Dynamic Information Research Articles

From disorder to order: A dynamic approach to mesophase formation in soft sphere model.

This study explores the dynamics of self-assembly and mesophase formation through molecular dynamics simulations of hexagonal and lamellar systems using a simplified coarse-grained model. We focus on characterizing the order-disorder transitions driven by temperature variations and emphasize the often overlooked disordered regime, which serves as a precursor to periodic mesoscale ordering. Our findings not only underscore the morphological richness of the disordered regime, comparable to that of its periodic counterparts, but also reveal the presence of clustering regimes within isotropic phases, thus corroborating prior experimental and theoretical observations. By employing the dynamic correlation coefficient, this work introduces a novel approach to understanding the fundamental mechanisms of mesophase formation, providing new insights into the complex dynamics of self-assembly.

Dynamics of microcompartment formation at the mitosis-to-G1 transition.

As cells exit mitosis and enter G1, mitotic chromosomes decompact and transcription is reestablished. Previously, Hi-C studies showed that essentially all interphase 3D genome features including A/B-compartments, TADs, and CTCF loops, are lost during mitosis. However, Hi-C remains insensitive to features such as microcompartments, nested focal interactions between cis-regulatory elements (CREs). We therefore applied Region Capture Micro-C to cells from mitosis to G1. Unexpectedly, we observe microcompartments in prometaphase, which further strengthen in ana/telophase before gradually weakening in G1. Loss of loop extrusion through condensin depletion differentially impacts microcompartments and large A/B-compartments, suggesting that they are partially distinct. Using polymer modeling, we show that microcompartment formation is favored by chromatin compaction and disfavored by loop extrusion activity, explaining why ana/telophase likely provides a particularly favorable environment. Our results suggest that CREs exhibit intrinsic homotypic affinity leading to microcompartment formation, which may explain transient transcriptional spiking observed upon mitotic exit.

Characterization of Silica Sand-Based Pervious Bricks and Their Performance under Stormwater Treatment

The acceleration of urbanization has disrupted natural water cycles, resulting in increased impervious urban surfaces and non-point source pollution from stormwater runoff. Addressing urban stormwater recharge has become crucial. This study introduces a novel silica sand-based permeable filtration material, investigating its surface characteristics, pore structure, permeability, and pollutant interception capabilities. The results demonstrate that hydrophilic binder coating modification of the permeable surface sand aggregate, combined with hydrophilic inorganic additives, having a porous structure with an average pore size of less than 50 μm and a porosity between 15% and 35%, significantly enhances surface hydrophilicity, achieving a permeation rate of up to 6.8 mL/(min·cm²). Moreover, it shows exceptional filtration and anti-clogging properties, achieving over 98% suspended solids interception and strong resistance to fouling. Dynamic biofilm formation experiments using simulated rain and domestic wastewater explore biofilm morphology and function on silica sand filtration well surfaces. Mature biofilms sustain COD removal efficiency exceeding 70%, with levels consistently below 50 mg/L, NH4+ decreasing to 2 mg N/L, and total nitrogen maintained below 10 mg N/L. The system features anoxic, anoxic, and aerobic zones, fostering synergistic organic matter and nitrogen removal by diverse microorganisms, enhancing pollutant mitigation. Silica sand-based permeable filtration material effectively mitigates urban stormwater runoff pollutants—suspended solids, organic matter, and nitrogen—offering an innovative solution for sponge city development and rainwater resource management.

Unveiling chlorine's role: How it shapes the formation and light-activated oxygen dynamics of chlorophenol-derived environmental persistent free radicals

Environmental persistent free radicals (EPFRs) derived from chlorophenols, triggered by light or heat exposure, pose significant ecological concerns, yet the impact of chlorine substituents on EPFRs formation and reactivity remains inadequately understood. Through an intentional synthesis of chlorophenol-derived EPFRs with varying chlorine contents and positioning, we elucidated the role of chlorine in the photoactivation of molecular oxygen. Our combined experimental and theoretical analysis reveals that these EPFRs are primarily oxygen-centered phenoxy radicals, establishing a direct link between chlorine substitution patterns and their ability to activate molecular oxygen under visible light. Increased chlorine content enhances EPFRs formation by elevating the positive charge on the phenolic hydroxyl group’s hydrogen atom, facilitating its removal. Moreover, the capability of EPFRs to activate molecular oxygen was directly correlated with chlorine content, with 2,3,5,6-tetrachlorophenol-derived EPFRs showcasing the highest activity. This activity is attributed to their structural propensity for TCSQ·- species generation. Furthermore, our study established a significant correlation between the toxicity and activity of EPFRs, emphasizing the critical role of halogen substituents in determining the reactivity of EPFRs. These insights contribute to our understanding of their environmental and toxicological ramifications, underscoring the imperative for continued research aimed at mitigating their detrimental impacts.

