Interface Acoustic Waves in 128° YX-LiNbO/SU-8/Overcoat Structures.

The propagation of interface acoustic waves (IAWs) in 128° YX-LiNbO/SU-8/overcoat structures was theoretically studied and experimentally investigated for different types of overcoat materials and thicknesses of the SU-8 adhesive layer. Three-dimensional finite element method analysis was performed using Comsol Multiphysics software to design an optimized multilayer configuration able to achieve an efficient guiding effect of the IAW at the LiNbO/overcoat interface. Numerical analysis results showed the following: (i) an overcoat faster than the piezoelectric half-space ensures that the wave propagation is confined mainly close to the surface of the LiNbO, although with minimal scattering in the overcoat; (ii) the presence of the SU-8, in addition to performing the essential function of an adhesive layer, can also promote the trapping of the acoustic energy toward the surface of the piezoelectric substrate; and (iii) the electromechanical coupling efficiency of the IAW is very close to that of the surface acoustic wave (SAW) along the bare LiNbO half-space.

Optimizing TMS dosimetry: evaluating the effective electric field as a novel metric.

Objective: This study introduces the effective electric field (Eeff) as a novel observable for transcranial magnetic stimulation (TMS) numerical dosimetry. Eeff represents the electric field component aligned with the local orientation of cortical and white matter neuronal elements. To assess the utility of Eeff as a predictive measure for TMS outcomes, we evaluated its correlation with TMS induced muscle responses and compared it against conventional observables, including the electric (E-)field magnitude, and its components normal and tangential to the cortical surface.

Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often associated with atypical visual attention patterns. In this paper, the Gaze-Based Autism Classifier (GBAC) is proposed, which is a Deep Neural Network model that leverages both data distillation and data attribution techniques to enhance ASD classification accuracy and explainability. Using data sampled by eye tracking sensors, the model identifies unique gaze behaviors linked to ASD and applies an explainability technique called TracIn for data attribution by computing self-influence scores to filter out noisy or anomalous training samples.

Experimental and Theoretical Analysis of Rayleigh and Leaky-Sezawa Waves Propagating in ZnO/Fused Silica Substrates.

Piezoelectric c-axis oriented zinc oxide (ZnO) thin films, from 1.8 up to 6.6 µm thick, have been grown by the radio frequency magnetron sputtering technique onto fused silica substrates.

Associations between fine particulate matter, gene expression, and promoter methylation in human bronchial epithelial cells exposed within a classroom under air-liquid interface.

Associations between indoor air pollution from fine particulate matter (PM with aerodynamic diameter d < 2.5 μm) and human health are poorly understood. Here, we analyse the concentration-response curves for fine and ultrafine PM, the gene expression, and the methylation patterns in human bronchial epithelial cells (BEAS-2B) exposed at the air-liquid interface (ALI) within a classroom in downtown Rome.

Modeling failures in smart grids by a bilinear logistic regression approach.

Modeling and recognizing events in complex systems through machine learning techniques is a challenging task. Especially if the model is constrained to be explainable and interpretable, while ensuring high levels of accuracy. In this paper, we adopt a bilinear logistic regression model in which the parameters are trained in a data-driven fashion on a real-world dataset of power grid failure data.

Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection.

Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder which affects a significant proportion of the population, with estimates suggesting that about 1 in 100 children worldwide are affected by ASD. This study introduces a new Deep Neural Network for identifying ASD in children through gait analysis, using features extracted from frames composing video recordings of their walking patterns. The innovative method presented herein is based on imagery and combines gait analysis and deep learning, offering a noninvasive and objective assessment of neurodevelopmental disorders while delivering high accuracy in ASD detection.

Optimized Leaky-Wave Antenna for Hyperthermia in Biological Tissue Theoretical Model.

In this paper, we exploit the enhanced penetration reachable through inhomogeneous waves to induce hyperthermia in biological tissues. We will present a leaky-wave antenna inspired by the Menzel antenna which has been shortened through opportune design and optimizations and that has been designed to optimize the penetration at the interface with the skin, allowing penetration in the skin layer at a constant temperature, and enhanced penetration in the overall structure considered. Past papers both numerically and analytically demonstrated the possibility of reducing the attenuation that the electromagnetic waves are subject to when travelling inside a lossy medium by using inhomogeneous waves.

An adaptive embedding procedure for time series forecasting with deep neural networks.

Nowadays, solving time series prediction problems is an open and challenging task. Many solutions are based on the implementation of deep neural architectures, which are able to analyze the structure of the time series and to carry out the prediction. In this work, we present a novel deep learning scheme based on an adaptive embedding mechanism.

Traffic Stream Analysis by Radar Sensors on Two-Lane Roads for Free-Moving and Constrained Vehicles Identification.

This paper focuses on the analysis of traffic streams on two-lane highways, which are crucial components of transportation networks. Traffic flow measurement technologies, such as detection stations, radar guns, or video cameras, have been used over the years to detect the level of traffic and the operating conditions. This type of sensor can record a large amount of data which is useful to evaluate and monitor road traffic conditions, and it is possible to identify free-moving and constrained vehicles by processing the collected data.

Evaluation of Bronze Electrode in Electrical Discharge Coating Process for Copper Coating.

