University of Basilicata

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    33498 research outputs found

    The Identity Value of Vernacular Productive Architecture Knowledge, Recovery and Enhancement of the Val D’Agri Water Mills

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    The Industrial Heritage (I.H) is made of rests of the industrial culture with its historical, technological, social, architectural and scientific elements – parts of buildings, machinery, laboratories, firms, mines and locations where pro- cessing and refining procedures took place, warehouses and shops, energy produc- tion and transfer sites. Such electricity is used for transport and all its infrastructure as it occurs for places where social activities take place such as accomodation, training or religion worship facilities. The patrimonialisation process of the I.H. is essential as it recognises a com- munity heritage made of memories and identity. The research is based on the Italian productive and vernacular patrimony, especially in the south of Italy, by taking into consideration the proto-industrial period – from the second half of the17th century to the second half of the 19th century – and analyses the organisa- tion of the industrial system before the real industrialisation Era starting in the19th century. Italy is rich in working places, in fact, in southern Italy agribusiness has a great impact with its range of productive activities connected to agricultural products processing. Indeed, the milling vernacular industry represented an economical development source between the 17th and the first half of 20th century, this is the reason why there are lots of mills and bakeries for bread. The study deeply analyses the economic and social impact of water mills – vernacular and productive architectures of rural areas used to process wheat into flour. The “Val D’Agri” presents a large number of water mills and is located in the south-west part of Basilicata region. Its name comes from the river crossing the area. This analysis considers the functional recovery of watermills with a multidisciplinar criterion

    Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection

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    Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%

    Antimicrobial activity of lipids extracted from Hermetia illucens reared on different substrates

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    Abstract: As the problem of antimicrobial resistance is constantly increasing, there is a renewed interest in antimicrobial products derived from natural sources, particularly obtained from innovative and eco-friendly materials. Insect lipids, due to their fatty acid composition, can be classified as natural antimicrobial compounds. In order to assess the antibacterial efficacy of Hermetia illucens lipids, we extracted this component from the larval stage, fed on different substrates and we characterized it. Moreover, we analyzed the fatty acid composition of the feeding substrate, to determine if and how it could affect the antimicrobial activity of the lipid component. The antimicrobial activity was evaluated against Gram-positive Micrococcus flavus and Gram-negative bacteria Escherichia coli. Analyzing the fatty acid profiles of larval lipids that showed activity against the two bacterial strains, we detected significant differences for C4:0, C10:0, C16:1, C18:3 n3 (ALA), and C20:1. The strongest antimicrobial activity was verified against Micrococcus flavus by lipids extracted from larvae reared on strawberry, tangerine, and fresh manure substrates, with growth inhibition zones ranged from 1.38 to 1.51 mm, while only the rearing on manure showed the effect against Escherichia coli. Notably, the fatty acid profile of H. illucens seems to not be really influenced by the substrate fatty acid profile, except for C18:0 and C18:2 CIS n6 (LA). This implies that other factors, such as the rearing conditions, larval development stages, and other nutrients such as carbohydrates, affect the amount of fatty acids in insects. Key points: • Feeding substrates influence larval lipids and fatty acids (FA) • Generally, there is no direct correlation between substrate FAs and the same larvae FAs • Specific FAs influence more the antimicrobial effect of BSF lipids Graphical abstract: [Figure not available: see fulltext.

    The new σ-IASI code for all sky radiative transfer calculations in the spectral range 10 to 2760 cm-1: σ-IASI/F2N

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    The paper describes a recently developed forward model (sigma-IASI/F2N) to produce spectral radiances from the far to near-infrared spectrum (100 to 2760 cm-1). The model is a pseudo-monochromatic radiative transfer tool that exploits lookup tables to compute the optical depths of atmospheric gas and clouds. Multiple scattering effects are accurately included in both the Far IR and Thermal IR by using a scaling method for cloud and aerosol radiative properties parameterized in terms of their effective radius, which allows them to be handled adopting the same formalism used in the clear sky. In this paper we apply a novel approach to a classical scaling method in the thermal IR relying on our improved parametrization of backscattering parameter over that used by Chou (Martinazzo et al., 2021), while in the Far IR a corrective term is introduced. The code is written in Fortran and runs on Unix-based (Linux and macOS) or MS Windows operating systems. sigma-IASI/F2N can be used to develop custom versions of fast-forward modules for satellite instruments working in the infrared spectral range, such as the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) and the Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) missions. We discuss the sigma-IASI/F2N performance in simulating a set of ECMWF analyses at the global scale. For this purpose, we compare observations from the Infrared Atmospheric Sounding Interferometer (IASI) flying on MetOp B, and C. Results show that sigma-IASI/F2N can easily ingest ECMWF analyses data and accurately reproduce cloud patterns. We also show that the difference between sigma-IASI/F2N simulations and corresponding IASI observations is below 1 K in the 8-12 um window region, which is mainly affected by the water vapor continuum absorption and weak lines, while for night-time clear sky, the differences are below 0.3 K. again within the same window region

    Le Abbadesse con giurisdizione “quasi-episcopale”

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    Perception of Ecosystem Services from Podolian Farming System in Marginal Areas of Southern Italy

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    Ecosystem services (ESs) are gaining increasing interest among researchers, local communities, and policymakers. The farming of Podolian cattle in the remote and marginal areas of southern Italy plays an important socio-economic role. This study aimed to assess the perception of ESs provided by Podolian cattle farming in the Basilicata region among the local actors and stakeholders. Using a questionnaire administered to 198 respondents, the level of agreement and disagreement was assessed on a 1–5 scale for the four main ESs: cultural, regulating, supporting, and provisioning. On average, the perception of ESs related to Podolian cattle farming was positive. However, the role of promoting tourism in the region was somewhat controversial, and the results in terms of regulation were divisive. The perception of provisioning services had a highly positive result. Further research is needed to better understand the role of Podolian cattle farming in the Basilicata region, providing more explicit guidance for local communities and policymakers

    Power-Aperture Resource Allocation for a MPAR with Communications Capabilities

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    Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of service (QoS) demanded by both line of sight (LOS) and reflective intelligent surfaces (RIS)-aided non line of sight (NLOS) search operations along with communications tasks. To this end, the ranges at which the cumulative detection probability and the channel capacity per bandwidth reach a desired value are introduced as task quality metrics for the search and communication functions, respectively. Then, to quantify the satisfaction level of each task, for each of them a bespoke utility function is defined to map the associated quality metric into the corresponding perceived utility. Hence, assigning different priority weights to each task, the resource allocation problem, in terms of radar power aperture (PAP) specification, is formulated as a constrained optimization problem whose solution optimizes the global radar QoS. Several simulations are conducted in scenarios of practical interest to prove the effectiveness of the approach


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