Seaweed Oligosaccharide Synergistic Silicate Improves the Resistance of Rice Plants to Lodging Stress under High Nitrogen Level

The objective of this study was to determine the effect of seaweed oligosaccharide synergistic silicate (Si) fertilizer (SOSSiF) on rice resistance to lodging stress. The results showed that a spraying SOSSiF decreased apparent lodging index and enhanced rice yield significantly under a high N level. The spraying test indicated that the apparent lodging rate of rice was the lowest when SOSSiF was sprayed for four times, and the dosage was 45 kg/ha each time. Morphological and anatomical analysis indicated that SOSSiF decreased plant height and the lower internode length of ZCSM and increased culm cross-sectional area and wall thickness of JNSM significantly compared with the control. Furthermore, SOSSiF enhanced bending strength of rice culm by 38.8% to 63.6%, and reduced lodging index by 36.8% to 42.6%. Chemical component analysis found that SOSSiF elevated the contents of soluble sugar, cellulose, Si, and lignin in the culms of ZCSM and JNSM. Correlation analysis revealed that the lodging index was positively correlated with the length of the lower internode, and was negatively correlated with culm bending strength and culm thickness. The above results suggested that spraying SOSSiF elevates culm contents of Si and lignin and enhances bending strength, thus improving rice lodging resistance and production

Investigation on thermal-hydraulic performance prediction of a new parallel-flow shell and tube heat exchanger with different surrogate models

The thermal-hydraulic performance of a new parallel-flow shell and tube heat exchanger (STHX) with equilateral cross-sectioned wire coil (HCBetwc-STHX) is investigated in turbulent regime. Four different surrogate models are established to predict the thermal-hydraulic performance. Their merits and drawbacks are illustrated. The results show that the Nuetwc/NuRRB and f etwc/f RRB are in the range of 1.1638–1.855 and 4.078–16.062, respectively. The precision of CFM is the lowest, whereas the precision of radial basis function + artificial neural network and Kriging model is the highest. A good balance can be achieved by response surface methodology between precision and cost. Finally, a general analysis procedure is presented for the predicting method of thermal-hydraulic performance of different STHX with relatively small cost and high precision

Seismic Anisotropy and Mantle Deformation Beneath the Central Sunda Plate

The central Sunda plate, which forms the core of Southeast Asia, has been extensively studied based on analyses of data mainly from surface geological observations. In contrast, largely due to the limited coverage by seismic stations in the area, a number of key issues associated with mantle structure and dynamics remain enigmatic. These can possibly be constrained by investigating seismic azimuthal anisotropy in the upper mantle. Here we employ the shear wave splitting technique on three P-to-S converted phases from the core-mantle boundary (PKS, SKKS, and SKS) recorded by 11 stations to systematically explore the spatial variation of azimuthal anisotropy beneath the central Sunda plate. Most of the Malay Peninsula is revealed to possess mostly trench-perpendicular fast orientations that can be attributed to mantle flow induced by the Indo-Australian subduction. In addition, the central part of the Malay Peninsula is characterized by a 2-layered model of anisotropy, which is possibly associated with the joint effects of lithospheric fabrics and a slab tear-induced toroidal flow. Absolute plate motion (APM)-parallel anisotropy is observed in northern Borneo and the Nansha Block, where APM-driven simple shear in the transitional layer between the partially coupled lithosphere and asthenosphere is mostly responsible for the observed anisotropy. The APM-induced flow may be locally modified by a fossil slab segment beneath Sabah

Subpixel Multilevel Scale Feature Learning and Adaptive Attention Constraint Fusion for Hyperspectral Image Classification

Convolutional neural networks (CNNs) play an important role in hyperspectral image (HSI) classification due to their powerful feature extraction ability. Multiscale information is an important means of enhancing the feature representation ability. However, current HSI classification models based on deep learning only use fixed patches as the network input, which may not well reflect the complexity and richness of HSIs. While the existing methods achieve good classification performance for large-scale scenes, the classification of boundary locations and small-scale scenes is still challenging. In addition, dimensional dislocation often exists in the feature fusion process, and the up/downsampling operation for feature alignment may introduce extra noise or result in feature loss. Aiming at the above issues, this paper deeply explores multiscale features, proposes an adaptive attention constraint fusion module for different scale features, and designs a semantic feature enhancement module for high-dimensional features. First, HSI data of two different spatial scales are fed into the model. For the two inputs, we upsample them using bilinear interpolation to obtain their subpixel data. The proposed multiscale feature extraction module is intended to extract the features of the above four parts of the data. For the extracted features, the multiscale attention fusion module is used for feature fusion, and then, the fused features are fed into the high-level feature semantic enhancement module. Finally, based on the fully connected layer and softmax layer, the prediction results of the proposed model are obtained. Experimental results on four public HSI databases verify that the proposed method outperforms several state-of-the-art methods

