Hydrogen vehicles: Impacts of DOE technical targets on market acceptance and societal benefits

Hydrogen vehicles (H2V), including H2 internal combustion engine, fuel cell and fuel cell plug-in hybrid, could greatly reduce petroleum consumption and greenhouse gas (GHG) emissions in the transportation sector. The U.S. Department of Energy has adopted targets for vehicle component technologies to address key technical barriers to widespread commercialization of H2Vs. This study estimates the market acceptance of H2Vs and the resulting societal benefits and subsidy in 41 scenarios that reflect a wide range of progress in meeting these technical targets. Important results include: (1) H2Vs could reach 20–70% market shares by 2050, depending on progress in achieving the technical targets. With a basic hydrogen infrastructure (∼5% hydrogen availability), the H2V market share is estimated to be 2–8%. Fuel cell and hydrogen costs are the most important factors affecting the long-term market shares of H2Vs. (2) Meeting all technical targets on time could result in about an 80% cut in petroleum use and a 62% (or 72% with aggressive electricity de-carbonization) reduction in GHG in 2050. (3) The required hydrogen infrastructure subsidy is estimated to range from $22 to$47 billion and the vehicle subsidy from $4 to$17 billion. (4) Long-term H2V market shares, societal benefits and hydrogen subsidies appear to be highly robust against delay in one target, if all other targets are met on time. R&D diversification could provide insurance for greater societal benefits. (5) Both H2Vs and plug-in electric vehicles could exceed 50% market shares by 2050, if all targets are met on time. The overlapping technology, the fuel cell plug-in hybrid electric vehicle, appears attractive both in the short and long runs, but for different reasons

Improving InSAR geodesy using global atmospheric models

Spatial and temporal variations of pressure, temperature and water vapor content in the atmosphere introduce significant confounding delays in Interferometric Synthetic Aperture Radar (InSAR) observations of ground deformation and bias estimatesof regional strain rates. Producing robust estimates of tropospheric delays remains one of the key challenges in increasing the accuracy of ground deformation measurements using InSAR. Recent studies revealed the efficiency of global atmospheric reanalysis to mitigate the impact of tropospheric delays, motivating further exploration of their potential. Here, we explore the effectiveness of these models in several geographic and tectonic settings on both single interferograms and time series analysis products. Both hydrostatic and wet contributions to the phase delay are important to account for. We validate these path delay corrections by comparing with estimates of vertically integrated atmospheric water vapor content derived from the passive multi-spectral imager MERIS, onboard the ENVISAT satellite. Generally, the performance of the prediction depends on the vigor of atmospheric turbulence. We discuss (1) how separating atmospheric and orbital contributions allows one to better measure long wavelength deformation, (2) how atmospheric delays affect measurements of surface deformation following earthquakes and (3) we show that such a method allows us to reduce biases in multi-year strain rate estimates by reducing the influence of unevenly sampled seasonal oscillations of the tropospheric delay

Quantifying automated vehicle benefits in reducing driving stress: a simulation experiment approach

Driving is a stressful activity because of the mental workload required to maneuver a vehicle in certain travel contexts, such as congested traffic, multi-modal networks requiring complex interaction with surrounding vehicles, and aggressive driving. Autonomous vehicles (AVs), on the other hand, can reduce the mental workload by performing most of the driving tasks and providing users with a comfortable ride. This study develops a pathway model to relate different health determinants, including travel reliability, safety, driving comfort, and value of time, to Autonomous vehicles driving and studies their impact on the value of driving stress. A case study example of Autonomous vehicles simulation is used to determine the impact of these health determinants. The value of driving stress in Autonomous vehicles is estimated as a function of the value of these individual health determinants. The results show that the perception of safe or unsafe driving in Autonomous vehicles is the most important factor in changing the perception of driving stress in Autonomous vehicles. Similarly, perceptions of comfortable driving in Autonomous vehicles and reduced workload with a higher value of time also reduce driving stress in Autonomous vehicles. These results allow Autonomous vehicles adoption models to explicitly consider driving stress reduction as a benefit and can improve understanding of Autonomous vehicles adoption, which may require quantitative analysis of underlying motivating benefits, including driving stress reduction

