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The research in this paper provides generalization and new ideas for research topics in computer-assisted medicine. The main improvement efforts in deep learning-based multimodal fusion schemes, which provide alternative directions and robust feature fitting performance for fusion schemes, are building complex structures, migrating knowledge or experience, processing and enhancing data, and targeting features for semantic correction based on contextual features. At the application level, the brain, liver, and lungs are the main targets of scientific research, so this paper surveys related work and analyzes the reasons for performance gains. Taken together, deep learning-based image fusion schemes can assist physicians in understanding information about lesion sites, lesion types, and sizes, providing an important basis for developing personalized treatment plans, which is important for improving diagnosis and specifying precise treatment plans. Therefore, the investigation of medical image fusion schemes is promising and beneficial.

The core task of medical image segmentation based on deep learning is to quickly obtain good results through low-cost auxiliary modules. The attention mechanism, relying on the interacting features of the neural network, is one of the lightweight schemes to focus on key features, which is inspired by the characteristics of selective filtering information in human vision. Through the investigation and analysis, this paper argues that the common attentional mechanisms can be mainly classified into four types according to their structure and form: (i) conventional attention based on feature interaction, (ii) multi-scale/multi-branch-based attention, (iii) Self-similarity attention based on key-value pair queries, (iv) hard attention, etc. Medical images contain poor and blur descriptions of contextual information than natural images. They are usually re-imaging by the feedback intensity of the medium signal since most of them have low contrast and uneven appearance, as well as contain noise and artifacts. In models based on deep learning, without the ability to focus on key descriptive information or features, it is difficult for well-designed models to perform theoretically. This paper shows that attention mechanisms can guide downstream medical image analysis tasks to master discernible expected features while filtering and suppressing irrelevant information to enhance the intensity of target features. Therefore, the network performance can be improved through continuous highly accurate feature spatial evolution.

The influence of unreasonable land consolidation on the ecology and landscape of rural land is a long-term process, which is almost irreversible. Therefore, the study of ecological land consolidation and agricultural landscape design is of great theoretical and practical significance for balancing the relationship among sustainable land use, sustainable development of agricultural production and ecological protection, improving rural environment and increasing ecological function value.

In this study, the yttrium trifluoride-doped polyacrylonitrile(PAN) based carbon nanofibers (YF

The development of modern computational fluid dynamics (CFD) technology provides many preliminary references for experimental design. In addition, the CFD calculation results verified by experiments can display enormous microdata in areas that are difficult to measure through experiments, as an extension. This can make measurements more reasonable and effective, shorten measurement times, and save manpower and capital significantly. Therefore, it is vital to verify the accuracy of CFD calculation results, especially in cases of complex structures and multiphase flows. The results of model tests can be utilized in a prototype experiment by properly designing a test section and selecting a working fluid if the Reynolds similarity criterion is met. Optical measurement technology is a noninvasive measurement method, and the impact on the flow field can be almost negligible. It is advised to use transparent materials and prepare a refractive index-matching (RIM) fluid to obtain a good optical path. Polymethyl methacrylate (PMMA) is widely used in flow field visualization experiments because of its good light transmission and mechanical strength. This review is aimed at introducing the current status of different flow field measurement techniques; moreover, it is intended to help the readers to become more familiar with the principles of RIM, the characteristics, applications, and usage suggestions of various RIM fluid schemes of PMMA, providing references for researchers in the design, preparation, and conducting stages of flow field visualization measurement experiments. This review is divided into five sections. In the introduction section, Chapter 1, relevant research developments and related results of flow field measurements are presented, followed by the innovations and benefits of this paper. In Chapter 2, the flow field visualization measurements are presented and a derivation is shown. In Chapter 3, some RIM fluid schemes of PMMA and their applications are given, which are very valuable for peers. In Chapter 4, the measurement and analysis of some physical properties are described. In the RIM process, it is necessary to focus on the RI, density, dynamic viscosity, compatibility, stability, safety, and cost of RIM fluids. These factors greatly impact the accuracy of experimental results, experimental progress, and safety of the experimenters. Based on the analysis and our practical experience, some suggestions are given for preparing and using RIM fluids. In the conclusion section, Chapter 5, the results and practical implications of this paper are summarized.

Financial fraud is the conscious misstatement and neglect of financial information by enterprises, which is often difficult to be detected due to the asymmetric nature of information. In recent years, frequent lightning in the capital market, especially the high incidence of financial fraud in listed companies in the pharmaceutical industry, has greatly jeopardized the market order. Therefore, the article discusses the common fraudulent means and differences that the two companies have in conjunction with the financial reports and related announcement information of Kangmei Pharmaceutical and Kangdexin, dissects the abnormal characteristics of identifying financial fraud risks, and proposes corresponding audit response ideas and methods to safeguard the capital market order and investors' interests.

Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations’ data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks.

In this paper, a contour error control strategy based on spatial iterative learning control (sILC) is developed for repetitive processing of five-axis computer numerical control (CNC) machine tools. A curve approximation method with an adaptive moving window is developed to achieve accurate tool position and orientation contour error estimation, and a five-axis contour error control strategy based on sILC is proposed. The compensation method is derived using an sILC algorithm to modify the geometric reference path instead of modifying the controller. The experimental results show that the proposed control strategy reduces contour errors of five-axis CNC machine tools, and it outperforms the traditional tracking error control.

We demonstrate that reaction temperature has a significant impact on the structures of two 1D CuII chain enantiomeric pairs and subsequently results in their completely distinct magnetic properties and large difference in NLO responses.

This review mainly focuses on the discussion of mechanisms behind improved potassium storage properties, starting from some specific optimization strategies, and presents a perspective for the further development of carbon anodes.

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneous computing and communication capabilities of different participants, asynchronous FL can avoid the stragglers effect in synchronous FL and adapts to scenarios with vast participants. Both staleness and non-IID data in asynchronous FL would reduce the model utility. However, there exists an inherent contradiction between the solutions to the two problems. That is, mitigating the staleness requires to select less but consistent gradients while coping with non-IID data demands more comprehensive gradients. To address the dilemma, this paper proposes a two-stage weighted $K$ asynchronous FL with adaptive learning rate (WKAFL). By selecting consistent gradients and adjusting learning rate adaptively, WKAFL utilizes stale gradients and mitigates the impact of non-IID data, which can achieve multifaceted enhancement in training speed, prediction accuracy and training stability. We also present the convergence analysis for WKAFL under the assumption of unbounded staleness to understand the impact of staleness and non-IID data. Experiments implemented on both benchmark and synthetic FL datasets show that WKAFL has better overall performance compared to existing algorithms.

Hemicellulose and its derivatives have attracted extensive attention as packaging materials and various methods have been utilized to improve its film formation properties. To make use of the byproduct in dissolving pulp production, hemicellulose collected from waste water was modified by carboxymethylation and blended with polyvinyl alcohol (PVA) to prepare composite film by solution casting method. Potassium cinnamate (PC) was further incorporated to endow the film with antibacterial activity. The properties of the composite films were characterized. Due to the good compatibility and intermolecular interactions, the composite film exhibited moderate oxygen barrier property (3.64-12.21 cm

Background: Cycloastragenol (CAG) is a sapogenin derived from the main bioactive constituents of Astragali Radix (AR). However, the current research on CAG metabolism in vivo and in vitro is still inadequate, and the metabolite cluster is incomplete due to incomplete analysis strategy. Objective: The objective of this study was to screen and identify the metabolic behavior of CAG in vivo and in vitro. Methods: A simple and rapid analysis strategy based on UHPLC-Q-Exactive Orbitrap mass spectrometry combined with data-mining processing technology was developed and used to screen and identify CAG metabolites in rat body fluids and tissues after oral administration. Results: As a result, a total of 82 metabolites were fully or partially characterized based on their accurate mass, characteristic fragment ions, retention times, corresponding Clog P values, and so on. Among the metabolites, 61 were not been reported in previous reports. These metabolites (6 metabolites in vitro and 91 in vivo) were generated through reactions of hydroxylation, glucuronidation, sulfation, hydrogenation, hydroxylation, demethylation, deisopropylation, dehydroxylation, ring cleavage, and carboxyl substitution and their composite reactions, and the hydroxylation might be the main metabolic reaction of CAG. In addition, the characteristic fragmentation pathways of CAG were summarized for the subsequent metabolite identification. Conclusion: The current study not only clarifies the metabolite cluster-based and metabolic regularity of CAG in vivo and in vitro, but also provides ideas for metabolism of other saponin compounds.

Rapid and accurate monitoring of the dynamic change of construction land is of great significance for strictly controlling the arbitrary growth of urban construction land and protecting agricultural and forestry land. In order to master the situation of land use change and conduct more efficient research on urban land use classification, this paper takes Weinan as the research object and uses Landsat8 data under the same classification conditions to analyze the image features of various land use types respectively by using minimum distance, maximum likelihood and random forest method, and carries out land use classification research. The research shows that for the selected research area, the maximum likelihood classification method has higher classification accuracy and time efficiency, while the minimum distance and random forest can not reflect its superior classification performance, and the overall accuracy is lower than the maximum likelihood classification method, and the maximum likelihood classification method has obvious advantages. Therefore, in the classification of remote sensing image, the maximum likelihood classification method is suitable when the data dimension is low.

The current domestic land consolidation project management has basically realized the regularization development, but with the overall popularization of green environmental protection concept and the continuous improvement of related work attention, the sustainable development of land ecological consolidation work is very important. For a long time, the imbalance between supply and demand of rural land use function is one of the main causes of rural problems. Based on the perspective of rural revitalization, this paper explains the mutual feed-back relationship between rural land consolidation and rural revitalization, and discusses how to achieve the balance between supply and demand of rural land use function through rural land consolidation, so as to promote rural revitalization.

Alginate oligosaccharide (AOS) is widely used in agriculture because of its many excellent biological properties. However, the possible beneficial effects of AOS and their underlying mechanisms are currently not well known in grass carp (Ctenopharyngodon idellus). Here, grass carp were fed diets supplemented with 5, 10, or 20 g/kg AOS for six weeks. HE and PAS staining showed that the diets of AOS significantly increased the number of goblet cells in the intestinal. According to transcriptome and quantitative real-time PCR (qRT-PCR) data, AOS-supplemented diets activated the expression of fat metabolism-related pathways and genes. The 16S rRNA sequencing results showed that supplementation with AOS affected the distribution and abundance of the gut bacterial assembly. qRT-PCR and activity assays revealed that the AOS diets significantly increased the antioxidant resistance in gut of grass carp, and down-regulated the expression of inflammatory and up-regulated anti-inflammatory cytokines. Finally, the Aeromonas hydrophila infection assay suggested that the mortality in the groups fed dietary AOS was slightly lower than that in the control. Therefore, supplementing the diet of grass carp with an appropriate amount of AOS can improve fat metabolism and immune responses and alter the intestinal bacterial community, which may help to fight bacterial infection.