Danielsconrad0110

The highest ionic conductivity achieved in this study was 2.0 × 10-5 S cm-1 at 30 °C. These results suggest that the design of the IL monomer provides the resultant polymer with high chain flexibility and a high IL density, and so it is effective for preparing TPILs with high ionic conductivities.Accurate and reliable evaluations of potential groundwater areas are of significance in the hydrogeological assessments of coalfields because water inrush disasters may be caused by unclear groundwater potential. A three-dimensional geological model of porosity based on deterministic modeling and a facies-controlled method are used to determine the groundwater potential of the coal measure aquifer. The modeling processes are as follows based on the interlayer and discontinuity (faults) data extracted from boreholes and geological maps, an integrated sequence framework model is developed. Using the results of sedimentary microfacies identification and the method of deterministic modeling, a sedimentary microfacies model is successfully established. Finally, based on facies-controlled and sequential Gaussian methods, an effective porosity model is established that can predict the groundwater potential. The predicted results show that sandstones sedimented in channel, point bar, and batture environments possess high effective porosity and strong groundwater potential; however, the sandstones sedimented in interdistributary bays, flood plains, and sand sheets possess low effective porosity. Model validation was performed based on the hydrological pumping test data collected from observation boreholes, drainage water inflow data from dewatered boreholes in the tunnel around workface, and the mine water inflow in tunnels and the workfaces. The validation analysis results show that the effective porosity and sedimentary facies were correlated with the actual flux. The predicted results are consistent with the actual flux data, validating the predicted model.To obtain sustainable economical oil production and recovery of investment, some oil fields adopted the strategy of multilayer commingling production at an early stage. This leads to interlayer interference and losing part of the recoverable reserves. In this paper, dynamic interference behaviors of arbitrary multilayer commingling production in heavy oil reservoirs are analyzed. Based on the non-Darcy flow equation, the Buckly-Leverett equation, and the material balance equation, a mathematical model of arbitrary multilayer commingling production is obtained. Oil and water relative permeability, saturation, and bottom hole flow pressure microelement and the iteration method are employed to solve the mathematical model in the time domain. The new model is verified by comparing the results from the typical black oil model using the Darcy law. The sensitivity analysis of critical parameters on interference behaviors, such as permeability, oil viscosity, effective drainage boundary, and voidage replacement ratio, is carried out. The model obtained in this paper can be used for oil and liquid productivity analysis during the overall process of commingling production and extended to be applied in numerical experiments with different combinations of typical parameters as well.Pharmaceutical science based on biological nanotechnology is developing rapidly in parallel with the development of nanomaterials and nanotechnology in general. Pectin is a natural polysaccharide obtainable from a wide range of sources. Here, we show that doxorubicin (DOX)-conjugated hydrophilic pectin (PET) comprising an amphiphilic polymer loaded with hydrophobic dihydroartemisinin (DHA) self-assemble into nanoparticles. Importantly, conjugated DOX and DHA could be released quickly in a weakly acidic environment by cleavage of the acid-sensitive acyl hydrazone bond. Confocal microscopy and flow cytometry confirmed that these PET-DOX/DHA nanoparticles efficiently delivered DOX into the nuclei of MCF-7 cells. Significant tumor growth reduction was monitored in a female C57BL/6 mouse model, showing that the PET-DOX/DHA nanoparticle-mediated drug delivery system inhibited tumor growth and may improve therapy. Thus, we have demonstrated that pectin may be useful in the design of materials for biomedical applications.The Internet environment has provided massive data to the actual industrial production process. It not only has large amounts of data but also has a high data dimension, which brings challenges to the traditional statistical process monitoring. Aiming at the nonlinearity and dynamics of industrial large-scale high-dimensional data, an efficient iterative multiple dynamic kernel principal component analysis (IMDKPCA) method is proposed to monitor the complex industrial process with super-large-scale high-dimensional data. In KPCA, a new KKT matrix is first created by using kernel matrix K. According to the properties of the symmetric matrix, the newly constructed matrix has the same eigenvector as the original matrix K; hence, each column of the matrix K can be used as the input sample of the iteration algorithm. After iterative operation, the kernel principal component can be deduced fleetly without the eigen decomposition. Because the kernel matrix is not stored in the algorithm beforehand, it can effectively reduce the computation complexity of the kernel. Especially for a tremendous data scale, the traditional eigen decomposition technology is no longer appropriate, yet the presented method can be solved quickly. The autoregressive moving average (ARMA) time series model and kernel principal component analysis (KPCA) are combined to build the IDKPCA model for dealing with the dynamics and nonlinearity in