Salient Properties Research Articles

Lightweight Mutually Authenticated Key Exchange with Physical Unclonable Functions

Authenticated key exchange is desired in scenarios where two participants must exchange sensitive information over an untrusted channel but do not trust each other at the outset of the exchange. As a unique hardware-based random oracle, physical unclonable functions (PUFs) can embed cryptographic hardness and binding properties needed for a secure, interactive authentication system. In this paper, we propose a lightweight protocol, termed PUF-MAKE, to achieve bilateral mutual authentication between two untrusted parties with the help of a trusted server and secure physical devices. At the end of the protocol, both parties are authenticated and possess a shared session key that they can use to encrypt sensitive information over an untrusted channel. The PUF’s underlying entropy hardness characteristics and the key-encryption-key (KEK) primitive act as the root of trust in the protocol’s construction. Other salient properties include a lightweight construction with minimal information stored on each device, a key refresh mechanism to ensure a fresh key is used for every authentication, and robustness against a wide range of attacks. We evaluate the protocol on a set of three FPGAs and a desktop server, with the computational complexity calculated as a function of primitive operations. A composable security model is proposed and analyzed considering a powerful adversary in control of all communications channels. In particular, session key confidentiality is proven through formal verification of the protocol under strong attacker (Dolev-Yao) assumptions, rendering it viable for high-security applications such as digital currency.

Social networks and stigma: The experiences of African immigrants living with HIV in the United States

Over the past two decades, research on the conceptualization, measurement, and refinement of stigma has grown extensively due to increasing recognition of stigma as a driver of adverse health outcomes. While these lines of research have generally recognized that stigma is enacted in social relationships, few analysts have characterized how the structure and composition of these relationships influence stigma, particularly among immigrant populations. In response, this paper integrates data from social network analysis and in-depth interviews to explore the experiences of and responses to stigma by African immigrants living with HIV in the United States.All participants reported that they anticipated, experienced, and internalized stigma within their personal networks. Many concealed their status and disclosed to only trusted associates, family members, and medical providers. Building on findings from previous studies, we found that the meanings and belief systems (particularly African cultural discourses that link HIV with mortality, immorality, retribution, and silence) matter for how stigma is assigned, enacted, experienced, and resisted. Our analyses also revealed that the structure of participants personal networks (i.e, the extent to which their associates were connected to each other, and how integrated/involved they were in these network relationships) intensified or diluted their exposure to stigmatizing discourses, depending on the composition (resourcefulness/quality) of their personal networks. Such network connections (i.e., social capital) translate into rewards if they are supportive and accepting, and costs if they stigmatize.By showing how individuals can use their social networks to stigmatize or support their peers, this study advances theoretical expositions on (1) how the meanings and belief systems held by individuals matter for understanding social network/structural processes,(2) how social networks shape how stigma is assigned, enacted, experienced and resisted,(3) the costs and downsides of social capital, which are often neglected through emphasis on its salutary impacts. Our findings underscore the need for interventions that leverage the salient properties of personal networks to understand, conceptualize, measure, and reduce stigma.

Green synthesis of reduced graphene oxide using Persea americana mill. extract: Characterization, oxygen reduction reaction and antibacterial application

Reduced graphene oxide (rGO) has garnered tremendous attention due to its salient properties that make it applicable in various electrochemical and environmental remediation fields. However, the commonly used chemical method to obtain rGO from graphene oxide (GO) involves toxic chemicals making both the process and product toxic and expensive. In the present work, the suitability of Avocado (Persea americana Mill) seed extract to reduce as-synthesized GO to rGO was investigated fully by optimising the reducing parameters such as the pH, temperature, amount of extract and reduction time. The formation of rGO from GO was confirmed with UV–Vis spectrophotometry (UV–Vis), Fourier Transform Infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), Transmission Electron Microscopy (TEM) and the thermogravimetric analysis (TGA). The optimal conditions for reducing GO with avocado seed extract are pH 8, 100 °C, and 12 h. The average thickness and width for the rGO was 5.55 nm and 11.39 nm accordingly. While GO exhibited a ζ of −29.9.3 mV, the reduced GO possessed a − 38.2 mV. Calculations from the Kotouckey-Levich's equation demonstrated that the obtained rGO can potentially be applied as an oxygen reduction reaction (ORR) catalyst for the generation of hydrogen peroxide since it follows a 2e− pathway mechanism where hydrogen peroxide is generated as an intermediate. Additionally, the avocado seed extract reduced GO demonstrated a slight growth inhibition of methicillin resistant S. aureus (MRSA).

