Filippo Accomando

Climate change is increasing the exposure of crops to drought stress, highlighting the need for integrative approaches to assess plant responses under field conditions. In this study, telomere length (TL) was evaluated in field-grown grapevine (Vitis vinifera L., cv. Aglianico) subjected to rainfed (RF) and controlled deficit irrigation (CDI) regimes. A qPCR-based protocol was applied together with physiological measurements, UAV-derived vegetation indices, and berry metabolomic profiling to investigate plant responses to different water regimes. Physiological and metabolomic analyses confirmed distinct responses between treatments, with rainfed vines showing more negative leaf water potential, lower stomatal conductance, and increased accumulation of stress-associated metabolites, including anthocyanins and abscisic acid. Linear mixed-effects modeling showed no significant difference in TL between treatments at the beginning of the experimental period (p = 0.198), whereas rainfed vines displayed significantly lower TL values than irrigated vines at the end of the growing season (p = 0.0009). TL decreased significantly over time in both treatments. The treatment × time interaction suggested a greater TL reduction in rainfed vines in the primary model (p = 0.064), and this effect was significant in a complete-pair sensitivity analysis (p = 0.036). These findings indicate an association between irrigation regime and telomere length variation under field conditions. The study provides preliminary evidence supporting the potential application of TL measurements for investigating plant responses to environmental stress in grapevine.

Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system’s behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period.

This study presents the first integrated magnetic and electromagnetic (EMI) survey of the Pianabella Basilica (Ostia, Italy), combining high-resolution magnetic gradient measurements with EMI mapping. The site, characterized by late-antique Christian architecture and funerary structures, provides a complex environment for testing non-invasive geophysical techniques. Magnetic data were acquired using the MagEx system (v.1.2.2558), a new prototype based on Micro-Fabricated Atomic Magnetometer (MFAM) technology, marking its first field deployment in archaeological prospection. Simultaneously, EMI measurements using the CMD-Mini Explorer provided data on apparent conductivity and in-phase components across three depth levels (0.5–1.8 m). The magnetic gradient map successfully delineated the Basilica’s planimetric outline, revealing anomalies (~20 nT/m) corresponding to masonry and internal enclosures. A significant anomaly (50–60 nT/m) north of the Basilica suggests a basalt-paved Roman road leading toward Porta Laurentina. EMI results corroborated these findings, with low-conductivity zones outlining walls and in-phase responses highlighting reused Roman building materials. Despite significant urban noise from a nearby railway and fences, this integrated approach enhanced interpretability and reduced ambiguity. These findings demonstrate the efficacy of next-generation magnetic gradiometry and EMI for high-resolution archaeological investigations, providing a new methodological benchmark for cultural heritage prospection.

Under pressure from climate change and the need to ensure production resilience, modern agriculture is shifting towards management strategies based on advanced monitoring systems. This approach, known as Agriculture 4.0 or 5.0, aims to optimize resource use and adapt to evolving conditions. However, the deployment of such tools cannot be incidental; to optimize resources and resilience, it is necessary to strategically determine the optimal positioning and the specific typology of sensors to be employed.Optimizing resources requires correct knowledge of the soil-plant-atmosphere (SPA) system. This objective demands a holistic understanding of the soil, plant, and atmosphere. Based on this knowledge, management decisions regarding nutrients and irrigation are made by subdividing the field into Management Zones or Homogeneous Zones (HZs). A detailed analysis of soil properties and crop responses allows for site-specific distribution of resources and the identification of Functional Homogeneous Zones (fHZs), which are directly related to SPA dynamic processes and ecosystem functions.The identification of HZs and fHZs passes through a critical characterization phase where geosciences play a transformative role. This contribution demonstrates how UAVs equipped with different cameras enable the classification of these zones through varying levels of complexity. These methods promote Agriculture 4.0 goals by optimizing field sensor deployment—significantly reducing costs—and improving resource allocation, which enhances farm incomes and systemic resilience.Geophysical investigations provide the spatial intelligence to plan sensor networks correctly, ensuring they capture actual landscape variability. We propose an integrated framework combining UAVs, geophysical surveys, and mechanistic modeling. UAVs with multispectral sensors and topographic tools monitor crop status and phenological plasticity with high precision. These data, integrated with proximal geophysical investigations like Electromagnetic Induction (EMI) and magnetic mapping, allow for non-invasive characterization of sub-surface soil heterogeneity. This approach reduces costs associated with blind sensor installation and trial-and-error management, providing a roadmap for Decision Support Systems (DSS). Through mechanistic modeling, the framework simulates water and nutrient dynamics, providing indicators of soil health and plant stress. Ultimately, UAV-based geophysics and hydropedological modeling represent transformative tools to safeguard ecosystem services and promote sustainable, economically viable agricultural landscapes.Keywords: Agriculture 4.0 & 5.0, UAV Support, Sensor Network Optimization, Geoscience, SPA Continuum, Climate Resilience.

