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A Machine Learning Platform that connects beamline scientists and machine learning (ML) researchers so that the ML researchers can quickly distribute their ML models to beamline scientists, and beamline scientists can give feedback to help ML researchers improve their models.
I am a professional scientist. What does that mean? I think it is safe to say it means that, to the best of my ability, I apply the scientist method to test hypotheses and gain a clearer understanding of the world around me. Furthermore, I am also a human being, and so when I am asked, as I often am, about how my research findings have influenced my day-to-day activities, I like to take a step back and think about what it means to be a scientist.
From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery
Oak Ridge National Laboratory (ORNL) offers the scientific community unique access to two types of world-class neutron sources at a single site - the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR). The 85-MW HFIR provides one of the highest steady-state neutron fluxes of any research reactor in the world, and the SNS is one of the world's most intense pulsed neutron beams. Management of these two resources is the responsibility of the Neutron Sciences Directorate (NScD). NScD commissioned this survey research to develop baseline information regarding awareness of and perceptions about neutron science. Specific areas of investigative interest include the following: (1) awareness levels among those in the scientific community about the two neutron sources that ORNL offers; (2) the level of understanding members of various scientific communities have regarding benefits that neutron scattering techniques offer; and (3) any perceptions that negatively impact utilization of the facilities. NScD leadership identified users of two light sources in North America - the Advanced Photon Source (APS) at Argonne National Laboratory and the National Synchrotron Light Source (NSLS) at Brookhaven National Laboratory - as key publics. Given the type of research in which these scientists engage, they would quite likely benefit from including the neutron techniques available at SNS and HFIR among their scientific investigation tools. The objective of the survey of users of APS, NSLS, SNS, and HFIR was to explore awareness of and perceptions regarding SNS and HFIR among those in selected scientific communities. Perceptions of SNS and FHIR will provide a foundation for strategic communication plan development and for developing key educational messages. The survey was conducted in two phases. The first phase included qualitative methods of (1) key stakeholder meetings; (2) online interviews with user administrators of APS and NSLS; and (3) one-on-one interviews and traditional and online focus groups with scientists. The latter include SNS, HFIR, and APS users as well as scientists at ORNL, some of whom had not yet used HFIR and/or SNS. These approaches informed development of the second phase, a quantitative online survey. The survey consisted of 16 questions and 7 demographic categorizations, 9 open-ended queries, and 153 pre-coded variables and took an average time of 18 minutes to complete. The survey was sent to 589 SNS/HFIR users, 1,819 NSLS users, and 2,587 APS users. A total of 899 individuals provided responses for this study: 240 from NSLS; 136 from SNS/HFIR; and 523 from APS. The overall response rate was 18%.
Multiscale, multirate scientific and engineering applications in the SciDAC portfolio possess resolution requirements that are practically inexhaustible and demand execution on the highest-capability computers available, which will soon reach the petascale. While the variety of applications is enormous, their needs for mathematical software infrastructure are surprisingly coincident; moreover the chief bottleneck is often the solver. At their current scalability limits, many applications spend a vast majority of their operations in solvers, due to solver algorithmic complexity that is superlinear in the problem size, whereas other phases scale linearly. Furthermore, the solver may be the phase of the simulation with the poorest parallel scalability, due to intrinsic global dependencies. This project brings together the providers of some of the world's most widely distributed, freely available, scalable solver software and focuses them on relieving this bottleneck for many specific applications within SciDAC, which are representative of many others outside. Solver software directly supported under TOPS includes: hypre, PETSc, SUNDIALS, SuperLU, TAO, and Trilinos. Transparent access is also provided to other solver software through the TOPS interface. The primary goals of TOPS are the development, testing, and dissemination of solver software, especially for systems governed by PDEs. Upon discretization, these systems possess mathematical structure that must be exploited for optimal scalability; therefore, application-targeted algorithmic research is included. TOPS software development includes attention to high performance as well as interoperability among the solver components. Support for integration of TOPS solvers into SciDAC applications is also directly supported by this proposal. The role of the UCSD PI in this overall CET, is one of direct interaction between the TOPS software partners and various DOE applications scientists' specifically toward magnetohydrodynamics (MHD) simulations with the Center for Extended Magnetohydrodynamic Modeling (CEMM) SciDAC and Applied Partial Differential Equations Center (APDEC) SciDAC, and toward core-collapse supernova simulations with the previous Terascale Supernova Initiative (TSI) SciDAC and in continued work on INCITE projects headed by Doug Swesty, SUNY Stony Brook. In addition to these DOE applications scientists, the UCSD PI works to bring leading-edge DOE solver technology to applications scientists in cosmology and large-scale galactic structure formation. Unfortunately, the funding for this grant ended after only two years of its five-year duration, in August 2008, due to difficulties at DOE in transferring the grant to the PI's new faculty position at Southern Methodist University. Therefore, this report only describes two years' worth of effort.
