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Chest pain symptoms are highly prevalent in emergency departments (EDs), where acute aortic syndrome (AAS) is a catastrophic cardiovascular emergency with a high fatality rate, especially when timely and accurate treatment is not administered. However, current triage practices in the ED can cause up to approximately half of patients with AAS to have an initially missed diagnosis or be misdiagnosed as having other acute chest pain conditions. Subsequently, these AAS patients will undergo clinically inaccurate or suboptimal differential diagnosis. Fortunately, even under these suboptimal protocols, nearly all these patients underwent non-contrast CT covering the aorta anatomy at the early stage of differential diagnosis. In this study, we developed an artificial intelligence model (DeepAAS) using non-contrast CT, which is highly accurate for identifying AAS and provides interpretable results to assist in clinical decision-making. Performance was assessed in two major phases: a multi-center retrospective study (n = 20,750) and an exploration in real-world emergency scenarios (n = 137,525). In the multi-center cohort, DeepAAS achieved a mean area under the receiver operating characteristic curve of 0.958 (95% CI 0.950-0.967). In the real-world cohort, DeepAAS detected 109 AAS patients with misguided initial suspicion, achieving 92.6% (95% CI 76.2%-97.5%) in mean sensitivity and 99.2% (95% CI 99.1%-99.3%) in mean specificity. Our AI model performed well on non-contrast CT at all applicable early stages of differential diagnosis workflows, effectively reduced the overall missed diagnosis and misdiagnosis rate from 48.8% to 4.8% and shortened the diagnosis time for patients with misguided initial suspicion from an average of 681.8 (74-11,820) mins to 68.5 (23-195) mins. DeepAAS could effectively fill the gap in the current clinical workflow without requiring additional tests.

We investigate the nonlinear stability problem for the two-dimensional Boussinesq system around the Poiseuille flow in a finite channel. The system has the characteristic of Navier-slip boundary condition for the velocity and Dirichlet boundary condition for the temperature, with a small viscosity $\nu$ and small thermal diffusion $\mu,$ respectively. More precisely, we prove that if the initial velocity and initial temperature satisfies$$||u_{0}-(1-y^2,0) ||_{H^{\frac{7}{2}+}}\leq c_0\min\left\lbrace \mu,\nu\right\rbrace ^{\frac{2}{3}}$$ and $$ ||\theta_{0}||_{H^1}+|||D_x|^{\frac{1}{8}}\theta_{0}||_{H^1}\leq c_1\min\left\lbrace \mu,\nu\right\rbrace ^{{\frac{31}{24}}}$$ for some small constants $c_0$ and $c_1$ which are both independent of $\mu,\nu$, then we can reach the conclusion that the velocity remains within $O\left( \min\left\lbrace \mu,\nu\right\rbrace ^{\frac{2}{3}}\right) $ of the Poiseuille flow; the temperature remains $O\left( \min\left\lbrace \mu,\nu\right\rbrace ^{\frac{31}{24}}\right) $ of the constant 0, and approaches to 0 as $t\rightarrow\infty.$, Comment: 27pages

In this paper, we investigate the transition threshold problem concerning the 2-D Navier-Stokes equations in the context of Couette flow $(y,0)$ at high Reynolds number $Re$ in whole space. By utilizing Green's function estimates for the linearized equations around Couette flow, we initially establish refined dissipation estimates for the linearized Navier-Stokes equations with a precise decay rate $(1+t)^{-1}.$ As an application, we prove that if the initial perturbation of vorticity satisfies$$\|\omega_{0}\|_{H^{1}\cap L^1}\leq c_0\nu^{\frac{3}{4}}$$ for some small constant $c_0$ independent of the viscosity $\nu$, then we can reach the conclusion that the solution remains within $O\left( \nu ^{\frac{3}{4}}\right) $ of the Couette flow., Comment: 20pages

Plasmonic lasers provide a paradigm-changing approach for the generation of coherent light at the nanoscale. In addition to the usual properties of coherent radiation, the emission of plasmonic lasers can feature high sensitivity to the surrounding environment, which makes this technology attractive for developing high-performance and highly-integrated sensing devices. Here, we investigate a plasmonic laser architecture based on a high-Q plasmonic crystal consisting of a periodic arrangement of nanoholes on a thin gold film cladded with an organic-dye-doped SiO$_2$ gain layer as the gain material. We report an extensive full-wave numerical analysis of the device's lasing performance and its application as a biochemical sensor, showing that the proposed design features excellent figures of merit for surface sensing that in principle can be over an order of magnitude larger than those of previously reported high-performance plasmonic biosensor architectures.

Atomic collapse can be observed in graphene because of its large "effective" fine structure constant, which enables this phenomenon to occur for an impurity charge as low as $Z_c\sim 1-2$. Here, we investigate the effect of the sublattice symmetry on molecular collapse in two spatially separated charge tunable vacancies, that are located on the same (A-A type) or different (A-B type) sublattices. We find that the broken sublattice symmetry: (1) does not affect the location of the main bonding and anti-bonding molecular collapse peaks, (2) but shifts the position of the satellite peaks, because they are a consequence of the breaking of the local sublattice symmetry, and (3) there are vacancy characteristic collapse peaks that only occur for A-B type vacancies, which can be employed to distinguish them experimentally from the A-A type. As the charge, energy, and separation distance increase, the additional collapse features merge with the main molecular collapse peaks. We show that the spatial distribution around the vacancy site of the collapse states allows us to differentiate the molecular from the frustrated collapse., Comment: 8 pages and 12 figures