One of the widely used non-traditional machines for machining of hard materials into complex shapes and different sizes is the electrical discharge machine (EDM). Recently, the EDM has been used for deposition by controlling the input parameters (current and duty cycle). This work was carried out to evaluate the readily available bronze (88% Cu + 12% Sn) electrode for deposition of copper material on titanium alloy.

Toward Minimal Complexity Models of Membrane Reactors for Hydrogen Production.

Membrane reactors are inherently two-dimensional systems that require complex models for an accurate description of the different transport phenomena involved. However, when their performance is limited by mass transport within the reactor rather than by the selective product permeation across the membrane, the 2D model may be significantly simplified. Here we extend results previously found for methane steam reforming membrane reactors to show that such simplified two-dimensional model admits either a straightforward analytical solution for the cross-section averaged concentration profile, or can be reduced to a 1D model with an enhanced Sherwood number, depending on the stoichiometry of the reaction considered.

Correction: Development of a transferable coarse-grained model of polydimethylsiloxane.

Correction for 'Development of a transferable coarse-grained model of polydimethylsiloxane' by Sonia Cambiaso , , 2022, https://doi.org/10.1039/d2sm00939k.

Effects of Environmental and Electric Perturbations on the pKa of Thioredoxin Cysteine 35: A Computational Study.

Here we present a theoretical-computational study dealing with the evaluation of the pKa of the Cysteine residues in Thioredoxin (TRX) and in its complex with the Thioredoxin-interacting protein (TXNIP). The free energy differences between the anionic and neutral form of the Cysteine 32 and 35 have been evaluated by means of the Perturbed Matrix Method with classical perturbations due to both the environment and an exogenous electric field as provided by Molecular Dynamics (MD) simulations. The evaluation of the free energies allowed us to show that the effect of the perturbing terms is to lower the pKa of Cysteine 32 and Cysteine 35 with respect to the free amino-acid.

Development of a transferable coarse-grained model of polydimethylsiloxane.

Polydimethylsiloxane (PDMS) is a popular silicon-based polymer with advanced applications in microfluidics and nanocomposites. The slow dynamics of polymer chains in such complex systems hinders molecular dynamics investigations based on all atom force fields. This limitation can be overcome by exploiting finely tuned coarse-grained (CG) models.

Evaluation of Mechanical and Microstructural Properties and Global Warming Potential of Green Concrete with Wheat Straw Ash and Silica Fume.

Cement and concrete are among the major contributors to CO emissions in modern society. Researchers have been investigating the possibility of replacing cement with industrial waste in concrete production to reduce its environmental impact. Therefore, the focus of this paper is on the effective use of wheat straw ash (WSA) together with silica fume (SF) as a cement substitute to produce high-performance and sustainable concrete.

Numerical and experimental analysis of Lagrangian dispersion in two-dimensional chaotic flows.

We present a review and a new assessment of the Lagrangian dispersion properties of a 2D model of chaotic advection and diffusion in a regular lattice of non stationary kinematic eddies. This model represents an ideal case for which it is possible to analyze the same system from three different perspectives: theory, modelling and experiments. At this regard, we examine absolute and relative Lagrangian dispersion for a kinematic flow, a hydrodynamic model (Delft3D), and a laboratory experiment, in terms of established dynamical system techniques, such as the measure of (Lagrangian) finite-scale Lyapunov exponents (FSLE).

Predicting blur visual discomfort for natural scenes by the loss of positional information.

The perception of blur due to accommodation failures, insufficient optical correction or imperfect image reproduction is a common source of visual discomfort, usually attributed to an anomalous and annoying distribution of the image spectrum in the spatial frequency domain. In the present paper, this discomfort is related to a loss of the localization accuracy of the observed patterns. It is assumed, as a starting perceptual principle, that the visual system is optimally adapted to pattern localization in a natural environment.

Environmental life cycle assessment of polyhydroxyalkanoates production from cheese whey.

Cheese whey (CW) is the main by-product of the dairy industry and is often considered one of the main agro-industrial biowaste streams to handle, especially within the European Union, where the diary activities play an essential role in the agrarian economy. In the paper, Life Cycle Assessment (LCA) is used to analyse the feasibility of producing polyhydroxyalkanoates (PHA) as the main output of an innovative CW valorisation route which is benchmarked against a conventional anaerobic digestion (AD) process. To this aim, the LCA inventory data are derived from lab-scale PHA accumulation tests performed on real CW, while data from the literature of concern are used for modelling both the PHA extraction from the accumulating biomass and for the alternative CW valorisation through AD.

A comparison of machine learning classifiers for smartphone-based gait analysis.

This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol.

On-Glass Integrated SU-8 Waveguide and Amorphous Silicon Photosensor for On-Chip Detection of Biomolecules: Feasibility Study on Hemoglobin Sensing.

An optoelectronic, integrated system-on-glass for on-chip detection of biomolecules is here presented. The system's working principle is based on the interaction, detected by a hydrogenated amorphous silicon photosensor, between a monochromatic light travelling in a SU-8 polymer optical waveguide and the biological solution under analysis. Optical simulations of the waveguide coupling to the thin-film photodiode with a specific design were carried out.

(Hyper)graph Kernels over Simplicial Complexes.

Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered.

Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations.

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces.