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This study introduces a structural-based training approach for CNN-based algorithms in single-molecule localization microscopy (SMLM) and 3D object reconstruction. We compare this approach with the traditional random-based training method, utilizing the LUENN package as our AI pipeline. The quantitative evaluation demonstrates significant improvements in detection rate and localization precision with the structural-based training approach, particularly in varying signal-to-noise ratios (SNRs). Moreover, the method effectively removes checkerboard artifacts, ensuring more accurate 3D reconstructions. Our findings highlight the potential of the structural-based training approach to advance super-resolution microscopy and deepen our understanding of complex biological systems at the nanoscale.

Single-Molecule Localization Microscopy (SMLM) has expanded our ability to visualize subcellular structures but is limited in its temporal resolution. Increasing emitter density will improve temporal resolution, but current analysis algorithms struggle as emitter images significantly overlap. Here we present a deep convolutional neural network called LUENN which utilizes a unique architecture that rejects the isolated emitter assumption; it can smoothly accommodate emitters that range from completely isolated to co-located. This architecture, alongside an accurate estimator of location uncertainty, extends the range of usable emitter densities by a factor of 6 to over 31 emitters per micrometer-squared with reduced penalty to localization precision and improved temporal resolution. Apart from providing uncertainty estimation, the algorithm improves usability in laboratories by reducing imaging times and easing requirements for successful experiments.

Rapid and accurate simulation of cerebral aneurysm flow modifications by flow diverters (FDs) can help improving patient-specific intervention and predicting treatment outcome. However, with explicit FD devices being placed in patient-specific aneurysm model, the computational domain must be resolved around the thin stent wires, leading to high computational cost in computational fluid dynamics (CFD). Classic homogeneous porous medium (PM) methods cannot accurately predict the post-stenting aneurysmal flow field due to the inhomogeneous FD wire distributions on anatomic arteries. We propose a novel approach that models the FD flow modification as a thin inhomogeneous porous medium (iPM). It improves over classic PM approaches in that, first, FD is treated as a screen, which is more accurate than the classic Darcy-Forchheimer relation based on 3D PM. second, the pressure drop is calculated using local FD geometric parameters across an inhomogeneous PM, which is more realistic. To test its accuracy and speed, we applied the iPM technique to simulate the post stenting flow field in three patient-specific aneurysms and compared the results against CFD simulations with explicit FD devices. The iPM CFD ran 500% faster than the explicit CFD while achieving 94%-99% accuracy. Thus iPM is a promising clinical bedside modeling tool to assist endovascular interventions with FD and stents., Comment: 21pages, 9figures

A Visual investigation of spray cone angle for different air-blast atomizers and flow conditions is described. Liquid jets are exposed to high gas stream with specific relative angle. Using high speed camera, spray cone angle over a range of Reynolds number 4x104 to 9x104 and Weber number 1 to 140 is studied, followed by laser-based diagnosis of particle distribution and Sauter mean diameter. The results show that for high Reynolds and Weber number, the cone angle is independent of flow condition, and is only dependent on geometry of atomizer, including orifice diameter with significant effect on cone angle, Sauter mean diameter and particle distribution

An experimental investigation of secondary atomization of spray in plain-jet airblast atomizers with a circular array of six liquid jets is studied. Crossflow of air with controlled velocity and pressure is applied to transverse jets. The liquid jets (Re=12000) are injected into airflow (Weg of 1250-3000) analyzed experimentally for different injection diameters of 0.8-1.6 mm. Particle size distribution measurement is carried out by Malvern Mastersizer X in the fully atomized region of the atomizer. The droplet size distribution in the secondary or final atomization stage is also modeled by the Finite stochastic Breakup model, which requires 4 parameters to be defined i.e. initial droplet diameter, maximum stable droplet size, minimum mass ratio, and droplet breakup probability. The modeling approximation is in very good agreement with the experimental result. This remarkable consistency between model and experiment is quite useful in terms of optimal atomization performance by considering the dynamics of the liquid jet.

In this article, the effect of geometrical parameters and flow conditions on the performance of a swirl atomizer is studied. Dimensional analysis and experimental investigations are utilized to define significant terms. The PDA system used for the measurements was able to supply information about the size, concentration, and particle velocity at each measurement location. The orifice diameter, the spiral cone angle, and also the flow Reynolds number, which is defined based on the injector orifice diameter, play an important role in spray quality, and their significance is summarized in a correlation. In order to achieve the appropriate combination of design variables that satisfy the design constraints, a GA-based program was used in a reverse analysis process. Finally, the advantages of human inspection were employed to provide true best performers from a small group of final answers.