An investigation of bicomponent polypropylene/poly(ethylene terephthalate) melt blown microfiber nonwovens

As mono-component melt blown (MB) nonwovens are finding more applications in filtration, absorbency, hygiene, and apparel, bicomponent (bico) MB nonwovens with side-by-side (S/S) cross-sectional fiber geometry are attracting significant attention from both industries and academic institutions. The S/S bico fiber provides the possibility to combine the advantages of the two polymers to produce unique fiber and web properties, such as greater fiber crimp, when the two polymers provide different shrinking behavior under heat. This work studied polypropylene/poly(ethylene terephthalate) (PP/PET) bico MB microfiber nonwovens, including the effects of processing conditions on fiber and web properties, process/structure/property relationships, and spinline dynamics. It was conducted in the 24-inch Reicofil® MB pilot line with bicomponent MB capability

Hexapartite steering based on a four-wave-mixing process with a spatially structured pump

Multipartite Einstein-Podolsky-Rosen (EPR) steering has been widely studied, for realizing safer quantum communication. The steering properties of six spatially separated beams from the four-wave-mixing process with a spatially structured pump are investigated. Behaviors of all (1+i)/(i+1)-mode (i=1,2,3) steerings are understandable, if the role of the corresponding relative interaction strengths are taken into account. Moreover, stronger collective multipartite steerings including five modes also can be obtained in our scheme, which has potential applications in ultra-secure multiuser quantum networks when the issue of trust is critical. By further discussing about all monogamy relations, it is noticed that the type-IV monogamy relations, which are naturally included in our model, are conditionally satisfied. Matrix representation is used to express the steerings for the first time, which is very useful to understand the monogomy relations intuitively. Different steering properties obtained in this compact phase-insensitive scheme have potential applications for different kinds of quantum communication tasks

Research on Personal Information Risk Assessment Model in Smart Cities

Personal information security plays fundamental and critical role in promotion of smart cities. By taking personal information, vulnerability and threat as basic elements for risk assessment, this article proposes a Markov method-based personal information security risk assessment model in smart cities with the core of threats (Li Hetian, 2007). Based on threat probability, threat consequence attribute and attribute value acquired through the Markov method, threat analysis, the multi-attribute decision-making theory and the expert grading method, this article calculates the objective threat indexes, which is then utilized for risk ranking, so as to provide scientific basis for formulating targeted personal information security risk management and control strategies

Synthesis of Indole-3-Acetic Acid Derivatives and a Urea Carboxylic Acid Derivative by Propylphosphonic Anhydride (T3P)

The purpose of medicinal chemistry is to efficiently create a variety of compounds with potential for pharmacological efficacy. To promote this diversity, indole-3-acetic acid, a common plant hormone, was used as the starting material for various reactions. The coupling reagent used for these reactions was propylphosphonic anhydride, or T3P, since it has demonstrated efficiency in selective amide formation under mild conditions and it is readily soluble. In the case of multiple viable reaction sites, the intended product will dimerize, as was the case in the synthesis of the compound labeled amide 2 when T3P coupled with both sites of piperazine. N-Hydroxysuccinimide, also referred to as HOSu and NHS, was used to decrease the reactivity of the carboxylic acid—T3P mixed anhydride, so it less readily formed the dimer. This increased the yield of the monomer. Pharmacological efficacy is more probable when synthesizing a chemotype with a known structure-activity relationship, or SAR. Urea carboxylic acid has been found to have antischistosomal activity. In an effort to synthesize a drug candidate with greater likelihood of pharmacological activity, a compound was synthesized from a urea carboxylic acid using T3P by the same method used to synthesize products from indole-3-acetic acid. Five compounds were synthesized using the T3P reagent in an attempt to expand the repository of potential drug candidates. The method for each compound was made largely similar, but it differed in the work-up and purification stages, as the acidity and polarity of the systems varied.https://digitalcommons.unmc.edu/surp2021/1061/thumbnail.jp

MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients

Overall survival (OS) time is one of the most important evaluation indices for gliomas situations. Multimodal Magnetic Resonance Imaging (MRI) scans play an important role in the study of glioma prognosis OS time. Several deep learning-based methods are proposed for the OS time prediction on multi-modal MRI problems. However, these methods usually fuse multi-modal information at the beginning or at the end of the deep learning networks and lack the fusion of features from different scales. In addition, the fusion at the end of networks always adapts global with global (eg. fully connected after concatenation of global average pooling output) or local with local (eg. bilinear pooling), which loses the information of local with global. In this paper, we propose a novel method for multi-modal OS time prediction of brain tumor patients, which contains an improved nonlocal features fusion module introduced on different scales. Our method obtains a relative 8.76% improvement over the current state-of-art method (0.6989 vs. 0.6426 on accuracy). Extensive testing demonstrates that our method could adapt to situations with missing modalities. The code is available at https://github.com/TangWen920812/mmmna-net.Comment: Accepted EMBC 202

RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans

In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost. Nevertheless, the coarse resolution may lead to difficulties in medical diagnosis by either physicians or computer-aided diagnosis algorithms. Deep learning-based volumetric super-resolution (SR) methods are feasible ways to improve resolution, with convolutional neural networks (CNN) at their core. Despite recent progress, these methods are limited by inherent properties of convolution operators, which ignore content relevance and cannot effectively model long-range dependencies. In addition, most of the existing methods use pseudo-paired volumes for training and evaluation, where pseudo low-resolution (LR) volumes are generated by a simple degradation of their high-resolution (HR) counterparts. However, the domain gap between pseudo- and real-LR volumes leads to the poor performance of these methods in practice. In this paper, we build the first public real-paired dataset RPLHR-CT as a benchmark for volumetric SR, and provide baseline results by re-implementing four state-of-the-art CNN-based methods. Considering the inherent shortcoming of CNN, we also propose a transformer volumetric super-resolution network (TVSRN) based on attention mechanisms, dispensing with convolutions entirely. This is the first research to use a pure transformer for CT volumetric SR. The experimental results show that TVSRN significantly outperforms all baselines on both PSNR and SSIM. Moreover, the TVSRN method achieves a better trade-off between the image quality, the number of parameters, and the running time. Data and code are available at https://github.com/smilenaxx/RPLHR-CT.Comment: Accepted MICCAI 202

Transformer Lesion Tracker

Evaluating lesion progression and treatment response via longitudinal lesion tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs and time consumption when lesion matching is done manually. Previous methods typically lack the integration of local and global information. In this work, we propose a transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, we design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We also develop a Registration-based Anatomical Attention Module (RAAM) to introduce anatomical information to CAT so that it can focus on useful feature knowledge. A Sparse Selection Strategy (SSS) is presented for selecting features and reducing memory footprint in Transformer training. In addition, we use a global regression to further improve model performance. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is available at https://github.com/TangWen920812/TLT.Comment: Accepted MICCAI 202