ALBERT

Description: The Molecular Electric Transducer (MET), widely applied for vibration measurement, has excellent sensitivity and dynamic response at low frequencies. The elastic membrane in the MET is a significant factor with an obvious effect on the performance of the MET in the low frequency domain and is the focus of this paper. In simulation experiments, the elastic membrane and the reaction cavity of the MET were analysed in a model based on the multiphysics finite element method. Meanwhile, the effects caused by the elastic membrane elements are verified in this paper. With the numerical simulation and practical experiments, a suitable elastic membrane can be designed for different cavity structures. Thus, the MET can exhibit the best dynamic response characteristics to measure the vibration signals. With the new method presented in this paper, it is possible to develop and optimize the characteristics of the MET effectively, and the dynamic characteristics of the MET can be improved in a thorough and systematic manner.

Description: Unprecedented dengue fever (DF) outbreaks impel China to develop useful disease control strategies. Integrated vector management (IVM) focuses on identifying vulnerable populations and interrupting human–vector contact; however, vulnerable populations have not been clearly identified in China. We conducted a case-control study during the initial stage of the 2014 DF outbreak in Guangzhou, China to assess risk factors for DF infection. Cases were randomly sampled from the National Notifiable Infectious Disease Reporting Information System (NNIDRIS). Controls were healthy individuals recruited from 17 DF infected communities through cluster sampling. A structured questionnaire on demographics, knowledge, practices, and living environment was administered to participants (165 cases; 492 controls). Logistic regression models identified characteristics of vulnerable populations. Awareness of dengue (OR = 0.08, 95% CI = 0.04–0.17), removing trash and stagnant water from around the residence (OR = 0.02, 95% CI = 0.00–0.17), and using mosquito repellent oils (OR = 0.36, 95% CI = 0.16–0.81) were protective factors. Living in an old flat or shed (OR = 2.38, 95% CI = 1.18–4.79) was a risk factor. Coils and bed nets were not protective due to incorrect knowledge of use. Using mosquito repellent oils and other protective measures can reduce vulnerability to DF infection.

Description: Haze has caused the deterioration of air quality and has ultimately affected the ecological environment. The contingent valuation method (CVM) is an important assessment method that is widely used in ecological economics. The public’s willingness to pay (WTP) for haze management and prevention can be analyzed using dichotomous choices. Here, the method is applied to study the valuation of haze management and prevention. Taking Jiangsu Province as an example, the non-market value is calculated by constructing the binary logistic model from questionnaire data, combined with a data-processing method: the sure independence screening (SIS) algorithm. The conclusions are as follows: (1) The public’s WTP for haze management and prevention is closely related to the monthly income of families and transport modality; (2) According to the CVM, the non-market value for haze management and prevention in Jiangsu is 7.645 billion yuan; (3) By the average estimate method (AEM), this value is 12.529 billion yuan, about 1.64 times the estimate from the CVM. This is because the AEM ignores the correlation among the influence factors and, therefore, overestimates the valuation of the services; (4) The CVM, combined with the SIS algorithm, does a better job in estimating the valuation of the services.

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: The bacterioplankton community composition’s (BCC) spatial and temporal variation patterns in three reservoirs (Shiyan, Xikeng, and LuoTian Reservoir) of similar trophic status in Bao’an District, Shenzhen (China), were investigated using PCR amplification of the 16S rDNA gene and the denaturing gradient gel electrophoresis (DGGE) techniques. Water samples were collected monthly in each reservoir during 12 consecutive months. Distinct differences were detected in band number, pattern, and density of DGGE at different sampling sites and time points. Analysis of the DGGE fingerprints showed that changes in the bacterial community structure mainly varied with seasons, and the patterns of change indicated that seasonal forces might have a more significant impact on the BCC than eutrophic status in the reservoirs, despite the similar Shannon-Weiner index among the three reservoirs. The sequences obtained from excised bands were affiliated with Cyanobacteria, Firmicutes, Bacteriodetes, Acidobacteria, Actinobacteria, Planctomycetes, and Proteobacteria.

Description: This paper presents a systematic pioneering study on the use of agricultural-purpose frequency domain reflectometry (FDR) sensors to monitor temperature and moisture of a subgrade in highway extension and reconstruction engineering. The principle of agricultural-purpose FDR sensors and the process for embedding this kind of sensors for subgrade engineering purposes are introduced. Based on field measured weather data, a numerical analysis model for temperature and moisture content in the subgrade’s soil is built. Comparisons of the temperature and moisture data obtained from numerical simulation and FDR-based measurements are conducted. The results show that: (1) the embedding method and process, data acquisition, and remote transmission presented are reasonable; (2) the temperature and moisture changes are coordinated with the atmospheric environment and they are also in close agreement with numerical calculations; (3) the change laws of both are consistent at positions where the subgrade is compacted uniformly. These results suggest that the data measured by the agricultural-purpose FDR sensors are reliable. The findings of this paper enable a new and effective real-time monitoring method for a subgrade’s temperature and moisture changes, and thus broaden the application of agricultural-purpose FDR sensors.