Design, synthesis and evaluation of polymeric and surfactant 4-(dialkylamino)pyridines as hydrolase models

In the present study, a number of 4-(dialkylamino)pyridines (DAAP), both polymeric and surfactant, have been synthesized and evaluated as hydrolase models. A polysiloxane with DAAP groups integrated into the polymer backbone has been shown to be a highly selective transacylase. It very selectively transfers the acyl group from a p-nitrophenyl alkanoate to a long chain primary alcohol in the presence of excess methanol and water. A series of polyamides with DAAP groups integrated into the polymer backbone have been synthesized and characterized. Study of catalysis of hydrolysis of p-nitrophenyl alkanoates by these polymeric DAAP\u27s has afforded the first direct evidence for involvement of an acylpyridinium ion in the hydrolysis pathway. Several anionic and neutral surfactant DAAP\u27s have been prepared and evaluated as catalysts for hydrolysis of both cationic surfactant and lipophilic p-nitrophenyl alkanoates. Electrostatic interactions between the anionic catalysts and the cationic substrates have been found to play a variety of roles in promoting hydrolysis. The major discovery is that multi-molecular aggregates are formed from the anionic surfactant catalysts and the cationic surfactant substrates as a result of both hydrophobic and electrostatic interactions. And reaction of one catalyst molecule with one substrate molecule within a multi-molecular aggregate are promoted by other catalyst as well as substrate molecules within the same aggregate. As a result, the rate of hydrolysis observed for our best pair of catalyst and substrate is much higher than that observed previously with similar substrates. In connection with our study on surfactant DAAP\u27s, a recent significant discovery has been re-examined. It was found that hexadecanoate ion aggregates instead of hydrolyzing a cationic surfactant amide. The basicity and catalytic activity of several structurally different DAAP\u27s have also been evaluated in the presence of cationic micelles. It was found that cationic micelles markedly lower both the basicity and catalytic activity of the DAAP\u27s. Therefore, the benefit of micellar catalysis comes at the expense of lowered activity of the functional groups. On the other hand, it was found that properly designed DAAP\u27s may serve as excellent probes for characterization of the cationic surface

DepthFormer: A High-Resolution Depth-Wise Transformer for Animal Pose Estimation

Animal pose estimation has important value in both theoretical research and practical applications, such as zoology and wildlife conservation. A simple but effective high-resolution Transformer model for animal pose estimation called DepthFormer is provided in this study to address the issue of large-scale models for multi-animal pose estimation being problematic with limited computing resources. We make good use of a multi-branch parallel design that can maintain high-resolution representations throughout the process. Along with two similarities, i.e., sparse connectivity and weight sharing between self-attention and depthwise convolution, we utilize the delicate structure of the Transformer and representative batch normalization to design a new basic block for reducing the number of parameters and the amount of computation required. In addition, four PoolFormer blocks are introduced after the parallel network to maintain good performance. Benchmark evaluation is performed on a public database named AP-10K, which contains 23 animal families and 54 species, and the results are compared with the other six state-of-the-art pose estimation networks. The results demonstrate that the performance of DepthFormer surpasses that of other popular lightweight networks (e.g., Lite-HRNet and HRFormer-Tiny) when performing this task. This work can provide effective technical support to accurately estimate animal poses with limited computing resources

DepthFormer: A High-Resolution Depth-Wise Transformer for Animal Pose Estimation

Animal pose estimation has important value in both theoretical research and practical applications, such as zoology and wildlife conservation. A simple but effective high-resolution Transformer model for animal pose estimation called DepthFormer is provided in this study to address the issue of large-scale models for multi-animal pose estimation being problematic with limited computing resources. We make good use of a multi-branch parallel design that can maintain high-resolution representations throughout the process. Along with two similarities, i.e., sparse connectivity and weight sharing between self-attention and depthwise convolution, we utilize the delicate structure of the Transformer and representative batch normalization to design a new basic block for reducing the number of parameters and the amount of computation required. In addition, four PoolFormer blocks are introduced after the parallel network to maintain good performance. Benchmark evaluation is performed on a public database named AP-10K, which contains 23 animal families and 54 species, and the results are compared with the other six state-of-the-art pose estimation networks. The results demonstrate that the performance of DepthFormer surpasses that of other popular lightweight networks (e.g., Lite-HRNet and HRFormer-Tiny) when performing this task. This work can provide effective technical support to accurately estimate animal poses with limited computing resources

RS-DARTS: A Convolutional Neural Architecture Search for Remote Sensing Image Scene Classification

Due to the superiority of convolutional neural networks, many deep learning methods have been used in image classification. The enormous difference between natural images and remote sensing images makes it difficult to directly utilize or modify existing CNN models for remote sensing scene classification tasks. In this article, a new paradigm is proposed that can automatically design a suitable CNN architecture for scene classification. A more efficient search framework, RS-DARTS, is adopted to find the optimal network architecture. This framework has two phases. In the search phase, some new strategies are presented, making the calculation process smoother, and better distinguishing the optimal and other operations. In addition, we added noise to suppress skip connections in order to close the gap between trained and validation processing and ensure classification accuracy. Moreover, a small part of the neural network is sampled to reduce the redundancy in exploring the network space and speed up the search processing. In the evaluation phase, the optimal cell architecture is stacked to construct the final network. Extensive experiments demonstrated the validity of the search strategy and the impressive classification performance of RS-DARTS on four public benchmark datasets. The proposed method showed more effectiveness than the manually designed CNN model and other methods of neural architecture search. Especially, in terms of search cost, RS-DARTS consumed less time than other NAS methods

