Mixing state and particle hygroscopicity of organic-dominated aerosols over the Pearl River Delta region in China

Simultaneous measurements of aerosol hygroscopicity and particle-phase chemical composition were performed at a suburban site over the Pearl River Delta region in the late summer of 2016 using a self-assembled hygroscopic tandem differential mobility analyzer (HTDMA) and an Aerodyne quadruple aerosol chemical speciation monitor (ACSM), respectively. The hygroscopic growth factor (HGF) of the Aitken mode (30 nm, 60 nm) and accumulation mode (100 nm, 145 nm) particles were obtained under 90% relative humidity (RH). An external mixture was observed for particles of every size during this study, with a dominant mode of more-hygroscopic (MH) particles, as aged aerosols dominated due to the anthropogenic influence. The HGF of lesshygroscopic (LH) mode particles increased, while their number fractions decreased during the daytime due to a reduced degree of external mixing that probably resulted from the condensation of gaseous species. These LH mode particles in the early morning or late afternoon could be possibly dominated by carbonaceous material emitted from local automobile exhaust during rush hours. During polluted days with air masses flowing mainly from the coastal areas, the chemical composition of aerosols had a clear diurnal variation and a strong correlation with the mean HGF. Closure analysis was carried out between the HTDMA-measured HGF and the ACSM-derived hygroscopicity using various approximations for the hygroscopic growth factor of organic compounds (HGF(org)). Considering the assumptions regarding the differences in the mass fraction of each component between PM1 and 145 nm particles, the hygroscopicity-composition closure was achieved using an HGF(org) of 1.26 for the organic material in the 145 nm particles and a simple linear relationship between the HGForg and the oxidation level inferred from the O : C ratio of the organic material was suggested. Compared with the results from other environments, HGF(org) obtained from our measurements appeared to be less sensitive to the variation of its oxidation level, which is, however, similar to the observations in the urban atmosphere of other megacities in China. This finding suggests that the anthropogenic precursors or the photooxidation mechanisms might differ significantly between the suburban and urban atmosphere in China and those in other background environments. This may lead to different characteristics of the oxidation products in secondary organic aerosols (SOA) and therefore to a different relationship between the HGF(org) and its O : C ratio.Peer reviewe

Orientation-dependent optic-fiber accelerometer based on excessively tilted fiber grating

An orientation-dependent optic-fiber accelerometer based on the excessively tilted fiber grating (ExTFG) inscribed in SM28 fiber is demonstrated, which is based on the optical power demodulation scheme. Without any complicated processing, the cladding mode resonances of the bare ExTFG show high sensitivity to slight perturbation of bending. Due to its excellent azimuth-related bending properties, such a bare ExTFG fixed on a simple cantilever beam has exhibited strong orientation-dependent vibration properties. The experimental results show that a TE mode of the sensor can provide a maximum acceleration sensitivity of 74.14 mV/g at 72 Hz and maximum orientation sensitivity of 9.1 mV/deg while, for a TM mode, a maximum acceleration sensitivity of 57.85 mV/g at 72 Hz and maximum orientation sensitivity of 7.4 mV/deg could be achieved. These unique properties enable the sensor to act as a vector accelerometer for applications in many vibration measurementfields

<b>Enhanced daytime secondary aerosol formation driven by gas-particle partitioning in downwind urban plumes</b>

This is a data set from a field campaign carried out during November and December at a rural site in the Pearl River Delta region, China. It includes particle number size distribution, production rate of OH and RO2, PMF results from AMS, thermograms measured by FIGAERO-CIMS, and oxidation state of organic vapors.</p

Topology Optimization Design of Multi-Input-Multi-Output Compliant Mechanisms with Hinge-Free Characteristic and Totally Decoupled Kinematics

