Diffusion-based Approach Research Articles

A Diffusion-Based Approach for Simulating Forward-in-Time State-Dependent Speciation and Extinction Dynamics.

We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and three-region geographic-state speciation-extinction (GeoSSE) models. We show that the species range state dynamics simulated under tree-based and diffusion-based processes are comparable. We derive a method to infer rate parameters that are compatible with given observed stationary state frequencies and obtain an analytical result to compute stationary state frequencies for a given set of rate parameters. We also describe a procedure to find the time to reach the stationary frequencies of a ClaSSE model using our diffusion-based approach, which we demonstrate using a worked example for a two-region GeoSSE model. Finally, we discuss how the diffusion framework can be applied to formalize relationships between evolutionary patterns and processes under state-dependent diversification scenarios.

Enhancing consistency in virtual try-on: A novel diffusion-based approach

In new technology scenarios, virtual try-on aims to integrate clothing onto body images naturally, enhancing shopping experience by simulating true effect of clothes. As the image resolution increases, we expect to improve the consistency of the result, i.e., to ensure that various elements of the image are harmonized in terms of color, shading, style, and texture in order to achieve a natural visual effect. Many studies based on Generative Adversarial Networks(GANs) struggle with consistency. They encounter challenges in accurately depicting the fabric of target garments, as well as natural shadows and folds, and sometimes exhibit visual discontinuities or inconsistencies. So, we propose a new approach CSD-VTON based on latent diffusion model. Considering that traditional UNet's computational primitives struggle to capture complex transformation relationships at the pixel level, we address this issue by concatenating the warped cloth images generated by the warping module with the noise image. Additionally, cascade feature extraction module is introduced to extract in-store garment features, which ensures the preservation of texture and details in the target garments. Finally, we incorporate the skip-connection supplementary module to compensate for the reconstruction error. We conducted experiments using the DressCode and VITON-HD datasets to demonstrate the effectiveness and superiority of our approach.

Attention-enhanced conditional-diffusion-based data synthesis for data augmentation in machine fault diagnosis

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis, impeding the development and broad adaptation of accurate and reliable deep-learning-based fault detection systems. To address these issues, we propose a novel attention-enhanced conditioning-guided diffusion-based approach for synthesizing additional training data. By generating noisy motor-current signals and employing a diffusion process to reduce noise levels, we obtain synthetic data that closely resembles the statistical properties and patterns of real-world data. The quality of the synthesized samples is assessed using a four-layer, pre-trained convolutional neural network, achieving an impressive classification rate of 96.39%. This demonstrates the effectiveness of our attention-enhanced conditioned diffusion-based model in synthesizing critical features for fault detection, making it well-suited for the introduced fault detection task. To further investigate the quality of the synthesized data samples, we implement a specific training strategy that uses the generated samples as training data. The trained diffusion model generates samples per class, which are then used to train a simple four-layer convolutional neural network for fault classification. Remarkably, the model achieves an accuracy of 98.96% when evaluated with real inverter signals, indicating that the generated samples effectively capture the distribution of real data. For further evaluation, multiple scenarios are created confirming the value of the additionally synthesized samples for real-world bearing fault detection. Our approach presents a well-suited solution to the data scarcity and class imbalance problem in machine fault diagnosis. The proposed method enhances the accuracy and reliability of fault diagnosis systems, offering valuable insights for various real-world condition monitoring applications.

Topological Plasma Transport from a Diffusion View

Recent studies have identified plasma as a topological material. Yet, these researches often depict plasma as a fluid governed by electromagnetic fields, i.e., a classical wave system. Indeed, plasma transport can be characterized by a unique diffusion process distinguished by its collective behaviors. We adopt a simplified diffusion-migration method to elucidate the topological plasma transport. Drawing parallels to the thermal conduction-convection system, we introduce a double-ring model to investigate the plasma density behaviors in the anti-parity-time reversal (APT) unbroken and broken phases. Subsequently, by augmenting the number of rings, we have established a coupled ring chain structure. This structure serves as a medium for realizing the APT symmetric one-dimensional (1D) reciprocal model, representing the simplest tight-binding model with a trivial topology. To develop a model featuring topological properties, we should modify the APT symmetric 1D reciprocal model from the following two aspects: hopping amplitude and onsite potential. From the hopping amplitude, we incorporate the non-reciprocity to facilitate the non-Hermitian skin effect, an intrinsic non-Hermitian topology. Meanwhile, from the onsite potential, the quasiperiodic modulation has been adopted onto the APT symmetric 1D reciprocal model. This APT symmetric 1D Aubry–André–Harper model is of topological nature. Additionally, we suggest the potential applications for these diffusive plasma topological states. This study establishes a diffusion-based approach to realize topological states in plasma, potentially inspiring further advancements in plasma physics.

