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The national opioid epidemic is a public health crisis. Thoracic surgery has also been associated with high incidence of new persistent opioid use. Our purpose was to describe the incidence and predictors of opioid use after lung cancer resection. Retrospective review of lung cancer resections from 2015 to 2018 was performed using the Ohio Automated Rx Reporting System. Opioid dosing was recorded as milligram morphine equivalents (MME). Patients were stratified by preoperative opioid use. Chronic preoperative opioid users (opioid dependent) filled > 120 days supply of opioid pain medication in the 12 months prior to surgery; intermittent opioid users filled 180 days were: chronic preoperative opioid use (OR 23.8, p

Healthcare institutions need modern and powerful technology to provide high-quality, cost-effective care to patients. However, despite the considerable progress in the computerization and digitization of medicine, efficient and robust management tools have yet to materialize. One important reason for this is the extreme complexity and variability of healthcare operations, the needs of which have outgrown conventional management. Machine learning algorithms, scalable and adaptive to complex patterns, may be particularly well suited to solving these problems. Two major advantages of machine learning—the power of building strong models from a large number of weakly predictive features, and the ability to identify key factors in complex feature sets—have a particularly direct connection to the principal operational challenges. The main goal of this work was to study this relationship using two major types of operational problems: predicting operational events, and identifying key workflow drivers. Using practical examples, we demonstrate how machine learning can improve human ability to understand and manage healthcare operations, leading to more efficient healthcare. While computerization and digitization of medicine have advanced substantially, management tools in healthcare have not yet benefited much from these developments due to the extreme complexity and variability of healthcare operations. The ability of machine learning algorithms to build strong models from a large number of weakly predictive features, and to identify key factors in complex feature sets, is tested in operational problems involving hospital datasets on workflow and patient waiting time.

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes., Proceedings of the 5th North American Particle Accelerator Conference, NAPAC2022, Albuquerque, NM, USA

We discuss design considerations for a circular ion trap based on electromagnetic and particle beam simulations. This is a circular radiofrequency quadrupole (rfq) being designed for quantum information applications. The circular rfq should have good electromagnetic properties to accumulate and store the beam for prolonged times, while providing apertures for laser cooling and lower voltage electrodes to provide control over the beam. We use the electromagnetic and particle-in-cell software VSim, which uses finite difference time-domain and particle-in-cell methods, together with high performance computing tools., Proceedings of the 13th International Particle Accelerator Conference, IPAC2022, Bangkok, Thailand

MeV ultrafast electron diffraction (MUED) is a pump-probe characterization technique to study ultrafast phenomena in materials with high temporal and spatial resolution. This complex instrument can be advanced into a turn-key, high-throughput tool with the aid of machine learning (ML) mechanisms and high-performance computing. The MUED instrument at the Accelerator Test Facility in Brookhaven National Laboratory was employed to test different ML approaches for both data analysis and control. We characterized different materials using MUED, mainly polycrystalline gold and single crystal Ta2NiS5. Diffraction patterns were acquired in single shot mode and convolutional neural network autoenconder models were evaluated for noise reduction and the reconstruction error was studied to identify anomalous diffraction patterns. Electron beam energy jitter was analyzed from single shot diffraction patterns to be used as a novel diagnostic tool. The MUED beamline was also simulated using VSim to construct a surrogate model for control of beam shape and energy. Progress towards ML-based controls leveraging off Argonne Leadership Computing Facility resources will also be presented., Proceedings of the 13th International Particle Accelerator Conference, IPAC2022, Bangkok, Thailand

MeV ultrafast electron diffraction (MUED) is a pump-probe material characterization technique to study ultrafast lattice dynamics with high temporal and spatial resolution. It is a relatively young technology that has the potential to shed light onto some of the most puzzling problems in physics. This complex instrument can be advanced into a turn-key high-throughput tool with the aid of machine learning (ML) mechanisms together with high-performance computing. The MUED instrument located in the Accelerator Test Facility of Brookhaven National Laboratory was employed in this work to test different ML approaches for both data analysis and control. We characterized three materials using MUED: graphite, black phosphorous and gold thin films. Diffraction patterns were acquired in single shot mode and different ML methodologies were applied to reduce image noise. Convolutional neural network autoenconder and variational autoenconder models were utilized to extract the noise features and increase the signal-to-noise ratio. The energy jitter of the electron beam was analyzed after noise reduction of the single shot diffraction patterns., Proceedings of the 31st International Linear Accelerator Conference, LINAC2022, Liverpool, UK

The MeV Ultrafast Electron Diffraction (MUED) instrument at Brookhaven National Laboratory is a unique capability for material science. As part of a plan to make MUED a high-throughput user facility, we are exploring instrumentation developments based on Machine Learning (ML). We are developing a surrogate model of MUED that can be used to support control tasks. The surrogate model will be based on beam simulations that are benchmarked to experimental observations. We use VSim to model the beam dynamics of the radio-frequency gun and Elegant to transport the beam through the rest of the beam-line. We also use High Performance Computing resources from Argonne Leadership Computing Facility to generate the data for the surrogate model based on the original simulation as well as training the ML model., Proceedings of the 5th North American Particle Accelerator Conference, NAPAC2022, Albuquerque, NM, USA

Electron diffraction is among the most complex and influential inventions of the last century and contributes to research in many areas of physics and engineering. Not only does it aid in problems like materials and plasma research, electron diffraction systems like the MeV ultra-fast electron diffraction(MUED) instrument at the Brookhaven National Lab(BNL) also present opportunities to explore/implement surrogate modeling methods using artificial intelligence/machine learning/deep learning algorithms. Running the MUED system requires extended periods of uninterrupted runtime, skilled operators, and many varying parameters that depend on the desired output. These problems lend themselves to techniques based on neural networks(NNs), which are suited to modeling, system controls, and analysis of time-varying/multi-parameter systems. NNs can be deployed in model-based control areas and can be used simulate control designs, planned experiments, and to simulate employment of new components. Surrogate models based on NNs provide fast and accurate results, ideal for real-time control systems during continuous operation and may be used to identify irregular beam behavior as they develop., Proceedings of the 12th International Particle Accelerator Conference, IPAC2021, Campinas, SP, Brazil

We discuss electromagnetic simulations of accelerating structures in a high performance computing (HPC) system. Our overarching goal is to resolve the linac operation in a large ensemble of initial beam conditions. This requires a symbiotic relation between the electromagnetic solver and HPC. The linac is being developed by Ion Linac Systems to produce a low-energy, high-current, proton beam. We use VSim, an electromagnetic solver and PIC software developed by Tech-X to determine the electromagnetic fundamental mode of operation of the accelerating structures and discuss its implementation at the THETA supercomputer in the Argonne Leadership Computing Facility., Proceedings of the 12th International Particle Accelerator Conference, IPAC2021, Campinas, SP, Brazil

We discuss the possible use of crystalline beams in storage rings for applications in quantum information science (QIS). Crystalline beams have been created in ion trap systems and proven to be useful as a computational basis for QIS applications. The same structures can be created in a storage ring, but the ions necessarily have a constant velocity and are rotating in a circular trap. The basic structures that are needed are ultracold crystalline beams, called ion Coulomb crystals (ICC’s). We will describe different applications of ICC’s for QIS, how QIS information is obtained and can be used for quantum computing, and some of the challenges that need to be resolved to realize practical QIS applications in storage rings., Proceedings of the 12th International Particle Accelerator Conference, IPAC2021, Campinas, SP, Brazil

During the last decade, the production of soft to hard x-rays (up to 25 keV) at XFEL facilities has enabled new developments in a broad range of disciplines. One caveat is that these instruments can require a large amount of real estate. For example, the XFEL driver is typically an electron beam linear accelerator (LINAC) and the need for higher electron beam energies capable of generating higher energy X-rays can require longer linacs; costs quickly become prohibitive, requiring state of art methods. One cost-saving measure is to produce a high accelerating gradient while reducing cavity size. Compact accelerating structures are also high-frequency. Here, we describe design concepts for a high-gradient, cryo-cooled LINAC for XFEL facilities in the C-band regime (~4-8 GHz). We are also exploring C-band for different applications including drivers for security applications. We investigate 2 different traveling wave (TW) geometries optimized for high-gradient operation as modeled with VSim software., Proceedings of the 12th International Particle Accelerator Conference, IPAC2021, Campinas, SP, Brazil

A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory., Proceedings of the 12th International Particle Accelerator Conference, IPAC2021, Campinas, SP, Brazil