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Canvas Studio – How to make simple videos - CANCELLED
Canvas Studio – How to make simple videos
Academic freedom in times of polarization
Agriculture in the future – society, safety and health
Moving Minds: Segmentation, Rhythm and Categorization of Action
Ayesha Jena's Licentiate Seminar
Funding
https://www.createhealth.lth.se/about/funding - 2026-04-19
Tailoring Broadband Large Chiral Response in All-Dielectric Twisted L-shape Metamaterial Platforms
This study presents the fabrication of all-dielectric silicon-based twisted L-shape metamaterials using a two-step electron-beam glancing angle deposition approach. Systematic chiroptical characterization and optimization are performed through Mueller matrix generalized spectroscopic ellipsometry and finite element modeling, demonstrating tunable, broadband chirality response with potential applic
Impact of Acceleration Voltage on Cathodoluminescence for Defect Identification in InGaN Quantum Wells
Micro-LEDs have emerged as a hot research topic due to their potential for next-generation display and lighting applications, necessitating advanced characterization techniques to optimize their performance. This study demonstrates the advantage of cathodoluminescence in the scanning electron microscope to be a critical tool for the optical characterization of InGaN platelets with red quantum well
Quantifying the Impact of Extreme Microclimate Conditions on the Hygrothermal Performance of Building Façades Under Climate Change
Accurate hygrothermal simulations are essential for assessing moisture safety in buildings, particularly under future climate conditions and extreme events. While the impacts of urban microclimates on energy performance, thermal comfort, and health have been extensively studied, their influence on hygrothermal simulations remains underexplored. This study addresses this gap by evaluating the impac
A Data Driven Approach for Resolving Time-dependent Differential Equations with Noise
We propose data-driven surrogate models to solve systems of time-dependent differential equations coupled with noise. Using a feedforward neural network, we separately learn the noise and solution, tackling approximations across regimes with bifurcations and rare events. Focusing on irregular data generated by a stochastic noise model on a one-dimensional spatial lattice coupled to a differential
