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Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. [ABSTRACT FROM AUTHOR]

Neurodevelopmental disorders exhibit recurrent facial features that can suggest the genetic diagnosis at a glance, but recognizing subtle dysmorphisms is a specialized skill that requires very long training. Face2Gene (FDNA Inc) is an innovative computer-aided phenotyping tool that analyses patient's portraits and suggests 30 candidate syndromes with similar morphology in a prioritized list. We hypothesized that the software could support even expert physicians in the diagnostic workup of genetic conditions. In this study, we assessed the performance of Face2Gene in an Italian dysmorphological pediatrics clinic. We uploaded two-dimensional face pictures of 145 children affected by genetic conditions with typical phenotypic traits. All diagnoses were previously confirmed by cytogenetic or molecular tests. Overall, the software's differential included the correct syndrome in most cases (98%). We evaluated the efficiency of the algorithm even considering the rareness of the genetic conditions. All "common" diagnoses were correctly identified, most of them with high diagnostic accuracy (93% in top-3 matches). Finally, the performance for the most common pediatric syndromes was calculated. Face2Gene performed well even for ultra-rare genetic conditions (75% within top-3 matches and 83% within top-10 matches). Expert geneticists maybe do not need computer support to recognize common syndromes, but our results prove that the tool can be useful not only for general pediatricians but also in dysmorphological clinics for ultra-rare genetic conditions., (© 2023 The Authors. American Journal of Medical Genetics Part A published by Wiley Periodicals LLC.)

The application of artificial intelligence (AI) in medical practice is spreading, especially in technologically dense fields such as radiology, which could consequently undergo profound transformations in the near future. This article aims to qualitatively explore the potential influence of AI technologies on the professional identity of radiologists. Drawing on 12 in‐depth interviews with a subgroup of radiologists who participated in a larger study, this article investigated (1) whether radiologists perceived AI as a threat to their decision‐making autonomy; and (2) how radiologists perceived the future of their profession compared to other health‐care professions. The findings revealed that while AI did not generally affect radiologists' decision‐making autonomy, it threatened their professional and epistemic authority. Two discursive strategies were identified to explain these findings. The first strategy emphasised radiologists' specific expertise and knowledge that extends beyond interpreting images, a task performed with high accuracy by AI machines. The second strategy underscored the fostering of radiologists' professional prestige through developing expertise in using AI technologies, a skill that would distinguish them from other clinicians who did not pose this knowledge. This study identifies AI machines as status objects and useful tools in performing boundary work in and around the radiological profession. [ABSTRACT FROM AUTHOR]

In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude ( M ) and distance ( R ) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the M - R scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal M - R pairs were compared to empirical curves defining the M - R bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible. [ABSTRACT FROM AUTHOR]

This work and manuscript focus on how 3D scanning methodologies and post-processing analyses may help us to gain a deeper investigation of cuneiform tablets beyond the written content. The dataset proposed herein is a key part of the archaeological collection preserved in the Musei Reali of Turin in Italy; these archaeological artefacts enclose further important semantic information extractable through detailed 3D documentation and 3D model filtering. In fact, this scanning process is a fundamental tool for better reading of sealing impressions beneath the cuneiform text, as well as for understanding micrometric evidence of the fingerprints of scribes. Most of the seal impressions were made before the writing (like a watermark), and thus, they are not detectable to the naked eye due to cuneiform signs above them as well as the state of preservation. In this regard, 3D scanning and post-processing analysis could help in the analysis of these nearly invisible features impressed on tablets. For this reason, this work is also based on how 3D analyses may support the identification of the unperceived and almost invisible features concealed in clay tablets. Analysis of fingerprints and the depths of the signs can tell us about the worker's strategies and the people beyond the artefacts. Three-dimensional models generated inside the Artec 3D ecosystem via Space Spider scanner and Artec Studio software were further investigated by applying specific filters and shaders. Digital light manipulation can reveal, through the dynamic displacement of light and shadows, particular details that can be deeply analysed with specific post-processing operations: for example, the MSII (multi-scale integral invariant) filter is a powerful tool exploited for revealing hidden and unperceived features such as fingerprints and sealing impressions (stratigraphically below cuneiform signs). Finally, the collected data will be handled twofold: in an open-access repository and through a common data environment (CDE) to aid in the data exchange process for project collaborators and common users. [ABSTRACT FROM AUTHOR]

