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1. Observations of Earth’s Normal Modes on Broadband Ocean Bottom Seismometers

   https://doi.org/10.3389/feart.2021.679958  

   Laske, Gabi (June 2021, Frontiers in Earth Science)

   null (Ed.)

   It is generally thought that high noise levels in the oceans inhibit the observation of long-period earthquake signals such as Earth’s normal modes on ocean bottom seismometers (OBSs). Here, we document the observation of Earth’s gravest modes at periods longer than 500 s (or frequencies below 2 mHz). We start with our own 2005–2007 Plume-Lithosphere-Undersea-Mantle Experiment (PLUME) near Hawaii that deployed a large number of broadband OBSs for the first time. We collected high-quality normal mode spectra for the great November 15, 2006 Kuril Islands earthquake on multiple OBSs. The random deployment of instruments from different OBS groups allows a direct comparison between different broadband seismometers. For this event, mode S 0 6 (1.038 mHz) consistently rises above the background noise at all OBSs that had a Nanometrics Trillium T-240 broadband seismometer. We also report observations of other deployments in the Pacific ocean that involved instruments of the U.S. OBS Instrument Pool (OBSIP) where we observe even mode S 0 4 (0.647 mHz). Earth’s normal modes were never the initial target of any OBS deployment, nor was any other ultra-low-frequency signal. However, given the high costs of an OBS campaign, the fact that data are openly available to future investigators not involved in the campaign, and the fact that seismology is evolving to investigate ever-new signals, this paper makes the case that the investment in a high-quality seismic sensor may be a wise one, even for a free-fall OBS. 

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2. A System-Dynamics Based Approach for Modeling Circular Economy Networks: Application to the Polyethylene Terephthalate (PET) Supply Chain

   https://doi.org/10.69997/sct.107833  

   Pert, Daniel; Torres, Ana Ins (July 2025, PSE press)

   The transition to a circular economy (CE) requires agents in circular supply chain (SC) networks to take a variety of different initiatives, many of which are dynamic in nature. We use a system dynamics (SD)-based approach to develop a generic framework for dynamic modeling of CE networks and propose a prototypical circular SC network by combining dynamic models for five actors: a manufacturer, consumer, material recovery facility (MRF), recycling facility, and the Earth. We apply this framework to the supply chain for Polyethylene Terephthalate (PET) plastic packaging by considering different scenarios over a 65-year time horizon in the US. We include both slow-down-the-loop initiatives (i.e., those that extend product use time through demand reduction or reuse) and close-the-loop initiatives (i.e., those that reintroduce product to the supply chain through recycling) by the consumer, as well as sorting and recycling capacity expansion. We find that, given the current recycling infrastructure in the U.S., slow-down-the-loop initiatives are more effective than close-the-loop initiatives for improving circularity and minimizing environmental impact. However, combining the two initiatives eliminates the need for capacity expansion and leads to the highest circularity in the shortest amount of time. 

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3. Wireless Mesh Networks as Community Hubs: Analysis of Small-Scale Wireless MeshNetworks and Community-Centered Technology Training

   Greig, Jamie A. (January 2018, Journal of information policy)

   This paper addresses two policy questions. Firstly, how might evolving technologies associated with broadband networks enhance or hinder marginalized or underserved population groups’ effective use and access to information resources? Secondly, how can we foster public hybrid broadband, situating broadband networks within existing communities, as a means to promote digital self-determination? This study finds that wireless mesh technology initiatives can create and foster community engagement through infrastructure deployment, maintenance and use; combat myths regarding marginalized demographics and technology, and provide marginalized communities with an opportunity to become decision-makers regarding communications technology infrastructure development. 

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4. Detection of Anomalies in Traffic Flows with Large Amounts of Missing Data

   https://doi.org/10.51387/23-NEJSDS20  

   He, Qing; Harrison, Charles W.; Huang, Hsin-Hsiung (January 2023, The New England Journal of Statistics in Data Science)

   Anomaly detection plays an important role in traffic operations and control. Missingness in spatial-temporal datasets prohibits anomaly detection algorithms from learning characteristic rules and patterns due to the lack of large amounts of data. This paper proposes an anomaly detection scheme for the 2021 Algorithms for Threat Detection (ATD) challenge based on Gaussian process models that generate features used in a logistic regression model which leads to high prediction accuracy for sparse traffic flow data with a large proportion of missingness. The dataset is provided by the National Science Foundation (NSF) in conjunction with the National Geospatial-Intelligence Agency (NGA), and it consists of thousands of labeled traffic flow records for 400 sensors from 2011 to 2020. Each sensor is purposely downsampled by NSF and NGA in order to simulate missing completely at random, and the missing rates are 99%, 98%, 95%, and 90%. Hence, it is challenging to detect anomalies from the sparse traffic flow data. The proposed scheme makes use of traffic patterns at different times of day and on different days of week to recover the complete data. The proposed anomaly detection scheme is computationally efficient by allowing parallel computation on different sensors. The proposed method is one of the two top performing algorithms in the 2021 ATD challenge. 

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5. Facial Recognition: Understanding Concerns and Privacy Attitudes Across Increasingly Diverse Deployment Scenarios

   Zhang, S.; Feng, Y.; Sadeh, N. (January 2021, USENIX Symposium on Usable Privacy and Security)

   null (Ed.)

   The rapid growth of facial recognition technology across ever more diverse contexts calls for a better understanding of how people feel about these deployments — whether they see value in them or are concerned about their privacy, and to what extent they have generally grown accustomed to them. We present a qualitative analysis of data gathered as part of a 10-day experience sampling study with 123 participants who were presented with realistic deployment scenarios of facial recognition as they went about their daily lives. Responses capturing their attitudes towards these deployments were collected both in situ and through daily evening surveys, in which participants were asked to reflect on their experiences and reactions. Ten follow-up interviews were conducted to further triangulate the data from the study. Our results highlight both the perceived benefits and concerns people express when faced with different facial recognition deployment scenarios. Participants reported concerns about the accuracy of the technology, including possible bias in its analysis, privacy concerns about the type of information being collected or inferred, and more generally, the dragnet effect resulting from the widespread deployment. Based on our findings, we discuss strategies and guidelines for informing the deployment of facial recognition, particularly focusing on ensuring that people are given adequate levels of transparency and control. 

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