Tootfinder

The number of #EV home charging point installations in Italy 🇮🇹 increased by 157% in October compared to last year, indicating that the uptake of EVs (incentivised by a new round of income-dependent grants) is reaching new users.

Le installazioni di wall box impennano grazie al Bonus auto: a ottobre +157% (ricerca ProntoPro)
Circa il 40% delle richieste nazionali proviene dalle cosiddette aree urbane funzionali (FUA), con Milano in testa al 15%

Ich freue mich auf die #nocode herum.
Und schön zu sehen: der Frauenant…

Close-up view of the audio-responsive LED floor installation I co-created for the KEF Muon launch in 2007 @ Sala del Cenacolo (Museo Nazionale della Scienza e della Tecnologia in Milano). The animation was repurposed from one of my first projects I created with Processing (in 2003)...
The 2 meter tall speakers were designed by Ross Lovegrove and made from milled solid aluminium...
The installation was created by Moving Brands (my old employer), a short video of the project/instal…

Characterizing Agent-Based Model Dynamics via $\epsilon$-Machines and Kolmogorov-Style Complexity
Roberto Garrone (University of Milano-Bicocca)
https://arxiv.org/abs/2510.12729

Characterizing Agent-Based Model Dynamics via $ε$-Machines and Kolmogorov-Style Complexity
We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $ε$-machine is reconstructed as a reference model that summarizes the system-wide informational regime. At the micro level, $ε$-machines are reconstructed for each caregiver-elder dyad and variable, and are complemented with algorithm-agnostic Kolmogorov-style mea…

A Noise Resilient Approach for Robust Hurst Exponent Estimation
Malith Premarathna (Department of Statistics, University of Nebraska-Lincoln), Fabrizio Ruggeri (CNR IMATI, Milano), Dixon Vimalajeewa (Department of Statistics, University of Nebraska-Lincoln)
https://arxiv.org/abs/2510.04811

A Noise Resilient Approach for Robust Hurst Exponent Estimation
Understanding signal behavior across scales is vital in areas such as natural phenomena analysis and financial modeling. A key property is self-similarity, quantified by the Hurst exponent (H), which reveals long-term dependencies. Wavelet-based methods are effective for estimating H due to their multi-scale analysis capability, but additive noise in real-world measurements often degrades accuracy. We propose Noise-Controlled ALPHEE (NC-ALPHEE), an enhancement of the Average Level-Pairwise Hurs…