Stress shielding (SS) around press-fit radial head arthroplasty (RHA) was recently reported as a cause of a new type of proximal radial neck resorption (PRNR). Very few studies have analyzed this phenomenon. No comprehensive classification is currently available. We thus decided to clinically and radiographically analyze 97 patients who underwent a press-fit RHA and who were followed up for a mean period of 72 months (range: 2–14 years). PRNR in the four quadrants of the radial neck was assessed. We designed a novel SS classification based on (1) the degree of resorption of the length of the radial neck and (2) the number of neck quadrants involved on the axial plane. The mean PRNR (mPRNR) was calculated as the mean resorption in the four quadrants. mPRNR was classified as mild (<3 mm), moderate (3 to 6 mm), and severe (>6 mm). Eighty-four percent of the patients presented PRNR. mPRNR was mild in 33% of the patients, moderate in 54%, and severe in 13%. In total, 6% of the patients with mild mPRNR displayed resorption in one quadrant, 18% displayed resorption in two quadrants, 4% displayed resorption in three quadrants, and 72% displayed resorption in four quadrants. All four quadrants were always involved in moderate or severe mPRNR, with no significant differences being detected between quadrants (p = 0.568). mPRNR has no apparent effect on the clinical results, complications, or RHA survival in the medium term. However, longer-term studies are needed to determine the effects of varying degrees of PRNR on implant failure. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, artificial intelligence has been used increasingly in medical practice. Technological progress has made it possible to digitize the slides of histological preparations, allowing the use of image processing and AI technologies in surgical pathology. This potentially reduces variability and improves the uniformity of some histological evaluations, such as cellularity assessment in bone marrow biopsies, which is traditionally performed visually by expert human observers, resulting in inter-observer and intra-observer variability. In this work, we developed an accurate AI-based tool for the automated quantification of cellularity in BMB histology and compared its performances with cellularity estimates from five expert hematopathologists. Our results showed the robustness of our model across users and two different scanners for digitized image generation. The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the use of an automated tool may reduce variability, improving the uniformity of the evaluation. The aim of this work is to develop an accurate AI-based tool for the automated quantification of cellularity in BMB histology. A total of 55 BMB histological slides, diagnosed as Ph- MPN between January 2018 and June 2023 from the archives of the Pathology Unit of University "Luigi Vanvitelli" in Naples (Italy), were scanned on Ventana DP200 or Epredia P1000 and exported as whole-slide images (WSIs). Fifteen BMBs were randomly selected to obtain a training set of AI-based tools. An expert pathologist and a trained resident performed annotations of hematopoietic tissue and adipose tissue, and annotations were exported as.tiff images and.png labels with two colors (black for hematopoietic tissue and yellow for adipose tissue). Subsequently, we developed a semantic segmentation model for hematopoietic tissue and adipose tissue. The remaining 40 BMBs were used for model verification. The performance of our model was compared with an evaluation of the cellularity of five expert hematopathologists and three trainees; we obtained an optimal concordance between our model and the expert pathologists' evaluation, with poorer concordance for trainees. There were no significant differences in cellularity assessments between two different scanners. [ABSTRACT FROM AUTHOR]

