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With the rapid developments of IoT technologies, a large amount of real-time data is collected and shared, having important impacts on many applications, such as business advertisement and decision-making assistance. However, most users are unwilling to share their personal data directly to any third party for either academic research or commercial analysis because personal data contains private or sensitive information, such as economic status or living habits. Balancing the utility of big data and users’ privacy is a vital issue in academia and industry. In this paper, a privacy-preserving raw data collection scheme for IoT is proposed, in which the participant's data is collected and obfuscated with the other participants’ data within a group in order to mask the individual's privacy. Specifically, individual data is kept in its raw format to enhance its value for the data consumer, while no other users outside of the user herself know the source of the collected data. In addition, no trusted authority (TA) is needed in our proposed scheme, which is more suitable for real-world applications. Moreover, efficiency analysis is performed by simulation, and the result shows that our proposed scheme is practical for IoT systems.

OBJECTIVES: To explore the current admittance situation of clinical teachers for masters of nursing specialist (MNS) postgraduates and to test the competence of clinical teachers in self-evaluation and other evaluations. METHODS: In this cross-sectional study, using a random number table, we chose 80 MNS postgraduates under clinical practice, their clinical teachers, and head nurses each from six hospitals in Hunan and Guangdong. The participants were tested on the basis of the Clinical Teachers' Competence Inventory of MNS Postgraduates. The competences of clinical teachers were evaluated by the three groups of participants. RESULTS: The aggregated scores of teacher competence as evaluated by the MNS postgraduates (181.33 ± 24.95) were lower than those assigned by both clinical teachers (190.75 ± 24.30) and their head nurses (198.53 ± 18.90), with significant differences in all dimensions except for clinical managing ability. The five highest rated items from all participants focused on the teachers' clinical nursing ability, and the five lowest rated items were mainly about their clinical research ability. CONCLUSION: The evaluation from MNS postgraduates is obviously lower than the self-evaluation of clinical teachers, and all participants are aware of the deficiency in research ability of the teachers. Thus, the admittance and examination of clinical teachers should be controlled strictly. Training should be carried out immediately to strengthen their comprehensive abilities, especially their research ability.

According to the experimental data, an amplitude probability density function (PDF) model of the slurry flow signal is built up for electromagnetic flowmeter (EMF) by the method combining statistical analysis with numerical fitting, in order to reveal the effect of slurry noise on the flow signal and describe the features of slurry flow signal. Based on this model, a signal reconstruction processing algorithm is proposed to deal with the output signal of EMF sensor for realizing the slurry flow measurement. At the same time, the high-low voltage switching mode based square-wave excitation method is presented for EMF so as to reduce the slurry noise interferences. A slurry-type EMF transmitter is developed with a DSP chip – TMS320F28335, to implement the signal processing algorithm and control function. Finally water flow calibrations and slurry flow experiments are conducted to verify the reliability and stability of the method and system. Experimental results show that its measurement accuracy of water flow is better than 0.5%, and its steady-state volatility of paper slurry is less than 3%, and its dynamic response time is less than 4 s.

Ischemic preconditioning protects against cerebral ischemia. Recent investigations indicated that acidic preconditioning (APC) protects against ischemia-induced cardiomyocytes injury. However, it is not clear whether APC can protect against cerebral ischemia. To address this issue, C57BL/6 mice were exposed 3 times at 10-min intervals to a normoxic atmosphere containing 20% CO(2) for 5 min before being further subjected to bilateral common carotid artery occlusion. APC reversed the ischemia-induced brain injury as revealed by improved performance in passive avoidance experiments and decreased neuron loss in the hippocampal CA1 region. Consistently, both APC-treated brain slices and primary cultured neurons were more resistant to oxygen-glucose-deprivation (OGD)-induced injury, in a pH- and time-dependent manner, as revealed by reversed cell/tissue viability. In addition, the APC treatment prevented OGD-induced mitochondrial transmembrane potential loss and apoptosis, which was inhibited by the mitochondrial permeability transport pore opener atractyloside. Taken together, these findings indicated that APC protects against ischemia-induced neuronal injury. The beneficial effects may be attributed, at least in part, to decreased mitochondria-dependent neuronal apoptosis.

This paper proposes a kind of identification method of block models to describe the nonlinear dynamic characteristics of sensors. The Volterra series are employed to analyze the block models and separate the higher-order Volterra kernels of the block models. The estimation method of frequency response function in the non-parametric form is used to identify the model of linear dynamic subsystem. And then coefficients of the nonlinear static subsystem are determined according to the nonlinear outputs of various orders. The identification method only needs the step or impact signal with different amplitudes as the input. These experimental data can be obtained in the calibration experiments of sensors easily. Considering the random noise or Gaussian noise in calibration experiments, the correlation function and bispectrum are used to reduce the influence of noise and improve the calculation precision. The method is applied to the hot-film mass air flow sensor for building its nonlinear dynamic model.

