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This study presents an automated methodology for evaluating micro-channels fabricated using a femtosecond laser on stainless steel substrates. We utilize 3D surface topography and metrological analyses to extract geometric features and detect fabrication defects. Standardized samples were analyzed using a light interferometer, and the resulting data were processed with Principal Component Analysis (PCA) and RANSAC algorithms to derive channel characteristics, such as depth, wall taper, and surface roughness. The proposed method identifies common defects, including bumps and V-defects, which can compromise the functionality of micro-channels. The effectiveness of the approach is validated by comparisons with commercial solutions. This automated procedure aims to enhance the reliability and precision of femtosecond laser micro-milling for industrial applications. The detected defects, combined with fabrication parameters, could be ingested in an AI-based process to optimize fabrication processes. [ABSTRACT FROM AUTHOR]

The sorption/desorption processes of the cationic herbicide paraquat (PQ) onto various clays, namely, kaolinite (KLN), illite (ILT), and montmorillonite (MNT), were investigated. After the attainment of sorption equilibrium, PQ was extracted from the clays by a double-stage desorption process utilizing an electro–ultrafiltration (EUF) procedure. The Freundlich isotherm model and a pseudo-first kinetic release model were found to adequately fit the sorption and desorption data, respectively. The experimental maximum sorbable amounts of paraquat were 5.56, 31.88, and 91.63 mg g−1 for KLN, ILT, and MNT, respectively, consistently with the order of magnitude of the cation-exchange capacity (CEC) of the clay minerals. The desorption experiments revealed that the amounts of PQ retained by the MNT samples were significantly larger than the respective amounts retained by KLN or ILT. The EUF-PQ desorption patterns of differently cation-saturated MNT samples indicated that the presence of monovalent cations could further hamper PQ release, while the opposite seemed to be true for divalent cations. Our results clearly show that a substantial aliquot of PQ is strongly retained by montmorillonite, probably via interlayering, thus suggesting that smectitic clays could act as a stable soil sink for cationic herbicides such as paraquat, favoring soil long-term contamination. [ABSTRACT FROM AUTHOR]

The evaluation of soil erosion rate, particularly in agricultural lands, is a crucial tool for long-term land management planning. This research utilized the soil and water assessment tool (SWAT) model to simulate soil erosion in a semiarid watershed located in South Portugal. To understand the evolution of the erosive phenomenon over time, soil erosion susceptibility maps for both historical and future periods were created. The historical period exhibited the highest average soil erosion for each land use, followed by the representative concentration pathways (RCPs) 8.5 and 4.5 scenarios. The differences in soil loss between these two RCPs were influenced by the slightly increasing trend of extreme events, particularly notable in RCP 8.5, leading to a higher maximum value of soil erosion. The research highlighted a tendency towards erosion in the agroforestry system known as "montado", specifically on Leptosols throughout the entire basin. The study confirmed that Leptosols are most susceptible to sediment loss due to their inherent characteristics. Additionally, both "montado" and farmed systems were found to negatively impact soil erosion rates if appropriate antierosion measures are not adopted. This underscores the importance of identifying all factors responsible for land degradation in Mediterranean watersheds. In conclusion, the study highlighted the significance of assessing soil erosion rates in agricultural areas for effective land management planning in the long run. The utilization of the SWAT model and the creation of susceptibility maps provide valuable insights into the erosive phenomenon's dynamics, urging the implementation of antierosion strategies to protect the soil and combat land degradation in the region. [ABSTRACT FROM AUTHOR]

Deep learning approaches have sparked much interest in the AI community during the last decade, becoming state-of-the-art in domains such as pattern recognition, computer vision, and data analysis. However, these methods are highly demanding in terms of training data, which is often a major issue in the geospatial and remote sensing fields. One possible solution to this problem comes from the Neuro-Symbolic Integration field (NeSy), where multiple methods have been defined to incorporate background knowledge into the neural network's learning pipeline. One such method is KENN (Knowledge Enhanced Neural Networks), which injects logical knowledge into the neural network's structure through additional final layers. Empirically, KENN showed comparable or better results than other NeSy frameworks in various tasks while being more scalable. Therefore, we propose the usage of KENN for point cloud semantic segmentation tasks, where it has immense potential to resolve issues with small sample sizes and unbalanced classes. While other works enforce the knowledge constraints in post-processing, to the best of our knowledge, no previous methods have injected inject such knowledge into the learning pipeline through the use of a NeSy framework. The experiment results over different datasets demonstrate that the introduction of knowledge rules enhances the performance of the original network and achieves state-of-the-art levels of accuracy, even with subideal training data. [ABSTRACT FROM AUTHOR]