Background: The recruitment-to-inflation ratio (R/I) has been recently proposed to bedside assess response to PEEP. The impact of PEEP on ventilator-induced lung injury depends on the extent of dynamic strain reduction. We hypothesized that R/I may reflect the potential for lung recruitment (i.e. recruitability) and, consequently, estimate the impact of PEEP on dynamic lung strain, both assessed through computed tomography scan. Methods: Fourteen lung-damaged pigs (lipopolysaccharide infusion) underwent ventilation at low (5 cmH2O) and high PEEP (i.e., PEEP generating a plateau pressure of 28–30 cmH2O). R/I was measured through a one-breath derecruitment maneuver from high to low PEEP. PEEP-induced changes in dynamic lung strain, difference in nonaerated lung tissue weight (tissue recruitment) and amount of gas entering previously nonaerated lung units (gas recruitment) were assessed through computed tomography scan. Tissue and gas recruitment were normalized to the weight and gas volume of previously ventilated lung areas at low PEEP (normalized-tissue recruitment and normalized-gas recruitment, respectively). Results: Between high (median [interquartile range] 20 cmH2O [18–21]) and low PEEP, median R/I was 1.08 [0.88–1.82], indicating high lung recruitability. Compared to low PEEP, tissue and gas recruitment at high PEEP were 246 g [182–288] and 385 ml [318–668], respectively. R/I was linearly related to normalized-gas recruitment (r = 0.90; [95% CI 0.71 to 0.97) and normalized-tissue recruitment (r = 0.69; [95% CI 0.25 to 0.89]). Dynamic lung strain was 0.37 [0.29–0.44] at high PEEP and 0.59 [0.46–0.80] at low PEEP (p < 0.001). R/I was significantly related to PEEP-induced reduction in dynamic (r = − 0.93; [95% CI − 0.78 to − 0.98]) and global lung strain (r = − 0.57; [95% CI − 0.05 to − 0.84]). No correlation was found between R/I and and PEEP-induced changes in static lung strain (r = 0.34; [95% CI − 0.23 to 0.74]). Conclusions: In a highly recruitable ARDS model, R/I reflects the potential for lung recruitment and well estimates the extent of PEEP-induced reduction in dynamic lung strain. [ABSTRACT FROM AUTHOR]

When evaluating the shear strength of rock mass discontinuities, certain challenges arise due to the difficulty in quantifying the roughness characteristics of surfaces and the strength of asperities. Recent research has focused on enhancing techniques for assessing these characteristics and exploring the application of laser scanning to aid in evaluating discontinuity features. The analysis of reflectivity values (I) obtained through a laser scanner survey presents an efficient method for assessing mechanical characteristics, such as joint compressive strength (JCS). Reflectivity measurements demonstrate correlations with Schmidt hammer rebound values (r). The laser scanner technique would enable the measurement of JCS without the direct application of the Schmidt hammer on rocks in areas where rebound values (r) measurements are unavailable. The use of a laser scanner allows for the acquisition of high-precision geometrical information concerning the 3D roughness and anisotropy of rock surfaces. In this study, an innovative technique was introduced that utilizes laser scanner data from six previous experimental surveys conducted on rock formations in Southern Italy. This technique facilitates the evaluation of roughness profiles, considering potential variations along kinematically admissible sliding directions, allowing for the estimation of the Joint Roughness Coefficient (JRC). This new methodology aids in evaluating the parameters of Barton's equation to determine the strength characteristics of rock mass discontinuities. [ABSTRACT FROM AUTHOR]

Smart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature. • Low-Power Edge-based insect monitoring system. • Insect detection and classification on resource-constrained microcontroller units (MCUs). • Extremely lightweight classification model for Halyomorpha Halys detection. • Joint improvement of hardware metrics without considerably compromising accuracy. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), commonly known as drones, present a cost-effective solution for the swift collection of data from vast and remote areas that are otherwise difficult to access. The Mediterranean Basin, known for being a hotspot for plant biodiversity, hosts several habitats and taxa of significant naturalistic value. However, many of these areas are often inaccessible to botanists, making exploration and research challenging. The aim of this paper is to involve the utilization of drone surveys and open-source software for botanical research. Our primary goal is to show the effectiveness of these tools in the field and demonstrate their practical application in Natura 2000 sites. The protected area chosen for this research is Rocca di Novara, situated in northeastern Sicily. Thanks to our drone investigations, we were able to capture images of a mountainside that is inaccessible to humans. This allowed us to observe the habitat of some species in detail. One of the most fascinating discoveries was the reappearance of Saxifraga callosa subsp. australis, which had not been confirmed in this area for over 140 years. Using drones for botanical research can boost field research, making monitoring easier and more cost-effective over time, especially in Natura 2000 sites. [ABSTRACT FROM AUTHOR]

