Discrete Approach Research Articles

Public preferences for edible invasive alien marine species - The Atlantic blue crab in southern Italy

Since 2014, the provision of the aquatic ecosystem services has been gradually affected due to the biological invasion of Callinectes sapidus (Rathbun, 1896, Crustacea, Decapoda, Portunidae), commonly known as Atlantic blue crab, across several lagoon-like locations in Italy. In addition, this serious aquatic invasive species, native of North American coasts, has already inflicted economic damage of about EUR 100 million to the Italian fishing and farming communities over the past year. To counter their severe and rapid spread, the Italian Government has encouraged the fishing communities to catch as many as possible and commercially exploit them for human consumption in an attempt to manage their expansion. Since there is an ongoing promotion for the consumption of blue crab by forging novel food businesses in Italy, this paper aims to predict the public preferences and their willingness to pay (WTP) towards this biological invader. For this purpose, a discrete choice experiment approach is used, by means of a multinomial logit model (MNL) and latent class model (LCM). The social field survey involves a representative sample of 440 valid respondents in Apulia Region, southern Italy. The descriptive statistics results reveal that 67.50% of the local citizens interviewed know about the blue crab invasion, while 29.09% of them have already consumed this seafood species. In addition, the MNL results show that the most appreciated attributes of the blue crabs by respondents are freshness and large size. Further, the LCM findings reveal two representative classes of Apulian consumers; the first group of citizens (70.9% of total respondents) expresses positive appreciation for consuming blue crabs, while the second group (29.1% of total respondents) is not willing to pay a premium price regarding this potential commercial activity. Furthermore, the econometric results show that the average value that Apulian’s (i.e. those belonging to Class/Group I of respondents) WTP for blue crabs’ consumption is about EUR 18 per kilogram. In this regard, this research has public and private implications and may reasonably promote the commercial exploitation of blue crabs, enhancing the reduction of its population density through human consumption as a promised management control strategy and forging a novel profitable business mainly for local small-scale artisanal fisheries.

Equilibrium dynamical models in the inner region of the Large Magellanic Cloud based on Gaia DR3 kinematics

The Large Magellanic Cloud (LMC) contains complex dynamics driven by both internal and external processes. The external forces are due to tidal interactions with the Small Magellanic Cloud and the Milky Way, while internally its dynamics mainly depend on the stellar, gas, and dark matter mass distributions. Despite this complexity, simple physical models often provide valuable insights into the primary driving factors. We used Gaia Data Release 3 (DR3) to explore how well equilibrium dynamical models based on the Jeans equations and the Schwarzschild orbit superposition method are able to describe the LMC's five-dimensional phase-space distribution and line-of-sight (LOS) velocity distribution, respectively. In the Schwarzschild model, we incorporated a triaxial bar component for the first time and derived the LMC's bar pattern speed. We fit comprehensive Jeans dynamical models to all Gaia DR3 stars with proper motion and LOS velocity measurements found in the footprint of the VISTA near-infrared survey of the Magellanic System using a discrete maximum likelihood approach. These models are very efficient at discriminating genuine LMC member stars from Milky Way foreground stars and background galaxies. They constrain the shape, orientation, and enclosed mass of the galaxy under the assumption of axisymmetry. We used the Jeans model results as a stepping stone to more complex two-component Schwarzschild models, which include an axisymmetric disc and a co-centric triaxial bar, which we fit to the LMC Gaia DR3 LOS velocity field using a $ minimisation approach. The Jeans models describe the rotation and velocity dispersion of the LMC disc well, and we find an inclination angle of $ circ circ $, line of nodes orientation of $ circ circ $, and an intrinsic thickness of the disc of $q_0^d = b a (minor to major axis ratio). However, bound to axisymmetry, these models fail to properly describe the kinematics in the central region of the galaxy dominated by the LMC bar. We used the derived disc orientation and the Gaia DR3 density image of the LMC to obtain the intrinsic shape of the bar. Using these two components as input to our Schwarzschild models, we performed orbit integration and weighting in a rotating reference frame fixed to the bar, deriving an independent measurement of the LMC bar pattern speed of $ Both the Jeans and Schwarzschild models predict the same enclosed mass distribution within a radius of $6.2$\,kpc of $ Msun.

