Evaluating the Impact of Algorithm Confidence Ratings on Human Decision Making in Visual Search.
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Superconducting qubits can be sensitive to abrupt energy deposits caused by cosmic rays and ambient radioactivity. Previous studies have focused on understanding possible correlated effects over time and distance due to cosmic rays. In this study, for the first time, we directly compare the response of a transmon qubit measured initially at the Fermilab SQMS above-ground facilities and then at the deep underground Gran Sasso Laboratory (INFN-LNGS, Italy). We observe same average qubit lifetime T$_1$ of roughly 80 microseconds at above and underground facilities. We then apply a fast decay detection protocol and investigate the time structure, sensitivity and relative rates of triggered events due to radiation versus intrinsic noise, comparing above and underground performance of several high-coherence qubits. Using gamma sources of variable activity we calibrate the response of the qubit to different levels of radiation in an environment with minimal background radiation. Results indicate that qubits respond to a strong gamma source and it is possible to detect particle impacts. However, when comparing above and underground results, we do not observe a difference in radiation induced-like events for these sapphire and niobium-based transmon qubits. We conclude that the majority of these events are not radiation related and to be attributed to other noise sources which by far dominate single qubit errors in modern transmon qubits.
Superconducting qubits can be sensitive to sudden energy deposits caused by ambient radioactivity and cosmic rays. Previous studies have focused on understanding possible correlated effects over time and distance due to this radiation. In this study‚ for the first time‚ we directly compare the response of a transmon qubit measured initially at the SQMS above-ground facility (Fermilab‚ Illinois‚ USA) and then at the deep underground Gran Sasso Laboratory (INFN-LNGS‚ Italy). We observe the same average qubit lifetime of roughly 80 microseconds at both facilities. We then apply a fast decay detection protocol and investigate the time structure and relative rates of triggered events due to radiation versus intrinsic noise‚ comparing the above and underground performance of several high-coherence qubits. Using gamma sources of variable intensity we calibrate the response of the qubit to different levels of radiation in an environment with minimal background radiation. Results indicate that qubits respond to a strong gamma source‚ and it is possible to detect particle impacts. However‚ we do not observe a difference in radiation-induced-like events when comparing the above and underground results for niobium-based transmon qubits with sapphire substrates. We conclude that the majority of these events are not radiation-related and are attributed to other noise sources‚ which by far dominate single-qubit errors in modern transmon qubits. [1] Dominicis‚ Roy et al. arXiv:2405.18355
Vehicles with advanced driver assistance systems (ADAS) like adaptive cruise control (ACC) could use connected (V2X) data to improve fuel economy Eco driving generates an energy efficient speed profile by minimizing acceleration events SwRI demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks by using nearby vehicle and traffic signal information How does eco driving impact vehicle emissions and aftertreatment (AT) systems?
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To facilitate the decarbonization of the power grid, fossil-fuel-based synchronous generators are gradually being retired and replaced by inverter-interfaced renewable energy resources. As synchronous machines get displaced, the characteristics of inter-area oscillation modes in electrical interconnections will change. This paper describes a model-based study of the impact of the retirement of synchronous fossil-fuel generators on the oscillatory characteristics of the US Western Interconnection (WI). Results show that if fossil-fuel-based synchronous machines are replaced by grid-forming inverters, then dominant inter-area modes seen in the WI today will change. New inter-area modes may appear due to the electromechanical energy exchange among remaining hydropower generators clustered in geographical proximity in the northwestern part of the WI. This observation indicates that the evolution in modal properties needs to be closely tracked as the resource mix in electrical interconnections changes.
This dataset was used to support machine learning model development for wildfire impacts on utility-scale solar in the United States. The dataset includes information regarding energy generation, PM2.5, clearness index, temperature, wind speed, precipitation, and site size.
