Explore the vast range of titles available.

Meteorological sensors deployed on ocean buoys frequently suffer from data loss or outliers due to electromagnetic interference and component failures caused by harsh weather and environmental conditions. Accurate reconstruction of corrupted buoy data remains a significant challenge, as conventional interpolation and imputation methods often fail to capture the inherent spatio-temporal dependencies in marine meteorological variables. To address this issue, this paper proposes a novel deep learning model that integrates Transformer and Graph Attention Network (GAT) architectures—termed the Spatio-Temporal Dual-Attention Network (ST-DAN). The model uses parallel computing to capture two aspects of the data: on one hand, it captures temporal dependencies through a Transformer enhanced by position encoding; on the other, it models inter-variable spatial correlations with a Graph Attention Network (GAT) based on a physically informed adjacency matrix, which dynamically adjusts the influence weights between variables to significantly enhance reconstruction accuracy. To evaluate the ST-DAN model, extensive experiments were conducted leveraging the ERA5 reanalysis dataset and in-situ observations from a Qingdao buoy, focusing on the reconstruction of temperature and wind speed data. The experiment result shows that ST-DAN outperformed baseline models (e.g., ARIMA, RNN, Bi-LSTM, and Transformer) across metrics including MAE, MSE, RMSE, and R². It indicates that the proposed model (ST-DAN) is off high robustness and achieves high-precision interpolation and anomaly correction for meteorological data.

Ocean current is one of the most important parameters in ocean observation, and ocean current measurement based on electromagnetic induction is becoming more and more important because of its advantages such as simple structure and high measurement accuracy. However, it is difficult to detect weak current signals in a complex marine environment. In this paper, an electromagnetic induction current measurement scheme based on lock-in amplification technology is proposed. Key technologies such as the evaluation of induced current intensity, overall design, circuit design, and orientation design of the current meter were studied. The prototype of the electromagnetic current meter was developed and tested in the laboratory and at sea. The repeatability of current velocity and current direction was higher than 1.5 cm/s and 1.5°, respectively. A comparison test between the electromagnetic current meter prototype and Nortek ADCP (Acoustic Doppler Current Profiler) installed on a buoy at sea was carried out, and the correlation coefficients of the current velocity and current direction datum were 0.90 and 0.96, respectively. Through continuous on-site and fault-free operations at sea, the experimental data show that the electromagnetic current meter has good adaptability at sea, which provides feasible technical and equipment support for ocean current observation.

Waves are crucial in ocean observation and research. Stereo vision-based wave measurement, offering non-contact, low-cost, and intelligent processing, is an emerging method. However, improving accuracy remains a challenge due to wave complexity. This paper presents a novel approach to measure wave height, period, and direction by combining deep learning-based stereo matching with feature matching techniques. To improve the discontinuity and low accuracy in disparity maps from traditional wave image matching algorithms, this paper proposes the use of a high-precision stereo matching method based on Pyramid Stereo Matching Network (PSM-Net).A 3D reconstruction method integrating Scale-Invariant Feature Transform (SIFT) with stereo matching was also introduced to overcome the limitations of template matching and interleaved spectrum methods, which only provide 2D data and fail to capture the full 3D motion of waves. This approach enables accurate wave direction measurement. Additionally, a six-degree-of-freedom platform was proposed to simulate waves, addressing the high costs and attenuation issues of traditional wave tank simulations. Experimental results show the prototype system achieves a wave height accuracy within 5%, period accuracy within 4%, and direction accuracy of ±2°, proving the method’s effectiveness and offering a new approach to stereo vision-based wave measurement.

The hydrodynamic performance of the floating body in seawater is very important. The wave buoy is a small buoy that measures wave parameters such as wave height and wave direction, for the non-powered wave buoy, the hydrodynamic performance mainly refers to the seaworthiness of the buoy body. Seakeeping refers to the motion law of the floating body in the wave. For the wave buoy, the seakeeping of the floating body has an important impact on the measurement of wave data. Therefore, the analysis of the hydrodynamic performance of the wave measurement float is an important reference for evaluating the performance of the wave buoy. In this paper, the hydrodynamic performance of cylindrical and spherical wave-finding buoys is compared and analyzed, and the influence of different structural forms on their hydrodynamic performance is analyzed, which provides a reference for optimizing the hydrodynamic performance of wave-measuring buoys.

