Search Results (101)

Improving the tracking accuracy and effectiveness of the pressurizer control system with respect to the reference signal is an effective method to enhance the safe and stable operation of nuclear reactors. This paper applies preview tracking control to the pressurizer control system. For the simplified control system model of the pressurizer, we first study its general structure, which can be characterized as a discrete-time system with state delay. Unlike conventional control systems, the system considered in this study features control inputs that are represented as cumulative sums of historical inputs. In order to design a preview tracking controller for such systems, we adopt the difference method and state augmentation technique and introduce an equality containing the reference signal and a discrete integrator to construct an augmented error system. Simultaneously, a performance signal is defined to evaluate the impact of external disturbances on system performance. Thus, the preview tracking control problem of the original system is reformulated as an $H_{\infty }$ control problem for the augmented error system. Subsequently, a memory-based state feedback controller is designed for the augmented error system. Then, by employing the Lyapunov function and linear matrix inequality (LMI), the $H_{\infty }$ preview tracking controller for the original system is derived. Finally, the proposed control strategy is applied to a pressurizer control system model, and numerical simulations are conducted to validate the effectiveness of the proposed controller by using MATLAB (R2023a, MathWorks, Natick, MA, USA). Full article

Unmanned boat distance detection is an important foundation for autonomous navigation tasks of unmanned boats. Monocular vision ranging has the advantages of low hardware equipment requirements, simple deployment, and high efficiency of distance detection. Unmanned boats can sense the real-time navigational situation of waters through monocular vision ranging, providing data support for their autonomous navigation. This paper establishes a framework for unmanned boat distance detection. The framework extracts and recognizes the features of an unmanned boat through Yolov11m-pose and selects the key points of the ship for physical distance mapping. Using the camera calibration to obtain the pixel focal length, the main point coordinates and other parameters are obtained. The number of pixel points in the image key point to the image center pixel and the actual distance of the camera from the horizontal plane are combined with the focal length of the camera for triangular similarity conversion. These data are fused with the camera pitch angle and other parameters to obtain the final distance. At the same time, experimental verification of the key point detection model demonstrates that it fully meets the requirements for unmanned boat ranging tasks, as assessed by Precision, Recall, mAP50, mAP50-95 and other indicators. These indicators show that Yolov11m-pose has a better accuracy in the key point detection task with an unmanned boat. The verification experiments also illustrate the accuracy of the key point-based physical distance mapping compared with the traditional detection frame-based physical distance mapping, which was assessed by the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The metrics show that key point-based unmanned boat distance mapping has greater accuracy in a variety of environmental situations, which verifies the effectiveness of this approach. Full article

Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection. Full article

As global energy demand continues to rise, accurate load forecasting has become increasingly crucial for power system operations. This study proposes a novel Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Fast Fourier Transform-inverted Transformer-Long Short-Term Memory (CEEMDAN-FFT-iTransformer-LSTM) methodological framework to address the challenges of component complexity and transient fluctuations in power load sequences. The framework initiates with CEEMDAN-based signal decomposition, which dissects the original load sequence into multiple intrinsic mode functions (IMFs) characterized by different temporal scales and frequencies, enabling differentiated processing of heterogeneous signal components. A subsequent application of Fast Fourier Transform (FFT) extracts discriminative frequency-domain features, thereby enriching the feature space with spectral information. The architecture employs an iTransformer module with multi-head self-attention mechanisms to capture high-frequency patterns in the most volatile IMFs, while a gated recurrent unit (LSTM) specializes in modeling low-frequency components with longer temporal dependencies. Experimental results demonstrate the proposed framework achieves superior performance with an average 80% improvement in R-squared ($R^2$), 40.1% lower Mean Absolute Error (MAE), and 54.1% reduced Mean Squared Error (RMSE) compared to other models. This advancement provides a robust computational tool for power grid operators, enabling optimal resource dispatch through enhanced prediction accuracy to reduce operational costs. The demonstrated capability to resolve multi-scale temporal dynamics suggests potential extensions to other forecasting tasks in energy systems involving complex temporal patterns. Full article

After hydraulic fracturing, hydraulic fractures and opened beddings are intertwined, which results in a complex fracture network in shale oil reservoirs. In addition, the migration of multi-phase fluids during fracturing and shut-in processes leads to complex flowback performance and brings difficulty to flowback strategies optimization. In this paper, taking the Daqing Gulong shale reservoir as an example, a numerical model, which considers oil–water–gas three-phase flow and the orthogonal fracture network, has been established for flowback period. The characteristics and influencing factors of flowback performance have been deeply studied, and the flowback modes of shale oil are reasonably optimized. Geological factors such as PTPG (pseudo-threshold pressure gradient), matrix permeability, and engineering factors such as opened bedding stress sensitivity, opened bedding permeability, and fracturing fluid distribution have obvious effects on the flowback performance, resulting in significant variations in production peaks, high production periods, and decline rates. Furthermore, three flowback modes distinguished by the BHP (bottom hole pressure) correspond to the three types of choke mode that have been optimized. This study reveals the main factors affecting the flowback performance. Meanwhile, the optimization method can be applied to optimize flowback strategies in Gulong and other similar shale reservoirs to obtain higher shale oil production. Full article

