SARS-CoV Infection in a Restaurant from Palm Civet

Contact with food animals was associated with SARS-CoV infection in the People’s Republic of China

Biodegradable Composites Based on Well-characterized Cellulose and Poly (Butyleneadipate-Co-Terephthalate)

As a biodegradable and flexible copolymer, poly(butyleneadipate-co-terephthalate) (PBAT) is used for packaging. However, high cost and limited properties restrict its applications. Because of the hydrophobic nature of PBAT, low mechanical properties are observed when PBAT and cellulosic fibers, which are hydrophilic, are used in blends. To increase the interfacial adhesion between cellulose and PBAT, γ-(2,3-epoxypropoxy) propytrimethoxysilane (KH560) was used as a reactive compatibilizer to modify cellulose. A one-step method was demonstrated for compounding and subsequent extrusion blowing, which is a simple and environmentally friendly approach to fabricate a series of K-Cellulose/PBAT (KH560-Cellulose/PBAT) composites. The morphology and structure of the cellulose were characterized by scanning electron microscopy, X-ray diffraction, and Fourier transform infrared spectrophotometry. Meanwhile, thermal analysis of the hybrids showed an improvement of the thermal stability of the composites with increased silanized cellulose content. In addition, the barrier properties of films are measured by water vapor permeability (WVP) and oxygen permeability (OP). Finally, after addition of reactive compatibilizer KH560, there was considerable improvement with increased the cellulose content, as shown through mechanical properties testing. Therefore, the composites prepared with these enhanced properties have great potential as substitutes for traditional commodity polymers

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery

Model selection for direct marketing : performance criteria and validation methods

Purpose – The purpose of this paper is to assess the performance of competing methods and model selection, which are non-trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward. Design/methodology/approach – This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k-fold cross-validation. Systematic experiments are conducted to compare their performance. Findings – The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten-fold cross-validation produces more accurate results than bootstrap validation. Practical implications – To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures. Originality/value – The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications

Ceiling temperature distribution and decay in tunnel fires: Effect of longitudinal velocity, bifurcated shaft exhaust and fire location

This paper establishes a model tunnel to investigate the impact of longitudinal velocity (u), bifurcated shaft exhaust velocity (BSEV) and fire location on ceiling temperature and decay. The experimental results show that a longitudinal velocity of 0.6 m/s can control the upstream high temperature within 2.5 m when the distance between fire and shaft (D) is 1.0 m, and further increase in longitudinal velocity has little effect on upstream temperature distribution. Downstream temperature profile should be divided into two cases according to the magnitude of longitudinal velocity: the difference between the temperature decay model in low-speed region (u ≤ 0.5 m/s) and that in the high-speed region (u > 0.5 m/s) is particularly obvious with D at 1.0 m, and the downstream temperature decay rate in the low-speed region is the slowest compared to all the working conditions in this paper. For D more than 1.0 m, the range of high temperature distribution increases with D for certain longitudinal velocities (0.6-0.7 m/s); however, at particularly large longitudinal velocity (0.8 m/s), D has almost no effect on the upstream temperature distribution. The effect of longitudinal velocity on upstream temperature is stronger than that of BSEV. The downstream ceiling temperature decay model is little affected by longitudinal velocity and BSEV with D more than 1.0 m. The temperature decay rate first decreases, then increases, and finally decreases again as the D increases. Existing temperature attenuation models cannot predict the temperature profile in longitudinally ventilated tunnels with BSEV, but the temperature decay model considering fire location proposed in this paper can provide a reference value for tunnels with synergistic ventilation of longitudinal ventilation and BSEV

An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery

A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research

A Condition-Monitoring Approach for Diesel Engines Based on an Adaptive VMD and Sparse Representation Theory

This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research