Dynamic Structure Research Articles

Stability analysis and soliton solutions of the (1+1)-dimensional nonlinear chiral Schrödinger equation in nuclear physics

The nonlinear Schrödinger equation equation is one of the most important physical models used in optical fiber theory to explain the transmission of an optical soliton. The field of chiral soliton propagation in nuclear physics is very interesting because of its numerous applications in communications and ultra-fast signal routing systems. The (1+1)-dimensional chiral dynamical structure that describes the soliton behaviour in data transmission is dealt with in this work using a variety of in-depth analytical techniques. This work has applications in particle physics, ionised science, nuclear physics, optics, and other applied mathematical sciences. We are able to develop a variety of solutions to demonstrate the behaviour of solitary wave structures, periodic soliton solutions, chiral soliton solutions, and bell-shaped soliton solutions with the use of applied techniques. Moreover, in order to verify the scientific calculations, the stability analysis for the observed solutions of the governing model is taken into consideration. In addition, the 3-dimensional, contour, and 2-dimensional visuals are supplied for a better understanding of the behaviour of the solutions. The employed strategies are dependable, uncomplicated, and effective; yet have not been utilised with the governing model in the literature that is now accessible. The resulting outcomes have impressive applications across a large number of study areas and computational physics phenomena representing real-world scenarios. The methods applied in this model are not utilized on the given models in previous literature so we can say that these describe the novelty of the work.

Giant Piezoelectric Effects of Topological Structures in Stretched Ferroelectric Membranes.

Freestanding ferroelectric oxide membranes emerge as a promising platform for exploring the interplay between topological polar ordering and dipolar interactions that are continuously tunable by strain. Our investigations combining density functional theory (DFT) and deep-learning-assisted molecular dynamics simulations demonstrate that DFT-predicted strain-driven morphotropic phase boundary involving monoclinic phases manifest as diverse domain structures at room temperatures, featuring continuous distributions of dipole orientations and mobile domain walls. Detailed analysis of dynamic structures reveals that the enhanced piezoelectric response observed in stretched PbTiO_{3} membranes results from small-angle rotations of dipoles at domain walls, distinct from conventional polarization rotation mechanism and adaptive phase theory inferred from static structures. We identify a ferroelectric topological structure, termed "dipole spiral," which exhibits a giant intrinsic piezoelectric response (>320 pC/N). This helical structure, possessing a rotational zero-energy mode, unlocks new possibilities for exploring chiral phonon dynamics and dipolar Dzyaloshinskii-Moriya-like interactions.

Constraint structure of the generalized Proca model in the Lagrangian formalism

We introduce a novel Lagrangian approach to analyze the dynamical structure of the generalized Proca theory (GP). This method incorporates the A-Z constraint structure of the model within the Lagrangian formalism, providing a precise determination of the degrees of freedom. Furthermore, we present a comprehensive analysis of the model's Hamiltonian constraint structure.

Dynamic swarms regulate the morphology and distribution of soft membrane domains.

We study the dynamic structure of lipid domain inclusions embedded within a phase-separated reconstituted lipid bilayer in contact with a swarming flow of gliding filamentous actin. Passive circular domains transition into highly deformed morphologies that continuously elongate, rotate, and pinch off into smaller fragments, leading to a dynamic steady state with ≈23× speedup in the relaxation of the intermediate scattering function compared with passive membrane domains driven by purely thermal forces. To corroborate experimental results, we develop a phase-field model of the lipid domains with two-way coupling to the Toner-Tu equations. We report phase domains that become entrained in the chaotic eddy patterns, with oscillating waves of domains that correlate with the dominant wavelengths of the Toner-Tu flow fields.

Thermal Effect Management via Entropy Variation Strategy to Improve the Catalyst Stability in Acetylene Hydrogenation.

