五代科举概述

五代科举机械地模仿唐代, 年年开考,每次录取的人都很少;几十年的考试过程中,既无特别轰动的丑闻与血淋淋的科场案,亦无所谓“盛事”;连所点状元一般在待遇上也没有什么特别之处;其制度上只有两点小的改革尝试值得注意。

Approach for epileptic EEG detection based on gradient boosting

The automatic seizure detection is significant for epilepsy diagnosis and it can alleviate the work intensity of inspecting prolonged electroencephalogram （EEG）. This paper presents and investigates a novel machine learning approach utilizing gradient boosting to detect seizures from long-term EEG. We apply relative fluctuation index to extract features of long-term intracranial EEG data. A classifier trained with the gradient boosting algorithm is adopted to discriminate the seizure and non-seizure EEG signals. Smoothing and collar technique are finally used as post-processing in order to improve the detection accuracy further. The seizure detection method is assessed on Freiburg EEG datasets from 21 patients. The experimental results indicate that the proposed method yields an average sensitivity of 94. 60% with a false detection rate of 0. 18/h.

Dynamic Changes of Nitrate Reductase Activity within 24 Hours

[Objective] The research aimed to study the circadian rhythm of nitrate re- ductase activity （NRA） in plant. [Method] The wheat plants at heading stage were used as the materials for the measurement of dynamic changes of nitrate reductase activity （NRA） within 24 h under the conditions of constant high temperature. [Resulti The fluctuation of NRA in wheat changed greatly from 20：00 pm to 11：00 am. The enzyme activity remained constant, but at 14：00 the enzyme activity was the high- est, higher than all the other time points except the enzyme activity measured at11：00. The enzyme activity was the lowest of 17：00, which was lower than all the other time points except the enzyme activity measured at 2：00. [Conclusion] There were autonomous rhythm changes of NRA in wheat in a certain degree.

Investigation on the automatic parameters extraction of pulse signals based on wavelet transform

This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis,quantitative methods are needed. To extract the parameters of pulse signals,the prerequisite is to detect the corners of pulse signals correctly. Up to now,the pulse parameters are mostly acquired by marking the pulse corners manually,which is an obstacle to modernize pulse diagnosis. Therefore,a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately,which can be expected to facilitate the modernization of pulse diagnosis.

Identification of diesel front sound source based on continuous wavelet transform

Acoustic signals from diesel engines contain useful information but also include considerable noise components To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for the characterization of engine acoustics. This paper first reviews CWT characteristics represented by short duration transient signals. Wavelet selection and CWT are then implemented and wavelet transform is used to analyze the major sources of the engine front's exterior radiation sound. The research provides a reliable basis for engineering practice to reduce vehicle sound level. Comparison of the identification results of the measured acoustic signals with the identification results of the measured surface vibration showed good agreement.

TORSIONAL OSCILLATION CHARACTERISTICS OF ROTARY SHAFTS BASED ON TORSION AND BENDING COUPLED VIBRATION

The torsional oscillation characteristics on the bending and torsioh coupled vibration of rotary shaft system were investigated using the elasto-dynamic theory and other mathematic methods, such as difference approach, Fourier transform, and wavelet transform. It is concluded that mass eccentricity and other exciting modalities affect the bending and torsion coupled vibration of rotary shafts. Torsional vibration caused by bending vibration features linearity along with the change of amplitude of bending vibration. Meanwhile, energy spectrum concentrates on high frequency area with the wavelet analysis.

Noether Symmetry and Noether Conserved Quantity of Nielsen Equation for Dynamical Systems of Relative Motion

Noether symmetry of Nielsen equation and Noether conserved quantity deduced directly from Noether symmetry for dynamical systems of the relative motion are studied. The definition and criteria of Noether symmetry of a Nielsen equation under the infinitesimal transformations of groups are given. Expression of Noether conserved quantity deduced directly from Noether symmetry of Nielsen equation for the system are obtained. Finally, an example is given to illustrate the application of the results.

Shrinking and Drying up of Baiyangdian Lake Wetland:A Natural or Human Cause?

