Zobrazeno 1 - 10
of 3 165
pro vyhledávání: '"P, Bjerregaard"'
Autor:
Mohammadi, Esmaeel, Ortiz-Arroyo, Daniel, Hansen, Aviaja Anna, Stokholm-Bjerregaard, Mikkel, Gros, Sebastien, Anand, Akhil S, Durdevic, Petar
Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These characteristics make conventional control methods, such as Proportiona
Externí odkaz:
http://arxiv.org/abs/2411.18305
Faithful quantum state transfer between telecom photons and microwave frequency mechanical oscillations necessitate a fast conversion rate and low thermal noise. Two-dimensional (2D) optomechanical crystals (OMCs) are favorable candidates that satisf
Externí odkaz:
http://arxiv.org/abs/2408.12474
The presence of gas pores in metal feedstock powder for additive manufacturing greatly affects the final AM product. Since current porosity analysis often involves lengthy X-ray computed tomography (XCT) scans with a full rotation around the sample,
Externí odkaz:
http://arxiv.org/abs/2408.02427
Autor:
Hansen, Laura Debel, Rani, Anju, Stokholm-Bjerregaard, Mikkel Algren, Stentoft, Peter Alexander, Arroyo, Daniel Ortiz, Durdevic, Petar
In this paper, we present two years of high-resolution nitrous oxide ($N_2O$) measurements for time series modeling and forecasting in wastewater treatment plants (WWTP). The dataset comprises frequent, real-time measurements from a full-scale WWTP,
Externí odkaz:
http://arxiv.org/abs/2407.05959
Autor:
Mohammadi, Esmaeel, Rani, Anju, Stokholm-Bjerregaard, Mikkel, Ortiz-Arroyo, Daniel, Durdevic, Petar
This paper introduces the Agtrup (BlueKolding) dataset, collected from Denmark's Agtrup wastewater treatment plant, specifically designed to enhance phosphorus removal via chemical and biological methods. This rich dataset is assembled through a high
Externí odkaz:
http://arxiv.org/abs/2407.05346
Autor:
Tan, Shawn Zheng Kai, Baksi, Shounak, Bjerregaard, Thomas Gade, Elangovan, Preethi, Gopalakrishnan, Thrishna Kuttikattu, Hric, Darko, Joumaa, Joffrey, Li, Beidi, Rabbani, Kashif, Venkatesan, Santhosh Kannan, Valdez, Joshua Daniel, Kuriakose, Saritha Vettikunnel
Biomedical data is growing exponentially, and managing it is increasingly challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data principles provide guidance, their adoption has proven difficult, especially in larger enterpris
Externí odkaz:
http://arxiv.org/abs/2405.05413
Autor:
Mohammadi, Esmaeel, Ortiz-Arroyo, Daniel, Stokholm-Bjerregaard, Mikkel, Hansen, Aviaja Anna, Durdevic, Petar
Even though Deep Reinforcement Learning (DRL) showed outstanding results in the fields of Robotics and Games, it is still challenging to implement it in the optimization of industrial processes like wastewater treatment. One of the challenges is the
Externí odkaz:
http://arxiv.org/abs/2403.15091
Autor:
Mohammadi, Esmaeel, Stokholm-Bjerregaard, Mikkel, Hansen, Aviaja Anna, Nielsen, Per Halkjær, Ortiz-Arroyo, Daniel, Durdevic, Petar
Publikováno v:
Engineering Applications of Artificial Intelligence 133 (2024) 107992
Phosphorus removal is vital in wastewater treatment to reduce reliance on limited resources. Deep reinforcement learning (DRL) is a machine learning technique that can optimize complex and nonlinear systems, including the processes in wastewater trea
Externí odkaz:
http://arxiv.org/abs/2401.12822
We demonstrate a memory for light based on optomechanically induced transparency. We achieve a long storage time by leveraging the ultra-low dissipation of a soft-clamped mechanical membrane resonator, which oscillates at MHz frequencies. At room tem
Externí odkaz:
http://arxiv.org/abs/2308.05206
Autor:
Morten Bjerregaard-Andersen, Jessica Da Silva, Rui Diogo, Ana Raquel Claro, Inês Ferro, Andreia Romana, Patrícia Rocha, Beatriz Sá, Goreti Lobarinhas, Sara Rolim, Claus Bogh Juhl, Kurt Højlund, Isabel Fernandes, Sónia Antunes, Maria Manuela Félix Calha, Guida Gama, Sofia Amálio, Mariana Figueiras, Teresa Silva, Margarida Rosado, Estela Ferrão, Luísa Arez, Ana Baptista, Adriana Martins Ferreira, Diana Alba, Carlos Godinho, Ana Luísa Leite, Maria de Lurdes Afonso Lopes, Maria Lurdes Sampaio, Joana Serra-Caetano, Eugenia Carvalho
Publikováno v:
BMC Endocrine Disorders, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background Viral respiratory infections may precipitate type 1 diabetes (T1D). A possible association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, and the incidence of T1D is b
Externí odkaz:
https://doaj.org/article/ec20d08584ec48a38a6f3648ae32f0e7
