Search Results (5,924)

The pollen wall protects pollen during dispersal and is critical for pollination recognition. In the Poaceae family, the pollen exine stereostructure exhibits a high degree of conservation with similar patterns across species. However, there remains controversy regarding the conservation of key factors involved in its formation among various Poaceae species. EPAD1, as a gene specific to the Poaceae family, and its orthologous genes play a conserved role in pollen wall formation in wheat and rice. However, they do not appear to have significant functions in maize. To further confirm the conserved function of EPAD1 in Poaceae, we performed an analysis on four EPAD1 orthologs from two distinct sub-clades within the Poaceae family. The two functional redundant barley EPAD1 genes (HvEPAD1 and HvEPAD2) from the BOP clade, along with the single copy of sorghum (SbEPAD1) and millet (SiEPAD1) from the PACMAD clade were examined. The CRISPR-Cas9-generated mutants all exhibited defects in pollen wall formation, consistent with previous findings on EPAD1 in rice and wheat. Interestingly, in barley, hvepad2 single mutant also showed apical spikelets abortion, aligning with a decreased expression level of HvEPAD1 and HvEPAD2 from the apical to the bottom of the spike. Our finding provides evidence that EPAD1 orthologs contribute to Poaceae specific pollen exine pattern formation via maintaining primexine integrity despite potential variations in copy numbers across different species. Full article

This paper proposes a novel object detection method to address the challenges posed by small objects and occlusion in object detection. This work is performed within the CenterNet framework, leveraging the MobileNetV3 backbone to model the input image’s abstract representation in a lightweight manner. A sparse convolutional skip connection is introduced in the bottleneck of MobileNetV3, specifically designed to adaptively suppress redundant and interfering information, thus enhancing feature extraction capabilities. A Dual-Path Bidirectional Feature Pyramid Network (DBi-FPN) is incorporated, allowing for high-level feature fusion through bidirectional flow and significantly improving the detection capabilities for small objects and occlusions. Task heads are applied within the feature space of multi-scale information merged by DBi-FPN, facilitating comprehensive consideration of multi-level representations. A bounding box-area loss function is also introduced, aimed at enhancing the model’s adaptability to object morphologies and geometric distortions. Extensive experiments on the PASCAL VOC 2007 and MS COCO 2017 datasets validate the competitiveness of our proposed method, particularly in real-time applications on resource-constrained devices. Our contributions offer promising avenues for enhancing the accuracy and robustness of object detection systems in complex scenarios. Full article

As a core component of electric vehicles, the state of health (SOH) of lithium-ion battery has a direct impact on vehicle performance and safety. Existing data-driven models primarily focus on feature extraction, often overlooking the processing of multi-level redundant information and the utilization of multi-stage battery features. To address the issues, this paper proposes a novel data-driven method, named multi-stage mixed attention U-Net (MMAU-Net), for SOH estimation, which is based on both the phased learning and an encoder–decoder structure. First, the geometric knee-point division method is proposed to divide the battery life cycle into multiple stages, which allows the model to learn the distinctive features of battery degradation at each stage. Second, to adeptly capture degraded features and reduce redundant information, we propose a mixed attention U-Net model for the SOH prediction task, which is constructed upon the fundamental U-Net backbone and is enhanced with time step attention and feature attention modules. The experimental results validate the proposed method’s feasibility and efficacy, demonstrating an acceptable performance across a spectrum of evaluative metrics. Consequently, this study offers a research within the domain of battery health management. Full article

The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes an automated method for scaffolding assembly quality inspection using point cloud data and feature selection algorithms. High-precision point cloud data of the scaffolding are captured by a Trimble X7 3D laser scanner. After registration with the forward design model, a 2D slicing comparison method is developed to measure geometric dimensions with an accuracy controlled within 0.1 mm. The collected data are used to build an SVM model for automated assembly quality inspection. To combat the curse of dimensionality associated with high-dimensional data, an optimized genetic algorithm is employed for the dimensionality reduction in the raw sample data, effectively eliminating data redundancy and significantly enhancing convergence speed and classification accuracy of the detection model. Case studies indicate that the proposed method can reduce feature dimensionality by 70% while simultaneously improving classification accuracy by 13.9%. The proposed method enables high-precision automated inspection of scaffolding assembly quality. By identifying the optimal feature subset, the method differentiates the priority of various structural parameters during inspection, providing insights for optimizing the quality inspection process. Full article