RS-DARTS: A Convolutional Neural Architecture Search for Remote Sensing Image Scene Classification

Due to the superiority of convolutional neural networks, many deep learning methods have been used in image classification. The enormous difference between natural images and remote sensing images makes it difficult to directly utilize or modify existing CNN models for remote sensing scene classification tasks. In this article, a new paradigm is proposed that can automatically design a suitable CNN architecture for scene classification. A more efficient search framework, RS-DARTS, is adopted to find the optimal network architecture. This framework has two phases. In the search phase, some new strategies are presented, making the calculation process smoother, and better distinguishing the optimal and other operations. In addition, we added noise to suppress skip connections in order to close the gap between trained and validation processing and ensure classification accuracy. Moreover, a small part of the neural network is sampled to reduce the redundancy in exploring the network space and speed up the search processing. In the evaluation phase, the optimal cell architecture is stacked to construct the final network. Extensive experiments demonstrated the validity of the search strategy and the impressive classification performance of RS-DARTS on four public benchmark datasets. The proposed method showed more effectiveness than the manually designed CNN model and other methods of neural architecture search. Especially, in terms of search cost, RS-DARTS consumed less time than other NAS methods

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

Macaque monkey is a rare substitute which plays an important role for human beings in relation to psychological and spiritual science research. It is essential for these studies to accurately estimate the pose information of macaque monkeys. Many large-scale models have achieved state-of-the-art results in pose macaque estimation. However, it is difficult to deploy when computing resources are limited. Combining the structure of high-resolution network and the design principle of light-weight network, we propose the attention-refined light-weight high-resolution network for macaque monkey pose estimation (HR-MPE). The multi-branch parallel structure is adopted to maintain high-resolution representation throughout the process. Moreover, a novel basic block is designed by a powerful transformer structure and polarized self-attention, where there is a simple structure and fewer parameters. Two attention refined blocks are added at the end of the parallel structure, which are composed of light-weight asymmetric convolutions and a triplet attention with almost no parameter, obtaining richer representation information. An unbiased data processing method is also utilized to obtain an accurate flipping result. The experiment is conducted on a macaque dataset containing more than 13,000 pictures. Our network has reached a 77.0 AP score, surpassing HRFormer with fewer parameters by 1.8 AP

Influence of Different Types of Obstacles on the Propagation of Premixed Methane-Air Flames in a Half-Open Tube

To understand the propagation characteristics of methane-air deflagration flames and in an obstacle-filled tube, a high-speed color video camera, photoelectric sensors, and pressure transducers were used to test the deflagration flame propagating parameters. The tests were run in a 1500 mm long plexiglass tube with a 100 × 100 mm square cross-section. The obstacles included four types of repeated baffles and five forms of solid structure obstacles. The results showed that: (1) the flame front was constantly distorted, stretched, and deformed by different types of obstacles and, consequently, the flame propagating parameters increased; (2) plates and triple prisms increased the speed of the flame and overpressure to the highest extent, whereas cuboids and quadrangulars exerted an intermediate effect. However, the effect of cylindrical obstacles was comparatively limited. It was suggested that the obstacle’s surface edge mutation or curvature changes were the main factors stimulating the flame acceleration; (3) the peak pressure of deflagration was relatively low near the ignition end, increased gradually until it reached the maximum at the middle of the tube, and decreased rapidly near the open end; and (4) the fixed obstacles in front of the flame exhibited a blocking effect on flame propagation during the initial stages; the flame speed and overpressure increased when the flame came into contact with the obstacles. This study is of significance because it explains the methane-air propagation mechanism induced by different types of obstacles. The findings have value for preventing or controlling gas explosion disasters

Effect analysis of the airflow field generated by ceiling fans on fire detectors using model experiments and Schlieren photography

Elderly care institutions in Taiwan are mostly equipped with ceiling fans to enhance their indoor ventilation. However, detectors are usually installed above ceiling fans. It is necessary to evaluate the influence of the airflow field on the functions of the detectors. The airflow fields generated by ceiling fans are complex due to the rotation of blades during operation. Using burning smokeless candles as the fire sources, the temperature distribution recorded by model experiments and the flow patterns recorded by Schlieren photography were analyzed. Effective fire detectors are important in institutions, as they can detect fires early. Unfortunately, the results of this study proved that the airflow fields significantly and negatively influence the functions of fire detectors. According to the results, the airflow generated by fans can destroy fire plume patterns and significantly reduce the temperature near the ceiling. The Schlieren images showed a significant entrained airflow. The rising heat was found to be taken by one side above the fans, causing the ceiling temperature distribution to change. The temperature was high on one side and low on the other side. Fire detectors are less likely to detect when the fire source is far from the ceiling fan