This report summarizes work carried out by the Earth System Grid Center for Enabling Technologies (ESG-CET) from October 1, 2010 through March 31, 2011. It discusses ESG-CET highlights for the reporting period, overall progress, period goals, and collaborations, and lists papers and presentations. To learn more about our project and to find previous reports, please visit the ESG-CET Web sites: http://esg-pcmdi.llnl.gov/ and/or https://wiki.ucar.edu/display/esgcet/Home. This report will be forwarded to managers in the Department of Energy (DOE) Scientific Discovery through Advanced Computing (SciDAC) program and the Office of Biological and Environmental Research (OBER), as well as national and international collaborators and stakeholders (e.g., those involved in the Coupled Model Intercomparison Project, phase 5 (CMIP5) for the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5); the Community Earth System Model (CESM); the Climate Science Computational End Station (CCES); SciDAC II: A Scalable and Extensible Earth System Model for Climate Change Science; the North American Regional Climate Change Assessment Program (NARCCAP); the Atmospheric Radiation Measurement (ARM) program; the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA)), and also to researchers working on a variety of other climate model and observation evaluation activities. The ESG-CET executive committee consists of Dean N. Williams, Lawrence Livermore National Laboratory (LLNL); Ian Foster, Argonne National Laboratory (ANL); and Don Middleton, National Center for Atmospheric Research (NCAR). The ESG-CET team is a group of researchers and scientists with diverse domain knowledge, whose home institutions include eight laboratories and two universities: ANL, Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), LLNL, NASA/Jet Propulsion Laboratory (JPL), NCAR, Oak Ridge National Laboratory (ORNL), Pacific Marine Environmental Laboratory (PMEL)/NOAA, Rensselaer Polytechnic Institute (RPI), and University of Southern California, Information Sciences Institute (USC/ISI). All ESG-CET work is accomplished under DOE open-source guidelines and in close collaboration with the project's stakeholders, domain researchers, and scientists. Through the ESG project, the ESG-CET team has developed and delivered a production environment for climate data from multiple climate model sources (e.g., CMIP (IPCC), CESM, ocean model data (e.g., Parallel Ocean Program), observation data (e.g., Atmospheric Infrared Sounder, Microwave Limb Sounder), and analysis and visualization tools) that serves a worldwide climate research community. Data holdings are distributed across multiple sites including LANL, LBNL, LLNL, NCAR, and ORNL as well as unfunded partners sites such as the Australian National University (ANU) National Computational Infrastructure (NCI), the British Atmospheric Data Center (BADC), the Geophysical Fluid Dynamics Laboratory/NOAA, the Max Planck Institute for Meteorology (MPI-M), the German Climate Computing Centre (DKRZ), and NASA/JPL. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users who want to understand it, process it, extract value from it, visualize it, and/or communicate it to others. This ongoing effort is extremely large and complex, but it will be incredibly valuable for building 'science gateways' to critical climate resources (such as CESM, CMIP5, ARM, NARCCAP, Atmospheric Infrared Sounder (AIRS), etc.) for processing the next IPCC assessment report. Continued ESG progress will result in a production-scale system that will empower scientists to attempt new and exciting data exchanges, which could ultimately lead to breakthrough climate science discoveries.
