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This chapter presents some of the biomaterials used in different clinical applications. Examples of current commercially available devices are presented, together with the advantages of the biomaterials used for each application and the rationale behind the choice.
Capacitors and inductors are introduced, along with their equivalent circuit laws. Switched RC circuits are thoroughly analyzed. The response of an RC circuit to a sinusoidal drive voltage is analyzed and leads to a discussion of high- and low-pass filters, phase shifters, integrators, and differentiators. The use of complex numbers in circuit analysis is introduced and applied to sinusoidally driven series RC, LR, and LRC circuits as well as the switched LRC circuit. Fourier analysis and its meaning are presented. The operation of transformers is introduced.
If more than one good linear approximation is available, then it is natural to try to exploit all of them simultaneously. This is called multiple linear cryptanalysis. The first part of this chapter discusses multiple linear cryptanalysis in general. The second part focuses on the special case with a set of masks that forms a vector space, which is called multidimensional linear cryptanalysis.
Finding linear trails with high absolute correlation quickly becomes tedious work, especially for ciphers with a more complicated structure than the example that we have worked with so far. Since the total number of trails is finite, finding linear trails with a maximal absolute correlation is an example of a combinatorial optimization problem. This chapter discusses three commonly used optimization methods: Matsui’s branch and bound method, mixed-integer linear programming, and satisfiability or satisfiability modulo theories. At the same time, the chapter introduces two additional example ciphers that follow a different design strategy.
The conventional $\textrm{e}^N$ laminar-to-turbulent transition-prediction method focuses on the relative growth rate, called the $N$ factor, and neglects receptivity. To improve predictions, Mack (1977) proposed the amplitude method to incorporate receptivity, nonlinear effects and broadband characteristics. Currently, the lack of accurate receptivity coefficients, estimates of initial disturbance amplitudes at the lower-branch neutral position, referred to as branch I (where the imaginary part of the spatial wavenumber is zero), hinders the application of the amplitude method. Although experimental- and numerical-receptivity analyses have been conducted previously, they rely on correlations or indirect approaches. For the purpose of direct evaluation, this study applies bi-orthogonal decomposition to direct numerical simulation (DNS) data of a hypersonic boundary layer over a blunt cone, extracting initial amplitudes of instability modes. The decomposition framework incorporates both boundary-layer and entropy-layer modes, enabling direct evaluation of receptivity coefficients at branch I. The decomposed modal amplitudes show reduced multimode interference and the receptivity coefficients have been computed to have fewer oscillations. With an overall greater magnitude, the receptivity coefficients suggest a possible earlier transition location than the previous numerical study by He & Zhong (2023 J. Spacecr. Rockets, vol. 60, no. 6, pp. 1927–1938). Additionally, a discrete entropy-layer mode is recovered, contributing to instability development alongside modes F and S. These findings support the use of bi-orthogonal decomposition as a practical tool for receptivity analysis and enhancement of the amplitude method in transition prediction.
The nonlinear growth of perturbations in hydrodynamic interfacial instabilities can be of particular importance in both scientific research (e.g. supernova explosion) and engineering applications (e.g. inertial confinement fusion). One of the most significant issues in these instabilities is the long-time nonlinear bubble evolution of a single-mode Rayleigh–Taylor instability (RTI), which remains as an unsolved and challenging problem since Taylor’s seminal work more than seven decades ago. Introduced in this paper is an analytical model for the long-time evolution of bubble velocity, curvature and vorticity, which is established by considering the vorticity accumulation around the bubble in a bilaterally rotational flow system under the classical planar potential-flow theory framework. The proposed theoretical model incorporates not only the classical linear, nonlinear and quasi-steady stages, but the late re-acceleration stage. Meanwhile, the new model can capture the phenomenon of secondary velocity saturation following the stage of bubble re-acceleration. The good agreement between the present model and numerical simulations for all density ratios and dimensions confirms that the accumulation in vorticity tends to break the early stage buoyancy-drag equilibrium mechanism and leads to the establishment of a new equilibrium in the late-stage RTI.
