Quantitative Analysis of Seeing with Height and Time at Muztagh-Ata Site Based on ERA5 Database

Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.

Advanced hepatocellular carcinoma in a patient with neurofibromatosis type 1 and malignant peripheral nerve sheath tumor

To the Editor: Neurofibromatosis type 1(NF-1), or Von Recklinghausen disease, is a genetic disorder inherited in an autosomal dominant pattern. It is caused by a mutation in the neurofibromin gene located on chromosome 17, accounting for 96% of all neurofibromatosis cases. This condition can affect multiple systems and often leads to the formation of tumors along the nervous system [1]. Patients with NF-1 may exhibit various symptoms, including Lisch nodules, neurofibromas, scoliosis, café au lait spots, learning disabilities, vision disorders, and epilepsy.

Cutting parameter optimization for one-step shaft excavation technique based on parallel cutting method

The outcome of the cutting blasting in a one-step shaft excavation is heavily related to the cutting parameters used for parallel cutting method. In this study, the relationships between the cutting parameters（such as the hole spacing L and the empty hole diameter D） and damage zones were investigated by numerical simulation. A damage state index γ was introduced and used to characterize the crushing and crack damage zones through a user-defined subroutine. Two indices, i.e., η1 and η2 that can reflect the cutting performance, were also introduced. The simulation results indicate that an optimal value of L can be obtained so that the η1 and η2 can reach their optimal states for the best cutting performance. A larger D results in better cutting performance when the L value maintains its best. In addition, the influences of the loading rate and the in-situ stress on the cutting performance were investigated. It is found that an explosive with a high loading rate is suit for cutting blasting. The propagation direction and the length of the tensile cracks are affected by the direction and the magnitude of the maximum principal stress.

On-demand inverse design of acoustic metamaterials using probabilistic generation network

On-demand inverse design of acoustic metamaterials(AMs),which aims to retrieve the optimal structure according to given requirements,is still a challenging task owing to the non-unique relationship between physical structures and spectral responses.Here,we propose a probabilistic generation network(PGN) model to unveil this implicit relationship and implement this concept with an acoustic magic-cube absorber.By employing the auto-encoder-like configuration composed of a gate recurrent unit(GRU) and a deep neural network,our PGN model encodes the required spectral response into a latent space.The memory or feedback loop contained in the proposed GRU allows it to effectively recognize sequence characteristics of a spectrum.The method of modeling the inverse problem and retrieving multiple meta structures in a probabilistic generative manner skillfully solves the one-to-many mapping issue that is intractable in deterministic models.Moreover,to meet different sound absorption requirements,we tailored several representative spectra with low-frequency sound absorption characteristics,generating highprecision(MAE<0.06) predicted spectra with multiple meta structures.To further verify the effective prediction of the proposed PGN strategy,the experiment was carried out in a tailored broadband example,whose results coincide with both theoretical and numerical ones.Compared with other 5 networks,the PGN model exhibits higher accuracy and efficiency.Our work offers flexible and diversified solutions for multivalued inverse problems,opening up avenues to realize the on-demand de sign of AMs.