Wastewater is one of the major sources of pollution in aquatic environments and its treatment is crucial to reduce risk and increase clean water availability. Constructed wetlands (CWs) are one of the most efficient, environmentally friendly, and less costly techniques for this purpose. This review aims to assess the state of the art on the use of CWs in removing environmental pollutants from wastewater in Italy in order to improve the current situation and provide background for future research and development work. To evaluate the CWs performances, 76 research works (2001–2023) were examined, and the parameters considered were the type of wastewater treated, pollutants removed, macrophytes, and the kinds of CWs utilized. The pollutant removal efficiencies of all CWs reviewed showed remarkable potential, even though there are biotic and abiotic factor-driven performance variations among them. The number of articles published showed an increasing trend over time, indicating the research progress of the application of CWs in wastewater treatment. This review highlighted that most of the investigated case studies referred to pilot CWs. This finding suggests that much more large-scale experiments should be conducted in the future to confirm the potential of CWs in eliminating pollutants from wastewater. [ABSTRACT FROM AUTHOR]

Soil rebuilding represents a major challenge in the recovery of abandoned quarries. In this study, we explored the possibility of using reconstructed Technosols, to achieve soil rebuilding goals at an abandoned quarry site. We first investigated the use of a mixture containing commercial manure and limestone debris (LD) as pedotechnomaterials for an "ad hoc" (re)constructed Technosol (CT), for the recovery of an opencast limestone quarry in one of most concentrated quarry areas in the world. In a field experiment, we tested and monitored different pedotechnosystems (PTSs) made up of constructed Technosol + pasture species + different Mediterranean plant species. Specifically, a control (CT, without any additional treatments) was compared to treatments with organic amendment (CTOA) and conventional fertilizers (CTCF). Data were collected over a 12-month period and included crop performance, plant nutritional state, soil physical-chemical parameters, and metabolites. Analysis of variance compared differences among treatments, while factor analysis (FA) interpreted multiple relationships while explaining observed variability. Results showed that CTOA had better soil physical-chemical properties, greater plant growth, and overall superior agronomic performances compared to all other treatments due to the improved substrate conditions. According to FA, these results appear related to the creation of fertile soil conditions, with most of the investigated metabolites (i) playing a pivotal role in observed outcomes, together with (ii) a clear potential in being considered as a reliable fingerprint for investigating plant responses in constructed PTSs. The proposed pedotechniques in CTOA development show a great potential for the full recovery of abandoned limestone quarries in degraded Mediterranean areas by providing an excellent medium for plant growth, facilitating environmental reclamation. [ABSTRACT FROM AUTHOR]

In recent years, massive digitisation of cultural heritage (CH) assets has become a focus of European programmes and initiatives. Among CH settings, attention is reserved to the immense and precious museum collections, whose digital 3D reproduction can support broader non-invasive analyses and stimulate the realisation of more attractive and interactive exhibitions. The reconstruction pipeline typically includes numerous processing steps when passive techniques are selected to deal with object digitisation. This article presents some insights on critical operations, which, based on our experience, can rule the quality of the final models and the reconstruction times for delivering 3D heritage results, while boosting the sustainability of digital cultural contents. The depth of field (DoF) problem is explored in the acquisition phase when surveying medium and small-sized objects. Techniques for deblurring images and masking object backgrounds are examined relative to the pre-processing stage. Some point cloud denoising and mesh simplification procedures are analysed in data post-processing. Hints on physically-based rendering (PBR) materials are also presented as closing operations of the reconstruction pipeline. This paper explores these processes mainly through experiments, providing a practical guide, tricks, and suggestions when tackling museum digitisation projects. [ABSTRACT FROM AUTHOR]

Tomato is the most widespread vegetable crop in the world. In Italy, tomatoes are mainly cultivated in the South and in the Campania region, precisely in the area called Agro Nocerino-Sarnese. This flatland is affected by an extreme level of environmental degradation, especially related to the Sarno River, where concentrations of Potential Toxic Elements (PTEs) have been found to be higher than the maximum permitted level. The aim of this study was to determine the PTEs uptake by roots and their translocation to the aerial parts of the plants of two cultivars of tomatoes (Pomodoro Giallo and San Marzano Cirio 3). To the purpose, samples of the two cultivars were grown both in pots with experimentally contaminated soil containing: Cr or Cd or Pb at extremely high concentrations and in pots with uncontaminated soils (control). Additionally, the antioxidant properties of the cultivars selected grown on uncontaminated/contaminated soils were assessed. The results showed that Cd was the contaminant that most significantly interfered with the growth of both cultivars of tomato plants, whereas Pb caused lower phenotypical damage. Cd translocation from root to the organs of tomato plants was observed in both cultivars. Specifically, the total amount of Cd found in stems and leaves was higher in the Pomodoro Giallo (254.4 mg/kg dry weight) than in the San Marzano Cirio 3 (165.8 mg/kg dry weight). Cd was the only PTE found in the fruits of both cultivars, with values of 6.1 and 3.9 mg/kg dry weight of Pomodoro Giallo and San Marzano Cirio 3, respectively. The fruits of tomato plants grown in PTEs-contaminated soil showed inhibition or stimulations of the radical scavenging activity compared to the fruits grown in uncontaminated soil. This study highlighted that, despite the relatively high experimental concentrations of PTEs, their translocation to the edible part was comparatively low or absent. [ABSTRACT FROM AUTHOR]

