Regions Of Space Research Articles

Informatics-based design of polyaniline-carbon nanotube thermoelectric nanocomposite using ANN and GA

Abstract Conducting polymer and carbon nanotube (CNT) based nanocomposites have emerged as prospective thermoelectric (TE) materials due to their potential application in flexible electronics. Non-conventional charge and heat transport in these nanocomposites, presents the possibility to enhance the TE conversion efficiency, given by ZT. However, the highly non-linear and complex association of structure and composition with the overall TE properties have hindered the development of any general strategy to develop high ZT nanocomposites. Here, we implement artificial neural network (ANN) and genetic algorithm (GA) to develop data driven models followed by optimization to design high efficiency nanocomposites based on CNT dispersed in polyaniline (PANI) matrix. Our models suggest that CNT concentration plays the most crucial role in determining ZT. Non-dominated Pareto optimal solutions consisting of different combinations of design variables are obtained by multi-objective optimization. Although a range of optimal solutions span over different regions of the search space, in general we note that longer CNTs boost Seebeck coefficient (S) and electrical conductivity (σ), and smaller length lowers thermal conductivity (k), while higher diameter of CNTs increase S and lowers σ and k. The results provide a general guideline for developing CNT-PANI nanocomposites with enhanced TE figure of merit.

The resonance effect for the CP asymmetry associated with the process $\omega \rightarrow \pi^{+}\pi^{-}\pi^{0}$

Abstract The direct CP asymmetry in the weak decay process of hadrons is commonly attributed to the weak phase of the CKM matrix and the indeterminate strong phase. We propose a method to generate a significant phase difference through the interference between $\rho$ and $\omega$ mesons, taking into account the G-parity allowed decay process of $\omega \rightarrow \pi^{+}\pi^{-}\pi^{0}$ and the G-parity suppressed decay process of $\rho^{0} \rightarrow \pi^{+}\pi^{-}\pi^{0}$ in B meson decays. 
It can lead to a significant CP asymmetry changes in the interference region. 
Besides, we also calculate the integral results of different phase space regions for the four-body decay process. We hope that our work can offer valuable theoretical guidance for future experimental investigations on CP asymmetry in these decays.Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

Multi-winged Lorenz attractors due to bifurcations of a periodic orbit with multipliers (−1,i,−i)

Abstract We show that bifurcations of periodic orbits with multipliers ( − 1 , i , − i ) can lead to the birth of pseudohyperbolic (i.e. robustly chaotic) Lorenz-like attractors of three different types: one is a discrete analogue of the classical Lorenz attractor, and the other two are new. We call them two- and four-winged ‘Simó angels’. These three attractors exist in an orientation-reversing, three-dimensional, quadratic Hénon map. Our analysis is based on a numerical study of a normal form for this bifurcation, a three-dimensional system of differential equations with a Z4-symmetry. We investigate bifurcations in the normal form and describe those responsible for the emergence of the Lorenz attractor and the continuous-time version of the Simó angels. Both for the normal form and the 3D Hénon map, we have found open regions in the parameter space where the attractors are pseudohyperbolic, implying that for every parameter value from these regions every orbit in the attractor has positive top Lyapunov exponent.

Quantum many-body scars in few-body dipole-dipole interactions

We simulate the dynamics of Rydberg atoms resonantly exchanging energy via two-, three-, and four-body dipole-dipole interactions in a one-dimensional array. Using simplified models of a realistic experimental system, we study the initial-state survival probability, mean level spacing, spread of entanglement, and properties of the energy eigenstates. By exploring a range of disorders and interaction strengths, we find regions in parameter space where the three- and four-body dynamics either fail to thermalize or do so slowly. The interplay between the stronger hopping and weaker field-tuned interactions gives rise to quantum many-body scar states, which play a critical role in slowing the dynamics of the three- and four-body interactions. Published by the American Physical Society 2024