A nitroguanidine propellant containing 4,4′,6,6′-tetra(azido)hydrazo-1,3,5-triazine (TAHT) was designed by replacing part of the nitroguanidine (NGu) in the nitroguanidine (NGu) propellant with TAHT. Three samples of the NGu propellant were prepared with different amounts of TAHT. The amounts of TAHT were 0%, 15%, and 20%, respectively. The effect of TAHT on the thermal behavior of the NGu propellant was studied by differential scanning calorimetry (DSC) and thermogravimetric analysis (TG). The apparent activation energy ($E_a$) of the decomposition reaction of the propellant samples was calculated using the Kissinger equation and the Ozawa equation. The thermal decomposition process of TAHT was studied using thermogravimetric–mass spectrometric–infrared (TG-MS-FTIR) triple technology. The results show that a small amount of TAHT slightly improves the thermal stability of the NGu propellant. TAHT can significantly reduce the mass loss rate of the propellant. Adding 20% of TAHT can reduce the maximum mass loss rate of the NGu propellant by 27%. It inhibits the thermal decomposition of the propellant. The $E_a$ values of the propellant calculated using the Kissinger equation were 192.8, 174.7, and 169.4 kJ·mol⁻¹, respectively, while the activation energies calculated using the Ozawa method were 190.7, 173.5, and 168.5 kJ·mol⁻¹, respectively. The consistency between these results indicates that adding TAHT can significantly reduce the thermal decomposition rate of the NGu propellant. During thermal decomposition, TAHT will generate a polyazide compound, and a dicyanopolymer is formed. This polyazide compound rapidly forms on the surface of the propellant, which explains the inhibiting effect of TAHT on the NGu propellant. Full article

The valorization of agricultural residues, particularly corn stover, represents a sustainable approach for resource utilization and protein production in which high-performing microbial strains are essential. This study systematically evaluated fungal lignocellulolytic capabilities during corn stover solid-state fermentation and employed atmospheric and room-temperature plasma (ARTP) mutagenesis to enhance the degradative capacity of Trichoderma longibrachiatum. Comparative screening revealed that T. longibrachiatum exhibited superior comprehensive degradation of the major lignocellulosic components compared to other tested strains. ARTP mutagenesis yielded mutant strain TL-MU07, which displayed significantly enhanced enzymatic capabilities with improvements in FPase (22.1%), CMCase (10.1%), and xylanase (16.1%) activities, resulting in increased cellulose degradation (14.6%) and protein accumulation (14.7%). Proteomic analysis revealed 289 significantly differentially expressed proteins, with pathway enrichment demonstrating enhancement of glycosaminoglycan degradation, amino sugar metabolism, and membrane remodeling. Key mechanistic adaptations included downregulation of Zn(2)-C6 transcriptional repressors, upregulation of detoxification enzymes (ALDH-like proteins), and enhanced secretory pathway components. The ARTP-derived mutant strain TL-MU07 represents a valuable microbial resource for agricultural waste bioconversion, offering enhanced lignocellulolytic capabilities for industrial applications while elucidating specific proteomic changes associated with improved biomass degradation efficiency for sustainable protein production in the circular bioeconomy. Full article

In cognitive radio, compressive spectrum sensing (CSS) is critical for efficient wideband spectrum sensing (WSS). However, traditional reconstruction algorithms exhibit suboptimal performance, and conventional WSS methods fail to fully capture the inherent structural information of wideband spectrum signals. Moreover, most existing deep learning-based approaches fail to effectively exploit the sparse structures of wideband spectrum signals, resulting in limited reconstruction performance. To overcome these limitations, we propose BEISTA-Net, a deep learning-based framework for reconstructing compressed wideband signals. BEISTA-Net integrates the iterative shrinkage-thresholding algorithm (ISTA) with deep learning, thereby extracting and enhancing the block sparsity features of wideband spectrum signals, which significantly improves reconstruction accuracy. Next, we propose BSWSS-Net, a lightweight network that efficiently leverages the sparse features of the reconstructed signal to enhance WSS performance. By jointly employing BEISTA-Net and BSWSS-Net, the challenges in CSS are effectively addressed. Extensive numerical experiments demonstrate that our proposed CSS method achieves state-of-the-art performance across both low and high signal-to-noise ratio scenarios. Full article