The integration of Unmanned Aerial Systems (UAS) into scientific research has established a crucial link between regional-scale satellite observations and high-resolution local-scale measurements. This study illustrates the operational framework and multi-disciplinary capabilities of GAIA iLAB (CNR), a laboratory designed to provide a structured guide for experimental activities in the geo-agro-environmental and geophysical sectors. By utilizing advanced technologies such as UAS, rovers, and in-situ acquisitions, GAIA iLAB addresses complex challenges ranging from precision agriculture to deep geophysical prospecting.The laboratory manages a diverse UAS fleet, including DJI Matrice 300 RTK and Matrice 600 PRO platforms, which serve as versatile vectors for a wide array of specialized sensors. The research topics covered by GAIA iLAB are structured into four primary pillars of equal scientific priority:Geophysics and Near-Surface Sensing: The lab conducts high-resolution magnetic and electromagnetic surveys. Utilizing drone-borne magnetometers (MagArrow) and vertical gradiometers (MagNimbus), the group investigates magnetization contrasts for archaeological research, geological-volcanological studies, and the search for buried structures. This is complemented by a Low-Frequency GPR system (Zond Aero LF) for urban geophysics and sub-surface investigations up to 10 meters deep.Geo-Environmental Monitoring: Using LiDAR (DJI Zenmuse L1) and high-resolution RGB cameras, GAIA iLAB performs detailed topographic reconstructions (DSM/DTM) to monitor hydrogeological instability, landslide movements, and seismic-tectonic processes.Advanced Agro-Environmental Research: The laboratory employs multispectral (Micasense Red-Edge M) and hyperspectral sensors (Senop HSC-2) to analyze vegetation health, crop water stress, and canopy temperature via thermal radiometric imaging (FLIR Vue Pro 640R).Electromagnetic Induction (EMI): Focused on the "Near Surface Zone," the lab utilizes FDEM sensors (CMD Explorer 6L) to characterize soil apparent electrical conductivity (ECa) and resistivity, essential for understanding soil-atmosphere interactions and anthropogenic impacts.The GAIA iLAB workflow ensures high-quality scientific output through rigorous flight planning (UgCS), strict adherence to EASA/ENAC regulations, and advanced data post-processing using SfM photogrammetry and LiDAR360 analysis. This integrated approach demonstrates the laboratory's potential to provide innovative solutions for environmental management and geophysical exploration.

Southern Italy is a tectonically active region of major geodynamic significance, where long-lived convergence, post-collisional extension, slab rollback, and crust-mantle decoupling generate strong lateral and vertical heterogeneity in lithology, temperature, fluids, and deformation. Here, we present an integrated 3D Finite Element (FE) thermo-rheological model developed within the TRHAM project activities, aimed at reconstructing the regional thermal and mechanical architecture of the crust within a physically consistent, data-constrained framework. The FE geometry synthesizes a large body of published geological and geophysical constraints, integrating surface geology, regional structural interpretations, and deep wellbore information, complemented by gravity and magnetic evidence. The thermal field is computed under coupled conductive-convective regimes by solving the fully coupled Fourier heat-conduction and Darcy-flow equations in porous media. Boundary conditions include an altitude-dependent surface temperature, prescribed basal heat flow at Moho depth, and laterally adiabatic conditions. Key thermal parameters are calibrated through a bounded optimization strategy against independent thermal observables, while explicitly accounting for resolution limits and non-uniqueness. Rheological calculations combine a frictional failure criterion for brittle deformation and power-law creep for ductile flow, incorporating spatially variable pore-fluid pressure ratios derived from the thermo-hydraulic solution. Strain-rate scenarios are guided by regional geodetic strain-rate constraints and GNSS-informed kinematic parameters. The resulting strength envelopes and yield-stress distributions show strong spatial variations in effective crustal strength and in the depth and geometry of the BDT, both along the Apennine belt and from the Tyrrhenian side to the Adriatic foreland. The model highlights the mechanical impact of inherited crustal architecture and fluid-assisted weakening, and reproduces a systematic contrast between the Apulian foreland and the Apenninic wedge consistent with regional deformation and seismicity patterns. Explicit fluid flow further emphasizes how crustal geometry modulates hydraulic connectivity and hydrological decoupling between Apulian and Apenninic domains, focusing infiltration/discharge and shaping surface heat-flow patterns.AcknowledgmentsThe activities are supported by the projects “Relation between 3D Thermo-Rheological Model and Seismic Hazard for Risk Mitigation in the Urban Areas of Southern Italy”, funded under the PRIN2022 PNRR initiative (code: P202299L2C), PRIN2022 PNRR, EU NextGenerationEU.

