Diffusion Trajectories Research Articles

Hydrogen diffusion mechanisms in the 4130X steel: An integrated study with electrochemical experiments and molecular dynamics simulations

In this work, hydrogen diffusion behavior and mechanisms in the 4130X steel influenced by temperature, locally high concentration, and grain boundary were studied by leveraging both electrochemical hydrogen permeation experiments and molecular dynamics simulations. It was revealed that the hydrogen diffusion coefficient of the 4130X steel was increased with increasing temperature and decreasing locally high hydrogen concentration. The grain boundaries with misorientation below 15° characterized by an electron backscatter diffraction map were identified as hydrogen trapping sites, thus rendering a lower mean square displacement of hydrogen atoms and localized hydrogen diffusion trajectories. Furthermore, at a high hydrogen concentration of 4 at. %, these grain boundaries were saturated by hydrogen atoms, and platelet-like hydrogen clusters were formed within the lattice, which further inhibited the diffusive motion of hydrogen atoms. These findings would deepen our understanding of hydrogen embrittlement mechanisms by establishing the connections between macroscopic permeation behavior and atomic-scale hydrogen diffusion in structural materials.

A quick parameter configuration tool for SCHISM’s ocean transport simulation of radioactive materials

Abstract A quick tool capable of processing output files generated by the SCHISM particle tracking module has been developed to handle complex data processing procedures and conduct parameter sensitivity testing. This tool performs calculations, considering the influence of half-lives and partition coefficients, for the conversion of activity concentrations, plots particle diffusion trajectories, conducts statistical analyses of regional particle amounts, and presents activity concentration within the defined area. Using the IAEA MODARIA program as an example, this study investigates the transport of radioactive 137Cs in the ocean following the Fukushima accident on March 11, 2011. A three-year diffusion simulation was conducted by incorporating data such as ocean currents, wind fields, and tides. The calculated results demonstrate a high level of consistency with measurements, and the calculated results in the low activity concentration fall within the variability range reported in the literature.

Change-point detection in anomalous-diffusion trajectories utilising machine-learning-based uncertainty estimates

Abstract When recording the movement of individual animals, cells or molecules one will often observe changes in their diffusive behaviour at certain points in time along their trajectory. In order to capture the different diffusive modes assembled in such heterogeneous trajectories it becomes necessary to segment them by determining these change-points. Such a change-point detection can be challenging for conventional statistical methods, especially when the changes are subtle. We here apply Bayesian Deep Learning to obtain point-wise estimates of not only the anomalous diffusion exponent but also the uncertainties in these predictions from a single anomalous diffusion trajectory generated according to four theoretical models of anomalous diffusion. We show that we are able to achieve an accuracy similar to single-mode (without change-points) predictions as well as a well calibrated uncertainty predictions of this accuracy. Additionally, we find that the predicted uncertainties feature interesting behaviour at the change-points leading us to examine the capabilities of these predictions for change-point detection. While the series of predicted uncertainties on their own are not sufficient to improve change-point detection, they do lead to a performance boost when applied in combination with the predicted anomalous diffusion exponents.

A multi-entity reinforced main path analysis: Heterogeneous network embedding considering knowledge proximity

Main path analysis (MPA) is an important approach in detecting the trajectory of knowledge diffusion in a specific research domain. Previous studies always focus on citation-based relationships, overlooking other structural forms in citation network. This study introduces a multi-entity reinforced MPA model by constructing a knowledge graph from paper metadata, including citations, authors, journals, and keywords. We construct heterogeneous network to reveal relationships among various entities. Different knowledge graph embedding models are employed to train the network, thereby obtaining entity and relation embeddings. The cosine similarity algorithm is adopted to measure the knowledge proximity between these embeddings. We take the Internet of Thing domain as an example to verify the performance of the multi-entity reinforced MPA through both quantitative and qualitative analysis. Our findings indicate that the adjusted MPA exhibits stronger topic relevance, demonstrating the effectiveness of the method in capturing complex knowledge relationships.