A peridynamics approach modeling gas flow in porous media with damaged regions

The study of gas flow within porous media, particularly those characterized by complex pore-fracture networks, is critical for applications in fields such as shale gas engineering, gas extraction, and hydrogeology. This paper proposes a convenient and novel peridynamics approach to describe gas flow behavior in fracture regions and transition regions between fractured and intact regions in porous media, where the bonds naturally describe the interactions between material points in damaged areas. For implementation, we interpolate the permeability of the bonds in the transition region by using local damage values, and adopt a meshfree discretization approach that does not require additional mesh refinement for damaged or fractured regions. Numerical examples are provided and compared with the traditional finite element method to verify the accuracy and effectiveness of the proposed method.

Age differences in emotional reactivity to facets of sadness and anger

ABSTRACT Emotional reactivity, based on the discrete emotions approach (DEA), is related to opportunities or constraints across development. While prior research suggests sadness to be more adaptive in old age and anger to be more adaptive in young adulthood, there may be facets within these discrete emotion categories that further expand the DEA framework: loss-based vs. failure-based sadness and frustration-based vs. moral violation-based anger. A sample of 49 younger adults (M age = 20.00, SD = 2.26) and 51 older adults (M age = 66.00, SD = 4.94) were asked to relive and describe an emotional memory associated with facets of sadness and anger. Emotional reactivity was operationalized through self-report ratings on distinct facet categories. Results revealed a significant age difference in emotional reactivity to moral violation-based anger, with older adults being more reactive than younger adults. No other significant age differences were observed. These findings are discussed in terms of how further distinctions across emotional facets can inform a better understanding of affective experience across adulthood and old age.

Power flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach

Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

A dynamic discrete choice modelling approach for forward-looking travel mode choices

In this paper, we present a systematic approach based on dynamic discrete choice models (DDCM) to investigate individuals’ forward-looking mode choice behaviours in daily travel tours with multiple destinations. We propose a novel network transformation model that encompasses the entire decision space of all feasible mode combinations for every observed trip/tour in the dataset. By applying the well-established Recursive Logit model structure commonly used in path choice modelling, we address the tour mode choice problem effectively and quantify forward looking considerations in the mode choice process. The proposed model captures the complex considerations individuals take into account when making mode choices. The network transformation incorporates downstream mode limitations into the preceding mode choice options, enabling us to model individuals’ forward-looking behaviour and gain insights into how considerations of future trips such as a shopping in the evening, or school pick-up trip influence previous mode choice decisions earlier in the day. Uncovering and quantifying these hidden forward-looking factors can help modellers better explain the private car usage usually observed for the entire sequences of daily trips, even in presence of competitive alternative modes. The proposed network transformation also enables us to measure the effect of the requirement/preference to return private vehicles (car, motorcycle, and bicycle) home on mode choices in home-bound trips, and subsequently, on the entire daily mode choice decisions. To validate the proposed model, we utilise the VISTA household travel survey data from the Melbourne Metropolitan area in Australia. The model is compared against baseline models, demonstrating its statistical advantages. Additionally, intuitive illustrations using the data showcase the model’s efficacy. From transport planning and policy perspective, tour-based mode choice modelling provides a more comprehensive and precise understanding of travel behaviour by considering the sequence of trips made by an individual. This can help capture the interactions and dependencies between different trips, which trip-based models may overlook. The proposed model is more suitable for analysing the effects of policy interventions like congestion pricing, public transport investments, or new mobility initiatives, as they can better represent the cascading effects of such policies across multiple trips.