Phenolic compounds (PCs) - with special reference to secondary plant metabolites - were characterised in two Mediterranean olive groves (Olea europaea L.). Representative pedological profiles were dug to identify and characterise the pedotype. Qualitative and quantitative analyses were carried out on soil core samples gathered at fixed depths (0-20 cm and 20-40 cm) and olive leaf methanol extracts by high-performance liquid chromatography with ultraviolet detection. The total PCs content reflected the soil organic carbon distribution, especially carbon of humic and fulvic acids, corroborating their crucial role in humification pathways. Among the analysed plant secondary metabolites, luteolin-4'-O-glucoside and verbascoside were the most abundant in leaves and soils, respectively. Most of the easily hydrolysed/metabolised phenols were not found in soils. Rutin and verbascoside, despite containing glucose, strongly persisted in the soil environment, probably due to their allelopathic effect. Oleuropein was not found in soils because it is highly soluble and mobile in the soil environment. Furthermore, the presence of clay in soil seemed to determine the accumulation of specific PCs. Our data suggest that PCs persistence in soil seems to be mainly determined by a balance between physicochemical and biochemical instability and allelopathic stability rather than their abundance in the plant. [ABSTRACT FROM AUTHOR]

In taking an integrated ethnopedological approach, this study aims to investigate the meaning of the distribution of the toponyms used in traditional and recent cartography of Sardinia (southern Italy). It is particularly, but not only, focused on those related to soil resources. Sardinia is particularly interesting in this respect, as its unique history, geography, and linguistic position makes it one of the Italian and Mediterranean regions with the greatest number of toponyms. This research investigated the toponyms belonging to an important sub-region of Sardinia, called Ogliastra (central-eastern Sardinia). The research was conducted through the following integrated approach: i) toponymy research and collection from different sources; ii) database creation and translation of toponyms from the Sardinian language (SL); iii) categorization of toponyms; and iv) graphical, statistical, and cartographic data processing. Distribution and diversity of toponyms were assessed using the compiled database, coupled with a geographical information system (GIS). Of around 7700 toponyms collected, 79% had already been reported in SL, while just 21% were in Italian. Of the toponyms in SL, 77% are of known meaning and 54% of these toponyms were characterized by a meaning directly and/or indirectly connected to specific environmental features. On the whole, morphology would appear to be the primary environmental factor able to explain the complex, articulated presence, distribution, and typology of the investigated toponyms. A least squares regression analysis of pedodiversity vs. topodiversity shows a very closed distribution, with an impressive high correlation index (R2 = 0.824). The principal factor analysis (PFA) shows that such a connection may be morphologically based, thereby confirming that pedodiversity and topodiversity are strongly linked each other. Overall, the research shows that an integrated ethnopedological approach, combining indigenous and scientific knowledge may be of great interest in order to mitigate the impressive phenomena of the indigenous knowledge lost. [ABSTRACT FROM AUTHOR]

In many Mediterranean regions industrial activities have drastically affected soil evolution. As a case study, reference is made to an important Chemical Industrial District of southern Italy (Sardinia). Its setting up and development resulted in the formation of human-altered and human-transported (HAHT) soils, largely through physical-mechanical excavation, transportation and rolling out the original soils (Palexeralfs, Haploxeralfs), as well as mixing and covering them with innocuous artifacts referred to as human-transported materials (HTM). On this basis, research began with a view to evaluating the anthropogenic processes in addition to the main morphological and physical-chemical characteristics of HAHT soils as compared with reference soils (RefS), their pedovariability and the classification of HAHT soils through Soil Taxonomy. The anthro-pedoturbation induced by mixing HTM to preexisting-presently buried- soils has dramatically disturbed the "natural" pedogenetic evolution, and driven the pro-isotropic processes leading the HAHT soils to much more simplified morphology and homogenization of chemical-physical features, with an impressive loss of pedovariability. They currently meet the requirements for a "mantle" classifiable as Alfic Xerarents, as an expression of the "entisolization" that induced a taxonomic shift from Alfisols to anthropogenic Entisols. [ABSTRACT FROM AUTHOR]

