Classification and production of polymeric foams among the systems for wound treatment

This work represents an overview on types of wounds according to their definition, classification and dressing treatments. Natural and synthetic polymeric wound dressings types have been analyzed, providing a historical overview, from ancient to modern times. Currently, there is a wide choice of materials for the treatment of wounds, such as hydrocolloids, polyure-thane and alginate patches, wafers, hydrogels and semi-permeable film dressings. These systems are often loaded with drugs such as antibiotics for the simultaneous delivery of drugs to prevent or cure infections caused by the exposition of blood vessel to open air. Among the presented tech-niques, a focus on foams has been provided, describing the most diffused branded products and their chemical, physical, biological and mechanical properties. Conventional and high-pressure methods for the production of foams for wound dressing are also analyzed in this work, with a proposed comparison in terms of process steps, efficiency and removal of solvent residue. Case studies, in vivo tests and models have been reported to identify the real applications of the produced foams

A cellular automaton for the factor of safety field in landslides modeling

Landslide inventories show that the statistical distribution of the area of recorded events is well described by a power law over a range of decades. To understand these distributions, we consider a cellular automaton to model a time and position dependent factor of safety. The model is able to reproduce the complex structure of landslide distribution, as experimentally reported. In particular, we investigate the role of the rate of change of the system dynamical variables, induced by an external drive, on landslide modeling and its implications on hazard assessment. As the rate is increased, the model has a crossover from a critical regime with power-laws to non power-law behaviors. We suggest that the detection of patterns of correlated domains in monitored regions can be crucial to identify the response of the system to perturbations, i.e., for hazard assessment.Comment: 4 pages, 3 figure

Estimating soil suction from electrical resistivity

Abstract. Soil suction and resistivity strongly depend on the degree of soil saturation and, therefore, both are used for estimating water content variations. The main difference between them is that soil suction is measured using tensiometers, which give point information, while resistivity is obtained by tomography surveys, which provide distributions of resistivity values in large volumes, although with less accuracy. In this paper, we have related soil suction to electrical resistivity with the aim of obtaining information about soil suction changes in large volumes, and not only for small areas around soil suction probes. We derived analytical relationships between soil matric suction and electrical resistivity by combining the empirical laws of van Genuchten and Archie. The obtained relationships were used to evaluate maps of soil suction values in different ashy layers originating in the explosive activity of the Mt Somma-Vesuvius volcano (southern Italy). Our findings provided a further example of the high potential of geophysical methods in contributing to more effective monitoring of soil stress conditions; this is of primary importance in areas where rainfall-induced landslides occur periodically

A sensitivity analysis for the adequacy assessment of a multi-state physics modeling approach for reliability analysis

In this work, a moment-independent Sensitivity Analysis (SA) based on Hellinger distance and Kullback-Leibler divergence is proposed to identify the component of a system most affecting its reliability (Diaconis et al., 1982; Gibbs et al., 2002; Di Maio et al., 2014). This result is used to adequately allocate modeling efforts on the most important component that, therefore, deserves a component-level Multi-State Physics Modeling (MSPM) to be integrated into a system-level model, to estimate the system failure probability

Robust multi-objective optimization of safety barriers performance parameters for NaTech scenarios risk assessment and management

Safety barriers are to be designed to bring the largest benefit in terms of accidental scenarios consequences mitigation at the most reasonable cost. In this paper, we formulate the problem of the identification of the optimal performance parameters of the barriers that can at the same time allow for the consequences mitigation of Natural Technological (NaTech) accidental scenarios at reasonable cost as a Multi-Objective Optimization (MOO) problem. The MOO is solved for a case study of literature, consisting in a chemical facility composed by three tanks filled with flammable substances and equipped with six safety barriers (active, passive and procedural), exposed to NaTech scenarios triggered by either severe floods or earthquakes. The performance of the barriers is evaluated by a phenomenological dynamic model that mimics the realistic response of the system. The uncertainty of the relevant parameters of the model (i.e., the response time of active and procedural barriers and the effectiveness of the barriers) is accounted for in the optimization, to provide robust solutions. Results for this case study suggest that the NaTech risk is optimally managed by improving the performances of four-out-of-six barriers (three active and one passive). Practical guidelines are provided to retrofit the safety barriers design

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies

Properties related to first countability and countable compactness in hyperspaces: a new approach

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Seismic resilience assessment of Small Modular Reactors by a Three-loop Monte Carlo Simulation

We develop a three-loop Monte Carlo Simulation (MCS) framework for the seismic resilience assessment of Small Modular Reactors (SMRs), embedding Probabilistic Seismic Hazard Analysis (PSHA), seismic fragility evaluation and multiple SMR units accident sequence analysis. A set of metrics are computed to capture different aspects of SMR resilience to earthquakes, specifically the ability to withstand seismic disruption, mitigate consequences and restore normal operation. The MCS framework allows accounting for the aleatory and epistemic uncertainties of the PSHA and fragility parameters. An application is given with regards to an advanced Nuclear Power Plant (aNPP) consisting of four reactor units of NuScale SMR design. A comparison is made to a conventional NPP (cNPP), i.e., a typical large reactor of equivalent generation capacity. Both plants are fictitiously located on the Garigliano nuclear site (southern Italy). The results show that resilient features of SMRs overcome cNPPs in terms of post-accident scenario mitigation and restoration capabilities

Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED)

Uniformizing (proximal) Δ-topologies

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