Discovering overlapping communities using a new diffusion approach based on core expanding and local depth traveling in social networks

This paper proposes a local diffusion-based approach to find overlapping communities in social networks based on label expansion using local depth first search and social influence information of nodes, called the LDLF algorithm. It is vital to start the diffusion process in local depth, traveling from specific core nodes based on their local topological features and strategic position for spreading community labels. Correspondingly, to avoid assigning excessive and unessential labels, the LDLF algorithm prudently removes redundant and less frequent labels for nodes with multiple labels. Finally, the proposed method finalizes the node's label based on the Hub Depressed index. Thanks to requiring only two iterations for label updating, the proposed LDLF algorithm runs in low time complexity while eliminating random behavior and achieving acceptable accuracy in finding overlapping communities for large-scale networks. The experiments on benchmark networks prove the effectiveness of the LDLF method compared to state-of-the-art approaches.

Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of papillary renal cell carcinoma

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data.

Mitigating the outbreak of an infectious disease over its life cycle: A diffusion-based approach.

We first qualitatively divide the cycle of an infectious disease outbreak into five distinct stages by following the adoption categorization from the diffusion theory. Next, we apply a standard mechanistic model, the susceptible-infected-recovered model, to simulate a variety of transmission scenarios and to quantify the benefits of various countermeasures. In particular, we apply the specific values of the newly infected to quantitatively divide an outbreak cycle into stages. We therefore reveal diverging patterns of countermeasures in different stages. The stage is critical in determining the evolutionary characteristics of the diffusion process. Our results show that it is necessary to employ appropriate diverse strategies in different stages over the life cycle of an infectious disease outbreak. In the early stages, we need to focus on prevention, early detection, and strict countermeasure (e.g., isolation and lockdown) for controlling an epidemic. It is better safe (i.e., stricter countermeasures) than sorry (i.e., let the virus spread out). There are two reasons why we should implement responsive and strict countermeasures in the early stages. The countermeasures are very effective, and the earlier the more total infected reduction over the whole cycle. The economic and societal burden for implementing countermeasures is relatively small due to limited affected areas, and the earlier the less burden. Both reasons change to the opposite in the late stages. The strategic focuses in the late stages become more delicate and balanced for two reasons: the same countermeasures become much less effective, and the society bears a much heavier burden. Strict countermeasures may become unnecessary, and we need to think about how to live with the infectious disease.

Revenue Management in Crowdfunding

Problem definition: We study the optimal design of crowdfunding campaigns and develop a model that maximizes revenue for a given crowdfunding campaign by optimizing both the pledge level sought from donors and the duration of the campaign. Academic/practical relevance: Our model explains the patterns of backer/donor arrival and pledging observed on crowdfunding platforms, such as Kickstarter. This model can be used to calibrate the revenue impact from using prespecified pledge levels or campaign durations. Methodology: We develop a theoretical model of the dynamics of the pledging process within the campaign duration, which employs a continuous-time, finite-horizon framework with two types of backer populations. Our model follows a diffusion-based approach and incorporates the structure of empirical observations. Results: We show that when campaign creators must follow an external standard for either the pledge level or the campaign duration, they should match low pledge levels with long campaign durations and high pledge levels with short campaign durations. We show that the optimal duration of a campaign, when not fixed by external constraints, depends on the composition of the backer population. Shorter campaigns are attuned to independent backers, and longer campaigns cater to herding backers. Managerial implications: Our analysis provides creators of crowdfunding campaigns with straightforward prescriptions for how to set the optimal pledge level and campaign duration. Platform managers could use the analysis to calibrate the arrival process of backers and creators’ campaign parameters. Funding: This work was supported by the Fishman-Davidson Center. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1147 .

