Spatial damping of propagating sausage waves in coronal cylinders

Sausage modes are important in coronal seismology. Spatially damped propagating sausage waves were recently observed in the solar atmosphere. We examine how wave leakage influences the spatial damping of sausage waves propagating along coronal structures modeled by a cylindrical density enhancement embedded in a uniform magnetic field. Working in the framework of cold magnetohydrodynamics, we solve the dispersion relation (DR) governing sausage waves for complex-valued longitudinal wavenumber $k$ at given real angular frequencies $\omega$. For validation purposes, we also provide analytical approximations to the DR in the low-frequency limit and in the vicinity of $\omega_{\rm c}$, the critical angular frequency separating trapped from leaky waves. In contrast to the standing case, propagating sausage waves are allowed for $\omega$ much lower than $\omega_{\rm c}$. However, while able to direct their energy upwards, these low-frequency waves are subject to substantial spatial attenuation. The spatial damping length shows little dependence on the density contrast between the cylinder and its surroundings, and depends only weakly on frequency. This spatial damping length is of the order of the cylinder radius for $\omega \lesssim 1.5 v_{\rm Ai}/a$, where $a$ and $v_{\rm Ai}$ are the cylinder radius and the Alfv\'en speed in the cylinder, respectively. We conclude that if a coronal cylinder is perturbed by symmetric boundary drivers (e.g., granular motions) with a broadband spectrum, wave leakage efficiently filters out the low-frequency components.Comment: 6 pages, 2 figures, to appear in Astronomy & Astrophysic

Suitability of Mycorrhiza-Defective Rice and Its Progenitor for Studies on the Control of Nitrogen Loss in Paddy Fields via Arbuscular Mycorrhiza

Employing mycorrhiza-defective mutants and their progenitors does not require inoculation or elimination of the resident microbial community in the experimental study of mycorrhizal soil ecology. We aimed to examine the suitability of mycorrhiza-defective rice (non-mycorrhizal, Oryza sativa L., cv. Nipponbare) and its progenitor (mycorrhizal) to evaluate nitrogen (N) loss control from paddy fields via arbuscular mycorrhizal (AM) fungi. We grew the two rice lines in soils with the full community of AM fungi and investigated root AM colonization. In the absence of AM fungi, we estimated rice N content, soil N concentration and microbial community on the basis of phospholipid fatty acids; we also quantified N loss via NH3 volatilization, N2O emission, runoff and leaching. In the presence of AM fungi, we did not find any evidence of AM colonization for non-mycorrhizal rice while mycorrhizal rice was colonized and percentage of root colonization was 17–24%. In the absence of AM fungi, the two rice lines had similar N content, soil N concentration and microbial community. Importantly, there was no significant difference in N loss via all the four pathways between mycorrhizal and non-mycorrhizal systems. This mycorrhizal/non-mycorrhizal rice pair is suitable for further research on the role of AM fungi in the control of soil N loss in paddy fields

Calculation and Analysis of the Instream Ecological Flow for the Irtysh River

AbstractInstream ecological flow is essential determinant of river health. In this paper, the monthly minimum flow calculation method, the (new) monthly frequency calculation method were applied to calculate and evaluate the minimum instream ecological flow and the optimal instream ecological flow for the Irtysh River, and the different criteria of instream ecological flow was calculated by the improved Tennant method. It is expected to provide a scientific basis for the reasonable allocation of water resource in Irtysh River basin by calculating the instream ecological flow

Research on bearing fault diagnosis technology based on machine learning

As industrial equipment complexity continues to rise, the importance of bearings within these systems has become more critical, given their pivotal role in equipment functionality. Bearing faults can result in severe production accidents and safety issues. Hence, there is an urgent need for advanced bearing fault diagnosis technology. This study concentrates on rolling bearings, analyzing their structural characteristics and key parameters to classify fault types—inner race faults, rolling element faults, and outer race faults. Utilizing a dataset of 80 sets of bearing factory data, time and frequency domain analyses are conducted, establishing seven feature parameters (five in the time domain and two in the frequency domain). This data is organized into a 7-dimensional matrix for subsequent analysis and model development. The K-Means algorithm is chosen for its effectiveness in automatically recognizing fault patterns in rolling bearings. Training on the 7-dimensional matrix identifies four clustering centers corresponding to normal conditions, inner race faults, rolling element faults, and outer race faults. The fault diagnosis system is implemented using Python, and algorithm optimization improves efficiency. The study concludes with insights drawn from the analysis and proposes optimization methods, which contributing to advancing bearing fault diagnosis technology, particularly addressing industrial equipment reliability and safety concerns