The catalytic transfer hydrogenation of furfural to the fuel additives 2‐methylfuran (2‐MF) and 2‐methyltetrahydrofuran (2‐MTHF) was investigated over various bimetallic catalysts in the presence of ...the hydrogen donor 2‐propanol. Of all the as‐prepared catalysts, bimetallic Cu‐Pd catalysts showed the highest catalytic activities towards the formation of 2‐MF and 2‐MTHF with a total yield of up to 83.9 % yield at 220 °C in 4 h. By modifying the Pd ratios in the Cu‐Pd catalyst, 2‐MF or 2‐MTHF could be obtained selectively as the prevailing product. The other reaction conditions also had a great influence on the product distribution. Mechanistic studies by reaction monitoring and intermediate conversion revealed that the reaction proceeded mainly through the hydrogenation of furfural to furfuryl alcohol, which was followed by deoxygenation to 2‐MF in parallel to deoxygenation/ring hydrogenation to 2‐MTHF. Finally, the catalyst showed a high reactivity and stability in five catalyst recycling runs, which represents a significant step forward toward the catalytic transfer hydrogenation of furfural.
Identifying the intermediates: The catalytic transfer hydrogenation of biomass‐derived furfural to fuel additives 2‐methylfuran and 2‐methyltetrahydrofuran is performed over a bimetallic Cu‐Pd catalyst in the presence of 2‐propanol. The reaction proceeds via the intermediate furfuryl alcohol, which is then deoxygenated/hydrogenated to the desired products.
Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low ...classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.
Chemical investigation of the fungus
sp. SCSIO Ind16F01 derived from deep-sea sediment sample afforded a new xanthone, 3,8-dihydroxy-2-methyl-9-oxoxanthene-4-carboxylic acid methyl ester (
) and a ...new chromone, coniochaetone J (
), together with three known xanthones, 8-hydroxy-6-methyl-9-oxo-9
-xanthene-1-carboxylic acid methyl ester (
), 7,8-dihydroxy-6-methyl-9-oxo-9
-xanthene-1-carboxylic acid methyl ester (
), 1,6,8-trihydroxy-3-(hydroxymethyl)anthraquinone (
), three known chromones, coniochaetone B (
), citrinolactones B (
), epiremisporine B (
), and four reported rare class of
-methyl quinolone lactams: quinolactacins B (
), C1 (
), and C2 (
), and quinolonimide (
). The structures of new compounds were determined by analysis of the NMR and MS spectroscopic data. Those isolated compounds were evaluated for their antiviral (EV71 and H3N2) and cytotoxic activities.
Traditional medical image segmentation methods have problems such as low segmentation accuracy and low adaptive ability. Therefore, many scholars have proposed a medical image segmentation method ...based on deep learning, which has achieved good results in the field of medical image segmentation. However, this type of method has the following problems in the application process: (1) Medical image segmentation target boundary positioning problem. Constrained by factors such as medical image contrast, heterogeneity, and boundary resolution, existing convolution models still cannot accurately locate boundaries. (2) Deep adaptability of deep learning network structure to medical images. Because medical images have more distinct and different feature information than natural images, the current deep learning-based medical segmentation methods have not fully considered this feature. In view of this, this paper proposes a multi-level boundary-aware RUNet segmentation model. The network structure consists of a U-Net-based segmentation network and a multi-level boundary detection network. It can solve the problem of boundary positioning. At the same time, in order to solve the problem of poor adaptability of deep learning network structures to medical images, this paper proposes to introduce a new interactive self-attention module into deep learning models. It can make the feature map get global information, and realize the effective extraction of medical image feature information. It solves the problem of weak matching between the deep learning network structure and medical images. Based on the above ideas, this paper proposes an image segmentation algorithm based on a multi-layer boundary perception-self-attention mechanism deep learning model. This method and other mainstream segmentation algorithms are used to perform experiments on related medical databases. The results show that the proposed method not only improves the segmentation effect significantly compared with traditional machine learning methods, but also improves it to a certain extent compared with other deep learning methods.