Percolating cosmic string networks from kination

We describe a new mechanism, whose ingredients are realized in string compactifications, for the formation of cosmic (super)string networks. Oscillating string loops grow when their tension μ decreases with time. If 2H+μ˙/μ<0, where H is the Hubble parameter, loops grow faster than the scale factor and an initial population of isolated small loops (for example, produced by nucleation) can grow, percolate, and form a network. This condition is satisfied for fundamental strings in the background of a kinating volume modulus rolling toward the asymptotic large volume region of moduli space. Such long kination epochs are motivated in string cosmology by both the electroweak hierarchy problem and the need to solve the overshoot problem. The tension of such a network today is set by the final vacuum; for phenomenologically appealing large volume scenario vacua, this would lead to a fundamental string network with Gμ∼10−10. Published by the American Physical Society 2024

Sequential Reactions of Acetylene with the Benzonitrile Radical Cation: New Insights into Structures and Rate Coefficients of the Covalent Ion Products.

Benzonitrile radical cations generated in ionizing environments such as solar nebulae and interstellar clouds can react with neutral molecules such as acetylene to form a variety of nitrogen-containing complex organics. Herein, we present results from mass-selected ion mobility experiments and coupled-cluster and DFT calculations for the sequential reactions of acetylene with the benzonitrile radical cation (C7NH5+•). The results reveal the formation of two covalently bonded adduct ions C9NH7+• and C11NH9+• with individual rate coefficients of 2.1(±0.4) × 10-11 cm3 s-1 and 1.1(±0.9) × 10-11 cm3 s-1, respectively measured at 334.5 K. The direct addition of acetylene onto the N atom of the benzonitrile cation results in the formation of a N-acetylene-benzonitrile+• radical cation with a calculated collision cross-section of 67.5 Å2 in perfect agreement with the measured cross-section of 67.5 Å2 of the C9NH7+• adduct. The measured collision cross-section of the second covalent adduct C11NH9+• (72.2 Å2) is also in excellent agreement with the calculated cross-section (71.2 Å2) of the lowest energy isomer of the C11NH9+• ion corresponding to the 2-phenylpyridine structure. The formation of the bicyclic 2-phenylpyridine radical cation is explained by the rapid conversion of the classical radical cation C11NH9+• into a distonic ion structure that can efficiently cyclize in an exothermic transformation to form the 2-phenylpyridine radical cation. This intriguing mechanism could explain the formation of N-containing complex organics in different regions of outer space. The current results are expected to have direct implications for the search for nitrogen-containing complex organics in space.

SHIFT@LHC: Searches for new physics with shifted interaction on a fixed target at the Large Hadron Collider

New low-mass particles with very small couplings to standard model particles that travel significant distances before decaying are interesting candidates to address some of the most intriguing questions of modern physics. In this paper, I propose to extend the LHC’s research program by installing a gaseous fixed target referred to as SHIFT at around 160 meters from the CMS interaction point. When the LHC proton beam collides with this target, interactions at a center of mass energy of ≈113 GeV would occur. The particles produced in such collisions, or their decay products, would travel through the rock and other material on their path, potentially reaching the CMS detector where they can be registered and studied. Such an approach would allow us to access otherwise uncovered regions of parameters phase space at a relatively low cost since it does not require constructing a new detector. Various aspects such as angular and lifetime coverage or material survival probability have been studied. The results are interpreted within two new physics models, namely, the Dark Photons and the Hidden Valley scenarios, and compared with the standard proton-proton physics program of CMS. A comparison is also made with the fixed target program at LHCb, as well as parasitic detectors such as FASER or MATHUSLA. The obtained results indicate that, despite assuming just 1% of the nominal CMS luminosity to be available to SHIFT, the physics reach could be extended by a factor of up to 150 (1000) for Dark Photon (Hidden Valley) scenarios, depending on the signal model parameters.

