SLWE-Net: An improved lightweight U-Net for Sargassum extraction from GOCI images.

The Sargassum bloom has severely impacted the ecological environment of the East China Sea and the Yellow Sea, causing significant economic losses. In recent years, deep learning has seen extensive development due to its outstanding feature extraction capabilities. However, the deep learning process typically involves a large number of parameters and computations. To address this issue, this paper proposes a lightweight deep learning network based on the U-Net framework, called SLWE-NET, which uses lightweight modules to replace the feature extraction modules in U-Net. In this experiment, SLWE-Net performed the best in both extraction accuracy and model lightweight. Compared to the formal U-Net, the number of parameters decreased by 65.83 %, the model size reduced from 94.97 MB to 32.51 MB, and the mIoU increased to 93.81 %. Therefore, the method proposed in this paper is beneficial for Sargassum extraction and provides a basis for operational monitoring.

An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image.

In the oil industry, oil spills occur due to offshore rig explosions, ship collisions, and other reasons. It is crucial to accurately and rapidly identify oil spills to protect marine ecosystems. Synthetic aperture radar (SAR) can all-weather and all-time work and provide a wealth of polarization information for identification of oil spills based on semantic segmentation model. However, the performance of classifiers in the semantic segmentation model has become a significant challenge to improving recognition ability. To solve this problem, an improved semantic segmentation model named DRSNet was proposed, which uses ResNet-50 as the backbone in DeepLabv3+ and support vector machines (SVM) as the classifier. The experiment was conducted using ten polarimetric features from SAR images and results demonstrate that the DRSNet performs best compared to other semantic segmentation models. Current work provides a valuable tool to enhance maritime emergency management capabilities.

Node-splitting optimized canonical correlation forest algorithm for sea fog detection using MODIS data.

In this paper, a node splitting optimized canonical correlation forest algorithm for sea fog detection is proposed by using active and passive satellites. The traditional canonical correlation forest (CCF) algorithm insufficiently accounts for the spectral characteristics and the reliability of each classifier during integration. To deal with the problem, the information gain rate of node entropy is used as the splitting criterion, and the spectral characteristics of clouds and fogs are also combined into the model generation process. The proposed algorithm was verified using the meteorological station data and compared with five state-of-the-art algorithms, which demonstrated that the algorithm has the best performance in sea fog detection and can identify mist better.

[Efficacy and Safety of Intensive Maintenance Chemotherapy Regimen for Treatment of Children and Adolescents with Lymphoblastic Lymphoma for Stage III-IV].

OBJECTIVE: To investigate the efficacy and safety of intensive maintenance chemotherapy regimen for treatment of children and adolescents with lymphoblastic lymphoma at stage Ⅲ-Ⅳ. METHODS: The clinical data of 87 children and adolescents with lymphoblastic lymphoma at stage Ⅲ-Ⅳ were analyzed retrospectively from July 2009 to July 2015. All patients received the treatment of modified NHL-BFM-90/95 regimen, and divided into 2 groups: the control group (62 patients) with conventional maintenance chemotherapy regimen, and the intensive regimen group (25 patients) with intensive maintenance chemotherapy regimen. The event-free survival (EFS) rate and overall survival (OS) rate during follow-up for 5 years, recurrence rate, mortality, and toxic and side effects were compared between 2 groups. RESULTS: There was no significant difference in the EFS rate and OS rate after follow-up for 5 years between 2 groups (P＞0.05). There was no significant difference in the EFS rate and OS rate after follow-up for 5 years between clinical stage for Ⅲ and Ⅳ, immunotyping for T-LBL and B-LBL and morderate risk and high risk in 2 groups (P＜0.05). There was no significant difference in the recurrence rate and mortality after followed-up between 2 groups (P＞0.05). The incidence of myelosuppression for Ⅲ-Ⅳ grade during maintenance therapy in intensive regimen group were significantly higher than that in control group (P＜0.05). CONCLUSION: Compared with conventional maintenance chemotherapy regimen, intensive maintenance chemotherapy regimen in the treatment of children and adolescents with lymphoblastic lymphoma for stage Ⅲ-Ⅳ possess the same survival benefit, but may cause increased severe bone marrow suppression risk.