High rate methanogenesis and nitrogenous component transformation in the high-solids anaerobic digestion of chicken manure enhanced by biogas recirculation ammonia stripping

High-solids anaerobic digestion is a promising technology for treating animal manure. However, the high nitrogen content in chicken manure leads to severe ammonia inhibition, thereby hindering the application of this technology for chicken manure. This study operated a high-solid anaerobic system feeding the reactor with 20 % total solids (TS) chicken manure for 650 days to investigate whether the anaerobic metabolic performance could be enhanced by adopting biogas recirculation ammonia stripping. The results show that, with ammonia stripping, the total ammonia nitrogen (TAN) was reduced from 8.2 to 3.0 g/L. The methanogenic activity was enhanced 1.3 – 2.9 fold. This led to a low level of volatile fatty acids (VFA, 0.6 g/L) and a high methane yield of 0.3 L/g-volatile solids (VS). Protein decomposition was facilitated under stripping conditions and correlated with a higher abundance of protein decomposition bacteria and enhanced methane production. The dynamics of ammonium formation and removal were illustrated for different stripping conditions. The ability to maintain low ammonium and the carbon and nitrogen balance was also verified. The proven effectiveness observed in this long continuous experiment may provide a foundation for further demonstrating the high-solids anaerobic digestion of chicken manure.

Backbone assignment of CcdB_G100T toxin from E.coli in complex with the toxin binding C-terminal domain of its cognate antitoxin CcdA.

The CcdAB system expressed in the E.coli cells is a prototypical example of the bacterial toxin-antitoxin (TA) systems that ensure the survival of the bacterial population under adverse environmental conditions. The solution and crystal structures of CcdA, CcdB and of CcdB in complex with the toxin-binding C-terminal domain of CcdA have been reported. Our interest lies in the dynamics of CcdB-CcdA complex formation. Solution NMR studies have shown that CcdB_G100T, in presence of saturating concentrations of CcdA-c, a truncated C-terminal fragment of CcdA exists in equilibrium between two major populations. Sequence specific backbone resonance assignments of both equilibrium forms of the ~ 27kDa complex, have been obtained from a suite of triple resonance NMR experiments acquired on 2H, 13C, 15N enriched samples of CcdB_G100T. Analysis of 1H, 13Cα, 13Cβ secondary chemical shifts, shows that both equilibrium forms of CcdB_G100T have five beta-strands and one alpha-helix as the major secondary structural elements in the tertiary structure. The results of these studies are presented below.

Modeling biological memory network by an autonomous and adaptive multi-agent system

At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships description of complex relationships and structures, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a multi-agent system (MAS) model based on the graph theory, each agent equipped with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information memory, modeling and simulation of the brain’s memory process. This decentralized approach transforms memory storage into the management of MAS paths, with each agent utilizing localized information for the dynamic formation and modification of these paths, different path refers to different memory instance. The model’s unique memory algorithm avoids a global view, instead relying on neighborhood-based interactions to enhance resource utilization. Emulating neuron electrophysiology, each agent’s adaptive learning behavior is represented through a microcircuit centered around a variable resistor. Using principles of Ohm’s and Kirchhoff’s laws, we validated the model’s efficacy in memorizing and retrieving data through computer simulations. This approach offers a plausible neurobiological explanation for memory realization and validates the memory trace theory at a system level.

