the residual haze being intensified during color correction and contrast enhancement operations. Recently, deep-learning-based approaches have achieved greatly improved performance. However, most existing networks focus on the characteristics of the RGB color space, while ignoring factors such as saturation and hue, which are more important to the human visual system. Considering the above research, we propose a two-step triple-color space feature fusion and reconstruction network (TCRN) for underwater image enhancement. Briefly, in the first step, we extract LAB, HSV, and RGB feature maps of the image via a parallel U-net-like network and introduce a dense pixel attention module (DPM) to filter the haze noise of the feature maps. In the second step, we first propose the utilization of fully connected layers to enhance the long-term dependence between high-dimensional features of different color spaces; then, a group structure is used to reconstruct specific spacial features. When applied to the UFO dataset, our method improved PSNR by 0.21% and SSIM by 0.1%, compared with the second-best method. Numerous experiments have shown that our TCRN brings competitive results compared with state-of-the-art methods in both qualitative and quantitative analyses. Full article

Three-dimensional point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we designed an augmented sampling and grouping module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to extract point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first tried to use a linearly normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module was adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that this method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which is 0.5% higher than PCT. Our network also achieved 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT. Full article

The UNUSUAL FLORAL ORGANS (UFO) gene is an essential regulatory factor of class B genes and plays a vital role in the process of inflorescence primordial and flower primordial development. The role of UFO genes in soybean was investigated to better understand the development of floral organs through gene cloning, expression analysis, and gene knockout. There are two copies of UFO genes in soybean and in situ hybridization, which have demonstrated similar expression patterns of the GmUFO1 and GmUFO2 genes in the flower primordium. The phenotypic observation of GmUFO1 knockout mutant lines (Gmufo1) showed an obvious alteration in the floral organ number and shape and mosaic organ formation. By contrast, GmUFO2 knockout mutant lines (Gmufo2) showed no obvious difference in the floral organs. However, the GmUFO1 and GmUFO2 double knockout lines (Gmufo1ufo2) showed more mosaic organs than the Gmufo1 lines, in addition to the alteration in the organ number and shape. Gene expression analysis also showed differences in the expression of major ABC function genes in the knockout lines. Based on the phenotypic and expression analysis, our results suggest the major role of GmUFO1 in the regulation of flower organ formation in soybeans and that GmUFO2 does not have any direct effect but might have an interaction role with GmUFO1 in the regulation of flower development. In conclusion, the present study identified UFO genes in soybean and improved our understanding of floral development, which could be useful for flower designs in hybrid soybean breeding. Full article

The System of Rice Intensification (SRI), an agro-ecological approach to rice cultivation developed in Madagascar, has generated considerable interest worldwide. Having not been developed at a research establishment but rather from observation and testing on farmers’ fields, SRI attracted considerable controversy, for example, with unwarranted objections that it lacked of scientific evidence, and being characterized as based on ‘unconfirmed field observations’ (UFOs). One 2004 article concluded that “the system of rice intensification does not fundamentally change the physiological yield potential of rice”. This assertion was not based on any physiological examination of rice plants grown using SRI methodology, however, or on any systematic comparison with what would be considered as best management practices (BMPs), recommended practices (RPs), or farmer practices (FPs). Other dismissals of SRI have had contestable data selection, analytical methods, and presentation of results. The published literature provides abundant evidence that the earlier evaluations of SRI were either not well-informed or objective, and possibly, they discouraged others from embarking on systematic evaluations of their own. This article examines the results of 78 studies in the published literature where SRI results were explicitly compared with RPs, including BMPs or FPs. The results from 27 countries, plus several large-scale evaluations, show that in 80% of the evaluations, grain yield was higher under SRI than with RPs or FPs. SRI gave 24% higher grain yield than RPs and 56% more than FPs, while reducing seed, water, and fertilizer inputs. Beyond the descriptive evidence, this paper considers that the phenotypical changes and physiological improvements in SRI-grown rice plants could account for the reported enhancement in yield. More research remains to be undertaken to elucidate casual mechanisms, but abundant evidence shows that this is a subject deserving considerable scientific effort. Full article

The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%. Full article

Flower bud differentiation is crucial to reproductive success in plants. In the present study, RNA-Seq and nutrients quantification were used to identify the stage-specific genes for flower bud differentiation with buds which characterize the marked change during flower bud formation from a widely grown Chinese cherry (Prunus pseudocerasus L.) cultivar ‘Manaohong’. A KEGG enrichment analysis revealed that the sugar metabolism pathways dynamically changed. The gradually decreasing trend in the contents of total sugar, soluble sugar and protein implies that the differentiation was an energy-consuming process. Changes in the contents of D-glucose and sorbitol were conformed with the gene expression trends of bglX and SORD, respectively, which at least partially reflects a key role of the two substances in the transition from physiological to morphological differentiation. Further, the WRKY and SBP families were also significantly differentially expressed during the vegetative-to-reproductive transition. In addition, floral meristem identity genes, e.g., AP1, AP3, PI, AGL6, SEP1, LFY, and UFO demonstrate involvement in the specification of the petal and stamen primordia, and FPF1 might promote the onset of morphological differentiation. Conclusively, the available evidence justifies the involvement of sugar metabolism in the flower bud differentiation of Chinese cherry, and the uncovered candidate genes are beneficial to further elucidate flower bud differentiation in cherries. Full article

Zika virus (ZIKV) compromises placental integrity, infecting the fetus. However, the mechanisms associated with ZIKV penetration into the placenta leading to fetal infection are unknown. Cystatin B (CSTB), the receptor for advanced glycation end products (RAGE), and tyrosine-protein kinase receptor UFO (AXL) have been implicated in ZIKV infection and inflammation. This work investigates CSTB, RAGE, and AXL receptor expression and activation pathways in ZIKV-infected placental tissues at term. The hypothesis is that there is overexpression of CSTB and increased inflammation affecting RAGE and AXL receptor expression in ZIKV-infected placentas. Pathological analyses of 22 placentas were performed to determine changes caused by ZIKV infection. Quantitative proteomics, immunofluorescence, and western blot were performed to analyze proteins and pathways affected by ZIKV infection in frozen placentas. The pathological analysis confirmed decreased size of capillaries, hyperplasia of Hofbauer cells, disruption in the trophoblast layer, cell agglutination, and ZIKV localization to the trophoblast layer. In addition, there was a significant decrease in CSTB, RAGE, and AXL expression and upregulation of caspase 1, tubulin beta, and heat shock protein 27. Modulation of these proteins and activation of inflammasome and pyroptosis pathways suggest targets for modulation of ZIKV infection in the placenta. Full article