Explore the vast range of titles available.

This study aimed to provide scientific evidence and strategic suggestions for coastline management and engineering practices through analysis of the characteristics and temporal and spatial variations of wind and waves. We selected a public dataset from National Marine Data Center (NMDC) pertaining to wind and waves, subsequently performing feature selection through an analysis of the correlations among the various elements within the datasets. Furthermore, we conducted a thorough analysis and discussion of the data that was missing from these datasets, ensuring a comprehensive understanding of their limitations and potential implications. After conducting thorough statistics and analysis of the measured data pertaining to waves and winds, we studied the temporal and spatial variations observed in coastal waves. This paper provided insights into how these waves evolve and change over different time periods and locations.

In this study, QRMHF-DNK (Quasi Reflection Metropolis Hasting Firefly- Deep Neural Kernel), a hybrid framework is designed to improve fake news detection on benchmark datasets. The framework integrates three main stages: data preprocessing to reduce sampling errors, feature selection using a swarm-based optimization strategy, and classification using a deep neural kernel model. This combination enables effective handling of high-dimensional textual data while accurately identifying informative features for classification. The proposed framework was evaluated on a publicly available Kaggle fake news dataset and compared with existing cooperative and multilingual deep learning methods. Experimental results show that QRMHF-DNK achieves a precision of 0.98 and recall of 0.95, with a sampling error of 0.0671%, indicating that the sampled data closely represents the true class distribution. These results demonstrate the effectiveness of the proposed approach on the evaluated dataset and suggest its potential applicability to fake news detection tasks, while further validation on additional datasets is left for future work.

[This corrects the article DOI: 10.1371/journal.pone.0201195.].

Objective and Impact Statement . In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on a single dataset, this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark location. Introduction . The accurate and automatic localization of anatomical landmarks plays an essential role in medical image analysis. However, recent deep learning-based methods only utilize limited data from a single dataset. It is promising and desirable to build a model learned from different regions which harnesses the power of big data. Methods . Our model consists of a local network and a global network, which capture local features and global features, respectively. The local network is a fully convolutional network built up with depth-wise separable convolutions, and the global network uses dilated convolution to enlarge the receptive field to model global dependencies. Results . We evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical regions. Extensive experimental results show that our model improves the detection accuracy compared to the state-of-the-art methods. Conclusion . Our model makes the first attempt to train a single network on multiple datasets for landmark detection. Experimental results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.

doi: 10.1038/nature19104 Corrigendum: Holocene shifts in the assembly of plant and animal communities implicate human impacts S. Kathleen Lyons, Kathryn L. Amatangelo, Anna K. Behrensmeyer, Antoine Bercovici, Jessica L. Blois, Matt Davis, William A. DiMichele, Andrew Du, Jussi T. Eronen, J. Tyler Faith, Gary R. Graves, Nathan Jud, conrad Labandeira, cindy V. Looy, brian McGill, Joshua H. Miller, David Patterson, Silvia Pineda-Munoz, Richard Potts, Brett Riddle, Rebecca Terry, Anikó Tóth, Werner Ulrich, Amelia Villaseñor, Scott Wing, Heidi Anderson, John Anderson, Donald Waller & Nicholas J. Gotelli Nature 529, 80-83 (2016); doi:10.1038/nature16447 It has come to our attention that in this Letter, there were some errors in the categorization of some of the modern datasets (R. Telford et al., personal communication).

The ColorChecker dataset is the most widely used dataset for evaluating and benchmarking illuminant-estimation algorithms. Although it is distributed with a 3-fold cross-validation partitioning, no procedure is defined on how to use it. In order to permit a fair comparison between illuminant-estimation algorithms, in this short correspondence we define a fair comparison procedure, showing that illuminant-estimation errors of state-of-the-art algorithms have been underestimated by up to 33%. We also compute the lower error bounds that can be reached on this dataset, which demonstrates that the existing algorithms have not yet reached their maximum performance potential.

The evaluation of clustering effects has been an important issue for a long time. How to effectively evaluate the clustering results of clustering algorithms is the key to the problem. The clustering effect evaluation is generally divided into internal clustering effect evaluation and external clustering effect evaluation. This paper focuses on the internal clustering effect evaluation, and proposes an improved index based on the Silhouette index and the Calinski-Harabasz index: Peak Weight Index (PWI). PWI combines the characteristics of Silhouette index and Calinski-Harabasz index, and takes the peak value of the two indexes as the impact point and gives appropriate weight within a certain range. Silhouette index and Calinski-Harabasz index will help improve the fluctuation of clustering results in the data set. Through the simulation experiments on four self-built influence data sets and two real data sets, it will prove that the PWI has excellent evaluation of clustering results.