Politecnio die Bari - Catalogo di prodotti della Ricerca
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Critical coupling in plasmonic chain for efficient energy trapping
No full textPlasmonic nanoparticles can concentrate energy at the nanometer scale, offering promising applications across multiple fields such as lab-on-chip technologies and photonic circuits. A crucial requirement for these applications is achieving efficient coupling between the nanoparticles and the excitation signal. Plasmonic nanoparticle chains can guide light at subwavelength scale and can be excited through coupling to a dielectric waveguide. In this manuscript, we propose a novel configuration for the plasmonic chain-dielectric waveguide structure that allows the chain to be freely positioned relative to the waveguide. We demonstrate the existence of a critical coupling regime between a silicon waveguide and a plasmonic chain, achieved through precise control of their separation. In this regime, the plasmonic chain transitions from its well-known transmission mode to a new cavity state, trapping 99% of the waveguide’s energy. This result paves the way for efficiently addressing nanostructures through integrated waveguides, enabling efficient optical nano-tweezers, sensors or nano-heaters
GRB 221009A: Observations with LST-1 of CTAO and Implications for Structured Jets in Long Gamma-Ray Bursts
No full textGRB 221009A is the brightest gamma-ray burst (GRB) observed to date. Extensive observations of its afterglow emission across the electromagnetic spectrum were performed, providing the first strong evidence of a jet with a nontrivial angular structure in a long GRB. We carried out an extensive observation campaign in very-high-energy (VHE) gamma rays with the first Large-Sized Telescope of the future Cherenkov Telescope Array Observatory starting on 2022 October 10, about 1 day after the burst. A dedicated analysis of the GRB 221009A data is performed to account for the different moonlight conditions under which data were recorded. We find an excess of gamma-like events with a statistical significance of 4.1σ during the observations taken 1.33 days after the burst, followed by background-compatible results for the later days. The results are compared with various models of afterglows from structured jets that are consistent with the published multiwavelength data but entail significant quantitative and qualitative differences in the VHE emission after 1 day. We disfavor models that imply VHE flux at 1 day considerably above 10−11 erg cm−2 s−1. Our late-time VHE observations can help disentangle the degeneracy among the models and provide valuable new insight into the structure of GRB jets
Enhancing the flexibility of decentralized energy resources through bi-level optimization in intra-day regional markets
No full textThis paper presents an innovative approach to an intra-day, intra-hourly Regional Flexibility Market (RFM) that enhances the utilization of Distributed Generation (DG) flexibility, including energy storage systems, electric vehicles, and photovoltaics. The market is managed by an Advanced Virtual Power Plant (AVPP), which acts as an intermediary and efficiently integrates DG flexibility into power system operations by coordinating transactions among DG aggregators. Beyond facilitating RFM trades, the AVPP also contributes to the Wholesale Flexibility Market (WFM) and helps mitigate short-term fluctuations within the Distribution Network (DN). To achieve an optimal market balance, a hierarchical market clearing mechanism is introduced, ensuring that DG flexibility is efficiently allocated while all participating entities gain economic benefits. The framework is modeled as a bilevel optimization problem with multiple lower-level decision processes, capturing the interactions between the AVPP and aggregators. While the AVPP at the upper level seeks to maximize its own profit, each lower-level problem represents an aggregator's strategic decision-making process. To enhance computational efficiency, the bilevel formulation is transformed into a single-level mixed-integer linear programming model and tested on a 119-bus DN. The results confirm that the framework effectively utilizes DG flexibility, increasing AVPP profit by 28% and reducing intra-hourly net-load deviations by 35%, thereby improving both economic efficiency and operational stability
Multi-channel add-drop filter using superimposed gratings in hybrid SiN-TFLN platform
No full textWe propose a novel grating-assisted contra-directional coupler for sparse wavelength division multiplexing (WDM) systems, utilizing a hybrid silicon nitride (SiN) and thin-film lithium niobate (TFLN) platform. The innovative design of this device is achieved by superimposing two grating structures with different periodicities, enabling simultaneous extraction of four distinct wavelengths. The four wavelengths with a channel spacing of 16 nm are collected using two output ports, each handling a pair of wavelengths. The integration of SiN on LN ensures efficient mode confinement and simplified fabrication. Numerical simulations of this structure demonstrate minimal insertion loss (∼1 dB), low crosstalk and robust performance to fabrication imperfections. Our results show that this unique device can offer a compact and scalable solution for sparse WDM applications
Digital healing gardens and metaverse for wellness
No full textThis paper explores the potential of phygital healing gardens—blending physical
and digital dimensions—and metaverse for enhancing psychological wellness in
university counselling environments. Traditionally linked to outdoor spaces, healing
gardens are reimagined here through virtual reality (VR) and artificial intelligence
(AI) within the metaverse, offering immersive, multisensory experiences accessible
indoors. Focusing on the MOEBIUS PRO-BEN project, the research examines how
digitally mediated nature can support students’ mental health by creating adaptive,
personalized therapeutic environments. By integrating environmental psychology,
