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The My Green LANL initiative was created to unify LANL employees in all research and work environments to reflect the highest standard of social and environmental responsibility. In combination with the My Green Lab non-profit organization and internal LANL efforts, the My Green LANL mission is to educate and emphasize the importance of practicing sustainable or ‘green’ efforts in both laboratory and office spaces.
The new millennium witnessed two revolutionary breakthroughs in ultrafast x-ray science: table-top XUV sources based on high harmonic generation in gases ushered in the attosecond era while facility-based x-ray free-electron lasers opened the path for intense, femtosecond hard x-rays. This award requested scholarship funds for a 2019 and 2023 School whose prime objective was the training of young scientists in these emerging complementary areas both relevant to DOE BES mission. The school entitled the “Frontiers of Attosecond and Ultrafast X-ray Science (FAXS)” (http://www.erice-attosecond.it/) was the second and fourth in a series, which began in 2017. The two Schools were held during March 10-16, 2019 and March 26-31, 2023 at the Ettore Majorana Foundation and Centre for Scientific Culture (http://www.ccsem.infn.it/) in Erice, Sicily. The DOE funds supported the registration fee for young scientists (graduate students and postdocs) from US institutions. The registration fees included School participation, lodging and meals over the duration of the School. Note, the third addition of the School was held in 2022 as a virtual event due to the pandemic, DOE funds were not necessary for this event. The FAXS School is a course of the 62th and 63rd International School of Quantum Electronics under the directorship of Prof. Diederik Wiersma (University of Florence). The Directors for the FAXS School are Louis DiMauro (The Ohio State University, USA) and Mauro Nisoli (Politecnico di Milano, Italy). The FAXS School program consisted of approximately 10 lectures by leading experts in attosecond and x-ray science (see attached list). Most lecturers delivered a series of three 1-hour lectures. The lecturers were required to spend the full 5 days at the school so to promote interaction with the students. The Erice Majorana Center venue accommodated ~75 young scientists. The registration fee covered the cost of participating in the school, lodging and meals for the entire duration of the school. The schedule consisted of lectures every morning and afternoon except for one afternoon that was reserved for an archaeological excursion. Every evening had a student/postdoc poster session and social gatherings at the Majorana Center to encourage further interaction of all participants and lecturers. The two FAXS Schools attracted an international group of young scientists. The DOE funds supported the registration fee for 11 students/postdocs from US institutions (5 supported in 2019 and 6 supported in 2023). The management of the DOE fellowships were administered through the Research Foundation of The Ohio State University. FAXS scholarships for European students/postdocs were provided by European funding sources administered by co-Director, Prof. Nisoli. All students/postdocs were encouraged to present a poster. Travel expenses to the FAXS School were the responsibility of the student/postdoc home institution.
The contamination legacy of industrialization, militarization, and nuclear arms race poses current or future risks to populations and the environment. Responsible parties and regulators make decisions regarding which sites to clean up, how, how much, and when. This study aimed to provide an information needs template to evaluate and reduce risks to human health when considering whether to initiate or delay remediation. This investigation focused on four aspects of timing and prioritization: 1) management, planning and implementation, 2) source terms, pathways, and exposures, 3) risks and receptors, and 4) external drivers. Within each type, issues were identified and described. Management class included personnel, health and safety data, funding, equipment, and structural integrity. Source term included contaminant sources, pathways, initiating events, and barriers to exposure. Risk included types and exposures to workers and general public. External drivers included regulatory framework, stakeholders, Congressional mandates, and economic and social contexts. Risk may increase over time as contamination spreads, enters aquifers, and reaches receptors, and may decline as radionuclides decay, and plumes dissipate. The overall objective was to provide a template of information that is useful to managers and regulators, and might be used by the public to understand the risks and benefits of re-prioritization cleanup.
COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5–24.8%) infection rate and 29.4% (95% CrI: 28.0–31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3–12.0%] vs 25.1% [95% CrI: 23.7–26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49–57%] vs 28% [95% CrI: 27–30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0–3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC’s social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.
