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NASA NTRS1993

Third Annual Foreign Acquisitions Workshop: Improving Access to Foreign Gray Literature

The theme of the Third Annual Foreign Acquisitions Workshop was the acquisition of and access to foreign (non-U.S.) gray literature. Individual presentations addressed general topics related to the value and scope of gray literature, specialized and foreign gray-literature sources, intellectual property issues, and U.S. Federal Agency activities. Additional topics focused on electronic access and evaluation techniques and the current and potential uses of networking technology. The workshop papers are presented in their entirety or in abstract or outline form. Appendices include a listing of databases that include foreign gray literature, a bibliography, and a report on U.S.-Japan cooperation in the use of scientific and technical information.

Source record↗
NASA NTRS2000

A Tool for Automatic Data Distribution for CFD Applications on Structured Grids

Development of HPF versions of NPB and ARC3D has shown that HPF provides an efficient, concise way to express parallelism and to organize data traffic. The use of HPF, as noted in the papers, requires an intimate knowledge of the applications and a detailed analysis of data affinity, data movement, and data granularity. To simplify and accelerate the task of developing HPF versions of existing CFD applications we have designed and implemented ADAPT (Automatic Data Alignment and Placement Tool). ADAPT analyzes a CFD application working on a single structured grid and generates HPF TEMPLATE, (RE)DISTRIBUTION, ALIGNMENT, and INDEPENDENT directives. The directives can be generated on the nest level, subroutine level, application level, or on the application interface level. ADAPT annotates an existing CFD FORTRAN application, performing computations on single or multiple grids. On each grid the application is considered as a sequence of operators, each applied to a set of variables defined in a particular grid domain. ADAPT automatically detects implicit operators (i.e., having data dependences) and explicit operators (without data dependences). For parallelization of an explicit operator ADAPT creates a template for the operator domain, aligns arrays used in the operator with the template, distributes the template, and declares the loops over the distributed dimensions as INDEPENDENT. For parallelization of an implicit operator, the distribution of the operator's domain should be consistent with the operator's dependences. Any dependence between sections distributed on different processors would preclude parallelization if the compiler does not have an ability to pipeline computations. If a data distribution is "orthogonal" to the dependences of an implicit operator, then the loop which implements the operator can be declared as INDEPENDENT. ADAPT starts with an analysis of array index expressions of the loop nests. For each pair of arrays referenced in an assignment statement, it generates an arc in the alignment graph and annotates it with an affinity relation. The template, alignment, and distribution directives for a particular loop nest are then derived from a transitive closure of the affinity relation. A compromise of data distributions in different nests and subroutines is achieved by merging annotated alignment graphs for adjacent nests/stibroutine calls in the nest/call graph of the application in the process called distribution lifting. ADAPT has been implemented as a C++ program running in conjunction with a parallelization tool called CAPTools. ADAPT uses the parse tree, interprocedural analysis and application database generated by CAPTools. It also uses the Directed Graph class, initially implemented in p2d2 (parallel debugger oi distributed programs), and some other classes supporting symbolic computations. ADAPT uses data distribution techniques described. ADAPT was tested with ARC3D and the FT benchmark and has demonstrated a code performance within a factor of 1.5 of handwritten versions.

Frumkin, Michael↗
NASA NTRS2001

Automation of a N-S S and C Database Generation for the Harrier in Ground Effect

A method of automating the generation of a time-dependent, Navier-Stokes static stability and control database for the Harrier aircraft in ground effect is outlined. Reusable, lightweight components arc described which allow different facets of the computational fluid dynamic simulation process to utilize a consistent interface to a remote database. These components also allow changes and customizations to easily be facilitated into the solution process to enhance performance, without relying upon third-party support. An analysis of the multi-level parallel solver OVERFLOW-MLP is presented, and the results indicate that it is feasible to utilize large numbers of processors (= 100) even with a grid system with relatively small number of cells (= 10(exp 6)). A more detailed discussion of the simulation process, as well as refined data for the scaling of the OVERFLOW-MLP flow solver will be included in the full paper.