technology, and spatial design, the study proposes scalable and replicable models
for digital healing gardens, aiming to bridge the gap between nature and therapeutic
needs in higher education contexts
Dynamic Analytical Model for Synchronous Homopolar Generators With Diode Rectifiers
No full textDespite being a promising solution for applications requiring a reliable and robust electromechanical energy conversion system, synchronous homopolar machines (SHMs) are constrained by their low power density and complex three-dimensional magnetic behaviour, which requires computationally intense 3D FE for their analysis and design optimisation. This challenge becomes even more pronounced in SHMs with split winding configurations and in applications involving highly non-linear loads. To address these limitations, this paper proposes an analytical model aimed at bridging these gaps. The proposed model provides a computationally efficient alternative to 3D finite element analysis, addressing key challenges such as dynamic performance estimation and magnetic sleeve saturation, fully capturing the influence of both space and time harmonics. As a vessel to investigate such enhanced capability, a challenging case study is considered, encompassing an SHM with a split winding configuration where each winding set supplies a DC load via two series-connected three-phase diode rectifiers. After a thorough introduction of the analytical modelling framework, its dynamic numerical implementation is described. Its predictions are validated through comparisons with 3D FEA and experimental results carried out on a 1.2 kW demonstrator, under various operating conditions. The proposed analytical approach, demonstrated here for dynamic performance evaluation even under highly non-linear loads, provides a foundation for rapid design optimisation of SHMs
Integrated Framework for Manufacturing, Design, and Monitoring of Composite-Bonded Joints: An Overview of the Results of the IDEA Project (MOST)
No full textThe IDEA project, developed in the frame of MOST—National Centre for Sustainable Mobility—addressed the growing need for reliable bonded joints in fibre-reinforced polymer composite structures used in transportation. Purely bonded joints are preferred for their lightweight and cost-efficient properties, but contamination and defect detection issues often make them unreliable. To solve this, the project developed innovative surface treatments, a methodology for the safe, optimized design of bonded joints, and structural health monitoring solutions, viable for real-time assessment. These advancements aim to increase the reliability and safety of bonded connections, helping industries adopt lighter, purely bonded joints over heavier, hybrid bonded/bolted options
Modeling Water Table Response in Apulia (Southern Italy) with Global and Local LSTM-Based Groundwater Forecasting
No full textFor effective groundwater resource management, it is essential to model the dynamic behaviour of aquifers in response to rainfall. Here, a methodological approach using a recurrent neural network, specifically a Long Short-Term Memory (LSTM) network, is used to model groundwater levels of the shallow porous aquifer in Southern Italy. This aquifer is recharged by local rainfall, which exhibits minimal variation across the catchment in terms of volume and temporal distribution. To gain a deeper understanding of the complex interactions between precipitation and groundwater levels within the aquifer, we used water level data from six wells. Although these wells were not directly correlated in terms of individual measurements, they were geographically located within the same shallow aquifer and exhibited a similar hydrogeological response. The trained model uses two variables, rainfall and groundwater levels, which are usually easily available. This approach allowed the model, during the training phase, to capture the general relationships and common dynamics present across the different time series of wells. This methodology was employed despite the geographical distinctions between the wells within the aquifer and the variable duration of their observed time series (ranging from 27 to 45 years). The results obtained were significant: the global model, trained with the simultaneous integration of data from all six wells, not only led to superior performance metrics but also highlighted its remarkable generalization capability in representing the hydrogeological system
Deep Learning Strategies for Semantic Segmentation in Robot-Assisted Radical Prostatectomy
No full textRobot-assisted radical prostatectomy (RARP) has become the most prevalent treatment for patients with organ-confined prostate cancer. Despite superior outcomes, suboptimal vesicourethral anastomosis (VUA) may lead to serious complications, including urinary leakage, prolonged catheterization, and extended hospitalization. A precise localization of both the surgical needle and the surrounding vesical and urethral tissues to coadapt is needed for fine-grained assessment of this task. Nonetheless, the identification of anatomical structures from endoscopic videos is difficult due to tissue distortions, changes in brightness, and instrument interferences. In this paper, we propose and compare two Deep Learning (DL) pipelines for the automatic segmentation of the mucosal layers and the suturing needle in real RARP videos by exploiting different architectures and training strategies. To train the models, we introduce a novel, annotated dataset collected from four VUA procedures. Experimental results show that the nnU-Net 2D model achieved the highest class-specific metrics, with a Dice Score of 0.663 for the mucosa class and 0.866 for the needle class, outperforming both transformer-based and baseline convolutional approaches on external validation video sequences. This work paves the way for computer-assisted tools that can objectively evaluate surgical performance during the critical phase of suturing tasks