ALBERT

Description: Aerosols and their radiative effects are of primary interest in climate research because of their vital influence on climate change. Dust aerosols are an important aerosol type in the North China Plain (NCP), mainly as a result of long-range transport, showing substantial spatiotemporal variations. By using measurements from the Aerosol Robotic Network (AERONET) between September 2004 and May 2017, and the space-borne Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol products, we investigated the properties of dust aerosols and their radiative effects at Xianghe (XH)—a suburban site in the NCP. Dust events occurred most frequently during spring (a total of 105 days) relative to the other three seasons (a total of 41 days) during the periods concerned. The dust aerosol optical depth (AOD) at 675 nm was at a maximum in spring (0.60 ± 0.44), followed (in decreasing order) by those in autumn (0.58 ± 0.39), summer (0.54 ± 0.15), and winter (0.53 ± 0.23). Cooling effects of dust aerosol radiative forcing (RF) at the bottom and top of the atmosphere tended to be strongest in spring (−96.72 ± 45.69 and −41.87 ± 19.66 Wm−2) compared to that in summer (−57.08 ± 18.54 and −25.54 ± 4.45 Wm−2), autumn (−72.01 ± 27.27 and −32.54 ± 15.18 Wm−2), and winter (−79.57 ± 32.96 and −37.05 ± 17.06 Wm−2). The back-trajectory analysis indicated that dust air mass at 500 m that arrived at XH generally originated from the Gobi and other deserts of northern China and Mongolia (59.8%), and followed by northwest China and Kazakhstan (37.2%); few dust cases came from northeast China (3.0%). A single-peaked structure with the maximum occurring at ~2 km was illustrated by all dust events and those sorted by their sources in three directions. Three typical dust events were specifically discussed to better reveal how long-range transport impacted the dust properties and radiative effects over the NCP. The results presented here are expected to improve our understanding of the physical properties of dust aerosols over the NCP and their major transport path and significant impacts on the regional solar radiation budget.

Description: Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.

Description: (1) Background: As the most common eye disease diagnosed in emergency departments, conjunctivitis has caused serious health and economic burdens worldwide. However, whether air pollution may be a risk factor for conjunctivitis is still inconsistent among current evidence. (2) Methods: We searched the literature on the relationship between air pollution and conjunctivitis in multiple English databases before 18 March 2019. Meta-analysis, meta-regression, and funnel plots were used to integrate the data, identify the sources of bias, and determine the publication bias, respectively. (3) Results: A total of 2450 papers were found, 12 of which were finally included. The pooled relative risk for each 10 μg/m3 increase of air pollution on conjunctivitis was 1.0006 (95%CI: 0.9993–1.0019) for CO, 1.0287 (1.0120–1.0457) for NO2, 1.0089 (1.0030–1.0149) for O3, 1.0004 (0.9976–1.0032) for PM2.5, 1.0033 (0.9982–1.0083) for PM10, and 1.0045 (0.9908–1.0185) for SO2. In the subgroup, PM2.5 and O3 had a greater impact on conjunctivitis risk in women than in men, and people 〈18 years old than those ≥18 years old. Relative humidity significantly modified the risk of O3 on conjunctivitis (p = 0.023), explaining 45% of the between-study heterogeneity. (4) Conclusion: Globally, air pollution has considerable health risks for conjunctivitis. Females and the youth were more vulnerable to PM2.5, NO2, and O3. Reductions of air pollution levels are still warranted to protect the vulnerable populations.

Description: High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF.

Description: This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the proposed delay estimation method under different time values was analyzed, and the optimal difference time values are given. To overcome the influences of digital signal sampling intervals on time delay estimation, a digital time delay estimation approach with low complexity and high accuracy is proposed. The performance of the proposed time delay estimation was analyzed in underwater multipath channels. Finally, the accuracy of the delay estimation using this proposed method was demonstrated by experiments.

