We present experimental results for the ionization of aniline and benzene molecules subjected to intense ultrashort laser pulses. Measured parent molecular ions yields were obtained using a recently developed technique capable of three-dimensional imaging of ion distributions within the focus of a laser beam. By selecting ions originating from the central region of the focus, where the spatial intensity distribution is nearly uniform, volumetric-free intensity-dependent ionization yields were obtained. The measured data revealed a previously unseen resonance-enhanced multiphoton ionization (REMPI)-like process. Comparison of benzene, aniline, and Xe ion yields demonstrates that the observed intensity-dependent structures are not due to geometric artifacts in the focus. Finally for intensities greater than ∼3 × 10 13 W/cm 2 , we attribute the ionization of aniline to a stepwise process going through the πσ * state which sits three photons above the ground state and two photons below the continuum.

We report on new flavor tagging algorithms developed to determine the quark-flavor content of bottom ( "Image missing") mesons at Belle II. The algorithms provide essential inputs for measurements of quark-flavor mixing and charge-parity violation. We validate and evaluate the performance of the algorithms using hadronic "Image missing" decays with flavor-specific final states reconstructed in a data set corresponding to an integrated luminosity of 62.8 fb$$^{-1}$$ - 1 , collected at the "Equation missing" resonance with the Belle II detector at the SuperKEKB collider. We measure the total effective tagging efficiency to be $$\begin{aligned} \varepsilon _\mathrm{eff} = \big (30.0 \pm 1.2(\text {stat}) \pm 0.4(\text {syst})\big )\% \end{aligned}$$ ε eff = ( 30.0 ± 1.2 ( stat ) ± 0.4 ( syst ) ) % for a category-based algorithm and $$\begin{aligned} \varepsilon _\mathrm{eff} = \big (28.8 \pm 1.2(\text {stat}) \pm 0.4(\text {syst})\big )\% \end{aligned}$$ ε eff = ( 28.8 ± 1.2 ( stat ) ± 0.4 ( syst ) ) % for a deep-learning-based algorithm.

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Artificial intelligence (AI) is the technique that enables computers to solve problems and perform tasks that traditionally require human intelligence. The availability of large amounts of medical data from electronic medical records and powerful modern microcomputers enables the development of AI in medicine. AI has proven its applicability in many different medical areas, such as drug discovery, diagnostic radiology and pathology, as well as interventional applications in cardiology and surgery. However, until today, AI is scarcely used in the clinical practice of anesthesiology. Although there has been a significant body of research published on AI applications for anesthesiology in the literature, the number of developed robot systems for commercial use or those ready for clinical trials remains limited. The limitations of AI systems are identified and discussed, which include incorrect medical data formatting, individual patient variability, the lack of ability of current AI systems, anesthesiologist inexperience in AI usage, system unreliability, unexplainable AI conclusions and strict regulations. In order to ensure anesthesiologists' trust in AI systems and improve their implementation in daily practice, strict quality control of the systems and algorithms should be undertaken. Further, anesthesiology personnel should play an integral role in the development of AI systems before we are able to see more AI integration in clinical anesthesiology.

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