FBDPN: CNN-Transformer hybrid feature boosting and differential pyramid network for underwater object detection

Despite advancements in underwater object detection (UOD) from optical underwater images in recent years, the task still poses significant challenges due to the chaotic underwater environment, as well as the substantial variations in scale and contour of objects. Existing deep learning-based schemes generally overlook the enhancement and refinement between multi-scale features of densely distributed underwater objects, leading to inaccurate localization and classification predictions with excessive information redundancy. To tackle the above issues, this article presents a novel feature boosting and differential pyramid network (FBDPN) for precise and efficient UOD. The salient properties of our paper are: (1) a heuristic feature pyramid network (FPN)-inspired architecture is constructed, which employs a convolutional neural network (CNN)-Transformer hybrid strategy to simultaneously facilitate the learning of multi-scale features and the capture of long-distance dependencies among pixels. (2) A neighborhood-scale feature boosting module (NSFBM) is developed to enhance contextual information between features of neighborhood scales. (3) A cross-scale feature differential module (CSFDM) is designed further to achieve effective information redundancy between features of different scales. Extensive experiments are conducted to reveal that our proposed FBDPN can outperform other state-of-the-art methods in both UOD performance and computational complexity. In addition, sufficient ablation studies are also performed to demonstrate the effectiveness of each component in our FBDPN. The source code is available at https://github.com/jixun-dmu/FBDPN.

Dynamic Logics of Diffusion and Link Changes on Social Networks

AbstractThis paper introduces a comprehensive logical framework to reason about threshold-driven diffusion and threshold-driven link change in social networks. It considers both monotonic dynamics, where agents can only adopt new features and create new connections, and non-monotonic dynamics, where agents may also abandon features or cut ties. Three types of operators are combined: one capturing diffusion only, one capturing link change only, and one capturing both at the same time. We first characterise the models on which diffusion of a unique feature and link change stabilise, whilst discussing salient properties of stable models with multiple spreading features. Second, we show that our operators (and any combination of them) are irreplaceable, in the sense that the sequences of model updates expressed by a combination of operators cannot always be expressed using any other operators. Finally, we analyse classes of models on which some operators can be replaced.

Faster but Not Sweeter: A Model of Escherichia coli Re-level Lipopolysaccharide for Martini 3 and a Martini 2 Version with Accelerated Kinetics.

Lipopolysaccharide (LPS) is a complex glycolipid molecule that is the main lipidic component of the outer leaflet of the outer membrane of Gram-negative bacteria. It has very limited lateral motion compared to phospholipids, which are more ubiquitous in biological membranes, including in the inner leaflet of the outer membrane of Gram-negative bacteria. The slow-moving nature of LPS can present a hurdle for molecular dynamics simulations, given that the (pragmatically) accessible timescales to simulations are currently limited to microseconds, during which LPS displays some conformational dynamics but hardly any lateral diffusion. Thus, it is not feasible to observe phenomena such as insertion of molecules, including antibiotics/antimicrobials, directly into the outer membrane from the extracellular side nor to observe LPS dissociating from proteins via molecular dynamics using currently available models at the atomistic and more coarse-grained levels of granularity. Here, we present a model of deep rough LPS compatible with the Martini 2 coarse-grained force field with scaled down nonbonded interactions to enable faster diffusion. We show that the faster-diffusing LPS model is able to reproduce the salient biophysical properties of the standard models, but due to its faster lateral motion, molecules are able to penetrate deeper into membranes containing the faster model. We show that the fast ReLPS model is able to reproduce experimentally determined patterns of interaction with outer membrane proteins while also allowing for LPS to associate and dissociate with proteins within microsecond timescales. We also complete the Martini 3 LPS toolkit for Escherichia coli by presenting a (standard) model of deep rough LPS for this force field.

