Publications
-
OptiPINN: A two-stage NSGA-II multi-objective optimization framework for solving 2D transient non-linear heat transfer problems with coupled convection and radiation
Transient non-linear heat transfer with coupled convection, radiation, and localized heat generation is challenging due to strong non-linearities, sharp thermal gradients, and asymmetric boundary conditions. Conventional Physics-Informed Neural Networks (PINNs) typically require manual tuning of the network architecture. The adjustment of weight coefficients for various loss terms is also time-consuming and can lead to unstable […]
-
Multi-objective Energy-efficient Flexible Flow Shop Scheduling Problem : Integrating Renewable Energy and Demand Response Programs
This study tackles an energy-efficient flexible flow shop scheduling problem motivated by the increasing need for sustainable manufacturing under volatile energy markets. The proposed framework jointly integrates photovoltaic generation, energy storage systems, Time-of-Use pricing, and demand bidding mechanisms, while explicitly accounting for machine On/Off strategies with realistic turn-on time constraints. A bi-objective mixed-integer linear programming […]
-
Integrating lean and green optimisation into decisionmaking frameworks in construction logistics: a systematic literature review
Logistics plays a decisive role in the performance of construction projects, owing to its central position within supply chains and the complexity of the trade-offs it involves. These decisions involve a wide range of stakeholders and require the reconciliation of operational, environmental and organisational constraints that are often interdependent. Against this backdrop, this study presents […]
-
Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence
We propose a momentum-guided federated split distillation framework for personalized, efficient, and autonomous temporal edge intelligence. We introduce TeRR-SAtt, our novel temporal reservoir student attention design that combines fixed reservoir representations, a lightweight temporal student, and personalized output modules. We also present AMGF, our anticipatory momentum-guided fusion mechanism that clusters clients through learning momentum and […]
-
Computational Efficiency through Momentum-Guided Split Distillation: Rethinking Federated Learning for Edge Autonomy
IoT ecosystems increasingly require adaptive intelligence under heterogeneous data and resource conditions [1]. In such distributed, privacy-sensitive, and evolving environments, Federated Learning (FL) enables collaborative edge intelligence without transmitting raw data [2]. However, IoT intelligence requires temporal reasoning to capture evolving and context-dependent dynamics [3]. This makes temporal reasoning valuable yet costly for constrained devices. […]
-
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 […]
-
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 […]
-
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 […]
-
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 […]
-
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. […]
-
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 […]
-
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 […]