The “cost of domestication” hypothesis suggests that the domestication of wild species increases the number, frequency, and/or proportion of deleterious genetic variants, potentially reducing their fitness in the wild. While extensively studied in domesticated species, this phenomenon remains understudied in fungi. Here, we used Saccharomyces cerevisiae, the world’s oldest domesticated fungus, as a model to investigate the genomic characteristics of deleterious variants arising from fungal domestication. Employing a graph-based pan-genome approach, we identified 1,297,761 single nucleotide polymorphisms (SNPs), 278,147 insertion/deletion events (indels; <30 bp), and 19,967 non-redundant structural variants (SVs; ≥30 bp) across 687 S. cerevisiae isolates. Comparing these variants with synonymous SNPs (sSNPs) as neutral controls, we found that the majority of the derived nonsynonymous SNPs (nSNPs), indels, and SVs were deleterious. Heterozygosity was positively correlated with the impact of deleterious SNPs, suggesting a role of genetic diversity in mitigating their effects. The domesticated isolates exhibited a higher additive burden of deleterious SNPs (dSNPs) than the wild isolates, but a lower burden of indels and SVs. Moreover, the domesticated S. cerevisiae showed reduced rates of adaptive evolution relative to the wild S. cerevisiae. In summary, deleterious variants tend to be heterozygous, which may mitigate their harmful effects, but they also constrain breeding potential. Addressing deleterious alleles and minimizing the genetic load are crucial considerations for future S. cerevisiae breeding efforts. Full article

Drosophila Hey is a basic helix–loop–helix–orange (bHLH-O) protein with an important role in the establishment of distinct identities of postmitotic cells. We have previously identified Hey as a transcriptional target and effector of Notch signalling during the asymmetric division of neuronal progenitors, generating neurons of two types, and we have shown that Notch-dependent expression of Hey also marks a subpopulation of the newborn enteroendocrine (EE) cells in the midgut primordium of the embryo. Here, we investigate the transcriptional regulation of Hey in neuronal and intestinal tissues. We isolated two genomic regions upstream of the promoter (HeyUP) and in the second intron (HeyIN2) of the Hey gene, based on the presence of binding motifs for Su(H), the transcription factor that mediates Notch activity. We found that both regions can direct the overlapping expression patterns of reporter transgenes recapitulating endogenous Hey expression. Moreover, we showed that while HeyIN2 represents a Notch-dependent enhancer, HeyUP confers both Notch-dependent and independent transcriptional regulation. We induced mutations that removed the Su(H) binding motifs in either region and then studied the enhancer functionality in the respective Hey mutant lines. Our results provide direct evidence that although both enhancers support Notch-dependent regulation of the Hey gene, their role is redundant, as a Hey loss-of-function lethal phenotype is observed only after deletion of all their Su(H) binding motifs by CRISPR/Cas9. Full article

In nephrotic syndrome, the podocyte filtration structures are damaged in a process called foot process effacement. This is mediated by the actin cytoskeleton; however, which actins are involved and how they interact with other filtration components, like the basement membrane, remains poorly understood. Here, we used the well-established Drosophila pericardial nephrocyte—the equivalent of podocytes in flies—knockdown models (RNAi) to study the interplay of the actin cytoskeleton (Act5C, Act57B, Act42A, and Act87E), alpha- and beta-integrin (basement membrane), and the slit diaphragm (Sns and Pyd). Knockdown of an actin gene led to variations of formation of actin stress fibers, the internalization of Sns, and a disrupted slit diaphragm cortical pattern. Notably, deficiency of Act5C, which resulted in complete absence of nephrocytes, could be partially mitigated by overexpressing Act42A or Act87E, suggesting at least partial functional redundancy. Integrin localized near the actin cytoskeleton as well as slit diaphragm components, but when the nephrocyte cytoskeleton or slit diaphragm was disrupted, this switched to colocalization, both at the surface and internalized in aggregates. Altogether, the data show that the interdependence of the slit diaphragm, actin cytoskeleton, and integrins is key to the structure and function of the Drosophila nephrocyte. Full article

The multi-scale representation of remote sensing images provides various levels of image information crucial for decision-making in GIS applications and plays a significant role in information processing, data analysis, and geographic modeling. Traditional methods for multi-scale representation of remote sensing images often struggle to simplify local details of individual targets while preserving the overall characteristics of target groups. These methods also encounter issues such as transitional texture distortion and rough final boundaries. This paper proposes a novel multi-scale representation method for remote sensing images based on computer vision techniques, which effectively maintains the overall characteristics of target groups. Initially, the K-means algorithm is employed to distinguish between islands and oceans. Subsequently, a superpixel segmentation algorithm is used to aggregate island groups and simplify the generated boundaries. Finally, texture synthesis and transfer are applied based on the original image to produce the aggregated island images. Evaluation metrics demonstrate that this method can generate multi-scale aggregated images of islands, effectively eliminate redundant information, and produce smooth boundaries. Full article