Citron (Citrus medica L. cv. Liscia-diamante), cultivated in the "Riviera dei Cedri" (southern Italy), is mainly utilized in the production of candied fruit and essential oils (EOs). Up to now, no information regarding the effect of storage temperatures on citron has been reported. Here, citron samples, after harvesting, were stored at different temperatures (5, 10 and 20 °C at 70% relative humidity) for two weeks, and the main postharvest quality parameters were evaluated. Moreover, EOs extracted from the stored samples were chemically characterized to reveal changes in the volatiles profile and antimicrobial activity. The EOs presented monoterpene hydrocarbons (87.1 to 96.3% of the total oil profile) as the most abundant compounds, followed by oxygenated metabolites ranging from 9.7 to 3.1% of the total pattern. Postharvest quality traits showed a good retention of green peel color during storage at 5 °C, while EOs from samples stored for 7 and 14 days at 10 and 20 °C, respectively, showed the highest antimicrobial activity against most assayed strains. The results indicated storage at 10 °C for 7 days as the most suitable for the preservation of the postharvest quality of the fruit and the antimicrobial activity of the extracted EOs. [ABSTRACT FROM AUTHOR]

Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network approach to forecast the convective cell trajectory and intensity, using, as an example, a region in northern Italy that is frequently hit by convective storms in spring and summer. The predictor input is constituted by radar-derived information about the center of gravity of the cell, its reflectivity (a proxy for the intensity of the precipitation), and the area affected by the storm. The essential characteristic of the proposed approach is that the neural network directly forecasts the evolution of the convective cell position and of the other features for the following hour at a 5-min temporal resolution without a relevant loss of accuracy in comparison to predictors trained for each specific variable at a particular time step. Besides its accuracy ( R 2 of the position is about 0.80 one hour in advance), this machine learning approach has clear advantages over the classical numerical weather predictors since it runs at orders of magnitude more rapidly, thus allowing for the implementation of a real-time early-warning system. [ABSTRACT FROM AUTHOR]

Coastal and underwater archaeological sites pose significant challenges in terms of investigation, conservation, valorisation, and management. These sites are often at risk due to climate change and various human-made impacts such as urban expansion, maritime pollution, and natural deterioration. However, advances in remote sensing (RS) and Earth observation (EO) technologies applied to cultural heritage (CH) sites have led to the development of various techniques for underwater cultural heritage (UCH) exploration. The aim of this work was the evaluation of an integrated methodological approach using ultra-high-resolution (UHR) bathymetric data to aid in the identification and interpretation of submerged archaeological contexts. The study focused on a selected area of the submerged Archaeological Park of Baia (Campi Flegrei, south Italy) as a test site. The study highlighted the potential of an approach based on UHR digital bathymetric model (DBM) derivatives and the use of machine learning and statistical techniques to automatically extract and discriminate features of archaeological interest from other components of the seabed substrate. The results achieved accuracy rates of around 90% and created a georeferenced vector map similar to that usually drawn by hand by archaeologists. [ABSTRACT FROM AUTHOR]

