Dynamic Information Research Articles

High-risk powered two-wheelers scenarios generation for autonomous vehicle testing using WGAN

Objective Autonomous vehicles (AVs) have the potential to revolutionize the future of mobility by significantly improving traffic safety. This study presents a novel method for validating the safety performance of AVs in high-risk scenarios involving powered 2-wheelers (PTWs). By generating high-risk scenarios using in-depth crash data, this study is devoted to addressing the challenge of public road scenarios in testing, which often lack the necessary complexity and risk to effectively evaluate the capabilities of AVs in high-risk situations. Method Our approach employs a Wasserstein generative adversarial network (WGAN) to generate high-risk scenes, particularly focusing on PTW scenarios. By extracting 314 car-to-PTW crashes from the China In-depth Mobility Safety Study–Traffic Accident database, we simulate outcomes using PC-Crash software. The data are divided into scenes at 0.1-s intervals, with WGAN generating numerous high-risk scenes. By using a cumulative distribution function (CDF), we sampled and analyzed the vehicle’s dynamic information to generate complete scenarios applicable to the test. The validation process involves using the SVL Simulator and the Baidu Apollo joint simulation platform to evaluate the AV’s driving behavior and interactions with PTWs. Results This study evaluates model generation results by comparing distributions using Wasserstein distance as an indicator. The generator converges after approximately 200 epochs, with the iterator converging quickly. Subsequently, 10,000 new scenes are then generated. The distribution of several key parameters in the generated scenes can be found to approximate that of the original scenes. After sampling, the usability of generated scenarios is 64.76%. Virtual simulations confirm the effectiveness of the scenario generation method, with a generated scenario crash rate of 16.50% closely reflecting the original rate of 15.0%, showcasing the method’s capacity to produce realistic and hazardous scenarios. Conclusions The experimental results suggest that these scenarios exhibit a level of risk similar to the original crashes and are effective for testing AVs. Consequently, the generated scenarios enhance the diversity of the scenario library and accelerate the overall testing process of AVs.

Atypical dynamic trust learning in individuals with high autistic traits in a multi-round trust game with multiple trustworthiness cues

BackgroundUnderstanding the trust-building process, especially in contexts requiring the integration of multiple trustworthiness cues, is a fundamental aspect of social interactions among individuals with high autistic traits (ATs). It can offer valuable insights into the social functioning of these individuals. MethodParticipants with high and low ATs (N = 120; 30 in each group for two experiments) completed a 10-round trust game that embedded various trustworthiness cues, including reputation, advice (Experiment 1 = direct advice; Experiment 2 = social-emotional advice), and reciprocity. Linear mixed-effects models with investment rates as the dependent variable were used to analyze the data. ResultsMethodologically, the experimental results elucidated the fundamental characteristics of dynamic trust formation within the context of multidimensional trust information. Furthermore, High-AT individuals showed assigned less weight to reciprocity but relied heavily on reputation, and exhibited maladaptive advice-taking. Unlike low-AT individuals, high-AT individuals did not adaptively reduce reputational effect over rounds. ConclusionsOur findings highlight the unique challenges faced by individuals with high ATs when navigating complex social interactions.

Chemoexcited Formation and Radiationless Decay Dynamics of Firefly Chromophore.

Multistate nonadiabatic dynamics combined with Mixed-Reference Spin-Flip Time-Dependent Density Functional Theory (MRSF-TDDFT) were performed to investigate the chemoexcitation dynamics of firefly dioxetanone (FDO- in S0) to oxyluciferin (OxyLH- in S1) and its subsequent decay dynamics. The formation of oxyluciferin occurs within approximately 100 fs and is primarily controlled by oscillatory CO2 decarboxylation. Unexpected radiationless decay from oxyluciferin was also observed, facilitated by intramolecular rotation. Simulations under three thermal conditions reveal that higher initial thermal energy not only enhances the formation of oxyluciferin but also increases radiationless decay by surpassing barriers to the ground state. Conversely, lower thermal energy conditions reduce oxyluciferin formation but suppress radiationless decay. These findings suggest that optimal conditions for higher chemiluminescence quantum yield involve initial high thermal energy to accelerate CO2 decarboxylation and gradual thermal dissipation to prevent intramolecular rotation of oxyluciferin. This approach could enhance the chemiluminescence quantum yield beyond the current limit of 40%, offering significant potential for applications in biological imaging and analytical chemistry.

