This study aims to utilize a deep convolutional neural network (DCNN) for synthesized CT image generation based on cone-beam CT (CBCT) and to apply the images to dose calculations for nasopharyngeal carcinoma (NPC). An encoder-decoder 2D U-Net neural network was produced. A total of 70 CBCT/CT paired images of NPC cancer patients were used for training (50), validation (10) and testing (10) datasets. The testing datasets were treated with the same prescription dose (70 Gy to PTVnx70, 68 Gy to PTVnd68, 62 Gy to the PTV62 and 54 Gy to the PTV54). The mean error (ME) and mean absolute error (MAE) for the true CT images were calculated for image quality evaluation of the synthesized CT. The dose-volume histogram (DVH) dose metric difference and 3D gamma pass rate for the true CT images were calculated for dose analysis, and the results were compared with those for the CBCT images (original CBCT images without any correction) and a patient-specific calibration (PSC) method. Compared with CBCT, the range of the MAE for synthesized CT images improved from (60, 120) to (6, 27) Hounsfield units (HU), and the ME improved from (−74, 51) to (−26, 4) HU. Compared with the true CT method, the average DVH dose metric differences for the CBCT, PSC and synthesized CT methods were 0.8%  ±  1.9%, 0.4%  ±  0.7% and 0.2%  ±  0.6%, respectively. The 1%/1 mm gamma pass rates within the body for the CBCT, PSC and synthesized CT methods were 90.8%  ±  6.2%, 94.1%  ±  4.4% and 95.5%  ±  1.6%, respectively, and the rates within the PTVnx70 were 80.3%  ±  16.6%, 87.9%  ±  19.7%, 98.6%  ±  2.9%, respectively. The DCNN model can generate high-quality synthesized CT images from CBCT images and be used for accurate dose calculations for NPC patients. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.

Summary The computational time for conventional flash calculations increases significantly with the number of components, making it impractical for use in many fine-grid compositional simulations and other applications. Previous research to increase flash-calculation speed has been limited to those with zero binary interaction parameters (BIPs) or approximate methods based on an eigenvalue analysis of the binary interaction matrix. Practical flash calculations, however, nearly always have some nonzero BIPs. Further, the accuracy and speed of the eigenvalue methods varies depending on the choice and number of the dominant eigenvalues. This paper presents a new and simple method for significantly increasing the speed of flash calculations for any number of nonzero BIPs. The approach requires the solution of up to six reduced parameters regardless of fluid complexity or the number of components and is based on decomposing the BIPs into two parameters using a simple quadratic expression. The new approach is exact in that the equilibrium-phase compositions for the same BIPs are identical to those with the conventional flash calculation; no eigenvalue analysis is required. Further, the new approach eliminates the Rachford-Rice procedure (1952) and is more robust than the conventional flash-calculation procedure. We demonstrate the new approach for several example fluids and show that speedup by a factor of approximately 3 to 20 is obtained over conventional flash calculations, depending on the number of components. Introduction Gas injection into oil reservoirs results in complex interactions of flow with phase behavior that often are not modeled accurately by black-oil simulation. This is especially true for miscible or nearly miscible floods in which significant mass transfer occurs between the hydrocarbon phases. Such floods are best modeled by compositional simulation. A significant disadvantage of compositional simulation, however, is that it is much more computationally intensive than black-oil simulation. The primary reason for the increased computational time is the result of solving repeated flash calculations with cubic equations of state (EOS). Research has shown that EOS flash calculations can occupy 50 to 70% of total computational time in compositional simulations (Stenby and Wang 1993; Chang 1990). This is also true for other applications, such as multiphase flow in pipelines. The use of fewer pseudocomponents can reduce the flash computation time, but fewer components results in less accuracy (Hong 1982; Liu 2001; Egwuenu et al. 2005). This is especially true in multicontact miscible displacements, in which miscibility is developed by a combined condensing/vaporizing drive process (Zick 1986; Johns et al. 1993; Egwuenu et al. 2005). Fluid characterization by pseudocomponent models can be improved by tuning to the analytical minimum miscibility enrichment or minimum miscibility pressure (Johns et al. 1994), but those models still require significant computational time, even for fewer pseudocomponents. Another way to reduce computation time is to reduce the number of gridblocks. With coarse grids, however, numerical dispersion is large, which may cloud the results (Solano et al. 2001). Ideally, fine grids should be used that better match the level of dispersion found at the field scale. More recently, methods have been examined to find reduced parameters for flash calculations. Michelsen (1982a, 1982b, 1986) significantly increased flash-calculation speed by finding three reduced parameters, regardless of the number of components. His method, however, assumes zero BIPs, which is too restrictive for real fluid characterization. Michelsen also gave a practical method for stability calculations using the tangent plane distance (TPD) (Michelsen 1982b).