The Okinawa Trough (OT) has been a focus of scientific research for many years due to the presence of vibrant hydrothermal and cold seep activity within its narrow basin. However, the spatial distribution and environmental drivers of microbial communities in OT sediments remain poorly understood. The present study aims to address this knowledge gap by investigating microbial diversity and abundance at ten different sampling sites in a transitional zone between hydrothermal vents and cold seeps in the OT. The microbial community at two sampling sites (G08 and G09) in close proximity to hydrothermal vents showed a high degree of similarity. However, lower bacterial and archaeal abundances were found in these sites. The archaeal groups, classified as Hydrothermarchaeota and Thermoplasmata, showed a comparatively higher relative abundance at these sites. In addition, ammonia-oxidizing archaea (AOA), from the family Nitrosopumilaceae, were found to have a higher relative abundance in the OT surface sediments at sampling sites G03, G04, G05, G06, and G07. This result suggests that ammonia oxidation may be actively occurring in these areas. Furthermore, Methylomirabilaceae, which are responsible for methane oxidation coupled with nitrite reduction, dominated three sampling sites (G07, G08, and G09), implying that N-DAMO may play an important role in mitigating methane emissions. Using the FAPROTAX database, we found that predicted prokaryotic microbial functional groups involved in methyl-reducing methanogenesis and hydrogenotrophic methanogenesis were most abundant at sites G08 and G09. At sampling sites G01 and G02, functional groups such as hydrocarbon degradation, methanotrophy, methanol oxidation, denitrification, sulfate respiration, and sulfur oxidation were more abundant. Nitrogen content is the most important environmental factor determining the bacterial and archaeal communities in the OT surface sediments. These results expand our knowledge of the spatial distribution of microbial communities in the transitional zone between hydrothermal vents and cold seeps in the OT. Full article

Determining the peak flowering dates of winter rapeseed is crucial for both increasing yields and developing tourism resources. Currently, the Normalized Difference Yellow Index (NDYI), widely used for monitoring these dates, faces stability and accuracy issues due to atmospheric interference and limited optical data during the flowering period. This research examines changes in remote-sensing parameters caused by canopy variations during winter rapeseed’s flowering period from crop canopy morphological characteristics and canopy optical properties. By integrating Sentinel-1 and Sentinel-2 data, a new spectral index, the Normalized Backscatter Yellow Vegetation Index (NBYVI), is introduced. The study uses phenological characteristics and the random forest classification algorithm to create a map of winter rapeseed in parts of the middle and lower reaches of the Yangtze River Basin, achieving a Kappa coefficient of 90.57%. It evaluates the effectiveness of crop morphological indices in monitoring growth stages and explores the impacts of elevation and latitude on the peak flowering dates of winter rapeseed. The error ranges for predicting the peak flowering dates with the NDYI (traditional optical index) and the VV (crop morphological index) are generally 2–7 days and 2–6 days, respectively, while the error range for the NBYVI index is generally 0–4 days, demonstrating superior stability and accuracy compared to the NDYI and VV indices. Full article

Surf during typhoon events poses severe threats to coastal infrastructure and public safety. Traditional monitoring approaches, including in situ sensors and numerical simulations, face inherent limitations in capturing surf impacts—sensors are constrained by point-based measurements, while simulations require intensive computational resources for real-time monitoring. Video-based monitoring offers promising potential for continuous surf observation, yet the development of deep learning models for surf detection remains underexplored, primarily due to the lack of high-quality training datasets from typhoon events. To bridge this gap, we propose a lightweight YOLO (You Only Look Once) based framework for real-time surf detection. A novel dataset of 2855 labeled images with surf annotations, collected from five typhoon events at the Chongwu Tide Gauge Station, captures diverse scenarios such as daytime, nighttime, and extreme weather conditions. The proposed YOLOv6n model achieved 99.3% $mA{P}_{50}$ at 161.8 FPS, outperforming both other YOLO variants and traditional two-stage detectors in accuracy and computational efficiency. Scaling analysis further revealed that YOLO models with 2–5 M parameters provide an optimal trade-off between accuracy and computational efficiency. These findings demonstrate the effectiveness of YOLO-based video monitoring systems for real-time surf detection, offering a practical and reliable solution for coastal hazard monitoring under extreme weather conditions. Full article

