Initial State Research Articles

Coupled hydromechanical modelling of cone penetration in layered liquefiable soils

Soil layering modifies cone penetration measurements when the cone is close to layer boundaries. Transition zone and thin-layer effects appear, complicating interpretation. To help identify the mechanisms underlying transition and thin-layer effects, several series of realistic simulations of cone penetration in layered soils are presented. Cone penetration tests are simulated using fully coupled hydromechanical models solved with the particle finite-element method. A constitutive model capable of representing flow liquefaction is employed to explore the effect of embedded layers with different initial state parameter and/or hydraulic conductivity than the host soil. Sensing and development distances for tip resistance and excess pore pressure are examined, as well as the effect of layering on dissipation tests. It is shown how distortion of layer interfaces by the cone is captured, explaining several characteristics of pore pressure and dissipation records. It is also shown that looser soil states may be hidden in the tip resistance trace by simultaneous changes in soil hydraulic conductivity.

On the role of poroelastic stressing and pore pressure diffusion in discrete fracture and fault system in triggering post-injection seismicity in enhanced geothermal systems

Injection-induced seismicity has become one of the most critical challenges for the widespread deployment of Enhanced Geothermal Systems (EGS). In particular, some EGS development projects have led to large, damaging earthquakes that unexpectedly occurred far off the stimulated reservoir region and, in particular, after stopping fluid injection. Yet, the causative mechanisms of these seismicity patterns remain highly elusive. Here, we identify a combination of mechanisms that could explain delayed seismicity in EGS sites by conducting fully-coupled hydromechanical simulations of the hydraulic stimulation of a naturally-fractured granitic reservoir. The model comprises a sparse network of long, variably-oriented fractures interacting with a nearby, critically-oriented fault. The results show that the presence of fractures introduces notable nonlinearities in the flow field and rock deformation and significantly expands the rock volume affected by fluid injection. First, the stimulated fracture network provides highly-permeable conduits for communicating elevated pore pressure over long distances. Second, the anisotropic expansion of fractures generates shear stress that is transmitted almost instantaneously across the reservoir. The pore pressure and stress perturbations can not only cause slip along fractures, inducing (micro)seismicity during injection, but also affect the stability of nearby faults, which may not necessarily be pressurized during injection. The transferred poroelastic stresses can increase or decrease the slip tendency along different fault segments. However, the fault may reactivate only after several months following injection when a progressive pore pressure diffusion modulated by the transient fault permeability evolution brings a critically-stressed fault segment to failure conditions. We also find that the spatiotemporal evolution of seismicity depends largely on the nearby fault orientation, hydromechanical properties, and hydraulic connection with the fracture network, as well as the initial state of stress. We conclude that accurate subsurface characterization and continuous monitoring during and after injection should allow for managing the risks posed by injection-induced seismicity and safely unlocking the immense potential for clean and sustainable geothermal energy.

Iterative learning control approach for linear discrete time-varying systems with different initial time points

Different from the existing iterative learning control (ILC) works for dynamical systems with iteration-varying trial lengths, in which the terminal time points are not identical, this paper presents an ILC approach for linear discrete time-varying (LDTV) systems with different initial time points. A piecewise ILC law is designed by using the segment compensation techniques in time axis. Through a rigorous mathematical analysis, it is theoretically proved that as the mathematical expectation of the iterative initial state equals to the desired initial state, the ILC tracking error at the maximum universal time interval can be driven into a bounded region in mathematical expectation sense. Particularly, the mathematical expectation of the ILC tracking error at the maximum universal time interval can even be driven to zero under a certain initial state condition. Numerical simulations are provided to verify the effectiveness of the proposed ILC scheme.

Comparative proteomic analysis of seed germination between allotetraploid cotton Gossypium hirsutum and Gossypium barbadense

