Università della Calabria
  • Rende, Calabria, Italy
Recent publications
Tasks and procedures involving lashing/unlashing operators have evident ergonomic criticalities but looking at the scientific background and on actual regulations there is a lack of attention toward procedures for a full ergonomic risk assessment. There are no scientific articles, no normative and standards that report ergonomic assessments for lashing and unlashing operations. According to this research gap, the proposed research seeks to contribute at different levels: carrying out a context analysis on how lashing and unlashing operations are carried out; identifying tools and methodologies that can support a comprehensive ergonomic analysis; combining the elements mentioned above in a risks assessment framework for ergonomic evaluation and prioritization. While pursuing such goals, the authors came up with a risk assessment framework based on simulation coupled with ergonomic methods and Analytical Hierarchy Process (AHP). An application of the framework has been conducted in an Italian container terminal in 2021. First, processes and tasks have been analyzed to develop a simulation model capable of reproducing the evolution over the time of the real system. As next step, the ergonomic issues related to lashing/unlashing operations have been identified by applying the ergonomic methods through the simulation model. Finally, AHP has been used to rank, in an analytical way, critical ergonomic operations and to establish priority of interventions. The identification of critical ergonomic issues along with their analytical prioritization provide operations management as well as normative and standard makers with meaningful inputs towards greater standardization of procedures, based on ergonomic factors, in container terminal sector.
Tracking the evolution in time of parameters, input and states of a structural dynamic system is often difficult, since their direct measurement can be problematic or even impossible. It is of great interest to estimate these quantities based on output-only data from a limited set of sensors. This work proposes an estimation technique for states, inputs and material parameters for structural dynamics models based on an Augmented Extended Kalman Filter. A parametric Model Order Reduction technique is proposed to construct a Reduced Order Model which maintains an explicit dependency on material parameters, enabling the parameter estimation thanks to a low computational cost and an efficient derivation of the linearized system. The choice of sensor configurations that ensure the observability of unknown quantities is discussed as well. The proposed methodology shows highly promising results and could be employed for model refinement or condition monitoring. The methodology is validated both numerically and experimentally, using data acquired on a scaled wind turbine blade, with errors on the estimated parameters lower than 3.5% with respect to experimentally identified parameter values.
The Oligocene Menilite Beds are considered the most important source rock for hydrocarbon accumulation in the Polish Carpathian region, whereas the Cretaceous Lgota Beds have been regarded as an additional potential source rock. Understanding their petrophysical and geochemical properties is essential for evaluating the hydrocarbon potential of these beds. This paper presents mineral and organic porosity characterization and focuses on factors responsible for the development of organic pores as a reflection of the depositional and diagenetic processes. Mudstones were evaluated as potential source and reservoir rocks, describing their diagenetic and thermal history and examining their effective porosity and permeability. The results show that the Lgota Beds mudstone in the Huczwice quarry is thermally mature (late oil/early gas window, Tmax 460–470 °C), containing type III kerogen and TOC between 0.68 wt% and 4.2 wt%, in contrast to the Menilite Beds mudstone (Monasterzec outcrop), which is thermally immature (Tmax<426 °C), containing type II kerogen and TOC content from 1.24 wt% to 8.7 wt%. The geochemical properties show that the Lgota mudstone is currently an ineffective source rock, whereas the Menilite mudstone can be a potential source rock. SEM-identified pores include mineral pores, organic pores and microfractures. Organic porosity is observable both in immature oil-prone type II kerogen and highly mature gas-prone type III kerogen. The amount of pores in organic matter increases with maturity, and no relation between TOC and organic porosity development has been observed. Palynofacies analysis showed that the Menilite and Lgota mudstones are dominated by amorphous organic matter, and that the Lgota mudstone also contains opaque woody material. MICP measurements indicate high (up to 15%) effective porosity values for the Menilite Beds and up to 8% for the Lgota Beds, with very low permeability values (<0.1 mD) in both cases. Isotherms obtained from nitrogen adsorption are type IV for the Lgota Beds and type II for the Menilite Beds, while the BET surface areas are around 13 m²/g and 3 m²/g, respectively. The Lgota Beds demonstrate advanced diagenetic processes such as compaction, cementation, dissolution, replacement, and transformation, which contributed to the significant reduction in porosity, while the Menilite Beds represent an early stage of burial with the prevailing impact of compaction and thus less destruction of original pores. Finally, the Menilite Beds from the Monasterzec outcrop do not demonstrate sufficient conditions for shale-oil/-gas source rock due to the lack of proper thermal maturity. Such criterion is fulfilled by the Lgota Beds in the Huczwice quarry, but due to very low hydrocarbon potential, the Lgota mudstone is an ineffective source rock. However, given the other petrophysical and geochemical properties, the analysed formations may constitute a basis for further research on the occurrence of unconventional reservoirs in the entire Carpathian region.
