Absolute contrast estimation for soft X-ray photon fluctuation spectroscopy using a variational droplet model

X-ray photon fluctuation spectroscopy using a two-pulse mode at the Linac Coherent Light Source has great potential for the study of quantum fluctuations in materials as it allows for exploration of low-energy physics. However, the complexity of the data analysis and interpretation still prevent recovering real-time results during an experiment, and can even complicate post-analysis processes. This is particularly true for high-spatial resolution applications using CCDs with small pixels, which can decrease the photon mapping accuracy resulting from the large electron cloud generation at the detector. Droplet algorithms endeavor to restore accurate photon maps, but the results can be altered by their hyper-parameters. We present numerical modeling tools through extensive simulations that mimic previous x-ray photon fluctuation spectroscopy experiments. By modification of a fast droplet algorithm, our results demonstrate how to optimize the precise parameters that lift the intrinsic counting degeneracy impeding accuracy in extracting the speckle contrast. These results allow for an absolute determination of the summed contrast from multi-pulse x-ray speckle diffraction, the modus operandi by which the correlation time for spontaneous fluctuations can be measured.

One-dimensional Droplet Model for Prediction of Jet-tip Height of Subcooling Mixing Jet for Cryogenic Propellant Storage Tank

This study proposes a one-dimensional droplet model to predict the jet-tip height of a subcooling mixing jet issuing from the bottom of a cryogenic propellant storage tank. Cryogenic liquids, such as liquid hydrogen and liquid oxygen, are used as propellants and oxidants in spacecraft propulsion systems that require long-term storage in a closed tank. However, thermal stratification forms near the gas-liquid interface during long-term storage of cryogens due to heat flowing into the tank from the surrounding environment. In addition, boil-off gas (BOG) is generated from the interface, which causes increased pressure in the tank. To reduce the BOG, it is effective to destroy the thermal stratification by mixing in the cold jet issuing from the bottom of the tank. Ground experiments using FC-72 and water as test fluids are conducted to investigate the behavior of the jet using the proposed one-dimensional spherical droplet model as the tip of the jet. The jet behavior is visualized using the Shadowgraph system and the height of the jet-tip is investigated under various experimental conditions. The proposed model is also verified by comparison with experimental data available in the literature. The results show that the proposed model aligns well with the experimental data.

Biophysical Studies of Cancer Cells’ Traverse-Vessel Behaviors Under Different Pressures Revealed Cells’ Motion State Transition

Circulating tumor cells (CTCs) survive in the bloodstream, seed, and invade to foster tumor metastasis. The arrest of CTCs is favored by permissive flow forces and geometrical constraints. Through the use of high-throughput microfluidic devices designed to mimic capillary-sized vessels, we applied different pressure differences to cancer cells and recorded cell traverse-vessel behaviors. Our results showed that cancer cells would transform from Newtonian droplet state to adhesion/migration state when cancer cells traverse in the artificial vessels. To explain these phenomena, a modified Newtonian droplet model was also proposed. These phenomena and the modified model may reveal how the CTCs in the blood seed and invade in the vessels under suitable conditions.

NEW SOLUTION TO THE DROPLET KERNEL MODEL

The droplet model of the nucleus is revived, for which an exact solution for an incompressible fluid is obtained using the hydrodynamic potential solution obtained from the Schrödinger equation. Moreover, for an incompressible fluid, there are formulas for the pressure or potential. There is the main part of the hydrodynamic potential, which is obtained by replacing the modulus of the inverse difference of vectors by the difference in moduli of the values of the vectors. The bulk of the potential is expressed in a finite formula with singularities. A formula is obtained for the integral containing the modulus of the difference between the exact values of the vectors minus the main part of the potential. This difference defines a continuous correction with the features taken into account. The main part of the potential at the boundary of the nucleus turned out to be infinitely large with an imaginary part, locking particles in the nucleus. In this case, the real part of the main potential decreases with decreasing radius, becomes negative, and determines the bound state. At half the radius of the nucleus, there is a linear term along the radius. At the zero radius, there is an infinite negative potential with an imaginary part. An expression for the quantum of the emitted energy is obtained. Note that the added mass was not used due to the rotational regime of the nucleus. An algorithm for calculating the spectrum of the kernel is proposed, and each state of the action of the kernel sn corresponds to n calculated frequencies, determined by n angles in the configuration space. The main space is n + 1 dimensional, and each dimension of space has its own energy. But without special means, the potential of the nucleus tends to infinity. It is necessary to introduce the imaginary degree of roughness of the corners, in expressions containing singularities, then the infinities disappear.

A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method

Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respiratory. Although various studies have provided enormous computational fluid simulations, most cases are too specific and quite challenging to combine with other similar studies directly. Therefore, this paper proposes a systematic approach to predict the droplet behavior for coughing cases using machine learning. The approach consists of three models, which are droplet generator, mask model, and free droplet model modeled using feedforward neural network (FFNN). The evaluation has shown that the three FFNNs models’ accuracies are relatively high, with R-values of more than 0.990. The model has successfully predicted the evaporation effect on the diameter reduction and the completely evaporated state, which can be considered unlearned cases for machine learning models. The predicted horizontal distance pattern also agrees with the data in the literature. In summary, the proposed approach has demonstrated the capability to predict the diameter pattern according to the experimental or previous work data at various mask face types.