Seed germination, a key initial event in the plant life cycle, directly affects cotton yield and quality. Gossypium barbadense and Gossypium hirsutum gradually evolved through polyploidization, resulting in different characteristics, and this interspecific variation lacks genetic and molecular explanation. This work aimed to compare the proteomes between G. barbadense and G. hirsutum during seed germination. Here, we identified 2740 proteins for G. barbadense and 3758 for G. hirsutum. In the initial state, proteins in two cotton involved similar bioprocess, such as sugar metabolism, DNA repairing, and ABA signaling pathway. However, in the post-germination stage, G. hirsutum expressed more protein related to redox homeostasis, peroxidase activity, and pathogen interactions. Analyzing the different expression patterns of 915 single-copy orthogroups between the two kinds of cotton indicated that most of the differentially expressed proteins in G. barbadense were related to carbon metabolism. In contrast, most proteins in G. hirsutum were associated with stress response. Besides that, by proteogenomic analysis, we found 349 putative non-canonical peptides, which may be involved in plant development. These results will help to understand the different characteristics of these two kinds of cotton, such as fiber quality, yield, and adaptability. Significance statementCotton is the predominant natural fiber crop worldwide; Gossypium barbadense and Gossypium hirsutum have evolved through polyploidization to produce differing traits. However, given their specific features, the divergence of mechanisms underlying seed germination between G. hirsutum and G. barbadense has not been discussed. Here, we explore what protein contributes to interspecific differences between G. barbadense and G. hirsutum during the seed germination period. This study helps to elucidate the evolution and domestication history of cotton polyploids and may allow breeders to understand their domestication history better and improve fiber quality and adaptability.

Assessment of carbon sequestration potential of mining areas under ecological restoration in China

Mining activities aggravate the ecological degradation and emission of greenhouse gases throughout the world, thereby affecting the global climate and posing a serious threat to the ecological safety. Vegetation restoration is considered to be an effective and sustainable strategy to improve the post-mining soil quality and functions. However, we still have a limited knowledge of the impact of vegetation restoration on carbon sequestration potential in mining areas. In this pursuit, the present study was envisaged to integrate the findings from studies on soil organic carbon (SOC) sequestration in mining areas under vegetation restoration with field monitoring data. The carbon sequestration potential under vegetation restoration in China's mining areas was estimated by using a machine learning model. The results showed that (1) Vegetation restoration exhibited a consistently positive impact on the changes in the SOC reserves. The carbon sequestration potential was the highest in mixed forests, followed by broad-leaved forests, coniferous forests, grassland, shrubland, and farmland; (2) The number of years of vegetation restoration and mean annual precipitation were found to be the important moderating variables affecting the SOC reserves in reclaimed soils in mining areas; (3) There were significant differences in the SOC sequestration potential under different vegetation restoration scenarios in mining areas in China. The SOC sequestration potential reached up to 9.86 million t C a−1, when the soil was restored to the initial state. Based on the meta-analysis, the maximal attainable SOC sequestration potential was found to be 4.26 million t C a−1. The SOC sequestration potential reached the highest level of 12.86 million t C a−1, when the optimal vegetation type in a given climate was restored. The results indicated the importance of vegetation restoration for improving the soil sequestration potential in mining areas. The time lag in carbon sequestration potential for different vegetation types in mining areas was also revealed. Our findings can assist the development of ecological restoration regimens in mining areas to mitigate the global climate change.

Synchronization and Random Attractors in Reaction Jump Processes

AbstractThis work explores a synchronization-like phenomenon induced by common noise for continuous-time Markov jump processes given by chemical reaction networks. Based on Gillespie’s stochastic simulation algorithm, a corresponding random dynamical system is formulated in a two-step procedure, at first for the states of the embedded discrete-time Markov chain and then for the augmented Markov chain including random jump times. We uncover a time-shifted synchronization in the sense that—after some initial waiting time—one trajectory exactly replicates another one with a certain time delay. Whether or not such a synchronization behavior occurs depends on the combination of the initial states. We prove this partial time-shifted synchronization for the special setting of a birth-death process by analyzing the corresponding two-point motion of the embedded Markov chain and determine the structure of the associated random attractor. In this context, we also provide general results on existence and form of random attractors for discrete-time, discrete-space random dynamical systems.

The Emergency Braking Game: a game theoretic approach for maneuvering in a dense crowd of pedestrians

We introduce an algorithm that maneuvers a vehicle through an area with randomly moving pedestrians. In non-critical situations, our strategy is to avoid pedestrians by steering, whereas dangerously moving pedestrians are avoided by braking, possibly coming to a complete stop. The distinction between non-critical and dangerous situations, as well as proof of safety, is based on a continuous optimization problem that we define. In this abstract problem, called Emergency Braking Game, one pedestrian is actively trying to collide with a continuously decelerating car. We show how to determine the outcome of the game based on the initial states of the car and the pedestrian. Using this information, our algorithm can initiate deceleration in the real scenario in time to avoid collision. The method’s safety is proven theoretically, and its efficiency is shown in simulations with randomly moving pedestrians.

