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Campos, Richard & Harvey, P. & Hou, Guangyang. (2023). Analytical fragility curves for trees subject to ice loading in a changing climate. 10.1080/23789689.2023.2202962. Recent severe ice storms across the United States severely damaged trees resulting in extensive electrical power outages. Furthermore, trees and branches can fall on nearby roads, blocking traffic flow and reducing the safety of drivers. In this study, trees subjected to ice loads were analyzed using the finite element method and Monte Carlo simulation to develop analytical fragility curves. Two-dimensional, fractal trees were constructed with randomly generated geometric and mechanical parameters for four deciduous tree species: Acer saccharum, Tilia americana, Fagus grandifolia, and Quercus alba. Two load case scenarios were considered – with and without the effects of leaves – which were then subjected to varying ice accumulation thicknesses. The resulting fragility curves suggest that leaves have a substantial impact on tree branch damage under ice loads, which is significant because of the increase in unseasonably early ice storms due to climate change. 

The significance of critical infrastructure systems in maintaining productivity is undeniable. However, such systems remain susceptible to external disturbances and cascading failures. Instead of operating independently, these physical systems, such as transportation and stormwater systems, form an interdependent system. This interdependence, particularly important during flooding, illustrates that the failure of a stormwater system can disrupt traffic networks. To explore the extent of such interdependency, this study investigates the transportation and stormwater networks in Norman, Oklahoma. Using network science theories and concepts of multilayered networks, this paper analyzes these systems, both individually and in combination. The study identifies closely located components in the road and stormwater networks using Moran's I spatial autocorrelation metric. Next, the connectivity of these networks is represented in a graph format to investigate the topological credentials (i.e., rank of relative importance) of the network components (i.e., water inlets, road intersections as nodes, and stormwater conduits, road segments as links). Moreover, such credentials further change by considering the weights of the network components (i.e., average daily traffic, water flow). The proximity-based connectivity considerations between these networks utilizing Moran's I significance score revealed a good indicator of spatial interdependency. When incorporating directionality, the multilayer network analysis highlights that highly central components tend to cluster spatially, unlike the undirected counterpart. The study also identifies vulnerable locations and network components in a combined network setting that differ from the networks in isolation. In doing so, the research reveals new insights governing the complex reliance of transportation systems on neighboring stormwater systems.

 

The Internet of Medical Things (IoMT) is a rapidly growing community of intelligent medical technologies dedicated to sensing, monitoring, and reporting patient vitals, often with the intent of communicating findings with healthcare professionals (HCPs). For the past two summers, 2020 and 2021, four undergraduate electrical/computer engineering and computer science students, and two high school STEM teachers, worked with two graduate student mentors to explore various IoMT use cases via their participation in a Research Experiences for Undergraduates (REU) and Teachers (RET) program. During both summers, the REU/RET program was conducted remotely over nine weeks, not including pre-summer engagement activities. These pre-summer activities were designed to promote and encourage healthy mentor-mentee interactions while also providing an additional opportunity for participants to acclimate to their research projects before the program start. Throughout this work, participants were able to gain or further develop skills in some of the following areas: Ethical Hacking, Data Science, Intrusion Detection Systems, Linux, Machine Learning, Networking, and Python, as well as interact with a designated smart device and testing environment. In the first summer, participants were assigned a smart glucose meter and tasked with 1) exploiting the potential threats associated with installing smart devices onto unsecured network configurations via address resolution protocol (ARP) poisoning, and 2) exploring social engineering tactics through cloning the device user application. Additionally, in the following summer, participants became acquainted with an existing IoMT dataset, developing an intrusion detection system (IDS) to accurately distinguish between normal and abnormal network packets due to a deployed Man-in-the-Middle (MitM) attack. The outputs of this work include: both sets of participants preparing verbal presentations, including demonstrations, and written papers outlining their results and experiences. After the project, participants should understand and implement a set of guidelines for utilizing IoMT devices more securely and with added privacy. 

