Time characteristics of aero-optical imaging degeneration of the ellipsoidal dome under variable operating conditions.

As a missile flies at high speed, the index and surface shape of the conformal dome will vary under the influence of the aero-optical effect, which will degenerate the performance of the seeker's imaging detection system. However, many previous studies on aero-optical imaging deterioration of optical domes were usually carried out under fixed operating conditions, which are not in line with the real flight scene of the missile. In addition, the aero-optical imaging degeneration of the dome is diverse as the flight time increases. Therefore, it is of great significance to study the time characteristics of aero-optical imaging degradation of optical domes under variable work conditions. In this study, taking an air-to-air missile as an example, the Zernike polynomials, wavefront aberration, Strehl ratio, and image simulation are applied to evaluate the aero-optical imaging deterioration of an ellipsoidal dome in the flight time range of 0-10 s under variable working conditions. The simulation results show that, as the flight time increases, (1) the dynamic range of tilt, defocus, astigmatism, coma, and wavefront aberration increase; and (2) the Strehl ratio and the peak signal-to-noise ratio (PSNR) of the simulated images decrease. Therefore, the influence of flight time on the aero-optical image degradation of the ellipsoidal dome gradually becomes more serious.

Elsinochrome A induces cell apoptosis and autophagy in photodynamic therapy.

Elsinochrome A (EA) is a perylene quinone natural photosensitizer, photosensitizer under light excitation generates reactive oxygen species (ROS) to induce apoptosis, so can be used for treating tumors, that is so-called photodynamic therapy (PDT). However, the molecular mechanism, especially related to apoptosis and autophagy, is still unclear. In this study, we aimed to explore the mechanism of EA-PDT-induced B16 cells apoptosis and autophagy. The action of EA-PDT on mitochondrial permeability transition pore (MPTP), mitochondrial membrane potential (MMP) and the mitochondrial function were researched by fluorescence technique and Extracellular Flux Analyzer. Illumina sequencing, tandem mass tags Quantitative Proteomics and Western Blot studied the mechanism at the gene and protein levels. The results indicated that EA-PDT had excellent phototoxicity in vitro. EA could bind to the mitochondria. EA-PDT for 5 min caused MPTP opening, MMP decreasing and abnormal mitochondrial function with a concentration-dependent characteristic. EA-PDT resulted in an increase intracellular ROS and the number of autophagosomes. Caspase2, caspase9 and tnf were upregulated, and bcl2, prkn, atg2, atg9 and atg10 were downregulated. Our results indicated that EA-PDT induced cell apoptosis and autophagy through the mediation of ROS/Atg/Parkin. This study can provide enlightenment for exploring potential targets of drug development for the PDT of melanoma.

Formate Additive for Efficient and Stable Methylammonium-Free Perovskite Solar Cells by Gas-Quenching.

In order to promote the commercialization of perovskite solar cells, gas-quenching is considered to be a promising technique for perovskite film fabrication. However, when handling with methylammonium-free (MA-free) perovskites, it is often difficult to obtain high-quality perovskite films by gas-quenching. Herein, formate additives are employed to regulate the crystallization of MA-free perovskite, and improve the quality of perovskite films. Different additives of formamidine formate (FAFO) and potassium formate (KFO) is compared to investigate the role of formate groups in the crystallization of perovskite films prepared by gas-quenching. The FAFO additive facilitates the perovskite crystallization in (001) orientation whereas KFO favors for (111) orientation. The MA-free device with addition of FAFO demonstrate a champion power conversion efficiency of 20.94 %, compared to that of 20.14 % for KFO devices. In addition, FAFO device also exhibits improved thermal stability in ambient condition without encapsulation, extending the T80 lifetime by 18 times compared to the pristine device.

Potential application of novel cadmium-tolerant bacteria in bioremediation of Cd-contaminated soil.

