Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets

The structural and photosynthetic characteristics of the exposed peduncle of wheat (Triticum aestivum L.): an important photosynthate source for grain-filling

<p>Abstract</p> <p>Background</p> <p>In wheat (<it>Triticum aestivum </it>L), the flag leaf has been thought of as the main source of assimilates for grain growth, whereas the peduncle has commonly been thought of as a transporting organ. The photosynthetic characteristics of the exposed peduncle have therefore been neglected. In this study, we investigated the anatomical traits of the exposed peduncle during wheat grain ontogenesis, and we compared the exposed peduncle to the flag leaf with respect to chloroplast ultrastructure, photosystem II (PSII) quantum yield, and phospho<it>enol</it>pyruvate carboxylase (PEPCase; EC 4.1.1.31) activity.</p> <p>Results</p> <p>Transmission electron microscope observations showed well-developed chloroplasts with numerous granum stacks at grain-filling stages 1, 2 and 3 in both the flag leaf and the exposed peduncle. In the exposed peduncle, the membranes constituting the thylakoids were very distinct and plentiful, but in the flag leaf, there was a sharp breakdown at stage 4 and complete disintegration of the thylakoid membranes at stage 5. PSII quantum yield assays revealed that the photosynthetic efficiency remained constant at stages 1, 2 and 3 and then declined in both organs. However, the decline occurred more dramatically in the flag leaf than in the exposed peduncle. An enzyme assay showed that at stages 1 and 2 the PEPCase activity was lower in the exposed peduncle than in the flag leaf; but at stages 3, 4 and 5 the value was higher in the exposed peduncle, with a particularly significant difference observed at stage 5. Subjecting the exposed part of the peduncle to darkness following anthesis reduced the rate of grain growth.</p> <p>Conclusion</p> <p>Our results suggest that the exposed peduncle is a photosynthetically active organ that produces photosynthates and thereby makes a crucial contribution to grain growth, particularly during the late stages of grain-filling.</p

The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery

The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep learning (DL) models for the semantic segmentation task using satellite imagery. In the past few years, DL-based models have achieved performance that meets expectations on image interpretation, due to the development of convolutional neural networks (CNNs). The main objective of this article is to present the details and the best-performing algorithms featured in this competition. The winning solutions are elaborated with state-of-the-art models like the Swin Transformer, SegFormer, and U-Net. Advanced machine learning techniques and strategies such as hard example mining, self-training, and mix-up data augmentation are also considered. Moreover, we describe the L4S benchmark data set in order to facilitate further comparisons, and report the results of the accuracy assessment online. The data is accessible on Future Development Leaderboard for future evaluation at https://www.iarai.ac.at/landslide4sense/challenge/ , and researchers are invited to submit more prediction results, evaluate the accuracy of their methods, compare them with those of other users, and, ideally, improve the landslide detection results reported in this article

Research on echo characteristics in remote detection with the pulse LiDAR of aerial targets under diverse atmospheric conditions

This paper utilizes the idea of theoretical analysis to introduce a method for echo characteristics in remote detection with the pulse LiDAR of aerial targets under atmospheric conditions. A Missiles and an aircraft are selected as simulation targets. The relation among mutual mapping of target surface elements can be directly gained by setting light source and target parameters. We discuss influences on atmospheric transport conditions, target shapes and detection conditions on echo characteristics. Atmospheric transport model is introduced as weather conditions, including sunny and cloudy days, with or without turbulence. Simulation results conclude that the outline of scanned waveform can invert the target shape. These provide a theoretical basis for improving the target detection and tracking performance

Effect of Aging Temperature on Precipitates Evolution and Mechanical Properties of GH4169 Superalloy

GH4169 is primarily strengthened through precipitation, with heat treatment serving as a crucial method for regulating the precipitates of the alloy. However, the impact of aging temperature on the microstructure and properties of GH4169 has not been thoroughly studied, hindering effective regulation of its microstructure and properties. This study systematically investigated the effects of aging temperature on the evolution of precipitates and mechanical properties of GH4169 alloy using various techniques such as OM, SEM, XRD and TEM. The results indicate that raising the aging temperature leads to an increase in the sizes of both the γ″ and γ′ phases in the alloy, as well as promoting the precipitation of δ phase at grain boundaries. Notably, the increase in γ″ phase size enhances the strength of the alloy, while the presence of δ phase is detrimental to its strength but greatly enhances its elongation. The yield strength of the alloy aged at 750 ℃ exhibits the highest yield strength, with values of 1135 MPa and 1050 MPa at room temperature and elevated temperature, respectively. As the aging temperature increases, the Portevin-Le Châtelier (PLC) effect during elevated temperature tensile tests at 650 ℃ gradually weakens. The PLC effect disappears almost completely when the aging temperature reaches 780 ℃

Triphenylphosphonium (TPP)-Conjugated Quinolone Analogs Displayed Significantly Enhanced Fungicidal Activity Superior to Its Parent Molecule

Although 1-hydroxy-4-quinolone derivatives, such as 2-heptyl-4-hydroxyquinoline-N-oxide (HQNO), aurachin C, and floxacrine, have been reported as effective cytochrome bc1 complex inhibitors, the bioactivity of these products is not ideal, presumably due to their low bioavailability in tissues, particularly their poor solubility and low mitochondrial accumulation. In order to overcome the drawbacks of these compounds and develop their use as agricultural fungicides acting by cytochrome bc1 inhibition, in this study, three novel mitochondria-targeting quinolone analogs (mitoQNOs) were designed and synthesized by conjugating triphenylphosphonium (TPP) with quinolone. They exhibited greatly enhanced fungicidal activity compared to the parent molecule, especially mitoQNO11, which showed high antifungal activity against Phytophthora capsici and Sclerotinia sclerotiorum with EC50 values of 7.42 and 4.43 μmol/L, respectively. In addition, mitoQNO11 could inhibit the activity of the cytochrome bc1 complex of P. capsici in a dose-dependent manner and effectively depress its respiration and ATP production. The greatly decreased mitochondrial membrane potential and massively generated reactive oxygen species (ROS) strongly suggested that the inhibition of complex III led to the leakage of free electrons, which resulted in the damage of the pathogen cell structure. The results of this study indicated that TPP-conjugated QNOs might be used as agricultural fungicides by conjugating them with TPP