Behaviors of hydrate cap formation via CO2-H2O collaborative injection:Applying to secure marine carbon storage

Leakage prevention of carbon dioxide (CO2) determines the safety of carbon marine geological storage. Hydrate caps have been proven to be one of the most effective structures for preventing CO2 leakage, which has attracted worldwide attention. However, the structure and formation behavior of hydrate caps during CO2 storage process are still unclear. In this study, a strategy of CO2-H2O co-injection was proposed to simulate the actual flow process and accelerate the hydrate cap formation rate. A series of CO2-H2O flow rates (0.25–10 mL/min) were employed. The results show that there will be four stages for the dynamic formation process of hydrate cap: fluid migration and diffusion, local hydrate formation, local area plugging, and hydrate cap formation. What's more, the water-gas flow ratio is inversely proportional to hydrate cap formation rate and positively proportional to the carbon storage efficiency. Meanwhile, the huge sealed capacity of hydrate cap was also proved in this work. The sequestration pressure of CO2 under hydrate cap exceeds 12 MPa, and the maximum carbon storage efficiency increases from 53.12% to 93.30%. Moreover, the macrostructure of hydrate cap consists of two layers with a certain hydrate saturation: the zero-permeability layer and the low-permeability layer. The zero-permeability layer determined the sealed capacity of hydrate cap. These findings may provide a theoretical foundation for the prevention of CO2 leakage in actual project of carbon marine geological storage.

Using In situ Transmission Electron Microscopy to Study Strong Metal‐Support Interactions in Heterogeneous Catalysis

AbstractPrecisely controlling the microstructure of supported metal catalysts and regulating metal‐support interactions at the atomic level are essential for achieving highly efficient heterogeneous catalysts. Strong metal‐support interaction (SMSI) not only stabilizes metal nanoparticles and improves their resistance to sintering but also modulates the electrical interaction between metal species and the support, optimizing the catalytic activity and selectivity. Therefore, understating the formation mechanism of SMSI and its dynamic evolution during the chemical reaction at the atomic scale is crucial for guiding the structural design and performance optimization of supported metal catalysts. Recent advancements in in situ transmission electron microscopy (TEM) have shed new light on these complex phenomena, providing deeper insights into the SMSI dynamics. Here, the research progress of in situ TEM investigation on SMSI in heterogeneous catalysis is systematically reviewed, focusing on the formation dynamics, structural evolution during the catalytic reactions, and regulation methods of SMSI. The significant advantages of in situ TEM technologies for SMSI research are also highlighted. Moreover, the challenges and probable development paths of in situ TEM studies on the SMSI are also provided.

Enhancing visual reinforcement learning with State–Action Representation

Despite the remarkable progress made in visual reinforcement learning (RL) in recent years, sample inefficiency remains a major challenge. Many existing approaches attempt to address this by extracting better representations from raw images using techniques like data augmentation or introducing some auxiliary tasks. However, these methods overlook the environmental dynamic information embedded in the collected transitions, which can be crucial for efficient control. In this paper, we present STAR: State-Action Representation Learning, a simple yet effective approach for visual continuous control. STAR learns a joint state–action representation by modeling the dynamics of the environment in the latent space. By incorporating the learned joint state–action representation into the critic, STAR enhances the value estimation with latent dynamics information. We theoretically show that the value function can still converge to the optima when involving additional representation inputs. On various challenging visual continuous control tasks from DeepMind Control Suite, STAR achieves significant improvements in sample efficiency compared to strong baseline algorithms.

Incremental attribute reduction for dynamic fuzzy decision information systems based on fuzzy knowledge granularity

Rough set-based attribute reduction is a powerful technique for data preprocessing in data mining. Knowledge granularity, as a reliable measure for assessing uncertainty in decision information systems (DIS), finds applicability in attribute reduction within such systems. Nevertheless, the limitation arises from the fact that static attribute reduction methods fail to effectively utilize the information contained in acquired data and promptly update knowledge due to the continuous evolution of data. In addition, existing incremental methods based on knowledge granularity are designed exclusively for symbolic data and lack the capability to handle real-valued data. Inspired by this, our study focuses on the attribute reduction approach for fuzzy decision information systems (FDIS) that encompass object variations by utilizing fuzzy knowledge granularity. Firstly, fuzzy knowledge granulation is defined to quantify uncertainty within FDIS, and utilized to determine the importance of attributes for attribute reduction. Additionally, the incremental mechanisms and attribute reduction algorithms are investigated for adding an object and an object set to FDIS, respectively. Moreover, an explication of how the incremental mechanism for increasing an object set can be viewed as a generalization of the mechanism used for a single object is provided. Finally, comparative experiments on various datasets are conducted to validate the effectiveness and efficiency of the proposed incremental algorithms. The results demonstrate that our algorithms achieve superior classification accuracy and while requiring minimal computing time when compared to the comparative algorithms.

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.