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Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea. [ABSTRACT FROM AUTHOR]

The irrational land use patterns in the Yellow River Delta (YRD) have resulted in an imbalance in ecological stoichiometry, leading to secondary salinization and soil degradation. However, there is limited knowledge about the long-term response of soil and enzyme stoichiometry to land use. This hampers our ability to optimize land use in the YRD to alleviate nutrient limitation and thus promote ecological stoichiometric balance. We investigated the stoichiometry of soil and enzyme carbon (C), nitrogen (N), and phosphorus (P) in three land use patterns (Alfalfa artificial grassland, AG; wheat–maize rotation field, WM; native grassland, PC) established for 19 years in the YRD. The results showed that the soil stoichiometry of the three land uses in the YRD was lower than the world and Chinese averages, indicating lower C and N levels. Nutrient limitations of soil microorganisms were C and P due to an enzyme C:N ratio greater than 1:1 and vector angle greater than 45°. The three land use patterns have different advantages in alleviating nutrient limitations in the YRD. AG promotes soil macroaggregate formation, reduces soil salt content, improves nutrient availability, and mitigates N limitation. This makes AG more conducive to improving the poor soil structure, high soil salinity, and stoichiometric imbalance in the YRD to mitigate local soil degradation and be suitable for long-term continuous cultivation. WM is beneficial for increasing soil total C content due to straw return. However, WM does not reduce soil salinity. WM is more suitable for intercropping or crop rotation to improve soil C content in the YRD. Although PC can alleviate soil microbial C limitation due to its significantly lower vector length than AG and WM, the low nutrient levels hindered its ability to alleviate local soil nutrient limitation. In conclusion, our study provides a theoretical basis for rational land use in the YRD to mitigate soil degradation. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

The determination of seepage field is the basis to design seepage drainage facilities and dam slopes in tailings pond. However, in the design of a dry-stack tailings pond with a long service life, previous research has been mostly limited to the influence of short-term rainfall, and a proper concept has not been formed to guide the calculation of the seepage field in the design of a dry-stack tailings pond under the cumulative effect of long-term periodic rainfall. The objective of this study is to propose a new concept to concisely determine the seepage field for the design of a dry-stack tailings pond under multi-year periodic rainfall. To this end, the calculation model of the seepage field of a dry-stack tailings pond under multiyear rainfall was established. The evolution process of the seepage field was studied by numerical simulation based on the calculation model and the final form of the seepage field evolution of a dry-stack tailings pond was found. Accordingly, a new concept, the relatively stable seepage field (RSSF) which can be used as the basis for seepage drainage facilities and the dam slope design of a dry-stack tailings pond, was proposed and named. Furthermore, the influencing factors of a relatively stable seepage field were studied by numerical simulation. The results show that: (1) the cumulative infiltration of long-term periodic rainfall is the main reason for the formation of the RSSF, and (2) under the condition of constant annual rainfall, the distribution of the RSSF has little to do with the selection of rainfall mode. Therefore, a new understanding has been formed from this article whereby the RSSF is an essential basis to be considered in the design of the dam slope and drainage system of a dry-stack tailings pond. [ABSTRACT FROM AUTHOR]

Lately, deep learning technology has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved. However, without fully sampled reference data for training, the current approaches may have limited abilities in recovering fine details or structures. To address this challenge, this paper proposes a self-supervised collaborative learning framework (SelfCoLearn) for accurate dynamic MR image reconstruction from undersampled k-space data directly. The proposed SelfCoLearn is equipped with three important components, namely, dual-network collaborative learning, reunderampling data augmentation and a special-designed co-training loss. The framework is flexible and can be integrated into various model-based iterative un-rolled networks. The proposed method has been evaluated on an in vivo dataset and was compared to four state-of-the-art methods. The results show that the proposed method possesses strong capabilities in capturing essential and inherent representations for direct reconstructions from the undersampled k-space data and thus enables high-quality and fast dynamic MR imaging. [ABSTRACT FROM AUTHOR]

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance. [ABSTRACT FROM AUTHOR]

Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. Unlike traditional traffic analysis methods which rely on low-resolution videos captured by road cameras, we capture 4K ( 3840 × 2178 ) traffic videos at a busy road intersection of a modern megacity by flying a unmanned aerial vehicle (UAV) during the rush hours. We then manually annotate locations and types of road vehicles. The proposed method consists of the following three steps: (1) vehicle detection and type recognition based on deep neural networks; (2) vehicle tracking by data association and vehicle trajectory modeling; (3) vehicle behavior recognition by nearest neighbor search and by bidirectional long short-term memory network, respectively. This paper also presents experimental results of the proposed framework in comparison with state-of-the-art approaches on the 4K testing traffic video, which demonstrated the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.