State wildlife management agencies in the United States have depended on a “user-pay” funding model for conservation efforts that relies on revenue from hunting license sales and a federal excise tax on firearms, ammunition, and archery equipment. Declines in hunting participation, however, jeopardize sustainability of the current funding model. Ensuring support among stakeholders for wildlife management and conservation may require expanding sources of funding and incorporating the perspectives and values of a diversifying constituency into decision making processes. We used a web-based survey of wildlife-associated recreationists in Michigan, USA to evaluate support for a range of conservation funding policies. Respondents self-identified primarily as hunters (n = 2,558) or wildlife watchers (n = 942). We used binary logistic regression to evaluate support for four conservation funding policy options: state sales tax, lottery proceeds, extractive industry revenue, and a user-based tax on outdoor gear (i.e., “backpack tax”). Determinants of support varied by type of policy and stakeholder characteristics. We found no statistically significant differences between hunters and wildlife watchers in their support for conservation funding policies when accounting for other variables such as wildlife value orientations, engagement in stewardship behaviors, age, and gender. The industry-based policy achieved the greatest level of approval, while the backpack tax had the lowest. Respondents were mixed in their support of the sales tax and lottery proceeds options. Cluster analysis revealed three homogenous groups related to conservation funding policies: “strong support,” “mixed/opposed,” and “anti-backpack tax.” Clusters differed in their support for conservation funding policies and on psychological and demographic variables. The “strong support” and “anti-backpack tax” groups differed in their levels of stewardship engagement, knowledge of conservation funding mechanisms, and support for the backpack tax option. The “mixed-opposed” group tended to be older, less educated, and less likely to be a member of a conservation organization. Results suggest support for conservation funding differs by policy type and social and psychological characteristics of stakeholders. Based on differences in policy support revealed in this study, we suggest a multi-tiered approach to funding conservation and building on support among wildlife stakeholders to mitigate the looming funding crisis for state wildlife agencies.
As concerns about social equity and clean energy rise, state and local governments, utilities, and non-profit organizations are offering at least 41 active programs in 21 states to promote solar adoption as a way to reduce energy bills for low- and moderate-income (LMI) households while meeting other policy goals such as job creation and clean energy generation. A new study from Berkeley Lab looks at how those programs are being evaluated. The report provides background on how they seek to address LMI household energy burdens and gives a brief discussion of the art and science of program evaluation, drawn from decades of experience in energy efficiency programs. It then turns to how LMI solar programs are currently being evaluated, highlighting trends among evaluation methods, metrics tracked, and best practices employed. A few programs are explored in more depth to illustrate notable practices that could be applied to other program evaluations. These LMI solar programs are mostly young, operate on tight budgets, and vary considerably in their design and stated goals. Consequently, well-designed program evaluation is critical to better understand what works, what could be improved, and how to maximize program impacts under budget constraints.
Potential economic and environmental benefits of increasing nitrogen-use efficiency (NUE) are widely recognized but scarcely quantified. This study quantifies the effects of increased NUE on 1) the national agricultural economy using a simulation model of US agriculture and 2) regional water quality effects using a biogeochemical model for the Arkansas-White-Red river basin. Here, national economic effects are reported for NUE improvement scenarios of 10%, 20%, 50%, and 100%, whereas regional water quality effects are estimated for a 20% NUE improvement scenario in the Arkansas-White-Red river basin. Simulating a 20% increase in NUE in row crops is shown to reduce N requirements by 1.4 million tonnes y-1 and increase farmer net profits by 1.6% ($743 million) per year by 2026 over the baseline simulation for the same period. For each 10% increase in NUE, annual farm revenues for commodity crops increased over the baseline by approximately $350 million per year by 2026. Changes in crop prices and land-use relative to the baseline were less than 2%. This suggests a net benefit even though fertilizer cost savings can result in increased cultivation of land, i.e., ‘Jevon's paradox’. Results from the biogeochemical model of the Arkansas-White-Red river basin suggest that a 20% increase in NUE corresponds to a 5.72% reduction in nitrate loadings to freshwaters, with higher reductions in agricultural watersheds. The value of these reductions was estimated as $43 ha-1, for a total of $15.3 to 136.7 million yr-1 in avoided water treatment costs. After estimating the social value of increased NUE, we conclude with a discussion of potential strategies to increase efficiency and the research needed to achieve this goal. These include perennialization of the agricultural landscape, genetic crop improvement, targeted fertilizer application, and manipulation of the plant-root microbiome.
As crewed spaceflight missions are set to explore greater distances from Earth and fly longer durations than ever before, we must expand our understanding of the impacts of prolonged isolation and confinement on the health, performance, and well-being of astronauts. In combination with flight research, ground-based analogs that simulate key mission conditions like the social and physical isolation associated with distance from Earth are essential research platforms for characterizing risk and developing validated and feasible countermeasures that support future crews and ground support teams. With several isolation and confinement analogs currently operating, and even more in development, there is great opportunity for a collaborative global analog environment to complement and optimize research efforts. The Research Operations and Integration team will moderate a panel discussion among analog operators and scientists who utilize isolation and confinement analogs. The focus of discussion will be the potential for strategic coordination, portability, and harmonization of science across different platforms; and identifying the valuable similarities and differences among isolation analogs as well as existing gaps within the analog community. A Q&A session will follow with attendees encouraged to ask questions and join in the discussion.