This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers' and agronomists' fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account. [ABSTRACT FROM AUTHOR]

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented. [ABSTRACT FROM AUTHOR]

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution. [ABSTRACT FROM AUTHOR]

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns). [ABSTRACT FROM AUTHOR]

• Deep rooting in Eucalyptus plantations is a common phenomenon in tropical soils. • Studies about the influence of edaphoclimatic conditions on tree growth are rare. • Soil water, fine root distribution and behavior were studied in a chronosequence. • Soil evolution plays a major rule in rooting and other plant/stand performances. • Oxisols assured better stand performances than poorly developed Entisols. Brazilian roundwood industry is one of the most important and productive in the world, with Eucalyptus plantations alone representing 73% of the total planted forests. Deep rooting in these plantations represents a more common phenomena than generally expected. However, there is still a lack of information on environmental factors that drive root growth in deep soil layers, with particular emphasis on edaphoclimatic conditions, and related consequence in terms of soil water behavior. As a part of a larger project, this research aimed to investigate soil water and fine root system distribution in deep tropical soils under a commercial Eucalyptus plantation chronosequence. Along a 2800-km gradient (from south- to north-east Brazil), 14 experimental areas were planted with a "plastic" clone (E. urophylla) and investigated in terms of climatic conditions, soil and water features, and plant/stand development for an entire 6-years rotation period. Fine roots distribution were investigated in one site (in Brazil) till to 20 m deep at 3, 9, 24, 48, and 65 months after planting. Results showed a fast displacement of the root front down to 75, 325, 825, 1250, and 1575 cm at month 3, 9, 24, 48, and 65 after planting, respectively. Fine root densities (g cm−3) and proportional water capture exponentially decreased with soil depth. Deep fine roots showed a relativelly higher efficiency in acquiring water than the shallower, denser roots. The relationship between stand height vs root front depth followed an exponential trend, suggesting that these stands developed relatively faster in height rather than in depth during the first 48 months, with the opposite characterizing plantation afterwards. Regardless of stand age, E. urophylla trees rapidly explored a considerable volume of soil at a relatively limited carbon cost. Multivariate statistics showed that edaphoclimatic conditions play a major role in Eucalyptus plant/stand development. This study outlined the major role played by soil development. From poorly developed sandy Entisols, to medium developed Inceptisols, and to most developed fine textured Oxisols, both plant growth and stand productivity greatly improved accordingly. This study suggests that soil type, together with other environmental factors, are likely to influence both the development and behavior of Eucalyptus plantations for an extent greater than commonly anticipated. [ABSTRACT FROM AUTHOR]

In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spectroradiometer. All soil variables, with the exception of silt, evidently affected some specific spectral features. Multivariate calibrations were performed to predict the soil properties from reflectance spectra. PLSR was used to calibrate the spectral data using two-thirds of samples for calibration and one-third for validation. Spectroscopic data were pre-processed [multiplicative scatter correction (MSC), standard normal variance (SNV), wavelet detrending (WD), first and second derivative transformation, and filtering] prior to multivariate calibration. The results revealed very good models (2.0 < RPD < 2.5) for the prediction of FC, PWP and sand, and excellent (RPD > 2.5) models for the prediction of clay and OC, whereas a poor (RPD < 1.4) prediction model was obtained for silt. [ABSTRACT FROM AUTHOR]

In recent years, the use of 3D models in cultural and archaeological heritage for documentation and dissemination purposes is increasing. The association of heterogeneous information to 3D data by means of automated segmentation and classification methods can help to characterize, describe and better interpret the object under study. Indeed, the high complexity of 3D data along with the large diversity of heritage assets themselves have constituted segmentation and classification methods as currently active research topics. Although machine learning methods brought great progress in this respect, few advances have been developed in relation to cultural heritage 3D data. Starting from the existing literature, this paper aims to develop, explore and validate reliable and efficient automated procedures for the classification of 3D data (point clouds or polygonal mesh models) of heritage scenarios. In more detail, the proposed solution works on 2D data ("texture-based" approach) or directly on the 3D data ("geometry-based approach) with supervised or unsupervised machine learning strategies. The method was applied and validated on four different archaeological/architectural scenarios. Experimental results demonstrate that the proposed approach is reliable and replicable and it is effective for restoration and documentation purposes, providing metric information e.g. of damaged areas to be restored. [ABSTRACT FROM AUTHOR]