Characterisation of oleoresins extracted from tomato waste by liquid and supercritical carbon dioxide

Tomato waste is characterised by high amounts of bioactive compounds, such as carotenoids, polyphenols and essential fatty acids. In this work, oleoresins were produced from tomato waste (skins + seeds) and skins using carbon dioxide under liquid and supercritical conditions. The use of ethanol as a co-solvent was also evaluated. Compositions of the oleoresins were analysed and compared to oleoresin obtained by means of an organic solvent, which was used as the control. Oleoresin extraction from the waste by liquid and supercritical CO₂ provided the highest yields (12.5%–12.9%). The extracts obtained from the skins with supercritical CO₂ without ethanol had the highest content of α- and γ-tocopherols (80 and 575 mg/100 g oleoresin, respectively), while lycopene and β-carotene were found at 205 and 75 mg/100 g oleoresin, respectively. The highest quantity of polyphenols (9305 mg GAE/100 g oleoresin) was found in the oleoresin extracted from skin by supercritical CO₂ with ethanol. All oleoresins were characterised by a high antioxidant activity, which improved with the synergy of carotenoids, tocopherols and polyphenols.     

Simultaneous Achievement of Open and Closed Loop Diagonal Dominance Through Constant Feedback

This paper proposes a new and low complexity technique for the simultaneous achievement of open and closed loop diagonal dominance for uncertain plants. The method is based on the introduction of a static high gain inner feedback which modifies the plant to attain a diagonal dominant open loop compensated plant. The sufficient, easy to use, conditions derived for the open loop diagonal dominance also assure the simultaneous achievement of closed loop diagonal dominance under soft assumptions on the diagonal elements of the decoupled controller robustly stabilizing the plant. These assumptions agree with the usual closed‐loop performance specifications, so that closed‐loop diagonal dominance is achieved without any extra complication on the controller design procedure. The main merits of the present method are: applicability to uncertain plants, ease of implementation and low computational cost. Three examples taken from the relevant literature, are given to show the effectiveness of the proposed approach.     

Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events

The availability and the quality of drinking water are key requirements for the well-being and the safety of a community, both in ordinary conditions and in case of disasters. Providing safe drinking water in emergency contributes to limit the intensity and the duration of crises, and is thus one of the main concerns for decision-makers, who operate under significant uncertainty. The present work proposes a Decision Support System for the emergency management of drinking water supply systems, integrating: i) a vulnerability assessment model based on Bayesian Belief Networks with the related uncertainty assessment model; ii) a model for impact, and related uncertainty assessment, based on Bayesian Belief Networks. The results of these models are jointly analyzed, providing decision-makers with a ranking of the priority of intervention. A GIS interface (G-Net) is developed to manage both input spatial information and results. The methodology is implemented in L’Aquila case study, discussing the potentialities associated to the use of the tool dealing with information and data uncertainty.     

Advances in Antimicrobial Microneedle Patches for Combating Infections

Skin infections caused by bacteria, viruses and fungi are difficult to treat by conventional topical administration because of poor drug penetration across the stratum corneum. This results in low bioavailability of drugs to the infection site, as well as the lack of prolonged release. Emerging antimicrobial transdermal and ocular microneedle patches have become promising medical devices for the delivery of various antibacterial, antifungal, and antiviral therapeutics. In the present review, skin anatomy and its barriers along with skin infection are discussed. Potential strategies for designing antimicrobial microneedles and their targeted therapy are outlined. Finally, biosensing microneedle patches associated with personalized drug therapy and selective toxicity toward specific microbial species are discussed.     

Amyloid Fibrils Aerogel for Sustainable Removal of Organic Contaminants from Water

