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

Privacy Preservation by Intermittent Transmission in Cooperative LQG Control Systems

In this paper, we study a cooperative linear quadratic Gaussian (LQG) control system with a single user and a server. In this system, the user runs a process and employs the server to meet the needs of computation. However, the user regards its state trajectories as privacy. Therefore, we propose a privacy scheme, in which the user sends data to the server intermittently. By this scheme, the server's received information of the user is reduced, and consequently the user's privacy is preserved. In this paper, we consider a periodic transmission scheme. We analyze the performance of privacy preservation and LQG control of different transmission periods. Under the given threshold of the control performance loss, a trade-off optimization problem is proposed. Finally, we give the solution to the optimization problem

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

Research on the Influence of Raceway Waviness on the Vibration Characteristics of Deep Groove Ball Bearings

The dynamics model of a 2-degree-of-freedom deep groove ball bearing is established by incorporating the raceway surface waviness model comprising multiple sinusoidal functions superposition. The model is solved using the fourth-order Runge-Kutta method to obtain the vibration characteristics including displacement, velocity, acceleration, and frequency of the bearing. Validation of the model is accomplished through comparison with theoretical vibration frequencies. The influence of the amplitude of waviness of the inner and outer ring raceway surfaces of deep groove ball bearings on the vibration displacement, peak-to-peak vibration displacement and root-mean-square vibration acceleration is analyzed, and the results show that as the amplitude of the inner and outer ring raceway surfaces waviness increases, all the vibration characteristic indexes increase, indicating that the vibration amplitude of the bearings as well as the energy of the waviness-induced shock waveforms increase with the increase of the amplitude of the waviness

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

Deformation characteristics and exploration potential of the West Kunlun foreland fold-and-thrust belt

The West Kunlun foreland is dominated by segmented fold-and-thrust belts with significant potential for hydrocarbon exploration, while the extent of exploration in this area has been relatively limited. In this paper, by conducting complex structural interpretation, the geometric and kinematic characteristics, as well as the variations in the segmented fold-and-thrust belts within this region are revealed. The West Kunlun foreland fold-and-thrust belts are divided into three structural segments, which exhibit distinct structural styles. The Pusha-Kedong segment in the east is characterized by large-scale northward propagation, with high-angle basement-involved faults in the root belt and thin-skinned thrusts in the front belt. Additionally, three-row anticlines developed in the middle to the upper structural layers. The Kashi-Yecheng segment, located in the middle, is characterized by strike-slip faults and basement-involved structural wedges transitioning to detachment structures. Within this segment, the Sugaite structure in the mountain front is a wedge structure composed of basement-involved faults and an upper back-thrust fault. Meanwhile, the Yingjisha structure in the thrust front consists of a fold in the lower part and a back-thrust system above it. The lower fold is controlled by the Cambrian detachment thrust, which terminates upward in the Paleogene, while the back-thrust faults truncate upper structural layers and terminate downwards in the Miocene strata. The Wupoer segment in the northwest is controlled by the Main Pamir Thrust and the Front Pamir Thrust, which are low angular forward thrust faults with an arc distribution. A piggyback basin has developed in the root belt and upper structural layer since the Pliocene. Based on the deformation characteristics and the accumulation of oil-gas reservoirs discovered so far, two types of oil and gas-rich thrust belts with different hydrocarbon exploration fields in the West Kunlun foreland are described.Document Type: Original articleCited as: Jiang, L., Dong, H., Li, Y., Zhao, W., Zhang, Y., Bo, D. Deformation characteristics and exploration potential of the West Kunlun foreland fold-and-thrust belt. Advances in Geo-Energy Research, 2024, 11(3): 181-193. https://doi.org/10.46690/ager.2024.03.0

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