With the increase in cadmium (Cd) release into the environment, it is necessary to find appropriate solutions to reduce soil Cd pollution. Microorganisms are a green and effective means for the remediation of Cd-contaminated soil. In this study, in a Cd-contaminated farmland, we screened and identified novel Cd-resistant strains, Paenarthrobactor nitroguajacolicus, Lysinibacillus fusiformis, Bacillus licheniformis, and Methyllobacium brachiatum, with minimum inhibitory concentrations of 100, 100, 50, and 50 mg/L, respectively, and added them each to pots containing Cd-contaminated rape plants to explore their remediation ability. The results showed that treatment with each of the four strains significantly increased the abundance of Nitrospirae, Firmicutes, Verrucomicrobia, and Patescibacterium in the rhizosphere soil of the plants. This led to changes in soil physical and chemical indices; pH; and available phosphorus, urease, and catalase activities, which were significantly negatively correlated with bioavailable Cd, reducing 28.74-58.82 % Cd enrichment to plants and 23.72-43.79 % Cd transport within plants, and reducing 5.52-10.68 % available cadmium in soil, effectively reducing the biotoxicity of Cd. Thus, this study suggests microbial remediation as a reliable option, forming a basis for the remediation of Cd-contaminated soil.

Synthesis of Liquid Hydrocarbon via Direct Hydrogenation of CO2 over FeCu-Based Bifunctional Catalyst Derived from Layered Double Hydroxides.

Here, we report a Na-promoted FeCu-based catalyst with excellent liquid hydrocarbon selectivity and catalytic activity. The physiochemical properties of the catalysts were comprehensively characterized by various characterization techniques. The characterization results indicate that the catalytic performance of the catalysts was closely related to the nature of the metal promoters. The Na-AlFeCu possessed the highest CO2 conversion due to enhanced CO2 adsorption of the catalysts by the introduction of Al species. The introduction of excess Mg promoter led to a strong methanation activity of the catalyst. Mn and Ga promoters exhibited high selectivity for light hydrocarbons due to their inhibition of iron carbides generation, resulting in a lack of chain growth capacity. The Na-ZnFeCu catalyst exhibited the optimal C5+ yield, owing to the fact that the Zn promoter improved the catalytic activity and liquid hydrocarbon selectivity by modulating the surface CO2 adsorption and carbide content. Carbon dioxide (CO2) hydrogenation to liquid fuel is considered a method for the utilization and conversion of CO2, whereas satisfactory activity and selectivity remains a challenge. This method provides a new idea for the catalytic hydrogenation of CO2 and from there the preparation of high-value-added products.

Imaging of Insect Hole in Living Tree Trunk Based on Joint Driven Algorithm of Electromagnetic Inverse Scattering.

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images.

Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation.

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.

Preoperative estimation of the survival of patients with unresectable hepatocellular carcinoma achieving complete response after conventional transcatheter arterial chemoembolization: assessments of clinical and LI-RADS MR features.

PURPOSE: To identify the associations of clinical and magnetic resonance (MR) features with overall survival (OS) in patients with unresectable hepatocellular carcinoma (HCC) achieving complete response (CR) after conventional transcatheter arterial chemoembolization (TACE) and to further develop an individual nomograph to estimate the survival probability. MATERIALS AND METHODS: A total of 112 patients with unresectable HCC treated with TACE as first-line treatment were retrospectively evaluated. Potential risk factors associated with OS were identified by univariate and multivariate Cox analyses. The survival model was developed by multivariate Cox proportional hazard model. The area under the receiver operating characteristic curve was calculated to assess the performance of each marker and of the whole model. Discrimination was performed using Kaplan-Meier curves, and the survival curves were compared by the log-rank test. A nomogram derived from the survival model was established. RESULTS: Multivariate Cox analyses indicated that nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and Barcelona clinic liver cancer (BCLC) stage were independent risk indicators associated with OS. The survival model showed acceptable diagnostic power, with an area under the curve (AUC) of 0.687. Kaplan-Meier curves demonstrated that the model discriminated well, as the high-risk and low-risk groups had median survival times of 21.6 months and 34.8 months, respectively (log-rank test, P = 0.01). CONCLUSIONS: Nonsmooth tumor margin, peritumoral enhancement, fat sparing in solid mass, and BCLC stage were potential biomarkers to evaluate the survival with favorable performance and discriminate HCC patients with CR under conventional TACE treatment.