Water contamination by organic pollutants is ubiquitous and hence a global concern due to detrimental effects on the environment and human health. Here, it is demonstrated that amyloid fibrils aerogels are ideal adsorbers for removing organic pollutants from water. To this end, amyloid fibrils prepared from β‑lactoglobulin, the major constituent of milk whey protein, are used as building blocks for the fabrication of the aerogels. The adsorption of Bentazone, Bisphenol A, and Ibuprofen, as model pollutants, is evaluated under quasi-static conditions, without use of energy or pressure. Through adsorption by amyloid fibrils aerogel, excellent removal efficiencies of 92%, 78%, and 98% are demonstrated for Bentazone, Bisphenol A, and Ibuprofen, respectively. Furthermore, the maximum adsorption capacity of amyloid fibrils aerogel for Bentazone, Bisphenol A, and Ibuprofen is 54.2, 50.6, and 69.6 mg g⁻¹, respectively. To shed light on the adsorption equilibrium process, adsorption isotherms, binding constants, saturation limits, and the effect of pH are evaluated. Finally, the regeneration of the aerogel over three consecutive cycles is studied, exhibiting high reusability with no significant changes in its removal performance. These results point at amyloid fibrils aerogels as a sustainable, efficient, and inexpensive technology for alleviating the ubiquitous water contamination by organic pollutants.     

Integrated Approach for Supporting Sustainable Water Resources Management of Irrigation Based on the WEFN Framework

Irrigated agriculture plays a vital role for the socio-economic development of the Mediterranean area, although it has significant impacts on both water and energy resources. Therefore, in a context in which water resources are also experiencing increasing pressures, there is an urgent need for supporting their sustainable management. This may be an extremely challenging task, especially at the local scale, due to the several interconnected dynamics affecting the state of a complex irrigation system. In fact, multiple actors are involved in decision-making processes, and the use of natural resources (and their mutual interactions) strongly depends on their behaviors, which affect the system as a whole. In this context, the present study proposes an integrated methodology, based on the Water Energy Food Nexus (WEFN), specifically focused on the sustainable management of water resources for irrigation. Firstly, a model based on Causal Loop Diagrams (CLD) is developed in order to get a deep insight into the key dynamics behind a complex irrigation system. Secondly, three indices based on the “footprint” concept are identified, in order to synthesize such dynamics. The integration of these two approaches support investigating the whole system and, particularly, understanding the influence of multiple decisional actors on it, as well as the role of a set of key drivers and constraints. This might also allow drawing some relevant conclusions, useful for supporting effective decisions oriented to a sustainable water resources management. Specific reference is made to a case study, the Capitanata irrigation system, located in the Southern Italy.

     

SEL: A unified algorithm for salient entity linking

The entity linking task consists in automatically identifying and linking the entities mentioned in a text to their uniform resource identifiers in a given knowledge base. This task is very challenging due to its natural language ambiguity. However, not all the entities mentioned in the document have the same utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in the document, also known as salient entities (SE), is attracting increasing interest. In this paper, we propose salient entity linking, a novel supervised 2‐step algorithm comprehensively addressing both entity linking and saliency detection. The first step is aimed at identifying a set of candidate entities that are likely to be mentioned in the document. The second step, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on 2 different data sets show that the proposed algorithm outperforms state‐of‐the‐art competitors and is able to detect SE with high accuracy. Furthermore, we used salient entity linking for extractive text summarization. We found that entity saliency can be incorporated into text summarizers to extract salient sentences from text. The resulting summarizers outperform well‐known summarization systems, proving the importance of using the SE information.     

Dynamic Spatial Equilibrium Models: an Application to the Natural Gas Spot Markets

Networks and Spatial Economics - European gas markets have gone through profound restructuring processes in the last 20 years. Directive 98/30/EC represented the first attempt to liberalize this...     

The multi-modal interface of Robot-Era multi-robot services tailored for the elderly

Socially assistive robotic platforms are now a realistic option for the long-term care of ageing populations. Elderly users may benefit from many services provided by robots operating in different environments, such as providing assistance inside apartments, serving in shared facilities of buildings or guiding people outdoors. In this paper, we present the experience gained within the EU FP7 ROBOT-ERA project towards the objective of implementing easy-to-use and acceptable service robotic system for the elderly. In particular, we detail the user-centred design and the experimental evaluation in realistic environments of a web-based multi-modal user interface tailored for elderly users of near future multi-robot services. Experimental results demonstrate positive evaluation of usability and willingness to use by elderly users, especially those less experienced with technological devices who could benefit more from the adoption of robotic services. Further analyses showed how multi-modal modes of interaction support more flexible and natural elderly–robot interaction, make clear the benefits for the users and, therefore, increase its acceptability. Finally, we provide insights and lessons learned from the extensive experimentation, which, to the best of our knowledge, is one of the largest experimentation of a multi-robot multi-service system so far.     

On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations, computing clusters and distributed cloud appliances.