Fatigue crack growth in functionally graded materials using an adaptive phase field method with cycle jump scheme

Functionally graded materials provide versatility in adjusting the volume fractions of constituent materials to meet specific design requirements. However, this customization often introduces mode-mixity at the crack tip, posing challenges in predicting fracture under cyclic loading with discrete approaches and computationally expensive with conventional phase-field fracture models. To address these issues, this paper introduces an adaptive phase-field fracture formulation with cycle jump scheme to elegantly predict fatigue crack nucleation and growth in functionally graded materials. Within this framework, the effective properties at a point are estimated using the Mori–Tanaka homogenization scheme, while the crack growth due to cyclic load is captured by incorporating an additional fatigue degradation parameter. Moreover, the computational efficiency of the proposed framework is improved through an adaptive mesh refinement and explicit cycle jump scheme. The adaptive refinement scheme utilizes an error indicator derived from both the displacement solution and phase-field variable. The adaptive refinement scheme is integrated with efficient quadtree decomposition, which generates a hierarchical mesh structure. Hanging nodes resulting from the quadtree decomposition are efficiently handled using a polygonal finite element method. The proposed framework is validated against experimental and numerical results reported in the literature. Furthermore, we investigate the fatigue crack growth resistance across a broad range of material gradation directions, gaining valuable insights and identifying functionally graded materials with high fatigue resistance.

Partial abrasion modeling and variable load characteristics analysis for high-speed load sensing electro-hydrostatic actuator pump of aircraft

The load-sensing electro-hydrostatic actuator (LS-EHA) allows to reduce the thermal output and enhance the dynamic characteristics of the more electric aircraft (MEA). In addition, the forecasting of the LS-EHA is crucial for its efficient functioning. This study proposes a novel model for the analysis of overturning and wear characterization of the slipper, while focusing on the partial abrasion. The transient thermoelastic hydrodynamic (TEHD) lubricating analysis of the slipper pair is solved by an analytical model along with the finite difference method. In the proposed partial abrasion model, the non-uniformity of the oil film thickness and the partial abrasion contour for the slipper bottom surface are considered through the discrete friction area approach in order to reach a higher precision. Afterward, the overturning behavior characteristics of the slipper and the variation law in its overturning behavior under different rotation speeds, load delivery pressures, and swashplate angles are analyzed. The obtained results demonstrate that the lubricating oil film field is unevenly distributed, and the overturning behavior of the slipper has certain variable load characteristics. Finally, the effectiveness and precision of the proposed partial abrasion model are validated through comparative simulation experiments and analysis.

Probability of early infection extinction depends linearly on the virus clearance rate.

We provide an in silico study of stochastic viral infection extinction from a pharmacokinetical viewpoint. Our work considers a non-specific antiviral drug that increases the virus clearance rate, and we investigate the effect of this drug on early infection extinction. Infection extinction data are generated by a hybrid multiscale framework that applies both continuous and discrete mathematical approaches. The central result of our paper is the observation, analysis and explanation of a linear relationship between the virus clearance rate and the probability of early infection extinction. The derivation behind this simple relationship is given by merging different mathematical toolboxes.

Robust concave utility maximization over chance constraints

This paper first studies an expected utility problem with chance constraints and incomplete information on a decision maker’s utility function. The model maximizes the worst-case expected utility of random outcome over a set of concave functions within a novel ambiguity set, while the underlying probability distribution is known with the assumption of a discretization of possible realizations for the uncertainty parameter. To obtain computationally tractable formulations, we employ a discretization approach to derive a max–min chance-constrained approximation of this problem. This approximation is further reformulated as a mixed-integer program. We show that the discrete approximation converges to the true counterpart under mild assumptions. We also present a row generation algorithm for optimizing the max–min program. A computational study for a bin-packing problem and a multi-item newsvendor problem is conducted to demonstrate the benefit of the proposed framework and the computational efficiency of our algorithm. We find that the row generation algorithm can significantly reduce the computational time, and our robust policy could achieve a better out-of-sample performance when compared with the non-robust policy and the one without the chance constraints.