K-Means is a well-known clustering algorithm whose goal is partitioning a number of data points into groups (clusters), so as to minimize dissimilarities of data, measured by some metric, within the same group. Due to its simplicity, K-Means is often used in machine learning unsupervised clustering applications. However, the execution performance of K-Means can easily become a bottleneck when dealing with very large datasets, paired with a great number of clusters, as those encountered in many big data ecosystems. Therefore, many efforts are reported in the literature devoted to a parallelization of K-Means, both on shared-nothing and shared-memory architectures. This paper proposes a novel approach to parallel K-Means on multi/many-core machines, which is based on the Theatre actor system developed in Java. The realization is based on message passing for synchronization among actors (workers) but also offers the possibility of sharing data, in a controlled and safe way, among the actors of the same computing node (theatre). The approach proves effective in delivering a high-performance execution. The paper first provides some background information about the basic K-Means algorithm and the Theatre architecture, then an actor-based parallel version of K-Means is described and experimented with.KeywordsK-Means clusteringActorsTheatreJavaHigh-performance computing
This study aims to analyse the thermal efficiency of wall elements with varying position-allocation-thickness of insulation, in the aspect of the optical properties of their external paint. A special focus has been placed on the role of solar reflectivity in wall coatings while taking into account the impact of the ambient environment at all cardinal points. In this light, the problem of urban environment warming must be addressed, while considering occupant reliance on air-conditioning. In the initial stage, the key research objective is to shed some light on the performance of analysed wall assemblies in terms of thermal sensitivity (decrement factor and time lag). On the other hand, at the targeting stage, our main intention is to demonstrate the eminence of solar heat-rejecting paints on the cooling power demand of wall arrangements. Furthermore, this work is extended to the assessment of the overall heating and cooling demands on an annual basis. A thermal-network model is developed within this framework to determine temperature variations and heat fluxes in the margins of the examined setups. The potential benefits of the suggested model are two-fold. Accordingly, the findings of the numerical analyses reveal the configurations and operating conditions proving the optimal dynamic thermal parameters and energy demand. Numerical simulations indicate that an optimal cooling power capacity is noticeable for wall surfaces covered with solar heat-rejecting paints; cooling saving can exceed 90% for highly solar-reflective surfaces. However, when it comes to unveiling the global performability of ultra-white paints the overall improvement of conditions may vary radically; a reflective paint will probably not be sufficient to counterbalance both heating and cooling concerns. In terms of annual heat transmission loads, results exhibit an optimal solar absorptivity of 0.35 for north/east/west facing walls and 0.75 for south-oriented walls. Also, within the confines of our attention, by the increase of insulation level, the energy benefit can reach up to 40% per annum.