Ocean scientists searching for isosurfaces and/or thresholds of interest in high resolution 3D datasets required a tedious and time-consuming interactive exploration experience. PISTON research and development activities are enabling ocean scientists to rapidly and interactively explore isosurfaces and thresholds in their large data sets using a simple slider with real time calculation and visualization of these features. Ocean Scientists can now visualize more features in less time, helping them gain a better understanding of the high resolution data sets they work with on a daily basis. Isosurface timings (512 3 grid): VTK 7.7 s, Parallel VTK (48-core) 1.3 s, PISTON OpenMP (48-core) 0.2 s, PISTON CUDA (Quadro 6000) 0.1 s.
Travel support was provided for a range of invited speakers, students, early-career, and developing-country, and key scientists who required financial assistance to participate, and would otherwise be unable to attend, to contribute to, and benefit from, this important event. This support also allowed participants to present their research findings, provide input to WCRP planning and plans, and encourage collaboration with other research scientists. In particular, the participation and engagement of regional scientists in the OSC helped to ensure communication and advocacy in identifying the climate research needs of the region and their inclusion in the WCRP long-range research priorities.
The Joint US-EC Short Course on Environmental Biotechnology is designed for several purposes. One of the central tenets is to bring together young scientists (at the late Ph.D. or early postdoctoral stages of their careers) in a forum that will set the groundwork for future overseas collaborative interactions. The course is also designed to give the scientists hands-on experience in modern, up-to-date biotechnological methods for the analysis of microbes and their activities pertinent to the remediation of pollutants in the environment. The 2011 course covered multiple theoretical and practical topics in environmental biotechnology. The practical part was centered around a full concise experiment to demonstrate the possibility for targeted remediation of contaminated soil. Experiments included chemical, microbiological, and molecular analyses of sediments and/or waters, contaminant bioavailability assessment, seeded bioremediation, gene probing, PCR amplification, microbial community analysis based on 16S rRNA gene diversity, and microarray analyses. Each of these topics is explained in detail. The practical part of the course was complemented with two lectures per day, given by distinguished scientists from the US and from Europe, covering a research area related to what the students are doing in the course.
The Lawrence Livermore National Laboratory (LLNL) Nuclear Forensics Summer Program is designed to give both undergraduate and graduate students an opportunity to come to LLNL for 8–10 weeks for a hands-on research experience. Students conduct research under the supervision of a staff scientist, attend a weekly lecture series, interact with other students, and present their work in poster format at the end of the program. Students also have the opportunity to meet staff scientists one-on-one, participate in LLNL facility tours (e.g., the National Ignition Facility and Center for Accelerator Mass Spectrometry) to gain a better understanding of the multi-disciplinary, on going science at LLNL. Currently called the Nuclear Forensics Summer Program, this program began 13 years ago as the Actinide Sciences Summer Program. The program is run within the Glenn T. Seaborg Institute in the Physical and Life Sciences Directorate at LLNL. The goal of the Nuclear Forensics Summer Program is to facilitate the training of the next generation of nuclear scientists and engineers to solve critical national security problems in the field of nuclear forensics and have the student experience conducting research at the Lab. We select students who are majoring in physics, chemistry, geology, mathematics, nuclear engineering, chemical engineering and environmental sciences. Students engage in research projects in the disciplines of actinide chemistry, radiochemistry, isotopic analysis, computational analysis, radiation detection, and nuclear engineering in order to strengthen the “pipeline” for future scientific disciplines critical to DHS (DNDO), NNSA.