Dynamic mechanism and structural property analysis of microfluidic-obtained pea protein fiber

Although protein-based fibers have been developed, the preparation of inexpensive, safe and environmentally friendly plant protein fibers is still a hot topic. Herein, we constructed the microfluidic chip to produce protein fiber using pea protein and a small amount of cellulose nanofibers. The results demonstrated that compared to pea protein alone, the pea protein-based fiber produced through microfluidic spinning exhibited a smooth and dense surface structure, good thermal stability, higher Zeta potential and antioxidant activity, and lower surface hydrophobicity and sensitization. Furthermore, we comprehensively revealed the dynamic formation mechanism of pea protein-based fiber. During the fiber preparation process, hydrodynamics led the protein and cellulose nanofibers to align gradually along the flow direction, enhancing their molecular orientation. Additionally, strong hydrodynamic shear force altered the protein structure, transitioning its spatial structure from random coil and α-helix to β-sheet, and promoting a binding of protein to cellulose nanofibers. This research may offer theoretical guidance for the application of pea protein-based fiber in the preparation of hypoallergenic protein-based fibers and the construction of foodborne delivery materials.

Competitive resource allocation on a network considering opinion dynamics with self-confidence evolution

The formation of public opinion is typically influenced by different stakeholders, such as governments and firms. Recently, various real-world problems related to the management of public opinion have emerged, necessitating stakeholders to strategically allocate resources on networks to achieve their objectives. To address this, it is imperative to consider the dynamics of opinion formation. Notably, in existing opinion dynamics models, individuals possess self-confidence parameters reflecting their adherence to historical opinions. However, most extant studies assume the individuals’ self-confidence levels remain constant over time, which cannot accurately capture the intricacies of human behavior. In response to this gap, we first introduce a self-confidence evolution model, which encompasses two influencing factors: the self-confidence levels of one's group mates and the passage of time. Furthermore, we present the social network DeGroot model with self-confidence evolution, and conduct some theoretical analyses. Moreover, we propose a game model to identify the optimal resource allocation strategies of players on a network. Finally, we provide sensitivity analyses, comparative studies, and a case study. This paper highlights the significance of incorporating self-confidence evolution into the process of opinion dynamics, and the results can provide valuable practical insights for players seeking to improve their optimal resource allocation on a network to more effectively manage public opinions.

Controlling factors of iron plaque formation and its adsorption of cadmium and arsenic throughout the entire life cycle of rice plants

Iron (Fe) plaque, which forms on the surface of rice roots, plays a crucial role in immobilizing heavy metal(loids), thus reducing their accumulation in rice plants. However, the principal factors influencing Fe plaque formation and its adsorption capacity for heavy metal(loid)s throughout the rice plant's lifecycle remain poorly understood. Thus, this study investigated the dynamics of Fe plaque formation and its ability to adsorb cadmium (Cd) and arsenic (As) across different growth stages, aiming to identify the key drivers behind these processes. The findings reveal that the rate of radial oxygen loss (ROL) and the abundance of plaque-associated microbes are the primary drivers of Fe plaque formation, with their relative importance ranging from 1.4% to 81%. Similarly, the adsorption of As by Fe plaque is principally determined by the rate of ROL and the quantity of Fe plaque, with subsequent effects from the total Fe in rhizospheric soil, arsenate-reducing bacteria, and organic matter-degrading bacteria. The relative importance of these factors ranges from 6.0% to 11.7%. By contrast, the adsorption of Cd onto Fe plaque is primarily affected by competition for adsorption sites with ammonium in soils and the presence of organic matter-degrading bacteria, contributing 25.5% and 23.5% to the adsorption process, respectively. These findings provide significant insights into the development of Fe plaque and its absorption of heavy metal(loid)s throughout the lifecycle of rice plants.

