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After standard seawalls have been built successfully, fishery ports become the structures most easily damaged during a typhoon. Estimating the risk of fishery ports against typhoons would be useful for identifying weaknesses and implementing corrective measures to protect fishing boats from a typhoon. This study describes a versatile methodology for conducting this type of quantitative assessment at fishery ports. The Dongsha fishery port in Zhejiang Province was selected as a case study to test the results derived from a high-precision Hydrodynamic Flexible Mesh model coupled with the Spectral Wave model. First, typhoon characteristics were assessed based on historical typhoons in the study area, and then, the wind, tide, storm surge, and waves were modeled and tide-surge interactions were investigated. Through comparisons of the destructive parameters from the typhoon assessment with the design and structural parameters of the fishery port, the level of the Dongsha fishery port against typhoons was determined to be 12, and the main weaknesses of the port’s defenses were found to be located near feature points T2, T3, T8, and T15. The results obtained demonstrate that the proposed methodology can be used to acquire valuable information on the risk of fishery ports against typhoons.

Additional file 2: Table S3. The list of primer sequences involved in qPCR.

Additional file 1: Table S2. The list of antibody and dilution ratio.

Due to the irregular surface of zebrafish larvae and the difficulty of fixing the body, there are high requirements for its injection target point and posture control. By establishing a zebrafish data set containing key points and three-dimensional posture annotations, a network model for zebrafish key point recognition is designed using convolutional neural networks. The posture angle information is further calculated through three-dimensional coordinate transformation. Experiments demonstrate that this method can obtain a more accurate zebrafish posture angle under the condition of posture change. In response to this method, an operating platform that can fix zebrafish juveniles and precisely adjust their posture is designed to lay the foundation for subsequent experimental operations.

This is the supporting information for a manuscript submitted to Journal of Geophysical Research: Solid Earth. It contains one text describing the methods used in this research, one supplementary figure, and four supplementary tables.&nbsp

Additional file 1: Supplementary Table 1. List of ICD codes for cause-specific mortality. Supplementary Table 2. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease. Supplementary Table 3. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease, sensitivity analysis excluding first 2 years of follow-up. Supplementary Table 4. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease, stratified by age. Supplementary Table 5. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease, stratified by sex. Supplementary Table 6. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease, stratified by race/ethnicity. Supplementary Table 7. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease, stratified by BMI. Supplementary Table 8. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease, stratified by physical activity. . Supplementary Table 9. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease, stratified by smoking status. Supplementary Table 10. Hazard ratios and 95% confidence intervals (CIs) of all-cause mortality and cause-specific mortality among participants with chronic kidney disease vs. no chronic kidney disease and corresponding E-values.

Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the mathematical foundation for deep learning. We investigated the convergence of deep ReLU networks and deep convolutional neural networks in two recent researches (arXiv:2107.12530, 2109.13542). Only the Rectified Linear Unit (ReLU) activation was studied therein, and the important pooling strategy was not considered. In this current work, we study the convergence of deep neural networks as the depth tends to infinity for two other important activation functions: the leaky ReLU and the sigmoid function. Pooling will also be studied. As a result, we prove that the sufficient condition established in arXiv:2107.12530, 2109.13542 is still sufficient for the leaky ReLU networks. For contractive activation functions such as the sigmoid function, we establish a weaker sufficient condition for uniform convergence of deep neural networks., Comment: incomplete

Various powerful deep neural network architectures have made great contribution to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networks (ResNets) are of particular importance because they demonstrated great usefulness in computer vision by winning the first place in many deep learning competitions. Also, ResNets were the first class of neural networks in the development history of deep learning that are really deep. It is of mathematical interest and practical meaning to understand the convergence of deep ResNets. We aim at characterizing the convergence of deep ResNets as the depth tends to infinity in terms of the parameters of the networks. Toward this purpose, we first give a matrix-vector description of general deep neural networks with shortcut connections and formulate an explicit expression for the networks by using the notions of activation domains and activation matrices. The convergence is then reduced to the convergence of two series involving infinite products of non-square matrices. By studying the two series, we establish a sufficient condition for pointwise convergence of ResNets. Our result is able to give justification for the design of ResNets. We also conduct experiments on benchmark machine learning data to verify our results.