The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.
Forest restoration has never been higher on policymakers' agendas. Complex and multi-dimensional arrangements across the urban–rural continuum challenge restorationists and require integrative approaches to strengthen environmental protection and increase restoration outcomes. It remains unclear if urban and rural forest restoration are moving towards or away from each other in practice and research, and whether comparing research outcomes can help stakeholders to gain a clearer understanding of the interconnectedness between the two fields. This study aims to identify the challenges and opportunities for enhancing forest restoration in both urban and rural systems by reviewing the scientific evidence, engaging with key stakeholders and using an urban–rural forest restoration framework. Using the Society for Ecological Restoration's International Principles as discussion topics, we highlight aspects of convergence and divergence between the two fields to broaden our understanding of forest restoration and promote integrative management approaches to address future forest conditions. Our findings reveal that urban and rural forest restoration have convergent and divergent aspects. We emphasise the importance of tailoring goals and objectives to specific contexts and the need to design different institutions and incentives based on the social and ecological needs and goals of stakeholders in different regions. Here we discuss the challenges of achieving high levels of ecological restoration and the need to go beyond traditional ecology to plan, implement, monitor, and adaptively manage restored forests. We suggest that rivers and watersheds could serve as a common ground linking rural and urban landscapes and that forest restoration could interact with other environmental protection measures. We note the potential for expanding the creative vision associated with increasing tree-containing environments in cities to generate more diverse and resilient forest restoration outcomes in rural settings. This study underscores the value of integrative management approaches in addressing future forest conditions across the urban–rural continuum. Our framework provides valuable insights for policymakers, researchers, and decision-makers to advance the field of forest restoration and address the challenges of restoration across the urban–rural continuum. The rural–urban interface serves as a convergence point for forest restoration, and both urban and rural fields can benefit from each other's expertise.
The premise of the workshop was that NASA and its associated science and exploration communities have not been as effective as they could be in communicating with the public about what NASA does or how its activities contribute to resolving critical problems on Earth. Although not explicitly stated, an underlying assumption seemed to be that if the public had a better understanding, it would be more supportive of NASA, which in turn could generate more political support for the organization. In the case of global climate change, the broader issue is how to convince the public of the magnitude of the problem and the need for solutions. The role of new social media tools like Facebook and Twitter in interacting with the public was an integral part of the discussion.
Background Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. Methods and findings We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality–no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77–0.81), and in early (AUC = 0.81 95% CI: 0.78–0.83) and late (AUC = 0.84, 95% CI: 0.78–0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60–64 years, overall mortality was estimated at 9% (95% CI: 6–11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3–7%) to 21% (95% CI: 12–31%), depending on sex and comorbid disease. Conclusion Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines.
According to the National Science Foundation, of the more than 4 million first university degrees awarded in science and engineering in 2006, students in China earned about 21%, those in the European Union earned about 19%, and those in the United States earned about 11%. Statistics like these are of great interest to NASA's Ares Projects, which are responsible for building the rockets for the U.S. Constellation Program to send humans beyond low-Earth orbit. Science, technology, engineering, and mathematics students are essential for the long-term sustainability of any space program. Since the Projects creation, the Ares Outreach Team has used a variety of STEM-related media, methods, and materials to engage students, educators, and the general public in Constellation's mission. Like Project Apollo, the nation s exploration destinations and the vehicles used to get there can inspire students to learn more about STEM. Ares has been particularly active in public outreach to schools in Northern Alabama; on the Internet via outreach and grade-specific educational materials; and in more informal social media settings such as YouTube and Facebook. These combined efforts remain integral to America s space program, regardless of its future direction.
Prior research has demonstrated that social determinants of health (SDoH) are major drivers of health outcomes and contributors to widespread health inequities. It was estimated that, in the United States, SDoH could be responsible for up to 40% of all preventable deaths, significantly higher than the 10-15% for which better medical care is responsible. Public health interventions that target SDoH are instrumental for improving health outcomes and reducing long-standing health inequities. Currently, most mainstream EHR vendors have implemented SDoH screeners in their EHR systems. However, the utility of the screeners is low, rendering patient-level SDoH still widely unavailable in the structured fields. SDoH are sometimes mentioned in free-text clinical notes (e.g., social context section) where natural language processing (NLP) can be applied to extract relevant information. Contextual-level SDoH can be identified from multiple data sources, many of which are publicly available and spatiotemporally linked to EHR data. As such, there is an opportunity for the KDDM research community to create innovative solutions to draw meaningful insights by creating and using rich data with SDoH to improve health outcomes while reducing disparities. In this workshop organized by AMIA Knowledge Discovery and Data Mining Working Group (AMIA KDDM WG), we will invite world-leading experts from academia, national laboratories, and life science industry with varied backgrounds in biomedical informatics, epidemiology, data science, machine learning, natural language processing, and pediatric cardiology to discuss the best practice of capturing, standardizing, and using SDoH information in various applications aiming at improving outcomes and health equity.