Mechanics of Materials: Multiscale Design of Advanced Materials and Structures

Materials can now be designed and architectured like structural components for targeted mechanical and physical properties. Structures and microstructures should not be studied independently and their design will benefit from a multiscale approach combining nonlinear continuum mechanics approaches and physical descriptions of elasticity, viscoplasticity, phase transformations and damage of microstructures, at various scales. The aim of the workshop was to gather outstanding junior and senior researchers in the various branches of mathematics, physics and engineering sciences suited to address the question of design of materials and structures by means of multiscale discrete and continuum approaches to their constitutive behavior. Examples include atomic or macroscopic lattices, random or periodic cellular materials, smart materials like shape memory alloys, 3D woven composites, acoustic and electromagnetic metamaterials, etc. Modern continuum mechanics relies on sophisticated constitutive laws for anisotropic materials exhibiting elastoviscoplastic behavior, still a field of intense research with new mathematical concepts. In particular size-dependent properties are addressed by resorting to generalized continua such as gradient or micromorphic and phase field models. The latter are attractive for the simulation of microstructure evolution coupled with mechanics, due to thermodynamic and metallurgical processes and damage. Scale transition and homogenization methods for continuous and discrete systems are required for the determination of effective material and structural behavior. Metamaterials are architectured materials specifically designed to achieve certain propagation and dispersion properties of elastic and plastic waves. Optimization strategies for the design of optimal architectures are involved in the design process. Target functions for optimization are now based on multicriteria (stiffness, strength, thermal expansion, transport properties, anisotropy etc.).

Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition.

Myoelectric control, the use of electromyogram (EMG) signals generated during muscle contractions to control a system or device, is a promising input, enabling always-available control for emerging ubiquitous computing applications. However, its widespread use has historically been limited by the need for user-specific machine learning models because of behavioural and physiological differences between users. Leveraging the publicly available 612-user EMG-EPN612 dataset, this work dispels this notion, showing that true zero-shot cross-user myoelectric control is achievable without user-specific training. By taking a discrete approach to classification (i.e., recognizing the entire dynamic gesture as a single event), a classification accuracy of 93.0% for six gestures was achieved on a set of 306 unseen users, showing that big data approaches can enable robust cross-user myoelectric control. By organizing the results into a series of mini-studies, this work provides an in-depth analysis of discrete cross-user models to answer unknown questions and uncover new research directions. In particular, this work explores the number of participants required to build cross-user models, the impact of transfer learning for fine-tuning these models, and the effects of under-represented end-user demographics in the training data, among other issues. Additionally, in order to further evaluate the performance of the developed cross-user models, a completely new dataset was created (using the same recording device) that includes known covariate factors such as cross-day use and limb-position variability. The results show that the large data models can effectively generalize to new datasets and mitigate the impact of common confounding factors that have historically limited the adoption of EMG-based inputs.

A Method for Analyzing Data from 1- and 2-Dimensional Relaxation and Diffusion NMR Experiments by Determination of their Expectation Values and Standard Deviations

A method for analyzing ill-posed multi-exponentially decaying data from 1- and 2-Dimensional experiments by Determination of the Expectation Values and their Standard Deviation has been developed. It combines a repeated use of the discrete Anahess approach for analyzing the dynamic data where a regrouping of the noise in the data is performed for each repetition. These resulting expectation values are used as initial and restricting values to produce a distribution using the Inverse Laplace transform, where position and volume of the distribution can then be reported with expectation value and standard deviation. The method is verified on synthetic data and tested on real data in both one and two dimensions.

An automatic simulation pipeline for coupled simulations of acoustic damping materials

AbstractFoamed materials are widely used to reduce noise due to their comparably good acoustic damping behavior. However, out of a large variety of these materials a suitable candidate has to be identified for each application. This is a challenging process that is typically guided by experiments and experience. While numerical simulations could support these experiments and reduce the effort to a great extent, no suitable discretization approach has yet been established that can fully capture the complex geometry of the foam. A fully resolved model is desirable in order to yield reliable predictions that can then be used to establish homogenized models. We established a monolithic coupling approach based on the finite cell method (FCM) that realizes a vibroacoustic simulation in this sense. The fluid and the structure domain are discretized by Cartesian grids and the geometry defined based on computed tomography scans is accounted for during the quadrature of the weak form. Our simulation in the time domain makes use of explicit time marching schemes and is therefore limited by a critical time step size. This is known to be arbitrarily low for discretizations with the FCM containing cells with arbitrarily small support. As a remedy against this we use the classical ‐stabilization technique and investigate its potentials and limitations.

