Analysis of Roadway Traffic During Hurricane Irma

Hurricane Irma struck the United States in 2017 and caused a massive evacuation across the State of Florida. This research uses traffic data collected across Florida to investigate the evacuation pattern during Hurricane Irma. Although many local governments issued evacuation orders before Hurricane Irma made landfall, the public may not follow the evacuation orders closely. They may choose to evacuate before the orders take effect. This thesis analyzes seven major regions, including the Florida Key, Southeast, Marco Island, Tampa, Hernando, Polk, and Orlando Regions. The objectives of this research are to (1) identify the evacuation start time, evacuation peak time and reentry time of each region and relate these times to information released time and Irma landfall time and (2) examine the road utilization by road types as Hurricane Irma approached. For the first objective, this research uses the traffic volume data to study the evacuation traffic pattern. Two methods, cumulative volume comparison and Wilcoxon Signed-Rank test, are provided and they work together to identify the evacuation start time of different regions. Also, the evacuation peak time and traffic reentry time are identified for each region, based on traffic volumes. For the second objective, this research calculates the volume to capacity ratio and density at different traffic count stations to examine the road utilization in three types of roads: Freeway, Multilane Highway and Two-lane Highway. The study explores the reasons why the volume to capacity ratio is less than 1.0 when the density indicates level of service (LOS) F. The results show that the evacuation started before evacuation orders took effect for the seven analysis regions. Volume to capacity and LOS analysis results show that Freeways were more frequently congested than Multilane highways and Two-Lane highways during evacuation

'THz Torch' wireless communications links

The low-cost 'THz Torch’ technology, which exploits the thermal infrared spectrum (ca. 10 to 100 THz), was recently introduced to provide secure low data rate communications links across short ranges. In this thesis, the channel model for 'THz Torch’ wireless communications links is redeveloped from a thermodynamics perspective. Novel optimization-based channel estimators are also proposed to calibrate parameters in the channel model. Based on these theoretical advances, a cognitive 'THz Torch’ receiver, which combines conventional digital communications with state-of-the-art deep learning techniques, is presented to achieve cognitive synchronization and demodulation. The newly reported 'THz Torch’ wireless link is capable of bypassing the thermal time constant constraints normally associated with both the thermal emitter and sensor, allowing truly asynchronous data transfer with direct electronic modulation. Experimental results obtained in both laboratory environments and field trials demonstrate step-change improvements in channel range, bit rate, bit error rate and demodulation speed. This work represents a paradigm shift in modulation-demodulation with a thermal-based physical layer and offers a practical solution for implementing future ubiquitous secure 'THz Torch’ wireless communications links. The cognitive receiver concept also has wide-ranging implications for future communications and sensor technologies, making them more resilient when operating in harsh environments.Open Acces

Electrochemically Modulated Generation/Delivery of Nitric Oxide (NO) from Nitrite for Biomedical Applications.

In this dissertation research, the development of a new electrochemically modulated NO generation/delivery approach was examined. Further, the potential application of this approach in devising advanced thromboresistant/bactericidal intravascular catheters and a new NO inhalation therapy system was explored. Nitric oxide can be generated from nitrite via two electrochemical approaches: 1) using a Cu0 wire and an applied anodic/cathodic potential pulse sequence to electrochemically reduce nitrite to NO (Chapter 2); and 2) using Pt/Au or other working electrodes and a soluble Cu(II)-ligand complex as mediator to reduce nitrite to NO (Chapter 3). The temporal pattern of NO generation can be precisely modulated in the latter system by the applied potential or current. This electrochemical NO release system was first incorporated within intravascular catheters, which exhibited much reduced clotting (~85 %) in vivo and significantly less (>99.9%) microbial biofilm in vitro compared to non-NO release control devices. Further, this NO release concept was combined with an amperometric oxygen sensor (PO2 sensor) within a dual-lumen catheter configuration (Chapter 4) for intravascular continuous monitoring of PO2 levels. Electrochemical NO release was fully compatible with PO2 sensing and yielded more accurate PO2 measurements (vs. controls) when implanted in arteries of pigs for 20 h. In Chapter 5, the electrochemical NO release catheters were used for controlled delivery of NO to elucidate the dosage effect of NO on mature P. aeruginosa biofilm. Fluxes of NO >0.5 × 10^-10 mol min-1 cm-2 showed 99% killing of the biofilm in 3 h, and such an effect was in synergy with added gentamicin. In Chapter 6, the new electrochemical NO delivery method was employed for developing a gas phase NO inhalation (INO) system. Relatively pure gas phase NO in the range of 1–150 ppmv can be created by this system. Finally, the partitioning and diffusion properties of NO within several biomedical polymers was examined (Chapter 7), with silicone rubber exhibiting the optimal transport of NO. Overall, electrochemical delivery of NO provides both a tool for fundamental biological studies, as well as a means to improve the biocompatibility of medical devices.PhDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120688/1/renhang_1.pd

Multipartite entanglement detection based on generalized state-dependent entropic uncertainty relation for multiple measurements

We present the generalized state-dependent entropic uncertainty relations for multiple measurement settings, and the optimal lower bound has been obtained by considering different measurement sequences. We then apply this uncertainty relation to witness entanglement, and give the experimentally accessible lower bounds on both bipartite and tripartite entanglements. This method of detecting entanglement is applied to physical systems of two particles on a one-dimensional lattice, GHZ-Werner states and W-Werner states. It is shown that, for measurements which are not in mutually unbiased bases, this new entropic uncertainty relation is superior to the previous state-independent one in entanglement detection. The results might play important roles in detecting multipartite entanglement experimentally.Comment: 7 pages,3 figure

The Latent Heat of Single Flavor Color Superconductivity in a Magnetic Field

We calculate the energy release associated with first-order phase transition between different types of single flavor color superconductivity in a magnetic field.Comment: Updated version accepted by PRD, with minor change

Promoting Collaborative Care: Relative Performance-based Payment Models for Hospitals and Post-acute Care Providers

Diagnosis-Related Group (DRG) and bundled payment models are widely used in healthcare reimbursement by entities like the Centers for Medicare & Medicaid Services (CMS) and insurance companies. However, these models were primarily designed for conditions managed by a single healthcare provider in a centralized manner, often overlooking the complexities of cases requiring post-acute care (PAC) following an initial hospital stay. This can result in inadequate incentives for effective care coordination between hospitals and PAC providers, especially when treatment decisions are decentralized. Motivated by the Comprehensive Care for Joint Replacement (CJR) payment model recently introduced by CMS, which holds hospitals accountable for the quality and cost of the entire CJR episode, including the cost of PAC, we propose simple payment models that incentivize hospitals and PAC providers to collaboratively enhance the cost efficiency and quality of care for such conditions. Our approach extends traditional payment models by introducing performance targets for all providers, encompassing the entire care episode. Using a game-theoretical model, we demonstrate that the proposed payment model elicits socially optimal actions from all providers, under various assumptions. Importantly, our models do not require detailed knowledge of the hospital-PAC network structure but rely solely on observed cost and quality outcomes within the entire system. Furthermore, while the CJR payment model represents a positive step forward, our analysis reveals potential areas for improvement. Specifically, we suggest that holding both hospitals and PAC providers financially accountable, instead of solely focusing on hospitals, would yield further enhancements in the care delivery model