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At the Jet Propulsion Laboratory, training developers are creating vector animations to instruct engineers in the operation of mission software.
Fact sheet describes the Federal Energy Management Program's training initiative and outlines its offerings for federal agencies.
We inter-compare the above-cloud aerosol optical depth (ACAOD) of biomass burning plumes retrieved from different A-train sensors, i.e., MODIS, CALIOP, POLDER, and OMI. These sensors have shown independent capabilities to detect and retrieve aerosol loading above marine boundary layer clouds--a kind of situation often found over the Southeast Atlantic Ocean during dry burning season. A systematic one-to-one comparison reveals that, in general, all passive sensors and CALIOP-based research methods derive comparable ACAOD with differences mostly within 0.2 over homogeneous cloud fields. The 532-nm ACAOD retrieved by CALIOP operational algorithm is largely underestimated; however, it’s 1064-nm AOD when converted to 500 nm shows closer agreement to the passive sensors. Given the different types of sensor measurements processed with different algorithms, the close agreement between them is encouraging. Due to lack of adequate direct measurements above cloud, the validation of satellite-based ACAOD retrievals remains an open challenge. The inter-satellite comparison, however, can be useful for the relative evaluation and consistency check.
A novel realistic 3D virtual reality (VR) application has been developed to allow medical imaging students at Queensland University of Technology to practice radiographic techniques independently outside the usual radiography laboratory. A flexible agile development methodology was used to create the software rapidly and effectively. A 3D gaming environment and realistic models were used to engender presence in the software while tutor-determined gold standards enabled students to compare their performance and learn in a problem-based learning pedagogy. Students reported high levels of satisfaction and perceived value and the software enabled up to 40 concurrent users to prepare for clinical practice. Student feedback also indicated that they found 3D to be of limited value in the desktop version compared to the usual 2D approach. A randomised comparison between groups receiving software-based and traditional practice measured performance in a formative role play with real equipment. The results of this work indicated superior performance with the equipment for the VR trained students (P = 0.0366) and confirmed the value of VR for enhancing 3D equipment-based problem-solving skills. Students practising projection techniques virtually performed better at role play assessments than students practising in a traditional radiography laboratory only. The application particularly helped with 3D equipment configuration, suggesting that teaching 3D problem solving is an ideal use of such medical equipment simulators. Ongoing development work aims to establish the role of VR software in preparing students for clinical practice with a range of medical imaging equipment.
Abstract Reliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction.
We introduce a three-dimensional (3D), fully tensor train (TT) assembled isogeometric analysis (IGA) framework, TT-IGA, for solving partial differential equations (PDEs). Our method reformulates IGA discrete operators into TT format, enabling efficient compression and computation. Geometry evaluations use the original NURBS description at sampling points and TT approximation is applied to geometry-derived coefficient fields and discrete operators. We demonstrate the effectiveness of the proposed TT-IGA framework on the three-dimensional Poisson equation, achieving substantial reductions in memory and computational cost without compromising solution quality.
Purpose: Radium was the foundation of brachytherapy in the early decades of the 20th century. Despite being a most precious and perilous substance, it was mislaid with surprising frequency. This essay explores how it was lost, the efforts taken to recover it, and measures instituted to prevent mishandling. Methods and Materials: Review of contemporary literature, government publications, archives, and lay press. Results: Radium is a particularly dangerous substance because of its long half-life, its gaseous daughter (radon), and the high-energy emissions of its decay products. Despite the hazard, it was unregulated for most of the century. Any physician could obtain and administer it, and protocols for safe handling were generally lacking. Change came with appreciation of the danger, regulation, mandated training, and the institution of a culture of accountability. Unfortunately, careless management of medical radionuclides remains a global hazard. Conclusion: Responsible stewardship of radioactive material was not a high priority, for practitioners or the federal government, for much of the 20{sup th} century. As a result, large quantities of radium had gone astray, possibly subjecting the general public to continued radiation exposure. Lessons from the radium era remain relevant, as medical radionuclides are still mishandled.