The objective of this study was to demonstrate the possible correlation of visible carcass contamination and abattoir aerosol quality with microbial hygiene criteria. A total of 279 bovine carcasses were analyzed on 23 different working days. The aerobic colony count and total coliforms on the carcasses were calculated together with the presence of Escherichia coli. To determine the visible contamination of carcasses, we used a 100 cm2 sheet of transparent, adhesive plastic material, applied to the side of the carcass, to collect all the particles, which were then counted against both black and white backgrounds. The daily particulate index in the abattoir aerosol was determined using an air sampler device. The results showed that aerobic colony counts, which ranged from 1.41 to 2.40 log cfu cm−2, total coliforms (from 0.00 to 0.73 log cfu cm−2), and E. coli presence (from 0.00% to 60% of the sampled carcasses per day) are not correlated with the carcasses' visual dirtiness or the aerosol quality. The factor analysis showed a correlation between the three groups of variables investigated: group 1, representing "aerosol quality", group 2, representing the "microbiology of the carcass", and group 3, the "visual dirtiness of the carcass". Thus, even though microbiology analysis is useful in diagnosing the microorganisms which the official veterinarian is unable to detect during the post-mortem inspection, it is ineffective in evaluating slaughtering procedures. Aerosol monitoring and the visual classification of carcass dirtiness, instead, could provide good indications of the slaughtering process and the quality of the abattoir environment, and guarantee control of manufacturing practices, protecting both animals' and operators' health. [ABSTRACT FROM AUTHOR]

As the need for ecosystem biodiversity assessment increases within the climate crisis framework, more and more studies using spectral variation hypothesis (SVH) are proposed to assess biodiversity at various scales. The SVH implies optical diversity (also called spectral diversity) is driven by light absorption dynamics associated with plant traits (PTs) variability (which is an indicator of functional diversity) which is, in turn, determined by biodiversity. In this study, we examined the relationship between PTs variability, optical diversity and α‐ and β‐diversity at different taxonomic ranks at the Monte Bondone grasslands, Trentino province, Italy. The results of the study showed that the PTs variability, at the α scale, was not correlated with biodiversity. On the other hand, the results observed at the community scale (β‐diversity) showed that the variation of some of the investigated biochemical and biophysical PTs was associated with the β‐diversity. We used the Mantel test to analyse the relationship between the PTs variability and species β‐diversity. The results showed a correlation coefficient of up to 0.50 between PTs variability and species β‐diversity. For higher taxonomic ranks such as family and functional groups, a slightly higher Spearman's correlation coefficient of up to 0.64 and 0.61 was observed, respectively. The SVH approach was also tested to estimate β‐diversity and we found that spectral diversity calculated by Spectral Angle Mapper showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity approach failed to estimate α‐diversity. These findings suggest that optical and PTs diversity approaches can be used to predict species diversity in the grasslands ecosystem where the species turnover is high. [ABSTRACT FROM AUTHOR]

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions. [ABSTRACT FROM AUTHOR]

The Euganean Thermal District, situated in North-East Italy, is one of Europe's largest and oldest thermal centres. The topical application of its therapeutic thermal muds is recognised by the Italian Health System as a beneficial treatment for patients suffering from arthro-rheumatic diseases. Polysaccharides produced by the mud microbiota have been recently identified as anti-inflammatory bioactive molecules. In this paper we analysed the efficacy of Microbial-Polysaccharides (M-PS) derived from mature muds obtained at different maturation temperatures, both within and outside the codified traditional mud maturation range. M-PSs were extracted from six mature muds produced by five spas of the Euganean Thermal District and investigated for their chemical properties, monosaccharide composition and in vivo anti-inflammatory potential, using the zebrafish model organism. Additionally, mature muds were characterized for their microbiota composition using Next-Generation Sequencing. The results showed that all M-PSs exhibit similar anti-inflammatory potential, referable to their comparable chemical composition. This consistency was observed despite changes in cyanobacteria populations, suggesting a possible role of the entire microbial community in shaping the properties of these biomolecules. These findings highlight the importance of scientific research in untangling the origins of the therapeutic efficacy of Euganean Thermal muds in the treatment of chronic inflammatory conditions. [ABSTRACT FROM AUTHOR]