Subpixel Multilevel Scale Feature Learning and Adaptive Attention Constraint Fusion for Hyperspectral Image Classification

Convolutional neural networks (CNNs) play an important role in hyperspectral image (HSI) classification due to their powerful feature extraction ability. Multiscale information is an important means of enhancing the feature representation ability. However, current HSI classification models based on deep learning only use fixed patches as the network input, which may not well reflect the complexity and richness of HSIs. While the existing methods achieve good classification performance for large-scale scenes, the classification of boundary locations and small-scale scenes is still challenging. In addition, dimensional dislocation often exists in the feature fusion process, and the up/downsampling operation for feature alignment may introduce extra noise or result in feature loss. Aiming at the above issues, this paper deeply explores multiscale features, proposes an adaptive attention constraint fusion module for different scale features, and designs a semantic feature enhancement module for high-dimensional features. First, HSI data of two different spatial scales are fed into the model. For the two inputs, we upsample them using bilinear interpolation to obtain their subpixel data. The proposed multiscale feature extraction module is intended to extract the features of the above four parts of the data. For the extracted features, the multiscale attention fusion module is used for feature fusion, and then, the fused features are fed into the high-level feature semantic enhancement module. Finally, based on the fully connected layer and softmax layer, the prediction results of the proposed model are obtained. Experimental results on four public HSI databases verify that the proposed method outperforms several state-of-the-art methods

Receiver Function Investigation of Crustal Structure in the Malawi and Luangwa Rift Zones and Adjacent Areas

Stacking over 2300 P-to-S receiver functions recorded by 33 SAFARI (Seismic Arrays for African Rift Initiation) broadband seismic stations that we installed in the vicinity of the Malawi and Luangwa rift zones (MRZ and LRZ, respectively) reveals significant variations of crustal thickness (32.8-46.3 km) and Vp/Vs (1.69-1.85). The resulting crustal stretching factor is about 1.05-1.08 for the MRZ, which is approximately 10-40% lower than that observed in the mature segments of the East African Rift System (EARS). The low stretching factor is consistent with the general absence of volcanism in the MRZ, and the relatively high Vp/Vs (≥ 1.81) beneath the southern MRZ, when combined with observations from previous studies, indicate the possible existence of crustal partial melting, elevated temperatures or fluid-filled deep crustal faults that are likely associated with lithospheric stretching. In sharp contrast with the southern MRZ, low Vp/Vs measurements in the range of 1.69-1.72 are observed along the western boundary of the northern MRZ, which could be attributable to the infiltration of magma-derived CO2 into the crust. The LRZ shows negligible crustal thinning and a Vp/Vs that is comparable to the globally averaged value for continental crust, suggesting a complete post-rifting recovery of crustal properties in terms of crustal thickness and Vp/Vs

Evaluating Rice Varieties for Suitability in a Rice–Fish Co-Culture System Based on Lodging Resistance and Grain Yield

Rice–fish co-cultures have been practiced for over 2000 years, and they have tremendous potential in terms of increasing food security and economic benefits. However, little research has been conducted into achieving stable yields and high lodging resistance with regard to rice while simultaneously promoting the harmonious and healthy growth of fish in rice–fish co-culture paddy fields. We conducted a field study aimed at selecting suitable rice varieties for rice–fish co-culture systems (encompassing both ratoon and main crop). This selection process was grounded in an evaluation of lodging resistance and grain yield among 33 rice varieties used throughout the studied region. The results revealed a range of lodging indices of the main crop for the second internode, spanning from 62.43 to 138.75, and the annual grain yield (main crop and ratoon crop) ranged from 7.17 to 13.10 t ha−1 within rice–fish co-culture systems. We found that the use of rice–fish co-culture farming could improve the milling quality, nutrient quality, and appearance quality of rice, though the improvement gained through co-culturing varied across rice varieties. Moreover, the lodging index of the three basal internodes of rice plants was significantly and positively correlated with the plant height and the culm fresh weight, but it was negatively correlated with the bending strength of the rice basal internodes. Additionally, the 33 tested rice varieties were clustered in accordance with their lodging resistance (i.e., high resistance with lodging indices 62.43–75.42; medium resistance with lodging indices 80.57–104.62; and low resistance with lodging indices 113.02–138.75) according to the hierarchical cluster analysis. The 33 rice varieties were also clustered in accordance with the annual (main crop and ratoon crop) grain yield (i.e., high yield with 11.17–13.10 t ha−1; medium yield with 10.15–10.83 t ha−1; and low yield with 7.16–9.88 t ha−1). In all, 11 rice varieties were identified by a comprehensive evaluation as suitable varieties for grain production in the rice–fish co-culture system. These varieties displayed favorable traits, including a high annual rice yield, strong lodging resistance, and good grain quality. This is the first study to systematically evaluate rice varieties based on grain yield, lodging resistance, and grain quality in rice–fish co-culture systems