Asymmetries and anisotropies are widespread in biological systems, including in the structure and dynamics of cilia and eukaryotic flagella. These microscopic, hair-like appendages exhibit asymmetric beating patterns that break time-reversal symmetry needed to facilitate fluid transport at the cellular level. The intrinsic anisotropies in ciliary structure can promote preferential beating directions, further influencing their dynamics. In this study, we employ numerical simulation and bifurcation analysis of a mathematical model of a filament driven by a follower force at its tip to explore how intrinsic curvature and direction-dependent bending stiffnesses impact filament dynamics. Our results show that while intrinsic curvature is indeed able to induce asymmetric beating patterns when filament motion is restricted to a plane, this beating is unstable to out-of-plane perturbations. Furthermore, we find that a three-dimensional whirling state seen for isotropic filament dynamics can be suppressed when sufficient asymmetry or anisotropy are introduced. Finally, for bending stiffness ratios as low as 2, we demonstrate that combining structural anisotropy with intrinsic curvature can stabilise asymmetric beating patterns, highlighting the crucial role of anisotropy in ciliary dynamics.
The motionless conducting state of liquid-metal convection with an applied vertical magnetic field confined in a vessel with insulating sidewalls becomes linearly unstable to wall modes through a supercritical pitchfork bifurcation. Nevertheless, we show that the transition proceeds subcritically, with stable finite-amplitude solutions with different symmetries existing at parameter values beneath this linear stability threshold. Under increased thermal driving, the branch born from the linear instability becomes unstable and solutions are attracted to the most subcritical branch, which follows a quasiperiodic route to chaos. Thus, we show that the transition to turbulence is controlled by this subcritical branch and hence turbulent solutions have no connection to the initial linear instability. This is further quantified by observing that the subcritical equilibrium solution sets the spatial symmetry of the turbulent mean flow and thus organises large-scale structures in the turbulent regime.
This paper presents the development of a graph autoencoder architecture capable of performing projection-based model-order reduction (PMOR) using a nonlinear manifold least-squares Petrov–Galerkin (LSPG) projection scheme. The architecture is particularly useful for advection-dominated flows modeled by unstructured meshes, as it provides a robust nonlinear mapping that can be leveraged in a PMOR setting. The presented graph autoencoder is constructed with a two-part process that consists of (1) generating a hierarchy of reduced graphs to emulate the compressive abilities of convolutional neural networks (CNNs) and (2) training a message passing operation at each step in the hierarchy of reduced graphs to emulate the filtering process of a CNN. The resulting framework provides improved flexibility over traditional CNN-based autoencoders because it is readily extendable to unstructured meshes. We provide an analysis of the interpretability of the graph autoencoder’s latent state variables, where we find that the Jacobian of the decoder for the proposed graph autoencoder provides interpretable mode shapes akin to traditional proper orthogonal decomposition modes. To highlight the capabilities of the proposed framework, which is named geometric deep least-squares Petrov–Galerkin (GD-LSPG), we benchmark the method on a one-dimensional Burgers’ model with a structured mesh and demonstrate the flexibility of GD-LSPG by deploying it on two test cases for two-dimensional Euler equations that use an unstructured mesh. The proposed framework is more flexible than using a traditional CNN-based autoencoder and provides considerable improvement in accuracy for very low-dimensional latent spaces in comparison with traditional affine projections.
Internal waves in a two-layer fluid with rotation are considered within the framework of Helfrich’s $f$-plane extension of the Miyata–Maltseva–Choi–Camassa model. We develop simultaneous asymptotic expansions for the evolving mean fields and deviations from them to describe a large class of uni-directional waves via the Ostrovsky equation, which fully decouples from mean-field variations. The latter generate additive inertial oscillations in the shear and in the phase of both the interfacial displacement and shear. Unlike conventional derivations leading to the Ostrovsky equation, our formulation does not impose the zero-mean constraints on the initial conditions of any variable. Using the constructed solutions, we model the evolution of quasi-periodic initial conditions close to the cnoidal wave solutions of the Korteweg–de Vries (KdV) equation but with local defects, both with and without rotation. We show that rotation leads to the emergence of bursts of internal waves and shear currents, qualitatively similar to the wavepackets generated from solitons and modulated cnoidal waves in earlier studies, but emerging much faster. We also show that cnoidal waves with expansion defects discussed in this work are generalised travelling waves of the KdV equation: they satisfy all conservation laws of the KdV equation (appropriately understood), as well as the Weirstrass–Erdmann corner condition for broken extremals of the associated variational problem and a natural weak formulation. Being smoothed in numerical simulations, they behave, in the absence of rotation, as long-lived states with no visible evolution, while rotation leads to the emergence of strong bursts.