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention.

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.

Optimizing the soil conservation service curve number model by accounting for rainfall characteristics: a case study of surface water sources in Beijing.

The upper catchment of the Miyun reservoir is an important drinking water source in Beijing. In recent years, researchers have used the soil conservation service curve number (SCS-CN) model to calculate surface runoff for the district. Although the runoff forecasting accuracy was unsatisfactory, the lack of understanding of rainfall processes and their influence on runoff may explain the observed deviations. Our study sought to optimize and assess the SCS-CN model simulation accuracy for the district by proposing an SCS-CN calculation method for each runoff event (CNt) based on observation data for 253 rainfall and runoff events from 7 plots in the Miyun Shixia watershed. This study elucidated a significant positive correlation between the ratio of CNt and the average SCS-CN (CN1), as well as the ratio of the maximum X-minute rainfall amount (PX) to the total rainfall amount for each rainfall event (P). Furthermore, a calculation method involving power function equations between CNt/CN1 and PX/P was proposed for CNt. When X = 5 min and the initial abstraction ratio (λ) = 0.01, the simulation performance of the optimized model was the highest, with a Nash-Sutcliffe efficiency coefficient of 0.791, which was significantly higher than that of the non-optimized SCS-CN model. The simulation performance for bare and cultivated land was higher than that of other land uses, with Ef values of 0.831 and 0.828, respectively. Future research should focus on improving the prediction accuracy of runoff events resulting from high-intensity and short-duration rainfall events.

MRI-Based Radiomics Models Developed With Features of the Whole Liver and Right Liver Lobe: Assessment of Hepatic Inflammatory Activity in Chronic Hepatic Disease.

BACKGROUND: The noninvasive assessment of hepatic inflammatory activity (HIA) is crucial for making clinical decisions and monitoring therapeutic efficacy in chronic liver disease (CLD). PURPOSE: To develop MRI-based radiomics models by extracting features from the whole liver and localized regions of the right liver lobe, compare the efficiency of two radiomics models, and further develop a radiomics nomogram for the assessment of HIA in CLD. STUDY TYPE: Retrospective. POPULATION: In all, 137 consecutive patients. FIELD STRENGTH/SEQUENCE: 1.5T/T2 -weighted imaging. ASSESSMENT: All patients (nonsignificant HIA, n = 98; significant HIA, n = 39) were randomly divided into a training (n = 95) and a test cohort (n = 42). Radiomics features were extracted from the regions covering the whole liver (ROI-w) and localized regions of the right liver lobe (ROI-r). Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses were used to select features and develop radiomics models. A combined model fusing the valuable radiomics features with clinical-radiological predictors was developed. Finally, a radiomics nomogram derived from the combined model was developed. STATISTICAL TESTS: Synthetic minority oversampling technique algorithm, LASSO, receiver operator characteristic curve, and calibration curve analysis were performed. RESULTS: The area under the curve (AUC), sensitivity, and specificity of the ROI-w radiomics model in assessing HIA were 0.858, 0.800, and 0.733, respectively. The ROI-r model were 0.844, 0.733, and 0.867, respectively. No differences were detected between the two radiomics models (P = 0.8329). The combined model fusing valuable ROI-w radiomics features, albumin, and periportal edema exhibited a promising performance (AUC, 0.911). The calibration curves showed good agreement between the actual observations and nomogram predictions. DATA CONCLUSION: The MRI-based radiomics models had a powerful ability to evaluate HIA and the ROI-w radiomics model was comparable to the ROI-r model. Moreover, the radiomics nomogram could be a favorable method to individually estimate HIA in CLD. J. MAGN. RESON. IMAGING 2020;52:1668-1678.

