Metron (United States)

We study the problem of adaptively obtaining am-bient acoustic measurements via an autonomous underwater vehicle, with the goal of characterizing the geoacoustic properties of the seabed. In contrast to the traditional adaptive sampling scenario, we are provided with sets of snapshots associated with each spatial location, making the problem one of unsupervised learning. We demonstrate how sets of snapshots can be used to obtain noisy pairwise similarities between locations, which can then be used to perform level set estimation to separate the seabed into two highly-distinct types. We propose an adaptive sampling policy that aims to directly reduce the number of locations whose level set membership is uncertain, as well as an approach to minimizing the distance traveled while sampling. Results on synthetic and real-world sediment data demonstrate the benefits of our approach in terms of both accuracy and distance traveled.

This article presents a theoretical analysis of optimally distinguishing among environmental parameters from ocean ambient sound. Recent approaches to this problem either focus on parameter estimation or attempt to classify the environment into one of many known types through machine learning. This classification problem is framed as one of hypothesis testing on the received ambient sound snapshots. The resulting test depends on the Kullback-Leibler divergence (KLD) between the distributions corresponding to different environments or sediment types. Analysis of the KLD shows the dependence on the signal-to-noise ratio, the underlying signal subspace, and the distribution of eigenvalues of the respective covariance matrices. This analysis provides insights into both when and why successful hypothesis testing is possible. Experiments demonstrate that our analysis provides insight as to why certain environmental parameters are more difficult to distinguish than others. Experiments on sediment types from the Naval Oceanographic Office Bottom Sediment type database show that certain types are indistinguishable for a given array configuration. Further, the KLD can be used to provide a quantitative alternative to examining bottom loss curves to predict array processing performance.

The judges for the 2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the four finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application describes the design and deployment of a generalized synthetic control, a powerful and innovative statistical method for identifying, in a noisy environment, retailing innovations that produce a small percentage improvement in a large volume of sales for Anheuser Busch Inbev. The remaining three papers describe an inverse control approach to allocating lung transplants that best meets targeted outcomes and has been implemented as the national lung allocation policy on March 9, 2023, across the United States; a human-centric, optimized parcel delivery system developed for Deutsche Post that saves money while meeting constraints learned dynamically from driver behavior; and an AI-based system developed for Alibaba that learns supplier behavior to improve replenishment ordering and inventory control. Supplemental Material: Full presentation videos with slides are available in the INFORMS Video Library at https://www.informs.org/Resource-Center/Video-Library and as electronic companions to the INFORMS Journal on Applied Analytics articles.

Pediatric low-grade glioma (pLGG) represents the most common brain tumor in childhood. Previous studies have reported that a therapeutic strategy on the basis of the association of bevacizumab alone (B) or in combination with irinotecan (BI) could produce rapid tumor response and clinical improvement in children with pLGG. Nevertheless, a majority of patients relapses shortly (median, 5 mo) after stopping B or BI treatment. We proposed metronomic maintenance with weekly vinblastine added after a 6 months induction of B/BI to prevent early relapse. Monocentric retrospective analysis of a patient with pLGG treated with B or BI for 6 months followed by a 12-month maintenance with weekly vinblastine (6 mg/m²) from October 2012 to September 2019 in a single institution. In total, 18 patients (7 males and 11 females) were identified. Because of progression during the B or BI induction 2/18 children were excluded. In total, 16 patients were analyzed with a median age of 10 years (range, 4 to 16 y). A total of 13 patients received BI and 3 patients received B alone. The mean duration of induction was 6.2 months (range, 2 to 12 mo). After induction 5/16 patients had a partial radiologic response, 11/16 patients had stable disease. All patients started maintenance (median duration, 12 mo; range, 3 to 12 mo). With a median follow-up of 3.9 years after the end of B or BI (range, 11 mo to 7.2 y), 15/16 patients were alive and 9/16 patients were progression-free. Seven of 16 children progressed with a median time to progression of 23 months (ranges, 5 to 39 mo). Three of 16 (18%) children progressed during vinblastine maintenance and 4/16 (25%) patients after the end of maintenance. After the total duration of treatment, clinical improvement was noted in 4 patients, 9 patients had stable symptoms, and only 3 patients progressed. One and 2-year event-free survival were, respectively, 81.2% and 56.2%. Two-year overall survival was 93.7%. We report here, the potential benefit and the improvement of progression-free survival by adding metronomic maintenance with weekly vinblastine after initial induction with B or BI in children with low-grade glioma.