One-dimensional photonic crystal enhancing spin-to-orbital angular momentum conversion for single-particle tracking

Single-particle tracking (SPT) is an immensely valuable technique for studying a variety of processes in the life sciences and physics. It can help researchers better understand the positions, paths, and interactions of single objects in systems that are highly dynamic or require imaging over an extended time. Here, we propose an all-dielectric one-dimensional photonic crystal (1D PC) that enhances spin-to-orbital angular momentum conversion for three-dimensional (3D) SPTs. This well-designed 1D PC can work as a substrate for optical microscopy. We introduce this effect into the interferometric scattering (iSCAT) technique, resulting in a double-helix point spread function (DH-PSF). DH-PSF provides more uniform Fisher information for 3D position estimation than the PSFs of conventional microscopy, such as encoding the axial position of a single particle in the angular orientation of DH-PSF lobes, thus providing a means for 3D SPT. This approach can address the challenge of iSCAT in 3D SPT because DH-PSF iSCAT will not experience multiple contrast inversions when a single particle travels along the axial direction. DH-PSF iSCAT microscopy was used to record the 3D trajectory of a single microbead attached to the flagellum, facilitating precise analysis of fluctuations in motor dynamics. Its ability to track single nanoparticles, such as 3D diffusion trajectories of 20 nm gold nanoparticles in glycerol solution, was also demonstrated. The DH-PSF iSCAT technique enabled by a 1D PC holds potential promise for future applications in physical, biological, and chemical science.

Quran Desecration Rallies in Scandinavia and the Netherlands: The Formation and Transnational Diffusion of an Anti-Muslim Protest Tactic

ABSTRACT Since 2019, Quran desecration rallies have proliferated across Northern Europe, leading to geopolitical tensions with Muslim-majority countries and domestic debates about freedom of speech. This article traces the diffusion trajectories of Quran desecration acts from their initiation in 2019 until November 2023 across Denmark, Norway, Sweden, and the Netherlands. Combining protest event analysis and process tracing based on mainstream media reporting and far-right social media, it shows how the desecration tactic spread from Denmark to remnants of the transnational Counter-Jihad Movement through a within-movement diffusion process involving both indirect and direct channels. Following Rasmus Paludan’s Quran-burning outside Turkey’s Embassy in Stockholm in January 2023, the tactic attracted massive international media attention. In Denmark and Sweden, the tactics’ repurposing toward geopolitical issues led to a spillover to actors unaffiliated with the far right, while in Norway and the Netherlands, the tactic hardly diffused beyond the anti-Islam activists. Drawing on far-right transnational diffusion literature, the article provides an empirically grounded and theoretically informed mapping of the recent wave of Quran desecration rallies, showing the various ways the same tactic spread throughout and into different domestic settings.

All roads lead to Rome: Understanding the diffusion trajectories of innovation twins

All roads lead to Rome: Understanding the diffusion trajectories of innovation twins

Fractional Lévy Stable Motion from a Segmentation Perspective

The segmentation analysis of the Golding–Cox mRNA dataset clarifies the description of these trajectories as a Fractional Lévy Stable Motion (FLSM). The FLSM method has several important advantages. Using only a few parameters, it allows for the detection of jumps in segmented trajectories with non-Gaussian confined parts. The value of each parameter indicates the contribution of confined segments. Non-Gaussian features in mRNA trajectories are attributed to trajectory segmentation. Each segment can be in one of the following diffusion modes: free diffusion, confined motion, and immobility. When free diffusion segments alternate with confined or immobile segments, the mean square displacement of the segmented trajectory resembles subdiffusion. Confined segments have both Gaussian (normal) and non-Gaussian statistics. If random trajectories are estimated as FLSM, they can exhibit either subdiffusion or Lévy diffusion. This approach can be useful for analyzing empirical data with non-Gaussian behavior, and statistical classification of diffusion trajectories helps reveal anomalous dynamics.

Combining text analytics and network path extraction to trace CSR in the social sciences: Intellectual structures and diffusion trajectories

AbstractCorporate social responsibility (CSR) has evolved over time into a mature interdisciplinary scientific field. However, there is a lack of research to explore the disciplinary association patterns and diffusion trajectories of scientific knowledge within this field. This research proposes a knowledge tracing framework that combines text analysis based on scientific texts and main path extraction based on citation networks to fill this gap. This framework is implemented in a dataset containing 15,949 papers related to CSR in the social sciences published from 1969 to 2022. This study identifies 19 main research directions within this field and their five growth trends. Combined with disciplinary categories of publication venues, both the disciplinary origins and disciplinary association patterns of these knowledge contents are also revealed. Besides that, three knowledge diffusion paths are obtained, which meticulously reveal the knowledge evolution and paradigm shift. Aside from the methodological novelty and result scarcity, this study also identifies research streams and potential opportunities. To begin with, CSR‐related studies have shown the tendency of microscopic and interdisciplinary. Then, related research methods show the tendency to be complicated and focus on causal logic. On this basis, some future agendas of CSR are also proposed in this work.

