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We introduce the Besov spaces B_(pθ)~Ω(S~(d-1)) of generalized smoothness on the sphere S~(d-1) and obtain the representation theorem, an embedding theorem, and the characterization using a frame. We also study the Kolmogorov, l...
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We introduce the Besov spaces B_(pθ)~Ω(S~(d-1)) of generalized smoothness on the sphere S~(d-1) and obtain the representation theorem, an embedding theorem, and the characterization using a frame. We also study the Kolmogorov, linear and Gelfand widths of Besov classes BB_(pθ)~Ω(S~(d-1))) of generalized smoothness in L_q(S~(d-1))) for 1 ≤ p,q ≤ ∞, and obtain their asymptotic orders.
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Abstract Internal moisture distribution seriously affects timber shrinkage relations during drying, and it is also an essential parameter for testing the validity of a three-dimensional heat and mass transfer model. Here, we prese...
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Abstract Internal moisture distribution seriously affects timber shrinkage relations during drying, and it is also an essential parameter for testing the validity of a three-dimensional heat and mass transfer model. Here, we present an experimental study of 3D changes in moisture content at different stages during constant temperature and humidity drying of two wood species, covering visible moisture movement evaporation surface, 2D and 3D moisture variations, moisture values in the dry and wet zones, and color changes. We show that the evaporating surface was a wet front, its influence on the wood 3D structure is in the tangential > radial > longitudinal direction, and the color difference ranges from 10.6 to 17.6 when the wet front is present. Moisture values for poplar and pine at the disappearance of the wet front ranged from 14.2–19.7% to 20.4–22.5%, respectively. In mass transfer during wood drying, wood dimensional simplification and homogenization in the drying model should not be used, and the moisture content in each wood thickness layer should be evaluated in a diversified manner.
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Clarifying the deformation behaviors of microstructures could greatly help us understand the precipitation-strengthening mechanism in alloys. However, it is still a formidable challenge to study the slow plastic deformation of all...
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Clarifying the deformation behaviors of microstructures could greatly help us understand the precipitation-strengthening mechanism in alloys. However, it is still a formidable challenge to study the slow plastic deformation of alloys at the atomic scale. In this work, the phase-field crystal method was used to investigate the interactions between precipitates, grain boundary, and dislocation during the deformation processes at different degrees of lattice misfits and strain rates. The results demonstrate that the pinning effect of precipitates becomes increasingly strong with the increase of lattice misfit at relatively slow deformation with a strain rate of 10?4. The cut regimen prevails under the interaction between coherent precipitates and dislocations. In the case of a large lattice misfit of 19.3%, the dislocations tend to move toward the incoherent phase interface and are absorbed. The deformation behavior of the precipitate-matrix phase interface was also investigated. Collaborative deformation is observed in coherent and semi-coherent interfaces, while incoherent precipitate deforms independently of the matrix grains. The faster deformations (strain rate is 10?2) with different lattice misfits all are characterized by the generation of a large number of dislocations and vacancies. The results contribute to important insights into the fundamental issue about how the microstructures of precipitation-strengthening alloys deform collaboratively or independently under different lattice misfits and deformation rates.
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Background:miR-100 has been reported to closely associate with gastric cancer (GC) initiation and progression. However, the underlying mechanism of miR-100-3p in GC is still largely unclear. In this study, we intend to study how m...
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Background:miR-100 has been reported to closely associate with gastric cancer (GC) initiation and progression. However, the underlying mechanism of miR-100-3p in GC is still largely unclear. In this study, we intend to study how miR-100-3p regulates GC malignancy.Methods:The expression levels of miR-100-3p in vitro (GES-1 and GC cell lines) and in vivo (cancerous and normal gastric tissues) were examined by quantitative real-time PCR (qRT-PCR). MTT and PE/Annexin V analyses were responsible for measurement of the effects of miR-100-3p on GC cell proliferation and apoptosis. Transwell assay with or without matrigel was used to examine the capacity of migration and invasion in GC cells. The interaction of miR-100-3p with bone morphogenetic protein receptor 2 (BMPR2) was confirmed through transcriptomics analysis and luciferase reporter assay. qRT-PCR and Western blot analyses were applied to determine the expression of ERK/AKT and Bax/Bcl2/Caspase3, which were responsible for the dysfunction of miR-100-3p.Results:miR-100-3p was down-regulated in GC cell lines and cancerous tissues, and was negatively correlated with BMPR2. Loss of miR-100-3p promoted tumor growth and BMPR2 expression. Consistently, the effects of miR-100-3p inhibition on GC cells were partially neutralized by knockdown of BMPR2. Over-expression of miR-100-3p simultaneously inhibited tumor growth and down-regulated BMPR2 expression. Consistently, over-expression of BMPR2 partially neutralized the effects of miR-100-3p over-expression. Further study demonstrated that BMPR2 mediated the effects downstream of miR-100-3p, which might indirectly regulate ERK/AKT and Bax/Bcl2/Caspase3 signaling pathways.Conclusion:miR-100-3p acted as a tumor-suppressor miRNA that down-regulated BMPR2, which consequently inhibited the ERK/AKT signaling and activated Bax/Bcl2/Caspase3 signaling. This finding provided novel insights into GC and could contribute to identify a new diagnostic and therapeutic target.? The Author(s) 2019.
