It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.
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A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.
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This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. The influences of the solid Earth and ocean tides and seasonal atmospheric variations are removed from the C04 series. The residuals are used for training of the ELM. The results of the prediction are compared with those from other prediction methods. The accuracy of the prediction is equal to or even better than that by other approaches. The most striking advantages of employing ELM instead of other algorithms are its noticeably reduced complexity and high computational efficiency.
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To evaluate the quality of Fructus Arctii, an accurate and reliable method of high-performance liquid chromatography/diode array detection—electrospray ionization—mass spectrometry (HPLC/DAD—ESI—MS) was developed. Nine compounds, including chlorogenic acid, caffeic acid, trans-p-hydroxycinnamic acid, arctiin, arctignan A, ethyl caffeate, matairesinol, arctigenin, and lappaol B, were determined simultaneously in 19 batches of Fructus Arctii samples collected from different localities. Nineteen common peaks were identified or tentatively assigned by comparing their mass spectrometric data with reference compounds, self-established compound library, and published literatures. Also, the 19 common peaks were selected as characteristic peaks to assess the similarity of chromatographic fingerprinting of these samples. Moreover, hierarchical clustering analysis (HCA) and principal components analysis (PCA) were successfully applied to demonstrate the variability of samples. The results indicated the content of nine compounds that varied greatly among the samples, and 19 samples collected from different localities could be discriminated. Furthermore, chlorogenic acid, arctiin, and arctigenin were found to be chemical markers for evaluating the quality of Fructus Arctii.
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Continuously scaling down of CMOS technology brings on low power but also reliability problems such as aggravated aging effects and process variations. They can influence and degrade the performance of integrated circuits. In recent years, reliability issues of 65nm CMOS node has been intensively studied. In this work, a reliability assessment approach considering aging mechanisms and parametric process variation induced delay fault is proposed in design loop. Negative bias temperature instability (NBTI) and hot carrier injection (HCI) induced degradation are simulated in 65nm flip-flops with different architectures. An example with simple combinational logic (65nm full adder) illustrates this approach for fault probability. It is concluded that process variations are more important comparing to aging effects induced degradation when designing low power digital flip-flops.
A high performance, ultra-low power, fully differentia 2nd-order continuous-time Σ∆ analogue-to-digital modulator for cardiac pacemakers is presented in this paper. The entire design procedure is described in detail from the high-level system synthesis in both discrete and continuous-time domain, to the low-level circuit implementation of key functional blocks of the modulator. The power consumption of the designed modulator is rated at 182nA from a 1.2V power supply, meeting the ultra-low power requirement of the cardiac pacemaker applications. A 65nm CMOS technology is employed to implement the Σ∆ modulator. The modulator achieves a simulated SNR of 53.8dB over a 400 Hz signal bandwidth, with 32KHz sampling frequency and an oversampling ratio of 40. The active area of the modulator is 0.45×0.50mm².
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Baihe Zhimu Tang (BZT) is a widely used traditional Chinese medicinal formula for the treatment of various diseases; however, its active components have remained unknown. In this study, high-performance liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS) methods were developed for the analysis of metabolic components of BZT. The HPLC experiments used a reversed-phase Shim-Pack VP-ODS C18 column with the column temperature at 35°C and a mobile phase system consisting of aqueous formic acid (0.05%, υ/υ) and acetonitrile using a gradient elution at the flow rate of 0.2 mL/min-1. The ESI-MS was operated with a single-quadrupole mass spectrometer in both negative and positive ion modes. After oral administration of extraction of BZT, the rat’s plasma, urine, and feces were also analyzed; 37 metabolic components including 23 original components and 14 transformative components of BZT were detected. The analysis of metabolic components in BZT by HPLC-ESI-MS methods could be a useful means of identifying the multi-components of BZT and understanding their possible metabolic mechanism of action in the body. The results also narrow the range of active compounds to be found in BZT, and pave the way for further pharmacology and active mechanism research.
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On the basis of the geological ranges of the Buchia and dinoflagellate cyst assemblages and their global correlation, two distinct biostratigraphical boundaries (event horizons) can be distinguished in the continu ous deposits spanning the Jurassic/Cretaceous boundary in the Dong'anzhen Formation of Dong'an, Raohe County and the Dongrong Formation of boreholes SB86-11 and SB79-1 of Suibin, Suibin County of eastern Heilongjiang, northeasteren China (Fig. 1.). Level 1. The base of the Buchia fischeriana - Buchia unschensis assemblage is characterized by the occurrence of Buchia (e.g. B. unschensis) with inversoid ontogenetic growth of the right valve, and the absence of the underlying B. russiensis, one of the index species of the Buchia russiensis - Buchia fischeriana assemblage, with obliquoid ontogenetic growth of the right valve (Fig. 1, column 2b). The top of the dinoflagellate cyst Amphorula delicate assemblage underlies, but it is near this boundary (Fig. 1, column 2a). This level basically corresponds to the currently accepted Tethyan version of the Jurassic/Cretaceous boundary and the working base of the Cretaceous as recommended by the International Commission on Stratigraphy (www.stratigraphy.org) (Fig. 1, column 1), and approximately corresponds to the boundary between the Boreal Middle-Upper Volgian substages (Fig. 1, column 3). This is because the base of the Buchia unschensis Zone or the base of the Buchia fischeriana - Buchia unschensis assemblage nearly coincides with that of the Boreal Craspedites exoticus Subzone or Craspedites okensis ammonite Zone, which in turn correspond closely to the base of Tethyan Lower Berriasian Berriasella jacobi ammonite Zone (Fig. 1, column 1), which is the index ammonite zone of the Tethyan or the international chronostratigraphic base of Berriasian, and thus the base of the Cretaceous (Fig. 1, column 1). Level 2. The base of the Buchia volgensis - Buchia cf. subokensis - Buchia cf. okensis - Buchia unschensis assemblage, is characterized by those easy-to-recognize large-sized Buchia with inversoid ontogenetic growth of the right valve and even of the left valve, and absence of Buchia fischeriana (Fig. 1, column 2b). The base of the Oligosphaeridium pulcherrimum dinoflagellate cyst assemblage probably corresponds approximately to the base of this Buchia assemblage (Fig. 1, column 2a). This level is very near the Boreal version of the Jurassic/Cretaceous boundary, which corresponds approximately to the base of the Tethyan middle part of Middle Berriasian. This is because the bases of both Buchia volgensis and Buchia cf. okensis in the assemblage of Buchia volgensis - Buchia cf. subokensis - Buchia cf. okensis - Buchia unschensis are closely coincident with the base of the upper Lower Berriasian Boreal Hectoroceras kochi Zone (Fig. 1, columns 2b, 3), and the Tethyan upper Berriasella privasensis Subzone of the Tirnovella occitanica Zone of the middle part Middle Berriasian, approximately corresponds to the middle of the Boreal Lower Berriasian Substage (Fig. 1, columns 1, 3).
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