Radius measurement via super-resolution microscopy enables the development of a variable radii proximity labeling platform

The elucidation of protein interaction networks is critical to understanding fundamental biology as well as developing new therapeutics. Proximity labeling platforms (PLPs) are state-of-the-art technologies that enable the discovery and delineation of biomolecular networks through the identification of protein-protein interactions. These platforms work via catalytic generation of reactive probes at a biological region of interest; these probes then diffuse through solution and covalently "tag" proximal biomolecules. The physical distance that the probes diffuse determines the effective labeling radius of the PLP and is a critical parameter that influences the scale and resolution of interactome mapping. As such, by expanding the degrees of labeling resolution offered by PLPs, it is possible to better capture the various size scales of interactomes. At present, however, there is little quantitative understanding of the labeling radii of different PLPs. Here, we report the development of a superresolution microscopy-based assay for the direct quantification of PLP labeling radii. Using this assay, we provide direct extracellular measurements of the labeling radii of state-of-the-art antibody-targeted PLPs, including the peroxidase-based phenoxy radical platform (269 ± 41 nm) and the high-resolution iridium-catalyzed µMap technology (54 ± 12 nm). Last, we apply these insights to the development of a molecular diffusion-based approach to tuning PLP resolution and introduce a new aryl-azide-based µMap platform with an intermediate labeling radius (80 ± 28 nm).

Modelling and simulations of reactor neutron noise induced by mechanical vibrations

Mechanical vibrations of core internals are among the main perturbations that induce oscillations in the neutron flux field, also known as neutron noise. In this work, different simulation models for the study of the influence of the mechanical vibrations of fuel assemblies on the neutron flux in the reactor core have been discussed. These methodologies employ the diffusion approximation, with or without a previous homogenization model, to simulate the neutron noise in the time or the frequency domain. The diffusion-based approach is expected to be less accurate in the vicinity of the vibrating fuel assemblies, but correct when considering distances larger than a few diffusion lengths away from the perturbation. All methodologies provide consistent results and can reproduce typical features of the neutron noise induced by mechanical vibrations of core components. First, FEMFFUSION can perform simulations in both the time and frequency domains. Second, CORE SIM + can be used to study various neutron noise scenarios in realistic three-dimensional reactor configurations. The third methodology is centred on using commercial codes as CASMO-5, SIMULATE-3 and SIMULATE-3K. This methodology allows time domain simulations of the neutron noise induced by different neutron noise sources in a nuclear reactor. Finally, a model for time-dependent geometry is implemented for the code system ATHLET/QUABOX-CUBBOX employing a cross-section-based approach for encoding water gap width variations at the reflector.

O-P03 A composite polymeric nanoparticle as a sensitiser for sonodynamic therapy (SDT)-based treatment of pancreatic cancer

Abstract Background Pancreatic cancer remains one of the most recalcitrant forms of cancer with poor prognosis and limited treatment options. SDT is a novel, targeted approach to the treatment of solid tumours. Based on the generation of cytotoxic reactive oxygen species (ROS) following the exposure of a sonosensitiser to ultrasound, the approach is designed to extracorporeally target less accessible lesions. Here we describe the production of a poly(lactic-co-glycolic acid) (PLGA), polyethyleneimine (PEI), Rose Bengal (RB) and indocyanine green (ICG) containing composite nanoparticles and describe their use in SDT-mediated treatment of pancreatic cancer using both in vitro and in vivo target models. Methods Nanoparticles were prepared using an oil in water emulsion and solvent diffusion-based approach. These were designated RB-ICGNP. In vitro SDT treatment consisted of exposing BxPC3 (human PDAC cells), T110029 (murine PDAC cells) or hPSC (immortalised human pancreatic stellate cells) to RB-ICGNP and subsequently treating with ultrasound for 30 s at a frequency of 1 MHz, a power density of 3.0 W/cm2 (SATP) using a duty cycle of 50% at a pulse repetition frequency of 100 Hz. For in vivo studies, BxPC3 (xenograft) and T110029 (syngeneic) tumours were treated with a power density of 3.5 W/cm2 ultrasound for 3.5 min. Results Conclusions Using in vitro and in vivo (human xenograft and murine syngeneic) models of pancreatic cancer, RB-ICGNP composite nanoparticles may be employed as a sensitiser for SDT-based treatment of pancreatic cancer. Since pancreatic stellate cells were more sensitive to SDT, the latter may have an impact on tumour stroma. Staining of residual tumour tissues from SDT-treated animals for connective tissue (stroma) confirmed the latter. Since tumour stroma presents a significant challenge to treatment of pancreatic cancer and represents a negative prognostic marker, the impact delivered by SDT may be exploited to potentiate alternative therapeutic approaches.