Climate Information Needs for Financial Decision Making (Final Report) This Department of Energy workshop award (grant #DE-SC0008480) provided primary support for the American Meteorological Society’s study on climate information needs for financial decision making. The goal of this study was to help advance societal decision making by examining the implications of climate variability and change on near-term financial investments. We explored four key topics: 1) the conditions and criteria that influence returns on investment of major financial decisions, 2) the climate sensitivity of financial decisions, 3) climate information needs of financial decision makers, and 4) potential new mechanisms to promote collaboration between scientists and financial decision makers. Better understanding of these four topics will help scientists provide the most useful information and enable financial decision makers to use scientific information most effectively. As a result, this study will enable leaders in business and government to make well-informed choices that help maximize long-term economic success and social wellbeing in the United States The outcomes of the study include a workshop, which brought together leaders from the scientific and financial decision making communities, a publication of the study report, and a public briefing of the results to the policy community. In addition, we will present the results to the scientific community at the AMS Annual Meeting in February, 2014. The study results were covered well by the media including Bloomberg News and E&E News. Upon request, we also briefed the Office of Science Technology Policy (OSTP) and the Council on Environmental Quality (CEQ) on the outcomes. We presented the results to the policy community through a public briefing in December on Capitol Hill. The full report is publicly available at www.ametsoc.org/cin. Summary of Key Findings The United States invests roughly $1.5 trillion U.S. dollars (USD) in capital assets each year across the public and private sectors (Orszag 2008; United States Census Bureau 2013). Extreme weather events create and exacerbate risks to these financial investments by contributing to: • Direct physical impacts on the investments themselves • Degradation of critical supporting infrastructure • Changes in the availability of key natural resources • Changes to workforce availability or capacity • Changes in the customer base • Supply chain disruptions • Legal liability • Shifts in the regulatory environment • Reductions in credit ratings Even small changes in weather can impact operations in critical economic sectors. As a result, maximizing returns on financial investments depends on accurately understanding and effectively accounting for these risks. Climate variability and change can either exacerbate existing risks or cause new sources of risk to emerge. Managing these risks most effectively will depend on scientific advances and increases in the capacity of financial decision makers to use the scientific knowledge that results. Barriers to using climate information must also be overcome. This study proposes three predefined levels of certainty for communicating about weather and climate risks: 1) possible (i.e., unknown likelihood or less than 50% chance of occurrence), 2) probable (greater than 50% chance of occurrence), and 3) effectively certain (at least 95% chance of occurrence). For example, it is effectively certain that a change in climate will alter weather patterns. It is probable that climate warming will cause increases in the intensity of some extreme events. It is possible that climate change will cause major and widespread disruptions to key planetary life-support services. Key recommendations of this study: 1) Identify climate-related risks and opportunities for financial decision making. 2) Create a framework to translate scientific information in clear and actionable terms for financial decision makers. 3) Analyze existing climate assessments and translate projected impacts into possible, probable, and effectively certain impacts. 4) Improve climate projections with respect to precipitation (timing, amount, and intensity), extreme events, and tails of probability distributions (i.e., low-probability but high-consequence events). 5) Increase spatial resolution of climate projections in order to provide climate information at the scale most relevant to financial investments. 6) Improve projections of the societal consequences of climate impacts through integrated assessments of physical, natural, and social sciences. 7) Create a user-friendly information repository and portal that provides easy access to information relevant to financial decision making. 8) Create and maintain opportunities to bring together financial decision makers, scientists, and service providers. Near-term financial decisions have long-term implications for the United States’ social and economic well-being that depend, in part, on climate variability and change. Investments will be most successful, and will advance the interests of society most effectively, if they are grounded in the best available knowledge & understanding.
The past three years of support by the Heavy Elements Chemistry Program have been highly productive in terms of advanced degrees awarded, currently supported graduate students, peer-reviewed publications, and presentations made at universities, national laboratories, and at international conferences. Ph.D. degrees were granted to Shuao Wang and Juan Diwu, who both went on to post-doctoral appointments at the Glenn T. Seaborg Center at Lawrence Berkeley National Laboratory with Jeff Long and Ken Raymond, respectively. Pius Adelani completed his Ph.D. with me and is now a post-doc with Peter C. Burns. Andrea Alsobrook finished her Ph.D. and is now a post-doc at Savannah River with Dave Hobbs. Anna Nelson completed her Ph.D. and is now a post-doc with Rod Ewing at the University of Michigan. As can be gleaned from this list, students supported by the Heavy Elements Chemistry grant have remained interested in actinide science after leaving my program. This follows in line with previous graduates in this program such as Richard E. Sykora, who did his post-doctoral work at Oak Ridge National Laboratory with R. G. Haire, and Amanda C. Bean, who is a staff scientist at Los Alamos National Laboratory, and Philip M. Almond and Thomas C. Shehee, who are both staff scientists at Savannah River National Laboratory, Gengbang Jin who is a staff scientist at Argonne National Lab, and Travis Bray who has been a post-doc at both LBNL and ANL. Clearly this program is serving as a pipe-line for students to enter into careers in the national laboratories. About half of my students depart the DOE complex for academia or industry. My undergraduate researchers also remain active in actinide chemistry after leaving my group. Dan Wells was a productive undergraduate of mine, and went on to pursue a Ph.D. on uranium and neptunium chalcogenides with Jim Ibers at Northwestern. After earning his Ph.D., he went directly into the nuclear industry.