The dynamic structure evolution of heterogeneous catalysts during reaction has gained great attention recently. However, controllably manipulating dynamic process and then feeding back catalyst design to extend the lifetime remains challenging. Herein, we proposed an entropy variation strategy to develop a dynamic CuZn-Co/HEOs catalyst, in which the non-active Co nano-islands play a crucial role in controlling thermal effect via timely capturing and utilizing reaction heat generated on the adjacent active CuZn alloys, thus solving the deactivation problem of Cu-based catalysts. Specifically, heat sensitive Co nano-islands experienced an entropy increasing process of slowly redispersion during the reaction. Under such heat dissipation effect, the CuZn-Co/HEOs catalyst exhibited 95.7% ethylene selectivity and amazing long-term stability (>530 h) in the typical exothermic acetylene hydrogenation. Aiming at cultivating it as a catalyst with promising industrial potential, we proposed a simple regeneration approach via an entropy decreasing process.

Thermal Effect Management via Entropy Variation Strategy to Improve the Catalyst Stability in Acetylene Hydrogenation

The dynamic structure evolution of heterogeneous catalysts during reaction has gained great attention recently. However, controllably manipulating dynamic process and then feeding back catalyst design to extend the lifetime remains challenging. Herein, we proposed an entropy variation strategy to develop a dynamic CuZn‐Co/HEOs catalyst, in which the non‐active Co nano‐islands play a crucial role in controlling thermal effect via timely capturing and utilizing reaction heat generated on the adjacent active CuZn alloys, thus solving the deactivation problem of Cu‐based catalysts. Specifically, heat sensitive Co nano‐islands experienced an entropy increasing process of slowly redispersion during the reaction. Under such heat dissipation effect, the CuZn‐Co/HEOs catalyst exhibited 95.7% ethylene selectivity and amazing long‐term stability (>530 h) in the typical exothermic acetylene hydrogenation. Aiming at cultivating it as a catalyst with promising industrial potential, we proposed a simple regeneration approach via an entropy decreasing process.

Landauer Principle and the Second Law in a Relativistic Communication Scenario.

The problem of formulating thermodynamics in a relativistic scenario remains unresolved, although many proposals exist in the literature. The challenge arises due to the intrinsic dynamic structure of spacetime as established by the general theory of relativity. With the discovery of the physical nature of information, which underpins Landauer's principle, we believe that information theory should play a role in understanding this problem. In this work, we contribute to this endeavour by considering a relativistic communication task between two partners, Alice and Bob, in a general Lorentzian spacetime. We then assume that the receiver, Bob, reversibly operates a local heat engine powered by information, and seek to determine the maximum amount of work he can extract from this device. As Bob cannot extract work for free, by applying both Landauer's principle and the second law of thermodynamics, we establish a bound on the energy Bob must spend to acquire the information in the first place. This bound is a function of the spacetime metric and the properties of the communication channel.

Dynamic compact data structure for temporal reachability with unsorted contact insertions

Abstract Temporal graphs represent interactions between entities over time. Deciding whether entities can reach each other through temporal paths is useful for various applications such as in communication networks and epidemiology. Previous works have studied the scenario in which addition of new interactions can happen at any point in time. A known strategy maintains, incrementally, a Timed Transitive Closure using a dynamic data structure composed of $O(n^{2})$ binary search trees containing non-nested time intervals. However, space usage for storing these trees grows rapidly as more interactions are inserted. In this paper, we introduce a compact data structure that represents each tree as two dynamic bit-vectors. Furthermore, we present two variants of this data structure: one representing bits in dynamic bit-vectors explicitly and the other representing only the active bits by encoding their consecutive distances. In our experiments, we observed that our data structure improves space usage while having similar time performance for incremental updates when comparing with the previous strategy. The first variant of our data structure gives the best space improvement when constructing Time Transitive Closures for temporally dense temporal graphs, and the second variant uses less space for temporally sparse temporal graphs.

Conformational analysis of a new peptide derived from feline immunodeficiency virus gp36 in SDS micelles: An NMR-MD based investigation.

