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This creation is used for standard energy performance tests of household and commercial refrigerators and other refrigeration equipment. It automates the door opening at a preset duration and velocity specified by the test procedure.
Abstract Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence–function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent’s understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.
Purpose: The purpose of this project was to develop a process that utilizes the onboard kV and MV electronic portal imaging devices (EPIDs) to perform rapid acceptance testing (AT) of linacs in order to improve efficiency and standardize AT equipment and processes. Methods: In this study a Varian TrueBeam linac equipped with an amorphous silicon based EPID (aSi1000) was used. The conventional set of AT tests and tolerances was used as a baseline guide, and a novel methodology was developed to perform as many tests as possible using EPID exclusively. The developer mode on Varian TrueBeam linac was used to automate the process. In the current AT process there are about 45 tests that call for customer demos. Many of the geometric tests such as jaw alignment and MLC positioning are performed with highly manual methods, such as using graph paper. The goal of the new methodology was to achieve quantitative testing while reducing variability in data acquisition, analysis and interpretation of the results. The developed process was validated on two machines at two different institutions. Results: At least 25 of the 45 (56%) tests which required customer demo can be streamlined and performed using EPIDs. More than half of the AT tests can be fully automated using the developer mode, while others still require some user interaction. Overall, the preliminary data shows that EPID-based linac AT can be performed in less than a day, compared to 2–3 days using conventional methods. Conclusions: Our preliminary results show that performance of onboard imagers is quite suitable for both geometric and dosimetric testing of TrueBeam systems. A standardized AT process can tremendously improve efficiency, and minimize the variability related to third party quality assurance (QA) equipment and the available onsite expertise. Research funding provided by Varian Medical Systems. Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.
High-temperature particle receivers are being developed to achieve temperatures in excess of 700 °C for advanced power cycles and solar thermochemical processes. This paper describes designs and features of a falling particle receiver system that has been evaluated and tested at the National Solar Thermal Test Facility at Sandia National Laboratories. These advanced designs are intended to reduce heat losses and increase the thermal efficiency. Novel features include aperture covers, active air flow, particle flow obstructions, and optimized receiver shapes that minimize advective heat losses, increase particle curtain opacity and uniformity, and reduce cavity wall temperatures. Control systems are implemented in recent on-sun tests to maintain a desired particle outlet temperature using an automated closed-loop proportional–integral–derivative controller. These tests demonstrate the ability to achieve and maintain particle outlet temperatures approaching 800 °C with efficiencies between 60 and 90%, depending on incident power, mass flow, and environmental conditions. Lessons learned regarding the testing of design features and overall receiver operation are also presented.
Purpose: The Varian EDGE machine is a new stereotactic platform, combining Calypso and VisionRT localization systems with a stereotactic linac. The system includes TrueBeam DeveloperMode, making possible the use of XML-scripting for automation of linac-related tasks. This study details the use of DeveloperMode to automate commissioning tasks for Varian EDGE, thereby improving efficiency and measurement consistency. Methods: XML-scripting was used for various commissioning tasks,including couch model verification,beam-scanning,and isocenter verification. For couch measurements, point measurements were acquired for several field sizes (2×2,4×4,10×10cm{sup 2}) at 42 gantry angles for two couch-models. Measurements were acquired with variations in couch position(rails in/out,couch shifted in each of motion axes) compared to treatment planning system(TPS)-calculated values,which were logged automatically through advanced planning interface(API) scripting functionality. For beam scanning, XML-scripts were used to create custom MLC-apertures. For isocenter verification, XML-scripts were used to automate various Winston-Lutz-type tests. Results: For couch measurements, the time required for each set of angles was approximately 9 minutes. Without scripting, each set required approximately 12 minutes. Automated measurements required only one physicist, while manual measurements required at least two physicists to handle linac positions/beams and data recording. MLC apertures were generated outside of the TPS,and with the .xml file format, double-checking without use of TPS/operator console was possible. Similar time efficiency gains were found for isocenter verification measurements Conclusion: The use of XML scripting in TrueBeam DeveloperMode allows for efficient and accurate data acquisition during commissioning. The efficiency improvement is most pronounced for iterative measurements, exemplified by the time savings for couch modeling measurements(approximately 10 hours). The scripting also allowed for creation of the files in advance without requiring access to TPS. The API scripting functionality enabled efficient creation/mining of TPS data. Finally, automation reduces the potential for human error in entering linac values at the machine console,and the script provides a log of measurements acquired for each session. This research was supported in part by a grant from Varian Medical Systems, Palo Alto, CA.
