Unprecedented Ambient Sulfur Trioxide (SO3) Detection : Possible Formation Mechanism and Atmospheric Implications

Sulfur trioxide (SO3) is a crucial compound for atmospheric sulfuric acid (H2SO4) formation, acid rain formation, and other atmospheric physicochemical processes. During the daytime, SO3 is mainly produced from the photo-oxidation of SO2 by OH radicals. However, the sources of SO3 during the early morning and night, when OH radicals are scarce, are not fully understood. We report results from two field measurements in urban Beijing during winter and summer 2019, using a nitrate-CI-APi-LTOF (chemical ionization-atmospheric pressure interface-long-time-offlight) mass spectrometer to detect atmospheric SO3 and H2SO4. Our results show the level of SO3 was higher during the winter than during the summer, with high SO3 levels observed especially during the early morning (similar to 05:00 to similar to 08:30) and night (similar to 18:00 to similar to 05:00 the next day). On the basis of analysis of SO2, NOx, black carbon, traffic flow, and atmospheric ions, we suggest SO3 could be formed from the catalytic oxidation of SO2 on the surface of traffic-related black carbon. This previously unidentified SO3 source results in significant H2SO4 formation in the early morning and thus promotes sub-2.5 nm particle formation. These findings will help in understanding urban SO3 and formulating policies to mitigate secondary particle formation in Chinese megacities.Peer reviewe

Research on Reliability Modeling, Allocation and Prediction of Chemical Production System

Reliability allocation and prediction play an important role in the chemical production system, controlling the significance of each production allocation and predicting system reliability to analyze the designs in each system or integrated system if meeting requirements or not. This paper takes an ethylene plant as an example to study reliability modeling, allocation and prediction of related system. It is aimed to allocate and predict each production system or unit. In terms of system reliability allocation, it finds out the reliability allocation R in integrated system is 0.76460 with improved fuzzy analytic hierarchy process (AHP). Due to the number higher than initial reliability value of 0.73886, it illustrates the integrated reliability allocation meets the design requirements. In terms of reliability prediction, the result is more accurate when using Bayes fuzzy reliability prediction to calculate system reliability level and it can reflect this kind of indeterminate small sample data better

Multimodal sparse time-frequency representation for underwater acoustic signals

Multiple features can be extracted from time-frequency representation (TFR) of signals for the purpose of acoustic event detection. However, many underwater acoustic signals are formed by multiple events (impulsive and tonal), which generates difficulty on the high-resolution TFR for each component. For the characterization of such different events, we propose an anisotropic chirplet transform to achieve the TFR with high energy concentration. Such transform applies a time-frequency varying Gaussian window to compensate the energy of each component while suppressing unwanted noise. Using a set of directional chirplet ridges from the obtained TFR, a structure-split-merge algorithm is designed to reconstruct a multimodal sparse representation, which provides instantaneous frequency and time features. Specifically, a pulsed-to-tonal ratio, based on these features, is computed to distinguish two acoustic signals. The presented method is validated using shallow water experimental underwater acoustic communication signals, and large sequences of harmonics and pulsed bursts from common whales

Research on Reliability Modeling, Allocation and Prediction of Chemical Production System

Reliability allocation and prediction play an important role in the chemical production system, controlling the significance of each production allocation and predicting system reliability to analyze the designs in each system or integrated system if meeting requirements or not. This paper takes an ethylene plant as an example to study reliability modeling, allocation and prediction of related system. It is aimed to allocate and predict each production system or unit. In terms of system reliability allocation, it finds out the reliability allocation R in integrated system is 0.76460 with improved fuzzy analytic hierarchy process (AHP). Due to the number higher than initial reliability value of 0.73886, it illustrates the integrated reliability allocation meets the design requirements. In terms of reliability prediction, the result is more accurate when using Bayes fuzzy reliability prediction to calculate system reliability level and it can reflect this kind of indeterminate small sample data better

Long-Term Trajectory Prediction for Oil Tankers via Grid-Based Clustering

Vessel trajectory prediction is an important step in route planning, which could help improve the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory prediction algorithm is proposed for oil tankers. The proposed algorithm extracts a set of waymark points that are representative of the key traveling patterns in an area of interest by applying DBSCAN clustering to historical AIS data. A novel path-finding algorithm is then developed to sequentially identify a subset of waymark points, from which the predicted trajectory to a fixed destination is produced. The proposed algorithm is tested using real data offered by the Danish Maritime Authority. Numerical results demonstrate that the proposed algorithm outperforms state-of-the-art vessel trajectory prediction algorithms and is able to make high-accuracy long-term trajectory predictions

A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization

This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithmcalled turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates theeffectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments

Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things

A full design of the Internet of Underwater Things (IoUT) with a high data rate is one of the greatest underwater communication difficulties due to the unavailability of a sustainable power source for the battery supplies of sensor nodes, electromagnetic spread weakness, and limited acoustic waves channel bandwidth. This paper presents a new energy-efficient communication scheme named Enhanced Fully Generalized Spatial Modulation (EFGSM) for the underwater acoustic channel, where the different number of active antennas used in Fully Generalized Spatial Modulation (FGSM) is combined with multiple signal constellations. The proposed EFGSM enhances energy efficiency over conventional schemes such as spatial modulation, generalized spatial modulation, and FGSM. In order to increase energy and spectral performance, the proposed technique conveys data bits not just by the number of active antenna’s index as in the existing traditional FGSM, but also using the type of signal constellation to increase the data bit rate and improve power saving without increasing the receiver’s complexity. The proposed EFGSM uses primary and secondary constellations as indexes to carry information, they are derived from others by geometric interpolation signal space. The performance of the suggested EFGSM is estimated and demonstrated through Monte Carlo simulation over an underwater acoustic channel. The simulation results confirm the advantage of the suggested EFGSM scheme not just regarding energy and spectral efficiency but also concerning the average bit error rate (ABER)

Data_Sheet_1_Progressive 3D biomedical image registration network based on deep self-calibration.PDF

Three dimensional deformable image registration (DIR) is a key enabling technique in building digital neuronal atlases of the brain, which can model the local non-linear deformation between a pair of biomedical images and align the anatomical structures of different samples into one spatial coordinate system. And thus, the DIR is always conducted following a preprocessing of global linear registration to remove the large global deformations. However, imperfect preprocessing may leave some large non-linear deformations that cannot be handled well by existing DIR methods. The recently proposed cascaded registration network gives a primary solution to deal with such large non-linear deformations, but still suffers from loss of image details caused by continuous interpolation (information loss problem). In this article, a progressive image registration strategy based on deep self-calibration is proposed to deal with the large non-linear deformations without causing information loss and introducing additional parameters. More importantly, we also propose a novel hierarchical registration strategy to quickly achieve accurate multi-scale progressive registration. In addition, our method can implicitly and reasonably implement dynamic dataset augmentation. We have evaluated the proposed method on both optical and MRI image datasets with obtaining promising results, which demonstrate the superior performance of the proposed method over several other state-of-the-art approaches for deformable image registration.</p