Multi-Step Passenger Flow Prediction for Urban Metro System Based on Spatial-Temporal Graph Neural Network

Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for predicting multistep passenger flow within metro stations. In the spatial dimension, previous research primarily focuses on local spatial dependencies, struggling to capture implicit global information. We propose a spatial modeling module that leverages a dynamic global attention network (DGAN) to capture dynamic global information from all-pair interactions, intricately fusing prior knowledge from the input graph with a graph convolutional network. In the temporal dimension, we design a temporal modeling module tailored to navigate the challenges of both long-term and recent-term temporal passenger flow patterns. This module consists of series decomposition blocks and locality-aware sparse attention (LSA) blocks to incorporate multiple local contexts and reduce computational complexities in long sequence modeling. Experiments conducted on both simulated and real-world datasets validate the exceptional predictive performance of our proposed model.

Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.

Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions-the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.

Multimaterial Digital-Light Processing of Metal-Organic Framework (MOF) Composites: A Versatile Tool for the Rapid Microfabrication of MOF-Based Devices.

Patterning Metal-Organic Frameworks (MOFs) is essential for their use in sensing, electronics, photonics, and encryption technologies. However, current lithography methods are limited in their ability to pattern more than two MOFs, hindering the potential for creating advanced multifunctional surfaces. Additionally, balancing design flexibility, simplicity, and cost often results in compromises. This study addresses these challenges by combining Digital-Light Processing (DLP) with a capillary-assisted stop-flow system to enable multimaterial MOF patterning. It demonstrates the desktop fabrication of multiplexed arbitrary micropatterns across cm-scale areas while preserving the MOF's pore accessibility. The ink, consisting of a MOF crystal suspension in a low volatile solvent, a mixture of high molecular weight oligomers, and a photoinitiator, is confined by capillarity in the DLP projection area and quickly exchanged using syringe pumps. The versatility of this method is demonstrated by the direct printing of a ZIF-8-based luminescent oxygen sensor, a 5-component dynamic information concealment method, and a PCN-224-based colorimetric sensor for amines, covering disparate pore and analyte sizes. The multi-MOF capabilities, simplicity, and accessibility of this strategy pave the way for the facile exploration of MOF materials across a wide range of applications, with the potential to significantly accelerate the design-to-application cycle of MOF-based devices.

Dynamic formation of the protein-lipid prefusion complex

Synaptic vesicles (SVs) fuse with the presynaptic membrane (PM) to release neuronal transmitters. The SV protein synaptotagmin 1 (Syt1) serves as a Ca2+ sensor for evoked fusion. Syt1 is thought to trigger fusion by penetrating the PM upon Ca2+ binding; however, the mechanistic detail of this process is still debated. Syt1 interacts with the SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) complex, a coiled-coil four-helical bundle that enables the SV-PM attachment. The SNARE-associated protein complexin (Cpx) promotes Ca2+-dependent fusion, possibly interacting with Syt1. We employed all-atom molecular dynamics to investigate the formation of the Syt1-SNARE-Cpx complex interacting with the lipid bilayers of the PM and SVs. Our simulations demonstrated that the PM-Syt1-SNARE-Cpx complex can transition to a “dead-end” state, wherein Syt1 attaches tightly to the PM but does not immerse into it, as opposed to a prefusion state, which has the tips of the Ca2+-bound C2 domains of Syt1 inserted into the PM. Our simulations unraveled the sequence of Syt1 conformational transitions, including the simultaneous docking of Syt1 to the SNARE-Cpx bundle and the PM, followed by Ca2+ chelation and the penetration of the tips of Syt1 domains into the PM, leading to the prefusion state of the protein-lipid complex. Importantly, we found that direct Syt1-Cpx interactions are required to promote these transitions. Thus, we developed the all-atom dynamic model of the conformational transitions that lead to the formation of the prefusion PM-Syt1-SNARE-Cpx complex. Our simulations also revealed an alternative dead-end state of the protein-lipid complex that can be formed if this pathway is disrupted.