Considered the lifeblood of Physics Division, postdoctoral researchers are a vital, energizing source of potential technical staff. “Our postdocs are the future of the Division,” said its leader Doug Fulton. Indeed, the majority of scientists recently hired by Physics Division were originally postdocs within the organization. Here, six former postdocs, now Physics Division staff scientists, reveal why they chose the Laboratory over other enticing employment offers, and describe their postdoc days being mentored by preeminent scientists, performing challenging experiments at distinctive facilities, and earning excellent pay. Among the Laboratory’s technical organizations, Physics Division has the fourth highest postdoc concentration, comprising nearly 11 percent of Los Alamos’s 393 postdocs. Over the course of up to three years, managers and postdocs get to size up each other. After completing their first year, postdocs are eligible to apply for staff positions in any Lab organization, and must demonstrate exceptional scientific abilities based on work performed, research published and presented, and interactions with colleagues. Becoming a Physics staff member is not guaranteed and competition is stiff, with division management selecting the most promising candidates, while considering diversity and the programmatic and strategic needs of its groups, the division, and the Laboratory.
The European Commission (EC)-United States (US) Task Force on Biotechnology Research has a longstanding joint Working Group on Biotechnology for the Environment whose mission is to foster collaborations between researchers in the European Union (EU) and US in the field of environmental biotechnology. A special focus of the Working Group is to increase scientific interchange between early career scientists in the US and EU. Such interactions initiate a foundation of respect and trust needed to develop long-term collaborations. In order to realize the full potential for the application of modern technologies to obtain a sustainable biosphere, it is vital to create conduits for knowledge exchange among scientists worldwide engaged in environmental microbial biotechnology research. Since its formation in 1994, the Working Group has organized many activities for early career scientists designed to promote this scientific exchange, including two week courses with hands-on research experience, intensive workshops of two or three days, and research scholar exchanges of one to six months. These interactions are focused on environmental problems that respect no international boundaries.
Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. Developing scalable parallel visualization algorithms is a key step enabling scientists to interact and visualize their data at this scale. However, at extreme scales, the datasets are so huge, there is not even enough time to view the data, let alone explore it with basic visualization methods. Automated tools are necessary for knowledge discovery -- to help sift through the information and isolate characteristic patterns, thereby enabling the scientist to study local interactions, the origin of features and their evolution in large volumes of data. These tools must be able to operate on data of this scale and work with the visualization process. In this project, we developed a framework for activity detection to allow scientists to model and extract spatio-temporal patterns from time-varying data.
Computational scientists must understand results from experimental, observational and computational simulation generated data to gain insights and perform knowledge discovery. As systems approach the petascale range, problems that were unimaginable a few years ago are within reach. With the increasing volume and complexity of data produced by ultra-scale simulations and high-throughput experiments, understanding the science is largely hampered by the lack of comprehensive I/O, storage, acceleration of data manipulation, analysis, and mining tools. Scientists require techniques, tools and infrastructure to facilitate better understanding of their data, in particular the ability to effectively perform complex data analysis, statistical analysis and knowledge discovery. The goal of this work is to enable more effective analysis of scientific datasets through the integration of enhancements in the I/O stack, from active storage support at the file system layer to MPI-IO and high-level I/O library layers. We propose to provide software components to accelerate data analytics, mining, I/O, and knowledge discovery for large-scale scientific applications, thereby increasing productivity of both scientists and the systems. Our approaches include 1) design the interfaces in high-level I/O libraries, such as parallel netCDF, for applications to activate data mining operations at the lower I/O layers; 2) Enhance MPI-IO runtime systems to incorporate the functionality developed as a part of the runtime system design; 3) Develop parallel data mining programs as part of runtime library for server-side file system in PVFS file system; and 4) Prototype an active storage cluster, which will utilize multicore CPUs, GPUs, and FPGAs to carry out the data mining workload.