Murman, Scott M.↗
NASA NTRS2014

Oxygen Isotope Evidence for the Relationship between CM and CO Chondrites: Could they Both Coexist on a Single Asteroid

Water played a critical role in the early evolution of asteroids and planets, as well as being an essential ingredient for life on Earth. However, despite its importance, the source of water in the inner solar system remains controversial. Delivery of water to Earth via comets is inconsistent with their relatively elevated D/H ratios, whereas carbonaceous chondrites (CCs) have more terrestrial-like D/H ratios [1]. Of the eight groups into which the CCs are divided, only three (CI, CM, CR) show evidence of extensive aqueous alteration. Of these, the CMs form the single most important group, representing 34% of all CC falls and a similar percentage of finds (Met. Bull. Database). CM material also dominates the population of CC clasts in extraterrestrial samples [2, 3]. The Antarctic micrometeorites population is also dominated by CM and CI-like material and similar particles may have transported water and volatiles to the early Earth [4]. CCs, and CMs in particular, offer the best opportunity for investigating the evolution of water reservoirs in the early solar system. An important aspect of this problem involves identifying the anhydrous silicate component which co-accreted with ice in the CM parent body. A genetic relationship between the essentially anhydrous CO group and the CMs was proposed on the basis of oxygen isotope evidence [5]. However, previous CM whole-rock oxygen isotope data scattered about a line of approximately 0.5 that did not intersect the field of CO chondrites [5]. Here we discuss new oxygen isotope data which provides additional constraints on the relationship between CO and CM chondrites.

Greenwood, R. C.↗
NASA NTRS2019

The Badhwar-O'Neill 2020 Model

The Badhwar-O'Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within ±15% of a large fraction of the available measurements (26,269 of 27,646 → 95%).

Slaba, Tony C.↗
NASA NTRS2020

The Badhwar-O'Neill 2020 Model

The Badhwar-O’Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high-precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within +15% of a large fraction of the available measurements (26,269 of 27,646  95%.

T C Slaba↗
NASA NTRSDate not supplied

Machine Learning Applications to Metal-Silicate Equilibria and their Insights into Core Formation

An extensive number of studies have experimentally investigated how elements distribute between metal and silicate phases, to better constrain core-mantle chemical equilibrium. Here, we present a new database compiling all (to our knowledge) experimental data on liquid metal-silicate partitioning from 118 peer-reviewed publications. We applied various machine learning techniques to gain further insights into these partitioning equilibria and their dependencies. We performed a network analysis to investigate the relationship between experiments and partition coefficients, which enables visualizing gaps in the experimental dataset and biases related to varying experimental conditions and analytical setup. In addition, semi-empirical thermodynamic models are commonly used to extrapolate these chemical reactions to the wide range of pressure, temperature and compositional conditions of planetary differentiation. These models are based on linear regressions that assume continuous relationship between partition coefficients and experimental variables. Here, we considered random forest regressions, which are algorithms based on ensembles of decision trees and does not consider continuous effects of each variable. The application of this regression significantly improves the prediction of metal-silicate partitioning for several elements including Ni, Si and Cr. We will show how this new approach improves our understanding of elemental exchange between metal and silicate and their implications for the Earth’s core formation.

siderophile element↗
NASA NTRS2020

The XRISM Science Data Center: Optimizing the Scientific Return from a Unique X-ray Observatory

The X-Ray Imaging and Spectroscopy Mission, XRISM, is currently scheduled to launch in 2022 with the objective of building on the brief, but significant, successes of the ASTRO-H (Hitomi) mission in solving outstanding astrophysical questions using high resolution X-ray spectroscopy. The XRISM Science Operations Team (SOT) consists of the JAXA-led Science Operations Center (SOC) and NASA-led Science Data Center (SDC), which work together to optimize the scientific output from the Resolve high-resolution spectrometer and the Xtend wide-field imager through planning and scheduling of observations, processing and distribution of data, development and distribution of software tools and the calibration database (CaldB), support of ground and in-flight calibration, and support of XRISM users in their scientific investigations of the energetic universe. Here, we summarize the roles and responsibilities of the SDC and its current status and future plans. The Resolve instrument poses particular challenges due to its unprecedented combination of high spectral resolution and throughput, broad spectral coverage, and relatively small field-of-view and large pixel-size. We highlight those challenges and how they are being met.