Red beds are well-known for recording stable natural remanent magnetization (NRM). However, discriminating primary NRM from secondary remanence in red beds is difficult. The Paleogene Nangqian red beds in eastern Tibetan Plateau variably record an overprint related to nearby magmatism and thus provide a great opportunity to characterize remagnetization in red beds. Through comprehensive rock magnetic, Mössbauer spectroscopic, and petrographic analyses, we find that remagnetization was controlled by temperature. Remagnetized red beds contain abundant authigenic hematite and goethite, with some larger grains unblocking at the Néel temperatures and the remainder (nanoparticles) unblocking at lower temperatures. In contrast, red beds retaining primary NRM are characterized by dominance of detrital hematite and magnetite, presence of fine-grained authigenic hematite, and absence of authigenic goethite and magnetite. High temperature behaviors of NRM/susceptibility are indicative of remagnetization, but the presence of goethite appears to be a more sensitive criterion for diagnosing remagnetization in red beds.

After standard seawalls have been built successfully, fishery ports become the structures most easily damaged during a typhoon. Assessments of the resilience of fishery ports to typhoon damage would be useful for identifying weaknesses and implementing corrective measures to protect fishing boats from a typhoon. This study describes a versatile methodology for conducting this type of quantitative assessment at fishery ports. The Dongsha fishery port in Zhejiang Province was selected as a case study to test the results derived from a high-precision Hydrodynamic Flexible Mesh model coupled with the Spectral Wave model. First, typhoon characteristics were assessed based on historical typhoons in the study area, and then, the wind, tide, storm surge, and waves were modeled and tide-surge interactions were investigated. Through comparisons of the destructive parameters from the typhoon assessment with the design and structural parameters of the fishery port, the resistance level of the Dongsha fishery port against typhoons was determined to be 12, and the main weaknesses of the port's defenses were found to be located near feature points T2, T3, T8, and T15. The results obtained demonstrate that the proposed methodology can be used to acquire valuable information on the resilience of fishery ports to typhoons.

Background Gastric cancer (GC) is the third most common cause of cancer deaths worldwide. In the present study, we aimed to identify novel GC biomarkers by integrating isobaric tags of relative and absolute quantitation (iTRAQ) for aberrantly expressed proteins in GC patients. Methods Using stable isotope tags, we labeled an initial discovery group comprising four paired gastric cancer and adjacent gastric tissue samples, and subjected them to LC‐ESI‐MS/MS. We used a validation set comprising 129 paired gastric cancer and adjacent gastric tissues from patients and benign healthy controls to validate the candidate targets. Results We identified two proteins, NAD(P)‐dependent steroid dehydrogenase‐like (NSDHL) and neutral cholesterol ester hydrolase 1 (NCEH1), that were significantly overexpressed in GC tissues. The sensitivity and specificity of NSDHL were 80.6% and 74.4%, respectively, in GC compared with a sensitivity of 25.6% in adjacent tissues and 24% in benign healthy controls. The area under the ROC curve (AUC) for NSDHL was 0.810 for GC detection. Overexpression of NSDHL in GC was significantly correlated with local tumor invasion. The sensitivity and specificity of NCEH1 were 77.5% and 73.6%, respectively, in GC compared with a sensitivity of 26.4% in adjacent tissues and 20% in benign controls. The AUC for NSDHL was 0.792. Overexpression of NCEH1 was significantly associated with tumor histological classification and local invasion. Moreover, a combined analysis of NSDHL and NCEH1 achieved a sensitivity and specificity of 85.7% and 83%, respectively, and the AUC was 0.872. The combined analysis of NSDHL and NCEH1 was significantly correlated with histological grade and TNM Ⅱ‐Ⅳ staging. Conclusions iTRAQ‐labeled quantitative proteomics represents a powerful method to identify novel cancer biomarkers. The present study identified NSDHL and NCEH1 as useful biomarkers for screening, diagnosis, and prognosis of patients with gastric cancer., In the present study, The AUCs for NSDHL and NCEH1 were 0.810 and 0.792. A combined analysis of NSDHL and NCEH1 achieved a sensitivity and specificity of 85.7% and 83%, and the AUC was 0.872. The combined analysis of NSDHL and NCEH1 significantly improved the AUC for detecting gastric cancer.

