Global Propagation Research Articles

Experimental and theoretical study on supramolecular encapsulation of molnupiravir by Cucurbit[7]uril: A potential formulating agent for COVID-19

Global propagation of the Novel Coronavirus Diseases-2019 (COVID-19) outbreak threatens the scientific community's ability to develop and learn effective SARS-CoV-2 treatment methods. Most of the current therapeutics have some form of side effects to treat COVID-19. The requirement for new medications emerges as a consequence of the evolution of new strain such as EG.5.1. Remdesivir, Molnupiravir, Favipiravir, Ribavirin, Baricitinib, Camostat, and other pharmaceuticals have been used to treat patients with COVID-19, among which only Molnupiravir was a novel synthesized agent while the rest were all repurposed medications. However, the side effects from these medications, such as vomiting, diarrhea, dizziness etc., necessitate immediate medical attention. To decrease the toxicity of some medications and increase their efficacy, cyclic macromolecules such as α/β-Cyclodextrin or Cucurbit[n]uril (n = 6–8) can play an important role by forming host–guest complexes. The drugs were coupled with several host molecules in the current investigation to examine their effectiveness against SARS-CoV-2 proteins via a molecular docking study, which revealed that Cucurbit[7]uril-molnupiravir had superior performance against SARS-CoV-2. The inclusion complex was synthesized and characterized via different characterization techniques to prove the formation of stable 1:1 complex. Furthermore, DFT studies also validate our experimental observations.

Node-based wave analysis method for the dynamic response and stiffness of long-span cable-stayed bridges

For dynamic analysis of long-span cable-stayed bridges (LCB) with high flexibility and long force transmission paths, wave analysis method offers unique advantages over modal analysis. However, conducting wave analysis for the entire LCB poses challenges due to the modeling complexity and the substantial computational cost associated with solving numerous wave equations. Therefore, a simple analytical approach for wave analysis of LCB is proposed, which entails the discretization of the entire bridge into waveguides and the subsequent recombination of the regularized local wave propagation models. The feasibility and strategy of solving the global propagation function using local double coordinate systems and specific node-based local wave propagation models are demonstrated. Additionally, based on the geometric and mechanical configuration of nodes in the LCB, local wave scattering and propagation models corresponding to the four standardized nodes types are proposed. A unified Node-Based Wave Analysis (NWA) method for LCB is developed, considering the viscoelastic behavior of cable-stayed beams according to the Kelvin-Voigt model to enhance structural realism. The reasonable damping coefficient in bridge is determined by means of theory and dynamic in situ test of three bridges. Finally, an extended case study is conducted on a typical two-pylon cable-stayed bridge, focusing on the impact of moving loads and material properties on wave characteristics. The results demonstrate the reliability of the proposed NWA method in predicting wave energy distribution, highlighting the suitability of wave analysis for dynamic analysis of LCB. In comparison to undamped structures, it has been found that the presence of damping results in a pair of additional flexural waves with a new dispersion relation in cable-stayed bridges. Furthermore, the study reveals the stiffness enhancement mechanism associated with wave dispersion characteristics in cable-stayed bridges.

Pose-Aided Video-Based Person Re-Identification via Recurrent Graph Convolutional Network

Existing methods for video-based person re-identification (ReID) mainly learn the appearance feature of a given pedestrian via a feature extractor and a feature aggregator. However, the appearance models would fail to learn a large inter-class variance when different pedestrians have similar appearances. Considering that different pedestrians have different walking postures and body proportions, we propose to learn the discriminative pose feature beyond the appearance feature for video retrieval. Specifically, we implement a two-branch architecture to separately learn the appearance feature and pose feature, and then concatenate them together for inference. To learn the pose feature, we first detect the pedestrian pose in each frame through an off-the-shelf pose detector, and construct a temporal graph using the pose sequence. We then exploit a recurrent graph convolutional network (RGCN) to learn the node embeddings of the temporal pose graph, which devises a global information propagation mechanism to simultaneously achieve the neighborhood aggregation of intra-frame nodes and message passing among inter-frame graphs. Finally, we propose a dual-attention method (DAM) consisting of node-attention and time-attention to obtain the temporal graph representation from the node embeddings, where the self-attention mechanism is employed to learn the importance of each node and each frame. We verify the proposed method on three video-based ReID datasets, i.e., Mars, DukeMTMC and iLIDS-VID, whose experimental results demonstrate that the learned pose feature can effectively improve the performance of existing appearance models.