QCD Running Coupling in the Nonperturbative and Near-Perturbative Regimes

We use analytic continuation to extend the gauge-gravity duality nonperturbative description of the strong force coupling into the transition, near-perturbative, regime where perturbative effects become important. By excluding the unphysical region in coupling space from the flow of singularities in the complex plane, we derive a specific relation between the scales relevant at large and short distances; this relation is uniquely fixed by requiring maximal analyticity. The unified effective coupling model gives an accurate description of the data in the nonperturbative and the near-perturbative regions. Published by the American Physical Society 2024

Enhanced artificial hummingbird algorithm with chaotic traversal flight

Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent in traditional meta-heuristic algorithms, this study presents the enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs chaotic ergodicity within the foundational framework of the conventional artificial hummingbird algorithm. This approach implements chaotic motion within local regions of the solution space, ensuring a thorough exploration of potential optima and preventing algorithmic stagnation at local maxima by guaranteeing a non-repetitive traversal of all search states. This study also analyzes the intrinsic mechanisms by which eight different chaotic mappings affect optimization performance, from the perspectives of invariant measures and traversal efficiency of ergodic chaotic motion. In comparative tests with 21 meta-heuristic algorithms on the CEC2014, CEC2019, and CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior optimization performance. Furthermore, the practicability and robustness of CEAHA have been confirmed in mechanical design optimization problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, and piston levers.

Axion baryogenesis puts a new spin on the Hubble tension

We show that a rotating axion field that makes a transition from a matterlike equation of state to a kinationlike equation of state around the epoch of recombination can significantly ameliorate the Hubble tension, i.e., the discrepancy between the determinations of the present-day expansion rate H0 from observations of the cosmic microwave background on one hand and type Ia supernovae on the other. We consider a specific, UV-complete model of such a rotating axion and find that it can relax the Hubble tension without exacerbating tensions in determinations of other cosmological parameters, in particular the amplitude of matter fluctuations S8. We subsequently demonstrate how this rotating axion model can also generate the baryon asymmetry of our Universe, by introducing a coupling of the axion field to right-handed neutrinos. This baryogenesis model predicts heavy neutral leptons that are most naturally within reach of future lepton colliders, but in finely tuned regions of parameter space may also be accessible at the high-luminosity LHC and the beam dump experiment SHiP. Published by the American Physical Society 2024

Generalization of neural network models for complex network dynamics

Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph. Data-driven approximations of differential equations present a promising alternative to traditional methods for uncovering a model of dynamical systems, especially in complex systems that lack explicit first principles. A recently employed machine learning tool for studying dynamics is neural networks, which can be used for solution finding or discovery of differential equations. However, deploying deep learning models in unfamiliar settings-such as predicting dynamics in unobserved state space regions or on novel graphs-can lead to spurious results. Focusing on complex systems whose dynamics are described with a system of first-order differential equations coupled through a graph, we study generalization of neural network predictions in settings where statistical properties of test data and training data are different. We find that neural networks can accurately predict dynamics beyond the immediate training setting within the domain of the training data. To identify when a model is unable to generalize to novel settings, we propose a statistical significance test.

Dynamic Analysis and FPGA Implementation of Fractional-Order Hopfield Networks with Memristive Synapse

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization.

Mitigating local minima in extracting optimal parameters for photovoltaic models: An optimizer leveraging multiple initial populations (OLMIP)

The accurate identification of parameters in photovoltaic models is of paramount importance to accurately predict the electrical behavior of photovoltaic systems and improve their performance. Nowadays, this task poses a multimodal optimization problem, aiming to find the best combination of parameters that fit the model to real data. This article presents a proposal for an Optimizer Leveraging Multiple Initial Populations (OLMIP) that aims to achieve optimal solutions while effectively avoiding undesirable local optima. By utilizing a separate evolution strategy involving four distinct initial populations, followed by the construction of an elite population, the algorithm can explore multiple regions of the search space and escape local minima. Experiments were conducted with four models: the single diode, double diode, triple diode from RTC France cell, and Photowatt-PWP201 module. The mean squared errors obtained are 9.860219E-04, 9.824849E-04, 9.824849E-04, and 2.425075 E−03, respectively. These results indicate that the algorithm achieves superior or comparable accuracy to that of six competitors. Furthermore, the statistical analysis of the results, including the Wilcoxon and Friedman tests, confirms the robustness and effectiveness of the approach used for parameter estimation in photovoltaic systems. These findings open up new prospects for the improvement and optimization of photovoltaic systems.