Numerical investigation of the thermal hydrofracturing behavior of the Callovo-Oxfordian claystone

This study addresses the thermal hydrofracturing behavior in claystone within the context of the high-level and intermediate-level long-lived radioactive waste disposal. The heat generated by the waste packages will lead to a temperature increment within the host formation, inducing a pore pressure build-up essentially due to the difference between the thermal expansion coefficient of the pore water and that of the solid skeleton. If the induced pore pressure build-up is too high, the host formation will experience tensile stresses, potentially exceeding its tensile strength and resulting in fracturing. Understanding of these processes and improving numerical models to reproduce them will help the design, optimization, and safety of the repository. Additionally, it will contribute to demonstrating robustness by showing that such processes are not expected to occur at the repository scale.This study was conducted as part of the DECOVALEX-2023 project and synthesizes the efforts of six research teams modelling laboratory thermal extension tests conducted on Callovo-Oxfordian claystone (COx) samples, as well as an in-situ thermal hydrofracturing experiment conducted at the Meuse/Haute-Marne Underground Research Laboratory in France. The teams used different numerical codes with different approaches, including continuum and discrete approaches, to model these two tests. The laboratory tests were used to calibrate the teams’ models, such as the fracturing criterion. The teams considered a thermo-hydromechanical formulation under saturated conditions. One of the key features of their models was the incorporation of changes in the hydraulic properties of the COx through hydromechanical coupling.The approaches developed by the teams demonstrated their capability to analyze and reproduce fracture initiation in the COx in terms of time of occurrence and location based on their respective stress analyses. However, attempts to reproduce fracture aperture or fracture propagation were less accurate and remain areas for future research, which were beyond the scope of this study.

Uniform exponential stability approximations of semi‐discretization schemes for two hybrid systems

The uniform exponential stabilities (UESs) of two hybrid control systems comprised of a wave equation and a second‐order ordinary differential equation are investigated in this study. Linear feedback law and local viscosity are considered, as are nonlinear feedback law and internal anti‐damping. The hybrid system is first reduced to a first‐order port‐Hamiltonian system with dynamical boundary conditions, and the resulting system is discretized using the average central‐difference scheme. Second, the UES of the discrete system is obtained without prior knowledge of the exponential stability of the continuous system. The frequency domain characterization of UES for a family of contractive semigroups and the discrete multiplier approach are used to validate the main conclusions. Finally, the Trotter–Kato theorem is used to perform a convergence study on the numerical approximation approach. Most notably, the exponential stability of the continuous system is derived by the convergence of energy and UES, which is a novel approach to studying the exponential stability of some complex systems. Numerical simulation is used to validate the effectiveness of the numerical approximating strategy.

Dynamic Effects Analysis in Fractional Memristor-Based Rulkov Neuron Model.

Mathematical models such as Fitzhugh-Nagoma and Hodgkin-Huxley models have been used to understand complex nervous systems. Still, due to their complexity, these models have made it challenging to analyze neural function. The discrete Rulkov model allows the analysis of neural function to facilitate the investigation of neuronal dynamics or others. This paper introduces a fractional memristor Rulkov neuron model and analyzes its dynamic effects, investigating how to improve neuron models by combining discrete memristors and fractional derivatives. These improvements include the more accurate generation of heritable properties compared to full-order models, the treatment of dynamic firing activity at multiple time scales for a single neuron, and the better performance of firing frequency responses in fractional designs compared to integer models. Initially, we combined a Rulkov neuron model with a memristor and evaluated all system parameters using bifurcation diagrams and the 0-1 chaos test. Subsequently, we applied a discrete fractional-order approach to the Rulkov memristor map. We investigated the impact of all parameters and the fractional order on the model and observed that the system exhibited various behaviors, including tonic firing, periodic firing, and chaotic firing. We also found that the more I tend towards the correct order, the more chaotic modes in the range of parameters. Following this, we coupled the proposed model with a similar one and assessed how the fractional order influences synchronization. Our results demonstrated that the fractional order significantly improves synchronization. The results of this research emphasize that the combination of memristor and discrete neurons provides an effective tool for modeling and estimating biophysical effects in neurons and artificial neural networks.

