Precision Viticulture (PV) is becoming an active and interdisciplinary research field since it requires solving interesting research issues to concretely answer the demands of specific use cases. A challenging problem in this context is the development of automatic methods for yield estimation. Computer vision methods can contribute to the accomplishment of this task, especially those that can replicate what winemakers do manually. In this paper, an automatic artificial intelligence method for grape bunch detection from RGB images is presented. A customized Convolutional Neural Network (CNN) is employed for pointwise classification of image pixels and the dependence of classification results on the type of input color channels and grapes color properties are studied. The advantage of using additional perception-based input features, such as luminance and visual contrast, is also evaluated, as well as the dependence of the method on the choice of the training set in terms of the amount of labeled data. The latter point has a significant impact on the practical use of the method on-site, its usability by non-expert users, and its adaptability to individual vineyards. Experimental results show that a properly trained CNN can discriminate and detect grape bunches even under uncontrolled acquisition conditions and with limited computational load, making the proposed method implementable on smart devices and suitable for on-site and real-time applications.

A nonlinear crime model is generalized by introducing self- and cross-diffusion terms. The effect of diffusion on the stability of non-negative constant steady states is applied. In particular, the cross-diffusion-driven instability, called Turing instability, is analyzed by linear stability analysis, and several Turing patterns driven by the cross-diffusion are studied through numerical investigations. When the Turing–Hopf conditions are satisfied, the type of instability highlighted in the ODE model persists in the PDE system, still showing an oscillatory behavior.

This Special Issue of Applied Numerical Mathematics contains selected and refereed papers presented at the 21st IMACS World Congress which was held during September 11–15, 2023 in Rome, Italy.

We study the process of thermal convection in jammed emulsions with a yield-stress rheology. We find that heat transfer occurs via an intermittent mechanism, whereby intense short-lived convective “heat bursts” are spaced out by long-lasting conductive periods. This behavior is the result of a sequence of fluidization-rigidity transitions, rooted in a nontrivial interplay between emulsion yield-stress rheology and plastic activity, which we characterize via a statistical analysis of the dynamics at the droplet scale. We also show that droplets’ coalescence induced during heat bursts leads to a spatially heterogeneous phase inversion of the emulsion which eventually supports a sustained convective state.

The paper concerns the weighted Hilbert transform of locally continuous functions on the semiaxis. By using a filtered de la Vallée Poussin type approximation polynomial recently introduced by the authors, it is proposed a new “truncated” product quadrature rule (VP- rule). Several error estimates are given for different smoothness degrees of the integrand ensuring the uniform convergence in Zygmund and Sobolev spaces. Moreover, new estimates are proved for the weighted Hilbert transform and for its approximation (L-rule) by means of the truncated Lagrange interpolation at the same Laguerre zeros. The theoretical results are validated by the numerical experiments that show a better performance of the VP-rule versus the L-rule.

We are concerned with the uniform approximation of functions of a generic reproducing kernel Hilbert space (RKHS). In this general context, classical approximations are given by Fourier orthogonal projections (if we know the Fourier coefficients) and their discrete versions (if we know the function values on well-distributed nodes). In case such approximations are not satisfactory, we propose to improve the approximation using the same data but combined with a new kernel function. For the resulting (both continuous and discrete) new approximations, theoretical estimates and concrete examples are given.

In this paper the interpolating rational functions introduced by Floater and Hormann are generalized leading to a whole new family of rational functions depending on γ, an additional positive integer parameter. For γ=1, the original Floater–Hormann interpolants are obtained. When γ>1 we prove that the new rational functions share a lot of the nice properties of the original Floater–Hormann functions. Indeed, for any configuration of nodes in a compact interval, they have no real poles, interpolate the given data, preserve the polynomials up to a certain fixed degree, and have a barycentric-type representation. Moreover, we estimate the associated Lebesgue constants in terms of the minimum (h∗) and maximum (h) distance between two consecutive nodes. It turns out that, in contrast to the original Floater–Hormann interpolants, for all γ>1 we get uniformly bounded Lebesgue constants in the case of equidistant and quasi-equidistant nodes configurations (i.e., when h∼h∗). For such configurations, as the number of nodes tends to infinity, we prove that the new interpolants (γ>1) uniformly converge to the interpolated function f, for any continuous function f and all γ>1. The same is not ensured by the original FH interpolants (γ=1). Moreover, we provide uniform and pointwise estimates of the approximation error for functions having different degrees of smoothness. Numerical experiments illustrate the theoretical results and show a better error profile for less smooth functions compared to the original Floater–Hormann interpolants.