In order to study the effects of wildfires on soil carbon dioxide (CO₂) emissions and microbial communities in planted forests, Pinus massoniana Lamb. and Cunninghamia lanceolata (Lamb.) Hook. forests were selected as the research subjects. Through a culture test with 60 days of indoor constant temperature, the soil physical and chemical properties, organic carbon mineralization, organic carbon components, enzyme activity, and microbial community structure changes of the two plantations after fire were analyzed. The results showed that wildfires significantly reduced soil CO₂ emissions from the Pinus massoniana forests and Cunninghamia lanceolata forests by 270.67 mg·kg⁻¹ and 470.40 mg·kg⁻¹, respectively, with Cunninghamia lanceolata forests exhibiting the greatest reduction in soil CO₂ emissions compared to unburned soils. Bioinformatics analysis revealed that the abundance of soil Proteobacteria in the Pinus massoniana and Cunninghamia lanceolata forests decreased by 6.00% and 4.55%, respectively, after wildfires. Additionally, redundancy analysis indicated a significant positive correlation between Proteobacteria and soil CO₂ emissions, suggesting that the decrease in Proteobacteria may inhibit soil CO₂ emissions. The Cunninghamia lanceolata forests exhibited a significant increase in soil available nutrients and inhibition of enzyme activities after the wildfire. Additionally, soil CO₂ emissions decreased more, indicating a stronger adaptive capacity to environmental changes following the wildfire. In summary, wildfire in the Cunninghamia lanceolata forests led to the most pronounced reduction in soil CO₂ emissions, thereby mitigating soil carbon emissions in the region. Full article

The monitoring of wind turbine (WT) systems allows operators to maximize their performance, consequently minimizing untimely shutdowns and related hazard situations while maximizing their efficiency. Indeed, the rational monitoring of WT ensures the identification of the main sources of risks at a proper time, such as internal or external failures, hence leading to an increase in their prevention by limiting the faults’ occurrence regarding the different components of wind turbines, achieving production objectives. In this context, the present paper develops a practical monitoring approach using a numerical fault-detection process for the pitch system based on a benchmark wind turbine (WT) model with the main aim of improving safety and security performance. Therefore, the proposed fault-diagnosis procedure deals with eventual faults occurring in the actuators and sensors of the pitch system. In this proposed approach, a simple, logical process is used to generate the correct residuals as fault information based on the redundancy in the actuators and sensors of the pitch sub-systems. The obtained results demonstrate the effectiveness of this proposed process for ensuring the tasks of the fault diagnosis and condition monitoring of the WT systems, and it can be a promising approach for avoiding major damage in such systems, leading to their operational stability and improved reliability and availability. Full article

The present work is devoted to the analysis of the most important reliability indicators of components of electrical devices of mining dump trucks, and analytical methods of their evaluation are proposed. A mathematical model for calculating the reliability of electrical devices integrated into the electrical systems of quarry dump trucks is presented. The model takes into account various loads arising in the process of operation and their influence on reliability reduction. Optimisation of maintenance and repair schedules of electrical equipment has revealed problems for research. One of them is the classification of electrical equipment by similar residual life, which allows the formation of effective repair and maintenance cycles. The analysis of statistical data on damages revealed the regularities of their occurrence, which is an important factor in assessing the reliability of electrical equipment in mining production. For quantitative assessment of reliability, it is proposed to use the parameter of the average expected operating time per failure. This parameter characterises the relative reliability of electrical equipment and is a determining factor of its reliability. The developed mathematical model of equipment failures with differentiation of maintained equipment by repeated service life allows flexible schedules of maintenance and repair to be created. The realisation of such cycles makes it possible to move from planned repairs to the system of repair according to the actual resource of the equipment. Full article

Due to the scarcity of modeling samples and the low prediction accuracy of the matte grade prediction model in the copper melting process, a new prediction method is proposed. This method is based on enhanced generative adversarial networks (EGANs) and random forests (RFs). Firstly, the maximum relevance minimum redundancy (MRMR) algorithm is utilized to screen the key influencing factors of matte grade and remove redundant information. Secondly, the GAN data augmentation model containing different activation functions is constructed. And, the generated data fusion criterion based on the root mean squared error (RMSE) and the coefficient of determination (R²) is designed, which can tap into the global character distributions of the copper melting data to improve the quality of the generated data. Finally, a matte grade prediction model based on RF is constructed, and the industrial data collected from the copper smelting process are used to verify the effectiveness of the model. The experimental results show that the proposed method can obtain high-quality generated data, and the prediction accuracy is better than other models. The R² is improved by at least 2.68%, and other indicators such as RMSE, mean absolute error (MAE), and mean absolute percentage error (MAPE) are significantly improved. Full article