Feline immunodeficiency virus (FIV) shares structural similarities with human immunodeficiency virus (HIV): the surface glycoprotein gp36 corresponds to the HIV gp41, which drives virus-host cell interactions and is targeted by the peptide entry inhibitor enfuvirtide. Following a similar drug design strategy for the development of an anti-FIV therapy, the present study investigates 627-646gp36 NHR, a peptide sequence derived from a region of gp36 that was previously found to interfere with the antiviral activity of the peptide C8, which instead derives from the gp36 MPER. CD, NMR, and MD simulations were employed to probe the conformational characteristics of 627-646gp36 NHR in the membrane-mimicking environment of SDS micelles. Our data show that 627-646gp36 NHR is characterized by three dynamic helix structures. MD simulations involving 627-646gp36 NHR, C8, and a larger protein, including the CHR and MPER regions, suggest that the interaction of C8 with the MPER region, the origin of the antiviral activity of C8, is disfavored in the presence of 627-646gp36 NHR in the simulation. This evidence can be useful for interpreting the molecular mechanism that leads to interference with the activity of C8, providing information on the folding/unfolding mechanism of the viral glycoprotein to design new strategies to inhibit viral entry.

Histone Tetrasome Dynamics Affects Chromatin Transcription.

During various DNA-centered processes in the cell nucleus, the minimal structural units of chromatin organization, nucleosomes, are often transiently converted to hexasomes and tetrasomes missing one or both H2A/H2B histone dimers, respectively. However, the structural and functional properties of the subnucleosomes and their impact on biological processes in the nuclei are poorly understood. Here, using biochemical approaches, molecular dynamics simulations, single-particle Förster resonance energy transfer (spFRET) microscopy and NMR spectroscopy, we have shown that, surprisingly, removal of both dimers from a nucleosome results in much higher mobility of both histones and DNA in the tetrasome. Accordingly, DNase I footprinting shows that DNA-histone interactions in tetrasomes are greatly compromised, resulting in formation of a much lower barrier to transcribing RNA polymerase II than nucleosomes. The data suggest that tetrasomes are remarkably dynamic structures and their formation can strongly affect various biological processes.

Improving Typhoon Muifa (2022) Forecasts with FY-3D and FY-3E MWHS-2 Satellite Data Assimilation under Clear Sky Conditions

This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon’s structure and providing a prediction of the typhoon’s intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system.

Revisiting the evolution of downhill thunderstorms over Beijing: a new perspective from a radar wind profiler mesonet

Abstract. Downhill thunderstorms frequently occur in Beijing during the rainy seasons, leading to substantial precipitation. The accurate intensity prediction of these events remains a challenge, partly attributed to insufficient observational studies that unveil the thermodynamic and dynamic structures along the vertical direction. This study provides a comprehensive methodology for identifying both enhanced and dissipated downhill thunderstorms. In addition, a radar wind profiler (RWP) mesonet has been built in Beijing to characterize the pre-storm environment downstream of the thunderstorms at the foothill. This involves deriving vertical distributions of high-resolution horizontal divergence and vertical motion from the horizontal wind profiles measured by the RWP mesonet. A case study of an enhanced downhill thunderstorm on 28 September 2018 is carried out for comparison with a dissipated downhill thunderstorm on 23 June 2018, supporting the notion that a deep convergence layer detected by the RWP mesonet, combined with the enhanced southerly flow, favors the intensification of thunderstorms. Statistical analyses based on radar reflectivity from April to September 2018–2021 have shown that a total of 63 thunderstorm events tend to be enhanced when entering the plain, accounting for about 66 % of the total number of downhill thunderstorm events. A critical region for intensified thunderstorms lies on the downslope side of the mountains west to Beijing. The evolution of a downhill storm is associated with the dynamic conditions over the plain compared to its initial morphology. Strong westerly winds and divergence in the middle of troposphere exert a critical influence on the enhancement of convection, while low-level divergence may lead to dissipation. The findings underscore the significant role of an RWP mesonet in elucidating the evolution of a downhill storm.