Multidrug resistance (MDR) has become a major obstacle to malignancies treatment by chemotherapeutic drugs, therefore, it is important to develop MDR reversal agents with high activity. We have previously found that the hederagenin (HD) derivative HBQ showed good tumor MDR reversal activity in vitro and in vivo but had poor solubility. In this study, to enhance the aqueous solubility and tumor MDR reversal activity of HBQ, three series of HD derivatives were designed and synthesized. Nitrogen-containing heterocyclic-substituted, PEGylated, and ring-A substituted derivatives significantly reversed the MDR phenotype of KBV (multidrug-resistant oral epidermoid carcinoma) cells toward paclitaxel at a concentration of 10 μM in MTT assays. The PEGylated derivatives 10c–10e had increased aqueous solubility compared with HBQ by 18–657 fold, while maintaining tumor MDR reversal activity. The most in vitro active compound 10c possessed good chemical stability to an esterase over 24 h and enhanced the sensitivity of KBV cells to paclitaxel and vincristine with IC50 values of 4.58 and 0.79 nM, respectively. Mechanism studies indicated that compound 10c increased the accumulation of P-glycoprotein (P-gp) substrates rhodamine 123 and Flutax1 in KBV cells and MCF-7T (paclitaxel-resistant breast carcinoma) cells, that is to say, compound 10c exerted the reversal effect of tumor MDR by inhibiting the efflux function of P-gp. Finally, the structure–activity relationships were further investigated by analyzing the relationship between structure and tumor MDR reversal activity of HD derivatives. This study highlights the potential of PEGylated HD derivatives such as compound 10c for the development of tumor MDR reversal agents and provides information for the further improvement of the aqueous solubility and tumor MDR reversal activity of HD derivatives in the future.

This study aimed to investigate the efficacy and safety of febuxostat in patients with radiolucent nephrolithiasis.From March 2016 to June 2018, data of 96 patients with radiolucent nephrolithiasis and hyperuricemia who referred to the Third Affiliated Hospital of Sun Yat-sen University were retrospectively analyzed. These patients were divided into allopurinol 300mg/d (control), febuxostat 40mg/d (F40) and 80mg/d (F80) groups respectively. All patients took potassium citrate as a combination treatment and had been followed up for at least 6 months. Before treatment and on after 1st, 3rd and 6th month, complete blood count, serum uric acid (sUA), hepatic and renal function as well as ultrasound were carried out. Arthritic and gastrointestinal symptoms were also monitored. Computed tomography was performed before treatment and 6 months after medication.Compared with allopurinol group, F40 group showed no difference in urate-lowering effect, while F80 had the best effect across all the visits (P0.01). At 6th month, 25(83.3%) cases of F80 group achieved sUA6mg/ dL, which was better than allopurinol group (18 cases, 58.1%) and F40 group (17 cases, 58.6%). In the dissolution effect of radiolucent calculi, F80 had the best effect, followed by F40 and then allopurinol (P0.05). No statistical difference was observed in adverse events among three groups.Febuxostat significantly decreased sUA, promoted radiolucent stone dissolution and reduced the total stone number, whereas it did not increase the adverse events.

Purpose MicroRNA-939 (miR-939) has crucial roles in several types of human cancer. However, the expression profile and precise functions of miR-939 in prostate cancer (PCa) are still unclear. This study aimed to determine miR-939 expression in PCa and explore its roles in PCa tumorigenesis. Methods miR-939 expression was determined in PCa tissues and cell lines using reverse transcription–quantitative polymerase chain reaction. Cell Counting Kit-8, colony formation, and flow cytometric assays were used to determine the role of miR-939 in PCa cell proliferation and apoptosis in vitro, whereas a tumor xenograft model was generated to evaluate the effect of miR-939 on tumor growth in vivo. Transwell assays were performed to investigate whether miR-939 affects the migration and invasiveness of PCa cells. Results miR-939 was found to be downregulated in PCa tissues and cell lines, and this downregulation was significantly correlated with tumor stage and lymphatic metastasis. Patients with PCa exhibiting low miR-939 expression had shorter overall survival than those exhibiting high miR-939 expression. Exogenous miR-939 expression suppressed PCa cell proliferation, colony formation, migration, and invasion in vitro; enhanced apoptosis in vitro; and decreased tumor growth in vivo. Investigation of the underlying molecular mechanisms revealed hepatoma-derived growth factor (HDGF) as a direct target gene of miR-939 in PCa. HDGF was found to be significantly upregulated in PCa tissues, and its expression was inversely correlated with miR-939 expression. HDGF silencing and miR-939 upregulation showed similar effects in PCa. Restored HDGF expression counteracted the tumor-suppressive activity of miR-939 overexpression in PCa cells. Furthermore, ectopic miR-939 expression inhibited the WNT/β-catenin pathway activation in PCa both in vitro and in vivo by downregulating HDGF. Conclusion miR-939 functions as a tumor suppressor during PCa tumorigenesis by directly targeting HDGF and deactivating the WNT/β-catenin pathway, suggesting the miR-939/HDGF/WNT/β-catenin pathway as an effective target for PCa therapy.

This thesis focuses on the development of high-performance phototransistors based on several solution-processed semiconductors. After giving an outline of the relevant theory and experimental methods, the thesis is divided into three experimental chapters. The first chapter presents the development of an ambipolar organic phototransistor based on [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM). The balanced electron and hole transport characteristics are achieved by fine-tuning of the injection electrode work function using a self-assembled monolayer. The ambipolar PC61BM phototransistor exhibits promising photoresponse in the UVA region, with maximum photosensitivity and responsivity of 9×103 and 3×103 A/W respectively. In addition, the phototransistor shows fast temporal response with switching times of

The Jacobian matrix is the core part of power flow analysis, which is the basis for power system planning and operations. This paper estimates the Jacobian matrix in high dimensional space. Firstly, theoretical analysis and model-based calculation of the Jacobian matrix are introduced to obtain the benchmark value. Then, the estimation algorithms based on least-squared errors and the deviation estimation based on the neural network are studied in detail, including the theories, equations, derivations, codes, advantages and disadvantages, and application scenes. The proposed algorithms are data-driven and sensitive to up-to-date topology parameters and state variables. The efforts are validate by comparing the results to benchmark values., Comment: submitted to IEEE

A Rayleigh scattering lidar for measuring the atmospheric density and temperature has been deployed at Zhongshan Station (69.4° S, 76.4° E), Antarctica. Lidar transmitter was a frequency doubled Nd:YAG laser with ~400 mJ pulse energy and 30 Hz repetition rate. A telescope with 0.8 m diameter pointing to the zenith direction served as the lidar receiver. This lidar was capable of profiling the density and temperature in the Upper Stratosphere and Lower Mesosphere (USLM) region. At the vertical resolution of 300 m and the temporal resolution of 30 min, the lidar measurement uncertainties, mainly due to the photon noise, were calculated to be within 1.5% and 1 K for density and temperature, respectively. Since March 2020, this lidar has been routinely operated at Zhongshan station for exploring the atmospheric density and temperature variations and wave propagation characteristics in the polar USLM region.

A novel control strategy based on Hammerstein model and neural network for the speedregulating system of the induction motor and inverter is proposed in this paper. First, Hammerstein model was used to model the speed-regulation system of the induction motor and inverter. Auto-regressive and moving average (ARMA) model was used to identify the dynamic linear module of Hammerstein model of the speed-regulating system. Second, the ARMA model was used as a reference model for identification of the inverse model of static nonlinear neural network (NN) module of Hammerstein model in the framework of the model reference adaptive control method. For the load disturbance issue, two control strategies, online learning neural network direct inverse control and the traditional PI close-loop control strategy were studied. Simulations show that the inverse control based on Hammerstein model and NN is effective and the online learning neural network direct inverse control strategy for the speed-regulating system with load disturbance has higher performance.

Wireless sensor network (WSN) is a typical kind of low-power and lossy network, in where ARQ (Automatic Repeat reQuest) schemes are often used to improve packets reliability. However, the ARQ related packets may incur significant load and consume more energy. This paper proposes a novel energy efficient ARQ protocol called ARQ+, which uses the nearest-first scheme and NAK aggregation scheme to reduce the amount and transmission hops of the ARQ related packets. Consequently, the energy consumption is significantly decreased. Theoretical analyses of ARQ+ on energy consumption, packet arrive ratio and latency are provided. Performance improvement of ARQ+ is validated by extensive simulations. They both show that ARQ+ has satisfactory energy efficiency, good packets arriving ratio and reasonable average packet delay comparing to traditional ARQ schemes.

This paper studies the problem of repairing secret sharing schemes, i.e., schemes that encode a message into $n$ shares, assigned to $n$ nodes, so that any $n-r$ nodes can decode the message but any colluding $z$ nodes cannot infer any information about the message. In the event of node failures so that shares held by the failed nodes are lost, the system needs to be repaired by reconstructing and reassigning the lost shares to the failed (or replacement) nodes. This can be achieved trivially by a trustworthy third-party that receives the shares of the available nodes, recompute and reassign the lost shares. The interesting question, studied in the paper, is how to repair without a trustworthy third-party. The main issue that arises is repair security: how to maintain the requirement that any colluding $z$ nodes, including the failed nodes, cannot learn any information about the message, during and after the repair process? We solve this secure repair problem from the perspective of secure multi-party computation. Specifically, we design generic repair schemes that can securely repair any (scalar or vector) linear secret sharing schemes. We prove a lower bound on the repair bandwidth of secure repair schemes and show that the proposed secure repair schemes achieve the optimal repair bandwidth up to a small constant factor when $n$ dominates $z$, or when the secret sharing scheme being repaired has optimal rate. We adopt a formal information-theoretic approach in our analysis and bounds. A main idea in our schemes is to allow a more flexible repair model than the straightforward one-round repair model implicitly assumed by existing secure regenerating codes. Particularly, the proposed secure repair schemes are simple and efficient two-round protocols.

In this paper, we study thefollowing generalized quasilinear Schrodinger equationsbeginalign*-textdivg²(u)∇ u)+g(u)g(u)∇ u²+V(x)u=h(u), x∈ R^N, endalign*where $N\ge~3,~$ $~g:\R\rightarrow\R^+$ is an even differentialfunction satisfying $~\lim_{t~\rightarrow~+\infty}\frac~{g(t)}{t^{\alpha~-1}}=~\beta~>0$ for some $\alpha~\ge~1$;$h:\R\rightarrow[0,+\infty)$ is a nonlinear function covering the case: $h(t)=|t|^{p-2}t$ $~(2

Freely falling of a plate contains very complicated unsteady characteristics. In the past decades, most of the studies focused on the mode and trajectoryofa freely falling plate. In this paper, we use PIV to study the instantaneous flow structure and analyze the force exerted on the plate and the influence of the flow on the motion of the plate. As the Reynolds number increases, the flow structure will be changed from the stable mode to the chaotic mode,and it accompanies with a mode switching of the freely falling plate. However, there is no necessarilycorresponding with the stable flow structure, even for the freely falling plate with a stable mode.

A framework is presented for unsupervised learning of representations based on infomax principle for large-scale neural populations. We use an asymptotic approximation to the Shannon's mutual information for a large neural population to demonstrate that a good initial approximation to the global information-theoretic optimum can be obtained by a hierarchical infomax method. Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases. As confirmed by numerical experiments, our method is robust and highly efficient for extracting salient features from input datasets. Compared with the main existing methods, our algorithm has a distinct advantage in both the training speed and the robustness of unsupervised representation learning. Furthermore, the proposed method is easily extended to the supervised or unsupervised model for training deep structure networks., Comment: 25 pages, 7 figures, 5th International Conference on Learning Representations (ICLR 2017)