Research and development in agricultural sector are becoming a crucial issue, especially to answer to growing global market needs and, in general, for rural innovation development. The innovation process involves stakeholders of all levels and rural development requires both personal farmers' characteristics along with favourable socio-political and infrastructural environment. Many countries and governments have executed innovation projects for agricultural firms, involving a number of actors from the public and private sectors. However, the literature lacks of studies that investigate the identification of the main factors that determine the agricultural entrepreneurs' probability to adopt new technologies during a crisis context. Thus, through the adoption of the Extended Theory of Planned Behaviour, this study aims at filling this lack. More specifically, the exploratory empirical analysis focuses on a sample of 130 agricultural entrepreneurs operating in a rural developing Italian region, during the historical context of global pandemic crisis of COVID-19. The results provided several insights showing the factors that influence the adoption of technologies, such as the Attitude to Environmental-Economic Sustainability and the Planned Behavioural Control. An important role is also assumed by the past farmer's technological experience. The paper offers implications for entrepreneurs and public government.
The recovery of raw materials represents one of the greatest challenges for a circular economy. Especially, the increased demand for lithium in the last years for its critical role in Li-ion batteries implies the need for green technology for Li recovery able to address market requests. Here, we devise and implement a new technology exploiting excitons-based light-to-heat conversion promoted by WS2 nanofillers in nanocomposite polymeric membranes for sunlight-driven photothermal membrane crystallization, applied for the efficient extraction of lithium from Li-rich brines. The activation of photothermal effects of excitonic nanofillers in the PVDF-WS2 nanocomposite enhances the evaporative flux of water under solar irradiation by 364 %, triggering the heterogeneous nucleation and the crystallization of LiCl salt, once achieved supersaturation. This new facile, economical, and green nanotechnology-enabled platform renews the interest in functional inks based on nanosheets of van der Waals semiconductors for the fabrication of functional nanocomposites, here exploited for the first time in the field of crystallization and the recovery of economically strategic minerals in a circular-economy paradigm. Moreover, these findings open up new opportunities for large-scale, efficient, and sustainable recovery of lithium (as well as other critical raw materials) for next-generation devices for the clean energy transition.
The structure of an integrated hydrodynamic modelling framework intended to describe the dynamics of surface irrigation is presented in this work. Specifically, the modelling framework, named IrriSurf2D, is constituted by a combination of three elements i.e. (i) high-resolution topographic data of ground surface, (ii) two-dimensional shallow water equations and (iii) one-dimensional Green-Ampt approach for describing infiltration process. The modelling framework was validated with a real case study where timings of waterfront advance and water depths on the field were monitored during a border irrigation event. The results show that IrriSurf2D was able to reproduce both the timings of waterfront advance and the maximum water depths with high accuracy, i.e. with average RMSE below 2 min and 3 cm, respectively. Model performance was robust and accurate even using literature parameters without a tailored calibration of infiltration and roughness parameters. Details of the digital terrain model, which affect the computational grid resolution, had a strong influence on the description of waterfront propagation: a coarse grid resolution (1 m²) was found inadequate for reproducing reliable timings of waterfront advance and water depths in the field, while with a finer grid (0.01 m²) as modelling input the simulation results appeared properly consistent with the observations. The modeling approach appears promising to describe the dynamics of border irrigation and paves the way for the development of an operational tool for improving the management of surface irrigation.
Due to its high toxicity and bioaccumulation tendency, tebuconazole (TBZ) is one of the ten substances posing the highest risk of harmful effects in aquatic ecosystems. The liver, a key compartment for xenobiotics detoxification, is also the organ in which TBZ mainly accumulates in fish. Herein, we investigated for the first time the morpho-functional changes induced in zebrafish (Danio rerio) liver after a short-term exposure (48, 96, and 192 hours) to a low, environmentally relevant concentration of TBZ (5 µg/L) to disclose the early effects under a realistic exposure scenario. We revealed that pathological alterations with varying degrees of severity could be detected in all the examined samples. The injuries become intense and irreversible with increased exposure time involving both hepatocytes and vascular components based on the degree of tissue changes. The main morphological alterations were: parenchyma dyschromia, macrophages infiltration, congestion of blood vessels, and sinusoids. TBZ exposure also resulted in a significant decrease in glycogen contents and hepatocyte dimensions, and the modulation of superoxide dismutase, an early indicator of oxidative stress. We demonstrated that even a very low dose of TBZ affects hepatic morphology and function, disrupting liver homeostasis and physiology.
