Testing and numerical simulation of a medium strength rock material under unconfined compression loading

This study aims to numerically and experimentally investigate the response of a medium strength rock material under unconfined compression loading up to failure. The unconfined compressive strength (UCS) is one of the most important parameters in characterising rock material behaviour. Hence the UCS is crucial in understanding the failure mechanism of fractured rocks. An effective approach to determine the UCS and to investigate the behaviours of rock materials under unconfined compression is essential in the majority of research fields of rock mechanics. The experimental configuration for the unconfined compression test, suggested by the protocols of the ASTM standard, has some limitations which affect the accuracy in determination of the real UCS. Among several alternative configurations proposed, the Mogi's configuration seems to be the most appropriate one. Therefore, the ASTM and Mogi's configurations were used to perform the tests on a medium strength rock material, i.e. Pietra Serena sandstone. The results using two configurations were discussed in terms of the differences. The tests were also replicated in LS-DYNA using a finite element method (FEM) coupled smooth particle hydrodynamics (SPH) technique. This technique is employed in this study due to its capabilities to cope with large deformation issues related to the rocks. An advanced material model, called the Karagozian and Case Concrete (KCC) model, is implemented in the numerical simulations. The KCC model consists of three independent fixed failure surfaces and it can consider the damage accumulation based on the current state of stress among these failure surfaces. An equation-of-state (EOS) is used in conjunction with KCC material model for decoupling the volumetric and deviatoric responses. The numerical and experimental results were finally compared with the focus on the stress–strain diagram and the failure patterns. The comparison shows that the numerical results are in good agreement with the experimental results.     

Catalysts for the Ethoxylation of Esters

The intermediates for the production of nonionic surfactants can be derived from petrochemicals and/or from renewable raw materials. In both, the role of catalysis is fundamental. In this paper the main results reported in the literature related with the catalysis for ethoxylation of fatty esters will be reviewed. The main open challenges for these technologies will be outlined.     

Copper(I/II), α/β‐Synuclein and Amyloid‐β: Menage à Trois?

Copper binding to α‐synuclein (aS) and to amyloid‐β (Ab) has been connected to Parkinson's and Alzheimer's disease (AD), respectively, because Cu ions can modulate the peptide aggregation, and these Cu ? peptide complexes can catalyse the production of reactive oxygen species (ROS). In a significant proportion of AD brains, aggregation of aS and Ab has been detected, and it was proposed that Ab and aS interact with each other. Thus, we investigated the potential interactions of Ab and aS through their binding of copper(I) and copper(II). Additionally, β‐synuclein (bS) was investigated, due to its additional methionine residue, a potential Cu^I ligand. We found that: 1) the peptides containing the Cu‐binding domains Ab1–16, aS1–15 and bS1–15 have similar affinities towards Cu^II and towards Cu^I, with Ab1–16 being slightly stronger, 2) in the case of Cu^I, the additional Met residue in bS1–15 increased the affinity slightly, 3) the exchange of Cu^I/II between the two peptides is rapid (≤ms), 4) a/bS1–15 and Ab1–16 form a heterodimeric complex with Cu^II, 5) Cu^I probably promotes a transient ternary complex, 6) the different Cu^I/II coordination of Ab1–16, aS1–15 and bS1–15 impacts the capacity to produce ROS and to oxidise catechol, and 7) when Ab1–16, aS1–15 and Cu are present, the ROS production more closely resembles that by Ab1–16. The work gives insights into the coordination chemistry of these related peptides, and the relevance of coordination differences, the ternary complex and ROS production are discussed.     

Kuwanon‐L as a New Allosteric HIV‐1 Integrase Inhibitor: Molecular Modeling and Biological Evaluation

HIV‐1 integrase (IN) active site inhibitors are the latest class of drugs approved for HIV treatment. The selection of IN strand‐transfer drug‐resistant HIV strains in patients supports the development of new agents that are active as allosteric IN inhibitors. Here, a docking‐based virtual screening has been applied to a small library of natural ligands to identify new allosteric IN inhibitors that target the sucrose binding pocket. From theoretical studies, kuwanon‐L emerged as the most promising binder and was thus selected for biological studies. Biochemical studies showed that kuwanon‐L is able to inhibit the HIV‐1 IN catalytic activity in the absence and in the presence of LEDGF/p75 protein, the IN dimerization, and the IN/LEDGF binding. Kuwanon‐L also inhibited HIV‐1 replication in cell cultures. Overall, docking and biochemical results suggest that kuwanon‐L binds to an allosteric binding pocket and can be considered an attractive lead for the development of new allosteric IN antiviral agents.     