As the spread of COVID19 in the US continues to grow, local and state officials face difficult decisions about when and how to transition to a “new normal.” The goal of this study is to project the number of COVID19 infections and resulting severe outcomes, and the need for hospital capacity under social distancing, particularly, shelter-in-place and voluntary quarantine for the State of Georgia. We developed an agent-based simulation model to project the infection spread. The model utilizes COVID19-specific parameters and data from Georgia on population interactions and demographics. The simulation study covered a seven and a half-month period, testing different social distancing scenarios, including baselines (no-intervention or school closure only) and combinations of shelter-in-place and voluntary quarantine with different timelines and compliance levels. The following outcomes are compared at the state and community levels: the number and percentage of cumulative and daily new symptomatic and asymptomatic infections, hospitalizations, and deaths; COVID19-related demand for hospital beds, ICU beds, and ventilators. The results suggest that shelter-in-place followed by voluntary quarantine reduced peak infections from approximately 180K under no intervention and 113K under school closure, respectively, to below 53K, and delayed the peak from April to July or later. Increasing shelter-in-place duration from four to five weeks yielded 2–9% and 3–11% decrease in cumulative infection and deaths, respectively. Regardless of the shelter-in-place duration, increasing voluntary quarantine compliance decreased daily new infections from almost 53K to 25K, and decreased cumulative infections by about 50%. The cumulative number of deaths ranged from 6,660 to 19,430 under different scenarios. Peak infection date varied across scenarios and counties; on average, increasing shelter-in-place duration delayed the peak day by 6 days. Overall, shelter-in-place followed by voluntary quarantine substantially reduced COVID19 infections, healthcare resource needs, and severe outcomes.

Abstract Thousands of school systems have been struggling with the decisions about how to safely and effectively deliver education during the fall semester of 2020, amid the COVID19 pandemic. The objective of this study is to evaluate the public health impact of reopening schools on the spread of COVID19. An agent-based simulation model was adapted and used to project the number of infections and deaths under multiple school reopening dates and scenarios, including different cohorts receiving in-person instruction on alternating days, only younger children returning to in-person instruction, regular schedule (all students receiving in-person instruction), and school closure (all students receiving online instruction). The study period was February 18th-November 24th, 2020 and the state of Georgia was used as a case study. Across all scenarios, the number of COVID19-related deaths ranged from approximately 17 to 22 thousand during the study period, and on the peak day, the number of new infections ranged from 43 to 68 thousand. An alternating school day schedule performed: (i) almost as well as keeping schools closed, with the infection attack rate ranging from 38.5% to 39.8% compared to that of 37.7% under school closure; (ii) slightly better than only allowing children 10 years or younger to return to in-person instruction. Delaying the reopening of schools had a minimal impact on reducing infections and deaths under most scenarios.Significance StatementThis study provides insights on the impact of various school reopening dates and scenarios on the spread of COVID19, incorporating differences between children and adults in terms of disease progression and community transmission. School districts are faced with these challenging decisions considering the complex tradeoffs of their impact between public health, education, and society. While the number of new COVID19 confirmed cases continue to increase in many states, so are concerns about the negative impact of school closures on the children’s education and development. The systematic analysis of school reopening scenarios provided in this study will support school systems in their decision-making regarding if, when, and how to return to in-person instruction.

This paper investigates the performance of multiple approaches, including maximum likelihood estimation (MLE), for determining the Doppler frequency of radar returns from clutter scatterers. Fields backscattered from clutter are modeled as a complex stationary random process with a specified temporal correlation function received in the presence of additive thermal noise at a specified clutter-to-noise ratio. The MLE implementation is formulated, and its results are then compared through a simulation study to those obtained from traditional pulse-pair processing and other fast Fourier transform based methods in terms of error statistics and computational complexity. The methods are also compared for measurements of sea clutter from an X -band radar system. The results of the study show that the MLE approach has modest advantages over other methods in certain conditions, but that the performance gains obtained may often be insufficient to justify the extra computational cost or a-priori knowledge requirements of the method.

In this paper, we consider a sensor system engaged in multiple tasks: target tracking, classification, and identification of target intent (friend or foe). A hierarchical fully adaptive radar (HFAR) approach is developed that autonomously balances the different task priorities so that the three tasks are completed to human-operator defined performance levels. The system is comprised of two first-tier perception-action cycles (PACs) separately performing tracking and classification and one second-tier PAC determining target intent. The first-tier PACs receive data from the system hardware sensors and pass tracking and classifier perceptions up to the second-tier PAC. The second-tier PAC receives performance goals from the system operator and passes hardware sensing requirements down to the first-tier PACs. Each PAC determines its next action based on its current perception, performance goals, and sensor costs. A simulation example is presented to demonstrate performance.