A static view of localities

This paper proposes alternative, effective characterizations for nets of automata of the location equivalence and preorder presented by Boudol et al. in the companion paper [BCHK]. Contrary to the technical development in the above given reference, where locations are dynamically associated to the subparts of a process in the operational semantics, the equivalence and preorder we propose are based on a static association of locations to the parallel components of a net. Following this static approach, it is possible to give these distributed nets a standard operational semantics which associates with each net a finite labelled transition system. Using this operational semantics for distributed nets, we introduce effective notions of equivalence and preorder which are shown to coincide with those proposed in [BCHK].     

FreesoundVR: soundscape composition in virtual reality using online sound repositories

Virtual Reality - The intersection between sound and music computing and Virtual Reality (VR) has grown significantly over the past decades, amounting to an established area of research today....     

Design and analysis of miniaturized all-optical binary to gray code converter using Y-shaped plasmonic waveguide for optical processors

In this paper, an all-optical miniaturized binary to gray code converter is designed and analyzed. The all-optical domain is now an alternative for electronic devices, where performance and speed are the key issues. Code converters are significantly used in digital data transmission in the areas of error detection and correction. Gray code is one of the cyclic codes, where the cyclic shift of each codeword is also a code word. An all-optical XOR gate, realized using a Y-shaped power combiner is used in this design to generate the desired gray code from the given binary code. The insertion loss and extinction ratio parameters are found to be 0.347 dB and 22.26 dB, respectively. The entire simulation is carried out using finite-difference time-domain method. The obtained practical results are verified mathematically using MATLAB.

     

Photonic-aware neural networks

Neural Computing and Applications - Photonics-based neural networks promise to outperform electronic counterparts, accelerating neural network computations while reducing power consumption and...     

A Novel Particle/Photon Detector Based on a Superconducting Proximity Array of Nanodots

The current frontiers in the investigation of high-energy particles demand for new detection methods. Higher sensitivity to low-energy deposition, high-energy resolution to identify events and improve the background rejection, and large detector masses have to be developed to detect even an individual particle that weakly interacts with ordinary matter. Here, we will describe the concept and the layout of a novel superconducting proximity array which show dynamic vortex Mott insulator to metal transitions, as an ultra-sensitive compact radiation-particle detector.     

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.     

Engineered Multifunctional Albumin‐Decorated Porous Silicon Nanoparticles for FcRn Translocation of Insulin

The last decade has seen remarkable advances in the development of drug delivery systems as alternative to parenteral injection‐based delivery of insulin. Neonatal Fc receptor (FcRn)‐mediated transcytosis has been recently proposed as a strategy to increase the transport of drugs across the intestinal epithelium. FcRn‐targeted nanoparticles (NPs) could hijack the FcRn transcytotic pathway and cross the epithelial cell layer. In this study, a novel nanoparticulate system for insulin delivery based on porous silicon NPs is proposed. After surface conjugation with albumin and loading with insulin, the NPs are encapsulated into a pH‐responsive polymeric particle by nanoprecipitation. The developed NP formulation shows controlled size and homogeneous size distribution. Transmission electron microscopy (TEM) images show successful encapsulation of the NPs into pH‐sensitive polymeric particles. No insulin release is detected at acidic conditions, but a controlled release profile is observed at intestinal pH. Toxicity studies show high compatibility of the NPs with intestinal cells. In vitro insulin permeation across the intestinal epithelium shows approximately fivefold increase when insulin is loaded into FcRn‐targeted NPs. Overall, these FcRn‐targeted NPs offer a toolbox in the development of targeted therapies for oral delivery of insulin.     

Dynamics and self-assembly of bio-functionalized gold nanoparticles in solution: Reactive molecular dynamics simulations

The self-assembling properties, stability, and dynamics of hybrid nanocarriers (gold nanoparticles (AuNPs) functionalized with cysteine-based peptides) in solution are studied through a series of classical molecular dynamics simulations based on a recently parametrized reactive force field. The results reveal, at the atomic level, all the details regarding the peptide adsorption mechanisms, nanoparticle stabilization, aggregation, and sintering. The data confirm and explain the experimental findings and disclose aspects that cannot be scrutinized by experiments. The biomolecules are both chemisorbed and physisorbed; self-interactions of the adsorbates and formation of stable networks of interconnected molecules on the AuNP surfaces limit substrate reconstructions, protect the AuNPs from the action of the solvent, and prevent direct interactions of the gold surfaces. The possibility of agglomeration of the functionalized nanoparticles, compared with the sintering of the bare supports in a water solution, is demonstrated through relatively long simulations and fast steered dynamics. The analysis of the trajectories reveals that the AuNPs were well stabilized by the peptides. This prevented particle sintering and kept the particles far apart; however, part of their chains could form interconnections (crosslinks) between neighboring gold vehicles. The excellent agreement of these results with the literature confirm the reliability of the method and its potential application to the modeling of more complex materials relevant to the biomedical sector.

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