Clustering algorithm for experimental datasets using global sensitivity-based affinity propagation (GSAP)

To minimize the uncertainty of the parameters in combustion kinetics models, Bayesian methods are commonly used for uncertainty constraints based on experimental data. With the rapid and substantial growth of experimental data, using all the experimental data for optimization is not only redundant and time-consuming, but it could also lead to data consistency problems. In this work, the global sensitivity-based affinity propagation method (GSAP) is proposed to cluster experimental datasets and to select representative experimental conditions. Specifically, the global sensitivity coefficient is first obtained through an analysis to characterize the sources of uncertainty in the kinetic model under different experimental conditions. The similarity coefficient, which is defined based on the global sensitivity, measures the resemblance between two experimental conditions. By exchanging messages calculated from similarity, affinity propagation enables the experimental dataset to be automatically clustered into several classes without specifying the number of classes in advance. This method innovatively introduces the consideration of model and experimental uncertainty under different conditions to obtain better optimization results. The correctness and effectiveness of the method are validated through clustering and optimizing on a laminar flame speed dataset of common C0C4 fuels. The dataset consisting of 288 experimental conditions has been automatically clustered into 27 categories, and an exemplar of each category is given. These exemplary conditions reflect the dominant chemistry behind their cluster. At the same time, these conditions have larger model prediction uncertainty and smaller experimental uncertainty to provide better Bayesian constraints. The uncertainty of the model parameters after Bayesian optimization is effectively constrained. The average uncertainty of model predictions across the dataset is reduced from 30 % to 10 % using only 27 exemplar conditions for optimization. While selecting experimental data for model optimization, the clustering strategies provided by this method also, in turn, help understand its underlying chemical essence.

Fundamental Experiment of Ignition and Flame Development in Turbulent Jet Ignition Ignited Methane Jet Flame

ABSTRACT In heavy-duty natural gas engines, diesel is usually injected before natural gas to initiate methane combustion. However, such an approach requires additional fuel and precise injection strategies. In the present work, an alternative approach of pre-chamber turbulent jet ignition (TJI) ignited methane jet flame is performed experimentally. The underlying mechanism of TJI-HPDI jet combustion is investigated in an optically visualized constant-volume combustion chamber with an active pre-chamber. It is found that with the increase of the injection-ignition delay (ti), the combustion process undergoes several transitions. In addition, the success ignition region and ignition failure region separated by ti and T MI are discerned on the ti-T MI diagram. As ti increases, there is an initial rise in both the mean flame front velocity and peak pressure, followed by a subsequent decrease This behavior highlights the TJI-HPDI system’s notable advantages in expanding the lean combustion limit and enhancing overall combustion characteristics compared to premixed combustion. These advantages can be attributed to the mixture stratification and turbulence. Furthermore, pre-chambers fueled with hydrogen can further improve the intensity and energy of the turbulent hot jet, which further decreases the critical injection-ignition delay to initiate global flame propagation in the TJI-HPDI system. The present work deals with real engineering technologies, and it will provide new insights into the design and control of TJI-HPDI engines, especially for heavy-duty natural gas engines.

Efficient parabolic equation based travel time computation

In this paper an efficient method for computing the travel time of an acoustic wave as a function of range using the parabolic equation model. The frequency derivative of the acoustic phase is the differential travel time associated with a propagation in range. By taking this difference across closely spaced frequencies (0.02 f0) and integrating in range, this method computes the travel time of the dominant acoustic arrival. The method compares well with other travel time methods for four different cases, including deep water (shallow source, axial source), upslope and shallow water, and a global 3D propagation environment.

