Hasil untuk "United States"

Julie Holihan, MD, MS, Devi Bavishi, MD, Kush Brahmbhatt, BS et al.

Efe Bozkir, Babette Bühler, Xiaoyuan Wu et al.

Augmented reality technology will likely be prevalent with more affordable head-mounted displays. Integrating novel interaction modalities, such as eye trackers into head-mounted displays could lead to collecting vast amounts of biometric data, which may allow inference of sensitive user attributes like health status or sexual preference, posing privacy issues. While previous works broadly examined privacy concerns about augmented reality, ours is the first to extensively explore privacy concerns on behavioral data, particularly eye tracking in augmented reality. We crowdsourced four survey studies in the United States (n1 = 48, n2 = 525) and Germany (n3 = 48, n4 = 525) to understand the impact of user attributes, augmented reality devices, use cases, data practices, and country on privacy concerns. Our findings indicate that participants are generally concerned about privacy when they know what inferences can be made based on the collected data. Despite the more prominent use of smartphones in daily life than augmented reality glasses, we found no indications of differing privacy concerns depending on the device type. In addition, our participants are more comfortable when a particular use case benefits them and less comfortable when other humans can consume their data. Furthermore, participants in the United States are less concerned about their privacy than those in Germany. Based on our findings, we provide several recommendations to practitioners and policymakers for privacy-aware augmented reality.

Jian Du

This dissertation presents a comprehensive comparative analysis of artificial intelligence governance frameworks across the European Union, United States, China, and IEEE technical standards, examining how different jurisdictions and organizations approach the challenge of promoting responsible and beneficial AI development. Using a qualitative research design based on systematic content analysis, the study identifies distinctive patterns in regulatory philosophy, implementation mechanisms, and global engagement strategies across these major AI governance ecosystems.

Daniele Barbera, Filippo Boni, Simone Dovetta et al.

We investigate the existence and the singular limit of normalized ground states for focusing doubly nonlinear Schrödinger equations with both standard and concentrated nonlinearities on two-dimensional square grids. First, we provide existence and non-existence results for such ground states depending on the values of the nonlinearity powers and on the structure of the set of vertices where the concentrated nonlinearities are located. Second, we prove that suitable piecewise-affine extensions of such states converge strongly in $H^1(\R^2)$ to ground states of corresponding doubly nonlinear models defined on the whole plane as the length of the edges in the grid tends to zero. This convergence is proved both for limit models with standard nonlinearities only and for models combining standard and singular nonlinearities concentrated on a line or on a strip.

Hao He, Timothy P Canty, Russell R Dickerson et al.

Between June 6 and 8, 2023, wildfires in Quebec, Canada generated massive smoke plumes that traveled long distances and deteriorated air quality across the Northeastern United States (US). Surface daily PM _2.5 observations exceeded 100 µ g m ^−3 , affecting major cities such as New York City and Philadelphia, while many areas lacked PM _2.5 monitors, making it difficult to assess local air quality conditions. To address this gap, we developed a WRF-CMAQ-BenMAP modeling system to provide rapid, spatially continuous estimates of wildfire-attributable PM _2.5 concentrations and associated health impacts, particularly benefiting regions lacking air quality monitoring. CMAQ simulations driven by two wildfire emissions datasets and two meteorological drivers showed good agreement with PM _2.5 observations, with linear regression results of R ^2 ∼0.6 and slope ∼0.9. We further quantified uncertainties introduced by varying emissions and meteorological drivers and found the choice of wildfire emissions dataset alone can alter PM _2.5 simulations by up to 40 µ g m ^−3 (∼40%). Short-term health impacts were evaluated using the BenMAP model. Validation against asthma-associated emergency department (ED) visits in New York State confirmed the framework’s ability to replicate real-world outcomes, with ED visits increased up to ∼40%. The modeling results identified counties most severely affected by wildfire plumes, the majority of which lack regulatory air quality monitors. Our approach highlights the value of integrated modeling for identifying vulnerable populations and delivering timely health burden estimates, regardless of local monitoring availability.

Bianca Forrester, Georgia Fisher, Louise A. Ellis et al.

