Foreign Direct Investment and Wage Inequality: Evidence from the People\u27s Republic of China

Based on theoretical analysis of effects of foreign direct investment (FDI) on the wage gap between foreign firms and domestic firms in the host country, we use data from Chinese Industrial Enterprises Database to measure these effects. Theoretical results show that the wage gap between foreign firms and domestic firms in the host country caused by the FDI labor transfer effect and technology spillover effect tends to increase then decrease, which implies an inverted U curve track. The empirical results show that the FDI has significant effects on the wage gap in the People’s Republic of China (PRC) during the observed time period. The contribution of the FDI to change of the wage gap is above 10%, which is in the second position among all observed factors. From the overall point of view, the contribution of the FDI tends to decrease. The reason is that the wage gap caused by the FDI has stepped into the decreasing stage. This means the wage gap between foreign firms and domestic firms currently has been on the latter part of the inverted U curve. The Chinese government should expand fields for FDI so as to decrease the wage gap between foreign firms and domestic firms. This policy implication should be helpful for the PRC to step over the “middle-income trap”

Molecular Characterization and Photochemical Transformation of Dissolved Organic Matter From Land to Ocean

Molecular characterization and photochemical transformation of dissolved organic matter (DOM) in both rivers and the ocean is the main research focus of this dissertation. Chemical characterization of DOM is hampered by the limited application of advanced techniques to desalt, concentrate, isolate and then molecularly characterize DOM. An affordable, commercially available mini-electrodialysis (mini-ED) system has been evaluated and recommended for the efficient desalting of small volume samples of seawater prior to analysis by electrospray Fourier transform ion cyclotron resonance mass spectrometry (ESI FTICR-MS). A high-recovery technique of DOM isolation – reverse osmosis coupled with electrodialysis (RO/ED) – was used to isolate DOM from various major oceanic water masses, prior to ESI FTICR-MS analysis. RO/ED isolated DOM samples share a significant number of common molecular formulas, accounting for 54-79% of formulas in each sample. MS peaks enriched in surface samples have higher H/C values than peaks enriched in deep samples. This enrichment pattern is likely due to the selective photo-degradation of aromatic compounds and the bio-production of aliphatic and carbohydrate-like compounds in surface waters, and the selective bio-degradation of aliphatic and carbohydrate-like compounds with increasing depth. MS peaks enriched in the North Pacific intermediate and deep DOM have significantly higher 0/C values than the North Atlantic oxygen minimum layer and deep DOM. This suggests oxidation of DOM, possibly via microbial activity during the ageing of DOM or the preferential remineralization of DOM from sinking particles at depth in the Pacific. Our studies show that terrestrial DOM exposed to simulated sunlight is altered to produce POM with a markedly different molecular composition enriched in newly-formed aliphatic and condensed aromatic molecules. This process is closely tied to the chemistry of iron, which primarily exists as dissolved Fe(II) and Fe(III)-organic complexes in initial DOM and photochemically matures to Fe(III) oxyhydroxides before co-precipitating out with POM. The newly formed condensed aromatic compounds resemble black carbon, which until now was thought to be produced only by combustion. These new molecules contribute a novel pool of Fe-rich, aliphatic and black carbon organic matter to sediments as the terrestrial DOM is transported through rivers

Centrifugal pump fault detection based on SWT and SVM

Centrifugal pumps, like other rotating equipment, produce vibration signals during operation. Vibration signals often contain pump state information. Therefore, we can obtain pump state information by using appropriate signal processing methods. Synchrosqueezing wavelet transform (SWT) is a new time-frequency analysis technology. It is an algorithm for rebuilding time-frequency signals, which is similar to the empirical mode decomposition method. It can improve the time-frequency resolution of the signal compared with wavelet transform. In this paper, the SWT is used to analyze the vibration signal of centrifugal pump and extract characteristics. The data shows that the SWT can effectively extract the information of signal in time domain and frequency domain. Then we use the Support Vector Machine (SVM) to classify the features and realize the fault diagnosis of centrifugal pump. The result proves that the fault diagnosis method based on the SWT and SVM

Rolling bearing fault detection based on local characteristic-scale decomposition and teager energy operator

In this paper, a rolling bearing fault detection method based on Local Characteristic-scale Decomposition (LCD) and Teager Energy operator (TEO) is proposed. Vibration signals is related to the bearing fault. However, the vibration signal of rolling bearing is nonlinear and has multiple components, which makes it difficult to analyze the signals by using traditional method such as the fast Fourier transform (FFT). LCD, a recently developed signal decomposition method, is especially capable for dealing with the complex signal by decomposing it into several intrinsic scale components (ISC). Furthermore, to extract fault diagnosis of the components, we used TEO to demodulate each ISC. The energy of fault feature frequencies was extracted as fault vector. The result shows that the method successfully diagnoses bearing fault

Fault diagnosis of electro-mechanical actuator based on WPD-STFT time-frequency entropy and PNN

Electro-mechanical actuators (EMAs) are increasingly being used as critical actuation devices of the aircraft. It will cause serious accidents once the fault of EMAs occurs, thus the fault diagnosis of EMAs is essential to maintain the normal operation of aircraft. In this paper, a method based on WPD-STFT time-frequency entropy and PNN is proposed to achieve fault diagnosis of EMAs by processing the vibration signals collected by the accelerometer installed in the EMAs. Firstly, the vibration signals are decomposed by wavelet packet to obtain the signal components of different frequency bands, the signal components are subjected to STFT and spectrograms are obtained. Then, time-frequency entropy is calculated and combined with principal component analysis (PCA) for dimension reduction as the feature vector. Finally, the probabilistic neural network (PNN) classifier is introduced to classify the fault modes. The experimental result shows that this method can accomplish the accurate fault diagnosis of EMAs. Moreover, the performance of the proposed WPD-STFT time-frequency entropy method has an advantage over that of WPD-PCA method or STFT combined with mass-moment entropy method for feature extraction

Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network

This paper presents an approach to bearing fault diagnosis based on the Teager energy operator (TEO) and Elman neural network. The TEO can estimate the total mechanical energy required to generate signals, thereby resulting in good time resolution and self-adaptability to transient signals. These attributes reflect the advantage of detecting signal impact characteristics. To detect the impact characteristics of the vibration signals of bearing faults, we used the TEO to extract the cyclical impact caused by bearing failure and applied the wavelet packet to reduce the noise of the Teager energy signal. This approach also enabled the extraction of bearing fault feature frequencies, which were identified using the fast Fourier transform of Teager energy. The feature frequencies of the inner and outer faults, as well as the ratio of resonance frequency band energy to total energy in the Teager spectrum, were extracted as feature vectors. In order to avoid a frequency leak error, the weighted Teager spectrum around the fault frequency was extracted as feature vector. These vectors were then used to train the Elman neural network and improve the robustness of the diagnostic algorithm. Experimental results indicate that the proposed approach effectively detects bearing faults under variable conditions

Online milling tool condition monitoring with a single continuous hidden Markov models approach

The health status evolving from normal to broken condition of a milling tool is needed as an object of assessment in condition-based maintenance. This paper proposes continuous hidden Markov models (CHMM) to assess the status of the tool online based on the normal dataset in the same case. A wavelet-packet decomposition technology is used to feature extraction and the CHMM is trained by Baum-Welch algorithm. Finally, we compute the log-likelihood based on the trained CHMM for abnormal detection and health assessment in real time during the milling process. A case study on tool state estimation demonstrates the effectiveness and potential of this methodology