Dual effect synergistically triggered Ce:(Y,Tb)3(Al,Mn)5O12 transparent ceramics enabling a high color-rendering index and excellent thermal stability for white LEDs

Ce:Y₃Al₅O₁₂ transparent ceramics (TCs) with appropriate emission light proportion and high thermal stability are significant to construct white light emitting diode devices with excellent chromaticity parameters. In this work, strategies of controlling crystal-field splitting around Ce³⁺ ion and doping orange-red emitting ion, were adopted to fabricate Ce:(Y,Tb)₃(Al,Mn)₅O₁₂ TCs via vacuum sintering technique. Notably, 85.4 % of the room-temperature luminescence intensity of the TC was retained at 150 °C, and the color rendering index was as high as 79.8. Furthermore, a 12 nm red shift and a 16.2 % increase of full width at half maximum were achieved owing to the synergistic effects of Tb³⁺ and Mn²⁺ ions. By combining TCs with a 460 nm blue chip, a warm white light with a low correlated color temperature of 4155 K was acquired. Meanwhile, the action mechanism of Tb³⁺ ion and the energy transfer between Ce³⁺ and Mn²⁺ ions were verified in prepared TCs.     

Matrix Manipulation of Directly-Synthesized PbS Quantum Dot Inks Enabled by Coordination Engineering

The direct-synthesis of conductive PbS quantum dot (QD) ink is facile, scalable, and low-cost, boosting the future commercialization of optoelectronics based on colloidal QDs. However, manipulating the QD matrix structures still is a challenge, which limits the corresponding QD solar cell performance. Here, for the first time a coordination-engineering strategy to finely adjust the matrix thickness around the QDs is presented, in which halogen salts are introduced into the reaction to convert the excessive insulating lead iodide into soluble iodoplumbate species. As a result, the obtained QD film exhibits shrunk insulating shells, leading to higher charge carrier transport and superior surface passivation compared to the control devices. A significantly improved power-conversion efficiency from 10.52% to 12.12% can be achieved after the matrix engineering. Therefore, the work shows high significance in promoting the practical application of directly synthesized PbS QD inks in large-area low-cost optoelectronic devices.     

基于MF-DCCA 的呼吸道疾病与大气污染物相关性分析

针对呼吸道系统疾病与大气 PM2:5、 SO2 浓度序列的相关性特征, 应用多重分形消除趋势波动分析法 (MF-DCCA), 对张家界市永定区呼吸道系统疾病患病人数与大气 PM2:5、 SO2 浓度序列进行了研究。结果发现该地区 呼吸道系统疾病患病人数与大气 PM2:5、 SO2 浓度的相关性具有长期持续特征和多重分形特征。随后对它们相关性 多重分形特征的动力来源进行了分析, 通过随机重排和相位随机处理, 结果表明在不同时间尺度上的长期持续性影响 是其主要动力来源。进一步研究发现该地区呼吸道系统疾病与大气 PM2:5、 SO2 浓度序列的相关性在四个季节均具 有长期持续性的多重分形特征, 且夏季多重分形特征相对强于其他季节。     

Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Modeling Sialidosis with Neural Precursor Cells Derived from Patient-Derived Induced Pluripotent Stem Cells

Sialidosis, caused by a genetic deficiency of the lysosomal sialidase gene (NEU1), is a systemic disease involving various tissues and organs, including the nervous system. Understanding the neurological dysfunction and pathology associated with sialidosis remains a challenge, partially due to the lack of a human model system. In this study, we have generated two types of induced pluripotent stem cells (iPSCs) with sialidosis-specific NEU1^G227R and NEU1^V275A/R347Q mutations (sialidosis-iPSCs), and further differentiated them into neural precursor cells (iNPCs). Characterization of NEU1^G227R- and NEU1^V275A/R347Q- mutated iNPCs derived from sialidosis-iPSCs (sialidosis-iNPCs) validated that sialidosis-iNPCs faithfully recapitulate key disease-specific phenotypes, including reduced NEU1 activity and impaired lysosomal and autophagic function. In particular, these cells showed defective differentiation into oligodendrocytes and astrocytes, while their neuronal differentiation was not notably affected. Importantly, we found that the phenotypic defects of sialidosis-iNPCs, such as impaired differentiation capacity, could be effectively rescued by the induction of autophagy with rapamycin. Our results demonstrate the first use of a sialidosis-iNPC model with NEU1^G227R- and NEU1^V275A/R347Q- mutation(s) to study the neurological defects of sialidosis, particularly those related to a defective autophagy–lysosome pathway, and may help accelerate the development of new drugs and therapeutics to combat sialidosis and other LSDs.     

Nanolayer CrAlN/TiSiN coating designed for tribological applications

With the goal to produce a hard and tough coating intended for tribological applications, CrAlN/TiSiN nanolayer coating was prepared by alternative deposition of CrAlN and TiSiN layers. In the first part of the article, a detailed study of phase composition, microstructure, and layer structure of CrAlN/TiSiN coating is presented. In the second part, its mechanical properties, fracture and tribological behavior are compared to the nanocomposite TiSiN coating. An industrial magnetron sputtering unit was used for coating deposition. X-ray photoelectron spectroscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy were used for compositional and microstructural analysis. Mechanical properties and fracture behavior were studied by instrumented indentation and focused ion beam techniques. Tribological properties were evaluated by ball-on-disk test in a linear reciprocal mode. A complex layer structure was found in the nanolayer coating. The TiSiN layers were epitaxially stabilized inside the coating which led to formation of dislocations at interfaces, to introduction of disturbances in the coating growth, and as a result, to development of fine-grained columnar microstructure. Indentation load required for the onset of fracture was twice lower for the nanolayer CrAlN/TiSiN, compared to the nanocomposite TiSiN coating. This agrees very well with their mechanical properties, with H³/E² being twice higher for the TiSiN coating. However, the nanolayer coating experienced less severe damage, which had a strong impact on tribological behavior. A magnitude of order lower wear rate and four times lower steady state friction coefficient were found for the nanolayer coating.     

Freeze-drying and mechanical redispersion of aqueous PEDOT:PSS

Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) films are attracting famous applications in antistatic coating, energy storage and conversion, printed electronics, and biomedical fields due to their conductivity, optical transparency and flexibility. However, PEDOT:PSS has poor dispersion stability during long-term storage and transport. Moreover, the dried PEDOT:PSS films are insoluble in any solvent and cannot be redispersed again. In comparison to bake drying, here, a feasible strategy to achieve mechanically redispersed PEDOT:PSS with the help of freeze-drying process was reported. The redispersed PEDOT:PSS can recover not only the initial characters such as pH, chemical composition, viscosity, and particle size under similar solid contents, but also conductivity and surface morphology of treated films. In addition, the treated film exhibits self-healing properties similar to pristine film in terms of mechanical and electrical properties. This technology enables reuse and overcomes the technical problems of PEDOT:PSS dispersion, realizing real-time processing to meet variable applications.     

结合句法依存信息的方面级情感分类

引入句法依存信息到原方面术语,提出一种新的方面术语表示方法,利用Glove词向量表示单词以及单词与单词之间的依存关系,构造出包含句法依存信息的依存关系邻接矩阵和依存关系表示矩阵,利用图卷积神经网络和多头注意力机制将句法依存信息融入到方面术语中,使得方面术语表达与上下文结构高度相关。将改进后的方面词术语表示替换到现有模型后,模型泛化能力得到有效提升。对比试验和分析结果表明:该方法具有有效性和泛化性。     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.