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🇺🇦 Auf radioeins läuft...
Bananafishbones:
🎵 Come To Sin
#NowPlaying #Bananafishbones
https://open.spotify.com/track/0jEvFyPTfXG6rHa1rxUU8l

Preparation Methods and Applications of Biomimetic Membranes
Ajit Seth, Sajal K. Ghosh, Veerendra K. Sharma
https://arxiv.org/abs/2509.25796 https://arxiv.…

Preparation Methods and Applications of Biomimetic Membranes
Model biomembrane systems play a crucial role in advancing biomedical research by providing simplified yet effective platforms for exploring complex biological mechanisms. These systems span a wide range of scales, from single-molecule-thick lipid monolayers to micron-sized giant unilamellar vesicles. Their efficacy and applicability largely depend on selecting an optimal model and an appropriate synthesis process. This chapter offers a comprehensive description of conventional synthesis techni…

BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives
Huayi Zhou, Kui Jia
https://arxiv.org/abs/2509.21256 https://arxiv.org/pdf…

BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives
Non-prehensile manipulation, encompassing ungraspable actions such as pushing, poking, and pivoting, represents a critical yet underexplored domain in robotics due to its contact-rich and analytically intractable nature. In this work, we revisit this problem from two novel perspectives. First, we move beyond the usual single-arm setup and the strong assumption of favorable external dexterity such as walls, ramps, or edges. Instead, we advocate a generalizable dual-arm configuration and establis…

RAPTOR-GEN: RApid PosTeriOR GENerator for Bayesian Learning in Biomanufacturing
Wandi Xu, Wei Xie
https://arxiv.org/abs/2509.20753 https://arxiv.org/pdf/25…

RAPTOR-GEN: RApid PosTeriOR GENerator for Bayesian Learning in Biomanufacturing
Biopharmaceutical manufacturing is vital to public health but lacks the agility for rapid, on-demand production of biotherapeutics due to the complexity and variability of bioprocesses. To overcome this, we introduce RApid PosTeriOR GENerator (RAPTOR-GEN), a mechanism-informed Bayesian learning framework designed to accelerate intelligent digital twin development from sparse and heterogeneous experimental data. This framework is built on a multi-scale probabilistic knowledge graph (pKG), formul…

🇺🇦 #NowPlaying on KEXP's #Early
Elias RŸnnenfelt:
🎵 Crush the Devils Head
#EliasRønnenfelt
https://eliasronnenfelt.bandcamp.com/track/crush-the-devils-head
https://open.spotify.com/track/2TSDOOv6cyynyAi1qcE3yw

🪖 Provisions from pond water? Researchers leverage biomanufacturing to produce food
https://phys.org/news/2025-09-provisions-pond-leverage-biomanufacturing-food.html

Provisions from pond water? Researchers leverage biomanufacturing to produce food
Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, are exploring a new approach to producing food on demand with unconventional materials. Through the Feedstocks for Food Production (FFP) project, APL is investigating ways to grow edible microbial food by harnessing nonpotable water.

Jeder Sticker an der Kaffeemaschine im Büro erzählt eine Geschichte!

Buddhas Tatzen sind sehr yummy! (Süße Bohnenfüllung inside)

Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing
Rajiv Kailasanathan, William R. Clements, Mohammad Reza Boskabadi, Shawn M. Gibford, Emmanouil Papadakis, Christopher J. Savoie, Seyed Soheil Mansouri
https://arxiv.org/abs/2508.21438

Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing
The development of continuous biomanufacturing processes requires robust and early anomaly detection, since even minor deviations can compromise yield and stability, leading to disruptions in scheduling, reduced weekly production, and diminished economic performance. These processes are inherently complex and exhibit non-linear dynamics with intricate relationships between process variables, thus making advanced methods for anomaly detection essential for efficient operation. In this work, we p…

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