A fuzzy logic and neuro-fuzzy system for classification of hydrometeor type based on polarimetric radar measurements is described in this paper. The hydrometeor classification system is implemented by using fuzzy logic and a neural network, where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the fuzzy sets in the fuzzy logic system according to prior knowledge. Five radar measurements, namely, horizontal reflectivity (Z H), differential reflec-tivity (Z DR), differential propagation phase shift (K DP), correlation coefficient [ HV (0)], and linear depolarization ratio (L DR), and corresponding altitude, have been used as input variables to the neuro-fuzzy network. The output of the neuro-fuzzy system is one of the many possible hydrometeor types: 1) drizzle, 2) rain, 3) dry and low density snow, 4) dry and high-density crystals, 5) wet and melting snow, 6) dry graupel, 7) wet graupel, 8) small hail, 9) large hail, and 10) a mixture of rain and hail. The neuro-fuzzy classifier is more advantageous than a simple neural network or a fuzzy logic classifier because it is more transparent (instead of a ''black box'') and can learn the parameter of the system from the past data (unlike a fuzzy logic system). The hydrometeor classifier has been applied to several case studies and the results are compared against in situ observations. FIG. 1. Block diagram of a general fuzzy logic system.

Foodborne pathogens like Listeria monocytogenes can cause various illnesses and pose a serious threat to public health. They produce species-specific microbial volatile organic compounds, i.e., the biomarkers, making it possible to indirectly measure microbial contamination in foodstuff. Herein, highly ordered mesoporous tungsten oxides with high surface areas and tunable pores have been synthesized and used as sensing materials to achieve an exceptionally sensitive and selective detection of trace Listeria monocytogenes. The mesoporous WO-based chemiresistive sensors exhibit a rapid response, superior sensitivity, and highly selective detection of 3-hydroxy-2-butanone. The chemical mechanism study reveals that acetic acid is the main product generated by the surface catalytic reaction of the biomarker molecule over mesoporous WO. Furthermore, by using the mesoporous WO-based sensors, a rapid bacteria detection was achieved, with a high sensitivity, a linear relationship in a broad range, and a high specificity for Listeria monocytogenes. Such a good gas sensing performance foresees the great potential application of mesoporous WO-based sensors for fast and effective detection of microbial contamination for the safety of food, water safety and public health.

High-sensitive measurement of radio-frequency (RF) electric field is available via the electromagnetically induced transparency (EIT) effect of Rydberg atom at room-temperature, which has been developed to be a promising atomic RF receiver. In this Letter, we investigate the credibility of the digital communication via this quantum-based antenna over the entire continuously tunable RF-carrier. Our experiment shows that digital communication at a rate of 500 kbps performs reliably within a tunable bandwidth of 200 MHz at carrier 10.22 GHz and a bit error rate (BER) appears out of this range, for example, the BER runs up to 15% at RF-detuning ±150 MHz. In the measurement, the time-variant RF field is retrieved by detecting the density of the probe laser at the center frequency of RF-induced symmetric or asymmetric Autler-Townes splitting in EIT. Prior to the digital test, we have studied the RF-receiving quality versus the physical ambiance and found that a choice of linear gain response to the RF-amplitude can suppress the signal distortion and the modulating signal is able to be decoded as fast as up to 500 kHz in the tunable bandwidth. Our checkout consolidates the physical foundation for a reliable communication and spectrum sensing over the broadband RFE-field signal in free-space can be captured by measuring the transmission of a probe laser in a condition of a Rydberg EIT. Owing to unique advantages of free-space RF field sensing, the quantum receiver has great significance compared with conventional electronics-based receivers, including but not limited to the weak signal, long-distance communication in free space or via a fiber link. All the principle experiments of communication were performed over carrier of an optimized resonant frequency of Rydberg states [11][12][13].

Recent research has shown that neural network techniques can be used successfully for ground rainfall estimation from radar measurements. The neural network is a nonparametric method for representing the relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The effectiveness of the rainfall estimation by using neural networks can be influenced by many factors such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, season change, location change, and so on. In this paper, a novel scheme of adaptively updating the structure and parameters of the neural network for rainfall estimation is presented. This adaptive neural network scheme enables the network to account for any variability in the relationship between radar measurements and precipitation estimation and also to incorporate new information to the network without retraining the complete network from the beginning. This precipitation estimation scheme is a good compromise between the competing demands of accuracy and generalization. Data collected by a Weather Surveillance Radar-1988 Doppler (WSR-88D) and a rain gauge network were used to evaluate the performance of the adaptive network for rainfall estimation. It is shown that the adaptive network can estimate rainfall fairly accurately. The implementation of the adaptive network is very efficient and convenient for real-time rainfall estimation to be used with WSR-88D.

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To discover the effect of environmental factors on pollinator visitation to flowering Medicago sativa, several field experiments were designed to examine the diurnal movement patterns of wild bee species in the Hexi Corridor of northwestern China. Our study results showed that Megachile abluta, M. spissula, and Xylocopa valga showed unimodal diurnal foraging behavior, whereas Andrena parvula and Anthophora melanognatha showed bimodal diurnal foraging behavior. Correlation analysis indicated that diurnal foraging activities of pollinators were significantly correlated with environmental factors. Correlations of foraging activities versus environmental factors for M. abluta, M. spissula, and X. valga best fit a linear model, whereas those of A. parvula and A. melanognatha best fit a parallel quadratic model. Results of this study indicated that solitary wild bees such as M. abluta, M. spissula, X. valga, A. parvula, and A. melanognatha are potential alfalfa pollinators in the Hexi Corridor. An understanding of the environmental factors that affect the behaviors of different wild bees foraging in alfalfa are basic to the utilization of solitary wild bees in a practical way for increased, or more consistent, pollination of alfalfa for seed production.

In China, filial piety, which usually refers to showing respect and obedience to parents, has exerted an important effect in the relationship between work stress and turnover intention. However, the mechanism behind this effect is still unclear. To address this gap in the existing literature, we developed and tested a moderated mediation model of the relationship that work stress shares with job satisfaction and turnover intention. In accordance with the dual filial piety model and the stress-moderation model, our hypothesized model predicted that the mediating effect of job satisfaction on the relationship between work stress and turnover intention would be moderated by reciprocal filial piety (RFP) and authoritarian filial piety (AFP). The analytic results of data that were obtained from 506 employees of manufacturing industries in China supported this model. Specifically, RFP and AFP, as a contextualized personality construct, positively moderated the direct relationship between work stress and turnover intention as well as the corresponding indirect effect through job satisfaction. In particular, RFP and AFP strengthened the positive effect of work stress on turnover intention. Based on these findings, recommendations to help employees fulfill their filial duties and reduce the effect of work stress on turnover intention among employees of Chinese manufacturing industries are delineated.