The U.S General Services Administration's (GSA's) Green Proving Ground program, in partnership with the National Renewable Energy Laboratory. completed a large pilot study of an Energy Management Information Systems (EMIS) with Automated System Optimization (ASO). Four test bed facilities, each with different building characteristics and systems, were chosen for the implementation of cloud-based EMIS with ASO. Depending on functionality, this tool can be extremely effective in energy management and energy optimization in buildings. The capabilities evaluated in the pilot ranged from energy savings and energy consumption predictions to evaluations of user acceptance, operability, and ease of installation. This report presents the methodology, lessons learned and best practices, and deployment recommendations for the GSA's portfolio of commercial office space, comprising more than 8,500 properties.
There is a requirement to measure a variety of challenging radioactive waste forms generated by the Chinese nuclear industry. These range from low activity waste to high activity waste with a high content of Cs-137 and waste in high density 200 litre drums. This paper describes the design, development and testing of an updated and automated Wide Range Segmented Gamma Scanner (WR-SGS) specifically configured for Chinese nuclear industry radioactive waste measuring requirements. The instrument retains the capabilities of the original SGS developed by Parker and Martin at the Los Alamos National Laboratory and now widely used for radioactive waste assay. It also incorporates additional features to improve measurement efficiency and reduce cost. These features include continuous helical scanning of the waste drum, and separate lifting pillars to lift the detector and the transmission source. In place of a transmission source shutter mechanism, the source is placed in a shielded safe when not in use. The design also incorporates a variable aperture collimator, and tungsten filters that can be deployed for the measurement of high activity, high dose-rate drums. Updated motion control electronics, based on the use of a programed logic controller have been incorporated to address potential future obsolescence. The WR-SGS employs a 40% high purity Germanium detector with electro-mechanical cooling and state of the art digital spectroscopy counting electronics for gamma ray spectrum data acquisition. Testing of the unit has been performed with both point sources and rod sources, which are traceable to international standards. Typical test drums, each with six distributed re-entrant tubes and with different density matrices, have been employed with the rod sources to validate both the calibration and the measurement performance of the WR-SGS for drums of different density. The measurement results have been compared with the reference decay corrected activity values and very good agreement is achieved, confirming the measurement performance of the instrument. (authors)
Purpose: Significant dosimetric benefits had been previously demonstrated in highly noncoplanar treatment plans. In this study, the authors developed and verified an individualized collision model for the purpose of delivering highly noncoplanar radiotherapy and tested the feasibility of total delivery automation with Varian TrueBeam developer mode. Methods: A hand-held 3D scanner was used to capture the surfaces of an anthropomorphic phantom and a human subject, which were positioned with a computer-aided design model of a TrueBeam machine to create a detailed virtual geometrical collision model. The collision model included gantry, collimator, and couch motion degrees of freedom. The accuracy of the 3D scanner was validated by scanning a rigid cubical phantom with known dimensions. The collision model was then validated by generating 300 linear accelerator orientations corresponding to 300 gantry-to-couch and gantry-to-phantom distances, and comparing the corresponding distance measurements to their corresponding models. The linear accelerator orientations reflected uniformly sampled noncoplanar beam angles to the head, lung, and prostate. The distance discrepancies between measurements on the physical and virtual systems were used to estimate treatment-site-specific safety buffer distances with 0.1%, 0.01%, and 0.001% probability of collision between the gantry and couch or phantom. Plans containing 20 noncoplanar beams to the brain, lung, and prostate optimized via an in-house noncoplanar radiotherapy platform were converted into XML script for automated delivery and the entire delivery was recorded and timed to demonstrate the feasibility of automated delivery. Results: The 3D scanner measured the dimension of the 14 cm cubic phantom within 0.5 mm. The maximal absolute discrepancy between machine and model measurements for gantry-to-couch and gantry-to-phantom was 0.95 and 2.97 cm, respectively. The reduced accuracy of gantry-to-phantom measurements was attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.
Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here an integrated approach is presented using a set of high-throughput (HTP) alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of Cr-Fe-Mn-Ni alloys are evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by 2 to 3 orders of magnitude. Based on the obtained results, a sacrificial protection mechanism is unveiled in the corrosion of Cr-Fe-Mn-Ni alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys.
We present an overview of a software system, Bellerophon, built to support a production-level HPC application called CHIMERA, which simulates core-collapse supernova events at the petascale. Developed over the last four years, Bellerophon enables CHIMERA's geographically dispersed team of collaborators to perform data analysis in near real-time. Its n-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a web- deliverable, cross-platform desktop application. In addition, Bellerophon addresses software engineering tasks for the CHIMERA team by providing an automated mechanism for performing regression testing on a variety of supercomputing platforms. Elements of the team's workflow management needs are met with software tools that dynamically generate code repository statistics, access important online resources, and monitor the current status of several supercomputing resources.
In-situ bright field transmission electron microscopy (TEM) nanomechanical tensile testing and in-situ automated crystallographic orientation mapping in TEM were combined to unravel the elementary mechanisms controlling the plasticity of ultrafine grained Aluminum freestanding thin films. The characterizations demonstrate that deformation proceeds with a transition from grain rotation to intragranular dislocation glide and starvation plasticity mechanism at about 1% deformation. The grain rotation is not affected by the character of the grain boundaries. No grain growth or twinning is detected.
I had the opportunity to work at Lawrence Livermore National Laboratory within the MEMS, Electronics, and Photonics Technologies group. I worked under the direct supervision of Dr. Adam Conway and Dr. Erik Swanberg on a semiconductor based gamma ray detector. My work involved automating the experimental setup and data analysis portion of the project to alleviate the researcher’s busy schedules. A semiconductor is a material that has conductivity in between a metal and ceramic. In layman’s terms, if you provide it with just enough energy, charge can flow inside of the material. If we impose some form of ionizing radiation such as a gamma ray, we can detect the amount of energy imparted on the system by measuring the amount of charge produced in the material. Figure 1 outlines the process that a semiconductor undergoes to allow us to detect ionizing radiation. Certain materials properties make them ideal for gamma ray detection such as the average atomic number, carrier properties, resistivity, and bandgap energy. I was working with Thallium Bromide (TlBr) detectors for room temperature detection of gamma rays, specifically looking at how different metal electrodes affected the stability of our system. My work involved automating the stress and voltage tests applied to the TlBr detectors and allowing for easy analysis of the data to determine if the system was still functional.
Data Science Infrastructure (DSI) Project offers a standardized but flexible way of storing data and associated metadata to support AI/ML and data intensive scientific workflows: Establish requirements via use case interviews; Develop DSI framework to support flexible, secure, and accessible data stores tailored to researcher needs; Augment existing institutional frameworks for code management, performance testing and file indexing via automated DSI interfaces; Deploy DSI framework to support key use cases in collaboration with domain scientists.