Abstract Introduction The COVID‐19 pandemic challenged primary care to rapidly innovate. In response, the Western Victorian Primary Health Network (WVPHN) developed a COVID‐19 online Community of Practice comprising general practitioners (GPs), practice nurses, pharmacists, aged care and disability workers, health administrators, public health experts, medical specialists, and consumers. This Experience Report describes our progress toward a durable organizational learning health system (LHS) model through the COVID‐19 pandemic crisis and beyond. Methods In March 2020, we commenced weekly Community of Practice sessions, adopting the Project ECHO (Extension of Community Health Outcomes) model for a virtual information‐sharing network that aims to bring clinicians together to develop collective knowledge. Our work was underpinned by the LHS framework proposed by Menear et al. and aligned with Kotter's eight‐step change model. Results There were four key phases in the development of our LHS: build a Community of Practice; facilitate iterative change; develop supportive organizational infrastructure; and establish a sustainable, ongoing LHS. In total, the Community of Practice supported 83 unique COVID‐19 ECHO sessions involving 3192 h of clinician participation and over 10 000 h of organizational commitment. Six larger sessions were run between March 2020 and September 2022 with 3192 attendances. New models of care and care pathways were codeveloped in sessions and network leaders contributed to the development of guidelines and policy advice. These innovations enabled WVPHN to lead the Australian state of Victoria on rates of COVID vaccine uptake and GP antiviral prescribing. Conclusion The COVID‐19 pandemic created a sense of urgency that helped stimulate a regional primary care‐based Community of Practice and LHS. A robust theoretical framework and established change management theory supported the purposeful implementation of our LHS. Reflection on challenges and successes may provide insights to support the implementation of LHS models in other primary care settings.

Nicole DeMarco, Shannon Carpenter, Brian Lee

Courtney Grimes, Ramón A Alvarez

Sudha, A. R. Usha Devi, B. N. Karthik et al.

The Bloch sphere provides an elegant way of visualizing a qubit. Analogous representation of the simplest composite state of two-qubits has attracted significant attention. Here we present a detailed mathematical analysis of the real-matrix parametrization and associated geometric picturization of arbitrary two-qubit states - up to their local SL2C equivalence, in terms of canonical ellipsoids inscribed within the Bloch sphere.

Paul A. Parker, Bruno Sansó

The Rapid Carbon Assessment, conducted by the U.S. Department of Agriculture, was implemented in order to obtain a representative sample of soil organic carbon across the contiguous United States. In conjunction with a statistical model, the dataset allows for mapping of soil carbon prediction across the U.S., however there are two primary challenges to such an effort. First, there exists a large degree of heterogeneity in the data, whereby both the first and second moments of the data generating process seem to vary both spatially and for different land-use categories. Second, the majority of the sampled locations do not actually have lab measured values for soil organic carbon. Rather, visible and near-infrared (VNIR) spectra were measured at most locations, which act as a proxy to help predict carbon content. Thus, we develop a heterogeneous model to analyze this data that allows both the mean and the variance to vary as a function of space as well as land-use category, while incorporating VNIR spectra as covariates. After a cross-validation study that establishes the effectiveness of the model, we construct a complete map of soil organic carbon for the contiguous U.S. along with uncertainty quantification.

Yi Jiang, Shohei Shimizu

While economic theory explains the linkages among the financial markets of different countries, empirical studies mainly verify the linkages through Granger causality, without considering latent variables or instantaneous effects. Their findings are inconsistent regarding the existence of causal linkages among financial markets, which might be attributed to differences in the focused markets, data periods, and methods applied. Our study adopts causal discovery methods including VAR-LiNGAM and LPCMCI with domain knowledge to explore the linkages among financial markets in Japan and the United States (US) for the post Covid-19 pandemic period under divergent monetary policy directions. The VAR-LiNGAM results reveal that the previous day's US market influences the following day's Japanese market for both stocks and bonds, and the bond markets of the previous day impact the following day's foreign exchange (FX) market directly and the following day's Japanese stock market indirectly. The LPCMCI results indicate the existence of potential latent confounders. Our results demonstrate that VAR-LiNGAM uniquely identifies the directed acyclic graph (DAG), and thus provides informative insight into the causal relationship when the assumptions are considered valid. Our study contributes to a better understanding of the linkages among financial markets in the analyzed data period by supporting the existence of linkages between Japan and the US for the same financial markets and among FX, stock, and bond markets, thus highlighting the importance of leveraging causal discovery methods in the financial domain.

Yalan Chen, Yi Wang, Fengsi Wei et al.

Magnetic reconnection is a process that can rapidly convert magnetic field energy into plasma thermal energy and kinetic energy, and it is also an important energy conversion mechanism in space physics, astrophysics and plasma physics. Research related to analytical solutions for time-dependent three-dimensional magnetic reconnection is extremely difficult. For decades, several mathematical descriptions have been developed regarding different reconnection mechanisms, in which the equations based on magnetohydrodynamics theory outside the reconnection diffusion region are widely accepted. However, the equation set cannot be analytically solved unless specified constraints are imposed or the equations are reduced. Based on previous analytical methods for kinematic stationary reconnection, here the analytical solutions for time-dependent kinematic three-dimensional magnetic reconnection are discussed. In contrast to the counter-rotating plasma flows that existed in steady-state reconnection, it is found that spiral plasma flows, which have never been reported before, can be generated if the magnetic field changes exponentially with time. These analyses reveal new scenarios for time-dependent kinematic three-dimensional magnetic reconnection, and the deduced analytical solutions could improve our understanding of the dynamics involved in reconnection processes, as well as the interactions between the magnetic field and plasma flows during magnetic reconnection.

