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The interplay between unconventional superconductivity and the ordering of charge/spin density wave is one of the most vital issues in both condensed matter physics and material science. The Ti-based compound BaTi2As2O, which can be seen as the parent phase of superconducting BaTi2Sb2O, has a layered structure with a space group P4/mmm, similar to that of cuprate and iron-based superconductors. This material exhibits a charge density wave (CDW) ordering transition revealed by an anomaly at around 200 K in transport measurements. Here, we report the synthesis and systematical study of the physical properties in Cr-doped BaTi(2-x)Cr(x)As2O (x = 0-0.154) and demonstrate that the transition temperature of the CDW ordering is suppressed gradually by the doped Cr element. The magnetization measurements confirm the evolution of the CDW ordering transition. These observed behaviors are similar to that observed in iron-based superconductors, but no superconductivity emerges down to 2 K. In addition, the first-principles calculations are also carried out for well-understanding the nature of experimental observations.
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Background: The benefits of home enteral nutrition (HEN) are increasingly recognized, with more scholars focusing on this field. This study aimed to comprehensively identify collaborative networks, analyze, and track research trends, focus on current hotspots, and accurately predict the forefront and focus of home enteral nutrition. Methods: A computer search of the Web of Science Core Collection (WoSCC) was conducted for studies related to home enteral nutrition published from January 1, 2004, to December 31, 2023, and select them in compliance with the PRISMA guidelines. The CiteSpace software was used for bibliometric visualization and comparative analysis of countries, institutions, journals, references, and keywords. Results: A total of 1,113 documents were included, showing a steady annual increase in publication volume. The United States and the Mayo Clinic were the top publishing country and institution, with 302 and 41 papers, respectively. "CLIN NUTR" had the highest number of publications, totaling 221, while "ESPEN guideline on home enteral nutrition" was the most cited reference, with 43 citations. The most prolific author was Manpreet S with 29 papers. Conclusion: The management of HEN is a current research hotspot. The safety of HEN and how to improve patient compliance are critical areas for researchers to consider. Future research could focus on these aspects. The blurring of boundaries between hospital and home care and how to utilize telemedicine technologies to serve more patients deserve in-depth exploration. Researchers worldwide should combine their unique characteristics and advantages to strengthen international cooperation.
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We propose a universal background subtraction framework based on the Arithmetic Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our ADNN model, the arithmetic distribution operations are utilized to introduce the arithmetic distribution layers, including the product distribution layer and the sum distribution layer. Furthermore, in order to improve the accuracy of the proposed approach, an improved Bayesian refinement model based on neighboring information, with a GPU implementation, is incorporated. In the forward pass and backpropagation of the proposed arithmetic distribution layers, histograms are considered as probability density functions rather than matrices. Thus, the proposed approach is able to utilize the probability information of the histogram and achieve promising results with a very simple architecture compared to traditional convolutional neural networks. Evaluations using standard benchmarks demonstrate the superiority of the proposed approach compared to state-of-the-art traditional and deep learning methods. To the best of our knowledge, this is the first method to propose network layers based on arithmetic distribution operations for learning distributions during background subtraction.
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Real-world video processing algorithms are often faced with the great challenges of processing the compressed videos instead of pristine videos. Despite the tremendous successes achieved in deep-learning based video super-resolution (SR), much less work has been dedicated to the SR of compressed videos. Herein, we propose a novel approach for compressed domain deep video SR by jointly leveraging the coding priors and deep priors. By exploiting the diverse and ready-made spatial and temporal coding priors (e.g., partition maps and motion vectors) extracted directly from the video bitstream in an effortless way, the video SR in the compressed domain allows us to accurately reconstruct the high resolution video with high flexibility and substantially economized computational complexity. More specifically, to incorporate the spatial coding prior, the Guided Spatial Feature Transform (GSFT) layer is proposed to modulate features of the prior with the guidance of the video information, making the prior features more fine-grained and content-adaptive. To incorporate the temporal coding prior, a guided soft alignment scheme is designed to generate local attention off-sets to compensate for decoded motion vectors. Our soft alignment scheme combines the merits of explicit and implicit motion modeling methods, rendering the alignment of features more effective for SR in terms of the computational complexity and robustness to inaccurate motion fields. Furthermore, to fully make use of the deep priors, the multi-scale fused features are generated from a scale-wise convolution reconstruction network for final SR video reconstruction. To promote the compressed domain video SR research, we build an extensive Compressed Videos with Coding Prior (CVCP) dataset, including compressed videos of diverse content and various coding priors extracted from the bitstream. Extensive experimental results show the effectiveness of coding priors in compressed domain video SR.
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In this work, the anion exchange resin (AER) was modified with a layer of glucose oxidase (GOD) and poly(diallyldimethylammonium chloride) (PDDA), respectively, via layer-by-layer electrostatic self-assembling strategy. The PDDA and GOD modified AER (PDDA/GOD/AER) was then packed into a home-made amperometric cell for flow injection analysis (FIA) of glucose. This design simplified the setup by integrating the enzyme reactor into the amperometric cell. And the AER in the cell behaved bifunctional, it was not only the support of enzymes, but also an anti-interference tool due to its retention effect toward ascorbic acid (AA) and uric acid (UA). A platinum modified porous titanium (Pt/PTi) electrode was utilized in the cell as the working electrode (WE), due to its large effective surface area it could increase the response by 8.3 times as compared with the planar pure platinum electrode. The proposed biosensor was very sensitive (22.4 microA cm(-2) mM(-1)) in glucose quantification, and the linear range was from 1 micromol L(-1) to 2 mmol L(-1) with the detection limit of 0.8 micromol L(-1). The biosensor was used for serum glucose determination, and the result obtained was satisfying. This work may have provided a reference design of the amperometric cell which could be adopted in other enzymatic-FIA biosensors.