Charge-lattice coupling and the dynamic structure of the U–O distribution in UO2+x

The different structures and behaviors of UO2+x observed in crystallographic and local structure measurements were examined by extended X-ray absorption fine structure (EXAFS) measurements of pristine UO2.0, p+ and He2+ irradiated UO2.0, and, at multiple temperatures, bulk U4O9 and U3O7 and thin film U4O9-δ on an epitaxial substrate. The disorder caused by irradiation is mostly limited to increased widths of the existing U–O/U pair distributions, with any new neighbor shells being minor. As has been previously reported, the disorder caused by oxidative addition to U4O9 and U3O7 is much more extensive, resulting in multisite U–O distributions and greater reduction of the U–U amplitude with different distributions in bulk and thin-film U4O9. This includes the significant spectral feature near R = 1.2 Å for all U4O9 and U3O7 samples fit with a U-oxo type moiety with a U–O distance around 1.7 Å. In addition to indicating that these anomalies only occur in mixed valence materials, this work confirms the continuous rearrangement of the U–O distributions from 10 to 250 K. Although these variations of the structure are not observed in crystallography, their prominence in the EXAFS indicates that the dynamic structure underlying these effects is an essential factor of these materials.

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information

Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics. On the other hand, the constructed initial static graph structure is more dependent on human labor and domain expertise, and the introduced errors cannot be corrected. To solve these problems, we introduce a dynamic graph structure framework (SIF) based on the complementarity of semantic and structural information. Specifically, for the first problem, SIF integrates the semantic and structural information of the text, and constructs two graph structures, structural information graph and semantic information graph, respectively, based on specialized knowledge, which complements the information between the two graph structures, provides rich semantic features for the downstream identification task, and helps to understanding of the contextual information between implicit sentiment semantics. To deal with the second issue, SIF dynamically learns the initial static graph structure to eliminate the noise information in the graph structure, preventing noise accumulation that affects the performance of the downstream identification task. We compare SIF with mainstream natural language processing methods in three publicly available datasets, all of which outperform the benchmark model. The accuracy on the Puns of day dataset, SemEval-2021 task 7 dataset, and Reddit dataset reaches 95.73%, 85.37%, and 65.36%, respectively. The experimental results demonstrate a good application scenario for our proposed method on implicit sentiment identification tasks.

Response of Cyclonic Eddies to Typhoon Surigae and Their Weakening Effect on the Kuroshio Current in the Western North Pacific Ocean

This study investigated the dynamic and thermal responses of cyclonic eddies (CEs) to Typhoon Surigae in the western North Pacific Ocean using satellite data and a coupled ocean–atmosphere model. Observations and simulations revealed that the typhoon enhanced the two preexisting CEs (C1 and C2). After the typhoon passed the two eddies, the sea surface height (SSH) lowered and the eddy velocity increased above 200 m. C1 was stretched with elliptical deformation accompanied by an SSH trough and jets on the sides of the typhoon track at the eddy edge. The comparative experiments indicated that the typhoon caused the SSH of C1 and C2 to lower by 53.52% and 25.14% compared to conditions without the typhoon, respectively, and the kinetic energy of C1 and C2 to increase by 12 times and 65.76%, respectively. The positive vorticity anomaly input from the typhoon to the CEs was the main mechanism for the enhancement of the CEs. The enhanced CEs modulated the typhoon-induced sea surface temperature (SST) cooling, causing the temperature within the eddies to decrease by upwelling and mixing, and the SST cooling became significant at the center of the CEs and propagated westward with the eddies. This study also revealed that typhoons can significantly perturb eddy dynamic structures by enhancing or generating cyclonic cold eddies and eradicating anticyclonic eddies, thereby weakening the Kuroshio Current transport via eddy–Kuroshio interactions.

Quantifying the uncertainty of LLM hallucination spreading in complex adaptive social networks

Large language models (LLMs) are becoming a significant source of content generation in social networks, which is a typical complex adaptive system (CAS). However, due to their hallucinatory nature, LLMs produce false information that can spread through social networks, which will impact the stability of the whole society. The uncertainty of LLMs false information spread within social networks is attributable to the diversity of individual behaviors, intricate interconnectivity, and dynamic network structures. Quantifying the uncertainty of false information spread by LLMs in social networks is beneficial for preemptively devising strategies to defend against threats. To address these challenges, we propose an LLMs hallucination-aware dynamic modeling method via agent-based probability distributions, spread popularity and community affiliation, to quantify the uncertain spreading of LLMs hallucination in social networks. We set up the node attributes and behaviors in the model based on real-world data. For evaluation, we consider the spreaders, informed people, discerning and unwilling non-spreaders as indicators, and quantified the spreading under different LLMs task situations, such as QA, dialogue, and summarization, as well as LLMs versions. Furthermore, we conduct experiments using real-world LLM hallucination data combined with social network features to ensure the validity of the proposed quantifying scheme.