MRI-Based Radiomics: Associations With the Recurrence-Free Survival of Patients With Hepatocellular Carcinoma Treated With Conventional Transcatheter Arterial Chemoembolization.

BACKGROUND: Preoperative estimation of hepatocellular carcinoma (HCC) recurrence after conventional transcatheter arterial chemoembolization (c-TACE) is crucial for subsequent follow-up and therapy decisions. PURPOSE: To evaluate the associations of radiomics models based on pretreatment contrast-enhanced MRI, a clinical-radiological model and a combined model with the recurrence-free survival (RFS) of patients with HCC after c-TACE, and to develop a radiomics nomogram for individual RFS estimations and risk stratification. STUDY TYPE: Retrospective. POPULATION: In all, 184 consecutive HCC patients. FIELD STRENGTH/SEQUENCE: 1.5T or 3.0T, including T2 WI, T1 WI, and contrast-enhanced T1 WI. ASSESSMENT: All HCC patients were randomly divided into the training (n = 110) and validation datasets (n = 74). Radiomics signatures capturing intratumoral and peritumoral expansion (1, 3, and 5 mm) were constructed, and the radiomics models were set up using least absolute shrinkage and selection operator (LASSO) Cox regression. Clinical-radiological features were identified by univariate and multivariate Cox regression. The clinical-radiological model and the combined model fusing the radiomics signature with the clinical-radiological risk factors were developed by a multivariate Cox proportional hazard model. A radiomics nomogram derived from the combined model was established. STATISTICAL TESTS: LASSO Cox regression, univariate and multivariate Cox regression, Kaplan-Meier analysis were performed. The discrimination performance of each model was quantified by the C-index. RESULTS: Among the different peritumoral expansion models, only the 3-mm peritumoral expansion model (C-index, 0.714) showed a comparable performance (P = 0.4087) to that of the portal venous phase intratumoral model (C-index, 0.727). The combined model showed the best performance and the C-index was 0.802. Kaplan-Meier analysis showed that the cutoff values of the combined model relative to a median value (1.7426) perfectly stratified these patients into high-risk and low-risk subgroups. DATA CONCLUSION: The combined model is more valuable than the clinical-radiological model or radiomics model alone for evaluating the RFS of HCC patients after c-TACE, and the radiomics nomogram can be used to preoperatively and individually estimate RFS. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;52:461-473.

Mussel-Inspired Biocoating for Improving the Adhesion of Dental Pulp Stem Cells in Dental Pulp Regeneration.

Dental pulp engineering possesses a promising perspective to replacing lost pulp in the root canal and restoring its functions. Stable adhesion of dental pulp stem cells (DPSCs) on the root canal dentin wall is a key element required for reconstruction of a functional odontoblast layer in dental pulp regeneration. To address this challenge, dopamine-modified hyaluronic acid (DA-HA) is coated on dentin to obtain a stable adhesion of DPSCs. The dopamine segment provides adhesion ability to the coating, and the hyaluronic acid increases the biocompatibility. The results show that DPSCs can adhere on the DA-HA coated dentin slice better than those without coating. Simultaneously, DPSCs proliferation can be further promoted on the prepared coating. Therefore, the DA-HA coating may provide a possible way to immobilize odontoblast cell onto dentin surface for pulp regeneration.

A Green Triboelectric Nano-Generator Composite of Degradable Cellulose, Piezoelectric Polymers of PVDF/PA6, and Nanoparticles of BaTiO3.

