Feedbacks between vegetation pattern and resource loss dramatically decrease ecosystem resilience and restoration potential in a simple dryland model

Conceptual frameworks of dryland degradation commonly include ecohydrological feedbacks between landscape spatial organization and resource loss, so that decreasing cover and size of vegetation patches result in higher water and soil losses, which lead to further vegetation loss. However, the impacts of these feedbacks on dryland dynamics in response to external stress have barely been tested. Using a spatially-explicit model, we represented feedbacks between vegetation pattern and landscape resource loss by establishing a negative dependence of plant establishment on the connectivity of runoff-source areas (e.g., bare soils). We assessed the impact of various feedback strengths on the response of dryland ecosystems to changing external conditions. In general, for a given external pressure, these connectivity-mediated feedbacks decrease vegetation cover at equilibrium, which indicates a decrease in ecosystem resistance. Along a gradient of gradual increase of environmental pressure (e.g., aridity), the connectivity-mediated feedbacks decrease the amount of pressure required to cause a critical shift to a degraded state (ecosystem resilience). If environmental conditions improve, these feedbacks increase the pressure release needed to achieve the ecosystem recovery (restoration potential). The impact of these feedbacks on dryland response to external stress is markedly non-linear, which relies on the non-linear negative relationship between bare-soil connectivity and vegetation cover. Modelling studies on dryland vegetation dynamics not accounting for the connectivity-mediated feedbacks studied here may overestimate the resistance, resilience and restoration potential of drylands in response to environmental and human pressures. Our results also suggest that changes in vegetation pattern and associated hydrological connectivity may be more informative early-warning indicators of dryland degradation than changes in vegetation cover.

On Modeling an Innovative Monitoring Network for Protecting and Managing Cultural Heritage from Risk Events

In this paper the model of an Innovative Monitoring Network involving properly connected nodes to develop an Information and Communication Technology (ICT) solution for preventive maintenance of historical centres from early warnings is proposed. It is well known that the protection of historical centres generally goes from a large-scale monitoring to a local one and it could be supported by a unique ICT solution. More in detail, the models of a virtually organized monitoring system could enable the implementation of automated analyses by presenting various alert levels. An adequate ICT solution tool would allow to define a monitoring network for a shared processing of data and results. Thus, a possible retrofit solution could be planned for pilot cases shared among the nodes of the network on the basis of a suitable procedure utilizing a retrofit catalogue. The final objective would consist in providing a model of an innovative tool to identify hazards, damages and possible retrofit solutions for historical centres, assuring an easy early warning support for stakeholders. The action could proactively target the needs and requirements of users, such as decision makers responsible for damage mitigation and safeguarding of cultural heritage assets.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

The Early Miocene “Bisciaro volcaniclastic event” (northern Apennines, Italy): a key study for the geodynamic evolution of the central-western Mediterranean

The Early Miocene Bisciaro Fm., a marly limestone succession cropping out widely in the Umbria–Romagna–Marche Apennines, is characterized by a high amount of volcaniclastic content, characterizing this unit as a peculiar event of the Adria Plate margin. Because of this volcaniclastic event, also recognizable in different sectors of the central-western Mediterranean chains, this formation is proposed as a “marker” for the geodynamic evolution of the area. In the Bisciaro Fm., the volcaniclastic supply starts with the “Raffaello” bed (Earliest Aquitanian) that marks the base of the formation and ends in the lower portion of the Schlier Fm. (Late Burdigalian–Langhian p.p.). Forty-one studied successions allowed the recognition of three main petrofacies: (1) Pyroclastic Deposits (volcanic materials more than 90 %) including the sub-petrofacies 1A, Vitroclastic/crystallo-vitroclastic tuffs; 1B, Bentonitic deposits; and 1C, Ocraceous and blackish layers; (2) Resedimented Syn-Eruptive Volcanogenic Deposits (volcanic material 30–90 %) including the sub-petrofacies 2A, High-density volcanogenic turbidites; 2B, Low-density volcanogenic turbidites; 2C, Crystal-rich volcanogenic deposits; and 2D, Glauconitic-rich volcaniclastites; (3) Mixing of Volcaniclastic Sediments with Marine Deposits (volcanic material 5–30 %, mixed with marine sediments: marls, calcareous marls, and marly limestones). Coeval volcaniclastic deposits recognizable in different tectonic units of the Apennines, Maghrebian, and Betic Chains show petrofacies and chemical–geochemical features related to a similar calc-alkaline magmatism. The characterization of this event led to the hypothesis of a co-genetic relationship between volcanic activity centres (primary volcanic systems) and depositional basins (depositional processes) in the Early Miocene palaeogeographic and palaeotectonic evolution of the central-western Mediterranean region. The results support the proposal of a geodynamic model of this area that considers previously proposed interpretations.