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Accurate snow water equivalent (SWE) products are vital for monitoring hydrological processes and managing water resources effectively. However, the coarse spatial resolution (typically at 25 km from passive microwave remote sensing images) of the existing SWE products cannot meet the needs of explicit hydrological modeling. Linear regression ignores the spatial autocorrelation (SA) in the variables, and the measure of SA in the data assimilation algorithm is not explicit. This study develops a Resolution-enhanced Multifactor Eigenvector Spatial Filtering (RM-ESF) method to estimate daily SWE in the western United States based on a 6.25 km enhanced-resolution passive microwave record. The RM-ESF method is based on a brightness temperature gradience algorithm, incorporating not only factors including geolocation, environmental, topographical, and snow features but also eigenvectors generated from a spatial weights matrix to take SA into account. The results indicate that the SWE estimation from the RM-ESF method obviously outperforms other SWE products given its overall highest correlation coefficient (0.72) and lowest RMSE (56.70 mm) and MAE (43.88 mm), compared with the AMSR2 (0.33, 131.38 mm, and 115.45 mm), GlobSnow3 (0.50, 100.03 mm, and 83.58 mm), NCA-LDAS (0.48, 98.80 mm, and 81.94 mm), and ERA5 (0.65, 67.33 mm, and 51.82 mm), respectively. The RM-ESF model considers SA effectively and estimates SWE at a resolution of 6.25 km, which provides a feasible and efficient approach for SWE estimation with higher precision and finer spatial resolution.

Anisotropic diffusion MRI techniques using single-shell or multi-shell acquisitions have been proposed as a means to overcome some limitations imposed by diffusion tensor imaging (DTI), especially in complex models of fibre orientation distribution in voxels. A long acquisition time for the angular resolution of diffusion MRI is a major obstacle to practical clinical implementations. In this paper, we propose a novel method to improve angular resolution of diffusion MRI acquisition using given diffusion gradient (DG) directions. First, we define a local diffusion pattern map of diffusion MR signals on a single shell in given DG directions. Using the local diffusion pattern map, we design a prediction scheme to determine the best DG direction to be synthesized within a nearest neighborhood DG directions group. Second, the local diffusion pattern map and the spherical distance on the shell are combined to determine a synthesized diffusion signal in the new DG direction. Using the synthesized and measured diffusion signals on a single sphere, we estimate a spin orientation distribution function (SDF) with human brain data. Although the proposed method is applied to SDF, a basic idea is to increase the angular resolution using the measured diffusion signals in various DG directions. The method can be applicable to different acquired multi-shell data or diffusion spectroscopic imaging (DSI) data. We validate the proposed method by comparing the recovered SDFs using the angular resolution enhanced diffusion signals with the recovered SDF using the measured diffusion data. The developed method provides an enhanced SDF resolution and improved multiple fiber structure by incorporating synthesized signals. The proposed method was also applied neurite orientation dispersion and density imaging (NODDI) using multi-shell acquisitions. •Novel method to improve angular resolution of DWI signals is proposed.•The method is based on local diffusion pattern maps of diffusion MR signals on a single and/or multi shells.•The method provides an enhanced SDF resolution and improved multiple fiber structures.•The proposed angular resolution improvement is achieved without increasing MR scan time.

Power quality analysis issues, especially the measurement of harmonic and interharmonic in cyber-physical energy systems, are addressed in this paper. As new situations are introduced to the power system, the impact of electric vehicles, distributed generation and renewable energy has introduced extra demands to distributed sensors, waveform-level information and power quality data analytics. Harmonics and interharmonics, as the most significant disturbances, require carefully designed detection methods for an accurate measurement of electric loads whose information is crucial to subsequent analyzing and control. This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement. The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis (RobustICA), then estimates the high-resolution frequency from three discrete Fourier transform (DFT) samples with little additional computation, and finally computes the amplitudes and phases with the adaptive linear neuron network. The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation, thus providing a deeper insight into the (inter)harmonic sources or even the whole system.

The self-processing module (SPM) is an internal segment of the FrpC protein (P415–F591) secreted by the pathogenic Gram-negative bacterium Neisseria meningitidis during meningococcal infection of human upper respiratory tract. SPM mediates ‘protein trans-splicing’, a unique natural mechanism for editing of proteins, which involves a calcium-dependent autocatalytic cleavage of the peptide bond between D414 and P415 and covalent linkage of the cleaved fragment through its carboxy-terminal group of D414 to ϵ -amino group of lysine residue within a neighboring polypeptide chain. We present an NMR resonance assignment of the calcium-free SPM, which displays characteristic features of intrinsically disordered proteins. Non-uniformly sampled 5D HN(CA)CONH, 4D HCBCACON, and HCBCANCO spectra were recorded to resolve poorly dispersed resonance frequencies of the disordered protein and 91 % of SPM residues were unambiguously assigned. Analysis of the chemical shifts revealed that two regions of the intrinsically disordered SPM (A95–S101 and R120–I127) have a tendency to form a helical structure, whereas the residues P1–D7 and G36–A40 have the propensity to adopt a β -structure.