In the framework of the planetary missions, the human exploration of the Martian surface is becoming a priority. The pathway to Mars is rooted through the development of various space missions of increasing complexity, which will lead to a long-term and sustainable human presence on the Moon. The Moon is therefore an intermediate and fundamental step for testing most of the new technologies required for sustainable human exploration of deep space.We here present an overview of two Italian National projects dedicated to the development of geophysical technologies for planetary exploration, including the “PROgramma di RIcerca Spaziale di base (PRORIS)” project, funded by the Italian Ministry of University and Research (MUR) and whose management has been assigned to the National Research Council of Italy (CNR) and the National Institute for Astrophysics (INAF), and the “Martian Analysis of Resources and Structures: Lava tubes, neAr-surface ice and aquifers Visibility and Assessment (MARS-LAVA)” project, led by CNR and INAF.The PRORIS project is mainly related to the Moon exploration with different activities for the development of methodologies and research prototypes, and the validation of the developed technologies in terrestrial analog environments. The MARS-LAVA project instead aims at developing, optimizing and testing a suite of geophysical sensors with high potential for Mars exploration, and to develop analysis techniques based on data fusion/integration and modeling, Both the projects involve the use of magnetometric and electromagnetic geophysical methods.In this contribution, we dedicate a particular focus to the multiscale analysis of magnetometric data for studying lava tubes, presenting the preliminary results of magnetometric surveys carried out at Italian lava tubes test-sites.These results underline the potential of the multiscale analysis of magnetometric data in the framework of the planetary exploration and the challenges that need to be overcome.

The interconnectedness and complexity of subsurface structures present challenges for their identification and visualisation. To address this, geophysicists routinely integrate multiple non-destructive sensing techniques to map underground utilities. These resulting sensor outputs are predominantly two-dimensional (2D) products, typically visualised as maps and sections, using 2D Geographic Information System (GIS) software [1], or more recently, as three-dimensional (3D) objects that encode depth information. However, despite the use of 3D representations, these visualisations are still commonly viewed via 2D projection media, such as monitors or mobile screens. Since these visualisations directly inform professional interpretation, it is essential to understand how stakeholders, those responsible for analysing, validating, and acting on geophysical data, engage with these platforms in practice.Advances in three-dimensional visualisation technologies, such as Extended Reality (XR), offer new opportunities to overcome these limitations. XR environments enable the integration of heterogeneous geophysical datasets within a single, interactive spatial framework, potentially enhancing spatial comprehension and interpretative accuracy. Recent studies have consequently begun exploring XR applications for subsurface and geophysical data visualisation [2]. A recent study [3] visualised drone-based Ground Penetrating Radar (GPR) and magnetometric data in a Virtual Reality (VR) prototype, identifying frame-rate instability and high GPU utilisation as key technical limitations.However, technical performance alone does not determine the success of a visualisation tool; stakeholder perspectives are critical to ensuring XR outputs align with the analytical requirements and decision-making practices of geophysical professionals. Building on prior work, the present study extends this prototype for preliminary user testing with six expert geophysical stakeholders. These participants were selected based on their extensive professional experience, ensuring the evaluation reflects real-world interpretative conditions rather than abstract usability testing. Feedback collected through semi-structured interviews was analysed thematically, yielding four key insights: (1) the necessity of adjustable colour maps to enhance data intensity interpretation; (2) the requirement for interactive selection of colour values to reveal metadata; (3) the importance of stakeholder-centred visualisation design; and (4) the implementation of a data catalogue to allow selective dataset visualisation.Future work will focus on refining the prototype based on these expert recommendations. This iterative process will involve a second round of evaluation to validate the updates, followed by pilot testing with broader stakeholder groups to evaluate the tool's effectiveness in real-world settings. Keywords: Extended Reality; Multi-sensor Datasets; Human-in-the-loop; Data Visualisation Acknowledgements: This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London. References[1] QGIS.org, "QGIS Geographic Information System," Http://Www.Qgis.Org, vol. 2026, 2026.[2] M. Janeras, J. Roca, J.A. Gili, O. Pedraza, G. Magnusson, M.A. Núñez-Andrés and K. Franklin, "Using Mixed Reality for the Visualization and Dissemination of Complex 3D Models in Geosciences—Application to the Montserrat Massif (Spain)," Geosciences, vol. 12, -10-07. 2022.[3] E.D. Atsakpo, F. Mercogliano, S. Uzor, P. Saadati, A. Barone, F. Accomando, R. Castaldo, I. Catapano, P. Tizzani and F. Tosti, "Visualising Multi-Modal Geophysical Data in Extended Reality," 2025 6th International Conference on Computer Vision and Data Mining (ICCVDM), pp. 195, -09-12. 2025.

