Tidligere arrangementer - Side 50

This talk is part of the Mechanics Lunch Seminar series. Bring-your-own-lunch and lots of questions.

Professor Justin William Wells, the Semiconductor physics section, UiO

NCMM Associate Investigator, Professor Karl Johan Malmberg, Group Leader of the Malmberg Lab at Oslo University Hospital will present his research as part of the NCMM Tuesday Seminar Series.

Johannes R?sok Eskilt, phd fellow at Institute of Theoretical Astrophysics, UiO.

By Andy Foote from NTNU, Norway

Abstract: This talk is about the non-integer (fractional) derivative, its mathematical formulation by Abel in 1823, and present-day applications in modeling power-law behavior. These applications are in acoustics of complex media like tissue and sediments as well as in rheology, turbulence, and dielectrics. It will build on my book ¡°Waves with Power-Law Attenuation¡±, Springer, 2019.

The talk will be streamed online. Please contact "timokoch at uio.no" for the Zoom link. This talk is part of the Mechanics Lunch Seminar series. Bring-your-own-lunch and lots of questions.

Duncan Watts, postdoctoral fellow at Institute of Theoretical Astrophysics, UiO.

Birgitte Stougaard Pedersen, Associate Professor at Aarhus University, will speak at RITMO's Seminar Series.

National coordinator for EATRIS, Anita Kavlie, will present as part of the NCMM Tuesday Seminar Series.

C*-algebra seminar talk by Johannes Christensen (KU Leuven)

Abstract: Gravitational settling of a droplet in air onto a soft substrate is a ubiquitous event relevant to many natural processes and engineering applications. We study this phenomenon by developing a three-phase lubrication model of droplet settling onto a solid substrate coated by a thin, soft compressible layer of elastic material. By combining scaling analysis, analytical methods and numerical simulations we elucidate how the resulting droplet dynamics is affected by the soft layer. We discuss extensions to droplet settlings onto thin viscous liquid films and elastic sheets. Our results provide new insight into the coupled interactions between droplets and solids coated by a thin film of a soft material.

Talk can be followed online on Zoom as well as in person. Please contact "timokoch at uio.no" for the Zoom link. This talk is part of the Mechanics Lunch Seminar series. Bring-your-own-lunch and lots of questions.

Mathew Galloway, Institute of Theoretical Astrophysics, University of Oslo.

China¡¯s main foreign policy initiatives are not directed at the West but prioritise south-south relations. In this seminar, we will learn more about China¡¯s orientation towards developing countries and the meaning of the concept of a ¡®more democratic world order¡¯ for the Chinese Communist Party.

Kriser r?sker opp i systemer og utl?ser nyskapning. Hvor god er v?r kriseh?ndtering? Hva skal til for at vi blir bedre? Overlever strategiene m?tet med virkeligheten?

Lectures by visiting guest researchers Mesele Yihune Tamene, Tilaye Wube Hailemariam, Nega Tassie Abate, and Gashaw Tilahun Desta

The Ports speaker series features Martin Danyluk, assistant professor of geography at the University of Nottingham.

Maintenance plays a crucial role in ships and especially in the vital electric propulsion system. Intelligent predictive maintenance idealistically aims at preventing system failures and minimizing needless repairs, i.e., predicting failure likelihood and time to failure while providing the crew explainable predictions and recommending the best action for timely intervention. This presentation will cover a relevant work in collaboration with Sensor Systems in BigInsight, particularly a paper published under https://doi.org/10.1109/TII.2022.3144177. The failure prediction approach is driven by event logs, which include warnings, alarms, and operational information that describe all the happenings onboard the ship. The failure prediction objective is turned into classification and regression tasks; however, the training data pose three challenges. The events are irregular textual messages. The training data samples are not labelled. The datasets are extremely imbalanced, due to sparse failure events and multiple failure modes. The problem is casted into a weakly supervised machine learning framework. In a multiple instance learning process, the ungiven data labels are learned recursively while fitting the model parameters using deterministic annealing. The overall approach was tested on real ship data, and it successively forecasted few propulsion failures with explainable causes.

Welcome to a new AMR related event organised by the Interdisciplinary Forum for Research on Antimicrobial resistance (INFRA) at Helsam.