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.     

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.     

Shape from Radiological Density

In this paper we propose a strategy to solve the problem of recovering the 3-D shape of anatomical structures from single X-ray images, i.e., the problem of Shape from Radiological Density (SFRD). In order to overcome the noninvertibility of the process of image generation, we formulate a minimal set of physical assumptions that are used to constrain SFRD and to transform it into a well-posed problem. Our shape recovery strategy requires the solution of four problems: (a) linearization of the process of X-ray image generation, (b) image segmentation, (c) estimation of a map of the local thickness of each anatomical structure of interest, and (d) recovery of the 3-D shape of each structure from its boundaries and thickness map. In this paper we assume that problems (a) and (b) have already been faced, and propose a solution for problems (c) and (d). Experimental results on synthetic images, X-ray images of phantoms, and real radiograms are reported.     

oDect: an RFID‐based object detection API to support applications development on mobile devices

The RFID technology is becoming ever more popular in the development of ubiquitous computing applications. A full exploitation of the RFID potential requires the study and implementation of human–computer interaction (HCI) modalities to be able to support wide usability by the target audience. This implies the need for programming methodologies specifically dedicated to support the easy and efficient prototyping of applications to have feedback from early tests with users. On the basis of our field‐working experience, we have designed oDect, a high‐level language and platform‐independent application programming interface (API), ad hoc designed to meet the needs of typical applications for mobile devices (smart phones and PDAs). oDect aims at allowing application developers to create their prototypes focusing on the needs of the final users, without having to care about the low‐level software that interacts with the RFID hardware. Further, in an end‐user developing (EUD) approach, oDect provides specific support for the application end‐user herself to cope with typical problems of RFID applications in detecting objects. We describe in detail the features of the API and discuss the findings of a test with four programmers, where we analyse and evaluate the use of the API in four sample applications. We also present results of an end‐user test, which investigated strengths and weaknesses of the territorial agenda (TA) concept. The TA is an RFID‐based citizen guide that aids—through time‐ and location‐based reminders—users in their daily activities in a city. The TA directly exploits EUD features of oDect, in particular concerning the possibility of linking detected objects with custom actions. Copyright © 2008 John Wiley & Sons, Ltd.     

Mass spectrometry in the diagnosis of thyroid disease and in the study of thyroid hormone metabolism

The importance of thyroid hormones in the regulation of development, growth, and energy metabolism is well known. Over the last decades, mass spectrometry has been extensively used to investigate thyroid hormone metabolism and to discover and characterize new molecules involved in thyroid hormones production, such as thyrotropin-releasing hormone. In the earlier period, the quantification methods, usually based on gas chromatography–mass spectrometry, were complicated and time consuming. They were mainly focused on basic research, and were not suitable for clinical diagnostics on a routine basis. The development of the modern mass spectrometers, mainly coupled to liquid chromatography, enabled simpler sample preparation procedures, and the accurate quantification of thyroid hormones, of their precursors, and of their metabolites in biological fluids, tissues, and cells became feasible. Nowadays, molecules of physiological and pathological interest can be assayed also for diagnostic purposes on a routine basis, and mass spectrometry is slowly entering the clinical laboratory. This review takes stock of the advancements in the field of thyroid metabolism that were carried out with mass spectrometry, with special focus on the use of this technique for the quantification of molecules involved in thyroid diseases.     

Redispersible Polymer Powders with High Bio-Based Content from Core–Shell Nanoparticles

Redispersible polymer powders (RDPPs), i.e., additives obtained from core–shell nanoparticles and commercialized in the form of a dry powder, find intensive application in the concrete industry. However, they are mainly produced from fossil resources. Therefore, the development of bio-based RDPPs is important to reduce the carbon footprint of these additives. In this work, two types of core–shell nanoparticles with a high percentage of bio-based content are synthesized and show to be good candidates as RDPPs. In the first case, up to 75% of bio-based content is obtained by combining lauryl acrylate, derived from coconut and palm kernel oil, as main core material, with isobornyl methacrylate, coming from pine resin, exploited to create the outer harder shell. In the second case, a degradable macromonomer obtained by the ring opening polymerization of lactide using 2-hydroxyethyl methacrylate as initiator is used as the core-forming monomer to obtain degradable RDPPs. In both cases, the particles are synthesized with a two-step emulsion polymerization process conducted in one pot and then spray-dried to obtain the RDPPs of interest. The morphology and redispersibility of the powders are characterized. Finally, their use as concrete additives is preliminarily assessed by evaluating their effect on changes in the surface morphologies of concrete specimens.     

Dynamic analyses of information encoding in neural ensembles

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.     

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.