Publications
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Forecasting short-term availability of bikes at sharing stations : a deep-learning approach
The growing popularity of bike-sharing systems represents a significant urban mobility trend, offering substantial economic, environmental, territorial and social benefits. The achievement of these advantages is contingent on the operational efficiency of the service. In this paper, the short-term bike availability forecasting at the station level is addressed using real-time open data from public bike […]
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Multi-criteria Evaluation of Digital Twins for Industry 5.0: Sustainability, Resilience and Human-Centricity
Digital twins (DTs) are a key enabler of Industry 5.0’s objective to reconcile operational performance with sustainability and human well-being. However, there is no widely adopted and reproducible evaluation framework for assessing the contributions of DT to these objectives. To address this gap, we first conducted a systematic literature review to identify current practices and […]
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AI-Based Energy Management of Grid Connected PV Systems in Positive Energy Buildings
In response to the growing importance of renewable energy in the global energy transition, this study addresses the critical need for efficient, real-time energy management in positive energy buildings by focusing on ultra-short-term prediction with a 5-minute prediction horizon. Utilizing a novel application of AI-driven dynamic control (AIDC) for intelligent real-time control, we employ the […]
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Hybrid flax/carbon bonded composite patches for strengthening of steel plates: layup and adhesive effect
Adhesive bonding of Fiber-Reinforced Polymer (FRP) patches is increasingly used to strengthen steel structures. Considering that carbon FRP (CFRP) and epoxy adhesives are the primary materials in industrial applications, this study explores the feasibility of hybridizing CFRPs with Flax FRPs (FFRPs) and the effects of using different adhesives on the mechanical performance of reinforced steel […]
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Mind the Domain Gap: Benchmarking Structure-from-Motion Methods on Paired Industrial Physical-Digital Twin Datasets to Enable Radiance Field Rendering
As industry transitions toward fully digitalized factories, mirroring physical environments in digital-twins has become essential for robotics, monitoring, and process automation, a capability that fundamentally relies on 6D camera pose estimation and 3D scene reconstruction. We propose an evaluation methodology to benchmark these tasks in paired physical and digital environments. Real-world acquisitions are performed on […]
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Data-driven prediction and surrogate-based parameter selection for density and surface roughness in SLM of 316L SS under data scarcity
Selective laser melting (SLM) of 316L stainless steel involves strongly nonlinear interactions between processing parameters and resulting part properties, which renders conventional trial-and-error optimization strategies inefficient. This study proposes a data-centric framework that combines machine learning with evolutionary optimization to accurately predict and computationally select candidate processing parameters for surface roughness Ra and relative density. […]
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Stable lead-free bismuth perovskite solar cells via thermally activated bilayer conversion
Lead-free bismuth-based metal halide perovskites are promising photovoltaic candidates due to their low toxicity, favorable bandgaps and intrinsic air stability. Scalable, reproducible fabrication remains challenging. We study post-deposition annealing of methylammonium bismuth iodide (MBI) bilayer films prepared by low-temperature thermal vapor deposition (LTTV), where BiI3 and MAI are sequentially evaporated at substrate temperatures as low […]
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Spectral densification and macroscopic phase delay of gravitational echoes from exotic compact objects
Gravitational-wave echoes from Exotic Compact Objects (ECOs) provide an observable probe for horizon-scale physics. Standard phenomenological models for these signals typically assume a constant Free Spectral Range, relying on the geometric optics approximation. In this work, we demonstrate that wave dispersion at the photon sphere induces a systematic deviation from this assumption, manifesting instead as […]
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A resilient model-free controller for power regulation and fatigue load reduction in floating offshore wind turbines
This paper introduces a model-free control strategy for floating offshore wind turbines (FOWTs), which utilizes a double cascade, two extended state observer (dCESO)-based active disturbance rejection controller (ADRC) to regulate the collective pitch angle of the turbine. The primary objectives are stabilizing the generated power and rotor speed at their rated values while mitigating damage […]
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Stable O(N) recursive boundary mapping for robust parametric analysis of dispersion in 2D N-layered WGM resonators
A self-normalizing analytical framework is established to evaluate the exact dispersion relations of whispering gallery modes (WGMs) in 2D N-layered cylindrical micro-resonators. By mapping a continuous radial boundary function through a recursive propagator, the catastrophic numerical cancellations inherent to traditional transfer matrix methods (TMMs) are avoided, particularly at high azimuthal orders in the strict evanescent […]
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Standardization of Quantitative Variables in ClustOfVar-Based Variable Clustering for Mixed Data
In many real-world datasets, individuals are described by both quantitative and qualitative variables. Clustering variables rather than individuals can provide valuable insight into the underlying structure of such mixed datasets. ClustOfVar is a technique for clustering variables proposed by [2] and widely used for mixed data, with both hierarchical and partitioning algorithms. The method aims […]
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Interpretable Feature Clustering for Dimensionality Reduction. An application to Explainable Remaining Useful Life Prediction
In prediction, variable selection allows to handle high-dimensionality, variable redundancy, multicollinearity and instability. Classical approaches, such as penalised supervised methods, like Lasso, may become unstable in the presence of strongly correlated variables and rely on specific modeling assumptions. Unsupervised feature clustering provides an alternative strategy for dimensionality reduction that is independent of the response variable. […]