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griDNA is an advanced monitoring and alerting technology for protecting electrical grids and other critical infrastructure from disruptions caused by cyberattacks or system failures. griDNA provides the holistic cyber-physical situational awareness that operators require for modern infrastructure challenges.
Being a Liquid Argon Time Projection Chamber (LAr-TPC) detector, ICARUS exploits a series of three parallel wire planes to measure and collect the ionization charge coming from an event inside the active volume. The wires are read out by A2795 custom boards, arranged in crates located on top of the two liquid Argon vessels. Each board is provided with two temperature sensors that allow temperature monitoring while the experiment is running: this is key in order to ensure data reliability and maintain hardware in safe conditions. Moreover, temperature-related problems may force unplanned shutdowns that result in losing valuable time for the experiment to run. In this work we are discussing the characterization of the ICARUS TPC board temperatures and the system we implemented in order to monitor them effectively. Among the tools used, Grafana results in a valid application for both displaying temperature data and sending alerts to the shifters whenever a pathological situation occurs.
UX lessons from designing the Alarms app (a tool operators rely on to track accelerator subsystems and respond to alerts). The talk focuses on how observation, empathy, and iterative design helped us balance information, attention, and safety.
The Remote Access Attack on Oldsmar Water Treatment Facility 2021 Precursor Analysis Report leverages publicly available information about the Oldsmar cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. On 5 February 2021, an adversary gained unauthorized remote access to Bruce T. Haddock Water Treatment Plant in Oldsmar, Florida, which provides treated water to 15,000 customers. The adversary accessed the facility’s Supervisory Control and Data Acquisition (SCADA) workstation and human machine interface (HMI) to change the chemical concentration of sodium hydroxide, commonly referred to as lye and used to regulate acidity levels, from 100 parts per million (PPM) to 11,100 PPM. The chemical was raised to lethal levels that if ingested could lead to serious soft tissue damage, burns, or even death. The facility, however, had redundancies and alarms in place to alert personnel of dangerous chemical levels, and facility officials stated it would have taken 24 to 36 hours for the chemical changes to affect the water supply. Researchers and analysts identified six unique techniques utilized during the attack with a total of 23 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Four of the identified techniques used during the Oldsmar cyber attack were precursors to the triggering event. Analysis identified 21 observables associated with these precursor techniques, 20 of which were assessed to have an increased likelihood of being perceived in the minutes preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.
Time-Domain and Multi-Messenger Astrophysics (TDAMM) is entering a fundamentally new phase characterized by an unprecedented increase in the rate and diversity of astrophysical transient detections. The community is transitioning from a discovery-limited to a follow-up-limited era, driven by major investments across electromagnetic, gravitational-wave, and neutrino observatories. Upcoming facilities such as the Vera C. Rubin Observatory, the Nancy Grace Roman Space Telescope, and wide-field survey instruments will produce a deluge of time-domain alerts, reaching millions of events per night. Simultaneously, upgrades to the gravitational-wave network (LVK O5 and beyond) and neutrino observatories (IceCube Gen2) will significantly increase the detection rates of non-electromagnetic messengers. New high-energy missions and expansions of the InterPlanetary Network (IPN) will further enhance discovery capabilities across the gamma-ray and X-ray regimes. This convergence of capabilities represents a transformative opportunity: for the first time, the community will routinely detect rare and high-impact events across multiple messengers. However, the scientific return from these discoveries will depend critically on the ability to rapidly identify, prioritize, and coordinate follow-up observations across a heterogeneous and globally distributed set of facilities.
Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.
In the teaching and learning events of our life, there are characters who play key roles. Teachers, mentors, relatives, novels, songs, newspaper columns, often help us understand the questions in our heads. However, we often reject them because they are messages or attitudes from older people and the generation gap does not allow us to understand them well. The times of covid-19 have us locked up at home. They alert us that precisely “elderly people” are a vulnerable group. Also, we have been asked to take care of grandparents. If we are lucky enough to have an internet connection, it seems that we can learn anything we want with just one click. This could lead us to explore only what we find attractive, easy and comfortable. With this testimony, I invite you to acknowledge on the role of the elderly in the learning of entire generations. I call for openness towards diverse voices and for the understanding that we all play a role in moving towards a more mature society.
