Search Results (468)

Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. Predicting its progression is crucial for preventing late-stage AMD, as it is an irreversible retinal disease. Both genetic factors and retinal images are instrumental in diagnosing and predicting AMD progression. Previous studies have explored automated diagnosis using single fundus images and genetic variants, but they often fail to utilize the valuable longitudinal data from multiple visits. Longitudinal retinal images offer a dynamic view of disease progression, yet standard Long Short-Term Memory (LSTM) models assume consistent time intervals between training and testing, limiting their effectiveness in real-world settings. To address this limitation, we propose time-varied Long Short-Term Memory (TV-LSTM), which accommodates irregular time intervals in longitudinal data. Our innovative approach enables the integration of both longitudinal fundus images and AMD-associated genetic variants for more precise progression prediction. Our TV-LSTM model achieved an AUC-ROC of 0.9479 and an AUC-PR of 0.8591 for predicting late AMD within two years, using data from four visits with varying time intervals. Full article

In order to correctly measure the shock wave pressure generated by a near-field explosion, and while considering the limitations of the measurement and calibration method of the current bar pressure sensor, an improved shock wave pressure measurement method was designed based on a bar pressure sensor combined with photon Doppler velocimetry (PDV) and strain measurement. By measuring the strain on the pressure bar and the particle velocity on the rear-end face, the shock wave pressure applied on the front-end face of the pressure bar was calculated based on one-dimensional stress wave theory. On the other hand, a calibration method was designed to validate the reliability of the test system. Based on the split-Hopkinson pressure bar (SHPB) loading experiment, the transmission characteristics of stress wave in the bar and the accuracy of the system test results were verified. The results indicated that the stress wave measurement results were consistent with the one-dimensional elementary theoretical calculation results of stress wave propagation in different wave-impedance materials, and the peak deviation measured by PDV and strain measurement method was less than 1.5%, which proved the accuracy of the test method and the feasibility of the calibration method. Full article

The combined application of fibers and lightweight aggregates (LWAs) represents an effective approach to achieving high-strength, lightweight concrete. To enhance the predictability of the mechanical properties of fiber-reinforced lightweight aggregate concrete (LWAC), this study conducts an in-depth investigation into its hydration characteristics. In this study, high-strength LWAC was developed by incorporating low water absorption LWAs, various volume fractions of basalt fiber (BF) (0.1%, 0.2%, and 0.3%), and a ternary cementitious system consisting of 70% cement, 20% fly ash, and 10% silica fume. The hydration-related properties were evaluated through isothermal calorimetry test and high-temperature calcination test. The results indicate that incorporating 0.1–0.3% fibers into the cementitious system delays the early hydration process, with a reduced peak heat release rate and a delayed peak heat release time compared to the control group. However, fitting the cumulative heat release over a 72-h period using the Knudsen equation suggests that BF has a minor impact on the final degree of hydration, with the difference in maximum heat release not exceeding 3%. Additionally, the calculation model for the final degree of hydration in the ternary binding system was also revised based on the maximum heat release at different water-to-binder ratios. The results for chemically bound water content show that compared with the pre-wetted LWA group, under identical net water content conditions, the non-pre-wetted LWA group exhibits a significant reduction at three days, with a decrease of 28.8%; while under identical total water content conditions it shows maximum reduction at ninety days with a decrease of 5%. This indicates that pre-wetted LWAs help maintain an effective water-to-binder ratio and facilitate continuous advancement in long-term hydration reactions. Based on these results, influence coefficients related to LWAs for both final degree of hydration and hydration rate were integrated into calculation models for degrees of hydration. Ultimately, this study verified reliability of strength prediction models based on degrees of hydration. Full article

Genetic transformation is an essential tool for investigating gene function and editing genomes. Kiwifruit, recognized as a significant global fresh fruit crop, holds considerable economic and nutritional importance. However, current genetic transformation techniques for kiwifruit are impeded by low efficiency, lengthy culture durations (a minimum of six months), and substantial labor requirements. In this research, we established an efficient system for shoot regeneration and the stable genetic transformation of the ‘Hayward’ cultivar, utilizing leaf explants in conjunction with two strains of Agrobacterium that harbor the expression vector pBI121-35S::GFP, which contains the green fluorescent protein (GFP) gene as a visible marker within the T-DNA region. Our results show that 93.3% of leaf explants responded positively to the regeneration medium, producing multiple independent adventitious shoots around the explants within a six-week period. Furthermore, over 71% of kanamycin-resistant plantlets exhibited robust GFP expression, and the entire transformation process was completed within four months of culture. Southern blot analysis confirmed the stable integration of GFP into the genome, while RT-PCR and fluorescence microscopy validated the sustained expression of GFP in mature plants. This efficient protocol for regeneration and transformation provides a solid foundation for micropropagation and the enhancement of desirable traits in kiwifruit through overexpression and gene silencing techniques. Full article