High-Q magnetic toroidal dipole resonance in all-dielectric metasurfaces

High quality (Q) factor toroidal dipole (TD) resonances have played an increasingly important role in enhancing light–matter interactions. Interestingly, TDs share a similar far-field distribution as the conventional electric/magnetic dipoles but have distinct near-field profiles from them. While most reported works focused on the electric TD, magnetic TDs (MTDs), particularly high-Q MTD, have not been fully explored yet. Here, we successfully realized a high-Q MTD by effectively harnessing the ultrahigh Q-factor guided mode resonances supported in an all-dielectric metasurface, that is, changing the interspacing between silicon nanobar dimers. Other salient properties include the stable resonance wavelength but a precisely tailored Q-factor by interspacing distance. A multipole decomposition analysis indicates that this mode is dominated by the MTD, where the electric fields are mainly confined within the dielectric nanostructures, while the induced magnetic dipole loops are connected head-to-tail. Finally, we experimentally demonstrated such high-Q MTD resonance by fabricating a series of silicon metasurfaces and measuring their transmission spectra. The MTD resonance is characterized by a sharp Fano resonance in the transmission spectrum. The maximum measured Q-factor is up to 5079. Our results provide useful guidance for realizing high-Q MTD and may find exciting applications in boosting light–matter interactions.

Spheroid models to elaborate the broken symmetry and equivalent volume of molecules in crystalline phase.

Dense packing of particles has provided powerful models to elaborate the important structural features of matter in various systems such as liquid, glassy, and crystalline phases. The simplest sphere packing models can represent and capture salient properties of the building blocks for covalent, metallic, and ionic crystals; it, however, becomes insufficient to reflect the broken symmetry of the commonly anisotropic molecules in molecular crystals. Here, we develop spheroid models with a minimal degree of anisotropy, which serve as a simple geometrical representation for a rich spectrum of molecules-including both isotropic and anisotropic, convex and concave ones-in crystalline phases. Our models are determined via an inverse packing approach: Given a molecular crystal, an optimal spheroid model is constructed using a contact diagram, which depicts the packing relationship between neighboring molecules within the crystal. The spheroid models are capable of accurately capturing the broken symmetry and characterizing the equivalent volume of molecules in the crystalline phases. Moreover, our model retrieves such molecular information from low-quality x-ray diffraction data with poorly resolved structures, and by using soft spheroids, it can also describe the packing behavior in cocrystals.

Deep Spectral Clustering With Constrained Laplacian Rank.

Spectral clustering (SC) is a well-performed and prevalent technique for data processing and analysis, which has attracted significant attention in the field of clustering. While the scalability and generalization ability of this method make it prohibitive for the large-scale dataset and the out-of-sample-extension problem. In this work, we propose a new efficient deep clustering architecture based on SC, named deep SC (DSC) with constrained Laplacian rank (DSCCLR). DSCCLR develops a self-adaptive affinity matrix with a clustering-friendly structure by constraining the Laplacian rank, which greatly mines the intrinsic relationships. Meanwhile, by introducing a simple fully connected network with an orthogonality constraint on the last layer, DSCCLR learns discriminative representations in a short training time. The proposed method has the following salient properties: 1) it overcomes limited generalization ability and scalability of the existing DSC methods; 2) it explores the intrinsic relationship between samples in the affinity matrix, which maintains the latent manifold of data as much as possible; and 3) it alleviates the complexity of eigendecomposition via a simple but effective fully connected network. The extensive empirical results demonstrate the superiorities of DSCCLR over other 17 clustering methods.

Monotonic Random Variables According to a Direction

In this paper, we introduce the concept of monotonicity according to a direction for a set of random variables. This concept extends well-known multivariate dependence notions, such as corner set monotonicity, and can be used to detect dependence in multivariate distributions not detected by other known concepts of dependence. Additionally, we establish relationships with other known multivariate dependence concepts, outline some of their salient properties, and provide several examples.

Thermoelectric Properties of Spray Coated n-Type PEDOT:PSS Film

Inorganic thermoelectric (TE) materials have gained significant attention because of their salient properties. However, they possess some significant drawbacks, including high production costs, high heat loss, and fragility. Recently, Organic conducting polymers presented a promising platform as an alternative TE material because of their great mechanical flexibility, high stretchability, and environmental friendliness. In this work, we report for the first time on the TE properties of n-PEDOT:PSS film prepared using spray coating technique. The structural, optical and TE properties of the obtained n-PEDOT:PSS thin film was investigated using X-ray diffraction spectroscopy, UV-vis spectroscopy and Seebeck coefficient measurement systems, respectively. The n-PEDOT:PSS layer showed excellent optical properties with a band gap ranges from 3.91 to 3.78. In addition, the Seebeck coefficient and power factor (PF) were obtained to be 1096.77 µVK-1 and 298.59 µWm-1K-2 respectively, making n-PEDOT:P PSS to be regarded as efficient TE material.

