Quantifying hurricane wind speed with undersea sound

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006Hurricanes, powerful storms with wind speeds that can exceed 80 m/s, are one of the most destructive natural disasters known to man. While current satellite technology has made it possible to effectively detect and track hurricanes, expensive 'hurricanehunting' aircraft are required to accurately classify their destructive power. Here we show that passive undersea acoustic techniques may provide a promising tool for accurately quantifying the destructive power of a hurricane and so may provide a safe and inexpensive alternative to aircraft-based techniques. It is well known that the crashing of wind-driven waves generates underwater noise in the 10 Hz to 10 kHz range. Theoretical and empirical evidence are combined to show that underwater acoustic sensing techniques may be valuable for measuring the wind speed and determining the destructive power of a hurricane. This is done by first developing a model for the acoustic intensity and mutual intensity in an ocean waveguide due to a hurricane and then determining the relationship between local wind speed and underwater acoustic intensity. Acoustic measurements of the underwater noise generated by hurricane Gert are correlated with meteorological data from reconnaissance aircraft and satellites to show that underwater noise intensity between 10 and 50 Hz is approximately proportional to the cube of the local wind speed. From this it is shown that it should be feasible to accurately measure the local wind speed and quantify the destructive power of a hurricane if its eye wall passes directly over a single underwater acoustic sensor. The potential advantages and disadvantages of the proposed acoustic method are weighed against those of currently employed techniques. It has also long been known that hurricanes generate microseisms in the 0.1 to 0.6 Hz frequency range through the non-linear interaction of ocean surface waves. Here we model microseisms generated by the spatially inhomogeneous waves of a hurricane with the non-linear wave equation where a second-order acoustic field is created by first-order ocean surface wave motion. We account for the propagation of microseismic noise through range-dependent waveguide environments from the deep ocean to a receiver on land. We compare estimates based on the ocean surface wave field measured in hurricane Bonnie with seismic measurements from Florida.Finally, I am grateful to have been awarded the Office of Naval Research Graduate Traineeship Award in Ocean Acoustics. I also thank the MIT Sea Grant office for funding portions of this research

Bayesian Online Learning of the Hazard Rate in Change-Point Problems

Change-point models are generative models of time-varying data in which the underlying generative parameters undergo discontinuous changes at different points in time known as change points. Changepoints often represent important events in the underlying processes, like a change in brain state reflected in EEG data or a change in the value of a company reflected in its stock price. However, change-points can be difficult to identify in noisy data streams. Previous attempts to identify change-points online using Bayesian inference relied on specifying in advance the rate at which they occur, called the hazard rate (h). This approach leads to predictions that can depend strongly on the choice of h and is unable to deal optimally with systems in which h is not constant in time. In this letter, we overcome these limitations by developing a hierarchical extension to earlier models. This approach allows h itself to be inferred from the data, which in turn helps to identify when change-points occur. We show that our approach can effectively identify change-points in both toy and real data sets with complex hazard rates and how it can be used as an ideal-observermodel for human and animal behavior when faced with rapidly changing inputs

End-functionalized glycopolymers as mimetics of chondroitin sulfate proteoglycans

Glycosaminoglycans are sulfated polysaccharides that play important roles in fundamental biological processes, such as cell division, viral invasion, cancer and neuroregeneration. The multivalent presentation of multiple glycosaminoglycan chains on proteoglycan scaffolds may profoundly influence their interactions with proteins and subsequent biological activity. However, the importance of this multivalent architecture remains largely unexplored, and few synthetic mimics exist for probing and manipulating glycosaminoglycan activity. Here, we describe a new class of end-functionalized ring-opening metathesis polymerization (ROMP) polymers that mimic the native-like, multivalent architecture found on chondroitin sulfate (CS) proteoglycans. We demonstrate that these glycopolymers can be readily integrated with microarray and surface plasmon resonance technology platforms, where they retain the ability to interact selectively with proteins. ROMP-based glycopolymers are part of a growing arsenal of chemical tools for probing the functions of glycosaminoglycans and for studying their interactions with proteins

Quantum nondemolition detection of a propagating microwave photon

The ability to nondestructively detect the presence of a single, traveling photon has been a long-standing goal in optics, with applications in quantum information and measurement. Realising such a detector is complicated by the fact that photon-photon interactions are typically very weak. At microwave frequencies, very strong effective photon-photon interactions in a waveguide have recently been demonstrated. Here we show how this type of interaction can be used to realize a quantum nondemolition measurement of a single propagating microwave photon. The scheme we propose uses a chain of solid-state 3-level systems (transmons), cascaded through circulators which suppress photon backscattering. Our theoretical analysis shows that microwave-photon detection with fidelity around 90% can be realized with existing technologies

Movement and Countermovement Dynamics Between the Religious Right and LGB Community Arising from Colorado’s Amendment 2

This sample of the case study of Equality Colorado will demonstrate how counter movements and litigation may limit and change how an organization surrounding a social movement acts. Colorado for Family Values helped pass Colorado’s Amendment 2 in 1992, which limited any present and future anti-discrimination legislation that would protect sexuality as a class. This ballot initiative passed by 53% of Colorado voters, and other states like Idaho and Oregon attempted to replicate this type of initiative. Amendment 2 challenged the LGB community and compelled the movement to collectively respond to the religious right with coalitions, pooled resources, and litigation. Equality Colorado, established in 1992, will exemplify how a social movement could respond to prejudicial legislation. One of Equality Colorado’s primary tactics was to reframe religion as inclusive of gay rights. It did not cede religion entirely to its opponents and attempted to delegitimize them by labeling them “radical right” as opposed to the more popular term “religious right” or “Christian Conservatives”. Additionally, Equality Colorado tried to compensate for the downsides of litigation by “translating” the legal terms to the general public and connecting litigators with the broader movement

Visualizing electrostatic gating effects in two-dimensional heterostructures

The ability to directly observe electronic band structure in modern nanoscale field-effect devices could transform understanding of their physics and function. One could, for example, visualize local changes in the electrical and chemical potentials as a gate voltage is applied. One could also study intriguing physical phenomena such as electrically induced topological transitions and many-body spectral reconstructions. Here we show that submicron angle-resolved photoemission (micro-ARPES) applied to two-dimensional (2D) van der Waals heterostructures affords this ability. In graphene devices, we observe a shift of the chemical potential by 0.6 eV across the Dirac point as a gate voltage is applied. In several 2D semiconductors we see the conduction band edge appear as electrons accumulate, establishing its energy and momentum, and observe significant band-gap renormalization at low densities. We also show that micro-ARPES and optical spectroscopy can be applied to a single device, allowing rigorous study of the relationship between gate-controlled electronic and excitonic properties.Comment: Original manuscript with 9 pages with 4 figures in main text, 5 pages with 4 figures in supplement. Substantially edited manuscript accepted at Natur

Directing Neuronal Signaling through Cell-Surface Glycan Engineering

The ability to tailor plasma membranes with specific glycans may enable the control of signaling events that are critical for proper development and function. We report a method to modify cell surfaces with specific sulfated chondroitin sulfate (CS) glycosaminoglycans using chemically modified liposomes. Neurons engineered to display CS-E-enriched polysaccharides exhibited increased activation of neurotrophin-mediated signaling pathways and enhanced axonal growth. This approach provides a facile, general route to tailor cell membranes with biologically active glycans and demonstrates the potential to direct important cellular events through cell-surface glycan engineering

Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists\u27 estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard