Morphology of Salina offshore (Southern Tyrrhenian Sea)

In this paper, we present the first complete morphological map of the Salina offshore at a scale of 1:100,000. The submarine flanks of the Salina edifice extend down to −650 to −1300 m, are steep and characterized by an uneven morphology due to the presence of volcanic and erosivedepositional features. The volcanic features cover ∼30% of the submarine portion and include volcanic cones and bedrock outcrops. The remaining ∼70% is affected by a wide series of erosive-depositional features. Among these, features related to Late Quaternary sea level fluctuations comprise the insular shelf surrounding the island and overlying submarine terraced depositional sequences. Mass-wasting features include landslide scars, channels, fanshaped deposits and waveforms. The presented map provides useful insights for a better understanding of the morphological evolution of the edific

Morphology of Lipari offshore (Southern Tyrrhenian Sea)

High-resolution multibeam bathymetry was recently collected around Lipari, the largest and most densely populated island of the Aeolian Archipelago (Southern Tyrrhenian Sea). The data were acquired within the context of marine geological studies performed in the area over the last 10 years. We present the first detailed morphological map of the Lipari offshore at 1:100,000 scale (Main Map). A rugged morphology characterizes the submarine portions of Lipari volcano, reflecting both volcanic and erosive-depositional processes. The volcanic features include cones, lava flows and bedrock outcrops. Erosive-depositional features include an insular shelf topped by submarine depositional terraces related to LateQuaternary sea-level fluctuations, as well as landslide scars, channelized features, fanshaped deposits and wavy bedforms. The different distribution of volcanic and erosivedepositional features on the various sectors of Lipari is mainly related to the older age of the western flank with respect to the eastern one. The map also provides insights for a first marine geohazard assessment of this active volcanic area

Composition of fungal communities in soil and endophytic in raspberry production systems.

Fungi play important roles as decomposers, plant symbionts and pathogens in soil. While endophytes are microorganisms that dwell within plant tissues and have a symbiotic association with the host. The structures of fungal communities in the soil and in endophytic association are dependent up complex interactions with the environment and the host. These two communities have a great influence on plant health and development. Using culture-independent fungal community profiling, we investigated the effects of fertilizer (composted dairy solids + mustard seed meal) on fungal communities in soil and endophytic in a raspberry production system. During the study we evaluated the impact of primer selection ITS1 vs ITS2. We characterized the communities for both spring and fall time periods. The results show that the soil communities are dominated by Ascomycota, and Basidiomycota in soil, while the endophytes were primarily Ascomycota. The relative abundances of certain taxa, such as Capnodiales, were more predominant in composted soil (8%) than the control (4%). There were no significant differences identified in the endophytic communities between the two treatments. Further research should elucidate the specific roles of these fungal taxa in raspberry soils and endophyte, and on the heath of the plant. To advance the ecological management of crop soils, understanding is needed of how beneficial microbial relationships can be fostered in these production systems

Algae-based biorefinery concept. An LCI analysis for a theoretical plant

Both micro and macro algae have a potential to be a valuable feedstock for biorefineries. The theoretical impact assessment of this kind of plant can be carried out through an LCA, which is a key tool in order to evaluate the potential environmental impact of a process throughout its entire life cycle. Hence, it is a priority to perform an LCI with the aim of gathering all the data and simulating all the unit process of a theoretical biorefinery. The Inventory ensures to obtain a simple and immediate way to represent several aspects of a biorefinery, e.g. productivity, environmental pressures, required resources in terms of raw materials and energy. One of the main aspects clearly shown in this study is the significant environmental pressures due to the cultivation and harvesting steps, for which it is desirable to consider a biomass collection from the environment, especially from areas where eutrophication phenomena are particularly recurrent. Another conclusion drawn from the study is that the total plant production per year appears very limited, if compared to any conventional refinery. The following approach can also provide a starting data set to perform a first approximate economic analysis of the costs/gains of the outlined project, and it could be used as a first concept design for the project development of a real plant

Enhancing intangible cultural heritage for sustainable tourism development in rural areas: the case of the “Marche food and wine memories” project (Italy)

In the context of increasing interest in the contribution made by culture to the implementation of the goals and targets of the 2030 Agenda for Sustainable Development, the present research investigates how intangible cultural heritage (ICH) can help sustainable tourism in rural areas. Adopting a case study methodology, we analyzed the “Marche Food and Wine Memories” project, an initiative promoted by CiùCiù, a winery based in Offida, a small village in the Marche region (Italy). After discussing the strategies and tools adopted to enhance rural heritage, the analysis focuses on the involvement of local communities and businesses in the different phases of the process. The research aimed to understand: (1) the project’s current contribution to the economic, social, cultural and environmental dimensions of sustainability; and (2) its strengths and weaknesses and possible future improvements. The research findings confirm the high potentialities of ICH-based initiatives for sustainable tourism development in rural areas, but also reveal the need to improve the level of networking with local businesses and highlight gaps in marketing and management skills. Finally, the results provide policy and managerial implications for similar ICH-based initiatives