Key message Clarification of the genome composition of the potato + eggplant somatic hybrids cooperated with transcriptome analysis efficiently identified the eggplant gene SmPGH1 that contributes to bacterial wilt resistance. The cultivated potato is susceptible and lacks resistance to bacterial wilt (BW), a soil-borne disease caused by Ralstonia solanacearum. It also has interspecies incompatibility within Solanaceae plants. Previously, we have successfully conducted the protoplast fusion of potato and eggplant and regenerated somatic hybrids that showing resistance to eggplant BW. For efficient use of these novel germplasm and improve BW resistance of cultivated potato, it is essential to dissect the genetic basis of the resistance to BW obtained from eggplant. The strategy of combining genome composition and transcriptome analysis was established to explore the gene that confers BW resistance to the hybrids. Genome composition of the 90 somatic hybrids was studied using genomic in situ hybridization coupled with 44 selected eggplant-specific SSRs (smSSRs). The analysis revealed a diverse set of genome combinations among the hybrids and showed a possibility of integration of alien genes along with the detection of 7 smSSRs linked to BW resistance (BW-linked SSRs) in the hybrids. Transcriptome comparison between the resistant and susceptible gene pools identified a BW resistance associated gene, smPGH1, which was significantly induced by R. solanacearum in the resistant pool. Remarkably, smPGH1 was co-localized with the BW-linked SSR emh01E15 on eggplant chromosome 9, which was further confirmed that smPGH1 was activated by R. solanacearum only in the resistant hybrids. Taken together, the identified gene smPGH1 and BW-linked SSRs have provided novel genetic resources that will aid in potato breeding for BW resistance.

Bacterial wilt (BW) caused by Ralstonia solanacearum is an important disease of many plant species especially Solanaceae. To compensate for lack of BW resistance in cultivated potato, we fused UV-treated protoplasts of a resistant eggplant variety with protoplasts of a susceptible potato clone to obtain 32 somatic hybrids. Although asymmetric protoplast fusion has the potential to transfer traits from distant species, introgression frequency and preference of alien fragments remain obscure, as well as the genetic basis for control of a trait. In the present research, the genome components of 32 somatic hybrids were determined by parent-specific SSRs. Each hybrid had integrated from one to eight alien chromosome fragments, providing a foundation for selection of BW resistance transmitted from eggplant. When the selected eggplant sequences were aligned with potato genome sequence it showed a similarity of 46.7 %, suggesting a large genetic distance between these two species. The results also revealed that introgression of eggplant fragments is non-selective, which may allow any part of alien chromosomes to be integrated. Distribution of eggplant loci in individual hybrids suggested a possible relationship between markers emk03O04, emi04P17 and emd13E02a and BW resistance, which are potential loci that control target traits and therefore deserve further investigation. With genome-wide selection of parent-specific molecular markers and sequence alignment, the present research substantiated interspecific introgression of a trait lacking in potato. Moreover, an efficient strategy was established to estimate genomic components of the somatic hybrids, and to explore the candidate loci that associate with target traits.

KEY MESSAGE : Bacterial wilt resistant somatic hybrids were obtained via protoplast fusion between potato and eggplant and three types of nuclear genomes were identified in the hybrids through GISH and SSR analysis. Cultivated potato (Solanum tuberosum L.) lacks resistance to bacterial wilt caused by Ralstonia solanacearum. Interspecific symmetric protoplast fusion was conducted to transfer bacterial wilt resistance from eggplant (S. melongena, 2n = 2x = 24) into dihaploid potato (2n = 2x = 24). In total, 34 somatic hybrids were obtained, and of these, 11 rooted and were tested for genome components and resistance to race 1 of R. solanacearum. The hybrids exhibited multiple ploidy levels and contained the dominant nuclear genome from the potato parent. Three types of nuclear genomes were identified in the hybrids through genomic in situ hybridization (GISH) and simple sequence repeat (SSR) analysis, including (1) the potato type of the tetraploids in which eggplant chromosomes could not be detected by GISH but their nuclear DNA was confirmed by SSR, (2) the biased type of the hexaploids in which the chromosome dosage was 2 potato:1 eggplant, and (3) the chromosome translocation type of the mixoploids and aneuploids that was characterized by various rates of translocations of nonhomologous chromosomes. Cytoplasmic genome analysis revealed that mitochondrial DNA of both parents coexisted and/or recombined in most of the hybrids. However, only potato chloroplast DNA was retained in the hybrids speculating a compatibility between cpDNA and nuclear genome of the cell. The pathogen inoculation assay suggested a successful transfer of bacterial wilt resistance from eggplant to the hybrids that provides potential resistance for potato breeding against bacterial wilt. The genome components characterized in present research may explain partially the inheritance behavior of the hybrids which is informative for potato improvement.