Mahmoud M. Ramadan, Yuanchen Wang, Pragya Tooteja

Hydrogen can play an important role for decarbonization. While hydrogen is usually produced through SMR, it can also be produced through water electrolysis which is cleaner. The relative cost and carbon intensity of hydrogen production through SMR and electrolysis vary throughout the United States because of differences in the grid. While many hydrogen cost models exist, no regional hydrogen study has been conducted across the US. We studied how the Levelized Cost of Hydrogen (LCOH) and carbon intensity for producing hydrogen vary across the US. We looked at electrolysis technologies (Alkaline, PEM, and SOEC) and compared them to SMR. In 2020, SMR with 90 percent CCUS has a lower average LCOH and carbon intensity for hydrogen production than electrolysis by SOEC. For states with cleaner grids, hydrogen produced through SOEC has a lower carbon intensity than hydrogen produced using SMR with 90 percent CCUS. Washington has one of the lowest carbon footprints and the lowest LCOH to produce hydrogen through electrolysis (alkaline). We predict that the LCOH for hydrogen production will be 3.2 USD per kg for Alkaline, 3.1 USD per kg for PEM, and 2.6 USD per kg for SOEC by 2050 with constant electricity prices. These projected LCOHs are still higher than the LCOH for hydrogen produced through SMR with 90 percent CCUS. If electricity costs decrease to 2c per kWh, we expect to reach cost-parity with SMR with 90 percent CCUS. The results suggest that significant investment in decarbonizing the grid and lowering the cost of electricity needs to be made to make electrolysis more competitive compared to SMR.

George A. Elliott, Klaus Thomsen

It is shown that any bundle of KMS state spaces which can occur for a flow on a unital separable C*-algebra with a trace state can also be realized by a flow on any given unital infinite-dimensional simple AF algebra with a tracial state space affinely homeomorphic to the fiber in the bundle over 0.

Nikhil Garg, Wes Gurnee, David Rothschild et al.

Every representative democracy must specify a mechanism under which voters choose their representatives. The most common mechanism in the United States -- Winner takes all single-member districts -- both enables substantial partisan gerrymandering and constrains `fair' redistricting, preventing proportional representation in legislatures. We study the design of \textit{multi-member districts (MMDs)}, in which each district elects multiple representatives, potentially through a non-Winner takes all voting rule. We carry out large-scale empirical analyses for the U.S. House of Representatives under MMDs with different social choice functions, under algorithmically generated maps optimized for either partisan benefit or proportionality. Doing so requires efficiently incorporating predicted partisan outcomes -- under various multi-winner social choice functions -- into an algorithm that optimizes over an ensemble of maps. We find that with three-member districts using Single Transferable Vote, fairness-minded independent commissions would be able to achieve proportional outcomes in every state up to rounding, \textit{and} advantage-seeking partisans would have their power to gerrymander significantly curtailed. Simultaneously, such districts would preserve geographic cohesion. Through simulation, we find that the insights are robust to cross-party voting. In the process, we advance a rich research agenda at the intersection of social choice and computational gerrymandering.

Marieke H Edwards, Linda van den Dries, Manfred Te Grotenhuis et al.

In order to prevent evictions, it is important to gain more insight into factors predicting whether or not tenants receive an eviction order. In this study, ten potential risk factors for evictions were tested. Tenants who were at risk of eviction due to rent arrears in five Dutch cities were interviewed using a structured questionnaire, and six months later their housing associations were asked to provide information about the tenants' current situation. Multiple logistic regression analyses with data on 344 tenants revealed that the amount of rent arrears was a strong predictor for receiving an eviction order. Furthermore, single tenants and tenants who had already been summoned to appear in court were more likely to receive an eviction order. These results can contribute to identifying households at risk of eviction at an early stage, and to develop targeted interventions to prevent evictions.

Mahesh Bashyal, Michael J. Mulvaney, Dewey Lee et al.