The Johnson Space Center (JSC), located in the southeast metropolitan region of Houston, TX is the prime NASA center for human spaceflight operations and astronaut training, but it also houses the unique collection of returned extraterrestrial samples, including lunar samples from the Apollo missions. The Center's location adjacent to Clear Lake and the Clear Creek watershed, an estuary of Galveston Bay, puts it at direct annual risk from hurricanes, but also from a number of other climate-related hazards including drought, floods, sea level rise, heat waves, and high wind events all assigned Threat Levels of 2 or 3 in the most recent NASA Center Disaster/Risk Matrix produced by the Climate Adaptation Science Investigator Working Group. Based on prior CASI workshops at other NASA centers, it is recognized that JSC is highly vulnerable to climate-change related hazards and has a need for adaptation strategies. We will present an overview of prior CASI-related work at JSC, including publication of a climate change and adaptation informational data brochure, and a Resilience and Adaptation to Climate Risks Workshop that was held at JSC in early March 2012. Major outcomes of that workshop that form a basis for work going forward are 1) a realization that JSC is embedded in a regional environmental and social context, and that potential climate change effects and adaptation strategies will not, and should not, be constrained by the Center fence line; 2) a desire to coordinate data collection and adaptation planning activities with interested stakeholders to form a regional climate change adaptation center that could facilitate interaction with CASI; 3) recognition that there is a wide array of basic data (remotely sensed, in situ, GIS/mapping, and historical) available through JSC and other stakeholders, but this data is not yet centrally accessible for planning purposes.
We have applied a future mission architecture, the Autonomous Nano-Technology Swarm (ANTS), to a proposed mission for in situ survey, or prospecting, of the asteroid belt, the Prospecting Asteroid Mission (PAM) as part of a NASA 2003 Revolutionary Aerospace Concept (RASC) study. ANTS architecture builds on and advances recent trends in robotics, artificial intelligence, and materials processing to minimize costs and maximize effectiveness of space operations. PAM and other applications have been proposed for the survey of inaccessible, high surface area populations of great interest from the standpoint of resources and/or solar system origin. The ANTS architecture is inspired by the success of social insect colonies, a success based on the division of labor within the colonies in two key ways: 1) within their specialties, individual specialists generally outperform generalists, and 2) with sufficiently efficient social interaction and coordination, the group of specialists generally outperforms the group of generalists. Thus systems designed as ANTS are built from potentially very large numbers of highly autonomous, yet socially interactive, elements. The architecture is self-similar in that elements and sub-elements of the system may also be recursively structured as ANTS on scales ranging from microscopic to interplanetary distances. Here, we analyze requirements for the mission application at the low gravity target end of the spectrum, the Prospecting Asteroid Mission (PAM), and for specialized autonomous operations which would support this mission. ANTS as applied to PAM involves the activities of hundreds of individual specialist 'sciencecraft'. Most of them, called Workers, carry and operate eight to nine different scientific instruments, as listed in the table, including spectrometers, ranging and radio science devices, and imagers. The remaining specialists, Messenger/Rulers, provide communication and coordination functions among specialists operating autonomously as individuals, team members, and subswarms.
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.
As the rehabilitation of infrastructure is outpaced by changes in the profile, frequency, and intensity of extreme weather events, infrastructure’s service disruptions and failures become increasingly likely. Safe-to-fail approaches for infrastructure planning and design improve the capacity of cities to adapt for uncertain climate futures by identifying social, ecological, and technological systems (SETS) capabilities to prepare for potential failure scenarios. In this work, we argue for transforming infrastructure planning and design to effectively utilize safe-to-fail approaches by navigating the opportunities and trade-offs of SETS resilience capabilities. From a technological vantage point, traditional infrastructure planning approaches account for social and ecological domains as external design conditions rather than embedded system characteristics. Safe-to-fail approaches directly challenge the isolation of the technological domain by necessitating a recognition that SETS domains are interconnected and interdependent in infrastructure systems, as such risks and system capabilities for resilience must be managed cohesively.