Molecular Dynamics Simulation of a Feather-Boa Model of a Bacterial Chromosome.

The chromosome of a bacterium consists of a mega-base pair-long circular DNA, which self-organizes within the micron-sized bacterial cell volume, compacting itself by three orders of magnitude. Unlike eukaryotic chromosomes, it lacks a nuclear membrane and freely floats in the cytosol confined by the cell membrane. It is believed that strong confinement, cross-linking by associated proteins, and molecular crowding all contribute to determine chromosome size and morphology. Modelling the chromosome simply as a circular polymer decorated with closed side loops in a cylindrical confining volume has been shown to already recapture some of the salient properties observed experimentally. Here we describe how a computer simulation can be set up to study structure and dynamics of bacterial chromosomes using this model.

Behaviour of concrete filled rectangular tube girders in bending

A detailed study of the behaviour of concrete-filled rectangular tubular flange girders (CFRFGs) was undertaken. The CFRFGs were steel beams in which the top flange plate is replaced with a concrete-filled steel section, resulting in greater load-carrying capacity and lateral torsional buckling resistance compared with a regular steel beam of similar proportions. They are complex members and their behaviour is governed by several interrelated parameters, which were studied in this work. Using the Abaqus program, a three-dimensional finite-element (FE) model was developed to investigate the flexural behaviour of simply supported CFRFGs. Using available experimental results, the computational model was validated and then used to study the influence of the most salient properties on the overall response. Based on a fundamental assessment of the behaviour, a simplified analytical model was developed to predict the capacity of these types of section in a way that is suitable for designers. The results were compared with those from the FE analysis and it was found that the analytical model is capable of providing an accurate depiction of the behaviour and the bending moment capacity.

A unified methodology for the power efficiency analysis of physical systems

In this paper, the problem of power efficiency evaluation for n-ports physical systems is investigated. The efficiency analysis that we perform highlights the necessary and sufficient conditions for the system to be passive, and outlines the guidelines for the efficiency maps computation. After addressing the problem from a formal point of view, the analysis is deepened for the case of two-ports linear and nonlinear physical systems, and for the cases of three and four-ports linear systems. The efficiency analysis and the computation of the efficiency maps are addressed as a function of the power variables characterizing all the energetic ports of the considered systems. Furthermore, the salient properties of the efficiency are highlighted and discussed. The theoretical analysis which is developed is then applied to some physical systems of interest for industries and engineers working in the electromechanical, hydraulic and automotive fields: a DC electric motor driving an hydraulic pump for the two-ports systems class, a single-stage planetary gear set for the three-ports systems class, and a Ravigneaux planetary gear set for the four-ports systems class.

Dust acoustic inertial Alfvénic nonlinear structures in an electron depleted dusty plasma

In this investigation, the propagation properties of dust inertial Alfvén wave (DIAW) solitons, rogons, and breather structures have been studied in a magnetised electron-depleted two fluids plasma consisting of negatively charged dust and positively charged ions. The Korteweg–de Vries (KdV) equation has been derived by using the reductive perturbation method (RPM) to study the properties of solitons. Further, the nonlinear Schrödinger equation (NLSE) has also been derived by employing a suitable transformation in the KdV equation. The evolution of various kinds of nonlinear structures such as rogons, Akhmediev breathers (AB), and Kuznetsov–Ma breathers (KM) has been elucidated from the different rational solutions of the nonlinear Schrödinger equation. The impact of various physical parameters, such as the dust plasma beta and the angle of propagation of different species, on the salient properties of DIAW solitons, rogons, and breather structures has been studied numerically.

Functional renormalisation group approach to shell models of turbulence

Shell models are simplified models of hydrodynamic turbulence, retaining only some essential features of the original equations, such as the non-linearity, symmetries and quadratic invariants. Yet, they were shown to reproduce the most salient properties of developed turbulence, in particular universal statistics and multi-scaling. We set up the functional renormalisation group (FRG) formalism to study generic shell models. In particular, we formulate an inverse RG flow, which consists in integrating out fluctuation modes from the large scales (small wavenumbers) to the small scales (large wavenumbers), which is physically grounded and has long been advocated in the context of turbulence. Focusing on the Sabra shell model, we study the effect of both a large-scale forcing, and a power-law forcing exerted at all scales. We show that these two types of forcing yield different fixed points, and thus correspond to distinct universality classes, characterised by different scaling exponents. We find that the power-law forcing leads to dimensional (K41-like) scaling, while the large-scale forcing entails anomalous scaling.