The shrinking and drying up of wetlands in arid and semiarid areas of China have been widely observed in the recent years, but there has been no consensus on whether the aggravation is caused by human activities or by global climate warming. For a better understanding of the cause, this study investigates the dynamic changes of Baiyangdian Lake wetland over the last 40 years. It is shown that since the 1980s, Baiyangdian Lake has suffered from an insufficient water input and shrunk considerably. By using SPSS 11.0, this study urines a detailed Analysis on the signifficance of the effects of the possible driving factors for the degradation. It is identified that the North China Plain has been warrnin~ up significantly in recent years, which causes a significant reduction in the precipitation and inflow to the lake. Although human disturbances such as the irrigation and storage of water in reservoirs do not play a decisive role, they accelerate the degradation and their effects should be minimized.

Analysis of dynamic of two-phase flow in small channel based on phase space reconstruction combined with data reduction sub-frequency band wavelet

A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.

Relationship between the Periodic Fluctuations of Pressure and Precipitation during a Rainstorm

The relationship between periodic fluctuations of pressure and precipitation was investigated using the continuous wavelet transform and wavelet coherence transform methods for a rainstorm case that occurred during 19–25 June 2005 in Guangdong Province, China. The results show that a period of 2–4 h existed in both pressure and precipitation fluctuations. Furthermore, this high frequency pressure fluctuation was closely related to the fluctuation of precipitation with a similar period, and the fluctuation of pressure was about two hours ahead of the precipitation fluctuation variation. Since the high frequency pressure fluctuation implies the existence of gravity wave activity, the results suggest that gravity wave activities with a 2–4 h period in the atmosphere exerted an important influence on precipitation during this rainstorm.

Benefit of everolimus as a monotherapy for a refractory breast cancer patient bearing multiple genetic mutations in the PI3K/AKT/mTOR signaling pathway

A postmenopausal patient with a diagnosis of estrogen receptor(ER)(+), progesterone receptor(PR)(+), and human epidermal growth factor receptor-2(HER2)(-) breast cancer was reported. The patient refused surgery and was resistant to conventional chemotherapy regimens. Computed tomography and the circulating tumor cell test indicated that the patient's tumor burden increased rapidly even after several chemotherapy sessions. Multiple genetic aberrances in the phosphatidylinositol3-kinases(PI3 K) signaling pathway were detected via next-generation sequencing(NGS)-based liquid biopsy, including a p. G1007 R missense mutation in exon 21 of PIK3 CA(33.61%), a p.L70 fs frameshift mutation in exon 3 of phosphatase and tension homolog deleted on chromosome ten(PTEN)(49.14%), and a p. D1542 Y missense mutation in exon 32 of mammalian target of rapamycin(m TOR)(1.66%). Therefore, only the m TOR inhibitor everolimus was administered to the patient. Partial remission(PR) was observed after 2 months, and sustained stable disease(SD) was observed after a year and a half. Subsequent sequencing showed that the mutation ratio of PIK3 CA decreased to 4.17%, and that the PTEN and m TOR mutations disappeared, which revealed the significant curative effect of everolimus. We report the first case of successful monotherapy treatment using everolimus in a patient with advanced breast cancer bearing mutations in genes involved in the PI3 K/ARK/m TOR signaling pathway. The success of this case highlights the invaluable clinical contribution of NGS-based liquid biopsy, as it successfully provided an optimal therapeutic target for the patient with advanced breast cancer.

Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic(sEMG) signals. In contrast to the existing methods,considering the non-stationary and nonlinear characteristics of EMG signals,to get the more separable feature set,we introduce the empirical mode decomposition(EMD) to decompose the original EMG signals into several intrinsic mode functions(IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines(LS-SVMs) ,the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore,compared with other classifiers using different features,the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.