Letrozole is one of the most prescribed drugs for the treatment of breast cancer in post-menopausal women, and it is endowed with selective peripheral aromatase inhibitory activity. The efficacy of this drug is also a consequence of its long-lasting activity, likely due to its metabolic stability. The reactivity of cyano groups in the letrozole structure could, however, lead to chemical derivatives still endowed with residual biological activity. Herein, the chemical degradation process of the drug was studied by coupling multivariate curve resolution and spectrophotometric methodologies in order to assess a detailed kinetic profile. Three main derivatives were identified after drug exposure to different degradation conditions, consisting of acid-base and oxidative environments and stressing light. Molecular docking confirmed the capability of these compounds to accommodate into the active site of the enzyme, suggesting that the sustained inhibitory activity of letrozole may be at least in part attributed to the degradation compounds.
The rapid and widespread deployment of green roofs requires the need to address their disposal and to assess the environmental impact of this phase of their life cycle to understand whether their current large-scale application may pose a problem. A review of the literature on green roofs environmental performance (particularly Life Cycle Assessment studies) has highlighted the lack of a standardized, commonly adopted, procedure for determining the treatments, recovery and/or disposal, to be assigned to waste from the removal of green roofs. In this regard, it should be mentioned that there is a lack of ad hoc legislation on the disposal of this technology (to the best of the authors’ knowledge, even at the international level). In this paper, an attempt procedure is introduced to identify the end-of-life scenario of green roofs that does not conflict with the current regulations regarding wastes. Specifically, the procedure relies on an “attempt classification” of the waste from individual green roof elements and the priority criterion for intervention. This procedure might thus be used temporarily by technicians, pending the issuance of guidelines specifically dedicated to green roofs disposal, to model their end-of-life and thus assess the environmental impact of this phase of the life cycle. The feasibility of this proposal was verified through a field application. Besides the methodological proposal, the results of the work indicated the need to review the current waste legislation and update it -at least the Italian one - to also consider new materials used in green transition technologies.
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
Cryptocurrencies are the new form of trade that has revolutionized how we look into our financial institutions. Bitcoin dominates the industry with the highest market share among the hundreds of other cryptocurrencies. However, high energy consumption leading to increasing carbon emission, prioritizing high-value transactions, and long waiting times are some of the flaws preventing it from reaching its full potential. Owing to the block rewards getting halved every four years, miners and researchers are fearful that this would be the breaking point of Bitcoin’s success. This article proposes an Industry-4.0-compliant next-generation Bitcoin architecture by introducing a dynamic and sustainable block concept. Along with our modified knapsack algorithms, i.e., priority-based 0/1 knapsack and advanced-priority-based 0/1 knapsack, we can ensure a balanced transaction selection, quicker verification, higher transaction throughput, reduced carbon emission, and increased earnings for the miners. Moreover, with the addition of only one of our proposed sustainable blocks, we can cut down verification times by 50% and increase throughput by 39%. We can also reduce carbon emissions per transaction by 61.3%, which would help reduce Bitcoins’ large carbon footprint, enabling us to approach greener digital transactions.
Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterwards, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the election day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users' publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.
Community deception is about hiding a target community that wants to remain below the radar of community detection algorithms. The goal is to devise algorithms that, given a maximum number of updates (e.g., edge additions and removal), strive to find the best way to perform such updates in order to hide the target community inside the community structure found by a detection algorithm. So far, community deception has only been studied for undirected networks, although many real-world networks (e.g., Twitter) are directed. One way to overcome this problem would be to treat the network as undirected. However, this approach discards potentially helpful information in the edge directions (e.g., A follows B does not imply that B follows A). The aim of this paper is threefold. First, to give an account of the state-of-the-art community deception techniques in undirected networks underlying their peculiarities. Second, to investigate the community deception problem in directed networks and to show how deception techniques proposed for undirected networks should be modified and adapted to work on directed networks. Third, to evaluate deception techniques both in undirected and directed networks. Our experimental evaluation on a variety of (large) directed networks shows that techniques that work well for undirected networks fail short when directly applied to directed networks, thus underlying the need for specific approaches.