Inference for partially observed epidemic dynamics guided by Kalman filtering techniques

Despite the recent development of methods dealing with partially observed epidemic dynamics (unobserved model coordinates, discrete and noisy outbreak data), limitations remain in practice, mainly related to the quantity of augmented data and calibration of numerous tuning parameters. In particular, as coordinates of dynamic epidemic models are coupled, the presence of unobserved coordinates leads to a statistically difficult problem. The aim is to propose an easy-to-use and general inference method that is able to tackle these issues. First, using the properties of epidemics in large populations, a two-layer model is constructed. Via a diffusion-based approach, a Gaussian approximation of the epidemic density-dependent Markovian jump process is obtained, representing the state model. The observational model, consisting of noisy observations of certain model coordinates, is approximated by Gaussian distributions. Then, an inference method based on an approximate likelihood using Kalman filtering recursion is developed to estimate parameters of both the state and observational models. The performance of estimators of key model parameters is assessed on simulated data of SIR epidemic dynamics for different scenarios with respect to the population size and the number of observations. This performance is compared with that obtained using the well-known maximum iterated filtering method. Finally, the inference method is applied to a real data set on an influenza outbreak in a British boarding school in 1978.

Metallurgical Modelling of Ti-6Al-4V for Welding Applications

Manufacturing processes such as welding subject the α/β titanium alloy Ti-6Al-4V to a wide range of temperatures and temperature rates, generating microstructure variations in the phases and in the precipitate dimensions. In this study, the metallurgical and numerical modelling of Ti-6Al-4V when subjected to a high energy density welding process was affected by a series of analytical equations coded in Sysweld commercial specialist FE welding software. Numerical predictions were compared with experimental results from laser welding tests on plates with different thicknesses, initial microstructural morphologies, and operating conditions. The evolution of the microstructure was described by using a diffusion-based approach when the material was operating in the α + β field, whilst empirical equations were used for temperatures above the β-transus temperature. Predictions made by the subroutines within the FE model were shown to match with reasonable trends when validated using experimental characterisation methods for various metallurgical features, including the α particle size, β grain size, martensitic needle thickness, and relative phase volume fractions.

Mobile Broadband for Inclusive Connectivity: What Deters the High-Capacity Deployment of 4G-LTE Innovation in India?

The global market uptake of the fourth generation-Long-Term Evolution (4G-LTE) Mobile Broadband (MoBro) services are steadily rising, leading to higher capital investments by mobile network operators (MNOs) to scale-up their infrastructure for meeting the impending demand for MoBro data the world over. However, MNOs face uncertainties about financial returns from such investments, owing to a host of technological and market-related factors, which impact the enablement of such 4G-LTE MoBro services. These challenges are clearly evident in the Indian market. Firstly, 4G-LTE subscribers in the rural India contribute to less than one-third of the overall market size. Secondly, India performs poorly in terms of minimum capacity requirements of the 4G-LTE MoBro services. Thirdly, the rise in per-user consumption of MoBro data does not translate into a similar rise in the MNOs’ revenue. Fourthly, the socio-economic-geographic segregation of India into twenty-two administrative zones (referred to as telecom circles) add to the complexities in the capital investment decisions of MNOs. To address the above challenges, we model various cost and profitability scenarios of a hypothetical MNO providing universal 4G-LTE deployment across the twenty-two telecom circles in India using the available spectrum bands. Our proposed model is firmly established in the “network investment economics” framework for telecom innovations. We adopt a technology diffusion-based approach to forecast the 4G-LTE subscribers in India. We focus on the requirements of 4G-LTE MoBro infrastructure investments, including the spectrum selection decisions by MNOs, and show that, for valuation of the spectrum, the policymakers in India need to take into account the potential of the spectrum in terms of financial returns to an MNO deploying 4G-LTE using that spectrum, as against merely valuing the spectrum based on primarily the technical characteristics of its carrier frequency and benchmarks of prior spectrum auction prices in a particular telecom circle. Finally, we also show that a nationwide 4G-LTE network, which is universal, inclusive, and adhering to the global standards in terms of service quality, can be financially lucrative for MNOs, if enabled by appropriate policies instituting collaborative frameworks for infrastructure sharing, and price rationalization of spectrum bands across the twenty-two telecom circles.