A wavelet function with good frequency response was adopted and the optimal decomposition level of wavelet transform was studied in order to improve the accuracy of fundamental wave extraction and frequency measurement in signal processing. In frequency measurements, an evaluating method was proposed to determine right maximal value points, and the fundamental frequency of signal was estimated by the least squares method, which could reduce the influence of low frequency noises. An interpolation method in parabola form was used to eliminate the error of the non-integral period sampling. A digital signal processing system was developed with a digital signal processor (DSP) to realise the wavelet filter and frequency measurement algorithms in real time. The testing results show that both the methods and system proposed by this paper are effective.

The current signal models of electromagnetic flowmeter under sinusoidal excitation cannot describe quantitatively the relationship among the sensor output, flow rate and exciting signal. A two-stage fitting method is proposed to build up the accurate signal model based on experimental data. The first step of the method is to approximate the relationship between the sensor output and the flow rate, and the second one is to fit the relationship between the model coefficients and the frequency or amplitude of exciting current. This method can determine the terms and coefficients of signal model, and reflect the effects of transformer and eddy current.

The hot-film/wire MAF sensors are widely used in automotive engines to measure the intake mass air flow for controlling the air fuel ratio due to their quick response, wide range and high reliability. However their non-linear dynamic characteristics influence the measurement accuracy of the hot-film/wire MAF sensors. In order to describe the sensor characteristics, this paper proposes a two-stage identification method based on the Hammerstein model and the experimental data of multi-amplitude step response. The modeling results show that the non-linear dynamic models of hot-film/wire MAF sensors are more accurate than the linear dynamic models, and convenient for sensor non-linear dynamic correction. This modeling method can also be applied to other sensors.

To determine a suitable initial water-soluble carbohydrate (WSC) content to make wheat straw natural fermentation successful and to study fermentation characteristics, glucose was used to adjust the initial WSC content to 1.4%, 4.0%, 5.0%, 6.0%, 7.0%, 8.0%, 9.0%, and 10.0% dry matter (DM) in the wheat straw. At 30 d of fermentation, there were three pHs: when the initial WSC content was 1.4%, the pH was 5.5; when the initial WSC contents were 4.0%, 5.0% and 6.0%, the pHs were near 5.1; and when the initial WSC contents were 7.0%, 8.0%, 9.0% and 10.0%, the pHs were near 4.0. The pattern of changes in WSC content during the fermentation was similar to that in pH. At 30 d of fermentation, there existed a dividing line in WSC remnants between the initial 6.0% WSC treatment and the initial 7.0% WSC treatment. When the initial WSC content was more than 7.0%, the remaining WSC content was more than 23.7 g/kg DM. When the initial WSC content was less than 6.0%, the remaining WSC content was less than 13.6 g/kg DM. Particularly for the 1.4% WSC treatment, the remaining WSC content was 2.1 g/kg DM. The results of the microbiological enumeration showed that with the increase in initial WSC content, the numbers of lactic acid bacteria (LAB) and other bacteria generally decreased. Denaturing gradient gel electrophoresis (DGGE) results showed that when the initial WSC content was beyond 7.0%, the LAB of the fermentation system were detected.

Inhibition of aggregation by Ro 44-9883, a potent and selective non-peptide GPIIb/IIIa antagonist, resulted in inhibition of serotonin secretion induced by weak agonists such as ADP or low doses of either thrombin receptor agonist peptide (TRAP) or collagen. In contrast, alpha granule secretion was inhibited to different extents dependent on donor, averaging 60% inhibition. Inhibition of serotonin secretion correlated with an inhibition of thromboxane A2 (TxA2) formation, both of which were overcome by higher doses of TRAP or collagen. Ro 44-9883 had no effect on the already reduced serotonin secretion and TxA2 formation in Glanzmann's thrombasthenic platelets. Restoration of serotonin secretion in the absence of aggregation requires both TxA2 and lysophosphatidic acid. In addition, Ro 44-9883 inhibition of TxA2 formation was not due to a lack of phospholipase A2 (PLA2) phosphorylation and activation as assayed in vitro. These results suggest that aggregation is required for weak or low dose agonist induced in vivo activity of PLA2, possibly by either regulating phospholipid substrate availability or interaction of PLA2 with platelet membranes.