Poly(ethylene glycol)-conjugated surfactants promote or inhibit aggregation of phospholipids

AbstractThe calcium-induced aggregation of dilauroyl phosphatidic acid (DLPA) suspensions, with or without added poly(ethylene oxide) (PEO)-conjugated surfactants containing 4 to 30 ethylene oxide subunits, were monitored by turbidity measurement and quasi-elastic light scattering (QLS). The aggregation was inhibited (protected) by the incorporated PEO surfactant for most samples, while a window for promotive effect was found for samples with low surface coverage by the PEO moiety of the incorporated surfactant. Promotion occurs only when the aggregation is slow and at a low level. The promotion is explained by the synergistic effect of PEO and divalent calcium cations when the steric repulsion is weak. The promotion/protection crossover is a display between the PEO/calcium synergistic effect and the steric repulsion

Segatron: Segment-Aware Transformer for Language Modeling and Understanding

Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated from the Transformer with richer positional information. To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model with memory extension and relative position encoding. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset. We further investigate the pre-training masked language modeling task with Segatron. Experimental results show that BERT pre-trained with Segatron (SegaBERT) can outperform BERT with vanilla Transformer on various NLP tasks, and outperforms RoBERTa on zero-shot sentence representation learning.Comment: Accepted by AAAI 202

Progressive Multi-Scale Residual Network for Single Image Super-Resolution

Multi-scale convolutional neural networks (CNNs) achieve significant success in single image super-resolution (SISR), which considers the comprehensive information from different receptive fields. However, recent multi-scale networks usually aim to build the hierarchical exploration with different sizes of filters, which lead to high computation complexity costs, and seldom focus on the inherent correlations among different scales. This paper converts the multi-scale exploration into a sequential manner, and proposes a progressive multi-scale residual network (PMRN) for SISR problem. Specifically, we devise a progressive multi-scale residual block (PMRB) to substitute the larger filters with small filter combinations, and gradually explore the hierarchical information. Furthermore, channel- and pixel-wise attention mechanism (CPA) is designed for finding the inherent correlations among image features with weighting and bias factors, which concentrates more on high-frequency information. Experimental results show that the proposed PMRN recovers structural textures more effectively with superior PSNR/SSIM results than other small networks. The extension model PMRN$^+$ with self-ensemble achieves competitive or better results than large networks with much fewer parameters and lower computation complexity.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction

Implicit neural representation has opened up new avenues for dynamic scene reconstruction and rendering. Nonetheless, state-of-the-art methods of dynamic neural rendering rely heavily on these implicit representations, which frequently struggle with accurately capturing the intricate details of objects in the scene. Furthermore, implicit methods struggle to achieve real-time rendering in general dynamic scenes, limiting their use in a wide range of tasks. To address the issues, we propose a deformable 3D Gaussians Splatting method that reconstructs scenes using explicit 3D Gaussians and learns Gaussians in canonical space with a deformation field to model monocular dynamic scenes. We also introduced a smoothing training mechanism with no extra overhead to mitigate the impact of inaccurate poses in real datasets on the smoothness of time interpolation tasks. Through differential gaussian rasterization, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed, making it well-suited for tasks such as novel-view synthesis, time synthesis, and real-time rendering

Iterative Network for Image Super-Resolution

Single image super-resolution (SISR), as a traditional ill-conditioned inverse problem, has been greatly revitalized by the recent development of convolutional neural networks (CNN). These CNN-based methods generally map a low-resolution image to its corresponding high-resolution version with sophisticated network structures and loss functions, showing impressive performances. This paper proposes a substantially different approach relying on the iterative optimization on HR space with an iterative super-resolution network (ISRN). We first analyze the observation model of image SR problem, inspiring a feasible solution by mimicking and fusing each iteration in a more general and efficient manner. Considering the drawbacks of batch normalization, we propose a feature normalization (FNorm) method to regulate the features in network. Furthermore, a novel block with F-Norm is developed to improve the network representation, termed as FNB. Residual-in-residual structure is proposed to form a very deep network, which groups FNBs with a long skip connection for better information delivery and stabling the training phase. Extensive experimental results on testing benchmarks with bicubic (BI) degradation show our ISRN can not only recover more structural information, but also achieve competitive or better PSNR/SSIM results with much fewer parameters compared to other works. Besides BI, we simulate the real-world degradation with blur-downscale (BD) and downscalenoise (DN). ISRN and its extension ISRN+ both achieve better performance than others with BD and DN degradation models.Comment: 12 pages, 14 figure

An efficient approach to separate CO2 using supersonic flows for carbon capture and storage