XRISM↗
DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗
DOE OSTI2017

Fault propagation and effects analysis for designing an online monitoring system for the secondary loop of a nuclear power plant part of a hybrid energy system - 393

This paper studies the propagation and effects of faults of critical components that pertain to the secondary loop of a nuclear power plant found in Nuclear Hybrid Energy Systems (NHES). This information is used to design an on-line monitoring (OLM) system which is capable of detecting and forecasting faults that are likely to occur during NHES operation. In this research, the causes, features, and effects of possible faults are investigated by simulating the propagation of faults in the secondary loop. The simulation is accomplished by using the Integrated System Failure Analysis (ISFA). ISFA is used for analyzing hardware and software faults during the conceptual design phase. In this paper, the models of system components required by ISFA are initially constructed. Then, the fault propagation analysis is implemented, which is conducted under the bounds set by acceptance criteria derived from the design of an OLM system. The result of the fault simulation is utilized to build a database for fault detection and diagnosis, provide preventive measures, and propose an optimization plan for the OLM system. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A Covid-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A COVID-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
DOE OSTI2019

The DAQ and control system for the CMS Phase-1 pixel detector upgrade

In 2017 a new pixel detector was installed in the CMS detector. This so-called Phase-1 pixel detector features four barrel layers in the central region and three disks per end in the forward regions. The upgraded pixel detector requires an upgraded data acquisition (DAQ) system to accept a new data format and larger event sizes. A new DAQ and control system has been developed based on a combination of custom and commercial microTCA parts. Custom mezzanine cards on standard carrier cards provide a front-end driver for readout, and two types of front-end controller for configuration and the distribution of clock and trigger signals. Before the installation of the detector the DAQ system underwent a series of integration tests, including readout of the pilot pixel detector, which was constructed with prototype Phase-1 electronics and operated in CMS from 2015 to 2016, quality assurance of the CMS Phase-1 detector during its assembly, and testing with the CMS Central DAQ. This paper describes the Phase-1 pixel DAQ and control system, along with the integration tests and results. A description of the operational experience and performance in data taking is included.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Bioblendstocks to Optimize Mixing Controlled Compression Ignition (MCCI) Engines

In this project, a team of researchers from the University of Massachusetts Lowell, the University of Maine, and Mainstream Engineering developed an integrated process for the product of bioblendstocks to optimize mixing controlled compression ignition (MCCI) engines. The objective was to improve the energy density, sooting propensity, and cetane number of base diesel fuel while maintaining cold weather behavior. The process converts woody biomass (e.g. sawmill residues) into bio-oil through selective fast pyrolysis; the bio-oil is then selectively upgraded to form selectively oxygenated, minimally-branched hydrocarbons using non-noble metal catalysts in combination with metal-catalyzed hydrogenation. Advanced predictive models, in conjunction with existing property databases, and experimental testing are used to evaluate overall bioblendstock properties and their impact on base diesel fuel. An iterative, targeted upgrading approach was implemented to optimize the proposed bioblendstock’s properties. Assessment methodologies included techno-economic analysis, life-cycle assessment, property testing, and engine testing. Ultimately, the project team successfully produced a viable bioblendstock while identifying critical process points related to scale-up efforts. It was found that producing pyrolysis oils at 500 degrees C and with pine particle sizes of 1-2 mm led to bio-oil with a higher yield (of approximately 45 wt%) and rich amounts of aromatic alcohols. The resultant pyrolysis oil was then upgraded using a sequence of mild hydrotreating, followed by catalytic etherification and esterification, followed by another final mild hydrotreating to produce a blendstock containing saturated species with a limited, but non-zero, amount of oxygen. The aromatic alcohols produced by pyrolysis were especially helpful in this regard, as the resulting bicycloethers and derivatives exhibited high cetane numbers. While most bulk properties of the bioblendstock met or exceeded targeted thresholds, viscosity and cloud point notably fell outside the expected range; this could be addressed by blending limits and/or through the use of additives that are commonplace in current refinding practices. Identification of a bioblendstock that can be produced economically at scale while improving the performance and emissions characteristics of internal combustion engines positively affects the economy by boosting domestic fuel production and the environment by decreasing harmful emissions and increasing efficiency.

