In the field of modern bidding, electronic bidding leads a new trend of development, convenience and efficiency and other significant advantages effectively promote the reform and innovation of China’s bidding field. Nowadays, most systems require a strong and trusted third party to guarantee the integrity and security of the system. However, with the development of blockchain technology and the rise of privacy protection, researchers has begun to emphasize the core concept of decentralization. This paper introduces a decentralized electronic bidding system based on blockchain and smart contract. The system uses blockchain to replace the traditional database and uses chaincode to process business logic. In data interaction, encryption techniques such as zero-knowledge proof based on graph isomorphism are used to improve privacy protection, which improves the anonymity of participants, the privacy of data transmission, and the traceability and verifiable of data. Compared with other electronic bidding systems, this system is more secure and efficient, and has the nature of anonymous operation, which fully protects the privacy information in the bidding process.

Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder–decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas’ boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring.

With the development of the Internet of Things and the popularity of smart terminal devices, mobile crowdsourcing systems are receiving more and more attention. However, the information overload of crowdsourcing platforms makes workers face difficulties in task selection. This paper proposes a task recommendation model based on the prediction of workers’ mobile trajectories. A recurrent neural network is used to obtain the movement pattern of workers and predict the next destination. In addition, an attention mechanism is added to the task recommendation model in order to capture records that are similar to candidate tasks and to obtain task selection preferences. Finally, we conduct experiments on two real datasets, Foursquare and AMT (Amazon Mechanical Turk), to verify the effectiveness of the proposed recommendation model.

Monitoring oil spills on coastal beaches using satellite imagery has received limited attention, primarily due to the lack of characteristic spectral data as well as constraints in spatial or temporal resolution. In this study, we employ both reflectance spectroscopy and CMOS-sensing imagery to detect and characterize different species of oil contaminants on sandy beaches and investigate their behavior throughout the weathering process. Laboratory and field measurements were conducted on oil-contaminated and clean beach samples with a high-resolution portable spectrometer and a highly sensitive CMOS camera. Predictive modeling of the reflectance spectra using LW-PLS, SVR, and SVM yielded R² values of 0.86 for oil concentration and 0.89 for weathering time, and achieved an oil species classification accuracy of 0.86. Furthermore, beach oil spills in the image dataset were detected using a DeepLabV3+ segmentation model with a ResNet-50 backbone, achieving a mean prediction accuracy of 98.73%. Finally, the segmentation model was successfully applied to accurately detect oil spill pollution on the beaches of Goa, India, confirming its field effectiveness. These reflectance spectroscopy and CMOS-sensing imagery technologies can provide critical data for calibrating remote sensing satellites, thereby offering direct technical support for targeted oil spill cleanup operations on beaches.