Extreme events related to climate change are increasing in intensity, frequency, and duration worldwide. Europe is identified as a heatwave hotspot, with trends three-to-four time faster than the northern mid-latitudes; effects of heatwaves are combined in urban contexts with the heat island phenomenon, making cities critical for climate risk prevention and management. Land surface temperature represents an essential parameter for assessing the intensity of thermal impact on urban ecosystems and on public health. This parameter is widely used to map and assess urban heat islands in light to support climate-resilient adaptation planning. The general aim of this study is to assess urban heat island intensity, during a significant heatwave, in a critical heat-related risk region in Southern Italy (Salento). Specific objectives are 1) assessing climate change trends for heat-related extremes (hot days and heatwaves), 2) calculating urban heat islands intensity at regional and urban scale, 3) assessing spatial relationships among thermal intensity and urban characteristics (soil sealing and surface albedo). Identification of heatwaves is based on climatological data and statistical analyses; spatial thermal analyses and correlations are based on Landsat-8 imagery while land cover data are derived from ortho-photos. Climate analyses show a notable increase of the maximum annual temperature of 0.5°C per decade, with an increase of eight hot days per decade. Spatial analyses on thermal impact highlight that urban heat island intensity is much lower within cities and towns than in rural areas, showing a "reverse effect" compared to the typical microclimatic characteristics of urban contexts. In fact, thermal intensity in the city of Lecce ranges from -11°C to 5.6°C. Also, by NDVI analyses, we found that permeable surfaces were 2°C higher than impermeable surfaces, with statistically significant differences. Results from albedo analysis suggest that the characteristics of building material in historical sectors of cities may play a crucial role in this "reverse effect" of urban heat islands. Further studies are required to better investigate the contribution of different factors in this context. [ABSTRACT FROM AUTHOR]

In the context of climate change, it is important to monitor the dynamics of the Earth's surface in order to prevent extreme weather phenomena such as floods and droughts. To this end, global meteorological forecasting is constantly being improved, with a recent breakthrough in deep learning methods. In this paper, we propose to adapt a recent weather forecasting architecture, called GraphCast, to a water resources forecasting task using high-resolution satellite image time series (SITS). Based on an intermediate mesh, the data geometry used within the network is adapted to match high spatial resolution data acquired in two-dimensional space. In particular, we introduce a predefined irregular mesh based on a segmentation map to guide the network's predictions and bring more detail to specific areas. We conduct experiments to forecast water resources index two months ahead on lakes and rivers in Italy and Spain. We demonstrate that our adaptation of GraphCast outperforms the existing frameworks designed for SITS analysis. It also showed stable results for the main hyperparameter, i.e., the number of superpixels. We conclude that adapting global meteorological forecasting methods to SITS settings can be beneficial for high spatial resolution predictions. [ABSTRACT FROM AUTHOR]

Mediterranean marine caves have been categorized as both biodiversity reservoirs and vulnerable habitats. However, only a few studies have focused on Porifera assemblages within marine caves along the Adriatic Apulian coast (southern Italy). In this study, the sponge fauna of the Rondinella cave, a semi-submerged marine cave along the coast of Bari (Southern Adriatic Sea), was investigated for the first time. The use of advanced image analysis in combination with targeted sampling has made it possible to determine the spatial distribution and diversity of Porifera along a transect from the entrance to the end of the cave. Data analysis clustered the stations into two groups, separated according to the distance from the entrance and corresponding to the cave entrance and the semi-dark zone. Sponges were found at all stations covering a considerable part of the substrate, with the highest cover values occurring in the semi-dark zone. A total of 54 sponge taxa were identified: 49 Demospongiae, 3 Homoscleromorpha, and 2 Calcarea. Six species are new records for the Apulian marine caves, one species represents a new record for marine caves, and two species are new findings for the southern Adriatic Sea. [ABSTRACT FROM AUTHOR]