09 BIOMASS FUELS↗
DOE OSTI2015

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

In this study, exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.

58 GEOSCIENCES↗
NASA NTRS2011

Reliability Impacts in Life Support Architecture and Technology Selection

Equivalent System Mass (ESM) and reliability estimates were performed for different life support architectures based primarily on International Space Station (ISS) technologies. The analysis was applied to a hypothetical 1-year deep-space mission. High-level fault trees were initially developed relating loss of life support functionality to the Loss of Crew (LOC) top event. System reliability was then expressed as the complement (nonoccurrence) this event and was increased through the addition of redundancy and spares, which added to the ESM. The reliability analysis assumed constant failure rates and used current projected values of the Mean Time Between Failures (MTBF) from an ISS database where available. Results were obtained showing the dependence of ESM on system reliability for each architecture. Although the analysis employed numerous simplifications and many of the input parameters are considered to have high uncertainty, the results strongly suggest that achieving necessary reliabilities for deep-space missions will add substantially to the life support system mass. As a point of reference, the reliability for a single-string architecture using the most regenerative combination of ISS technologies without unscheduled replacement spares was estimated to be less than 1%. The results also demonstrate how adding technologies in a serial manner to increase system closure forces the reliability of other life support technologies to increase in order to meet the system reliability requirement. This increase in reliability results in increased mass for multiple technologies through the need for additional spares. Alternative parallel architecture approaches and approaches with the potential to do more with less are discussed. The tall poles in life support ESM are also reexamined in light of estimated reliability impacts.

Lange, Kevin E.↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

A novel mutation MT-COIII m.9267G>C and MT-COI m.5913G>A mutation in mitochondrial genes in a Tunisian family with maternally inherited diabetes and deafness (MIDD) associated with sever nephropathy

Mitochondrial diabetes (MD) is a heterogeneous disorder characterized by a chronic hyperglycemia, maternal transmission and its association with a bilateral hearing impairment. Several studies reported mutations in mitochondrial genes as potentially pathogenic for diabetes, since mitochondrial oxidative phosphorylation plays an important role in glucose-stimulated insulin secretion from beta cells. In the present report, we studied a Tunisian family with mitochondrial diabetes (MD) and deafness associated with nephropathy. The mutational analysis screening revealed the presence of a novel heteroplasmic mutation m.9276G>C in the mitochondrial COIII gene, detected in mtDNA extracted from leukocytes of a mother and her two daughters indicating that this mutation is maternally transmitted and suggest its implication in the observed phenotype. Bioinformatic tools showed that m.9267G>C mutation (p.A21P) is « deleterious » and it can modify the function and the stability of the MT-COIII protein by affecting the assembly of mitochondrial COX subunits and the translocation of protons then reducing the activity of the respective OXPHOS complexes of ATP synthesis. The nonsynonymous mutation (p.A21P) has not been reported before, it is the first mutation described in the COXIII gene which is related to insulin dependent mitochondrial diabetes and deafness and could be specific to the Tunisian population. The m.9267G>C mutation was present with a nonsynonymous inherited mitochondrial homoplasmic variation MT-COI m.5913 G>A (D4N) responsible of high blood pressure, a clinical feature detected in all explored patients. - Highlights: • MT-COX3 m.9267G>C (p.A21P), heteroplasmic substitution, is not reported in any database. • m.9267G>C can be responsible of the MIDD associated with nephropaty. • This substitution can modify the function and the stability of the MT-CO3 protein. • This substitution can modify MT-CO3 structure (2D and 3D). • MT-COX3 m.9267G>C is associated with MT-CO1 m.5913G>A a homoplasmic substitution.

60 APPLIED LIFE SCIENCES↗