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The deep research should be implemented, due to the higher frequency of the accidents in the laboratory of universities. According to relevant literature statistics, fire and explosion accidents account for a high proportion of the total number of college accidents. In order to effectively prevent the occurrence of fire and explosion accidents in Colleges and universities, taking the explosion-proof cabinet in the drug room of the Department of energy and chemical engineering of East China University of science and Technology as the research object, the risk control factors of the laboratory were analyzed, and the risk assessment was carried out by using the fault tree analysis method and regional quantitative risk analysis method. The results showed that the minimum path set and the minimum cut set of the fault tree were obtained by using the fault tree analysis method, as well as the structural importance. In terms of probabilistic risk assessment, compared with the situation without control, the fire risk of laboratory explosion-proof cabinet is greatly reduced after adopting safety measures.

Gait rhythm disturbances due to abnormal strides indicate the degenerative mobility regulation of motor neurons affected by Parkinson's disease (PD). The aim of this work is to compute the approximate entropy (ApEn), normalized symbolic entropy (NSE), and signal turns count (STC) parameters for the measurements of stride fluctuations in PD. Generalized linear regression analysis (GLRA) and support vector machine (SVM) techniques were employed to implement nonlinear gait pattern classifications. The classification performance was evaluated in terms of overall accuracy, sensitivity, specificity, precision, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic (ROC) curve. Our experimental results indicated that the ApEn, NSE, and STC parameters computed from the stride series of PD patients were all significantly larger (Wilcoxon rank-sum test: p

Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn) and average stride interval (ASI) parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-WhitneyUtest (p<0.01) in children of 3–14 years old, which indicates the stride irregularity has been significantly ameliorated with the body growth. The original and normalized ASI values are also significantly changing when comparing between any two groups of young (aged 3–5 years), middle (aged 6–8 years), and elder (aged 10–14 years) children. Such results suggest that healthy children may better modulate their gait cadence rhythm with the development of their musculoskeletal and neurological systems. In addition, the AdaBoost.M2 and Bagging algorithms were used to effectively distinguish the children’s gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%), recall (≥0.8), and precision (≥0.8077).

Analysis of quantified voice patterns is useful in the detection and assessment of dysphonia and related phonation disorders. In this paper, we first study the linear correlations between 22 voice parameters of fundamental frequency variability, amplitude variations, and nonlinear measures. The highly correlated vocal parameters are combined by using the linear discriminant analysis method. Based on the probability density functions estimated by the Parzen-window technique, we propose an interclass probability risk (ICPR) method to select the vocal parameters with small ICPR values as dominant features and compare with the modified Kullback-Leibler divergence (MKLD) feature selection approach. The experimental results show that the generalized logistic regression analysis (GLRA), support vector machine (SVM), and Bagging ensemble algorithm input with the ICPR features can provide better classification results than the same classifiers with the MKLD selected features. The SVM is much better at distinguishing normal vocal patterns with a specificity of 0.8542. Among the three classification methods, the Bagging ensemble algorithm with ICPR features can identify 90.77% vocal patterns, with the highest sensitivity of 0.9796 and largest area value of 0.9558 under the receiver operating characteristic curve. The classification results demonstrate the effectiveness of our feature selection and pattern analysis methods for dysphonic voice detection and measurement.

High-quality CdTeSe colloidal nanocrystals with gradient distribution of components, consisting of Te-rich inner cores and Se-rich outer shells, were synthesized in a “green” solvent paraffin via a noninjection one-pot approach with the use of cadmium oxide (CdO), elemental tellurium, and elemental selenium as Cd, Te, and Se sources, respectively. All of these reactants were loaded at room temperature. This features synthetic reproducibility and large-scale capability. The bandgap engineering of the obtained CdTeSe QDs can be conveniently realized through the variation of growth temperature. Red- to near-infrared-emitting (620–780 nm) QDs with nearly identical particle sizes can be obtained when the reaction temperature was changed from 180 to 280 °C with the fixation of precursor feed ratio at 5Cd–0.5Te–0.5Se. The as-prepared CdTeSe QDs exhibit PL QY as high as 53%. The resulting CdTeSe QDs were characterized by UV–vis and photoluminescence spectroscopy, powder X-ray diffraction, transmission electron microscopy, and inductively coupled plasma atomic emission spectroscopy.

Colloidal oil-soluble Bi 2 O 3 quantum dots (QDs) are synthesized through an alcoholysis route in organic media. Water-soluble Bi 2 O 3 QDs are then obtained from the initial oil-soluble QDs through phase transfer by surface modification with mercaptopropionic acid. X-ray diffraction and transmission electron microscopy studies show that the crystallinity is enhanced and particles grow larger after phase transfer. The water-soluble Bi 2 O 3 QDs exhibit excellent photocatalytic activity for the degradation of methyl orange at a wide range of pH values. After several cycles, the Bi 2 O 3 QDs retain high degradation efficiency, Simulation according to the first-order reaction dynamics indicates that the degradation reaction may follow complicated quasi-homogeneous photocatalysis.

Graphical abstractDisplay Omitted HighlightsSymbolic entropy, approximate entropy, and fuzzy entropy are used to measure the irregularity of knee vibroarthrographic signals.Mean, standard deviation, and root-mean-squared value parameters of the signal envelope amplitude are computed to characterize the signal fluctuations.Gender-dependent statistics of entropy and envelope amplitude parameters are discussed. Background and objectiveInjury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint. MethodsThe symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint. The quadratic discriminant analysis (QDA), generalized logistic regression analysis (GLRA), and support vector machine (SVM) methods were used to perform signal pattern classifications. ResultsThe experimental results showed that the patients with cartilage pathology (CP) possess larger SyEn and ApEn, but smaller FuzzyEn, over the statistical significance level of the Wilcoxon rank-sum test (p

In this paper, we derive forward autoregressive models to describe the stochastic process underlying stride interval series related to idiopathic Parkinson's disease. The parameters of the autoregressive model that specify pole locations in the complex z-plane were used as dominant features for the separation of gait series of healthy subjects and patients with Parkinson's disease. Based on the autoregressive parameters, linear discriminant analysis and support vector machines can provide classification accurate rates over 74% and area larger than 0.8 under the receiver operating characteristic curve. The results obtained show that the autoregressive model parameters could be useful for classification of stride series.

Decomposition of local field potential (LFP) signals with different oscillatory rhythms is useful for analysis of various neuronal activities in mice. In this paper, we first removed the power-line interference with high signal fidelity by using a notch filter with infinite impulse response. Next, we applied the ensemble empirical mode decomposition (EEMD) method to separate the LFP signal into low-frequency, Delta, Theta, Beta, Gamma, Ripple, and high-frequency oscillations, in the form of different intrinsic mode functions (IMFs). The LFP signal components with different frequency bands were identified and then reconstructed from the IMFs within the same frequency range by analyzing their power spectral ratios (PSRs). Then, normalized autocorrelation functions of the resting respiratory signal and the reconstructed Delta oscillations were computed to estimate the corresponding power spectral densities by means of the Fourier transform. The results of LFP signal decomposition and oscillatory rhythm reconstruction demonstrated the effectiveness of the EEMD and PSR analysis methods. The coherence analysis results indicate that the primary periodicity peak of the Delta LFP component is definitely linked to that of resting respiration in an awake mouse. Our major contribution is to establish a novel LFP signal separation and identification procedure by combining the EEMD method with appropriate parameter setting and the power spectral analysis approach.