Control of a Wind Turbine Working in the Intermediate Velocity Zone: A Model Free Discrete Time Approach

Control of a Wind Turbine Working in the Intermediate Velocity Zone: A Model Free Discrete Time Approach

Bridging micro nature with macro behaviors for granular thermal mechanics

The connection between micro-level characteristics and macroscopic properties in granular heat transfer and mechanics is fundamental and crucial. This study proposes a novel discrete element approach incorporating granular heat transfer, contact bonding, and granular stress tensor models to investigate the mechanical and thermal responses of continuum media composed of constituent spheres. Eight benchmark tests were devised to bridge the long-standing gap between micro and macro properties in granular materials. Through these tests, the numerical solutions obtained from discrete element modeling match well with existing analytical or finite element solutions derived from continuum-based theory. This validation underscores the rationality and reliability of the granular heat transfer model, contact bonding model, and granular stress tensor model. Moreover, the study highlights the consistency between continuum-based theory and discontinuum-based theory. A minor distinction between continuum-based models and discrete element models emerges near the boundaries due to variations in the specification of boundary conditions. This discrepancy can be clarified by Saint-Venant's Principle, thus validating the accuracy of the microscale heat transfer and mechanics theory for granular materials. Five mono-disperse packing structures, including simple cubic (SC), body-centered cubic (BCC), face-centered cubic (FCC), hexagonal close packing (HCP), and random packing (Random), were further analyzed to examine their influence on heat transfer performance. Numerical results reveal that higher coordination numbers and solid volume fractions correspond to higher apparent thermal conductivity of granular assemblies, thus elucidating the connection between micro packing configurations and macroscopic heat transfer properties. The apparent thermal conductivity for different crystal configurations follows the sequence: HCP ≒ FCC > BCC ≒ Random > SC. To improve the accuracy and physical relevance of the proposed model, the effect of particle contact area needs to be further incorporated into the granular heat transfer model.

Discrete differential geometry-based model for nonlinear analysis of axisymmetric shells

In this paper, we propose a novel one-dimensional (1D) discrete differential geometry (DDG)-based numerical method for geometrically nonlinear mechanics analysis (e.g., buckling and snapping) of axisymmetric shell structures. Our numerical model leverages differential geometry principles to accurately capture the complex nonlinear deformation patterns exhibited by axisymmetric shells. By discretizing the axisymmetric shell into interconnected 1D elements along the meridional direction, the in-plane stretching and out-of-bending potentials are formulated based on the geometric principles of 1D nodes and edges under the Kirchhoff–Love hypothesis, and elastic force vector and associated Hessian matrix required by equations of motion are later derived based on symbolic calculation. Through extensive validation with available theoretical solutions and finite element method (FEM) simulations in literature, our model demonstrates high accuracy in predicting the nonlinear behavior of axisymmetric shells. Importantly, compared to the classical theoretical model and three-dimensional (3D) FEM simulation, our model is highly computationally efficient, making it suitable for large-scale real-time simulations of nonlinear problems of shell structures such as instability and snap-through phenomena. Moreover, our framework can easily incorporate complex loading conditions, e.g., boundary nonlinear contact and multi-physics actuation, which play an essential role in the use of engineering applications, such as soft robots and flexible devices. This study demonstrates that the simplicity and effectiveness of the 1D discrete differential geometry-based approach render it a powerful tool for engineers and researchers interested in nonlinear mechanics analysis of axisymmetric shells, with potential applications in various engineering fields.