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The article proposes a new vector analysis based model predictive control (VAMPC) method for multilevel converters with four different switch configurations. It is based on track of an error vector using fundamental state vectors,...
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The article proposes a new vector analysis based model predictive control (VAMPC) method for multilevel converters with four different switch configurations. It is based on track of an error vector using fundamental state vectors, and can achieve deadbeat control of four switching states multilevel converters with multiple control objectives. Compared with conventional finite control set model predictive control (FCS-MPC) method, VAMPC can achieve excellent spectrum characteristics in the case of a slight increase in computational burden. The VAMPC method is experimentally validated on a 100-kHz GaN-based three-level flying capacitor (FC) converter. Compared with the FCS-MPC, VAMPC leads to significant reduction in steady-state control errors. For example, the inductor current total harmonic distortion of the FC converter is now reduced by more than 60%.
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With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. T...
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With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.
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In this article, two continuous control set model predictive control (CCS-MPC) methods with one-step prediction horizon for three-level flying capacitor boost converter (FCBC) that can provide constant switching frequency are pres...
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In this article, two continuous control set model predictive control (CCS-MPC) methods with one-step prediction horizon for three-level flying capacitor boost converter (FCBC) that can provide constant switching frequency are presented. The first one is explicit model predictive control (EMPC), which can provide explicit solution. In order to further reduce the complexity and the computational burden of EMPC while meeting the control requirements, a simple model predictive control (SMPC) is proposed as the second CCS-MPC method. The proposed CCS-MPC will be used as the inner-loop controller to control the inductor current and flying capacitor (FC) voltage. Besides, a suitable outer-loop control strategy for the output voltage is proposed to provide a suitable inductor current reference to improve the dynamic response of the output voltage while avoiding overcurrent. Compared with the previous control method for three-level FCBC, the proposed control method has good dynamic performance and stronger ability to resist input voltage disturbance. The effectiveness of the proposed method is verified by simulation and experimental results.
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The stable structures of Ag–Au and Cu–Au clusters with 1 : 1, 1 : 3 and 3 : 1 compositions with up to 108 atoms are obtained using a modified adaptive immune optimization algorithm with Gupta potential. The dominant motifs of Ag...
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The stable structures of Ag–Au and Cu–Au clusters with 1 : 1, 1 : 3 and 3 : 1 compositions with up to 108 atoms are obtained using a modified adaptive immune optimization algorithm with Gupta potential. The dominant motifs of Ag–Au and Cu–Au clusters are decahedron and icosahedron, respectively. However, in Ag-rich Ag–Au clusters, more icosahedra are found, and in Cu-rich Cu–Au clusters, there exist several decahedral motifs. Four Leary tetrahedral motifs are predicted. Cu core Au shell configurations are predicted in Cu–Au clusters. In Ag–Au clusters, most Ag atoms are on the surface, but partial ones are located in the inner shell, while Au atoms are interconnected in the middle shell.
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Discontinuities due to weather conditions and daylight affect solar drying, so to address that, a working mechanism of air energy-assisted solar drying was developed in this study. An air-energy assisted solar dryer was used to dr...
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Discontinuities due to weather conditions and daylight affect solar drying, so to address that, a working mechanism of air energy-assisted solar drying was developed in this study. An air-energy assisted solar dryer was used to dry timber, from a green average moisture content of 183%-11.3% in 161.5 h, where the moisture diffusion coefficient ranged from 1.28 ×10-10 m2/s to 6.41 ×10-9 m2/s. The effective solar radiation intensity during drying ranged from 221.3 to 750.3 W/m2. The overall drying efficiency was 30.7%, and the effective solar heat supply ratio was 58.5%. The specific moisture extraction rate and specific energy consumption were measured within a range of 0.32-3.0 kg/kWh and 924 - 11390 kJ/kg, respectively. The average power consumption per m3 of wood volume was 254.2 kW h/m3. The results showed that the energy-assisted solar dryer was suitable for a condition with low radiation and various weather conditions.
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There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical...
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There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this article, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop simulation environment. An artificial neural network (ANN) is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the input elements. The basic concept, ANN structure, offline training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement (e.g., 2.11 times fewer slice LUTs and 2.06 times fewer DSPs) while offering a control performance same as the conventional MPC.
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