Abstract: A method for preparing biochar and hydrogen by utilizing anaerobic fermentation byproducts, the method including: (1) mixing a first straw, seeding sludge and distilled water, and then carrying out anaerobic fermentation to obtain a mixed product after fermentation; (2) performing separation on the mixed product to obtain a second straw and biogas slurry; and (3) carbonizing the second straw to obtain biochar, and collecting gas after a pressurized catalytic reaction on the biogas slurry to obtain hydrogen.

Abstract: Systems, methods, and other embodiments associated with phase cycled magnetic resonance spectroscopic imaging (PCSI). According to one embodiment, a method includes applying an excitation radio frequency (RF) pulse having a low flip angle to a sample. The method further includes adjusting the phase of the RF to sweep through a frequency range based, at least in part, on PCSI. Sampling is then performed in the frequency range. The method also includes receiving a set of data based, at least in part, on the sampling in the frequency range.

Abstract: Apparatus, methods, and other embodiments associated with the spectral analysis of T2 spectral data are described. One example magnetic resonance imaging (MRI) method includes accessing a data set comprising T2 spectral data associated with a magnetic resonance imaging (MRI) signal received from an object. The method also includes decomposing the T2 spectral data with multi-exponential functions to determine T2 spectra corresponding to the T2 spectral data. The T2 spectra are portioned into a plurality of intervals defined by the T2 spectral data acquired in a predetermined time period.

Abstract: Apparatus, methods, and other embodiments associated with the spectral analysis of T2 spectral data are described. One example magnetic resonance imaging (MRI) method includes accessing a data set comprising T2 spectral data associated with a magnetic resonance imaging (MRI) signal received from an object. The method also includes decomposing the T2 spectral data with multi-exponential functions to determine T2 spectra corresponding to the T2 spectral data. The T2 spectra are portioned into a plurality of intervals defined by the T2 spectral data acquired in a predetermined time period.

Abstract: Apparatus, methods, and other embodiments associated with the spectral analysis of T2 spectral data are described. One example magnetic resonance imaging (MRI) method includes accessing a data set comprising T2 spectral data associated with a magnetic resonance imaging (MRI) signal received from an object. The T2 spectral data is decomposed with multi-exponential functions to determine T2 spectra. The T2 spectral amplitude is regularized with a regularized constant. The regularized constant is sufficiently large to smooth the T2 spectra so adjacent pixels of the T2 spectral data have similar characteristics. The T2 spectral data is weighted so that the regularization is uniformly weighted for the spectral amplitudes. The T2 spectra is partitioned into intervals corresponding to myelin water fraction (MWF), tissue water fraction (TWF), Long T2 water fraction (LWF), and cerebrospinal fluid fraction (CSF). Parametric maps are generated based, at least in part, on the T2 spectral data.

Abstract: Apparatus, methods, and other embodiments associated with the spectral analysis of T2 spectral data are described. One example magnetic resonance imaging (MRI) method includes accessing a data set comprising T2 spectral data associated with a magnetic resonance imaging (MRI) signal received from an object. The T2 spectral data is decomposed with multi-exponential functions to determine T2 spectra. The T2 spectral amplitude is regularized with a regularized constant. The regularized constant is sufficiently large to smooth the T2 spectra so adjacent pixels of the T2 spectral data have similar characteristics. The T2 spectral data is weighted so that the regularization is uniformly weighted for the spectral amplitudes. The T2 spectra is partitioned into intervals corresponding to myelin water fraction (MWF), tissue water fraction (TWF), Long T2 water fraction (LWF), and cerebrospinal fluid fraction (CSF). Parametric maps are generated based, at least in part, on the T2 spectral data.

Abstract: A method of performing parallel image reconstruction of undersampled image data in k-space. A defined partitioning of a k-space region into a plurality of segments is received. A segment of the plurality of segments is identified wherein data is sampled at less than a Nyquist rate. First imaging data is sampled at the Nyquist rate. A reconstruction coefficient is calculated for at least a portion of the identified segment using the sampled first imaging data. Second imaging data is sampled at less than the Nyquist rate. A value for a missing k-space sample in the identified segment is predicted using the calculated reconstruction coefficient and the sampled second imaging data. An image of the image area is defined using the predicted value and the received second dataset.

Abstract: A method of performing parallel image reconstruction of undersampled image data in k-space. A defined partitioning of a k-space region into a plurality of segments is received. A segment of the plurality of segments is identified wherein data is sampled at less than a Nyquist rate. First imaging data is sampled at the Nyquist rate. A reconstruction coefficient is calculated for at least a portion of the identified segment using the sampled first imaging data. Second imaging data is sampled at less than the Nyquist rate. A value for a missing k-space sample in the identified segment is predicted using the calculated reconstruction coefficient and the sampled second imaging data. An image of the image area is defined using the predicted value and the received second dataset.