As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities (Social Digital Twins, SDTs) at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement, while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.
During an epidemic, decision-makers in public health need accurate predictions of the future case numbers, in order to control the spread of new cases and allow efficient resource planning for hospital needs and capacities. In particular, considering that infectious diseases are spread through human-human transmissions, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents the design and implementation of a predictive approach, based on spatial analysis and regressive models, to discover spatio-temporal predictive epidemic patterns from mobility and infection data. The experimental evaluation, performed on mobility and COVID-19 data collected in the city of Chicago, is aimed to assess the effectiveness of the approach in a real-world scenario.
Super-resolution spectral methods are applied and compared to improve the estimation result provided by biomedical microwave resonant sensors. In particular, the resolution of resonant sensors is revealed to be significantly improved, despite their intrinsic low quality factor. Excellent robustness against noise is also demonstrated. Algorithms are first validated on ad hoc synthetic data mimicking the response of a resonant sensor. Additionally, experimental validation is carried out by using data coming from a microwave resonant sensor, which is specifically designed for blood-glucose monitoring.
The open centre turbine can be easily deployed with a kite-like mooring system, which is promising to harvest renewable marine resources due to its higher energy conversion efficiency, and lower cost, as well as the minimum impacts on the submarine environment. However, the multi-turbines deployment is still a great challenge due to the interaction between the wake flow generated by each device. Understanding the wake development is critical to implementing a strategy for multi-turbine deployment and minimizing the impact of the array on the submarine environment, the shore bed and the coast. This work aims to define the wake morphology and the multi-device configuration for the open centre turbines by applying the computational fluid dynamic (CFD) analysis, and the Jensen model, validated by scaled experiments for traditional turbines. The first step of the research deals with a stand-alone fully resolved turbine geometry. The annular rotor works like a Venturi channel: the flow passing through the central hole rises its velocity and reduces the pressure behind the rotor plane. The induced suction effect reduces the tangential flow velocity components, containing either the wake radial expansion to 1.6R (turbine radius) and axial extension to 6D (turbine diameter). The wake takes a cylindrical shape and the flow field outside this cylinder can be assumed as undisturbed. The second step of research deals with the study of an optimal multi-device layout. The parameters to be found are the distance between rotors’ rows and turbines’ wheelbase, under the condition that the power of each turbine is almost equal. For a 2 staggered turbines layout, a wheelbase of 3D and a distance between rows of 5D allow for keeping the devices’ performances constant, being, in both cases, the array Cp = 0.414. For 3 turbines in two staggered rows, the optimal configuration is characterized by a wheelbase of 1.5D and a distance between rows of 3D, with an array Cp = 0.413. The key to this performance is the cylindrical wake generated by the open centre rotor geometry: in the multi-device configuration any turbine is decoupled, so there is no mutual disturbance even at reduced inter-device clearances.
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3,955 members
Alberto Di Renzo
  • Department of Computer Engineering, Modelling, Electronics and Systems (DIMES)
Maurizio la rocca
  • Department of Business Administration and Law
Giancarlo Fortino
  • Department of Computer Engineering, Modelling, Electronics and Systems (DIMES)
Rocco Servidio
  • Department of Cultures Education and Society
Giuseppe Carbone
  • Department of Mechanical, Energy and Management Engineering
via Ponte Bucci, 87036, Rende, Calabria, Italy
Head of institution
Prof. Nicola LEONE
+39 0984493234
+39 0984493271