On brittle fracture resistance determination of chromium stainless steel irradiated in ion accelerator

The possibility of brittle fracture resistance (BFR) determination is investigated with mechanical testing for ion-irradiated chromium stainless steel. To consider the ion irradiation effect on material properties disc specimens in initial and irradiated states were tested by mechanical loading until brittle fracture. The micro- hardness of specimens before and after ion irradiation was also determined. It was found that the microhardness of ion-irradiated steel is higher than the microhardness of steel in initial state. However, critical loads for brittle fracture of disc specimens are practically equal for initial and ion-irradiated states. The performed calculations, SEM investigations and also analysis of mechanisms of cleavage microcrack nucleation in disc specimen surfaces allowed us to explain the brittle fracture test results. As fracture tests do not allow us to estimate BFR, it is proposed to estimate BFR by microhardness measurement results.

High-Precision large deformation measurement of array SAR based on FBG strain monitoring and initial state reconstruction

Array Synthetic Aperture Radar (SAR) as the novel generation airborne remote sensing system is installed below the wing, has received more attention because it can realize all-weather three-dimensional (3-D) imaging. Its high-precision imaging needs to measure large deformation of wing accurately. The exiting methods based on Fiber Bragg grating (FBG) sensors ignore the effects of wing initial state, which will generate non-negligible non-linear measurement error. To solve this problem, a high-precision large deformation measurement method of array SAR based on FBG strain monitoring and initial state reconstruction is proposed. Firstly, initial angle reconstruction approach is presented to obtain initial state. Then, bending strain measurement method is used to obtain continuous strain. Finally, large deformation model based on initial angle and bending strain is established to achieve large deformation. Experimental results indicate proposed method can effectively measure large deformation, and can acquire the higher displacement accuracy compared to traditional method.

Fiber-level modeling of 3D braided preforms using virtual braiding method

High fidelity geometry models of the preforms are essential in mechanical property analysis for composite materials. The paper presents a virtual braiding method to generate digital material twins of 3D braided preforms. The four-step 3D braiding process is simulated based on the concept of virtual fiber and according to the rule of yarn movement. Fiber-level models of the preform are constructed which display the micro-scale architectures explicitly, including compressive deformations of yarn cross-sections and variations of yarn paths. The Micro-CT test is employed to investigate the internal fiber structures of 3D braided preform and quantitatively verify the accuracy of the proposed modeling method. Influence of the braiding parameters, such as yarn tension and arrangement angle in the initial state on geometric parameters of the preform is then investigated.

Optimal Resource Assignment in Hybrid Microgrids Based on Demand Response Proposals

The energy consumption of buildings has been affected by the increase in new loads, which is where emerging technologies have become important. In this sense, microgrids have become a solution that has reduced the loadability of power systems. Thus, the Salesian Polytechnic University in Quito has implemented a hybrid microgrid with three photovoltaic plants (PV), two battery storage systems (BESS), and a connection to the public grid. This research shows a methodology to minimize the energy consumption of the public grid by taking advantage of the existing resources in the microgrid through the allocation of resources and demand management, for which a domotic system based on a z-wave protocol was implemented to monitor and control the loads. The initial state and the state after the implementation of the management equipment were compared, and the reduction of electricity consumption in the public grid was quantified, which was around 63%.

Solid-lubrication performance of Ti3C2Tx - Effect of tribo-chemistry and exfoliation

Multi-layer Ti3C2Tx coatings have demonstrated an outstanding wear performance with excellent durability due to beneficial tribo-layers formed. However, the involved formation processes dependent on the tribological conditions and coating thickness are yet to be fully explored. Therefore, we spray-coated Ti3C2Tx multi-layer particles onto stainless steel substrates to create coatings with two different thicknesses and tested their solid lubrication performance with different normal loads (100 and 200 mN) and sliding frequencies (1 and 2.4 Hz) using linear-reciprocating ball-on-disk tribometry. We demonstrate that MXenes’ tribological performance depends on their initial state (delaminated few-layer vs. multi-layer particles), coating thickness, applied load and sliding frequency. Specifically, the best behavior is observed for thinner multi-layer coatings tested at the lower frequency. In contrast, coatings made of delaminated few-layer MXene are not as effective as their multi-layer counterparts. Our high-resolution interface characterization by transmission electron microscopy revealed unambiguous differences regarding the uniformity and chemistry of the formed tribo-layers as well as the degree of tribo-induced MXenes’ exfoliation. Atomistic insights into the exfoliation process and molecular dynamic simulations quantitatively backed up our experimental results regarding coating thickness and velocity dependency. This ultimately demonstrates that MXenes’ tribological performance is governed by the underlying tribo-chemistry and their exfoliation ability during rubbing.