Molecular biomarkers are increasingly used in agriculture to provide objective indicators of plant health and responses to environmental stress. In grapevine (Vitis vinifera L.), genomic biomarkers are particularly valuable as they integrate physiological and environmental information. Among these, telomere length has emerged as a rapid and cost-effective biomarker, already applied in several plant and animal systems.Telomeres are repetitive DNA sequences located at chromosome ends that preserve genomic stability and are known to respond dynamically to aging and stress conditions. In this study, telomere length was quantified by quantitative real-time PCR (qPCR), using a grapevine-specific single-copy reference gene to ensure reliable measurements.The protocol was applied to Aglianico grapevines subjected to different water regimes (irrigated and non-irrigated) under identical soil conditions in a high-quality wine production area of southern Italy (Taurasi DOCG area, Tenuta Donna Elvira, Montemiletto, AV). Results obtained over two consecutive growing seasons allowed evaluation of telomere length dynamics across years. Consistent differences associated with water regime were observed between seasons, supporting telomere length as a sensitive indicator of vine well-being and environmental pressure.These findings strengthen the potential of telomere length as a genomic biomarker for long-term monitoring of grapevine health under climate-related stress conditions.

This study investigates how complex volcanic systems undergo major behavioral shifts, focusing on the Solfatara–Pisciarelli (SP) hydrothermal-magmatic area within the Campi Flegrei caldera (Southern Italy). The SP system is one of the most active zones of the caldera, characterized by persistent degassing, seismic swarms, strong hydrothermal circulation and long-term ground uplift. These processes arise from nonlinear interactions between magmatic inputs, fluid migration, and shallow hydrothermal pressurization, making the identification of critical transitions particularly challenging.To address this, we developed an integrated analytical framework combining Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA). MFPA models nonlinear and time-lagged associations among key monitoring parameters—vertical ground deformation, seismicity, CO₂ flux, geochemical equilibrium variables, and thermal signals—while GCPA identifies the temporal moments when multiple variables collectively show systemic reorganization.Analysis of multi-year (2018–2024) geophysical and geochemical datasets revealed that deformation is strongly associated with seismicity, equilibrium pressures of hydrothermal gases, heat flow, and CO₂ flux. Incorporating time-lagged deformation improved model accuracy and reduced unexplained variance, highlighting delayed cause–effect couplings between deformation and fluid-dynamic processes. The model confirms seismicity as the most stable explanatory parameter, consistent with sustained fracturing and fluid pressurization in the shallow system.GCPA identified two major critical transitions:CP1 – 30 November 2020, dominated by thermal–chemical reorganization and increased gas-system pressurization.CP2 – 1 April 2023, reflecting a more open and multiparametric regime where deformation, temperature, seismicity, heat flux, and CO₂ emissions contribute comparably to system evolution.These transitions align with independent geodetic evidence suggesting migration and reconfiguration of the shallow overpressure source beneath the SP area. The integrated MFPA–GCPA approach thus reconstructs how systemic changes propagate across geophysical and geochemical variables, providing retrospective insight into the onset and progression of unrest phases.This framework offers several advantages over classical or non-parametric approaches: interpretability of functional relationships, explicit treatment of nonlinearities and time lags, and the ability to detect collective regime shifts rather than isolated anomalies. Although not predictive, the method provides a quantitative basis for identifying critical phases in volcanic systems and may be adapted to other densely monitored calderas. With higher-resolution and real-time data streams, it could support early indications of evolving unrest and enrich next-generation volcano-monitoring strategies.