In this paper, we designed an end-to-end spectral-spatial residual network (SSRN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this ...network, the spectral and spatial residual blocks consecutively learn discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). The proposed SSRN is a supervised deep learning framework that alleviates the declining-accuracy phenomenon of other deep learning models. Specifically, the residual blocks connect every other 3-D convolutional layer through identity mapping, which facilitates the backpropagation of gradients. Furthermore, we impose batch normalization on every convolutional layer to regularize the learning process and improve the classification performance of trained models. Quantitative and qualitative results demonstrate that the SSRN achieved the state-of-the-art HSI classification accuracy in agricultural, rural-urban, and urban data sets: Indian Pines, Kennedy Space Center, and University of Pavia.
Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing ...uneven, unstructured, noisy, and massive 3-D point clouds are a challenging and tedious task. In this article, we provide a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification. Although several published research articles focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on DL applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this article is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3-D deep architectures, the remarkable DL applications in 3-D semantic segmentation, object detection, and classification; specific data sets, evaluation metrics, and the state-of-the-art performance. Finally, we conclude the remaining challenges and future researches.
The study of bilingualism and multilingualism has gained increasing prominence in the context of globalization. As a result, research on code switching has garnered growing attention, leading to a ...substantial number of published papers in recent decades. To gain insights into the current status and potential trends of psycholinguistic research on code switching, this study conducted a bibliometric analysis of 1,293 articles focusing on code switching from 1968 to 2022. The analysis was performed using bibliometrix, a bibliometric software package in R. The results of the analysis indicated that code switching between English and other languages, the role of inhibition ability, and the processing mechanisms of highly proficient bilinguals were prominent hot topics in the field of code-switching research. Additionally, the processing of grammatical gender, bilingual language production, and plurilingualism were identified as potential emerging research trends. By presenting a macroscopic landscape of research on code switching, this study aims to provide readers with a comprehensive overview and serve as a beneficial reference for researchers in this field.
Air-conditioning systems account for 40–60% of the energy consumption of buildings, and most of this figure corresponds to the cooling and dehumidification process of air-conditioning units. Compared ...with traditional compressed air-conditioning systems, solid adsorption dehumidification systems possess good potential to improve indoor air quality and reduce buildings’ energy consumption. However, there are still some problems that prevent the use of solid adsorption dehumidification systems, such as their complexity and high regeneration temperatures. The key to solving the above problem is the discovery of more suitable adsorbents. In this paper, poly N-isopropylacrylamide/polypyrrole (PNIPAm/PPy) hydrogel was selected as the research object, and the performance of the dehumidification material and its potential for application in solid dehumidification systems were studied. It was found that the pore structure of PNIPAm/PPy was relatively complex and that there were abundant pores with uneven pore sizes. The minimum pore size was about 4 μm, while the maximum pore size was about 25 μm, and the pore sizes were mostly distributed between 8 and 20 μm. Abundant and dense pores ensure good hygroscopic and water-releasing properties of the resulting hydrogel. The PPy inside the hydrogel acts as both a hygroscopic and photothermal agent. In an environment with a relative humidity of 90%, 60%, and 50%, the hygroscopic efficiency of PNIPAm/PPy reached 80% in about 75, 100, and 120 min, and the corresponding unit equilibrium hygroscopic capacity values were 3.85 g/g, 3.72 g/g, and 3.71 g/g, respectively. In the initial stage, the moisture absorption increased with the increase in time; then, the increase in moisture absorption decreased. When the temperature was below 40 °C, the hygroscopic performance of PNIPAm/PPy was almost temperature-independent. The PNIPAm/PPy with different thicknesses showed similar moisture absorption efficiency. The lowest desorption temperature of PNIPAm/PPy was 40 °C, which indicates that low-grade energy can be used for material desorption. And the higher the temperature, the faster the desorption rate of PNIPAm/PPy and the higher the desorption amount. It can be seen that the PNIPAm/PPy hydrogel presents good desorption performance and can be used repeatedly.
Background
Tyrosine kinase inhibitors (TKI) have been highlighted for the therapy of non-small-cell lung cancer (NSCLC), due to their capability of efficiently blocking signal pathway of epidermal ...growth factor receptor (EGFR) which causes the inhibition and apoptosis of NSCLC cells. However, EGFR-TKIs have poor aqueous solubility and severe side effects arising from the difficulty in control of biodistribution. In this study, folate-functionalized nanoparticles (FA-NPs) are designed and fabricated to load EGFR-TKI through flash nanoprecipitation (FNP) strategy, which could enhance the tumor-targeting drug delivery and reduced drug accumulation and side effects to normal tissues.
Results
Herein, the EGFR-TKI loaded FA-NPs are constructed by FNP, with FA decorated dextran-
b
-polylactide as polymeric stabilizer and gefitinib as TKI. The fast mixing and co-precipitation in FNP provide FA-NPs with well-defined particle size, narrow size distribution and high drug loading content. The FA-NPs exhibit efficient uptake and cytotoxicity in HCC827 NSCLC cells, and reduced uptake and cytotoxicity in normal cells comparing with free gefitinib. In vivo evaluation of gefitinib-loaded FA-NPs confirms the selective drug delivery and accumulation, leading to enhanced inhibition on NSCLC tumor and simultaneously diminished side effects to normal tissues.
Conclusion
The facile design of FA-NPs by FNP and their achieved performance in vitro and in vivo evaluations offer new therapeutic opportunities for treatment of non-small cell lung cancer.
Graphical Abstract
Recent geometric deep learning works define convolution operations in local regions and have enjoyed remarkable success on non-Euclidean data, including graph and point clouds. However, the ...high-level geometric correlations between the input and its neighboring coordinates or features are not fully exploited, resulting in suboptimal segmentation performance. In this article, we propose a novel graph convolution architecture, which we term as Taylor Gaussian mixture model (GMM) network (TGNet), to efficiently learn expressive and compositional local geometric features from point clouds. The TGNet is composed of basic geometric units, TGConv, that conduct local convolution on irregular point sets and are parametrized by a family of filters. Specifically, these filters are defined as the products of the local point features and the neighboring geometric features extracted from local coordinates. These geometric features are expressed by Gaussian weighted Taylor kernels. Then, a parametric pooling layer aggregates TGConv features to generate new feature vectors for each point. TGNet employs TGConv on multiscale neighborhoods to extract coarse-to-fine semantic deep features while improving its scale invariance. Additionally, a conditional random field (CRF) is adopted within the output layer to further improve the segmentation results. Using three point cloud data sets, qualitative and quantitative experimental results demonstrate that the proposed method achieves 62.2% average accuracy on ScanNet, 57.8% and 68.17% mean intersection over union (mIoU) on Stanford Large-Scale 3D Indoor Spaces (S3DIS) and Paris-Lille-3D data sets, respectively.
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