Driven by the rapid development of e-commerce and intelligent logistics, the volume of express delivery services has surged, making the efficient and accurate identification of shipping information a core requirement for automatic sorting systems. However, traditional Optical Character Recognition (OCR) technology struggles to meet the accuracy and real-time demands of complex logistics scenarios due to challenges such as image distortion, uneven illumination, and field overlap. This paper proposes a three-level collaborative recognition method based on deep learning that facilitates structured information extraction through regional normalization, dual-path parallel extraction, and a dynamic matching mechanism. First, the geometric distortion associated with contour detection and the lightweight direction classification model has been improved. Second, by integrating the enhanced YOLOv5s for key area localization with the upgraded PaddleOCR for full-text character extraction, a dual-path parallel architecture for positioning and recognition has been constructed. Finally, a dynamic space–semantic joint matching module has been designed that incorporates anti-offset IoU metrics and hierarchical semantic regularization constraints, thereby enhancing matching robustness through density-adaptive weight adjustment. Experimental results indicate that the accuracy of this method on a self-constructed dataset is 89.5%, with an F1 score of 90.1%, representing a 24.2% improvement over traditional OCR methods. The dynamic matching mechanism elevates the average accuracy of YOLOv5s from 78.5% to 89.7%, surpassing the Faster R-CNN benchmark model while maintaining a real-time processing efficiency of 76 FPS. This study offers a lightweight and highly robust solution for the efficient extraction of order information in complex logistics scenarios, significantly advancing the intelligent upgrading of sorting systems. Full article

The rapid evolution of pathogens and the limited genetic diversity of hosts are two major factors contributing to the plant pathogenic phenomenon known as the loss of disease resistance in maize (Zea mays L.). It has emerged as a significant biological stressor threatening the global food supplies and security. Based on previous cross-species homologous gene screening assays conducted in the laboratory, this study identified the maize disease-resistance candidate gene ZmNLR-7 to investigate the maize immune regulation mechanism against Bipolaris maydis. Subcellular localization assays confirmed that the ZmNLR-7 protein is localized in the plasma membrane and nucleus, and phylogenetic analysis revealed that it contains a conserved NB-ARC domain. Analysis of tissue expression patterns revealed that ZmNLR-7 was expressed in all maize tissues, with the highest expression level (5.11 times) exhibited in the leaves, and that its transcription level peaked at 11.92 times 48 h post Bipolaris maydis infection. Upon inoculating the ZmNLR-7 EMS mutants with Bipolaris maydis, the disease index was increased to 33.89 and 43.33, respectively, and the lesion expansion rate was higher than that in the wild type, indicating enhanced susceptibility to southern corn leaf blight. Physiological index measurements revealed a disturbance of ROS metabolism in ZmNLR-7 EMS mutants, with SOD activity decreased by approximately 30% and 55%, and POD activity decreased by 18% and 22%. Moreover, H₂O₂ content decreased, while lipid peroxide MDA accumulation increased. Transcriptomic analysis revealed a significant inhibition of the expression of the key genes NPR1 and ACS6 in the SA/ET signaling pathway and a decrease in the expression of disease-related genes ERF1 and PR1. This study established a new paradigm for the study of NLR protein-mediated plant immune mechanisms and provided target genes for molecular breeding of disease resistance in maize. Overall, these findings provide the first evidence that ZmNLR-7 confers resistance to southern corn leaf blight in maize by synergistically regulating ROS homeostasis, SA/ET signal transduction, and downstream defense gene expression networks. Full article