A new multi-constraint optimization model with the weighted objective function is proposed to design the multi-input-multi-output (MIMO) compliant mechanisms. The main feature of this work is that both the two notable problems related to the de facto hinge and the movement coupling are tackled simultaneously in the topological synthesis of MIMO compliant mechanisms. To be specific, the first problem is the severe stress concentration in the flexible hinge areas, and it is solved by the introduction of input and output compliances into the objective function, which could facilitate the optimization to strike a good balance between structural flexibility and stiffness. The second problem is the high degree of control complexity caused by the coupled outputs and inputs, and it is addressed by achieving the complete decoupling with two groups of extra constraints that are used to suppress the input coupling and the output coupling, respectively. As the most common and effective topology optimization method, the Solid Isotropic Material with Penalization (SIMP)-based density method is adopted here to obtain the optimized configurations. After the analytical sensitivity deduction related to the weighted objective function and constraints, two typical numerical examples are presented to demonstrate the validity of the proposed topology optimization framework in designing the hinge-free and completely decoupled MIMO compliant mechanisms

A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each “descriptor” corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global “feature”, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5–10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O(n) for real-time person re-ID applications

A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation

Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each &ldquo;descriptor&rdquo; corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global &ldquo;feature&rdquo;, i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5&ndash;10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as O(n) for real-time person re-ID applications

Application of a fluorescent probe for the online measurement of PM-bound reactive oxygen species in chamber and ambient studies

This manuscript details the application of a profluorescent nitroxide (PFN) for the online quantification of radical concentrations on particulate matter (PM) using an improved Particle Into Nitroxide Quencher (PINQ). A miniature flow-through fluorimeter developed specifically for use with the 9,10-bis(phenylethynyl)anthracene-nitroxide (BPEAnit) probe was integrated into the PINQ, along with automated gas phase corrections through periodic high efficiency particle arrestor (HEPA) filtering. The resulting instrument is capable of unattended sampling and was operated with a minimum time resolution of 2.5 min. Details of the fluorimeter design and examples of data processing are provided, and results from a chamber study of side-stream cigarette smoke and ambient monitoring campaign in Guangzhou, China are presented. Primary cigarette smoke was shown to have both short-lived (t1/2 = 27 min) and long-lived (t1/2 = indefinite) PM-bound reactive oxygen species (ROS) components which had previously only been observed in secondary organic aerosol (SOA).</p

Genetic fate mapping of transient cell fate reveals N-cadherin activity and function in tumor metastasis

Genetic lineage tracing unravels cell fate and plasticity in development, tissue homeostasis, and diseases. However, it remains technically challenging to trace temporary or transient cell fate, such as epithelial-to-mesenchymal transition (EMT) in tumor metastasis. Here, we generated a genetic fate-mapping system for temporally seamless tracing of transient cell fate. Highlighting its immediate application, we used it to study EMT gene activity from the local primary tumor to a distant metastatic site in vivo. In a spontaneous breast-to-lung metastasis model, we found that primary tumor cells activated vimentin and N-cadherin in situ, but only N-cadherin was activated and functionally required during metastasis. Tumor cells that have ever expressed N-cadherin constituted the majority of metastases in lungs, and functional deletion of N-cad significantly reduced metastasis. The seamless genetic recording system described here provides an alternative way for understanding transient cell fate and plasticity in biological processes.This study was supported by the National Key Research & Development Program of China (grant nos. 2019YFA011040, 2019YFA080200, 2018YFA010810, 2018YFA0107900, 2017YFC1001303, and 2016YFC1300600), Strategic Priority Research Program of the Chinese Academy of Sciences (CAS, grant nos. XDB19000000 and XDA16010507), National Science Foundation of China (grant nos. 31730112, 91849202, 31625019, 31730112 and 31900625), Key Project of Frontier Sciences of CAS (grant no. QYZDB-SSW-SMC003), International Cooperation Fund of CAS, Shanghai Science and Technology Commission (grant nos. 19JC1415700, 19YF1455300, 19ZR1479800, and 20QC1401000), China Postdoctoral Science Foundation (grant nos. 2018M640430 and 2019M660100), National Postdoctoral Program for Innovative Talents (grant nos. BX20180338 and BX20190343), the Pearl River Talent Recruitment Program of Guagdong Province (grant no. 2017ZT07S347), Royal Society-Newton Advanced Fellowship, and Sanofi-SIBS Fellowship.Peer reviewe