Black swan events in infectious disease describe rare but devastatingly large outbreaks. While experts are skeptical that such events are predictable, it might be possible to identify the warning signs of a black swan event. Specifically, following the initiation of an outbreak, key differentiating features could serve as alerts. Such features could be derived from meta-analyses of large outbreaks for multiple infectious diseases. We hypothesized there may be common features among the pathogen, environment, and host epidemiological triad that characterize an infectious disease black swan event. Using Los Alamos National Laboratory’s tool, Analytics for Investigation of Disease Outbreaks, we investigated historical disease outbreak information and anomalous events for several infectious diseases. By studying 32 different infectious diseases and global outbreaks, we observed that in the past 20–30 years, there have been potential black swan events in the majority of infectious diseases analyzed. Importantly, these potential black swan events cannot be attributed to the first introduction of the disease to a susceptible host population. This paper describes our observations and perspectives and illustrates the value of broad analysis of data across the infectious disease realm, providing insights that may not be possible when we focus on singular infectious agents or diseases. Data analytics could be developed to warn health authorities at the beginning of an outbreak of an impending black swan event. Such tools could complement traditional epidemiological modeling to help forecast future large outbreaks and facilitate timely warning and effective, targeted resource allocation for mitigation efforts.
Treatment options are lacking to prevent photoreceptor death and subsequent vision loss. Previously, we demonstrated that reprogramming metabolism via the pharmacologic activation of PKM2 is a novel photoreceptor neuroprotective strategy. However, the features of the tool compound used in those studies, ML-265, preclude its advancement as an intraocular, clinical candidate. This study sought to develop the next generation of small-molecule PKM2 activators, aimed specifically for delivery into the eye. Compounds were developed that replaced the thienopyrrolopyridazinone core of ML-265 and modified the aniline and methyl sulfoxide functional groups. Compound 2 demonstrated that structural changes to the ML-265 scaffold are tolerated from a potency and efficacy standpoint, allow for a similar binding mode to the target, and circumvent apoptosis in models of outer retinal stress. To overcome the low solubility and problematic functional groups of ML-265, compound 2’s efficacious and versatile core structure for the incorporation of diverse functional groups was then utilized to develop novel PKM2 activators with improved solubility, lack of structural alerts, and retained potency. No other molecules are in the pharmaceutical pipeline for the metabolic reprogramming of photoreceptors. Thus, this study is the first to cultivate the next generation of novel, structurally diverse, small-molecule PKM2 activators for delivery into the eye.
We report detailed follow-up observations of eight cataclysmic variable stars (CVs) that are apparently AM Her stars, also called polars. For all, we either determine orbital periods for the first time or improve on existing determinations. The seven for which we have spectra show the high-amplitude radial-velocity curves and prominent He ii λ4686 emission lines characteristic of strongly magnetic CVs, and their periods, which range from 81 to 219 minutes, are also typical for AM Her stars. Two objects from the Gaia-alerts index, Gaia18aot and Gaia18aya, are newly identified as CVs. Another, RX J0636.3+6554, eclipses deeply, while CSS 080228:081210+040352 shows a sharp dip that is apparently a partial eclipse. The spectrum of Gaia18aya has a cyclotron harmonic near λ5500 Å that constrains the surface field to ∼49 MG or greater.
AM CVn systems are a rare type of accreting binary that consists of a white dwarf and a helium-rich, degenerate donor star. Using the Zwicky Transient Facility (ZTF), we searched for new AM CVn systems by focusing on blue, outbursting stars. We first selected outbursting stars using the ZTF alerts. We cross matched the candidates with Gaia and Pan-STARRS catalogs. The initial selection of candidates based on the Gaia BP-RP contains 1751 unknown objects. We used the Pan-STARRS g-r and r-i color in combination with the Gaia color to identify 59 strong AM CVn candidates. We obtained identification spectra of 35 sources, of which 18 are high-priority candidates, and discovered nine new AM CVn systems and one magnetic CV that shows only He-ii lines. Using the outburst recurrence time, we estimate the orbital periods of the nine new AM CVn systems that are in the range of 29–50 minutes. We conclude that targeted follow up of blue, outbursting sources is an efficient method to find new AM CVn systems and we plan to follow up all candidates we identified to systematically study the population of outbursting AM CVn systems.