The paper is concerned with a generalization of Floater–Hormann (briefly FH) rational interpolation recently introduced by the authors. Compared with the original FH interpolants, the generalized ones depend on an additional integer parameter γ>1, that, in the limit case γ=1 returns the classical FH definition. Here we focus on the general case of an arbitrary distribution of nodes and, for any γ>1, we estimate the sup norm of the error in terms of the maximum (h) and minimum (h∗) distance between two consecutive nodes. In the special case of equidistant (h=h∗) or quasi–equidistant (h≈h∗) nodes, the new estimate improves previous results requiring some theoretical restrictions on γ which are not needed as shown by the numerical tests carried out to validate the theory.

Quasiparticles are low-energy excitations with important roles in condensed matter physics. An intriguing example is provided by Majorana quasiparticles, which are equivalent to their antiparticles. Despite being implicated in neutrino oscillations and topological superconductivity, their experimental realizations remain very rare. Here, we propose a purely classical realization of Majorana fermions in terms of three-dimensional disclination lines in active nematics. The underlying reason is the well-known equivalence, in 3D, between a + 1 / 2 local defect profile and a - 1 / 2 profile, which acts as its antiparticle. The mapping also requires proving that defect profiles transform as spinors, and activity is needed to overcome the elastic cost associated with these excitations, so they spontaneously appear in steady state. We combine topological considerations and numerics to show that active nematics under confinement spontaneously create in their interior topologically charged disclination lines and loops, akin to Majorana quasiparticles with finite momentum. Within a long channel, the phenomenology we observe resembles that of the Kitaev chain, as Majorana-like states appear near the boundaries, while a delocalized topological excitation arises in the form of a chiral disclination line. The analogy between 3D nematic defects and topological quasiparticles further suggests that active turbulence can be viewed as a topological phase, where defects percolate to form delocalized topological quasiparticles similar to those observed in the channel. We propose that three-dimensional active disclinations can be used to probe the physics of Majorana spinors at much larger scale than that for which they were originally introduced, potentially facilitating their experimental study.

High-resolution water vapor fields retrieved over Iberia during the passage of storm Barbara (October 19–20, 2020) by Sentinel-1 and assimilated by the Weanther Research & Forecasting Model (WRF) reveal a substantial positive impact on water vapor forecasting. Due to the path followed by the storm across Iberia, from its southwestern to the northeastern corners, and the geometry of Sentinel-1 data acquisition, it is possible to show, for the first time, the potential added value of precipitable water vapor (PWV) obtained by the Interferometric Synthetic-Aperture Radar (InSAR) technique, as a data source for both the forecast and validation of meteorological forecasts of synoptic-scale storms. Results indicate that data assimilated in the InSARfootprint positively impact the downstream forecasts up to the northeastern boundary, about 850km and 12 hours away, with improved skill scores of the water vapor distribution and improved forecasts of rain.

This article presents a study of the relationship among decorrelation phase in synthetic aperture radar (SAR) interferogram, soil moisture, and water content in vegetation with the aim of mitigating the contribution of decorrelation phase in SAR interferometry estimates of terrain displacements. A methodology for the mitigation of the phase bias based on the temporal variation of the vegetation water content is presented. Decorrelation phases are computed using time series of Sentinel-1 images and compared with in situ measurements of soil moisture. It is shown that soil moisture can partially explain the observed values of decorrelation phases pointing out the role of vegetation water content. A new model is proposed to compute the contribution of vegetation to the decorrelation phase based on the normalized difference water index (NDWI) index. The methodology is applied to all short temporal baseline interferograms obtained from the time series of Sentinel-1 SAR images, using the NDWI maps generated from Sentinel-2 multispectral images. The cumulative displacement is computed by integrating the short temporal baseline interferograms, corrected for the land cover and soil moisture changes. It is shown that the proposed methodology can reduce the variance of estimated cumulative displacement in areas covered by vegetation.

Accuracy of radar interferometry is often hindered by the atmospheric phase screen (APS). To address this limitation, the geostatistical approach known as Kriging has been employed to predict APS from sparse observations for compensation purposes. In this article, we propose an enhanced Kriging approach to achieve more accurate APS predictions in ground-based (GB) radar interferometry applications. Specifically, the Kriging system is augmented with a time-series measure through correlation analysis, effectively leveraging spatiotemporal information for APS prediction. The validity of the introduced Kriging method in the APS compensation framework was tested with Ku-band GB radar datasets collected over two different mountainous sites. A comparison of this method with simple Kriging reveals a noticeable improvement in APS prediction accuracy and temporal phase stability.