Signal detection and classification tasks, especially in the realm of audio, suffer from difficulties in capturing long short-term dependencies and effectively utilizing samples. Firstly, audio signal detection and classification need to classify audio signals and detect their onset and offset times; therefore, obtaining long short-term dependencies is necessary. The methods based on RNNs have high time complexity and dilated convolution-based methods suffer from the “gridding issue” challenge; thus, the HDC-Inception module is proposed to efficiently extract long short-term dependencies. Combining the advantages of the Inception module and a hybrid dilated convolution (HDC) framework, the HDC-Inception module can both alleviate the “gridding issue” and obtain long short-term dependencies. Secondly, datasets have large numbers of silent segments and too many samples for some signal types, which are redundant and less difficult to detect, and, therefore, should not be overly prioritized. Thus, selecting effective samples and guiding the training based on them is of great importance. Inspired by support vector machine (SVM), combining soft margin SVM and cross-entropy loss (CE loss), the soft margin CE loss is proposed. Soft margin CE loss can adaptively select support vectors (effective samples) in datasets and guide training based on the selected samples. To utilize datasets more sufficiently, a hybrid CE loss is proposed. Using the benefits of soft margin CE loss and CE loss, hybrid CE loss guides the training with all samples and gives weight to support vectors. Soft margin CE loss and hybrid CE loss can be extended to most classification tasks and offer a wide range of applications and great potential. Full article

Synthetic aperture radar (SAR) is a technique widely used in the field of ship detection. However, due to the high ship density, fore-ground-background imbalance, and varying target sizes, achieving lightweight and high-precision multiscale ship object detection remains a significant challenge. In response to these challenges, this research presents YOLO-MSD, a multiscale SAR ship detection method. Firstly, we propose a Deep Poly Kernel Backbone Network (DPK-Net) that utilizes the Optimized Convolution (OC) Module to reduce data redundancy and the Poly Kernel (PK) Module to improve the feature extraction capability and scale adaptability. Secondly, we design a BiLevel Spatial Attention Module (BSAM), which consists of the BiLevel Routing Attention (BRA) and the Spatial Attention Module. The BRA is first utilized to capture global information. Then, the Spatial Attention Module is used to improve the network’s ability to localize the target and capture high-quality detailed information. Finally, we adopt a Powerful-IoU (P-IoU) loss function, which can adjust to the ship size adaptively, effectively guiding the anchor box to achieve faster and more accurate detection. Using HRSID and SSDD as experimental datasets, mAP of 90.2% and 98.8% are achieved, respectively, outperforming the baseline by 5.9% and 6.2% with a model size of 12.3 M. Furthermore, the network exhibits excellent performance across various ship scales. Full article

Plant ecological strategies are essential for assessing habitat stress and disturbance and evaluating community productivity. These strategies provide theoretical frameworks for maintaining the natural state of vegetation and enhancing productivity. The functional traits of leaves reflect a plant’s responses to environmental changes and contribute to understanding ecosystem stability, providing a basis for species diversity maintenance and effective conservation efforts. The Wuyishan National Park, a biodiversity hotspot in China, is a focal point for ecological research. Its evergreen, broad-leaved forest, the zonal vegetation of Mt. Wuyi, underpins plant diversity protection in the region. This study investigates the CSR (competitor, stress-tolerator, ruderal) strategy of 126 species on Wuyi Mountain to elucidate prevalent ecological strategies. The main ecological strategy of plants in the study area is the CS (competitor, stress-tolerator) strategy. The species exhibit nine categories. The most abundant ecological strategy is S/CS (plants from Fagaceae), accounting for 38%, followed by S/CSR at 23% (plants from Theaceae), CS at 20% (plants from Fagaceae and Theaceae), and the remaining strategies collectively at 19%. The different growth habit categories showed variations in the CSR strategies. The trees clustered around a CS median strategy, with no R-selected trees observed. Shrubs and lianas centered around an S/CSR strategy, while grasses and understory shrubs clustered around CS/CSR. Redundancy analysis results indicate that leaf functional traits are primarily influenced by temperature, suggesting that temperature is the key environmental factor driving the differentiation of plant functional traits. This study provides insights into the ecological strategies of plant species in the Mt. Wuyi region, highlighting the importance of considering both biotic and abiotic factors in maintaining biodiversity and ecosystem stability. Full article