Abstract In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe ( r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021).
We report on the observations of gamma-ray burst (GRB) 190114C by the Fermi Gamma-ray Space Telescope and the Neil Gehrels Swift Observatory . The prompt gamma-ray emission was detected by the Fermi GRB Monitor (GBM), the Fermi Large Area Telescope (LAT), and the Swift Burst Alert Telescope (BAT) and the long-lived afterglow emission was subsequently observed by the GBM, LAT, Swift X-ray Telescope (XRT), and Swift UV Optical Telescope. The early-time observations reveal multiple emission components that evolve independently, with a delayed power-law component that exhibits significant spectral attenuation above 40 MeV in the first few seconds of the burst. This power-law component transitions to a harder spectrum that is consistent with the afterglow emission observed by the XRT at later times. This afterglow component is clearly identifiable in the GBM and BAT light curves as a slowly fading emission component on which the rest of the prompt emission is superimposed. As a result, we are able to observe the transition from internal-shock- to external-shock-dominated emission. We find that the temporal and spectral evolution of the broadband afterglow emission can be well modeled as synchrotron emission from a forward shock propagating into a wind-like circumstellar environment. We estimate the initial bulk Lorentz factor using the observed high-energy spectral cutoff. Considering the onset of the afterglow component, we constrain the deceleration radius at which this forward shock begins to radiate in order to estimate the maximum synchrotron energy as a function of time. We find that even in the LAT energy range, there exist high-energy photons that are in tension with the theoretical maximum energy that can be achieved through synchrotron emission from a shock. These violations of the maximum synchrotron energy are further compounded by the detection of very high-energy (VHE) emission above 300 GeV by MAGIC concurrent with our observations. We conclude that the observations of VHE photons from GRB 190114C necessitates either an additional emission mechanism at very high energies that is hidden in the synchrotron component in the LAT energy range, an acceleration mechanism that imparts energy to the particles at a rate that is faster than the electron synchrotron energy-loss rate, or revisions of the fundamental assumptions used in estimating the maximum photon energy attainable through the synchrotron process.
Theory predicts that a supermassive black hole binary (SMBHB) could be observed as a luminous active galactic nucleus (AGN) that periodically varies on the order of its orbital timescale. In X-rays, periodic variations could be caused by mechanisms including relativistic Doppler boosting and shocks. In this paper we present the first systematic search for periodic AGNs using 941 hard X-ray light curves (14–195 keV) from the first 105 months of the Swift Burst Alert Telescope (BAT) survey (2004–2013). We do not find evidence for periodic AGNs in Swift-BAT, including the previously reported SMBHB candidate MCG+11–11–032. We find that the null detection is consistent with the combination of the upper-limit binary population in AGNs in our adopted model, their expected periodic variability amplitudes, and the BAT survey characteristics. We have also investigated the detectability of SMBHBs against normal AGN X-ray variability in the context of the extended ROentgen Survey with an Imaging Telescope Array (eROSITA) survey. Under our assumptions of a binary population and the periodic signals they produce, which have long periods of hundreds of days, up to 13% true periodic binaries can be robustly distinguished from normal variable AGNs with the ideal uniform sampling. However, we demonstrate that realistic eROSITA sampling is likely to be insensitive to long-period binaries because longer observing gaps reduce their detectability. In contrast, large observing gaps do not diminish the prospect of detecting binaries of short, few-day periods, as 19% can be successfully recovered, the vast majority of which can be identified by the first half of the survey.