Thermodynamic stability and ionic conductivity in lithium–germanium binary system

Lithium–germanium binary compounds are promising anode materials for secondary lithium-ion batteries due to their high capacity, low operating voltage, and high electronic conductivity of lithiated Ge. For their successful application in batteries, it is essential to know the temperature stability of different Li–Ge phases and the variation of their ionic conductivity depending on the operating temperatures of the batteries. This work aims to comprehensively study the thermodynamic stability and ionic conductivity in Li–Ge binary compounds using a combination of first-principle computations and machine-learning interatomic potentials. We calculated convex hulls of the Li–Ge system at various temperatures and a temperature–composition phase diagram was obtained, delineating stability fields of each phase. Our calculations show that at temperatures higher than 590 K, LiGe undergoes a I41/a–P4/mmm transition, which leads to a change in the ionic conductivity. We show that all stable and metastable Li–Ge compounds have high ionic conductivity, but LiGe and Li7Ge12 have the lowest lithium diffusion. Trajectories of diffusion and Ge arrangements depend on lithium concentration. Based on advanced theoretical approaches, this study provides insights for the development of Li–Ge materials in lithium-ion and lithium-metal battery applications.

Multifractal spectral features enhance classification of anomalous diffusion.

Anomalous diffusion processes, characterized by their nonstandard scaling of the mean-squared displacement, pose a unique challenge in classification and characterization. In a previous study [Mangalam et al., Phys. Rev. Res. 5, 023144 (2023)2643-156410.1103/PhysRevResearch.5.023144], we established a comprehensive framework for understanding anomalous diffusion using multifractal formalism. The present study delves into the potential of multifractal spectral features for effectively distinguishing anomalous diffusion trajectories from five widely used models: fractional Brownian motion, scaled Brownian motion, continuous-time random walk, annealed transient time motion, and Lévy walk. We generate extensive datasets comprising 10^{6} trajectories from these five anomalous diffusion models and extract multiple multifractal spectra from each trajectory to accomplish this. Our investigation entails a thorough analysis of neural network performance, encompassing features derived from varying numbers of spectra. We also explore the integration of multifractal spectra into traditional feature datasets, enabling us to assess their impact comprehensively. To ensure a statistically meaningful comparison, we categorize features into concept groups and train neural networks using features from each designated group. Notably, several feature groups demonstrate similar levels of accuracy, with the highest performance observed in groups utilizing moving-window characteristics and p varation features. Multifractal spectral features, particularly those derived from three spectra involving different timescales and cutoffs, closely follow, highlighting their robust discriminatory potential. Remarkably, a neural network exclusively trained on features from a single multifractal spectrum exhibits commendable performance, surpassing other feature groups. In summary, our findings underscore the diverse and potent efficacy of multifractal spectral features in enhancing the predictive capacity of machine learning to classify anomalous diffusion processes.

Influence of interdisciplinarity of scientific papers on the durability of citation diffusion: A perspective from citation discontinuance

Whether interdisciplinarity leads to greater success in research remains a question that is still unresolved. Investigating the impact of interdisciplinarity of scientific papers on the durability of their citation diffusion is of significant importance. Combining the concept of discontinuance in the theory of innovation diffusion and citation trajectory scenarios, this study proposes the definition and measurement indicators of Citation Discontinuance (CD), and examines the feasibility of using CD as a descriptor for the durability of citation diffusion. Using CD-related features as the dependent variable, hierarchical multiple regression is employed to explore the influence of interdisciplinarity of scientific papers on the durability of citation diffusion. The findings reveal that CD is commonly observed in citation diffusion and can serve as an indicator for describing the durability of citation diffusion. From the perspective of CD, the interdisciplinarity of scientific papers shows a positive impact on the durability of citation diffusion. This effect will also vary by discipline.

The Prospective Applications of Arising Nanostructured Dielectric Materials in Storage of Energy: A Comprehensive Review

Background: The manufacture and study of innovative materials that enable the availability of relevant technologies are vital in light of the energy demands of various human activities and the need for a substantial shift in the energy matrix. Objective: A strategy based on the creation of enhanced applications for batteries has been devised to reduce the conversion, storage, and feeding of renewable energy like fuel cells and electrochemical capacitors. Methods: Conductive polymers (CP) can be utilised instead of traditional inorganic chemicals. Electrochemical energy storage devices with similar capabilities can be built using approaches based on the production of composite materials and nanostructures. Results: CP's nanostructuring is notable for its concentration on synergistic coupling with other materials, which sets it apart from other nanostructures that have been developed in the preceding two decades. This is due to the fact that, when paired with other materials, their distinctive morphology and adaptability significantly enhance performance in areas like the suppression of ionic diffusion trajectories, electronic transport and the improvement of ion penetrability and intercalation spaces. Conclusion: The present study forecasts the wide-ranging modern applications of diverse nanostructured dielectric materials along with its future prospectives. The potential contributions of nanostructured carbon nanotubes to the development of innovative materials for energy storage devices are also critically discussed in this context, which delivers a summary of the present state of information on this emerging topic.