Background: In Italy, malaria was endemic until the 1970s, when it was declared eradicated by WHO. Nowadays, with the persistence of competent mosquito populations, the effect of climate change, and increased possibility of importing malaria parasites from endemic counties due to growing migration, a malaria resurgence in Italy has become more likely. Hence, enhancing the understanding of the current distribution of the Anopheles maculipennis complex and the factors that influence the presence of this malaria vector is crucial, especially in Northern Italy, characterised by a high density of both human population and livestock. Methods: To assess the presence and abundance of malaria vectors, a 4-year field survey in the plain areas of Lombardy and Emilia-Romagna region in Italy was conducted. Every sampling point was characterised in space by the land use in a 500-m radius and in time considering meteorological data collected in the short and long time periods before sampling. We combined the results of a linear regression model with a random forest analysis to understand the relative importance of the investigated niche dimensions in determining Anopheles mosquito presence and abundance. Results: The estimated normalised variable importance indicates that rice fields were the most important land use class explaining the presence of Anopheles, followed by transitional woodlands and shrubland. Farm buildings were the third variable in terms of importance, likely because of the presence of animal shelters, followed by urbanised land. The two most important meteorological variables influencing the abundance of Anopheles in our study area were mean temperature in the 24 h before the sampling date and the sum of degree-days with temperature between 18 °C and 30 °C in the 14 days before the sampling date. Conclusions: The results obtained in this study could be helpful in predicting the risk of autochthonous malaria transmission, based on local information on land cover classes that might facilitate the presence of malaria vectors and presence of short- and medium-term meteorological conditions favourable to mosquito development and activity. The results can support the design of vector control measures through environmental management. [ABSTRACT FROM AUTHOR]

Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of Popillia using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances. [ABSTRACT FROM AUTHOR]

The impacts of floods on environmental assets are often not assessed. In this communication, we reflect on this issue by analysing the reported environmental consequences of the 2023 Emilia-Romagna floods. The information on the environmental impacts is constructed by collecting data from reports, press releases, and interviews in the aftermath of the events. The most frequently reported damage involves water resources and water-related ecosystems, with cultural and supporting ecosystem services particularly affected. Indirect effects in time and space, intrinsic recovery capacity, cascade impacts on socio-economic systems, and the lack of established monitoring activities appear to be the most challenging aspects for future research. [ABSTRACT FROM AUTHOR]

Nowadays, European program Copernicus' Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service called Sen4MUN for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions. The analysis was focused on the Aosta Valley region, North West Italy. A comparison between the Ordinary Workflow (OW) and the suggested Sen4MUN approach was performed. OW is based on statistical survey and municipality declaration, while Sen4MUN is based on geospatial deep learning techniques on aerial imagery (to extract roads and buildings to get real estate units) and yearly Land Cover map components according to European EAGLE guidelines. Both methods are based on land cover components which represent the input on which the financial coefficients for assigning contributions are applied. In both approaches, buffers are applied onto urban class (LCb). This buffer was performed according to the EEA-ISPRA soil consumption guidelines to avoid underestimating some areas that are difficult to map. In the case of Sen4MUN, this is applied to overcome Sentinel sensor limits and spectral mixing issues, while in the case of OW, this is due to limits in the survey method itself. Finally, a validation was performed assuming as truth the approach defined by law as the standard, i.e., OW, although it has limitations. MAEs involving LCb, road lengths and real estate units demonstrate the effectiveness of Sen4MUN. The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration. [ABSTRACT FROM AUTHOR]

In the Northern Apennines, the Internal Ligurian Units are considered deformed and metamorphosed fragments of the Ligure-Piemontese oceanic basin. In this paper, we report on the temperature and pressure conditions of the metamorphic peak for four Internal Ligurian Units, estimated using different geothermometers and geobarometers based on the white mica and chlorite compositions. These minerals were formed during the D1 deformation phase in the pre-Oligocene. The results indicate that the Portello and Gottero units are both characterized by metamorphic conditions pertaining to low blueschists facies, while the Colli-Tavarone and Bracco-Val Graveglia Units show a lower metamorphic imprint that produces assemblages of prehnite-pumpellyite facies. The estimated geothermal gradient for the metamorphic peak achieved by the analyzed Internal Ligurian Units during the D1 phase is 7–15 °C/Km, which is indicative of deformation in a subduction setting. Under these conditions, the D1 phase developed in these units as a result of underplating at the base of the accretionary wedge during the closure of the Ligure-Piemontese basin. These data indicate a close geodynamic correlation among the Internal Ligurian Units and the ophiolite-bearing units of the Alps. [ABSTRACT FROM AUTHOR]