Enhanced Chaotic Pseudorandom Number Generation Using Multiple Bernoulli Maps with Field Programmable Gate Array Optimizations

Certain methods for implementing chaotic maps can lead to dynamic degradation of the generated number sequences. To solve such a problem, we develop a method for generating pseudorandom number sequences based on multiple one-dimensional chaotic maps. In particular, we introduce a Bernoulli chaotic map that utilizes function transformations and constraints on its control parameter, covering complementary regions of the phase space. This approach allows the generation of chaotic number sequences with a wide coverage of phase space, thereby increasing the uncertainty in the number sequence generation process. Moreover, by incorporating a scaling factor and a sine function, we develop a robust chaotic map, called the Sine-Multiple Modified Bernoulli Chaotic Map (SM-MBCM), which ensures a high degree of randomness, validated through statistical mechanics analysis tools. Using the SM-MBCM, we propose a chaotic PRNG (CPRNG) and evaluate its quality through correlation coefficient analysis, key sensitivity tests, statistical and entropy analysis, key space evaluation, linear complexity analysis, and performance tests. Furthermore, we present an FPGA-based implementation scheme that leverages equivalent MBCM variants to optimize the electronic implementation process. Finally, we compare the proposed system with existing designs in terms of throughput and key space.

Identification of Crashworthy Designs Combining Active Learning and the Solution Space Methodology

Abstract This study introduces a novel methodology for vehicle development under crashworthiness constraints. We propose coupling the solution space method (SSM) with active learning reliability (ALR) to map global requirements, i.e., safety requirements on the whole vehicle, to the design parameters associated with a component. To this purpose, we use a classifier to distinguish between the design that fulfills the requirements, the safe domain, and those that do not, the failure domain. This classifier is trained on finite element simulations, exploiting the learning strategies used by ALR to efficiently and precisely identify the border between the two domains and the information provided on these domains by the SSM. We then provide an exemplary application where the efficiency of the method is shown: the safe domain is identified with 270 samples and an average total error of 2.5%. The methodology we propose here is an efficient method to identify safe designs at a comparatively low computational budget. To the best of our knowledge, there is currently no methodology available that can identify regions in the design space that result in designs satisfying the local requirements set by the SSM due to the complexity and strong nonlinearity of crashworthiness simulations. The proposed coupling exploits the information of SSM and the capabilities of ALR to provide a fast mapping between the global requirements and the design parameters, which can, in turn, be made available to the designers to inexpensively evaluate the crashworthiness of new shapes and component features.

A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements

Abstract We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III–V elements. The functional is machine-learned with Gaussian process regression (GPR) from data computed with Kohn-Sham DFT with plane wave bases and local pseudopotentials. The dataset includes multiple phases of unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In. A total of 433 materials were used for training, and 18 strained structures were used for each material. Averaged (over the unit cell) kinetic energy density is fitted as a function of averaged terms of the 4th order gradient expansion and the product of the density and effective potential. The kinetic energy predicted by the model allows reproducing energy-volume curves around equilibrium geometry with good accuracy. We show that the GPR model beats linear and polynomial regressions. We also find that unary compounds sample a wider region of the descriptor space than binary and ternary compounds, and it is therefore important to include them in the training set; a GPR model trained on a small number of unary compounds is able to extrapolate relatively well to binary and ternary compounds but not vice versa.