In recent years, there has been growing interest regarding the impact of human movement quality on health. However, assessing movement quality outside of laboratories or clinics remains challenging. This study aimed to evaluate the capabilities of consumer-grade wearables to assess movement quality and to consider optimal sensor locations. Twenty-two participants wore Polar Verity Sense magnetic, angular rate, and gravity (MARG) sensors on their chest and both wrists, thighs, and ankles, while performing repeated bodyweight movements. The Madgwick sensor-fusion algorithm was utilized to obtain three-dimensional orientations. Concurrent validity, quantified using the root-mean-square-error (RMSE), was established against a Vicon optical motion capture system following time-synchronization and coordinate-system alignment. The chest sensors demonstrated the highest accuracies overall, with mean RMSE ($ {\mathrm{RMSE}}_{\mathrm{mean}} $) less than 9.0° across all movements. In contrast, the wrist sensors varied considerably ($ 5.5\hskip-2pt {}^{\circ}\le {\mathrm{RMSE}}_{\mathrm{mean}}\le 139.1\hskip-2pt {}^{\circ} $). Ankle and thigh sensors yielded mixed results, with the $ {\mathrm{RMSE}}_{\mathrm{mean}} $ ranging from 2.0° to 40.0°. Notably, yaw angles consistently demonstrated higher discrepancies overall, while pitch and roll were relatively more stable. This study highlights the potential of consumer-grade MARG sensors to increase the real-world applicability and accessibility of complex biomechanical models. It also accentuates the requirement for strategic sensor placement and refined calibration and postprocessing methods to ensure accuracy.
We study the stability of a steady Eckart streaming jet flowing in a closed cylindrical cavity. This configuration is a generic representation of industrial processes where driving flows in a cavity by means of acoustic forcing offers a contactless way of stirring or controlling flows. Successfully doing so, however, requires sufficient insight into the topology induced by the acoustic beam. This, in turn, raises the more fundamental question of whether the basic jet topology is stable and, when it is not, of the alternative states that end up being acoustically forced. To answer these questions, we consider a flow forced by an axisymmetric diffracting beam of attenuated sound waves emitted by a plane circular transducer at one cavity end. At the opposite end, the jet impingement drives recirculating structures spanning nearly the entire cavity radius. We rely on linear stability analysis (LSA) together with three-dimensional nonlinear simulations to identify the flow destabilisation mechanisms and to determine the bifurcation criticalities. We show that flow destabilisation is closely related to the impingement-driven recirculating structures, and that the ratio $C_R$ between the cavity and the maximum beam radii plays a key role on the flow stability. In total, we identified four mode types destabilising the flow. For $4 \leqslant C_R \leqslant 6$, a non-oscillatory perturbation rooted in the jet impingement triggers a supercritical bifurcation. For $C_R = 3$, the flow destabilises through a subcritical non-oscillatory bifurcation and we explain the topological change of the unstable perturbation by analysing its critical points. Further reducing $C_R$ increases the shear within the flow and gradually moves the instability origin to the shear layer between impingement-induced vortices: for $C_R = 2$, an unstable travelling wave grows out of a subcritical bifurcation, which becomes supercritical for $C_R=1$. For each geometry, the nonlinear three-dimensional (3-D) simulations confirm both the topology and the growth rate of the unstable perturbation returned by LSA. This study offers fundamental insight into the stability of acoustically driven flows in general, but also opens possible pathways to either induce turbulence acoustically or to avoid it in realistic configurations.