Purpose: To conduct a nationwide survey to evaluate the current status of resident mentorship in radiation oncology. Methods and Materials: An anonymous electronic questionnaire was sent to all residents and recent graduates at US Accreditation Council for Graduate Medical Education–accredited radiation oncology residency programs, identified in the member directory of the Association of Residents in Radiation Oncology. Factors predictive of having a mentor and satisfaction with the mentorship experience were identified using univariate and multivariate analyses. Results: The survey response rate was 25%, with 85% of respondents reporting that mentorship plays a critical role in residency training, whereas only 53% had a current mentor. Larger programs (≥10 faculty, P=.004; and ≥10 residents, P<.001) were more likely to offer a formal mentorship program, which makes it more likely for residents to have an active mentor (88% vs 44%). Residents in a formal mentoring program reported being more satisfied with the overall mentorship experience (univariate odds ratio 8.77, P<.001; multivariate odds ratio 5, P<.001). On multivariate analysis, women were less likely to be satisfied with the mentorship experience. Conclusions: This is the first survey focusing on the status of residency mentorship in radiation oncology. Our survey highlights the unmet need for mentorship in residency programs.
Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu] 2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu] 2+ -catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.
In this letter, we investigate the tradeoff between reliability and beam training overhead in cooperative millimeter wave (mmWave) unmanned aerial vehicle (UAV) communications. In particular, we analyze how outage probability varies with the number of neighboring UAVs being probed as potential relays for relay selection. Unlike existing work, the limited number of UAV beams as well as UAV orientations are explicitly considered in our analysis. Here we derive the outage probability in closed form and verify our analysis via simulations. The results facilitate us to understand the fundamental limits of cooperative mmWave UAV communications.
Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an efficient framework to simulate hierarchically some challenging stochastic circuits/systems that include high-dimensional subsystems. Due to the high parameter dimensionality, it is challenging to both extract surrogate models at the low level of the design hierarchy and to handle them in the high-level simulation. In this paper, we develop an efficient analysis of variance-based stochastic circuit/microelectromechanical systems simulator to efficiently extract the surrogate models at the low level. In order to avoid the curse of dimensionality, we employ tensor-train decomposition at the high level to construct the basis functions and Gauss quadrature points. As a demonstration, we verify our algorithm on a stochastic oscillator with four MEMS capacitors and 184 random parameters. This challenging example is efficiently simulated by our simulator at the cost of only 10min in MATLAB on a regular personal computer.
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
Purpose: To develop and test clear illustrated instructions for training of monthly mechanical QA of medical linear accelerators. Methods: Illustrated instructions were created for monthly mechanical QA with tolerance tabulated, and underwent several steps of review and refinement. Testers with zero QA experience were then recruited from our radiotherapy department (1 student, 2 computational scientists and 8 dosimetrists). The following parameters were progressively de-calibrated on a Varian C-series linac: Group A = gantry angle, ceiling laser position, X1 jaw position, couch longitudinal position, physical graticule position (5 testers); Group B = Group A + wall laser position, couch lateral and vertical position, collimator angle (3 testers); Group C = Group B + couch angle, wall laser angle, and optical distance indicator (3 testers). Testers were taught how to use the linac, and then used the instructions to try to identify these errors. A physicist observed each session, giving support on machine operation, as necessary. The instructions were further tested with groups of therapists, graduate students and physics residents at multiple institutions. We have also changed the language of the instructions to simulate using the instructions with non-English speakers. Results: Testers were able to follow the instructions. They determined gantry, collimator and couch angle errors within 0.4, 0.3, and 0.9degrees of the actual changed values, respectively. Laser positions were determined within 1mm, and jaw positions within 2mm. Couch position errors were determined within 2 and 3mm for lateral/longitudinal and vertical errors, respectively. Accessory positioning errors were determined within 1mm. ODI errors were determined within 2mm when comparing with distance sticks, and 6mm when using blocks, indicating that distance sticks should be the preferred approach for inexperienced staff. Conclusion: Inexperienced users were able to follow these instructions, and catch errors within the criteria suggested by AAPM TG142 for linacs used for IMRT.