The superhydrophobic coatings for outdoor use need to be exposed to sunlight for a long time; therefore, their UV-aging resistances are crucial in practical applications. In this study, the primary product of titanium dioxide (P-TiO₂) was used as the raw material. Nano-silica (SiO₂) was coated onto the surface of P-TiO₂ by the acid precipitation method to prepare P-TiO₂-SiO₂ composite particles. Then, they were modified and sprayed simply to obtain a superhydrophobic P-TiO₂-SiO₂/HDTMS coating. The results indicated that amorphous nano-SiO₂ was coated on the P-TiO₂ surface, forming a micro–nano binary structure, which was the essential structure to form superhydrophobic coatings. Additionally, the UV-aging property of P-TiO₂ was significantly enhanced after being coated with SiO₂. After continuous UV irradiation for 30 days, the color difference (ΔE*) and yellowing index (Δb*) values of the coating prepared with P-TiO₂-SiO₂ increased from 0 to 0.75 and 0.23, respectively. In contrast, the ΔE* and Δb* of the coating prepared with P-TiO₂ increased from 0 to 1.68 and 0.74, respectively. It was clear that the yellowing degree of the P-TiO₂-SiO₂ coating was lower than that of P-TiO₂, and its UV-aging resistance was significantly improved. After modification with HDTMS, the P-TiO₂-SiO₂ coating formed a superhydrophobic P-TiO₂-SiO₂/HDTMS coating. The water contact angle (WCA) and water slide angle (WSA) on the surface of the coating were 154.9° and 1.3°, respectively. Furthermore, the coating demonstrated excellent UV-aging resistance. After continuous UV irradiation for 45 days, the WCA on the coating surface remained above 150°. Under the same conditions, the WCAs of the P-TiO₂/HDTMS coating decreased from more than 150° to 15.3°. This indicated that the retention of surface hydrophobicity of the P-TiO₂-SiO₂/HDTMS coating was longer than that of P-TiO₂/HDTMS, and the P-TiO₂-SiO₂/HDTMS coating’s UV-aging resistance was greater. The superhydrophobic P-TiO₂-SiO₂/HDTMS self-cleaning coating reported in this study exhibited outstanding UV-aging resistance, and it had the potential for long-term outdoor use. Full article

As a globally significant economic crop, pepper (Capsicum annuum L.) plants display excessive plant height (etiolation) in greenhouse production under an undesirable environment, leading to lodging-prone plants with reduced stress resistance. In the present study, we provided supplementary ultraviolet-B (UV-B, 280–315 nm) light to pepper plants grown in a greenhouse to assess the influences of UV-B on pepper growth, with an emphasis on the molecular mechanisms mediated through the gibberellin (GA) signaling pathway. The results indicated that UV-B significantly decreased the plant height and the fresh weight of pepper plants. However, no significant differences were observed in the chlorophyll content of pepper plants grown under natural light and supplementary UV-B radiation. The results of the transcriptomic and metabolomic analyses indicated that differentially expressed genes (DEGs) were significantly enriched in plant hormone signal transduction and that UV radiation altered the gibberellin synthesis pathway of pepper plants. Specifically, the GA3 content of the pepper plants grown with UV-B radiation decreased by 39.1% compared with those grown without supplementary UV-B radiation; however, the opposite trend was observed in GA34, GA7, and GA51 contents. In conclusion, UV-B exposure significantly reduced plant height, a phenotypic response mechanistically linked to an alteration in GA homeostasis, which may be caused by a decrease in GA3 content. Our study elucidated the interplay between UV-B and gibberellin biosynthesis in pepper morphogenesis, offering a theoretical rationale for developing UV-B photoregulation technologies as alternatives to chemical growth inhibitors. Full article

Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article

Accurate wind-speed prediction plays an important role in improving the operation stability of wind-power generation systems. However, the inherent complexity of meteorological dynamics poses a major challenge to forecasting accuracy. In order to overcome these limitations, we propose a new hybrid framework, which combines variational mode decomposition (VMD) for signal processing, enhanced quantum particle swarm optimization (e-QPSO), an improved walking optimization algorithm (IHOA) for feature selection and the long short-term memory (LSTM) network, and which finally establishes a reliable prediction architecture. The purpose of this paper is to optimize VMD by using the e-QPSO algorithm to improve the problems of excessive filtering or error filtering caused by parameter problems in VMD, as the noise signal cannot be filtered completely, and the number of sources cannot be accurately estimated. The IHOA algorithm is used to find the optimal hyperparameters of LSTM to improve the learning efficiency of neurons and improve the fitting ability of the model. The proposed e-QPSO-VMD-IHOA-LSTM model is compared with six established benchmark models to verify its predictive ability. The effectiveness of the model is verified by experiments using the hourly wind-speed data measured in four seasons in Changchun in 2023. The MAPE values of the four datasets were 0.0460, 0.0212, 0.0263, and 0.0371, respectively. The results show that e-QPSO-VMD effectively processes the data and avoids the problem of error filtering, while IHOA effectively optimizes the LSTM parameters and improves prediction performance. Full article

Large-scale scene rendering faces challenges in managing massive scene data and mitigating rendering latency caused by suboptimal loading sequences. Although current approaches utilize Level of Detail (LOD) for dynamic resource loading, two limitations remain. One is loading priority, which does not adequately consider the factors affecting visual effects such as LOD selection and visible area. The other is the insufficient trade-off between rendering quality and loading latency. To this end, we propose a loading prioritization metric called Vision Degree (VD), derived from LOD selection, loading time, and the trade-off between rendering quality and loading latency. During rendering, VDs are sorted in descending order to achieve an optimized loading and unloading sequence. At the same time, a compensation factor is proposed to further compensate for the visual loss caused by the reduced LOD level and to optimize the rendering effect. Finally, we optimize the initial viewpoint selection by minimizing the average model-to-viewpoint distance, thereby reducing the initial scene loading time. Experimental results demonstrate that our method reduces the rendering latency by 24–29% compared with the existing Area-of-Interest (AOI)-based loading strategy, while maintaining comparable visual quality. Full article