We study the sub-ionospheric VLF transmitter signals recorded by the Austrian Graz station in the year 2020. Those radio signals are known to propagate in the Earth-ionosphere waveguide between the ground and lower ionosphere. The Austrian Graz facility (geographic coordinates: 15.46 degrees E, 47.03 degrees N) can receive such sub-ionospheric transmitter signals, particularly those propagating above earthquake (EQ) regions in the southern part of Europe. We consider in this work the transmitter amplitude variations recorded a few weeks before the occurrence of two EQs in Croatia at a distance less than 200 km from Graz VLF facility. The selected EQs happened on 22 March 2020 and 29 December 2020, with magnitudes of Mw5.4 and Mw6.4, respectively, epicenters localized close to Zagreb (16.02 degrees E, 45.87 degrees N; 16.21 degrees E, 45.42 degrees N), and with focuses of depth smaller than 10 km. In our study we emphasize the anomaly fluctuations before/after the sunrise times, sunset times, and the cross-correlation of transmitter signals. We attempt to evaluate and to estimate the latitudinal and the longitudinal expansions of the ionospheric disturbances related to the seismic preparation areas.

Fully developed turbulence is a universal and scale-invariant chaotic state characterized by an energy cascade from large to small scales at which the cascade is eventually arrested by dissipation1–6. Here we show how to harness these seemingly structureless turbulent cascades to generate patterns. Pattern formation entails a process of wavelength selection, which can usually be traced to the linear instability of a homogeneous state7. By contrast, the mechanism we propose here is fully nonlinear. It is triggered by the non-dissipative arrest of turbulent cascades: energy piles up at an intermediate scale, which is neither the system size nor the smallest scales at which energy is usually dissipated. Using a combination of theory and large-scale simulations, we show that the tunable wavelength of these cascade-induced patterns can be set by a non-dissipative transport coefficient called odd viscosity, ubiquitous in chiral fluids ranging from bioactive to quantum systems8–12. Odd viscosity, which acts as a scale-dependent Coriolis-like force, leads to a two-dimensionalization of the flow at small scales, in contrast with rotating fluids in which a two-dimensionalization occurs at large scales4. Apart from odd viscosity fluids, we discuss how cascade-induced patterns can arise in natural systems, including atmospheric flows13–19, stellar plasma such as the solar wind20–22, or the pulverization and coagulation of objects or droplets in which mass rather than energy cascades23–25.

Small bubbles in fluids rise to the surface due to Archimede’s force. Remarkably, in turbulent flows this process is severely hindered by the presence of vortex filaments, which act as moving potential wells, dynamically trapping light particles and bubbles. Quantifying the statistical weights and roles of vortex filaments in turbulence is, however, still an outstanding experimental and computational challenge due to their small scale, fast chaotic motion, and transient nature. Here we show that, under the influence of a modulated oscillatory forcing, the collective bubble behavior switches from a dynamically localized to a delocalized state. Additionally, we find that by varying the forcing frequency and amplitude, a remarkable resonant phenomenon between light particles and small-scale vortex filaments emerges, likening particle behavior to a forced damped oscillator. We discuss how these externally actuated bubbles can be used as a type of microscopic probe to investigate the space-time statistical properties of the smallest turbulence scales, allowing to quantitatively measure physical characteristics of vortex filaments. We develop a superposition model that is in excellent agreement with the simulation data of the particle dynamics which reveals the fraction of localized/delocalized particles as well as characteristics of the potential landscape induced by vortices in turbulence. Our approach paves the way for innovative ways to accurately measure turbulent properties and to the possibility to control light particles and bubble motions in turbulence with potential applications to oceanography, medical imaging, drug/gene delivery, chemical reactions, wastewater treatment, and industrial mixing.