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Time series experiments were used to explore the fluid redox impact on the corrosion of Q125, 316L, and C276 steels in low-ionic-strength and neutral water at temperature and pressure conditions associated with low-temperature geothermal systems. After exposing polished samples of each steel grade to an oxidizing (H2O2) and a reducing (Zn-doped) fluid for intervals of 24 h, 1 week, and 6 weeks, the atomic force microscopy results revealed general corrosion for Q125, while 316L and C276 exhibited pitting, crevice expansion, and edge attack corrosion. Secondary depositional features are frequently found as topographic highs, adjacent to pitting corrosion. These features may be identified as there is a very strong spatial correlation between the height retrace and phase retrace surface maps. All steels became progressively rougher over time after exposure to both fluids, while the corrosion rates were more complex. Samples exposed to the reducing fluid experienced an increase in the corrosion rate over time, while C276 and 316L experienced a decrease in the corrosion rate. Finally, a novel data validation technique was developed to address the intrinsic scalability of corrosion. The results indicate that the AFM scan area does not affect the measured surface roughness over nearly three orders of magnitude.
Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO2) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO2 yearly, which is approximately 30% of the total CO2 emitted by internal combustion engine (ICE) cars in the region.
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The Colorado Geological Survey and the co-authors of this paper were awarded one of 15 NASA Broad Agency Announcement (BAA) grants in 2001. The project focuses on the use of hyperspectral remote sensing to map acid-generating minerals that affect water quality within a watershed, and to identify the relative contributions of natural and anthropogenic sources to that drainage. A further objective is to define the most cost-effective remote sensing instrument configuration for this application.
A radiative transfer model for estimating snow water equivalent (SWE, mm) from satellite-observed brightness temperature (K) at 19 and 37 GHz (respectively, T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)) over partially forested area is presented, as an extension of a previously published model, by considering scattering of radiation within the canopy. For the specific case of dense vegetation covering fractional area f, the model can be written as, SWE = alpha{ A. delta (T(sub B(sub, sat)) + B - C. f}/(l f), where delta T(sub B(sub, sat)), is the difference of T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)), alpha(mm/K) is the slope of SWE vs. brightness temperature difference at 19 and 37 GHz that would be obtained by ignoring the presence of atmosphere, delta(T(sub B)sub g)), for a homogeneous snow cover (which varies with grain size). The parameters A, B, and C, are determined primarily by atmospheric characteristics, and for a likely range of atmospheric conditions appear to be in the range of, respectively, 1.15-1.63, 0.69-2.84 K and 0.59-2.39 K. Ignoring atmospheric correction would introduce bias towards underestimation of SWE (and also, snow cover area and snow depth). Increasing cloud liquid water path (L) has the effect of increasing A, and ignoring this variation of A with L would have the impact of biasing the estimate of SWE (and snow extent). Such biasing is further exacerbated with increasing f, because of the appearance of term (l-f) in the denominator. The impact of ignoring the intercept parameters (B and C) would be noticeable at low values of SWE (appearing as a bias towards underestimation of SWE), which has been determined to be about 6 mm for average environmental conditions. The uncertainty in estimating SWE due to variations in the atmospheric characteristics is likely to be less than 15%, but could be up to 25% for non-vegetated snow-covered areas. Better estimates of SWE (and snow extent) would be obtained by adjusting the parameters of the above model to regional differences in the atmospheric characteristics. The biases in determining SWE arising due to variations in atmospheric conditions and due to changes in fractional forest cover are not independent, since they interact as {A/(l-f)}. The present calculations also show that improvement in determining snow cover area from the microwave data is likely to occur when these data are corrected for atmospheric effects, as demonstrated by a specific case study.
When the Space Shuttle Orbiter incurs damage on its windowpane during flight from particles traveling at hypervelocity speeds, it produces a distinctive damage that reduces the overall strength of the pane. This damage has the potential to increase the risk associated with a safe return to Earth. Engineers at Boeing and NASA/JSC are called to Mission Control to evaluate the damage and provide an assessment on the risk to the crew. Historically, damages like these were categorized as "accepted risk" associated with manned spaceflight, and as long as the glass was intact, engineers gave a "go ahead" for entry for the Orbiter. Since the Columbia accident, managers have given more scrutiny to these assessments, and this has caused the Orbiter window engineers to capitalize on new methods of assessments for these damages. This presentation will describe the original methodology that was used to asses the damages, and introduce a philosophy new to the Shuttle program for assessing structural damage, reliability/risk-based engineering. The presentation will also present a new, recently adopted method for assessing the damage and providing management with a reasonable assessment on the realities of the risk to the crew and vehicle for return.