Description: Whether bacterioplankton are assembled in the same way as microeukaryotes is a key question that has been answered only partially in microbial ecology. In particular, relating distribution patterns to the underlying ecological processes for plankton communities in highly dynamic ecosystems, such as river–reservoirs subjected to anthropogenic impacts, remains largely unstudied. Here, we analyzed taxonomic distribution patterns, and unraveled community assembly processes underlying the core and random bacterioplankton and microeukaryotes from a subtropical river–reservoir system. These plankton domains were modelled using the spatial abundance distributions (SpADs) of the operational taxonomic units (OTUs) as a proxy for abundant and rare taxa delineation. Both bacterioplankton and microeukaryote plankton communities exhibited significant distance–decay relationships, and samples were grouped depending on reservoir or river habitats. The neutral community model showed that 35–45% of the plankton community variation could be explained by neutral processes. The phylogenetic null model revealed that dispersal limitation accounted for the largest percentage of pairwise comparisons (42–68%), followed by environmental selection (18–25%). We concluded that similar prevalence of ecological processes acting on particular subsets of the bacterioplankton and microeukaryotes might have resulted from similar responses to environmental change, potentially induced by human activities in the watershed.

Description: Lots of studies on gold precipitation mechanisms have focused on fluid inclusions within quartz. However, the trace elements in quartz reflect the properties of the ore fluid, and a comparison of the trace element content in different types of quartz can reveal the precipitation mechanism. The Jinqingding gold deposit is the largest gold deposit in the Muping–Rushan gold belt and contains the largest single sulfide–quartz vein type orebody in the gold belt. This study distinguished four types of quartz in this orebody through field work and investigations of the mineralogy and cathodoluminescence (CL) of the quartz and crosscutting relationships as seen under a microscope. In situ studies via electron probe micro-analyzer (EPMA) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) were used to determine the trace element content of the different quartz types. Type Qa displayed a comb structure in the field and zoning under the microscope and in CL. Milky white and smoke grey Qb was the most common quartz type and hosted the most sulfide and gold. Qc was Qa and Qb quartz that recrystallized around pyrite or overgrew and appeared different from Qa and Qb in CL images. Qd occurred within fractures in pyrite. Qa formed prior to the mineralization of gold, and Qd formed post-mineralization. Qb and Qc provided information regarding the ore fluid during mineralization. Sericites occurred with pyrite in fractures in the quartz, and some, along with free gold, filled in fractures in pyrite. Free gold occurred within Qa, Qb, Qc, and in brittle fractures in pyrite. Qc had the lowest Al content of all of the quartz types. As Al content is related to the acidity of the ore fluid in previous study, this indicated an acidity decrease during mineralization, which could be attributed to the sericitization. Sericitization could indicate a potential gold occurrence. The Ti content decreased from Qb to Qc, indicating a decrease in temperature during quartz overgrowth formation. Change in acidity and cooling can therefore be identified as possible causes of gold precipitation in the sulfide–quartz vein type in the Jinqingding gold deposit.

Description: Unsteady Reynolds-averaged Navier–Stokes simulations of an axial-flow pump for waterjet propulsion are carried out at model scale, and the numerical uncertainties are analyzed mainly according to the procedure recommended by the twenty-eighth International Towing Tank Conference. The two-layer realizable k-ε model is adopted for turbulence closure, and the flow in viscous sub-layer is resolved. The governing equations are discretized with second-order schemes in space and first-order scheme in time and solved by the semi-implicit method for pressure-linked equations. The computational domain is discretized into block-structured hexahedral cells. For an axial-flow pump consisting of a seven-bladed rotor and a nine-bladed stator, the uncertainty analysis is conducted by using three sets of successively refined grids and time steps. In terms of the head and power over a range of flow rates, it is verified that the simulation uncertainty is less than 4.3%, and the validation is successfully achieved at an uncertainty level of 4.4% except for the lowest flow rate. Besides this, the simulated flow features around rotor blade tips and between the stator and rotor blade rows are investigated.

Description: In order to examine the geometric characteristics of BOF (blast oxygen furnace) slag coarse aggregate, the aggregate image measurement system (AIMS) was used to analyze the sphericity, gradient angularity and micro texture. Both volumetric and mechanical properties were studied to evaluate the influence of geometric characteristics of BOF slag coarse aggregate on asphalt concrete. The experimental results show that the BOF slag coarse aggregate has the characteristics of high sphericity, good angular performance and rough surface texture. The geometric characteristics of BOF slag has obvious influence on the volume performance of asphalt concrete. the higher sphericity of BOF slag causes an increase of the air voids of asphalt mixture. BOF slag coarse aggregate can effectively improve the road performances of asphalt concrete. BOF slag’s higher sphericity and angularity improve the moisture damage resistance and rutting resistance of asphalt concrete. Results indicate that better angularity can slightly enhance the moisture resistance property of asphalt concrete, but excessively high angularity of BOF slag coarse aggregates reduces the anti-rutting properties of asphalt mixture.

Description: Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.