The mitigation of CO2 emissions is an effective measure to solve the climate change issue. In the present study, we propose an alternative approach for CO2 capture by employing supersonic flows. For this purpose, we first develop a computational fluid dynamics (CFD) model to predict the CO2 condensing flow in a supersonic nozzle. Adding two transport equations to describe the liquid fraction and droplet number, the detailed numerical model can describe the heat and mass transfer characteristics during the CO2 phase change process under the supersonic expansion conditions. A comparative study is performed to evaluate the effect of CO2 condensation using the condensation model and dry gas assumption. The results show that the developed CFD model predicts accurately the distribution of the static temperature contrary to the dry gas assumption. Furthermore, the condensing flow model predicts a CO2 liquid fraction up to 18.6% of the total mass, which leads to the release of the latent heat to the vapour phase. The investigation performed in this study suggests that the CO2 condensation in supersonic flows provides an efficient and eco-friendly way to mitigate the CO2 emissions to the environment

Effect of superabsorbent polymer on mechanical properties of cement stabilized base and its mechanism

Superabsorbent polymers (SAPs) are cross-linked polymers that can absorb and retain large amounts of water. In recent years, a growing interest was seen in applying SAPs in concrete to improve its performance due to its efficiency in mitigating shrinkage. This paper presents findings in a study on effect of SAPs on performance of cement-treated base (CTB), using the experience of internal curing of concrete. CTB specimens with and without SAPs were prepared and tested in the laboratory. Tests conducted include mechanical property testing, dry shrinkage testing, differential thermal analysis, mercury intrusion porosimetry and scanning electron microscope testing. It was found that 7-day and 28-day unconfined compressive strength of CTB specimens with SAPs was higher than regular CTB specimens. 28d compressive strength of CTB specimens with SAPs made by Static pressure method was 5.87 MPa, which is 27% higher than that of regular CTB specimens. Drying shrinkage of CTB specimens with SAPs was decreased by 52.5% comparing with regular CTB specimens. Through the microstructure analysis it was found that CTB specimens with SAPs could produce more hydration products, which is also the reason for the strength improvement

A Framework for Calculating the Failure Probability of Natural Gas Pipeline

Reliability based design and assessment (RBDA) technique is a developing direction of natural gas pipeline design method. In this paper, a framework for calculating the failure probability of natural gas pipeline is proposed. First, Java reflection mechanism is used in the management of the limit state functions, which enables the separation of the limit state algorithms and the calculations of the failure probability. Under this framework, more newly developed equations can be add into the library of the software readily. Second, a Monte Carlo reliability analysis algorithm capable of incorporating the basic input parameters and limit state functions is used to calculate failure probability of pipelines. Third, a post data processing algorithm is used to improve the efficiency. Finally, an example on natural gas pipeline is presented to illustrate the availability and effectiveness of the software. Experimental results indicate the ability of the proposed framework for pipeline quality control

ADriver-I: A General World Model for Autonomous Driving

Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular design tends to introduce a substantial amount of redundancy. Recently, multimodal large language models (MLLM) and diffusion techniques have demonstrated their superior performance on comprehension and generation ability. In this paper, we first introduce the concept of interleaved vision-action pair, which unifies the format of visual features and control signals. Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I. It takes the vision-action pairs as inputs and autoregressively predicts the control signal of the current frame. The generated control signals together with the historical vision-action pairs are further conditioned to predict the future frames. With the predicted next frame, ADriver-I performs further control signal prediction. Such a process can be repeated infinite times, ADriver-I achieves autonomous driving in the world created by itself. Extensive experiments are conducted on nuScenes and our large-scale private datasets. ADriver-I shows impressive performance compared to several constructed baselines. We hope our ADriver-I can provide some new insights for future autonomous driving and embodied intelligence.Comment: Tech Repor

Culture-related grief beliefs of Chinese Shidu parents: Development and psychometric properties of a new scale

Background: In China, parents whose only-child dies and who have no living or adopted child are called Shidu parents. Negative thinking is assumed to contribute to the development of emotional problems in bereavement. Because grief cognitions are likely influenced by the concepts of Chinese traditional culture (e.g., family continuation), Shidu parents may hold specific culture-related grief beliefs about themselves or the world, which, in turn, could impede their recovery. Objective: This study developed a questionnaire assessing the culture-related grief beliefs of Shidu parents and examined its psychometric properties. Methods: This newly developed questionnaire was administered to the combined sample of 313 Shidu parents. Exploratory (n = 164) and confirmatory factor analysis (n = 149) were conducted. Psychometric properties of the questionnaire were evaluated. Results: Exploratory factor analysis revealed three distinct factors (filial piety belief, destiny belief and perceived stigma), generating a nine-item culture-related grief beliefs of Shidu parents questionnaire (CBSQ). Confirmatory factor analysis verified the three-factor structure (chi(2)(24) = 39.103, p = 0.027, chi(2)/df = 1.630, CFI = .980, TLI = .970, RMSEA = .065, SRMR = .052). Internal consistency and temporal stability were adequate. Convergent, discriminant and concurrent validity were supported. Conclusions: This study highlights the importance of extending the concept of grief cognitions to include culture-specific beliefs, and provides a first measurement tool to assess culture-related grief beliefs after only-child loss, which can be used in future research with Shidu parents.National Social Science Fund of China [16ZDA233]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]