We present a novel method to detect variable astrophysical objects and transient phenomena using anomalous excess scatter in repeated measurements from public catalogs of Gaia DR2 and Zwicky Transient Facility (ZTF) DR3 photometry. We first provide a generalized, all-sky proxy for variability using only Gaia DR2 photometry, calibrated to white dwarf stars. To ensure more robust candidate detection, we further employ a method combining Gaia with ZTF photometry and alerts. To demonstrate its efficacy, we apply this latter technique to a sample of roughly 12,100 white dwarfs within 200 pc centered on the ZZ Ceti instability strip, where hydrogen-atmosphere white dwarfs are known to pulsate. Here, by inspecting the top 1% of the samples ranked by these methods, we demonstrate that both the Gaia-only and ZTF-informed techniques are highly effective at identifying known and new variable white dwarfs, which we verify using follow-up, high-speed photometry. We confirm variability in all 33 out of 33 (100%) observed white dwarfs within our top 1% highest-ranked candidates, both inside and outside the ZZ Ceti instability strip. In addition to dozens of new pulsating white dwarfs, we also identify five white dwarfs highly likely to show transiting planetary debris; if confirmed, these systems would more than triple the number of white dwarfs known to host transiting debris.
We derive the most up-to-date Swift-Burst Alert Telescope (BAT) blazar luminosity function in the 14–195 keV range, making use of a clean sample of 118 blazars detected in the BAT 105 month survey catalog, with newly obtained redshifts from the BAT Active Galatic Nucleus Spectroscopic Survey. We determine the best-fit X-ray luminosity function for the whole blazar population, as well as for flat-spectrum radio quasars (FSRQs) alone. The main results are: (1) at any redshift, BAT detects the most luminous blazars, above any possible break in their luminosity distribution, which means we cannot differentiate between density and luminosity evolution; (2) the whole blazar population, dominated by FSRQs, evolves positively up to redshift z ~ 4.3, confirming earlier results and implying lower number densities of blazars at higher redshifts than previously estimated. The contribution of this source class to the cosmic X-ray background at 14–195 keV can range from 5%–18%, while possibly accounting for 100% of the MeV background. We also derived the average 14 keV–10 GeV spectral energy distribution for BAT blazars, which allows us to predict the number counts of sources in the MeV range, as well as the expected number of high-energy (>100 TeV) neutrinos. A mission like COSI will detect 40 MeV blazars, of which two may have coincident neutrino detections. Finally, taking into account beaming selection effects, the distribution and properties of the parent population of these extragalactic jets are derived. We find that the distribution of viewing angles is quite narrow, with most sources aligned within <5° of the line of sight. Moreover, the average Lorentz factor, $\langle$Γ$\rangle$ = 8–12, is lower than previously suggested for these powerful sources.
The traditional description of the orbital evolution of compact-object binaries, like double white dwarfs (DWDs), assumes that the system is driven only by gravitational-wave (GW) radiation. However, the high magnetic fields with intensities of up to gigagausses measured in WDs alert a potential role of the electromagnetic (EM) emission in the evolution of DWDs. We evaluate the orbital dynamics of DWDs under the effects of GW radiation, tidal synchronization, and EM emission by a unipolar inductor generated by the magnetic primary and the relative motion of the nonmagnetic secondary. We show that the EM emission can affect the orbital dynamics for magnetic fields larger than megagausses. We applied the model to two known DWDs, SDSS J0651+2844 and ZTF J1539+5027, for which the GW radiation alone does not fully account for the measured orbital decay rate. We obtain upper limits to the primary's magnetic field strength, over which the EM emission causes an orbital decay faster than observed. The contribution of tidal locking and the EM emission is comparable, and together they can contribute up to 20% to the measured orbital decay rate. We show that the gravitational waveform for a DWD modeled as purely driven by GWs and including tidal interactions and EM emission can have large relative dephasing detectable in the mHz regime of frequencies relevant for space-based detectors like LISA. Therefore, including physics besides GW radiation in the waveform templates is essential to calibrate the GW detectors using known sources, e.g., ZTF J1539+5027, and to infer binary parameters.
In the quest for high-energy neutrino sources, the Astrophysical Multimessenger Observatory Network has implemented a new search by combining data from the High Altitude Water Cherenkov (HAWC) Observatory and the Astronomy with a Neutrino Telescope and Abyss environmental RESearch (ANTARES) neutrino telescope. Using the same analysis strategy as in a previous detector combination of HAWC and IceCube data, we perform a search for coincidences in HAWC and ANTARES events that are below the threshold for sending public alerts in each individual detector. Data were collected between 2015 July and 2020 February with a live time of 4.39 yr. Over this time period, three coincident events with an estimated false-alarm rate of <1 coincidence per year were found. This number is consistent with background expectations.