The potential of a coherent microwave radar for infrastructure health monitoring has been investigated over the past decade. Microwave radar measuring based on interferometry processing is a non-invasive technique that can measure the line-of-sight (LOS) displacements of large infrastructure with sub-millimeter precision and provide the corresponding frequency spectrum. It has the capability to estimate infrastructure vibration simultaneously and remotely with high accuracy and repeatability, which serves the long-term serviceability of bridge structures within the context of the long-term sustainability of civil engineering infrastructure management. In this paper, we present three types of microwave radar systems employed to monitor the displacement of bridges in Japan and Italy. A technique that fuses polarimetric analysis and the interferometry technique for bridge monitoring is proposed. Monitoring results achieved with full polarimetric real aperture radar (RAR), step-frequency continuous-wave (SFCW)-based linear synthetic aperture, and multi-input multi-output (MIMO) array sensors are also presented. The results reveal bridge dynamic responses under different loading conditions, including wind, vehicular traffic, and passing trains, and show that microwave sensor interferometry can be utilized to monitor the dynamics of bridge structures with unprecedented spatial and temporal resolution. This paper demonstrates that microwave sensor interferometry with efficient, cost-effective, and non-destructive properties is a serious contender to employment as a sustainable infrastructure monitoring technology serving the sustainable development agenda.

The interferometric synthetic aperture radar (InSAR) technique has demonstrated its ability to capture temporal variations in tropospheric water vapor, providing a valuable source of information for numerical weather prediction (NWP) models. Integrating InSAR data into NWP models has the potential to significantly enhance their forecasting capabilities, especially for predicting local extreme weather events. The challenge lies in extracting a single epoch from the InSAR differential observations. In this work, we introduced a method based on the least-squares approach to estimate single epochs using the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWFs) as a first guess. By leveraging ERA5 data, distinct atmospheric components can be disentangled without additional assumptions or external measurements. Since ERA5 is globally available at 1-h temporal resolution, the proposed method can be applied in remote areas without in situ data, providing improved high-resolution maps at all times (day/night) and in all weather conditions.

The health monitoring of infrastructure is vital for ensuring the safety and structural integrity of bridges. Recently, ground-based real aperture radar (GB-RAR) systems have been successfully utilized in the dynamic and static monitoring of bridges. In this study, a comprehensive and innovative approach is presented to monitor the vertical deformation of a long-span metallic railway bridge and a reinforced concrete Shinkansen bridge in Japan using a polarimetric GB-RAR system. Distinct from conventional signal processing procedures, the proposed method omits the coherent scatterer selection step. Instead, polarization analysis is employed to evaluate the properties of scatterers and identify those corresponding to bridge sections requiring monitoring, while considering the structural characteristics of the bridge. Simultaneously, the signal-to-noise ratio for monitoring is enhanced by combining co-polarization responses from scatterers. Furthermore, the radar look angle is determined by accounting for the spatial configuration of the survey and the polarization orientation angle. Lastly, vertical deformation is assessed by projecting line of sight deformation in the vertical direction. The findings reveal the dynamic responses of the two bridges under diverse loading conditions, which include the transit of a low-speed train and a high-speed Shinkansen bullet train. The results demonstrate that the polarimetric GB-RAR interferometry technique, coupled with the developed algorithms, can be effectively applied to monitor any type of bridge with unparalleled spatial and temporal resolutions.

This work presents the analysis model of the study data available in the LMS platforms specifically designed to analyze potential critical issues as a functional indicator for the possible achievement of the training objectives and completion of the course. The illustrated system highlights how the use of statistical indicators and predictability can be an effective tool for the early identification of possible critical issues in the field of training results, as well as design and organizational inconsistencies that can weigh on the effectiveness of the training system made available. Our work explains how adopting a data analysis model applied to training environments provides the tutoring system with adequate information on potential critical issues to favor targeted interventions on the participants to prevent risks of training ineffectiveness. At the same time, it analyzes the global quality of the courses made available through a perspective of data exploration that starts from the learning experience and enhances the data already present in the LMS platforms.

The medical discourse, entails the analysis of the modalities, far from unbiased, by which hypotheses and results are laid out in the dissemination of findings in scientific publications, giving different emphases on the background, relevance, robustness, and assumptions that the audience should take for granted. While this concept is extensively studied in socio-anthropology, it remains generally overlooked within the scientific community conducting the research. Yet, analyzing the discourse is crucial for several reasons: to frame policies that take into account an appropriately large screen of medical opportunities, to avoid overseeing promising but less walked paths, to grasp different types of representations of diseases, therapies, patients, and other stakeholders, understanding and being aware of how these very terms are conditioned by time, culture and so on. While socio-anthropologists traditionally use manual curation methods, automated approaches like topic modeling offer a complementary way to explore the vast and ever-growing body of medical literature. In this work, we propose a complementary analysis of the medical discourse regarding the therapies offered for rheumatoid arthritis using topic modeling and large language model-based emotion and sentiment analysis.