Many low- and middle-income countries lack the resources and services to manage cancer, from screening and diagnosis to radiation therapy planning, treatment and quality assurance. The challenges in upgrading or introducing the needed services are enormous, and include severe shortages in equipment and trained staff. In this symposium, we will describe examples of technology and scientific research that have the potential to impact all these areas. These include: (1) the development of high-quality/low-cost colposcopes for cervical cancer screening, (2) the application of automated radiotherapy treatment planning to reduce staffing shortages, (3) the development of a novel radiotherapy treatment unit, and (4) utilizing a cloud-based infrastructure to facilitate collaboration and QA. Learning Objectives: Understand some of the issues in cancer care in low- resource environments, including shortages in staff and equipment, and inadequate physical infrastructure for advanced radiotherapy. Understand the challenges in developing and deploying diagnostic and treatment devices and services for low-resource environments. Understand some of the emerging technological solutions for cancer management in LMICs. NCI; L. Court, NIH, Varian, Elekta; I. Feain, Ilana Feain is founder and CTO of Nano-X Pty Ltd.
Within military training and operational environments, individuals from diverse backgrounds share common spaces, follow structured routines and diets, and engage in physically demanding tasks. While there has been interest in leveraging microbiome features to predict and improve military health and performance, the longitudinal convergence of microbiomes in such constrained environments has not been established. To assess the degree of microbiome convergence, we performed shotgun metagenomic sequencing on swab samples from a military trainee cohort. Samples were taken across four different body sites, three timepoints, and two spatially distinct platoons. We observed evidence of convergence in one platoon, whereby similarity in microbiome composition increased over time, with numerous differentially abundant species. We found no indication of strain transfer between individuals, suggesting that convergence was influenced by external environmental factors, diet, and lifestyle. Microbial shifts observed in the convergence process included a decrease in fungal species, such as Malassezia restricta in nasal cavities, and a decrease in Prevotella species at inguinal regions across time. Shifts in multiple Corynebacterium species were also observed with varying magnitudes depending on the body site. Overall, we provide preliminary evidence of convergence of host microbial communities in military-associated environments that were distinguishable using shotgun metagenomic sequencing approaches. The data presented here on microbiome convergence, dynamics, and stability may inform risk-based mitigation in congregate military settings facilitating development of targeted microbial, dietary, or other interventions to optimize health and performance of military populations.
The tensor-train (TT) decomposition expresses a tensor in a data-sparse format used in molecular simulations, high-order correlation functions, and optimization. In this paper, we propose four parallelizable algorithms that compute the TT format from various tensor inputs: (1) Parallel-TTSVD for traditional format, (2) PSTT and its variants for streaming data, (3) Tucker2TT for Tucker format, and (4) TT-fADI for solutions of Sylvester tensor equations. We provide theoretical guarantees of accuracy, parallelization methods, scaling analysis, and numerical results. For example, for a d-dimension tensor in $\mathbb{R}$ $n\times∙∙∙$$\times$$n$ a two-sided sketching algorithm PSTT2 is shown to have a memory complexity of $O(n^{[d/2]})$, improving upon $O(n^{d—1})$ from previous algorithms.
Accelerator science and technology is inherently an integrative discipline that combines aspects of physics, computational science, electrical and mechanical engineering. As few universities offer full academic programs, the education of accelerator physicists and engineers for the future has primarily relied on a combination of on-the-job training supplemented with intense courses at regional accelerator schools. This paper describes the approaches being used to satisfy the educational interests of a growing number of interested physicists and engineers.
Using the wind data collected at a location in Fort Wainwright’s Donnelly Training Area (DTA) near the Cold Regions Test Center (CRTC) test track, Pacific Northwest National Laboratory (PNNL) estimated the gross and net energy productions that proposed turbine models would have produced exposed to the wind resource measured at the meteorological tower (met tower) location during the year of measurement. Calculations are based on the proposed turbine models’ standard atmospheric conditions power curves, the annual average wind speeds, wind shear estimates, and standard industry assumptions.