Application of machine learning to experimental design in quantum mechanics

The recent advances in Machine Learning hold great promises for the field of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, which is typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system the agent is interacting with, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system and only some parameters of the evolution or of the initial state are unknown. We present here a general Machine Learning technique that can optimize the precision of quantum sensors, and in doing so it exploits the knowledge we have on the system. We have developed a Python package to automate a broad class of optimizations that can be found in the tasks of quantum parameter estimation, quantum metrology and quantum hypothesis testing. What the agent is learning here is an optimal adaptive strategy, that, on the basis of the previous outcomes, decides the next measurements to perform. It works both for Bayesian estimation and for frequentist estimation. The user is required to implement the physics of the system to be studied and state which parameters in the experiment are controllable and which are unknowns. The functions of the library allow then to easily train a neural network agent for optimizing the precision of the sensor, by simulating the experiment. We have explored some applications of this technique to magnetometry on NV centers (both DC and AC), to state discrimination in quantum optics and to phase estimation. So far, we were able to certify better results than the current state-of-the-art controls for many examples. The Machine Learning technique developed here can be applied in all those scenarios where the quantum system is well characterized and relatively simple and small. In these cases, we can squeeze every last bit of information from a quantum sensor by controlling it with a neural network appropriately trained.

Coherent transient exciton transport in disordered polaritonic wires

Abstract Excitation energy transport can be significantly enhanced by strong light–matter interactions. In the present work, we explore intriguing features of coherent transient exciton wave packet dynamics on a lossless disordered polaritonic wire. Our main results can be understood in terms of the effective exciton group velocity, a new quantity we obtain from the polariton dispersion. Under weak and moderate disorder, we find that the early wave packet spread velocity is controlled by the overlap of the initial exciton momentum distribution and its effective group velocity. Conversely, when disorder is stronger, the initial state is nearly irrelevant, and red-shifted cavities support excitons with greater mobility. Our findings provide guiding principles for optimizing ultrafast coherent exciton transport based on the magnitude of disorder and the polariton dispersion. The presented perspectives may be valuable for understanding and designing new polaritonic platforms for enhanced exciton energy transport.

Taming the Rotating Wave Approximation

The interaction between light and matter is one of the oldest research areas of quantum mechanics, and a field that just keeps on delivering new insights and applications. With the arrival of cavity and circuit quantum electrodynamics we can now achieve strong light-matter couplings which form the basis of most implementations of quantum technology. But quantum information processing also has high demands requiring total error rates of fractions of percentage in order to be scalable (fault-tolerant) to useful applications. Since errors can also arise from modelling, this has brought into center stage one of the key approximations of quantum theory, the Rotating Wave Approximation (RWA) of the quantum Rabi model, leading to the Jaynes-Cummings Hamiltonian. While the RWA is often very good and incredibly useful to understand light-matter interactions, there is also growing experimental evidence of regimes where it is a bad approximation. Here, we ask and answer a harder question: for which experimental parameters is the RWA, although perhaps qualitatively adequate, already not good enough to match the demands of scalable quantum technology? For example, when is the error at least, and when at most, 1%? To answer this, we develop rigorous non-perturbative bounds taming the RWA. We find that these bounds not only depend, as expected, on the ratio of the coupling strength and the oscillator frequency, but also on the average number of photons in the initial state. This confirms recent experiments on photon-dressed Bloch-Siegert shifts. We argue that with experiments reporting controllable cavity states with hundreds of photons and with quantum error correcting codes exploring more and more of Fock space, this state-dependency of the RWA is increasingly relevant for the field of quantum computation, and our results pave the way towards a better understanding of those experiments.

Comparative studies of spectroscopic properties and surface structure of solid solutions of CaF2:SrF2:YbF3 crystals and ceramics

The optical properties of laser ceramics based on 65CaF2:30SrF2:5YbF3 have been studied and compared with a single crystal of the same composition when exposed to 60Co γ-rays. The spectral characteristics of the non-irradiated samples were similar. During and after γ-ray irradiation of the 65CaF2:30SrF2:5YbF3 crystal and ceramic samples, Yb3+→Yb2+ transformations occur as well as configurationally Yb3+→Yb3+ transitions between the different states of the Yb3+ ions with the participation of the interstitial Fi ions. Our analysis of the state of the surface of the single crystal and nanoceramics showed that the roughness on the surface of the ceramics was improved, while on the surface of the single crystal it worsened when compared to its initial state.