Diffusion-kurtosis magnetic resonance imaging of the brain in the differential diagnostics of metastases of tumors of various primary localization

Background: Metastatic brain lesions lead to the most unfavorable prognosis for the course of an oncological disease. Most often, brain metastases arise from primary tumors such as lung cancer, breast cancer, and melanoma. Of particular interest are groups of secondary intracranial tumors without an identified primary focus. Methods of non-invasive differential diagnosis based on a possible histological affiliation, including diffusion-kurtosis magnetic resonance imaging, can improve the diagnostic search for the primary tumor. Aim: The aim of this study is to improve the quality of differential diagnosis for brain metastases of tumors of different primary localization by introducing the diffusion-kurtosis magnetic resonance imaging technique into the magnetic resonance scanning protocol. Methods: Our work included studies of 60 patients who underwent examination and treatment at the N.N. Blokhin National Research Medical Center of Oncology of the Ministry of Health of Russia from October 2019 to March 2022. According to magnetic resonance imaging, metastatic formations were detected in the brain of the patients, with different localizations of the primary tumor. 20 patients were diagnosed with lung cancer (33.3%), 20 patients with breast cancer (33.3%) and 20 patients with melanomas (33.3%). We evaluated the tumor size, diffusion and kurtosis parameters, such as the mean kurtosis, axial kurtosis, radial kurtosis, kurtosis anisotropy, radial diffusion, and fractional anisotropy, and relative anisotropy, axial diffusion of the extra-axonal fluid, radial diffusion of the extra-axonal fluid, axonal fluid fraction, and tortuosity of the diffusion trajectory. Results: Statistically significantly (p 0.05) differing parameters of diffusion and kurtosis in the comparative evaluation of the above indicators were identified in the structure associated with melanoma and lung cancer axial diffusion, fractional anisotropy, relative anisotropy, radial kurtosis and tortuosity of the diffusion trajectory, as well as in the structure of melanoma and breast cancer — axial diffusion, axonal fluid fraction, fractional anisotropy, axial diffusion of extra-axonal fluid, mean kurtosis, relative anisotropy, radial kurtosis and tortuosity of the diffusion trajectory. Conclusion: Diffusion-kurtosis magnetic resonance imaging is a promising technique that allows obtaining additional differential information in the case of metastatic lesions of the brain matter, especially those from an undetected primary focus.

Semantic segmentation of anomalous diffusion using deep convolutional networks

Heterogeneous dynamics commonly emerges in anomalous diffusion with intermittent transitions of diffusion states but proves challenging to identify using conventional statistical methods. To effectively capture these transient changes of diffusion states, we propose a deep learning model (U-AnDi) for the semantic segmentation of anomalous diffusion trajectories. This model is developed with the dilated causal convolution (DCC), gated activation unit (GAU), and U-Net architecture. The study addresses two key subtasks related to trajectory segmentation and changepoint detection, concentrating on variations in diffusion exponents and dynamic models. Additionally, extended analyses are conducted on the segmentation of single-model trajectories, multistate biological trajectories, and anomalous diffusion with added correlation functions. By rationally designing comparative models and evaluating the performance of U-AnDi against these models, we discover that U-AnDi consistently outperforms other models across all segmentation tasks, thereby affirming its superiority in the field. This performance edge also sheds light on the interpretability of U-AnDi's core components: DCC, GAU, and U-Net. The clarity with which these components contribute to U-AnDi's success underscores their congruence with the intrinsic physics underlying anomalous diffusion. Furthermore, our model is examined using real-world anomalous diffusion data: the diffusion of transmembrane proteins on cell membrane surfaces, and the segmentation results are highly consistent with experimental observations. Our findings could offer a heuristic deep learning solution for the detection of heterogeneous dynamics in single-molecule/particle tracking experiments, and have the potential to be generalized as a universal scheme for time-series segmentation. Published by the American Physical Society 2024

Investigating diffusion and convergence trajectory of hydrogen storage technology based on patent analysis