Unidirectional links prefer local firing propagation in the neural network of C. elegans

Understanding the mechanisms of brain functions is currently a hot topic but most studies have been focused on the aspects of synchronization and various partial synchronization. We here study this problem from a new angle of signal propagation, where the local (global) propagation may be related to the status of health (mental disorder). We make a case study on the neural network of C. elegans by presenting a framework where one node is chosen as the source node to generate signals and all the other nodes are chosen as the target nodes. We interestingly find that the existence of the unidirectional links is the key element to generate the local firing propagation and the configuration of the unidirectional links will seriously influence the range of firing propagation. In details, our findings include: (i) The unidirectional links prefer the local firing propagation, while the bidirectional links prefer the global firing propagation. (ii) Multiple source nodes will enhance the firing propagation, where the range of firing propagation for choosing the multiple source nodes from different communities is slightly larger than that from the same community. And (iii) a resonance effect of the range of firing propagation on coupling strength is observed, i.e. there is an optimal coupling strength. Moreover, a brief theoretical analysis is provided to explain these results.

The cellular lives of Wolbachia.

Wolbachia are successful Gram-negative bacterial endosymbionts, globally infecting a large fraction of arthropod species and filarial nematodes. Efficient vertical transmission, the capacity for horizontal transmission, manipulation of host reproduction and enhancement of host fitness can promote the spread both within and between species. Wolbachia are abundant and can occupy extraordinary diverse and evolutionary distant host species, suggesting that they have evolved to engage and manipulate highly conserved core cellular processes. Here, we review recent studies identifying Wolbachia-host interactions at the molecular and cellular levels. We explore how Wolbachia interact with a wide array of host cytoplasmic and nuclear components in order to thrive in a diversity of cell types and cellular environments. This endosymbiont has also evolved the ability to precisely target and manipulate specific phases of the host cell cycle. The remarkable diversity of cellular interactions distinguishes Wolbachia from other endosymbionts and is largely responsible for facilitating its global propagation through host populations. Finally, we describe how insights into Wolbachia-host cellular interactions have led to promising applications in controlling insect-borne and filarial nematode-based diseases.

Very accurate time propagation of coupled Schrödinger equations for femto- and attosecond physics and chemistry, with C++ source code

In this article, I present a very fast and high-precision (up to 33 decimal places) C++ implementation of the semi-global time propagation algorithm for a system of coupled Schrödinger equations with a time-dependent Hamiltonian. It can be used to describe time-dependent processes in molecular systems after excitation by femto- and attosecond laser pulses. It also works with an arbitrary user supplied Hamiltonian and can be used for nonlinear problems. The semi-global algorithm is briefly presented, the C++ implementation is described and five sample simulations are shown. The accompanying C++ source code package is included. The high precision benchmark (long double and float128) shows the estimated calculation costs. The presented method turns out to be faster and more accurate than the global Chebyshev propagator. Program summaryProgram Title: SemiGlobalCppCPC Library link to program files:https://doi.org/10.17632/429rszyc65.1Developer's repository link:http://gitlab.com/cosurgi/SemiGlobalCppLicensing provisions: GNU General Public License 2Programming language: C++Nature of problem: The femto- and attosecond chemistry requires fast and high precision computation tools for quantum dynamics. Conventional software has problems with providing high precision calculation results (up to 33 significant digits), especially when the computation has to be as fast as possible.Solution method: This software fills in the gap by providing the semi-global algorithm [1–3] for arbitrary number of coupled electronic states for the time dependent Hamiltonian and nonlinear inhomogeneous source term. It is implemented in a way that allows computation with precision of 15, 18 or 33 significant digits, where the computation speed can be directly controlled by setting the required error tolerance. The semi-global algorithm [1–3] is implemented in C++ providing a 10× speed boost compared to original publication of semi-global algorithm implemented in Matlab/Octave [1–3].