Identification of IPMC nonlinear model via single and multi-objective optimization algorithms

Ionic Polymer–Metal Composites (IPMCs) are electro-active polymers transforming mechanical forces into electric signals and vice versa. This paper proposes an improved electro-mechanical grey-box model for IPMC membrane working as actuator. In particular the IPMC nonlinearity has been characterized through experimentation and included within the electric model. Moreover identification of the model parameters has been performed via optimization algorithms using both single- and multi-objective formulation. Minimization was attained via the Nelder–Mead simplex and the Genetic Algorithms considering as cost functions the error between the experimental and modeled absorbed current and the error between experimental and modeled displacement. The obtained results for the different formulations have been then compared.     

Identification and analysis of isothiocyanates and new acylated anthocyanins in the juice of Raphanus sativus cv. Sango sprouts

The freeze-dried sprouts’ juice of Raphanus sativus (L.) cv. Sango was prepared and analysed for the first time. HPLC analysis of total isothiocyanates, after protein displacement, resulted in 77.8 ± 3.0 μmol/g of dry juice while GC–MS analysis of hexane and acetone extracts showed E- and Z-raphasatin (8.9 and 0.11 μmol/g, respectively) and sulforaphene (11.7 μmol/g), summing up to 20.7 ± 1.7 μmol/g of free isothiocyanates. Sprouts’ juice contained an unprecedented wealth of anthocyanins and a new fractionation methodology allowed us to isolate 34 mg/g of acylated anthocyanins (28.3 ± 1.9 μmol/g), belonging selectively to the cyanidin family. Analysis was performed by HPLC–PDA–ESI–MSⁿ and extended to deacylated anthocyanins and aglycones, obtained, respectively, by alkaline and acid hydrolysis. This study identified 70 anthocyanins, 19 of which have never been described before and 32 of which are reported here in R. sativus for the first time. Sango radish sprouts are exceptional dietary sources of heath-promoting micronutrients.     

Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon

Artificial Neural Networks (ANNs) have been successfully employed for predicting and forecasting groundwater levels up to some time steps ahead. In this paper, we present an application of feed forward neural networks (FFNs) for long period simulations of hourly groundwater levels in a coastal unconfined aquifer sited in the Lagoon of Venice, Italy. After initialising the model with groundwater elevations observed at a given time, the developed FNN should able to reproduce water level variations using only the external input variables, which have been identified as rainfall and evapotranspiration. To achieve this purpose, the models are first calibrated on a training dataset to perform 1-h ahead predictions of future groundwater levels using past observed groundwater levels and external inputs. Simulations are then produced on another data set by iteratively feeding back the predicted groundwater levels, along with real external data. The results show that the developed FNN can accurately reproduce groundwater depths of the shallow aquifer for several months. The study suggests that such network can be used as a viable alternative to physical-based models to simulate the responses of the aquifer under plausible future scenarios or to reconstruct long periods of missing observations provided past data for the influencing variables is available.     

Exact Schema Theory for Genetic Programming and Variable-Length Genetic Algorithms with One-Point Crossover

A few schema theorems for genetic programming (GP) have been proposed in the literature in the last few years. Since they consider schema survival and disruption only, they can only provide a lower bound for the expected value of the number of instances of a given schema at the next generation rather than an exact value. This paper presents theoretical results for GP with one-point crossover which overcome this problem. First, we give an exact formulation for the expected number of instances of a schema at the next generation in terms of microscopic quantities. Due to this formulation we are then able to provide an improved version of an earlier GP schema theorem in which some (but not all) schema creation events are accounted for. Then, we extend this result to obtain an exact formulation in terms of macroscopic quantities which makes all the mechanisms of schema creation explicit. This theorem allows the exact formulation of the notion of effective fitness in GP and opens the way to future work on GP convergence, population sizing, operator biases, and bloat, to mention only some of the possibilities.     

Online Portable Microcantilever Biosensors for Salmonella enterica Serotype Enteritidis Detection

The micro- and nano-technologies coupled with a deep knowledge of organic/inorganic interfaces guarantee an exceptional sensitivity and specificity of the sensor, while the lab-on-a-chip platform reduces assay times and limits sampling and/or sample preparation, providing compact and portable objects. Therefore, the development of innovative biosensors such as antibody-immobilized microcantilevers can overcome the evident limits of nowadays technologies, such as time consuming, expensiveness, difficult automation, low sensitivity, accuracy, and precision for quantitative methods. The present study proposes two device designs for the detection of food pathogens, exploiting an antibody-immobilized microcantilever biosensors, a novel class of mass detectors. For the first one, we integrated the mechanical sensors on a microfluidic platform (lab-on-a-chip) to perform online analysis, directly in liquid environment. We showed that our portable biosensors could easily detect the presence of pathogenic bacteria such as Salmonella enterica serotype enteritidis in concentration 10⁵ cfu/mL in just 40 min, without any enrichment and/or sample preparation. To increase the mass sensitivity of our analysis, we also fabricated microstructures optimized for vibrating in vacuum environment. Using a dip-and-dry technique, we showed that, in such configuration, the experimental limit of detection is as low as 10³ cfu/mL. Due to the extremely small volumes needed, our biosensors operating in vacuum have the potentiality of detecting the presence or absence of a single cell.