Suitable colonization materials are a pursued target in marine restoration programs. Known for making nutrients available while reducing pollutants and the risk of pathogens in terrestrial ecosystems, Biochar and Bioferment materials of organic origin were tested during a two-year experiment. We tested the efficacy of these materials for restoration purposes through experimental concrete tiles treated with Biochar (B) and Bioferment (F) and tiles made of concrete, which were used as controls (Ct) for the colonization of marine organisms in the marine protected area of Porto Cesareo, Southern Italy (20 m depth). Tiles were monitored for photographs from October 2019 to September 2021. Initially, Biochar treatment presented a higher percentage of total benthic cover (81.23 ± 2.76, median ± SE), differing from Bioferment treatment and control tiles (45.65 ± 5.43 and 47.95 ± 3.69, respectively). Significant interaction between treatments and times suggests changes in community structure related to Polychaeta cover increase in Bioferment and control materials from the second monitoring time. Furthermore, the underwater instability of Bioferment on the tiles could explain the similarity with control tiles in marine organisms' covers. Hence, Biochar is shown to be a material with optimal stability in seawater, demonstrating greater capacity for marine organisms' colonization in less time compared to the other two materials. [ABSTRACT FROM AUTHOR]

Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψstem). In this study, we carried out field measurements of Ψstem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψstem measurements were utilized to calculate vegetation indices (VIs). Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R2 = 0.78). This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψstem avoiding time-consuming and resource-intensive fieldwork. [ABSTRACT FROM AUTHOR]

Temperature training (TT) with short-term mild increase in incubation temperature in the hatcher can lead to acute and long-term improvements in performance and robustness. In order to make this method safer for use in practice, a monitoring and control system is needed that can non-invasively detect changes in physiological parameters of the embryo during TT with high sensitivity and thus enable fine-tuning of the TT. Temperature training was applied at embryonic days 17 to 20. The incubation temperature (IT) was increased by 1°C for 2 hours per day, starting from the normal IT of 37.3°C. The trials were conducted with Ross 308 hatching eggs (control and TT-group with 30 eggs each) in a hatcher with a capacity of up to 50 eggs (FIEM, MG50H MINILCD, Italy). Highly sensitive temperature sensors of the type EPCOS B57540G with a resolution of 0.01°C have been fixed to the egg shell. It was assumed that the change in heart rate as information about the state of the embryo is hidden in the noise of the measurement signal (change in egg shell temperature, sampling rate 100/sec). The results were used to fine-tune the TT. Based on the hatching results (hatching rate, chick quality, chick weight), after 35 days the growth performance (feed intake, growth rate, body mass), and parameters as mortality, heterophils to lymphocytes ratio (HLR), the success of the fine-tuning of the TT was evaluated. As physiological parameter periodic changes in heart rate (HR) were recorded and used for TT fine tuning. When exceeding a critical value of the HR (300 b/min) the increase in IT during stimulation was reduced by 0.4 K. The success of this fine tuning could be shown by the recorded production and physiological parameters. The animals that were experienced by TT had a similar good hatching result compared to the control (e.g. hatching rate of 90% in both groups; chick quality 9.7 ± 0.6 vers. 9.6 ± 0.7) and achieved higher body weights. HLR in the TT-group was improved (0.63 ± 0.30 vers. 0.76 ± 0.46) and in agreement with previous studies. Mortality was zero in both groups. Conclusion: The developed sensor system for fine tuning of TT could be a method to make TT safer for practice transfer. In a next step a technical solution has to be developed to adapt the method for commercial hatchers. [ABSTRACT FROM AUTHOR]