Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications

Numerical Simulation for Exploring the Effect of Viscosity on Single-screw Extrusion Process of Propellant

AbstractSingle-screw extrusion process of propellant has the characteristics of multiple accidents, complicated rheological parameters and difficult measurement of real-time conditions, however, the process details can be reproduced by simulation conveniently and intuitively. In this paper, the POLYFLOW simulation platform was used to model and analyze the single-screw extrusion process of propellant through the application of Finite Element Analysis on extrusion of plastic. The distributions and changes of viscosity in extrusion process, which were taken as the starting point to study the threshold and distribution of pressure, temperature and other sensitive parameters, were obtained. The simulation shows that the risk at the screw edge is higher because of severe mixing and plasticizing process, and the viscous heating is up to 1.4×105 W · m-3. Parameters under different speed conditions were studied as well, which provide guidance for the coordination of security and economy in production

Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction

Proximal gradient-based optimization is one of the most common strategies for solving image inverse problems as well as easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on second Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high/low frequency image features,but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction.Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration and post-processing. In the image restoration step,a cascade geometric incremental learning module is designed to compensate for the missing texture information from different geometric spectral decomposition domains. Inspired by overlap-tile strategy, we also develop a post-processing module to remove the block-effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable,an adaptive initialization technique of physical-parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority. Our source codes are available at https://github.com/fanxiaohong/Nest-DGIL.Comment: 15 page

Chinese Herb and Formulas for Promoting Blood Circulation and Removing Blood Stasis and Antiplatelet Therapies

Atherothrombosis, which directly threatens people's health and lives, is the main cause of morbidity and mortality all over the world. Platelets play a key role in the development of acute coronary syndromes (ACSs) and contribute to cardiovascular events. Oral antiplatelet drugs are a milestone in the therapy of cardiovascular atherothrombotic diseases. In recent years, many reports have shown the possibility that “resistance” to oral anti-platelet drugs and many adverse reactions, such as serious bleeding risk, which provides an impetus for developing new anti-platelet drugs possesses highly efficiency and fewer adverse effects. Study on the blood stasis syndrome and promoting blood circulation and removing blood stasis is the most active field of research of integration of traditional and western medicine in China. Blood-stasis syndrome and platelet activation have close relationship, many Chinese herb and formulas for promoting blood circulation and removing blood stasis possess definite anti-platelet effect. This paper covers the progress of anti-platelet mechanism of Chinese herb and formulas for promoting blood circulation and removing blood stasis and is to be deeply discussed in further research

Tris[2-eth­oxy-6-(methyl­imino­meth­yl)phenolato-κ2 N,O 1]cobalt(III) monohydrate

In the title compound, [Co(C10H12NO2)3]·H2O, the CoIII ion is coordinated by three O atoms and three N atoms from three bidentate 2-eth­oxy-6-(methyl­imino­meth­yl)phenolate ligands in a slightly distorted octa­hedral environment. The water mol­ecule connects two ligands by O—H⋯O hydrogen bonds. One terminal methyl group is disordered over two positions, with site-occupancy factors of 0.412 (15) and 0.588 (15)

Determining Singularity-Free Inner Workspace through Offline Conversion of Assembly Modes for a 3-RRR PPM

The existing singularity avoidance methods have deficiencies, such as the conditionality of the online conversion of the assembly modes (AMs) and the kinematically redundant manipulator with the predicament of the prototype design and added complexity of the mechanism. To address these issues, a method to determine a singularity-free inner workspace through offline conversion of the AMs of the 3-RRR planar parallel manipulator (PPM) is presented. Based on the geometric relations among rods of the manipulator during the occurrence of singularity, and the singular points at or near the boundary of the workspace are permitted, the AMs and ranges of the orientation angle of the moving platform corresponding to the inner singularity-free workspace are determined through a three-dimensional search method. The simulation and experimental comparisons indicate that singular-free paths related to the constant or variable orientation angle of the moving platform can be planned on the singularity-free inner workspace

BMP9-Induced Osteogenetic Differentiation and Bone Formation of Muscle-Derived Stem Cells

Efficient osteogenetic differentiation and bone formation from muscle-derived stem cells (MDSCs) should have potential clinical applications in treating nonunion fracture healing or bone defects. Here, we investigate osteogenetic differentiation ability of MDSCs induced by bone morphogenetic protein 9 (BMP9) in vitro and bone formation ability in rabbit radius defects repairing model. Rabbit's MDSCs were extracted by type I collagenase and trypsin methods, and BMP9 was introduced into MDSCs by infection with recombinant adenovirus. Effects of BMP9-induced osteogenetic differentiation of MDSCs were identified with alkaline phosphatase (ALP) activity and expression of later marker. In stem-cell implantation assay, MDSCs have also shown valuable potential bone formation ability induced by BMP9 in rabbit radius defects repairing test. Taken together, our findings suggest that MDSCs are potentiated osteogenetic stem cells which can be induced by BMP9 to treat large segmental bone defects, nonunion fracture, and/or osteoporotic fracture

A Survey on Multimodal Large Language Models

Multimodal Large Language Model (MLLM) recently has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of MLLM, such as writing stories based on images and OCR-free math reasoning, are rare in traditional methods, suggesting a potential path to artificial general intelligence. In this paper, we aim to trace and summarize the recent progress of MLLM. First of all, we present the formulation of MLLM and delineate its related concepts. Then, we discuss the key techniques and applications, including Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning (M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning (LAVR). Finally, we discuss existing challenges and point out promising research directions. In light of the fact that the era of MLLM has only just begun, we will keep updating this survey and hope it can inspire more research. An associated GitHub link collecting the latest papers is available at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.Comment: Project page:https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model