Faddeev–Jackiw Hamiltonian formulation for general exotic bi-gravity

General exotic bi-gravity, obtained in Ozkan et al. (Phys Rev Lett 123(3):031303, 2019), is a unitary parity-preserving model which describes two interacting spin-two fields in three-dimensional spacetime. Adopting a symplectic viewpoint, we investigate the dynamical structure of general exotic bi-gravity theory. In particular, by exploiting the properties of the corresponding pre-symplectic matrix and its associated zero-modes, we explicitly derive all constraints of the theory, including the integrability conditions and scalar relationships between all the parameters and fields defining the model. Then, as an application, these scalar relationships are used for studying the anti-de Sitter background. After that, we derive the gauge transformations for the dynamical variables from the structure of the remaining zero-modes, meaning that such zero-modes are indeed the generators of the gauge symmetry of the theory. Finally, by switching off one of the four coupling constants βn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta _{n}$$\\end{document} and assuming the invertibility of some of the dreibeins, we find that the general exotic bi-gravity theory has two physical degrees of freedom.

The significance of personalized medicine in healthcare services of the 21st century: a brief literature review

In modern healthcare services, patient safety is a primary goal. With technological advancements, the complexity of healthcare services increases, which in turn creates increased pressure on health professionals during decision-making processes and can lead to errors worldwide. Communication gaps, human factors, patient-originated issues, technical failures, and inadequate policies have been identified as the main causes of medical errors. While research shows that errors stem from human nature and are inevitable, it is emphasized that it is possible to develop methods that enhance patient safety. Utilizing digital technologies to improve the quality and efficiency of healthcare services is a crucial strategy. Innovations such as wearable technologies, mobile devices, digital media-delivered education and consulting services, telehealth applications, 3D printers, clinical decision support systems, and implantable biosensors encompass advancements in the digital health field. This research aims to analyze the complex and dynamic structure of healthcare services in the 21st century, especially considering the opportunities presented by the integration of artificial intelligence and genomic data, within the scope of the relevant literature.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Lytic transglycosylase repertoire diversity enables intrinsic antibiotic resistance and daughter cell separation in Escherichia coli under acidic stress.

Peptidoglycan (PG) is an important architectural element that imparts physical toughness and rigidity to the bacterial envelope. It is also a dynamic structure that undergoes continuous turnover or autolysis. Escherichia coli possesses redundant PG degradation enzymes responsible for PG turnover; however, the advantage afforded by the existence of numerous PG degradation enzymes remains incompletely understood. In this study, we elucidated the physiological roles of MltE and MltC, members of the lytic transglycosylase (LTG) family that catalyze the cleavage of glycosidic bonds between disaccharide subunits within PG strands. MltE and MltC are acidic LTGs that exhibit increased enzymatic activity and protein levels under acidic pH conditions, respectively, and deletion of these two LTGs results in a pronounced growth defect at acidic pH. Furthermore, inactivation of these two LTGs induces increased susceptibility at acidic pH against various antibiotics, particularly vancomycin, which seems to be partially caused by elevated membrane permeability. Intriguingly, inactivation of these LTGs induces a chaining morphology, indicative of daughter cell separation defects, only under acidic pH conditions. Simultaneous deletion of PG amidases, known contributors to daughter cell separation, exacerbates the chaining phenotype at acidic pH. This suggests that the two LTGs may participate in the cleavage of glycan strands between daughter cells under acidic pH conditions. Collectively, our findings highlight the role of LTG repertoire diversity in facilitating bacterial survival and antibiotic resistance under stressful conditions.