Abstract Radio emission from interplanetary shocks, planetary foreshocks, and some solar flares occurs in the so-called “plasma emission” framework. The generally accepted scenario begins with electrostatic Langmuir waves that are driven by a suprathermal electron beam on the Landau resonance. These Langmuir waves then mode-convert to freely propagating electromagnetic emissions at the local plasma frequency f pe and/or its harmonic 2 f pe . However, the details of the physics of mode conversion are unclear, and so far the magnetic component of the plasma waves has not been definitively measured. Several spacecraft have measured quasi-monochromatic Langmuir or slow extraordinary modes (sometimes called z -modes) in the solar wind. These coherent waves are expected to have a weak magnetic component, which has never been observed in an unambiguous way. Here we report on the direct measurement of the magnetic signature of these waves using the Search Coil Magnetometer sensor of the Parker Solar Probe/FIELDS instrument. Using simulations of wave propagation in an inhomogeneous plasma, we show that the appearance of the magnetic component of the slow extraordinary mode is highly influenced by the presence of density inhomogeneities that occasionally cause the refractive index to drop to low values where the wave has strong electromagnetic properties. 

Abstract The Van Allen Probes Electric Fields and Waves (EFW) instrument provided measurements of electric fields and spacecraft floating potentials over a wide dynamic range from DC to 6.5 kHz near the equatorial plane of the inner magnetosphere between 600 km altitude and 5.8 Re geocentric distance from October 2012 to November 2019. The two identical instruments provided data to investigate the quasi-static and low frequency fields that drive large-scale convection, waves induced by interplanetary shock impacts that result in rapid relativistic particle energization, ultra-low frequency (ULF) MHD waves which can drive radial diffusion, and higher frequency wave fields and time domain structures that provide particle pitch angle scattering and energization. In addition, measurements of the spacecraft potential provided a density estimate in cold plasmas ( $<20~\text{eV}$ < 20 eV ) from 10 to $3000~\text{cm}^{-3}$ 3000 cm − 3 . The EFW instrument provided analog electric field signals to EMFISIS for wave analysis, and it received 3d analog signals from the EMFISIS search coil sensors for inclusion in high time resolution waveform data. The electric fields and potentials were measured by current-biased spherical sensors deployed at the end of four 50 m booms in the spacecraft spin plane (spin period $\sim11~\text{sec}$ ∼ 11 sec ) and a pair of stacer booms with a total tip-tip separation of 15 m along the spin axis. Survey waveform measurements at 16 and/or 32 S/sec (with a nominal uncertainty of 0.3 mV/m over the prime mission) were available continuously while burst waveform captures at up to 16,384 S/sec provided high frequency waveforms. This post-mission paper provides the reader with information useful for accessing, understanding and using EFW data. Selected science results are discussed and used to highlight instrument capabilities. Science quantities, data quality and error sources, and analysis routines are documented. 

Abstract In van der Holst et al. (2019), we modeled the solar corona and inner heliosphere of the first encounter of NASA’s Parker Solar Probe (PSP) using the Alfvén Wave Solar atmosphere Model (AWSoM) with Air Force Data Assimilative Photospheric flux Transport–Global Oscillation Network Group magnetograms, and made predictions of the state of the solar wind plasma for the first encounter. AWSoM uses low-frequency Alfvén wave turbulence to address the coronal heating and acceleration. Here, we revise our simulations, by introducing improvements in the energy partitioning of the wave dissipation to the electron and anisotropic proton heating and using a better grid design. We compare the new AWSoM results with the PSP data and find improved agreement with the magnetic field, turbulence level, and parallel proton plasma beta. To deduce the sources of the solar wind observed by PSP, we use the AWSoM model to determine the field line connectivity between PSP locations near the perihelion at 2018 November 6 UT 03:27 and the solar surface. Close to the perihelion, the field lines trace back to a negative-polarity region about the equator.