Summary
The dual‐porosity model is usually employed to simulate the flow in fractured reservoirs. However, its original form for the multiphase flow does not consider the displacement effect under ...macropressure gradient. Especially for the incompressible multiphase flow, it predicts zero transfer term between fracture and matrix, which is unreasonable. To improve this, a modified double‐porosity model is proposed for incompressible two‐phase flow, in which the displacement effect is considered and the corresponding shape factor is derived. For the anisotropic case, the shape factor of displacement depends upon the velocity direction. The accuracy and the efficiency of the proposed dual‐porosity model are indicated through numerical tests.
Estrogen has been postulated to contribute to the development and progression of lung cancer. We examined the epidemiologic evidence, explored the characteristics of estrogen receptors (ER) in lung ...adenocarcinoma, and investigated the effect of estrogen on lung cancer cell migration, including the signaling pathway involved. For epidemiologic evidence, a total of 1434 consecutive non‐small cell lung cancer patients who underwent standardized staging and homogenous treatment were prospectively enrolled from January 2002 to December 2008, and followed until December 2012. The possible prognostic factors to be analyzed included stage, age, gender, menopausal status, smoking history and histology. For laboratory study, lung cancer cell lines A549 and PE089 and malignant pleural effusions from the patients with lung adenocarcinoma were used. We found that the premenopausal patients had more advanced disease and a shorter survival among the never‐smoking female patients with lung adenocarcinoma. ERβ was the predominant ER in the lung cancer cell lines. We proposed a different pathway that estrogen upregulated the expression of osteopontin and then promoted cell migration through αvβ3 integrin binding and activated MEK‐ERK signaling pathway, which is a common downstream pathway with epidermal growth factor receptor (EGFR) activation. An additive effect of ER antagonists and EGFR antagonists on the inhibition of cell migration was also noted. Our results suggest that estrogen adversely affects the prognosis of patients with lung adenocarcinoma. Osteopontin contributed to the cross‐talk between ER and EGFR signaling pathways. Estrogen, with its receptor, has the potential to be a prognosticator and a therapeutic target in lung cancer.
Estrogen up‐regulates osteopontin expression and promotes lung cancer cell migration via the MEK/ERK signaling pathway. Osteopontin contributes to the cross‐talk between estrogen receptor and epidermal growth factor receptor signaling pathways.
Summary
This study aims to investigate influences of different acylations (acetyl, propionyl and butyryl) with different degrees of substitution (DS) on physicochemical properties and digestion ...characteristics of Arenga pinnata starches (APS). The presence of acyl protons was confirmed by 1H NMR and FT‐IR spectroscopy of acylated APS. The viscosities, swelling power, thermal properties and crystallinity of acylated APS were lower than APS, but these characteristics tended to increase with the increase of DS. Acylation could effectively regulate the digestibility of starch granules. The content of resistant starch in acylated APS increased from 66.58% to 80.92%. With the DS increasing, acylation strengthened the rearrangement and aggregation behaviour of APS molecular chains, making the crystal structure and double helix structure of amylopectin crystal region more orderly, which might lead to changes in the pasting properties and digestibility. It was possible to evaluate the effectiveness of low‐substituted acylated APS.
Three acylated (acetyl, propionyl and butyryl) APS with low DS were prepared and characterized. Acylation enhanced molecular ordered structure of the modified APS with low DS. The viscosity, SP of acylated APS in low DS were decreased with increasing DS and Acylation effectively regulated digestibility of APS, especially the anti‐digestibility.