In this paper, a kind of green triboelectric nano-generator based on natural degradable cellulose is proposed. Different kinds of regenerated cellulose composite layers are prepared by a blending doping method, and then assembled with poly(tetrafluoroethylene) (PTFE) thin films to form tribioelectric nanogenerator (TENG). The results show that the open circuit output voltage and the short circuit output current using a pure cellulose membrane is 7.925 V and 1.095 µA. After adding a certain amount of polyamide (PA6)/polyvinylidene fluoride (PVDF)/barium titanate (BaTiO3), the open circuit output voltage peak and the peak short circuit output current increases by 254.43% (to 20.155 V) and 548.04% (to 6.001 µA). The surface morphology, elemental composition and functional group of different cellulose layers are characterized by Scanning Electronic Microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and tested by the electrochemical analyze. Moreover, after multiple assembly and rectification processing, the electrical output performance shows that the peak value of open-circuit output voltage and the peak value of short circuit output current increases by 132.06% and 116.13%. Within 500 s of the charge-discharge test, the single peak charge reached 3.114 V, and the two peak charges reached 3.840 V. The results demonstrate that the nano-generator based on cellulose showed good stability and reliability, and the application and development of natural biomaterials represented by cellulose are greatly promoted in miniature electronic sensing area.

Net Primary Productivity Loss under different drought levels in different grassland ecosystems.

Drought is one of the most prominent natural threats to grassland productivity, although the magnitude of this threat is uncertain due to the different drought-levels. However, drought-productivity dynamics has not yet received much attention. It is necessary to establish the method to evaluate quantitatively the effect of different drought-levels on grassland productivity. To better understand the impact of different drought-levels on productivity dynamics, an assessment method to assess the quantitative effects of different drought-levels on grassland productivity was proposed based-on long-term observation data, standardized precipitation index (SPI) and Biome-BGC process model. Based-on assessment indicator of net primary productivity (NPP), NPP loss caused by moderate, severe and extreme drought was dramatically different in grasslands with a significant exponential change with gradient of different drought-levels. Furthermore, NPP loss variation in different grassland types under the same drought level was significantly different. Besides, the effect of drought on NPP gradually decreased by an exponential relationship in desert, typical and meadow steppe. However, the percentage of NPP loss in desert, typical and meadow steppe reduced by 20.5%, 13.1% and 17.5% with U-shaped, respectively. Meanwhile, our results can offer scientific basis to improve assessment impact of extreme climate events used by ecosystem model and data, and cope with carbon cycling management and climate change.

High-performance biocompatible nano-biocomposite artificial muscles based on a renewable ionic electrolyte made of cellulose dissolved in ionic liquid.

In this work, high-performance biocompatible nano-biocomposite artificial muscles were developed via various thicknesses of renewable microporous ionic electrolytes (ICEs) made of natural biopolymer cellulose dissolved in ionic liquid with excellent ionic conductivity and flexibility. The changing thickness experiments illustrated that 0.7 mm thick ICEs could deliver outstanding areal capacitance of 44.708 mF cm-2 and ionic conductivity of 79.7 µS cm-1, as well as minimum resistance of 1.61 Ω. The current density changed from 1 to 10 Ag-1, and improvements were achieved in energy density (from 3.88 to 21.25 Wh kg-1) and power density (from 2.63 to 5.51 KW kg-1). The voltage window widened from 0.5 to 1 V, and improvements were gained in energy density (from 4.13 to 22.01 Wh kg-1) and power density (from 1.25 to 2.81 KW kg-1). Moreover, good flexibility of 0.7 mm thick ICE with porosity of 89.61% and elastic modulus of 74.38 MPa was discovered. Electromechanical experiments demonstrated from the above results that the maximum peak displacement with 0.3 mm ICE was 5.33 mm at 5 V 0.02 Hz sine wave voltage, and the maximum displacement and force with 0.7 mm ICE was 17.44 mm and 5.93 mN at 5 V DC voltages. These findings suggest that the explored excellent ionic conductivity and flexibility of ICEs holds great promise for the further study of high-performance green actuators.

Bioinspired Supramolecular Lubricating Hydrogel Induced by Shear Force.