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MgH2 has attracted intensive interests as one of the most promising hydrogen storage materials. Nevertheless, the high desorption temperature, sluggish kinetics, and rapid capacity decay hamper its commercial application. Herein, 2D TiO2 nanosheets with abundant oxygen vacancies are used to fabricate a flower-like MgH2/TiO2 heterostructure with enhanced hydrogen storage performances. Particularly, the onset hydrogen desorption temperature of the MgH2/TiO2 heterostructure is lowered down to 180 °C (295 °C for blank MgH2). The initial desorption rate of MgH2/TiO2 reaches 2.116 wt% min−1 at 300 °C, 35 times of the blank MgH2 under the same conditions. Moreover, the capacity retention is as high as 98.5% after 100 cycles at 300 °C, remarkably higher than those of the previously reported MgH2-TiO2 composites. Both in situ HRTEM observations and ex situ XPS analyses confirm that the synergistic effects from multi-valance of Ti species, accelerated electron transportation caused by oxygen vacancies, formation of catalytic Mg-Ti oxides, and stabilized MgH2 NPs confined by TiO2 nanosheets contribute to the high stability and kinetically accelerated hydrogen storage performances of the composite. The strategy of using 2D substrates with abundant defects to support nano-sized energy storage materials to build heterostructure is therefore promising for the design of high-performance energy materials.

Abstract-We address low-complexity, highly-accurate frequency offset estimation for multi-band orthogonal frequency division multiplexing (MB-OFDM) based ultra-wide band systems in time-invariant as well as time-variant channels. We investigate the unique characteristics of MB-OFDM systems, namely, different frequency offsets, channel responses, received energies, and preamble structures in different frequency bands. Utilizing them, we develop frequency offset estimators based on the best linear unbiased estimation principle. If compared to the reference estimators, our proposed methods achieve significantly better estimation performance (4 to 6.4 dB (5 to 20 dB) estimation mean-square error advantage in the time-invariant (time-variant) channels) for all preamble patterns of the MB-OFDM system in [8].

Purpose The purpose of this paper is to know airflow field and its distribution of pneumatic compact spinning systems. Complete compact spinning (CCS) and four-line rollers compact spinning (FRCS) are both two kinds of pneumatic compact spinning systems, which utilizes airflow in condensing equipment to condense fiber bundle and improve yarn properties. Design/methodology/approach The paper opted for an exploratory study using finite element method, the airflow field in the condensing area of CCS and FRCS were simulated. First, a periodic movement of the fibers in bundle in condensing area was detected, and the yarn tracks were described veritably under the high-speed-video-camera and AutoCAD Software. Then the physical models of the condensing zone were constructed according to the physical parameters of the practical system. The simulation of airflow velocities were extracted along the yarn tracks using ANSYS Software. Finally, the numerical results were verified by spinning experiments. Findings The results show that the negative velocity component along the Y-axis helps keeping beneficial hairiness. CCS has higher negative velocity value and more abundant beneficial hairiness than FRCS. The velocity component in the X-axis direction has a direct effect on yarn evenness. For the same liner density of CCS and FRCS, the larger the value of the velocity component on X-axis is, the better the yarn evenness is. For 9.7tex, CCS has larger velocity component in the X-axis direction and better yarn evenness than FRCS, showing that CCS is more suitable for spinning fine count yarn. The velocity component in the Z-axis direction has a direct effect on breaking strength. CCS has little velocity component in the Z-axis direction and little breaking strength than FRCS. Originality/value To know airflow field and its distribution by finite element method is helpful to investigate the condensing principles of the fiber bundle and improve yarn properties.

A methodology for estimating the region of attraction for autonomous nonlinear systems is developed. The methodology is based on a proof that the region of attraction can be estimated accurately by the zero sublevel set of an implicit function which is the viscosity solution of a time-dependent Hamilton–Jacobi equation. The methodology starts with a given initial domain and yields a sequence of region of attraction estimates by tracking the evolution of the implicit function. The resulting sequence is contained in and converges to the exact region of attraction. While alternative iterative methods for estimating the region of attraction have been proposed, the methodology proposed in this paper can compute the region of attraction to achieve any desired accuracy in a dimensionally independent and efficient way. An implementation of the proposed methodology has been developed in the Matlab environment. The correctness and efficiency of the methodology are verified through a few examples.

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