Predictive value of first-trimester GPR120 levels in gestational diabetes mellitus

BackgroundEarly diagnosis of gestational diabetes mellitus (GDM) reduces the risk of unfavorable perinatal and maternal consequences. Currently, there are no recognized biomarkers or clinical prediction models for use in clinical practice to diagnosing GDM during early pregnancy. The purpose of this research is to detect the serum G-protein coupled receptor 120 (GPR120) levels during early pregnancy and construct a model for predicting GDM.MethodsThis prospective cohort study was implemented at the Women’s Hospital of Jiangnan University between November 2019 and November 2022. All clinical indicators were assessed at the Hospital Laboratory. GPR120 expression was measured in white blood cells through quantitative PCR. Thereafter, the least absolute shrinkage and selection operator (LASSO) regression analysis technique was employed for optimizing the selection of the variables, while the multivariate logistic regression technique was implemented for constructing the nomogram model to anticipate the risk of GDM. The calibration curve analysis, area under the receiver operating characteristic curve (AUC) analysis, and the decision curve analysis (DCA) were conducted for assessing the performance of the constructed nomogram.ResultsHerein, we included a total of 250 pregnant women (125 with GDM). The results showed that the GDM group showed significantly higher GPR120 expression levels in their first trimester compared to the normal pregnancy group (p < 0.05). LASSO and multivariate regression analyses were carried out to construct a GDM nomogram during the first trimester. The indicators used in the nomogram included fasting plasma glucose, total cholesterol, lipoproteins, and GPR120 levels. The nomogram exhibited good performance in the training (AUC 0.996, 95% confidence interval [CI] = 0.989-0.999) and validation sets (AUC=0.992) for predicting GDM. The Akaike Information Criterion of the nomogram was 37.961. The nomogram showed a cutoff value of 0.714 (sensitivity = 0.989; specificity = 0.977). The nomogram displayed good calibration and discrimination, while the DCA was conducted for validating the clinical applicability of the nomogram.ConclusionsThe patients in the GDM group showed a high GPR120 expression level during the first trimester. Therefore, GPR120 expression could be used as an effective biomarker for predicting the onset of GDM. The nomogram incorporating GPR120 levels in early pregnancy showed good predictive ability for the onset of GDM

Learning Accurate Entropy Model with Global Reference for Image Compression

In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate. The proposed method scans decoded latents and then finds the most relevant latent to assist the distribution estimating of the current latent. A by-product of this work is the innovation of a mean-shifting GDN module that further improves the performance. Experimental results demonstrate that the proposed model outperforms the rate-distortion performance of most of the state-of-the-art methods in the industry

The impact of reliable range estimation on battery electric vehicle feasibility

Range limitation is a significant obstacle to market acceptance of battery electric vehicles (BEVs). Range anxiety is exacerbated when drivers could not reliably predict the remaining battery range or when their journeys were unexpectedly extended. This paper quantifies the impact of reliable range estimation on BEV feasibility using GPS-tracked travel survey data, collected over an 18-month period (from November 2004 to April 2006) in the Seattle metropolitan area. BEV feasibility is quantified as the number of days when travel adaption is needed if a driver replaces a conventional gasoline vehicle (CGV) with a BEV. The distribution of BEV range is estimated based on the real-world fuel efficiency data. A driver is assumed to choose between using a BEV or a substitute gasoline vehicle, based on the cumulative prospect theory (CPT). BEV is considered feasible for a particular driver if he/she needs to use a substitute vehicle on less than 0.5% of the travel days. By varying the values of some CPT parameter, the percentage of BEV feasible vehicles could change from less than 5% to 25%. The numerical results also show that with a 50% reduction in the standard deviation and 50% increase in the mean of the BEV range distribution BEV feasibility increases from less than 5% of the sampled drivers to 30%