Abstract Brassica carinata (carinata), a non‐food oilseed feedstock mainly used for biofuel, is a relatively new alternative winter crop in the southeastern (SE) United States (US). However, there are limited N rate and N application timing data available at the regional scale. These data are needed to expand production in the SE US. An N rate study was conducted during the winter–spring growing seasons during 2017–2018 and 2018–2019 in Florida, US, and at three locations during 2018–2019 in Georgia, US, to quantify the effects of N rate (0, 45, 90, 134, and 179 kg N ha−1) on carinata nutrient uptake, biomass, seed yield, and seed chemical composition. Seed yield showed a linear response up to 134 kg N ha−1. Seed protein and glucosinolate concentrations decreased from 0 to 90 kg N ha−1, then increased from 90 to 179 kg N ha−1. Seed oil concentration was inversely related to seed protein concentration. A two‐factor N application timing study (4 N application timing: at‐plant, pre‐bolting, at‐plant + pre‐bolting, at‐plant + pre‐bolting + bolting × 4 N rates: 0, 45, 90, and 134 kg N ha−1) was conducted in Georgia, US, over three site‐years to quantify the effect of N application timing on yield and agronomic and economic optimum N rates (AONR and EONR, respectively). All split applications increased AONR by at least 10 kg N ha−1 compared to a single at‐plant application. A two‐split N application was more profitable than either a single N application or a three‐split N application based on marginal return. A two‐way split application (at‐plant + pre‐bolting) at 134 kg N ha−1 is recommended to optimize yield and economical production. Based on uncertainty analyses, the 50% credible interval of EONR occurred between 116 and 152 kg N ha−1, with a median estimate at 130 kg N ha−1.

Dongmi Kim, Ashley Taneja, John W Schiemann et al.

Introduction: The large numbers of torture survivors suffering from post-traumatic stress disorder and other psychological trauma in the United States suggests pharmacists should be aware of, and attentive to possible drug therapies for this population. Method: To this end, we systematically review the literature on pharmacotherapy for survivors of torture. Published literature that assessed use of pharmacotherapy in torture victims were searched from MEDLINE, Cochrane Library, PsychInfo and CINAHL. Search terms “torture,” “pharmacotherapy,” “depression OR PTSD,” “refugee OR asylum seekers” and “treatment or rehabilitation” were utilized. Results: Review of controlled and uncontrolled studies reveal that antidepressants are the most widely studied medications, particularly sertraline, a selective-serotonin reuptake inhibitor, in the torture survivor population expanding to refugees and asylum seekers. Anti-adrenergic medications were used as adjunctive treatment in some uncontrolled studies. In randomized controlled trials, pharmacotherapy did not differentiate from placebo in reducing symptoms. Uncontrolled trials had yielded variable outcomes from pharmacotherapy. Discussion: There is lack of strong evidence in supporting the use of pharmacotherapy for the treatment of post-traumatic stress disorder and major depressive disorder. Heterogeneity in the study design, patient ethnicity and the social and political status at the time of the study may have contributed in the variable clinical responses to pharmacotherapy.

Qianqian Sun, Yixuan Pan, Weiyi Zhou et al.

As a highly infectious respiratory disease, COVID-19 has become a pandemic that threatens global health. Without an effective treatment, non-pharmaceutical interventions, such as travel restrictions, have been widely promoted to mitigate the outbreak. Current studies analyze mobility metrics such as travel distance; however, there is a lack of research on interzonal travel flow and its impact on the pandemic. Our study specifically focuses on the inter-county mobility pattern and its influence on the COVID-19 spread in the United States. To retrieve real-world mobility patterns, we utilize an integrated set of mobile device location data including over 100 million anonymous devices. We first investigate the nationwide temporal trend and spatial distribution of inter-county mobility. Then we zoom in on the epicenter of the U.S. outbreak, New York City, and evaluate the impacts of its outflow on other counties. Finally, we develop a "log-linear double-risk" model at the county level to quantify the influence of both "external risk" imported by inter-county mobility flows and the "internal risk" defined as the vulnerability of a county in terms of population with high-risk phenotypes. Our study enhances the situation awareness of inter-county mobility in the U.S. and can help improve non-pharmaceutical interventions for COVID-19.

Tomoko Matsuda, Hikoyu Suzuki, Norichika Ogata

Worldwide Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is disrupting in the economy and anxiety of people. The public anxiety has increased the psychological burden on government and healthcare professionals, resulting in a government worker suicide in Japan. The terrified people are asking the government for border measures. However, are border measures possible for this virus? By analyzing 48 almost complete virus genome sequences, we found out that the viruses that invaded Taiwan, the United States, and Japan were introduced independently. We identified thirteen parsimony-informative sites and three groups (CTC, TCC, and TCT). Viruses found outside China did not form a monophyletic clade, opposite to previous study. These results suggest the difficulty of implementing effective border measures against this virus.