The conceptual design of mission-tailored aircraft is increasingly shifting towards system of systems (SoS) perspectives that account for system interactions using a holistic view. Agent-based modelling and simulation (ABMS) is a common approach for analysing an SoS, but the behaviour of its agents tends to be defined by rigid behaviour trees. The present work aims to evaluate the suitability of a prompt-engineered large language model (LLM) acting as the Incident Commander (IC), replacing the fixed behaviour trees that govern the agents’ decisions. The research contributes by developing a prompting framework for operational guidelines, constraints, and priorities to obtain an LLM commander within a wildfire suppression, SoS capable of replicating human decisions. By enabling agents in a simulation model with decision-making capabilities closer to those expected from humans, the commander’s decisions and potential emergent patterns can be translated into more defined requirements for aircraft conceptual design (ACD) (e.g., endurance, payload, sensors, communications, or turnaround requirements). Results showed that an LLM commander facilitated adaptive and context-aware decisions that can be analysed via decision logs. The results allow designers to derive aircraft requirements for their specific roles from operational outcomes rather than a priori assumptions, linking SoS mission needs and ACD parameters.
The crystal structure of racemic afoxolaner has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Afoxolaner crystallizes in space group P21/a (#14) with a = 9.6014(6), b = 14.0100(11), c = 39.477(10) Å, β = 94.389(7)°, V = 5,294.7(17) Å3, and Z = 8 at 298 K. The crystal structure consists of layers of molecules parallel to the ab-plane. The boundaries of the layers are rich in halogens. Within the layers, there is parallel stacking of rings along both the a- and b-axes. Two classical N–H···O hydrogen bonds link the two independent molecules into dimers. The powder pattern has been submitted to the International Centre for Diffraction Data (ICDD®) for inclusion in the Powder Diffraction File™ (PDF®).
We use scanning-tomographic particle image velocimetry introduced by Casey, Sakakibara & Thoroddsen (Phys. Fluids, vol. 25 (2), 2013, p. 025102) to measure the volumetric velocity field in a fully turbulent round jet. The experiments are performed for ${Re}=2640,\, 5280$ and $10\,700.$ Using Fourier-based proper orthogonal decomposition (POD), the dominant modes that describe the velocity and vorticity fields are extracted. We employ a new method of averaging POD modes from different experimental runs using a phase-synchronisation with respect to a common basis. For the dominant azimuthal wavenumber $m=1,$ the first and second POD modes of the axial velocity have opposite signs and appear as embracing helical structures, with opposite twist, while, for the same parameters, POD modes of the radial velocity extend to the axis of symmetry and, interestingly, also show a helical shape. The $(m=1)$-POD modes for the azimuthal vorticity appear as two separate structures, consisting of C-shaped loops in the region away from the axis and helically twisted axial tubes close to the axis. The corresponding axial vorticity modes are cone-like and appear as inclined streaks of alternate sign in the $r$–$z$-plane, similar to velocity streaks seen in wall-bounded shear flows. Temporal analysis of the dynamics shows that a $(m=1)$ two-mode velocity POD representation precesses with a Strouhal number of approximately $St=0.05,$ while the same reconstruction based on vorticity POD modes has a slightly higher Strouhal number of $St=0.06.$
Reinforcement learning (RL) has demonstrated computational efficiency and autonomy in solving unmanned aerial vehicle (UAV) obstacle avoidance problems. However, practical applications still remain challenges, such as three-dimensional manoeuvres, dynamic obstacles and kinematic constraints. This paper proposes a real-time obstacle avoidance method based on RL and a kinematic model, where the RL framework outputs 3D-axis velocity to enable continuous UAV manoeuver control. To perceive large-scale, highly dynamic obstacles, we establish a 3D safety separation model and construct a modular observation matrix to enhance perception capability. The Soft Actor-Critic (SAC) algorithm is adopted to enhance stochastic exploration in high-dimensional state space while incorporating flight uncertainty. Under simulation, the proposed method effectively avoids dynamic obstacles. The optimised state space boosts learning speed and performance. This provides an effective solution for UAV autonomous obstacle avoidance in complex environments.