P-DRL: A Framework for Multi-UAVs Dynamic Formation Control under Operational Uncertainty and Unknown Environment

P-DRL: A Framework for Multi-UAVs Dynamic Formation Control under Operational Uncertainty and Unknown Environment

Emergence of condensation patterns in kinetic equations for opinion dynamics

In this work, we define a class of models to understand the impact of population size on opinion formation dynamics, a phenomenon usually related to group conformity. To this end, we introduce a new kinetic model in which the interaction frequency is weighted by the kinetic density. In the quasi-invariant regime, this model reduces to a Kaniadakis–Quarati-type equation with nonlinear drift, originally introduced for the dynamics of bosons in a spatially homogeneous setting. From the obtained PDE for the evolution of the opinion density, we determine the regime of parameters for which a critical mass exists and triggers blow-up of the solution. Therefore, the model is capable of describing strong conformity phenomena in cases where the total density of individuals holding a given opinion exceeds a fixed critical size. In the final part, several numerical experiments demonstrate the features of the introduced class of models and the related consensus effects.

Motional Resistance as Highly Selective Descriptor to Probe Dynamic Formation of Surface Films on Zinc Anode

AbstractZinc anodes are expected as a promising alternative to lithium‐based anodes in energy storage systems due to their low cost, high theoretical capacity, and environmental friendliness. However, the development of efficient and stable zinc anode requires a fundamental understanding of the interfacial processes occurring during zinc deposition and dissolution cycling. In this study, we employed electrochemical quartz crystal microbalance (EQCM) analysis to investigate the potential‐dependent formation and decomposition of surface films on zinc metal anodes in sulfate‐based aqueous electrolytes. Changes in frequency and motional resistance served as complementary descriptors, with motional resistance being a highly selective indicator for probing dynamic surface film formation driven by side reactions at the zinc anode. While the frequency change provided the overall changes in the mass of both zinc metal and surface films, changes in the motional resistance selectively reflected the amount and nature of the visco‐elastic interface that comprise the surface films. The two descriptors provide quantitative and complementary means to discover the complex interfacial processes such as the formation of surface visco‐elastic films, guiding to the development of more stable and efficient zinc‐based electrochemical systems.

N-Glycosylation-Induced Pathologic Protein Conformations as a Tool to Guide the Selection of Biologically Active Small Molecules.

Post-translational modifications such as protein N-glycosylation, significantly influence cellular processes. Dysregulated N-glycosylation, exemplified in Grp94, a member of the Hsp90 family, leads to structural changes and the formation of epichaperomes, contributing to pathologies. Targeting N-glycosylation-induced conformations offers opportunities for developing selective chemical tools and drugs for these pathologic forms of chaperones. We here demonstrate how a specific Grp94 conformation induced by N-glycosylation, identified previously via molecular dynamics simulations, rationalizes the distinct behavior of similar ligands. Integrating dynamic ligand unbinding information with SAR development, we differentiate ligands productively engaging the pathologic Grp94 conformers from those that are not. Additionally, analyzing binding site stereoelectronic properties and QSAR models using cytotoxicity data unveils relationships between chemical, conformational properties, and biological activities. These findings facilitate the design of ligands targeting specific Grp94 conformations induced by abnormal glycosylation, selectively disrupting pathogenic protein networks while sparing normal mechanisms.

Dynamic predictive maintenance strategy for multi‐component system based on LSTM and hierarchical clustering

AbstractIn recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice.

Optical Conductivity and Photo-Induced Polaronic Formation in Co2MnGa Topological Semimetal.

Topological materials occupy an important place in the quantum materials family due to their peculiar low-energy electrodynamics, hosting emergent magneto-electrical, and nonlinear optical responses. This manuscript reports on the optical responses for the magnetic topological nodal semimetal Co2MnGa, studied in a thin film geometry at various thicknesses. The thickness-dependent optical conductivity is investigated, observing a substantial dependence of the electronic band structure on thickness. Additionally, details on the ultrafast response of the low energy excitations in the terahertz frequency are reported by employing optical pump-terahertz probe (OPTP) spectroscopy. In particular, the photocarrier dynamics of Co2MnGa thin films is studied at varying pump fluence, pump wavelength, and film thickness, observing a negative THz photoconductivity which is assigned to a dynamical formation of large polarons in thematerial.