To study the factors affecting Penaeus vannamei production in small-scale greenhouse ponds, four ponds in Jiangmen, Guangdong Province, China were selected. This study investigated the variation in the characteristics of bacterial communities and pathogens in pond water and shrimp intestines, as well as water quality factors during the culture stage. Multivariate linear regression equations were used to analyse the potential factors affecting production. The nitrite concentration reached its peak in the mid-culture stage, with a maximum of 16.3 mg·L⁻¹, whereas total nitrogen and salinity were highest in the late culture stage, reaching 48.4 mg·L⁻¹ and 26, respectively. The dominant bacteria in the pond water were Marivita and Rhodobacteraceae, whereas in the shrimp intestines, they were Bacillus and Candidatus Bacilloplasma. The nitrifying bacteria in the pond water were dominated by Nitrosomonas and Nitrobacter. Pathogens detected in the pond water included acute hepatopancreatic necrosis disease (AHPND), Enterocytozoon hepatopenaei (EHP), and white spot syndrome virus (WSSV). The counts of EHP and the relative abundance of Ardenticatenales_norank and Marivita in the pond were the main factors affecting the shrimp production (p < 0.01). This study indicates that establishing optimal bacterial communities, such as Marivita, Nitrobacter, and Rhodobacteraceae, and controlling the counts of EHP and AHPND pathogens is crucial for regulating the pond environment and enhancing production. Full article

Aluminum (Al) toxicity is a major constraint to crop productivity in acidic soils, frequently encountered in banana-growing regions. This study investigates physiological and biochemical responses to Al stress in two Cavendish banana genotypes, Baodao and Baxi (Musa acuminata L.), which exhibit contrasting levels of Al tolerance. Banana plantlets were grown hydroponically under three AlCl₃ concentrations (0, 100, and 500 μM) for 24, 48, and 72 h. Root elongation was progressively inhibited with increasing Al concentrations, with Baodao showing greater inhibition than Baxi. Al primarily accumulated in roots and displayed genotype-specific distribution patterns: Baodao concentrated more Al in root tips, suggesting lower exclusion efficiency. In contrast, Baxi, the Al-tolerant genotype, translocated Al from roots to shoots more effectively, indicating potential sequestration mechanisms in less sensitive tissues. Al stress influenced enzyme activities, with Baxi exhibiting higher phosphoenolpyruvate carboxylase and citrate synthase activities at 100 µM Al, while both genotypes showed similar reductions at 500 µM. Baodao experienced more pronounced reductions in H⁺-ATPase activity. At 100 µM Al, Baxi retained higher levels of key nutrients (P, Zn, Mg, Mn, Fe, K, and B) in essential tissues than Baodao. However, nutrient levels were reduced in both genotypes at 500 µM Al. These findings highlight Baxi’s superior resilience under Al stress, making it a suitable genotype for cultivation and breeding in acidic soils. Full article

Melatonin is a critical regulator of biological rhythms across organisms, transducing light signals into neuroendocrine signals that facilitate reproductive regulation in response to environmental cues. However, the precise mechanisms through which melatonin regulates reproduction in fish require further investigation. In this study, we employed molecular and organizational biological techniques to examine the expression patterns of melatonin and its five receptor subtypes (LcMTNR1A1, LcMTNR1A2, LcMTNR1B1, LcMTNR1B2, and LcMTNR1C) in various tissues of the large yellow croaker (Larimichthys crocea). Our results revealed significant expression of all receptors in the pituitary and testes, with distinct gender differences, including a lack of expression in the ovary. Moreover, our data indicate that melatonin and its receptors are primarily expressed during stage III, highlighting their role in sexual maturity. Enzyme- linked immunosorbent assay (ELISA) results further demonstrated that in vitro melatonin incubation in the brain of L. crocea influenced gonadotropin-releasing hormone (GnRH) and testosterone secretion in a dose-dependent manner, suggesting actions beyond the classical hypothalamic–pituitary–gonadal (HPG) axis. Overall, our findings provide new evidence supporting the role of the melatonin system in reproductive regulation in marine teleosts. Full article

To avoid additional component losses while significantly improving the energy conversion efficiency of battery energy storage systems, the application of series-connected partial power converter (S-PPC) technology in battery energy storage systems is investigated in this study. In the S-PPC battery energy storage system configuration, coupling effects exist between the dc-link side and the battery-series side. The impedance modeling of a battery energy storage system is performed while taking these coupling effects into consideration. To address the instability observed during battery discharge conditions, an impedance reshaping control strategy that is suitable for the S-PPC battery energy storage system is proposed. The proposed method focuses on adjusting the input impedance of the load converter within a limited frequency band centered on the system’s oscillation frequency. This targeted approach significantly improves the stability of the system while ensuring ease of implementation and maintaining high reliability. Finally, the experimental results validate the theoretical analysis. Full article