Safe and flexible hydrogen storage technology (HST) emerges as a crucial element in driving the industrialization of hydrogen energy. Consequently, HSTs are being extensively investigated globally, with an increasing number diffusing beyond national boundaries through transnational patent applications. However, the absence of an evolutionary analysis constrains the exploration of technological opportunities in HST. In this study, we unveil the evolutionary trajectory and innovation trends of HST by analyzing its global diffusion and convergence network, utilizing patent family and IPC co-occurrence data spanning from 1993 to 2022. Our results indicate that the transnational diffusion of HST is concentrated in core countries. Japan and France emerged as dominant source countries, while China, Europe, Korea, and Canada were identified as main target countries for HST diffusion. Notably, in the past decade, China and France have experienced significant expansion in filing HST patents. However, China filed a relatively limited number of patent families, suggesting a varied level of engagement in transnational HST diffusion. Hydrogen fuel cells, storage alloys, vessels, and distribution systems constitute the primary technological domains of HST. In the initial stages, fuel cells and alloy materials dominated HST development, often converging with metal powders and heat treatment. However, since 2003, F17C, related to storage vessels, has evolved as a focal point of convergence, involving the integration of pressure tanks, filling and discharging systems, liquid transportation, gas purification, and rare earth materials. Overall, this research offers novel insights for strategizing future HST innovations.

Demand Response Scheduling Considering Pollutant Diffusion Uncertainty of Industrial Customers

The demand response scheduling scheme requires the consideration of both the industrial customers' economic cost and the environmental influences from pollutants. However, the diffusion process of the latter, although of paramount importance, is typically ignored in the existing literature. To address this issue, we propose a demand response scheduling scheme that not only precisely simulates the diffusion process through a spatio-temporal diffusion model, but incorporates the uncertainty into the diffusion trajectories via a Markov decision process. This enables the schedule-maker optimally select the industrial customers to participate in the demand response with a minimum cost while reducing the environmental influences of the pollutants simultaneously. Using it, a deep reinforcement learning approach is further advocated in the optimization procedure to improve the scalability of the proposed method. Simulation results on the modified IEEE-118 test system reveal the validity of the proposed method.

Состояние фуллерена C60 в двухкомпонентной смеси газов

In this paper, the non-equilibrium state of a gaseous molecular system containing nitrogen, helium, and fullerenes is studied in the framework of the model of classical molecular dynamics. In such systems, the non-equilibrium state is determined by different states of single components. Here, the heavy fraction of the fullerenes can induce the internal non-equilibrium state associated with the difference between its translational and rotational temperatures. This paper proposes an original method for calculating the rotations of fullerenes in space, which does not use Euler angles and therefore has no consequential restrictions. The diffusion trajectories of fullerene particles are calculated, and the energy of their rotation is determined as the average temperature in the system of 300 K. The calculated results show that the internal non-equilibrium state of the fullerene fraction becomes apparent at pressures less than 10 atm. At pressures of a few tens of atmospheres, the rotational temperature of fullerenes coincides with their translational temperature.

Examining The Diffusion Trajectory Of Technology Awareness Initiative In An IT Organization: Insights For Persona Based Organizational Capability Building

This study investigates the diffusion patterns of technology awareness campaigns within a large IT organization. It uses a 21-month cybersecurity awareness initiative as a case study to explore the relationship between awareness diffusion and employee personas (Tech Enthusiasts, Visionaries, Pragmatics, Conservatives, and Skeptics) based on the frameworks of Everett Rogers' "Diffusion of Innovation" theory and Geoffrey Moore's "Crossing the Chasm". While the traditional diffusion pattern suggests a bell curve, the analysis reveals a trajectory characterized by multiple, gradually diminishing spikes. An employee survey revealed that the dominance of Tech Enthusiasts, due to the IT-oriented nature of the workforce, contributed to this deviation. Furthermore, the study identified two "chasms" – a deviation from Moore's single-chasm model. Key stakeholder interviews provided insights into how the organization successfully bridged these chasms. Based on these findings, the study offers practical recommendations for organizations planning and implementing awareness campaigns. Overall, this research provides valuable insights for organizations aiming to develop their workforce capabilities in new technologies and methodologies.

The Dalit Panthers: A Voice From the Below

This article investigates the process of the domestication of ideas by the Dalit intellectuals leading to its radical assertion of ‘Dalit Consciousness’. A chance encounter with an article about the US Black Panthers in the Time magazine proved to be a wake-up call for the subalterns in India. The present paper centres around the ideologies adopted by the Dalit Panthers, the syncretism of ideas of B. R. Ambedkar, Jyotirao Phule and Karl Marx. Delving deeper into Ambedkar’s American academic background and his intellectual odyssey at Columbia University during the Harlem Renaissance, the article will look into the politics of emancipation, exchanges and similarities of both the intellectuals, W. E. B. Du Bois and B. R. Ambedkar as well as the influence of the Black Power movement on the Dalit movement and its literature. Tracing the trajectory of intersectional political and ideological diffusion between the two movements, this article, in addition to the above observations, will draw parallels by underlining the texts and themes of oppression present in both caste and race.