Sustainable Operation and Management of a Dynamic Supply Chain under the Framework of a Community with a Shared Future for Mankind

The values of a community with a shared future for mankind include the views of common interests, sustainable development, and global governance. This article will fully consider introducing the value concept of a community with a shared future into the operation and management of dynamic supply chains. Based on the optimal information fusion mechanism of artificial intelligence, this article aims to examine the operation and management of dynamic supply chains within the framework of a community with a shared future for mankind. The core idea is to consider the common interests among enterprises, establish a global collaborative operation concept for upstream, midstream, and downstream enterprises, and achieve the goal of sustainable development. Firstly, a type of composite dynamic supply chain model is considered, in which the total inventory of each node in the supply chain is further subdivided into raw material inventory and finished product inventory. At the same time, we have considered factors such as the signing of procurement contracts between core enterprises and upstream enterprises, as well as the signing of supply contracts between core enterprises and downstream enterprises. Secondly, the static and dynamic monitoring information of the enterprise has been established. We use steady-state Kalman filtering theory to obtain dynamic reference signals for upstream enterprises, core enterprises, and downstream enterprises. Based on the optimal information fusion processing mechanism of artificial intelligence, the coefficient weighting method is used to obtain the optimal fusion signals of upstream enterprises, core enterprises, and downstream enterprises. Once again, through high-quality switching strategies, enterprises can achieve in-order switching, improve production efficiency, reduce downtime, enhance their competitiveness and responsiveness, and transform the dynamic supply chain, including order switching, into a discrete-time linear switching system for processing. Fourthly, sufficient conditions, robustness analysis results, and inventory control criteria for the solvability of dynamic supply chain H∞ with order switching are provided. Finally, data analysis is conducted using historical order information from three fruit companies to verify the validity and feasibility of the conclusions in this article and to improve the performance of the dynamic supply chain system. The research findings of this article enrich the exploration of the operation and management of dynamic supply chains and the construction of a community with a shared future for mankind.

Dynamics of cosmic string formation with emission of gravitational waves: a toy model

Starting from an extended theory of general relativity with boundary terms included, we study a toy model to describe the dynamical collapse of an infinite cylinder with positive spatial curvature from which a cosmic string is formed after collapsing, and we examine the production of gravitational waves (GW) emitted during the collapse. The background dynamics of the radial collapse is described by a massive scalar field which departs from the equilibrium when the scalar field climbs through the quadratic potential. During the collapse the radial pressure is considered as null, so that the kinetic and the potential energy densities are equaled. The components of the GW modes +,×and B, for the polarization tensor, are analytically obtained.

Carbon Storage Dynamics in Lower Shimentan Formation of the Qiantang Sag, East China Sea Shelf Basin: Stratigraphy, Reservoir – Cap Analysis, and Source – Sink Compatibility

Carbon Storage Dynamics in Lower Shimentan Formation of the Qiantang Sag, East China Sea Shelf Basin: Stratigraphy, Reservoir – Cap Analysis, and Source – Sink Compatibility

Dynamics of HCN, HNC, and HNCO Formation in the 193 nm Photodissociation of Formamide.

Formamide (NH2CHO) is the simplest molecule containing a peptide linkage [-NH-C(═O)-], and it plays an essential role in the study of prebiotic chemistry. Exposure to UV irradiation allows formamide to decompose and act as a prebiotic feedstock in the formation of nucleobases and other necessary starting materials. The photodissociation mechanism of gaseous formamide at 193 nm is studied using (a) chirped-pulse Fourier transform millimeter-wave spectroscopy in the 260-290 GHz spectral region in a room-temperature flow-tube reactor at 1 μbar pressure, (b) a combination of electronic structure theory, transition state theory, and quasiclassical trajectories, and (c) the Active Thermochemical Tables. The HCN and HNC photoproducts of hydrogenated (NH2CHO) and deuterated (NH2CDO and ND2CHO) formamide precursors are examined to gain insight into the photodissociation mechanism. The theoretical investigation has characterized the main pathway leading to each of the HCN/HNC isomers from the precursor isotopologues. The theoretical branching ratio [HNC]/[HCN] = 2.1 for nascent photofragments agrees with the experiment. The effect of the postphotolysis HNC ↔ HCN isomerization on the [HNC]/[HCN] ratio is predicted. We report the experimental branching ratio [HNCO]: ([HNC] + [HCN]) = 12 ± 3 and propose that most of HNCO originates from dissociation on the S1 electronic state of formamide.