TOI-5005 b: A super-Neptune in the savanna near the ridge

The Neptunian desert and savanna have recently been found to be separated by a ridge, an overdensity of planets in the period range of simeq 3--5 days. These features are thought to be shaped by dynamical and atmospheric processes. However, their roles are not yet well understood. Our aim was to confirm and characterize the super-Neptune TESS candidate TOI-5005.01, which orbits a moderately bright (V = 11.8) solar-type star (G2 V) with an orbital period of 6.3 days. With these properties, TOI-5005.01 is located in the Neptunian savanna near the ridge. We used Bayesian inference to analyse 38 HARPS radial velocity measurements, three sectors of TESS photometry, and two PEST and TRAPPIST-South transits. We tested a set of models involving eccentric and circular orbits, long-term drifts, and Gaussian processes to account for correlated stellar and instrumental noise. We computed the Bayesian evidence to find the model that best represents our dataset and infer the orbital and physical properties of the system. We confirm TOI-5005 b to be a transiting super-Neptune with a radius of $R_ p oplus $ ($R_ p J $) and a mass of $M_ p oplus $ ($M_ p J $), which corresponds to a mean density of $ p g \ $. Our internal structure modelling indicates that the core mass fraction (CMF = $0.74^ $) and envelope metal mass fraction ($Z_ env $) of TOI-5005 b are degenerate, but the overall metal mass fraction is well constrained to a value slightly lower than that of Neptune and Uranus ($Z_ planet $). The $Z_ planet $/$Z_ star $ ratio is consistent with the well-known mass-metallicity relation, which suggests that TOI-5005 b was formed via core accretion. We also estimated the present-day atmospheric mass-loss rate of TOI-5005 b, but found contrasting predictions depending on the choice of photoevaporation model ($0.013 oplus $ Gyr$^ $ vs $0.17 oplus $ Gyr$^ $). At a population level, we find statistical evidence ($p$-value = $0.0092^ $) that planets in the savanna such as TOI-5005 b tend to show lower densities than planets in the ridge, with a dividing line around 1 $ g \ $, which supports the hypothesis of different evolutionary pathways populating the two regimes. TOI-5005 b is located in a region of the period-radius space that is key to studying the transition between the Neptunian ridge and the savanna. It orbits the brightest star of all such planets known today, which makes it a target of interest for atmospheric and orbital architecture observations that will bring a clearer picture of its overall evolution.

Prospects for Light Dark Matter Searches at Large-Volume Neutrino Detectors.

We propose a new approach to search for light dark matter (DM), with keV-GeV mass, via inelastic nucleus scattering at large-volume neutrino detectors such as Borexino, DUNE, Super-K, Hyper-K, and JUNO. The approach uses inelastic nuclear scattering of cosmic-ray boosted DM, enabling a low-background search for DM in these experiments. Large neutrino detectors, with higher thresholds than dark matter detectors, can be used, since the nuclear deexcitation lines are O(10) MeV. Using a hadrophilic dark-gauge-boson-portal model as a benchmark, we show that the nuclear inelastic channels generally provide better sensitivity than the elastic scattering for a large region of light DM parameter space.

Diagnosing tracer transport in convective penetration of a stably stratified layer

We use large-eddy simulations to study the penetration of a buoyant plume carrying a passive tracer into a stably stratified layer with constant buoyancy frequency. Using a buoyancy-tracer volume distribution, we develop a method for objectively partitioning plume fluid in buoyancy-tracer space into three regions, each of which corresponds to a coherent region in physical space. Specifically, we identify a source region where undiluted plume fluid enters the stratified layer, a transport region where much of the transition from undiluted to mixed fluid occurs in the plume cap and an accumulation region corresponding to a radially spreading intrusion. This method enables quantification of different measures of turbulence and mixing within each of the three regions, including potential energy and turbulent kinetic energy dissipation rates, an activity parameter and the instantaneous mixing efficiency. We find that the most intense buoyancy gradients lie in a thin layer at the cap of the penetrating plume. This provides the primary stage of mixing between plume and environment and exhibits a mixing efficiency around 50 %. Newly generated mixtures of environmental and plume fluid join the intrusion and experience relatively weak turbulence and buoyancy gradients. As the intrusion spreads radially, environmental fluid surrounding the intrusion is mixed into the intrusion with moderate mixing efficiency. This dominates the volume of environmental fluid entrained into the region containing plume fluid. However, the ‘strongest’ entrainment, as measured by the specific entrainment rate, is largest in the plume cap, where the most buoyant environmental fluid is entrained.

Duality of spaces and the origin of integral reflection conditions

The dualism between direct and reciprocal space is at the origin of well known relations between basis vectors in the two spaces. It is shown that when a coordinate system corresponding to a non-primitive unit cell is adopted, this dualism has to be handled with care. In particular, the reciprocal of a non-primitive unit cell is not a unit cell but a region in reciprocal space that does not represent a unit of repetition by translation. The basis vectors do not correspond to reciprocal-space cell lengths, contrary to what is stated even in the core CIF dictionary. The corresponding unit cell is a multiple of this region. The broken correspondence between basis vectors and unit cell is at the origin of the integral reflection conditions.