An assessment of sustainability metrics for waste-to-liquid fuel pathways for a low carbon circular economy

The nexus of solid waste management problems and energy crisis necessitates the utilization of lignocellulosic biomass agro residues and municipal solid wastes for fuel and energy generation. Thermochemical technologies like pyrolysis, hydrothermal liquefaction and gasification are promising options to produce bio-oil, bio-crude and syngas from biomass, which can be further catalytically upgraded to hydrocarbon fuels. While there are technical merits associated with these processes and the products derived from them, the sustainability analysis of the processes from a systems level is imperative to evaluate their commercial viability. This perspective article presents fundamental understanding, importance and analysis of the different sustainability metrics based on mass and energy. The salient properties of biomass and wastes, and that of various fuels derived from them are presented. The three technologies are evaluated based on key metrics such as energy recovery/efficiency, E-factor, process mass intensity and CO2 footprint. The E-factor for different technologies follows the trend: hydrothermal liquefaction (0.14) ≈ gasification (0.13) < pyrolysis (0.5). However, it is clear from the analysis that the E-factor and GHG emissions of the HTL process, a promising feedstock-agnostic pathway, can be further reduced by valorizing the organic-laden aqueous phase and upgrading the bio-crude to hydrocarbons. The key results of process technoeconomics are presented and assessed from a sustainability viewpoint. This study recommends the use of simple sustainability metrics in research works on thermochemical conversion so that the results from different studies can be compared on a common sustainability platform.

Using the Soundscape Code to compare coastal marine habitats in the Gulf of Maine

Identifying similarities and differences in soundscape properties among coastal marine habitats is valuable for determining indicators of habitat composition, assessing functional connectivity among habitats, and informing management decisions regarding the soundscapes of these habitats. The “Soundscape Code,” proposed and developed by Dylan Wilford, enables rapid calculation of values for four salient soundscape properties: amplitude, impulsivity, periodicity, and dissimilarity. This enables multivariate statistical analyses to quantitatively compare soundscapes in different habitat types and geographic regions. The objective of the current work was to determine whether geographic region or habitat type accounts for more variability in coastal soundscape properties. Passive acoustic recordings were acquired in three different habitat types (sand, macroalgae, and eelgrass dominated substrates), in each of four different geographic locations along the New Hampshire/Maine coastline, to compare the soundscapes of habitats with varied biological and geophysical substrate composition. Results indicate that geographic location accounted for more variability in the soundscapes than habitat type, suggesting that habitats’ local connectivity outweighs acoustic and biological uniformity of the same habitat type over broader spatial scales. Future analyses will incorporate metagenomic data for predictive modeling of habitat composition through the combined use of passive acoustic monitoring and metabarcoding of seawater samples.

4H-Silicon Carbide as an Acoustic Material for MEMS.

This paper discusses the potential of 4H-SiC as a superior acoustic material for MEMS, particularly for high-performance resonator and extreme environments applications. Through a comparison of the crystalline structure along with the mechanical, acoustic, electrical, and thermal properties of 4H with respect to other SiC polytypes and silicon, it is shown that 4H-SiC possesses salient properties for MEMS applications, including its transverse isotropy and small phonon scattering dissipation. The utility and implementation of bonded SiC on insulator (4H-SiCOI) substrates as an emerging MEMS technology platform are presented. Additionally, this paper reports on the temperature-dependent mechanical properties of 4H-SiC, including the temperature coefficient of frequency and quality factor for Lame mode resonators. Finally, the 4H-SiC MEMS fabrication including its deep reactive ion etching is discussed. This paper provides valuable insights into the potential of 4H-SiC as a mechano-acoustic material and provides a foundation for future research in the field.

A novel approach to find feature selection using modified fuzzy C-mean

Feature selection, also known as dimensionality reduction, is a common preprocessing step in the fields of pattern recognition, data mining, and machine learning. This is a critical issue when mining high-dimensional, massive data sets. Preprocessing the data before analysis to acquire a smaller collection of representative features and keeping the optimal salient properties of the data leads to more compactness of the models trained and better generalization, as well as a decrease in processing time. Therefore, the standard for dimension reduction is to save just the information that is most important from the original data, as determined by certain optimality criteria. In this paper we are going to form a new framework include the combination of MFCM and APSO, to find the HIDS.