We present the first coordinated study using two lidars at two separate locations to characterize a 1 h mesoscale gravity wave event in the mesopause region. The simultaneous observations were made with the Student Training and Atmospheric Research (STAR) Na Doppler lidar at Boulder, CO, and the Utah State University Na Doppler lidar and temperature mapper at Logan, UT, on 27 November 2013. The high precision possessed by the STAR lidar enabled these waves to be detected in vertical wind. The mean wave amplitudes are ~0.44 m/s in vertical wind and ~1% in relative temperature at altitudes of 82–107 km. Those in the zonal and meridional winds are 6.1 and 5.2 m/s averaged from 84 to 99 km. The horizontal and vertical wavelengths inferred from the mapper and lidars are ~219 ± 4 and 16.0 ± 0.3 km, respectively. The intrinsic period is ~1.3 h for the airglow layer, Doppler shifted by a mean wind of ~17 m/s. The wave packet propagates from Logan to Boulder with an azimuth angle of ~135° clockwise from north and an elevation angle of ~ 3° from the horizon. The observed phase difference between the two locations can be explained by the traveling time of the 1 h wave from Logan to Boulder, which is about ~2.4 h. The wave polarization relations are examined through the simultaneous quantifications of the three wind components and temperature. This study has developed a systematic methodology for fully characterizing mesoscale gravity waves, inspecting their intrinsic properties and validating the derivation of horizontal wave structures by applying multiple instruments from coordinated stations.

As the problem of global warming is being more and more serious, the low-carbon power technologies have aroused wide concerned. Because the Micro-grid technology is one of the main technologies to realize mass application of distributed generation, it's very important to put the low-carbon factor into the generation planning of micro-grid. This paper use the analysis method of levelized cost of electricity(LCOE) to compare the variation trends of power supplies which use different energy sources to generate in the future, and to build the Energy Price Equilibrium point analysis model (EPE) of high-carbon energy and low-carbon energy. Based on the research above, while consider the energy prices trend, the randomness and waviness of wind and solar energy, build the full-life cycle and dynamic model of optimal sizing in an Integrated Power Generation System. In three situations which include law-carbon scene, equilibrium and high-carbon scene, use the modified particle swarm Method to solve the optimization. According to simulation, the optimal sizes of power generation are different when planning period start from different initial year. The results show that the balance scene is most suitable for the clean energy consumption, which lead an equilibrium point of energy consumption and carbon consumption. The reasonable policy guidance can promote transformation from high carbon scenario to a low-carbon scenario, which makes the power grid planning programs to meet the needs of sustainable development more effective.

A homogeneous thin plate often flutters while falling through a fluid under gravity. The center of gravity of the plate moves back-and-forth horizontally and the plate tilting angle oscillates symmetrically from the horizontal. Here we show that such a scenario is qualitatively changed for a plate with noncoinciding centers of gravity and buoyancy due to an inhomogeneous mass distribution. Mismatch of the centers causes an external torque that breaks the symmetry of rotational motion, shifts the mean tilting position from the horizontal, and leads to a net horizontal plate displacement. In laboratory experiments with a Reynolds number around 1500, we found that the net horizontal displacement scales linearly with the separation between the centers up to a critical value, beyond which the plate falls vertically in an edge-on configuration with the heavier side downward. Experimental results are compared to predictions of a quasi-steady numerical model. Our work demonstrates that motion of freely moving objects in a fluid depends sensitively on external torques, which potentially can be used as an effective control method.

The engine vibration signals characters are extracted using wavelet packet technology. A model of wavelet neural networks is constructed based on wavelet frame theory and neural networks technology. Then multiresolution analysis is used to choose and optimize the wavelet neuron. The model is validated through the testing that simulates the faults of engine valve clearance. The experimental results show that the proposed automobile engine fault diagnostic model based on wavelet neural networks can diagnose the engine fault effectively.

The advances in the growth of pseudomorphic silicon-germanium epitaxial layers combined with the strong need for high-speed devices have led to increased interest in silicon-based heterojunction field-effect transistors. Here we present a kind of strained SiGe-channel P-MOSFET which can offer better performance compared to the Si device. When applying to the sample with W/L value of 14/7, a 30% improvement can be achieved in transconductance.