Profiling industrial vehicle duties using CAN bus signal segmentation and clustering

Industrial vehicles working in construction sites show rather heterogeneous usage patterns. Depending on its type, model, and context of usage, the vehicle workload may vary from light to heavy with variable periodicity. Duties summarize the current state of a vehicle according to its usage level. They are usually set up manually vehicle by vehicle according to the specifications of the manufacturer. To automate the definition of per-vehicle duty levels, this paper explores the use of clustering techniques applied to CAN bus signals. It first performs a segmentation of the CAN bus signals to identify specific working cycles. Then, it clusters the segments to support the definition of vehicle-specific duty levels. The preliminary results, acquired on real vehicle usage data, show the applicability of the proposed approach

Disaggregating non-volatile memory for throughput-oriented genomics workloads

Massive exploitation of next-generation sequencing technologies requires dealing with both: huge amounts of data and complex bioinformatics pipelines. Computing architectures have evolved to deal with these problems, enabling approaches that were unfeasible years ago: accelerators and Non-Volatile Memories (NVM) are becoming widely used to enhance the most demanding workloads. However, bioinformatics workloads are usually part of bigger pipelines with different and dynamic needs in terms of resources. The introduction of Software Defined Infrastructures (SDI) for data centers provides roots to dramatically increase the efficiency in the management of infrastructures. SDI enables new ways to structure hardware resources through disaggregation, and provides new hardware composability and sharing mechanisms to deploy workloads in more flexible ways. In this paper we study a state-of-the-art genomics application, SMUFIN, aiming to address the challenges of future HPC facilities.This work is partially supported by the European Research Council (ERC) under the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitivity (TIN2015-65316-P) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

The Open AUC Project

Progress in analytical ultracentrifugation (AUC) has been hindered by obstructions to hardware innovation and by software incompatibility. In this paper, we announce and outline the Open AUC Project. The goals of the Open AUC Project are to stimulate AUC innovation by improving instrumentation, detectors, acquisition and analysis software, and collaborative tools. These improvements are needed for the next generation of AUC-based research. The Open AUC Project combines on-going work from several different groups. A new base instrument is described, one that is designed from the ground up to be an analytical ultracentrifuge. This machine offers an open architecture, hardware standards, and application programming interfaces for detector developers. All software will use the GNU Public License to assure that intellectual property is available in open source format. The Open AUC strategy facilitates collaborations, encourages sharing, and eliminates the chronic impediments that have plagued AUC innovation for the last 20 years. This ultracentrifuge will be equipped with multiple and interchangeable optical tracks so that state-of-the-art electronics and improved detectors will be available for a variety of optical systems. The instrument will be complemented by a new rotor, enhanced data acquisition and analysis software, as well as collaboration software. Described here are the instrument, the modular software components, and a standardized database that will encourage and ease integration of data analysis and interpretation software

Life Cycle Modeling of Technologies and Strategies for a Sustainable Freight System in California

California’s freight transportation system is a vital part of the state’s economy but is a significant contributor to greenhouse gas emissions and generates an even higher portion of regional and local air pollution. The state’s primary strategy for reducing emissions from the on-road freight sector relies on deploying new vehicle and fuel technologies, such as electric medium- and heavy-duty vehicles. The market for electric truck technologies is developing rapidly.The goal of this research is to quantify the life cycle environmental impacts and life cycle costs for on-road goods movement in California to estimate the abatement potential and economic costs and benefits of electrifying California’s freight truck sector. The study compares the emissions and costs of urban conventional gasoline and diesel Class 3–8 vehicles with electric heavy-duty vehicles (i.e., electric trucks) for a range of freight and commercial vocations. A model of freight vehicle operations is developed based on representative vehicle location data, and linked with life cycle emissions inventory, technology cost, and pollution health damage cost data. The model is then used to assess energy and capacity requirements for electric trucks and battery systems and explore the impacts of a range of charging strategies and vehicle duty cycles (i.e., vocations) on energy, costs, and emissions between 2020 and 2040.Where emissions occur, and how emissions of different pollutants are affected by factors including vocation, duty cycle, powertrain configuration, and fuel pathway, will influence the effectiveness and economic costs of emissions reduction strategies. On a per mile basis, replacing a conventional gasoline or diesel truck can reduce CO2-equivalent (CO2e) emissions by 50%–75% compared to conventional gas and diesel vehicles. Statewide, 100% electrification of in-state Class 8 vehicles by 2040 could reduce annual CO2e emissions by nearly than 30% (50 million metric tonnes per year), and electrification of Class 3 trucks statewide would likely half current PM2.5 emissions from transportation. The costs of emissions abatement from truck electrification ranged from $0.25 to$182 per metric tonne of CO2e for trucks deployed in 2020, but these costs are likely to fall dramatically by 2040. Full electrification of the in-state registered Class 3–8 vehicle fleet by 2040 would significantly reduce criteria pollutants and aerosols emissions; this in turn could reduce pollution related damages in the state by $507 million per year by 2025, and by some$1.6 billion by 2040.View the NCST Project Webpag

The NASA Cubesat Missions Flying on Artemis-1

In 2021, the Space Launch Services (SLS) Artemis-1 mission will carry thirteen 6U CubeSats into deep space. Three of those payloads are NASA missions performing a variety of unique deep-space science experiments. The three NASA CubeSat missions are BioSentinel, Lunar Flashlight and NEAScout. The BioSentinel mission will measure deep-space radiation effects on DNA using yeast organisms. It is a six-month mission in a heliocentric orbit. BioSentinel was designed, built, tested and operated out of NASA Ames Research Center. Lunar Flashlight’s mission is to look for surface water ice in the permanently shadowed regions near the south pole of the Moon and test out new small spacecraft technologies. The spacecraft was developed at the Jet Propulsion Laboratory as a technology demonstration mission with support from the Marshall Space Flight Center, the Goddard Space Flight Center and Georgia Institute of Technology. NEA Scout’s mission is twofold: to demonstrate solar sail deployment and spacecraft navigation using the sail to detect, track, fly by, and characterize a near earth asteroid. NEA Scout was developed by NASA’s Marshall Space Flight Center in partnership with the Jet Propulsion Laboratory, and support from the Goddard Space Flight Center, Lyndon B. Johnson Space Center, and Langley Research Center

Business Model Innovation and exaptation: a new way of innovating in SMEs

Although research underlines the need for SMEs to innovate their Business Model, they face considerable challenges in exploring external business opportunities and experimenting/developing their available resources in unexpected ways. We posit that one way that SMEs can innovate their Business Model is through exaptation, a discontinuous evolutionary process that allows utilizing and adapting existing resources in new application domains. Using a case study approach, we investigate the case of a SME that has successfully innovated its Business Model through exaptation. We then discuss how three key exaptation processes lead to value creation, delivery and capturing, thus supporting Business Model Innovation in SMEs