A novel stabilization approach for small signal disturbance of power system with time-varying delay

Small signal instability may cause severe accidents for power system if it can not be dear correctly and timely. How to maintain power system stable under small signal disturbance is a big challenge for power system operators and dispatchers. Time delay existing in signal transmission process makes the problem more complex. Conventional eigenvalue analysis method neglects time delay influence and can not precisely describe power system dynamic behaviors. In this work, a modified small signal stability model considering time varying delay influence was constructed and a new time delay controller was proposed to stabilize power system under disturbance. By Lyapunov-Krasovskii function, the control law in the form of nonlinear matrix inequality （NLMI） was derived. Considering synthesis method limitation for time delay controller at present, both parameter adjustment method by using linear matrix inequality （LMI） solver and iteration searching method by solving nonlinear minimization problem were suggested to design the controller. Simulation tests were carried out on synchronous-machine infinite-bus power system. Satisfactory test results verify the correctness of the proposed model and the feasibility of the stabilization approach.

Fourier and wavelet transformations application to fault detection of induction motor with stator current

Fault detection of an induction motor was carried out using the information of the stator current. After synchronizing the actual data, Fourier and wavelet transformations were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The most reliable phase current among the three phase currents was selected using an approach that employs the fuzzy entropy measure. Data were trained with a neural network system, and the fault detection algorithm was verified using the unknown data. Results of the proposed approach based on Fourier and wavelet transformations indicate that the faults can be properly classified into six categories. The training error is 5.3×10-7, and the average test error is 0.103.

Wavelet Analysis of Earthquake Activity in the West of the Chinese Mainland and Its Adjacent Area1

Wavelets are a useful tool for analyzing the time-frequency of a non-stable series and are widely applied in many fields. The process of earthquake preparation and occurrence is a non-linear process. In the paper, the wavelet method is used to analyze the series of earthquake data for the time period from 1900 to 2003 in the west of the Chinese mainland and its adjacent area (WCMAA), and to obtain the characteristic information for different time scales. In the past 103 years, there were four primary periods of regional earthquake activity in the area with durations of 42, 22, 7 and 14 years, respectively and the intensity of earthquake activity changing with time. It doesn’t make sense to talk about active or quiet periods of earthquake activity unless it is based on a specific time scale. In addition, the tendency analysis of earthquake activity using the primary period of seismic activity and wavelet coefficients of varied time scales indicates that the earthquake activity in this region will be high in the forthcoming years.

Short Term Wind Power Prediction Using Wavelet Transform and ARIMA

A sustainable production of electricity is essential for low carbon green growth in South Korea. Although wind energy is unlimited in potential, both intermittency and volatility should be tackled for smart grid integration in future. To cope with this, many works have been done for wind speed and power forecasting. It is shown that statistical techniques are useful for short-term forecasting of wind power. This paper presents a statistical wind speed forecasting. The wavelet decomposition is employed as a de-noising technique. An illustration will be given by real-world dataset. According to the result, the MAD （mean absolute deviation） is improved as much as 10%.

Analysis of Electroencephalogram Based on Wavelet Spectrum and Wavelet Entropy

Based on discrete wavelet transform, both relative wavelet energy （RWE） and segment wavelet entropy （SWE） of electroencephalogram （EEG） are defined in this paper. The RWE provides quantitatively the information about the relative energy associated with different frequency bands present in the EEG. The SWE carries information about the degree of order or disorder associated with different time segment of EEG evolution, which can determine the time-segment loealizations of abnormal dynamic processes of brain activity due to the localization characteristics of the wavelet transform. The experimental results show that the RWE and SWE are different between epileptic EEGs and normal EEGs, which demonstrate that the RWE and the SWE are helpful to analyze the dynamic behavior of different EEGs.

Dynamic change of xylanase activity and gene expression during wheat germination on As（III） stress

Through water cultivating method, the dynamic changes of xylanase activity in seed, root and plumule of wheat with different As （III） concentration treatment were studied. The results indicated that the order of average xylanase activity was seed〉plumule〉root. With the increasing concentration of As （III）, the xylanase activity elevated first then dropped in seed, but it descended first then ascended in root and plumule. As the sampling time prolonged, the xylanase activity of seeds climbed first then dropped on the four as （III） concentration, the same trend also appeared in pulume, as the as （Ill） concentration went up, the xylanase activity moved up simultaneity. Semi-quantity Reverse Transcription Polymerase Chain Reaction was used in the study, the results indicated that, the xylanase gene began to express at 132 h on 0 mg/L As （III） concentration and at 120h on other concentration in the leaves of wheat.

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.