Air Pressure Perturbations in Karst Caves and Waters after the Hunga Tonga–Hunga Ha’apai Volcano Eruption on 15 January 2022

Microclimatic monitoring (air T, air pressure, CO2, ventilation, humidity, methane, and radon) in selected show caves in Slovenia has been a continuous process for more than 10 years, a process that aims to supervise the use of the caves for tourism in the sense of sustainable environmental management. After the cataclysmic eruption of the Hunga Tonga–Hunga Ha’apai (HTHH) volcano on 15 January 2022, global propagation of ionospheric disturbances was reported worldwide as barometric pressure changes and seismic noise events. Weather stations in Slovenia reported 2–4 hPa changes in atmospheric pressure 16 h after the eruption at 20:30 CET (19:30 UTC). Changes in atmospheric pressure were also detected at 15 air monitoring sites in 3 different caves (20–120 m below the surface), at 8 water monitoring sites in 4 different caves (1–10 m below the water surface), and on the surface (4 air and 2 water monitoring sites), where we identified a small but significant increase in atmospheric pressure of <1 hPa, with the highest signal at 21:00 CET (20:00 UTC). At some cave monitoring locations, air T fell during this global event induced by a far-field volcanic eruption. Cave CO2, methane, and radon measurements did not show significant changes related to the eruption. This is the first evidence of atmospheric pressure changes due to the HTHH volcano eruption in karst caves and waters.

Opportune detections of global P-wave propagation from microseisms interferometry

Opportune detections of global P-wave propagation from microseisms interferometry

About the compatibility between the perturbational complexity index andthe global neuronal workspace theory ofconsciousness.

This paper investigates the compatibility between the theoretical framework of the global neuronal workspace theory (GNWT) of conscious processing and the perturbational complexity index (PCI). Even if it has been introduced within the framework of a concurrent theory (i.e.Integrated Information Theory), PCI appears, in principle, compatible with the main tenet of GNWT, which is a conscious process that depends on a long-range connection between different cortical regions, more specifically on the amplification, global propagation, and integration of brain signals. Notwithstanding this basic compatibility, a number of limited compatibilities and apparent differences emerge. This paper starts from the description of brain complexity, a notion that is crucial for PCI, to then summary of the main features of PCI and the main tenets of GNWT. Against this background, the text explores the compatibility between PCI and GNWT. It concludes that GNWT and PCI are fundamentally compatible, even though there are some partial disagreements and some points to further examine.

Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability.

Dependencies in the global food production network can lead to shortages in numerous regions, as demonstrated by the impacts of the Russia-Ukraine conflict on global food supplies. Here we reveal the losses of 125 food products after a localized shock to agricultural production in 192 countries and territories using a multilayer network model of trade (direct) and conversion of food products (indirect), thereby quantifying 108 shock transmissions. We find that a complete agricultural production loss in Ukraine has heterogeneous impacts on other countries, causing relative losses of up to 89% in sunflower oil and 85% in maize via direct effects and up to 25% in poultry meat via indirect impacts. Whereas previous studies often treated products in isolation and did not account for product conversion during production, the present model considers the global propagation of local supply shocks along both production and trade relations, allowing for a comparison of different response strategies.

Cell protrusions and contractions generate long-range membrane tension propagation

Membrane tension is thought to be a long-range integrator of cell physiology. Membrane tension has been proposed to enable cell polarity during migration through front-back coordination and long-range protrusion competition. These roles necessitate effective tension transmission across the cell. However, conflicting observations have left the field divided as to whether cell membranes support or resist tension propagation. This discrepancy likely originates from the use of exogenous forces that may not accurately mimic endogenous forces. We overcome this complication by leveraging optogenetics to directly control localized actin-based protrusions or actomyosin contractions while simultaneously monitoring the propagation of membrane tension using dual-trap optical tweezers. Surprisingly, actin-driven protrusions and actomyosin contractions both elicit rapid global membrane tension propagation, whereas forces applied to cell membranes alone do not. We present a simple unifying mechanical model in which mechanical forces that engage the actin cortex drive rapid, robust membrane tension propagation through long-range membrane flows.

Lowermost Mantle Structure Beneath the Central Pacific Ocean: Ultralow Velocity Zones and Seismic Anisotropy

AbstractUltralow velocity zones (ULVZs) and seismic anisotropy are both commonly detected in the lowermost mantle at the edges of the two antipodal large low velocity provinces (LLVPs). The preferential occurrences of both ULVZs and anisotropy at LLVP edges are potentially connected to deep mantle dynamics; however, the two phenomena are typically investigated separately. Here we use waveforms from three deep earthquakes to jointly investigate ULVZ structure and lowermost mantle anisotropy near an edge of the Pacific LLVP to the southeast of Hawaii. We model global wave propagation through candidate lowermost mantle structures using AxiSEM3D. Two structures that cause ULVZ‐characteristic postcursors in our data are identified and are modeled as cylindrical ULVZs with radii of ∼1° and ∼3° and velocity reductions of ∼36% and ∼20%. One of these features has not been detected before. The ULVZs are located to the south of Hawaii and are part of the previously detected complex low velocity structure at the base of the mantle in our study region. The waveforms also reveal that, to first order, the base of the mantle in our study region is a broad and thin region of modestly low velocities. Measurements of Sdiff shear wave splitting reveal evidence for lowermost mantle anisotropy that is approximately co‐located with ULVZ material. Our measurements of co‐located anisotropy and ULVZ material suggest plausible geodynamic scenarios for flow in the deep mantle near the Pacific LLVP edge.

An Efficient Phase Unwrapping Method Based on Unscented Kalman Filter

In this paper, we proposed a phase unwrapping method which combines with unscented Kalman filter, pixel classification and an efficient path-following strategy. The characteristics of proposed method as: (1) the path-following strategy speeds up the process of phase unwrapping without decreasing the accuracy; (2) the reliability of each pixel will be graded according to the position of residue and pixel classification strategy; (3) different from the traditional methods, the proposed method can perform filtering and phase unwrapping at the same time to prevent global propagation of error. In addition, we also introduce a signal model which can obtain a similar correlation map by only using a wrapped phase image when without the master-slave image. The results on synthetic data and real data show that the proposed method can obtain better results.

A reliability analysis technique for complex systems with retaining fuzzy information

AbstractThis paper proposed a method for the reliability analysis of systems characterized by fuzzy failure probabilities and intricate failure behaviors, while retaining the fuzzy information throughout the analysis process. Specifically, we introduced a combinational modeling method that integrates generalized stochastic Petri nets (GSPN) and dynamic fault trees (DFT) to capture the dynamic failure behaviors and address the limitations of DFT modeling. This combinational approach is capable of capturing more sophisticated failure behaviors, such as competing failures and global failure propagation patterns. Furthermore, we propose an Monte Carlo technique based on endpoint sampling to enable quantitative analysis of GSPN‐based composite models, which preserves the fuzzy fault information of the system. Finally, we demonstrate the effectiveness of the proposed method through an example of a flight control system.

Atmospheric and Ionospheric Responses to Hunga‐Tonga Volcano Eruption Simulated by WACCM‐X

AbstractHigh‐resolution Whole Atmosphere Community Climate Model with thermosphere/ionosphere extension is used to simulate the responses to the Hunga‐Tonga volcano eruption on 15 January 2022. Global propagation of the Lamb wave L’0 and L’1 pseudomodes are reproduced in the simulation, with the exponential growth of wave amplitudes with altitudes. The wavefront is vertical up to the lower thermosphere, and tilts outward above. These features are consistent with theoretical results. With simulated surface pressure perturbation agreeing with observations (∼100–250 Pa), thermospheric wind perturbations over 100 ms−1 are comparable with reported satellite and ground‐based observations. Traveling ionospheric disturbances in the total electron contents from the simulation show good agreement with observations, including magnitude and propagating speed and evidence of conjugacy in the first 1–2 hr after eruption. Conjugacy in E × B drift, on the other hand, is more persistent.

Global well-posedness for 2D Euler-Boussinesq equations with temperature-dependent thermal diffusivity

In the present paper, we consider the two-dimensional Euler-Boussinesq equations with temperature-dependent thermal diffusivity. More precisely, we prove the global-in-time existence and uniqueness for this system under Yudovich-type initial data. In addition, the global propagation of striated regularity is also considered.

A new channel for global volatility propagation

We compare two channels for global impact on local volatility: the direct channel in which global variables affect the expected value of local volatility, and a new channel in which they affect local volatility persistence. Using 21 equity indices in 17 developed economies, we show that (1) global variables are the main determinants of local volatility persistence; (2) the volatility-persistence channel contributes far more to local volatility variations than the direct channel; and (3) global average return contributes far more to local volatility variations than global average volatility. The global impact on local volatility persistence help explain the commonality in global volatility dynamics.