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: Two scale-model tests were separately conducted in standard Toyoura sand with relative density of 50% and 80%. The effect of sand relative density on pile-soil interaction was investigated through the response of a laterally loaded pile and the sand movement around the pile. At a displacement of 3.6 mm of the loading point, the applied loads in loose and dense sand were 4.775 N and 21.025 N, respectively, and the maximum moment and soil resistance of the pile in dense sand were over 4 times those in loose sand. However, the deflection of the pile in dense sand was less than that in loose sand; additionally, the depth of zero deflection in dense sand was also less than that in loose sand. At the same time, the maximum displacements of loose sand in the vertical profile and ground surface were over 1.5 times those of dense sand. These characteristics occurred because the relative stiffness ratio of soil and pile increased as the relative density increased, which caused the behavior of the pile in dense sand to be elastic rather than rigid. In addition, the compacted sand particles did not move as easily as the loose sand particles.

Description: The Schiff bases N-(5-phenylthiazole-2-yl)-2-hydroxylnaphthaldehydehydrazone (1), N-(4′-chloro-5-phenylthiazole-2-yl)-2-hydroxylnaphthaldehydehydrazone (2), and N-(4′-nitro-5-phenylthiazole-2-yl)-2-hydroxylnaphthaldehydehydrazone (3) were synthesized. These compounds were characterized by using IR, 1H NMR, 13C NMR, and MS. The photochromism of the compounds was investigated by IR and UV-visible spectrometry which is time variable under irradiation of 254 nm UV light. The thermochromism of the compounds was studied using temperature-variable IR, UV-visible spectrometry, TG, and differential scanning calorimetry (DSC). The results suggested that compound 2 showed thermochromism properties and compounds 2 and 3 displayed photochromism properties. The relationship between the substituents species and photochromic or thermochromic properties of these compounds was revealed as well.

Description: GC-MS method combined with FTIR techniques by the analysis of polysaccharide was applied to evaluate the similarity between wild (W) and tissue-cultured (TC) Dendrobium huoshanense (DHS), Dendrobium officinale (DO), and Dendrobium moniliforme (DM) as well as 3 wild Dendrobium spp.: Dendrobium henanense (DHN), Dendrobium loddigesii (DL), and Dendrobium crepidatum (DC). Eight monosaccharides involving xylose, arabinose, rhamnose, glucose, mannose, fructose, galactose, and galacturonic acid were identified in the polysaccharide from each Dendrobium sample while the contents of the monosugars varied remarkably across origins and species. Further similarity evaluation based on GC-MS data showed that the values of different origins of DHS, DO, and DM were 0.831, 0.865, and 0.884, respectively, while the values ranged from 0.475 to 0.837 across species. FTIR files of the polysaccharides revealed that the similarity coefficients between W and TC-DHS, DO, and DM were 88.7%, 86.8%, and 88.5%, respectively, in contrast to the similarity coefficients varying from 57.4% to 82.6% across species. These results suggested that the structures of polysaccharides between different origins of the investigated Dendrobiums might be higher than what we had supposed.

Description: With the development of sensors, it is possible to embed many sensors within a certain space, which makes the monitor and alarm system with multisensor possible. There are two important parameters in a monitor and alarm system, namely, the false alarm rate and the missed alarm rate. In this work, a method for selecting optimal number of sensors in the sensor array is presented to improve the credibility. The influence factors of the weights and the false alarm rate and the missed alarm rate of one sensor and total number of sensors are discussed. An experimental setup was developed. The monitoring methods of common strategies and the proposed optimal number of sensors strategy are compared graphically by the receiver operating characteristic curves and the area under receiver operating characteristic curve values. The receiver operating characteristic curves graphically prove that the optimal number of sensors’ method presents the best performance, and it is shown that the optimal number of sensors’ method has the highest area under receiver operating characteristic value (0.9631). This method may aid future users of the monitor and alarm system by providing an optimal number of sensors strategy for high credibility.

Description: A highly efficient and robust scheme is proposed for analyzing electromagnetic scattering from electrically large arbitrary shaped conductors in a half space. This scheme is based on the electric field integral equation (EFIE) with a half-space Green’s function. The precorrected fast Fourier transform (p-FFT) is first extended to a half space for general three-dimensional scattering problems. A novel enhanced dual threshold incomplete LU factorization (ILUT) is then constructed as an effective preconditioner to improve the convergence of the half-space EFIE. Inspired by the idea of the improved electric field integral operator (IEFIO), the geometrical-optics current/principle value term of the magnetic field integral equation is used as a physical perturbation to stabilize the traditional ILUT perconditioning matrix. The high accuracy of EFIE is maintained, yet good calculating efficiency comparable to the combined field integral equation (CFIE) can be achieved. Furthermore, this approach can be applied to arbitrary geometrical structures including open surfaces and requires no extra types of Sommerfeld integrals needed in the half-space CFIE. Numerical examples are presented to demonstrate the high performance of the proposed solver among several other approaches in typical half-space problems.