High serum osteopontin levels are associated with prevalent fractures and worse lipid profile in post-menopausal women with type 2 diabetes

Purpose: Patients with type 2 diabetes (T2DM) have increased fracture risk. Osteopontin (OPN) is a protein involved in bone remodeling and inflammation. The aim of this study was to evaluate the association of OPN with fracture prevalence and with metabolic parameters in post-menopausal women with T2DM. Methods: Sixty-four post-menopausal women with T2DM (age 67.0 ± 7.8 years, diabetes duration 8.9 ± 6.7 years), enrolled in a previous study, were followed up (3.6 ± 0.9 years). Previous fragility fractures were recorded. The FRAX score (without BMD) was calculated and biochemical parameters (plasma glucose, HbA1c, lipid profile and renal function) were assessed. Serum 25OH-vitamin D, calcium, PTH and OPN were evaluated at baseline. The association between OPN and fracture prevalence at baseline was evaluated by a logistic model. Results: OPN levels were higher in patients with previous fractures (n.25) than in patients without previous fractures at baseline (n.39) (p = 0.006). The odds of having fractures at baseline increased by 6.7 (1.9–31.4, 95% CI, p = 0.007) for each increase of 1 ng/ml in OPN levels, after adjustment for vitamin D and HbA1c levels. Fracture incidence was 4.7%. Higher OPN associated with a decrease in HDL-cholesterol (p = 0.048), after adjustment for age, basal HDL-cholesterol, basal and follow-up HbA1c and follow-up duration. 25OH-vitamin D associated with an increase in FRAX-estimated probability of hip fracture at follow-up (p = 0.029), after adjustment for age, 25OH-vitamin D and time. Conclusions: In post-menopausal women with T2DM, OPN might be a useful marker of fracture and worse lipid profile

Development of interconnected silicon micro-evaporators for the on-detector electronics cooling of the future ITS detector in the ALICE experiment at LHC

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.The design of the future High Energy Physics (HEP) particle detectors for the upgrade of the LHC (Large Hadron Collider) experiments at CERN (European Organization for Nuclear Research) is pushing technological frontiers to the limit trying to reach unprecedented accuracy in particles identification and particle production dynamics in ultra-relativistic hadron collisions. The thermal management of the on-detector electronics and the development of low mass integrated cooling systems have become a crucial task in the design of silicon tracking detectors for HEP applications. In this paper, we present a novel concept of low mass interconnected silicon microchannel devices for the future Inner Tracking System of the ALICE (A Large Ion Collider Experiment) detector at LHC. This innovative design achieves the requirements of the detector while minimizing the total material budget

Dynamic control strategies for a solar-ORC system using first-law dynamic and data-driven machine learning models

In this study, we developed and assessed the potential of dynamic control strategies for a domestic scale 1-kW solar thermal power system based on a non-recuperated organic Rankine cycle (ORC) engine coupled to a solar energy system. Such solar-driven systems suffer from part-load performance deterioration due to diurnal and inter-seasonal fluctuations in solar irradiance and ambient temperature. Real-time control strategies for adjusting the operating parameters of these systems have shown great potential to optimise their transient response to time-varying conditions, thus allowing significant gains in the power output delivered by the system. Dynamic model predictive control strategies rely on the development of computationally efficient, fast-solving models. In contrast, traditional physics-based dynamic process models are often too complex to be used for real-time controls. Machine learning techniques (MLTs), especially deep learning artificial neural networks (ANN), have been applied successfully for controlling and optimising nonlinear dynamic systems. In this study, the solar system was controlled using a fuzzy logic controller with optimised decision parameters for maximum solar energy absorption. For the sake of obtaining the optimal ORC thermal efficiency at any instantaneous time, particularly during part-load operation, the first-law ORC model was first replaced by a fast-solving feedforward network model, which was then integrated with a multi-objective genetic algorithm, such that the optimal ORC operating parameters can be obtained. Despite the fact that the feedforward network model was trained using steady-state ORC performance data, it showed comparable results compared with the first-principle model in the dynamic context, with a mean absolute error of 3.3 percent for power prediction and 0.186 percentage points for efficiency prediction