Bioinspired lubricating materials are great challenge toward artificial joints. In this contribution, we synthesize a bioinspired hydrogel by combining a thixotropic supramolecular network and polymer double network, exhibiting a unique shear-responsive lubricating property. The disassembly of the N-fluorenylmethoxycarbonyl-l-tryptophan supramolecular network triggered by shear force will endow lubricating function to the hydrogel; meanwhile PAAm and PVA double network acts as the supporting skeleton with high mechanical property. This work will bring new insight on the design of artificial lubricating joint.

Generalized inverse matrix-exterior penalty function (GIM-EPF) algorithm for data processing of multi-wavelength pyrometer (MWP).

The unknown emissivity of materials is a huge obstacle in multi-wavelength pyrometry (MWP). It leads to a set of ill-posed equations that cannot be directly inverted to obtain the true temperature from a set of multi-wavelength measurements. Constraint optimization algorithms such as the gradient projection (GP) and internal penalty function (IPF) algorithms provide solutions without any emissivity model assumptions but require a narrow fixed emissivity range and an appropriate initial emissivity input value, otherwise, accuracy and computational efficiency are greatly affected. Here, we propose a generalized inverse matrix-exterior penalty function (GIM-EPF) algorithm to realize an efficient and accurate inversion without limiting the emissivity range in advance. First, a set of emissivities is obtained by the generalized inverse matrix method; these emissivities are used as initial values in the exterior penalty function iteration algorithm, from which temperature and spectral emissivity are obtained. Simulation results show that the GIM-EPF algorithm provides results superior to IPF, especially in computational efficiency. The proposed GIM-EPF method is 8 times faster than the IPF method with a 0.56% relative error at the 1800 K true temperature. The GIM-EPF method also allows near real-time diagnosis of rocket exhaust flame temperature. Rocket nozzle temperature experiment results show that the temperatures derived by the GIM-EPF algorithm agree well with the theoretical design temperature and the IPF inversion temperature.

The Influence of Radial Undersampling Schemes on Compressed Sensing in Cardiac DTI.

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image quality. Radial undersampling is one of the most popular non-Cartesian k-space sampling schemes, since it has relatively lower sensitivity to motion than Cartesian trajectories, and artifacts from linear reconstruction are more noise-like. Therefore, radial imaging is a promising strategy of undersampling to accelerate acquisitions. The purpose of this study is to investigate various radial sampling schemes as well as reconstructions using compressed sensing (CS). In particular, we propose two randomly perturbed radial undersampling schemes: golden-angle and random angle. The proposed methods are compared with existing radial undersampling methods, including uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle. The results on both simulated and real human cardiac diffusion weighted (DW) images show that, for the same amount of k-space data, randomly sampling around a random radial line results in better reconstruction quality for DTI indices, such as fractional anisotropy (FA), mean diffusivities (MD), and that the randomly perturbed golden-angle undersampling yields the best results for cardiac CS-DTI image reconstruction.

Directly data processing algorithm for multi-wavelength pyrometer (MWP).

Data processing of multi-wavelength pyrometer (MWP) is a difficult problem because unknown emissivity. So far some solutions developed generally assumed particular mathematical relations for emissivity versus wavelength or emissivity versus temperature. Due to the deviation between the hypothesis and actual situation, the inversion results can be seriously affected. So directly data processing algorithm of MWP that does not need to assume the spectral emissivity model in advance is main aim of the study. Two new data processing algorithms of MWP, Gradient Projection (GP) algorithm and Internal Penalty Function (IPF) algorithm, each of which does not require to fix emissivity model in advance, are proposed. The novelty core idea is that data processing problem of MWP is transformed into constraint optimization problem, then it can be solved by GP or IPF algorithms. By comparison of simulation results for some typical spectral emissivity models, it is found that IPF algorithm is superior to GP algorithm in terms of accuracy and efficiency. Rocket nozzle temperature experiment results show that true temperature inversion results from IPF algorithm agree well with the theoretical design temperature as well. So the proposed combination IPF algorithm with MWP is expected to be a directly data processing algorithm to clear up the unknown emissivity obstacle for MWP.