10th Anniversary of Plants—Recent Advances and Perspectives is a scientific paper collection specially published on the anniversary of Plants. Covering all major areas of plant science, it is a valuable guide through current achievements and future discoveries in this scientific field.

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10th Anniversary of Plants—Recent Advances and Perspectives is a scientific paper collection specially published on the anniversary of Plants. Covering all major areas of plant science, it is a valuable guide through current achievements and future discoveries in this scientific field.

Listed by Unglue.it.

10th Anniversary of Plants—Recent Advances and Perspectives is a scientific paper collection specially published on the anniversary of Plants. Covering all major areas of plant science, it is a valuable guide through current achievements and future discoveries in this scientific field.

Listed by Unglue.it.

This Special Issue contains selected papers from works presented at the 11th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space, which was held from the 1st until the 3rd of September 2021. About 420 remote participants contributed to a high-level scientific gathering providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Nineteen interesting papers, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue.

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In this Open Access publication Claudia Lemke develops a comprehensive Multi-Level Sustainable Development Index (MLSDI) that is applicable to micro, meso, and macro objects by conducting methodological and empirical research. Multi-level comparability is crucial because the Sustainable Development Goals (SDGs) at macro level can only be achieved if micro and meso objects contribute. The author shows that a novel information-theoretic algorithm outperforms established multivariate statistical weighting methods such as the principal component analysis (PCA). Overcoming further methodological shortcomings of previous sustainable development indices, the MLSDI avoids misled managerial and political decision making.

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This Special Issue “Advances in Sedimentology and Coastal and Marine Geology” brought together thirteen research articles and one review article, improving the state of the art of the corresponding sub-disciplines. Beach rocks, hybrid event beds, Quaternary marine sedimentation, and shallow gas represent important topics highlighted in this Special Issue. Different areas locations have been studied in the articles included, including both Mediterranean and extra-Mediterranean areas. In the Mediterranean areas, the Bay of Naples and the Cilento offshore (Southern Tyrrhenian Sea, Italy) have been studied, along with the Red Sea (Saudi Arabia). The content of this Special Issue significantly improves enhances previous knowledge of the marine geological and sedimentological processes of continental margins. The audience will include academics and researchers in the field of geology, geotechnical engineers, as well as geological scholars.

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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

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This reprint provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These techniques are computationally intensive and require the help of high-performance computing devices. With the development of devices such as GPUs, remote sensing technology, and aerial sensing technology, it is possible to monitor the Earth with high-resolution images and to obtain vast amounts of Earth observation data. The papers published in this reprint describe recent advances in big data processing and artificial intelligence-based technologies for remote sensing technology.

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With the development of computer technology and communication technology, various industries have collected a large amount of data in different forms, so-called big data. How to obtain valuable knowledge from these data is a very challenging task. Machine learning is such a direct and effective method for big data analytics. In recent years, a variety of advanced machine learning technologies have emerged, and they continue to play important roles in the era of big data. Considering advanced machine learning and big data together, we have selected a series of relevant works in this Special Issue to showcase the latest research advancements in this field. Specifically, a total of thirty-three articles are included in this Special Issue, which can be roughly categorized into six groups: time series analysis, evolutionary computation, pattern recognition, computer vision, image encryption, and others.

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This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes.

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