This final technical report provides a description and results of the design, development, testing, and validation of the Automated Resilience Management System (ARMS) solution, which integrates different systems to collect, store and process relevant information from the power distribution system to automatically manage the operation of multiple energy assets to enhance the resilience of the power grid against high-impact physical incidents (e.g., hurricanes) and cyber threats. The developed solution comprises a suite of novel AI-based algorithms designed to first detect, locate, and classify anomalies by collecting and analyzing data from multiple monitoring and control devices
A fault detection and diagnostics (FDD) tool is a type of energy management and information system that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Building operators on the leading edge of technology adoption use FDD tools to enable median whole-building portfolio savings of 8%. Although FDD tools can inform operators of operational faults, currently an action is always required to correct the faults to generate energy savings. A subset of faults, however, such as biased sensors, can be addressed automatically, eliminating the need for staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability and technical and economic performance of FDD technologies. This paper presents the development of nine innovative fault auto-correction algorithms for Heating, Ventilation, and Air Conditioning pi(HVAC) systems. When the auto-correction routine is triggered, it overwrites control setpoints or other variables to implement the intended changes. It also discusses the implementation of the auto-correction algorithms in commercial FDD software products, the integration of these strategies with building automation systems and their preliminary testing.
Measuring in situ soil fluxes of carbon dioxide (CO 2 ), methane(CH 4 ), and nitrous oxide (N 2 O) continuously at highfrequency requires appropriate technology. We tested the combination of acommercial automated soil CO 2 flux chamber system (LI-8100A) with a CH 4 and N 2 O analyzer (Picarro G2308) in a tropicalrainforest for 4 months. A chamber closure time of 2 min was sufficient fora reliable estimation of CO 2 and CH 4 fluxes (100 %and 98.5 % of fluxes were above minimum detectable flux – MDF,respectively). This closure time was generally not suitable for a reliableestimation of the low N 2 O fluxes in this ecosystem but wassufficient for detecting rare major peak events. A closure time of 25 minwas more appropriate for reliable estimation of most N 2 O fluxes(85.6 % of measured fluxes are above MDF ± 0.002 nmol m -2 s -1 ). Our study highlights the importance ofadjusted closure time for each gas.
We present an overview of a software system, Bellerophon, built to support a production-level HPC application called CHIMERA, which simulates core-collapse supernova events at the petascale. Developed over the last four years, Bellerophon enables CHIMERA s geographically dispersed team of collaborators to perform data analysis in near real-time. Its n-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a web-deliverable, cross-platform desktop application. In addition, Bellerophon addresses software engineering tasks for the CHIMERA team by providing an automated mechanism for performing regression testing on a variety of supercomputing platforms. Elements of the team s workflow management needs are met with software tools that dynamically generate code repository statistics, access important online resources, and monitor the current status of several supercomputing resources.
Ransomware, a class of self-propagating malware that uses encryption to hold the victims’ data ransom, has emerged in recent years as one of the most dangerous cyber threats, with widespread damage; e.g., zero-day ransomware WannaCry has caused world-wide catastrophe, from knocking U.K. National Health Service hospitals offline to shutting down a Honda Motor Company in Japan [1]. Our close collaboration with security operations of large enterprises reveals that defense against ransomware relies on tedious analysis from high-volume systems logs of the first few infections. Sandbox analysis of freshly captured malware is also commonplace in operation. We introduce a method to identify and rank the most discriminating ransomware features from a set of ambient (non-attack) system logs and at least one log stream containing both ambient and ransomware behavior. These ranked features reveal a set of malware actions that are produced automatically from system logs, and can help automate tedious manual analysis. We test our approach using WannaCry and two polymorphic samples by producing logs with Cuckoo Sandbox during both ambient, and ambient plus ransomware executions. Our goal is to extract the features of the malware from the logs with only knowledge that malware was present. We compare outputs with a detailed analysis of WannaCry allowing validation of the algorithm’s feature extraction and provide analysis of the method’s robustness to variations of input data—changing quality/quantity of ambient data and testing polymorphic ransomware. Most notably, our patterns are accurate and unwavering when generated from polymorphic WannaCry copies, on which 63 (of 63 tested) antivirus (AV) products fail.