Cluster based wireless sensor networks have been widely used due to the good performance. However, in so many cluster based protocols, because of the complexity of the problem, theoretical analysis ...and optimization remain difficult to develop. This paper studies the performance optimization of four protocols theoretically. They are LEACH (Low Energy Adaptive Clustering Hierarchy), MLEACH (Multi-hop LEACH), HEED (Hybrid Energy-Efficient Distributed Clustering Approach), and UCR (Unequal Cluster based Routing). The maximum FIRST node DIED TIME (FDT) and the maximum ALL node DIED TIME (ADT) are obtained for the first time in this paper, as well as the optimal parameters which maximize the network lifetime. Different from previous analysis of network lifetime, this paper analyzes the node energy consumption in different regions through the differential analysis method. Thus, the optimal parameters which maximize the lifetime can be obtained and the detailed energy consumption in different regions at different time can be also obtained. Moreover, we can obtain the time and space evolution of the network, from a steady state (without any death) to a non-steady state (with some death of nodes), and then to the final situation (all nodes die). Therefore, we are fully aware of the network status from spatial and temporal analysis. Additionally, the correctness of the theoretical analysis in this paper is proved by the Omnet++ experiment results. This conclusion can be an effective guideline for the deployment and optimization of cluster based networks.
► The first node and All node died time can be calculated for cluster based WSNs. ► The region and occurrence time of energy hole can be also obtained. ► The optimal parameters which maximize the lifetime of WSNs can be obtained. ► Node density has little impact on energy consumption for cluster based WSNs.
The impact of mitral valve prolapse (MVP) and mitral regurgitation (MR) on physical performance has not been examined. Of 1,808 physically fit Asian military males, we compared the physical fitness ...between 62 subjects with MVP (MVP(+)) and 1,311 age- and anthropometrics-matched controls from the 1,746 participants without MVP (MVP(-)). MVP and MR grade were defined based on the American Society of Echocardiography criteria. Aerobic endurance capacity was evaluated by a 3000-m run and muscular endurance capacity was separately evaluated by 2-min sit-ups and 2-min push-ups. Analysis of covariance was used to determine the difference between groups. As compared to the MVP(-), the MVP(+) completed the 3000-m run test faster (839.2 ± 65.3 sec vs. 866.6 ± 86.8 sec, p = 0.019), but did fewer push-ups (41.3 ± 3.92 vs. 48.0 ± 10.1, p = 0.02) and similar sit-ups within 2 min. Of the MVP(+), those with any MR (trivial, mild or moderate) completed the 3000-m run test faster than those without MR (830.6 ± 61.7 sec vs. 877.2 ± 61.7 sec, p = 0.02). Our findings suggest that in physically active Asian military males, the MVP(+) may have greater aerobic endurance capacity but lower muscular endurance capacity than the MVP(-). The presence of MR may play a role for the MVP(+) to have greater aerobic endurance capacity.
Traditional object recognition algorithms cannot meet the requirements of object recognition accuracy in the actual warehousing and logistics field. In recent years, the rapid development of the deep ...learning theory has provided a technical approach for solving the above problems, and a number of object recognition algorithms has been proposed based on deep learning, which have been promoted and applied. However, deep learning has the following problems in the application process of object recognition: First, the nonlinear modeling ability of the activation function in the deep learning model is poor; second, the deep learning model has a large number of repeated pooling operations during which information is lost. In view of these shortcomings, this paper proposes multiple-parameter exponential linear units with uniform and learnable parameter forms and introduces two learned parameters in the exponential linear unit (ELU), enabling it to represent piecewise linear and exponential nonlinear functions. Therefore, the ELU has good nonlinear modeling capabilities. At the same time, to improve the problem of losing information in the large number of repeated pooling operations, this paper proposes a new global convolutional neural network structure. This network structure makes full use of the local and global information of different layer feature maps in the network. It can reduce the problem of losing feature information in the large number of pooling operations. Based on the above ideas, this paper suggests an object recognition algorithm based on the optimized nonlinear activation function-global convolutional neural network. Experiments were carried out on the CIFAR100 dataset and the ImageNet dataset using the object recognition algorithm proposed in this paper. The results show that the object recognition method suggested in this paper not only has a better recognition accuracy than traditional machine learning and other deep learning models but also has a good stability and robustness.
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