The Center for Interface Science: Solar Electric Materials (CISSEM) was funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences (BES) from August 1, 2009 – December 31, 2014 under Award Number DE-SC0001084, as part of a broad set of Energy Frontier Research Centers (EFRCs) designed to underpin the development of economical energy conversion platforms for the 21st century. CISSEM successfully integrated the research groups of 19 principal investigators at The University of Arizona (the lead institution), the Georgia Institute of Technology, Princeton University, the University of Washington, and the National Renewable Energy Laboratory (NREL) into a coordinated and synergistic program, while also building a highly productive collaboration with the SLAC National Accelerator Laboratory. Our mission was to advance the understanding of interface science underlying solar energy conversion technologies based on organic and organic-inorganic hybrid materials – specifically in organic photovoltaic solar cells (OPVs); and to inspire, recruit and train future scientists and leaders in the basic science of solar electric energy conversion. CISSEM researchers focused on establishing a foundational understanding of the electronic properties of interfaces in area-scalable, thin-film photovoltaic platforms. Metal oxide interlayers used in OPVs to improve the efficiency of charge harvesting at electrodes was our central focus. A key feature of CISSEM research has been our ability to develop a comprehensive understanding of interfaces and interfacial processes at the atomic and molecular scales. This is a scientific foundation for thin-film photovoltaic technologies and our nation’s pursuit of lowering the costs of transforming the sun’s energy into electricity. Our efforts combined: i) theoretical modeling; ii) new materials development; iii) developing new measurement science approaches to characterize composition, molecular and supramolecular structure, band edge energies, electrical properties, and charge harvesting or injection; and iv) integrating our use-inspired new materials and enhanced knowledge of interfaces and interfacial processes into OPV platforms. The strengths of the characterization methodologies developed in CISSEM were recognized within the EFRC network, and were a major component of our interactions with other DOE-funded programs including EFRCs. CISSEM research has resulted in a legacy of 120+ peer-reviewed publications describing our basic science. Much of this highly collaborative research will now be built upon at CISSEM member institutions, with other extramural funding sources. Furthermore, the state-of-the-art facilities and expertise created for modern interface science, especially as they pertain to energy conversion and energy storage challenges, will ensure their broadest continued impact. DOE EFRC funding has positively impacted and enhanced the training and development of more than 140 graduate students, postdoctoral researchers and research scientists at the five CISSEM institutions, and students from three Colorado universities associated with NREL. Our legacy also includes these student, postdoctoral researcher and scientist alumni who have taken positions of impact and responsibility in technology industries, government agencies and academia in the U.S., Asia and Europe.
Shielding for the APS will be such that the individual radiation worker dose will be as low as reasonably achievable (ALARA). The ALARA goals for the APS are to keep the total of the work-related radiation exposure (exposure coming from other than natural or medical sources) as far below 5 person mSv/yr (500 person-mrem/yr), collective total effective dose equivalent, as reasonably achievable. For an individual APS radiation worker, the goal is to keep the maximum occupational total effective dose equivalent of any one employee as far below 2 mSv/yr as reasonably achievable. The ALARA goal for APS beamline scientists is to keep the total of the work-related radiation exposure (exposure coming from other than natural or medical sources) as far below 1 person mSv/yr (100 person-mrem/yr), collective total effective dose equivalent, as reasonably achievable. For an individual APS beamline scientist, the goal is to keep the maximum occupational total effective dose equivalent of any one scientist as far below 0.5 mSv/yr (50 mrem/yr) as reasonably achievable. The dose is actively monitored by the radiation monitors on the storage ring wall in each sector and by the frequent area surveys performed by the health physics personnel. For cases in which surveys indicate elevated hourly dose rates that may impact worker exposure, additional local shielding is provided to reduce the radiation field to an acceptable level. Passive area monitors are used throughout the facility to integrate doses in various areas. The results are analyzed for trends of increased doses, and shielding in these areas is evaluated and improved, as appropriate. The APS policy for on-site nonradiation workers in the vicinity of the APS facilities requires that the average nonradiation worker dose be below 0.2 mSv/yr (20 mrem/yr). In addition, the dose at the site boundary from all pathways is required to be below 0.1 mSv/yr (10 mrem/yr). For future modifications of the facility, the doses shall be evaluated and additional shielding provided to meet the policy requirements.