Quantifying Effusion Rates at Active Volcanoes through Integrated Time-Lapse Laser Scanning and Photography

During volcanic eruptions, measurements of the rate at which magma is erupted underpin hazard assessments. For eruptions dominated by the effusion of lava, estimates are often made using satellite data; here, in a case study at Mount Etna (Sicily), we make the first measurements based on terrestrial laser scanning (TLS), and we also include explosive products. During the study period (17–21 July, 2012), regular strombolian explosions were occurring within the Bocca Nuova crater, producing a ~50 m high scoria cone and a small lava flow field. TLS surveys over multi-day intervals determined a mean cone growth rate (effusive and explosive products) of ~0.24 m3s-1. Differences between 0.3-m-resolution DEMs acquired at 10-minute intervals captured the evolution of a breakout lava flow lobe advancing at 0.01–0.03 m3s-1. Partial occlusion within the crater prevented similar measurement of the main flow, but integrating TLS data with time-lapse imagery enabled lava viscosity (7.4 × 105 Pa s) to be derived from surface velocities and, hence, a flux of 0.11 m3s-1 to be calculated. The total dense-rock equivalent magma discharge estimates range from ~0.1 to ~0.2 m3s-1 over the measurement period, and suggest that simultaneous estimates from satellite data are somewhat overestimated. Our results support the use of integrated TLS and time-lapse photography for ground-truthing space-based measurements and highlight the value of interactive image analysis when automated approaches such as particle image velocimetry (PIV) fail

The infrastructure powering IBM's Gen AI model development

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonill

OCLC on the Responsive Web

Patrons and staff are increasingly using mobile and tablet devices to access their library’s online catalog. Chapter 6 of Library Technology Reports (vol. 51, no. 7), “Mobile Devices: Service with Intention,” describes OCLC’s responsive web design (RWD) methodology in the development of its new discovery interfaces. RWD allows designers and developers to create one Web experience that can be dynamically optimized for any device’s screen size. The use of RWD has allowed OCLC to meet many observed and projected user needs with one design. This chapter details OCLC’s process and the insights and challenges encountered during development. The primary challenge identified is the issue of content priority. It is impossible to fit the same amount of content on both a desktop and mobile screen. With smaller screens in mind, the team was forced to prioritize the content of every screen, taking user needs and expectations into account. The team developed a content priority workshop to help in this process, the details of which are shared in this chapter. Testing of the designs will also be discussed and came in the form of usability tests, analytics, and responses from a beta site