The increasing concentration of greenhouse gases is amplifying the global risk of drought on crop productivity. This study sought to investigate the effects of drought on the growth of maize (Zea mays L.) seedlings. A total of 78 maize hybrids were employed in this study to replicate drought conditions through the potting method. The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. The eight phenotypic traits include the fresh and dry weights of both the aboveground and underground parts. Three machine learning methods—random forest (RF), K-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)—were employed to systematically analyze the relevant traits of maize seedlings’ drought tolerance and to assess their predictive performance in this regard. The findings indicated that plant height, aboveground weight, and chlorophyll content constituted the primary indices for phenotyping maize seedlings under drought conditions. The XGBoost model demonstrated optimal performance in the classification (AUC = 0.993) and regression (R² = 0.863) tasks, establishing itself as the most effective prediction model. This study provides a foundation for the feasibility and reliability of screening drought-tolerant maize varieties and refining precision breeding strategies. Full article

With the rapid development of marine renewable energy, especially offshore photovoltaic systems, the problem of biofouling of photovoltaic equipment in the marine environment has become increasingly prominent. The attachment of marine organisms such as algae will significantly affect the photoelectric conversion efficiency of photovoltaic panels, thereby reducing the stability and economy of the system. In this study, a composite siloxane coating was designed and prepared. Methyltrimethoxysilane (MTMS) was used as the organosilicon component. The negative potential of the coating was significantly enhanced by incorporating hexagonal boron nitride nanosheets (h-BNNS). This negative potential and the negative charge on the surface of marine organisms, especially algae, would produce electrostatic repulsion, which can effectively reduce the attachment of organisms. The results show that the prepared coating exhibits excellent performance in anti-biofouling, adhesion, chemical stability, transparency, and self-cleaning properties. The transparency of the coating reached 92.7%. After immersion with Chlorella for 28 days, the coverage percentage on the coating surface was only 0.98%, while the coverage percentage on the blank sample was 23.25%. The corrosion resistance and salt resistance of the coating also ensure its stability in complex marine environments, and it has broad application prospects. Full article

As the core component of modern mechanical transmission, the precision rotary motion mechanism and its drive system have wide applications in aerospace, robotics, and other fields. This article systematically reviews the design principles, performance characteristics, and research progress of various rotational motion mechanisms and their driving technologies. The working principles, advantages, disadvantages, and applicable scenarios of gears, drive belts, sprockets, camshafts, ratchet claw mechanisms, and linkage mechanisms were analyzed in terms of traditional mechanisms. In terms of new mechanisms, we focused on exploring the innovative design and application potential of intermittent indexing mechanisms, magnetic gears, 3D-printed spherical gears, and multi-link mechanisms. In addition, the paper compared the performance differences of electric, hydraulic, pneumatic, and piezoelectric drive methods. Research has shown that through material innovation, structural optimization, and intelligent control, there is still significant room for improvement in the load capacity, accuracy, and reliability of precision rotary motion mechanisms, providing theoretical support and practical reference for innovative design and engineering applications of future mechanical transmission technologies. Full article

Traditional clustering methods are often ineffective in extracting relevant features from high-dimensional, nonlinear near-infrared (NIR) spectra, resulting in poor accuracy of detecting lactose-free milk adulteration. In this paper, we introduce a clustering model based on Gram angular field and convolutional depth manifold (GAF-ConvDuc). The Gram angular field accentuates variations in spectral absorption peaks, while convolution depth manifold clustering captures local features between adjacent wavelengths, reducing the influence of noise and enhancing clustering accuracy. Experiments were performed on samples from 2250 milk spectra using the GAF-ConvDuc model. Compared to K-means, the silhouette coefficient (SC) increased from 0.109 to 0.571, standardized mutual information index (NMI) increased from 0.696 to 0.921, the Adjusted Randindex (ARI) increased from 0.543 to 0.836, and accuracy (ACC) increased from 67.2% to 88.9%. Experimental results indicate that our method is superior to K-means, Variational Autoencoder (VAE) clustering, and other approaches. Without requiring pre-labeled data, the model achieves higher inter-cluster separation and more distinct clustering boundaries. These findings offer a robust solution for detecting lactose-free milk adulteration, crucial for food safety oversight. Full article