the industrial process. Eventually, it is applied to monitor faults in the penicillin fermentation process and compared with MKPCA to certify the accuracy and applicability of the proposed method.The present study measured the antioxidant properties of 15 commercial tea samples as expressed by the oxygen radical absorbance capacity (ORAC) hydro, ORAC lipo, and ferric reducing antioxidant power (FRAP) indexes. The main antioxidant compounds known to be present in tea are several catechins and catechin gallates, gallic acid, theaflavin and some theaflavin gallates, and theogallin. In this study, only gallic acid and the four most common catechins (epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate) were analyzed in the tea samples. In addition, caffeine levels were measured. The ORAC and FRAP values for these compounds were also determined. The levels of theaflavin, theaflavin gallates, and theogallin were not measured since these compounds are present at relatively low levels in tea. The ORAC (and FRAP) indexes for each tea sample were also calculated based on the content of individual antioxidant compounds and their ORAC and FRAP indexes. Correlations between the experimental ORAC (and FRAP) and the calculated values were further obtained. The correlations were poor, with R 2 = 0.3657 for ORAC hydro, R 2 = 0.2794 for ORAC lipo, and R 2 = 0.6929 for FRAP. The poor correlation between the overall catechin content and the experimental ORAC values in tea infusions was previously reported in the literature. The present study directly calculated the expected ORAC index from individual antioxidant components and reached the same result of poor correlation. For FRAP values, no comparison was previously reported in the literature. The poor correlations were not well explained, indicating that the cause of the antioxidant character of tea is more complex than simply produced by the main catechins.The search for suitable strategies to manufacture self-healable nitrile rubber (NBR) composites is the most promising part in the industrial field of polar rubber research. In recent years, some important strategies, specifically, metal-ligand coordination bond formation, ionic bond formation, and dynamic hydrogen bond formation, have been utilized to develop duly self-healable NBR composites. This paper reviews the continuous advancement in the research field related to self-healable NBR composites by considering healing strategies and healing conditions. RAD1901 Special attention is given to understand the healing mechanism in reversibly cross-linked NBR systems. The healing efficiency of a cross-linked NBR network is usually dependent on the definite interaction between functional groups of NBR and a cross-linking agent. Finally, the results obtained from successful studies suggest that self-healing technology has incredible potential to increase the sustainability and lifetime of NBR-based rubber products.The purpose of this study was to determine the types, proportions, and energies of secondary particle interactions in a Compton camera (CC) during the delivery of clinical proton beams. The delivery of clinical proton pencil beams ranging from 70 to 200 MeV incident on a water phantom was simulated using Geant4 software (version 10.4). The simulation included a CC similar to the configuration of a Polaris J3 CC designed to image prompt gammas (PGs) emitted during proton beam irradiation for the purpose of in vivo range verification. The interaction positions and energies of secondary particles in each CC detector module were scored. For a 150-MeV proton beam, a total of 156,688(575) secondary particles per 108 protons, primarily composed of gamma rays (46.31%), neutrons (41.37%), and electrons (8.88%), were found to reach the camera modules, and 79.37% of these particles interacted with the modules. Strategies for using CCs for proton range verification should include methods of reducing the large neutron backgrounds and low-energy non-PG radiation. The proportions of interaction types by module from this study may provide information useful for background suppression.We propose a forward-backward splitting algorithm to integrate deep learning into maximum-a-posteriori (MAP) positron emission tomography (PET) image reconstruction. The MAP reconstruction is split into regularization, expectation-maximization (EM), and a weighted fusion. For regularization, the use of either a Bowsher prior (using Markov-random fields) or a residual learning unit (using convolutional-neural networks) were considered. For the latter, our proposed forward-backward splitting EM (FBSEM), accelerated with ordered subsets (OS), was unrolled into a recurrent-neural network in which network parameters (including regularization strength) are shared across all states and learned during PET reconstruction. Our network was trained and evaluated using PET-only (FBSEM-p) and PET-MR (FBSEM-pm) datasets for low-dose simulations and short-duration in-vivo brain imaging. It was compared to OSEM, Bowsher MAPEM, and a post-reconstruction U-Net denoising trained on the same PET-only (Unet-p) or PET-MR (Unet-pm) datasets. For simulations, FBSEM-p(m) and Unet-p(m) nets achieved a comparable performance, on average, 14.4% and 13.4% normalized root-mean square error (NRMSE), respectively; and both outperformed OSEM and MAPEM methods (with 20.7% and 17.7% NRMSE, respectively). For in-vivo datasets, FBSEM-p(m), Unet-p(m), MAPEM, and OSEM methods achieved average root-sum-of-squared errors of 3.9%, 5.7%, 5.9%, and 7.8% in different brain regions, respectively. In conclusion, the studied U-Net denoising method achieved a comparable performance to a representative implementation of the FBSEM net.