Physics-informed neural nets for control of dynamical systems

Physics-informed neural networks (PINNs) incorporate established physical principles into the training of deep neural networks, ensuring that they adhere to the underlying physics of the process while reducing the need for labeled data, since the desired output is not a prerequisite for physics-informed training. For modeling systems described by Ordinary Differential Equations (ODEs), traditional PINNs typically take continuous time as an input variable and produce the solution to the corresponding ODE. However, in their original form, PINNs neither accommodate control inputs nor do they effectively simulate variable long-range intervals without experiencing a significant decline in prediction accuracy. In this context, this work introduces a novel framework known as “Physics-Informed Neural Nets for Control” (PINC). PINC presents an innovative PINN-based architecture tailored to control problems, capable of simulating longer-range time horizons that are not predetermined during training. This increased flexibility sets it apart from traditional PINNs. The variable simulation time is achieved by adding inputs to the PINC network that convey the initial condition and the control signal for a particular time interval. Simulating variable long-range intervals involves running the PINC net across a sequence of shorter intervals. In this autoregressive process, the network predictions are linked in a self-feedback mode, with the initial state (input) of the next interval set to the last predicted state (network output) of the previous interval. We showcase the effectiveness of our proposal in identifying and controlling three nonlinear dynamic systems: the Van der Pol oscillator, the four-tank system, and an electric submersible pump. Crucially, these experiments demonstrate that learning the dynamics of these systems can be achieved without relying on any sample collected from the actual process, and it offers faster inference speed compared to numerical simulations.

A hybrid quantum algorithm to detect conical intersections

Conical intersections are topologically protected crossings between the potential energy surfaces of a molecular Hamiltonian, known to play an important role in chemical processes such as photoisomerization and non-radiative relaxation. They are characterized by a non-zero Berry phase, which is a topological invariant defined on a closed path in atomic coordinate space, taking the value π when the path encircles the intersection manifold. In this work, we show that for real molecular Hamiltonians, the Berry phase can be obtained by tracing a local optimum of a variational ansatz along the chosen path and estimating the overlap between the initial and final state with a control-free Hadamard test. Moreover, by discretizing the path into N points, we can use N single Newton-Raphson steps to update our state non-variationally. Finally, since the Berry phase can only take two discrete values (0 or π), our procedure succeeds even for a cumulative error bounded by a constant; this allows us to bound the total sampling cost and to readily verify the success of the procedure. We demonstrate numerically the application of our algorithm on small toy models of the formaldimine molecule (H2C=NH).

Brownian oscillator with time-dependent strength: a delta function protocol

We consider a classical Brownian oscillator of mass m driven from an arbitrary initial state by varying the stiffness k(t) of the harmonic potential according to the protocol k(t)=k0+aδ(t) , involving the Dirac delta function. The microscopic work performed on the oscillator is shown to be W=(a2/2m)q2−aqv , where q and v are the coordinate and velocity in the initial state. If the initial distribution of q and v is the equilibrium one with temperature T, the average work is ⟨W⟩=a2T/(2mk0) and the distribution f(W) has the form of the product of exponential and modified Bessel functions. The distribution is asymmetric and diverges as W → 0. The system’s response for t > 0 is evaluated for specific models.

Cooperative control of a DNA origami force sensor

Biomolecular systems are dependent on a complex interplay of forces. Modern force spectroscopy techniques provide means of interrogating these forces, but they are not optimized for studies in constrained environments as they require attachment to micron-scale probes such as beads or cantilevers. Nanomechanical devices are a promising alternative, but this requires versatile designs that can be tuned to respond to a wide range of forces. We investigate the properties of a nanoscale force sensitive DNA origami device which is highly customizable in geometry, functionalization, and mechanical properties. The device, referred to as the NanoDyn, has a binary (open or closed) response to an applied force by undergoing a reversible structural transition. The transition force is tuned with minor alterations of 1 to 3 DNA oligonucleotides and spans tens of picoNewtons (pN). The DNA oligonucleotide design parameters also strongly influence the efficiency of resetting the initial state, with higher stability devices (≳10 pN) resetting more reliably during repeated force-loading cycles. Finally, we show the opening force is tunable in real time by adding a single DNA oligonucleotide. These results establish the potential of the NanoDyn as a versatile force sensor and provide fundamental insights into how design parameters modulate mechanical and dynamic properties.