The paper presents a hybrid edge‑cloud digital twin for welfare‑constrained environmental control in poultry farms. It combines distributed sensing, on‑device state estimation, a physics‑based thermodynamic model with a learned residual, and model predictive control to adaptively manage temperature and ammonia levels. Experiments in a broiler testbed show significant improvements: temperature prediction error drops from 1.8 °C to 0.4 °C, ammonia violations fall by 90 %, and communication needs are reduced 30‑fold, with a Domain Transfer Score of 0.92 indicating strong cross‑facility robustness.
By Suresh Neethirajan
arXiv:2607. 00431v1 Announce Type: new Abstract: Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost.
By Md Rakibul Haque, Shireen Elhabian, Warren Woodrich Pettine
arXiv:2606. 24986v1 Announce Type: new Abstract: Automated cattle posture-classification systems frequently report near-perfect accuracy, yet their robustness under realistic deployment conditions remains largely unknown.
By Leutrim Uka, Severino Pinto, Gundula Hoffmann, Marina M. -C. H\"ohne
arXiv:2607. 03585v1 Announce Type: new Abstract: Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from heterogeneous and non-stationary time-series data.
By Quang Hung Pham, Ryad Zemouri, Martin Gagnon, Luc Vouligny
arXiv:2608. 09943v1 Announce Type: cross Abstract: Monitoring livestock behaviour under extensive conditions would provide valuable insights to assess animal adaption to environmental perturbations in agroecological systems (e.
By Lucile Riaboff (GenPhySE, INRAE), Ny Aina Andriamampandry (GenPhySE, GenPhySE), Jean-Fran\c{c}ois Bompa (GenPhySE, GenPhySE), Mathias Aletru (GenPhySE, GenPhySE), Christian Durand (UEF), S\'ebastien Douls (UEF), Ga\"etan Bonnafe (UEF), Morgane Costes-Thir\'e (GenPhySE, GenPhySE), Guillaume Delosi\`eres (GenPhySE, GenPhySE), Jean- Marc Mongrelet (GenPhySE, GenPhySE), Enzo Niro (GenPhySE, GenPhySE), N\'emuel Tadi (GenPhySE, GenPhySE), S\'everine Deretz (DEPT GA, UEF, INRAE), Sara Parisot (UEF), Margot Lamarque (UEF), Dominique Hazard (GenPhySE), Emilie Cobo (GenPhySE)
CAMOS is a coupled oscillatory state‑space model designed for multimodal clinical time‑series that are irregularly sampled and often incomplete. It addresses a representational limitation of linear state‑space models by allowing the transition operator to depend on which modalities are present, using a bank of second‑order oscillators coupled through a gated matrix. On the ADNI dataset, CAMOS outperforms both uncoupled oscillatory models and clinical fusion models in same‑visit staging, landmark prediction, and longitudinal forecasting, and uniquely avoids collapsing to the majority class when transferred zero‑shot to OASIS‑3.
By Maxx Richard Rahman, Mostafa Hammouda, Wolfgang Maass