Modelling summertime ozone in North China

A high-resolution nested air quality model, the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), was applied to simulate the ozone concentration in North China from 15 May 2017 to 22 June 2017 during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme measurement campaign and to study an ozone pollution event at the end of May 2017. The model reproduced the meteorological parameters of temperature, relative humidity, wind speed and wind direction well over the period. The key air pollutants of ozone, NOx, SO2, PM2.5 and PM10 are captured reasonable well compared with observations. Results suggest that the ozone simulation matches the observation and simulations of NO2 and SO2 are generally satisfactory using emissions scaled down based on previous studies of anthropogenic emissions changes in China. The model underestimated the peaks in ozone concentration, especially on heavily polluted days, which remain a challenge for modelling ozone and may be attributed to model weaknesses in representing the diurnal cycle of NO and the observed VOC and isoprene. We carried out sensitivity studies investigating how NOx, VOC and isoprene emissions changes affects the simulation of ozone, and improved the ozone simulation of the peaks with 50% increased VOC emissions and doubled isoprene emission. We speculate that the underestimation of VOC emissions or the reactivities of VOC in the model could be reasons for the underestimation of the peaks in ozone concentration and further investigation is needed to improve the simulation of ozone concentrations. We also note that increasing isoprene emission factors can increase the isoprene concentration and improve simulation of ozone concentration, but that simulation of isoprene can still be improved, highlighting the need for investigations of isoprene emissions and model simulation of isoprene. This study describes the effects of different emissions, analyses the weaknesses of the model in simulating ozone and proposes the need for further research on VOC and isoprene simulations

Online Influence Maximization (Extended Version)

Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or "seed nodes"), with the hope that they will convince their friends to buy it. One way to formalize marketers' objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete. In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users' feedback is used to update influence information. We adopt the Explore-Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users' feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.Comment: 13 pages. To appear in KDD 2015. Extended versio

Application of Radiant Floor Heating in Large Space Buildings with Significant Cold Air Infiltration through Door Openings

Radiant Floor Heating System (RFHS) has been commonly used in railway stations in cold regions of China for its advantages in thermal comfort and energy efficiency. However, the uneven distribution and extremely cold area of the heating floor, caused by cold air infiltration through door openings, are commonly found in our filed measurements. This impact is not considered in the standardized design methods, resulting in an underestimation of the design heat flux. In this paper, CFD simulations are used to quantify the impacts of natural infiltration on surface heat transfer process. Model validation was made against field measurements. 13 simulations were performed for different speeds. As a result, the mean radiant heat flux at floor surface decreased by 36.8% as the infiltration air speed increased from 0.05 m/s to 1.2 m/s, and the noneffective area increased more than 16 times. This result highlights a significant influence of natural infiltration. Regression models were finally developed as a simple method for rough estimation of this impact on radiation, which can make up the limitations of current methods and inform designers to improve their initial design of RFHS when natural infiltration is present

Sodalite-like carbon based superconductors with Tc about 77 K at ambient pressure

The attainment of superconductivity at room temperature is a longstanding aspiration for both experimental and theoretical scientists. Materials exhibiting superconductivity under ambient conditions would have significant applications. Here, we report two metastable phases of sodalite-like carbon based superconductors, GaC6 and GeC6, at ambient pressure using the CALYPSO structural search method and first-principles calculations. Our calculations reveal that both GaC6 and GeC6 compounds have Im[3 with combining macron]m symmetry and are dynamically stable at ambient pressure with Tc values up to the boiling point of liquid nitrogen. The underlying mechanisms indicate that the guest Ga and Ge atoms play a dual role in enhancing the structural stability and concurrently acting as electron donors, thereby modulating the electronic properties of the C24 covalent frameworks, i.e. from insulating states to superconducting states. The present results offer insights into the exploration of novel high temperature superconductors under ambient conditions

Controllable electromechanical stability of a torsional micromirror actuator with piezoelectric composite structure under capillary force

Various types of micro/nano functional devices are being widely designed as optical switches, micro scanners, micromirrors and other core optical devices. The continuing miniaturization of the functional devices makes the size dependence of electromechanical property significant in micro/nano scale due to the sharp increase of surface interactions such as capillary force from liquid bridge, van der Waals and Casimir forces from quantum fluctuations. The surface interactions can cause the pull-in instability, adhesion between parts, and even failure of device. This work provides an active control method to avoid the pull-in instability of an electrostatically driven circular micromirror by applying voltage on a torsional piezoelectric composite structure. The influences of the three types are compared of dispersion forces on the electromechanical stability of the micromirror actuator. A comprehensive electromechanical model of a torsional piezoelectric beam was established to numerically investigate the electromechanical coupling of the micromirror. The results show that the influence of capillary force on the stability of the micromirror is as significant as van der Waals force and Casimir force. By introducing piezoelectric nanoplates into the laminated torsional structure, the micromirror stability can be controlled based on the piezoelectric effect of the torsional piezoelectric composite structure. This work can contribute to the structural optimization design and manufacture of micromirror systems.Cited as: Liu, M., Chen, Y., Cheng, W., Chen, S., Yu, T., Yang, W. Controllable electromechanical stability of a torsional micromirror actuator with piezoelectric composite structure under capillary force. Capillarity, 2022, 5(3): 51-64. https://doi.org/10.46690/capi.2022.03.0

Multiscale Superpixel Structured Difference Graph Convolutional Network for VL Representation

Within the multimodal field, the key to integrating vision and language lies in establishing a good alignment strategy. Recently, benefiting from the success of self-supervised learning, significant progress has been made in multimodal semantic representation based on pre-trained models for vision and language. However, there is still room for improvement in visual semantic representation. The lack of spatial semantic coherence and vulnerability to noise makes it challenging for current pixel or patch-based methods to accurately extract complex scene boundaries. To this end, this paper develops superpixel as a comprehensive compact representation of learnable image data, which effectively reduces the number of visual primitives for subsequent processing by clustering perceptually similar pixels. To mine more precise topological relations, we propose a Multiscale Difference Graph Convolutional Network (MDGCN). It parses the entire image as a fine-to-coarse hierarchical structure of constituent visual patterns, and captures multiscale features by progressively merging adjacent superpixels as graph nodes. Moreover, we predict the differences between adjacent nodes through the graph structure, facilitating key information aggregation of graph nodes to reason actual semantic relations. Afterward, we design a multi-level fusion rule in a bottom-up manner to avoid understanding deviation by learning complementary spatial information at different regional scales. Our proposed method can be well applied to multiple downstream task learning. Extensive experiments demonstrate that our method is competitive with other state-of-the-art methods in visual reasoning. Our code will be released upon publication

AFPN: Asymptotic Feature Pyramid Network for Object Detection

Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at \href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}

Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions

Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R-2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.Peer reviewe