Degradation of basalt fiber–reinforced polymer bars in seawater and sea sand concrete environment

Although numerous experimental and analytical investigations on the environmental effects on basalt fiber–reinforced polymer bars were carried out, degradation of the basalt fiber–reinforced polymer bar in seawater and sea sand concrete environment has been insufficiently analyzed. This work presents two distinct numerical approaches, degradation rate–based approach and diffusion-based approach, to investigate the durability of basalt fiber–reinforced polymer bars in seawater and sea sand concrete solution subjected to various temperatures (32°C, 40°C, 48°C, and 55°C). The degradation of the material was quantified using a simplified two-dimensional model of a homogenized basalt fiber–reinforced polymer bar in COMSOL Multiphysics software. Fickian diffusion provides basis for modeling diffusion-based approach. The findings from both the approaches suggested that the basalt fiber–reinforced polymer bar becomes more susceptible to degradation as the exposure temperature increases and results in greater geometrical deformities. The comparisons of experimental data, analytical solutions, and numerical results showcase that the present numerical models can predict the degradation of a basalt fiber–reinforced polymer bar in a seawater and sea sand concrete environment.

Microstructural Modeling of the \u03b1\u2009+\u2009\u03b2 Phase in Ti-6Al-4V: A Diffusion-Based Approach

Complex heat treatment operations and advanced manufacturing processes such as laser or electron-beam welding will see the metallic workpiece experience a considerable range of temperatures and heating/cooling rates. These intrinsic conditions will have a significant bearing upon the microstructure of the material, and in turn upon the thermo-mechanical properties. In this work, a diffusion-based approach to model the growth and shrinkage of precipitates in the alpha + beta field of the common titanium alloy Ti-6Al-4V is established. Further, the numerical model is extended using a JMA-type approach to explore the dependency of the beta-transus temperature on extremely high heating rates, whereby dissolution alone is too slow to accurately describe the alpha to beta-phase transformation. Experimental heat treatments at rates of 5, 50, and 500 °C/s were performed, and metallographic analysis of the samples was used to validate the numerical modeling framework predictions for lamellar shrinkage, while data from the literature has been used to evaluate the numerical modeling framework for the growth of equiaxed microstructures. The agreement between measurements and numerical predictions was found to be good.

Using Cartograms for Visualizing extended Floating Car Data (xFCD)

Abstract. This study assesses the usefulness of cartograms when visualizing extended Floating Car Data (xFCD). Cartograms deform regions in a map proportionally to assigned values. We apply this method for visualizing highresolution extended Floating Car Data (xFCD). Elaborating on this, we perform a case study in Mönchengladbach, Germany using 1.8 Million record points containing information about carbon dioxide (CO2) emissions based on an xFCD dataset. Utilizing a diffusion-based approach, we compute cartograms. Findings indicate a good suitability for identifying areas with a higher (or lower) average emission of CO2. We provide a documented workflow to compute cartograms based on parameters from an extended floating car dataset. The quality and spatial distribution of the basic dataset turns out to be important. Choosing the correct spatial subdivision of the research area as a basis for deforming areas is significant as it strongly influences the visual output.

Particle by Particle Kinetic Monte Carlo Tracking of Reaction and Mass Transfer Events in Miniemulsion Free Radical Polymerization

In many studies on emulsion polymerization emphasis is on a constant average particle size and an average monomer concentration for all particles, and the zero–one or pseudobulk approximation is applied. Based on kinetic Monte Carlo simulations, it is demonstrated that the kinetics need to be followed particle by particle while acknowledging the difference in radical and monomer concentration per particle, in contrast to the conventional Smith–Ewart methodology. Focus is on miniemulsion free radical polymerization, with physically reasonable values for all parameters and considering interphase mass transfer of initiator and monomer molecules and initiator-derived, monomeric and oligomeric radicals, following a diffusion-based approach. The temporal evolutions of the chain length distribution (CLD) and the particle size distribution (PSD) are correlated and affected by radical exit/entry and monomer mass transfer. The results unveil unprecedented insight into dispersed phase radical polymerizations and ope...

Diffusion-based swelling in elastomers under low- and high-salinity brine

Swelling elastomers are designed to swell when immersed into fluids like water, oil, or acid. The mechanism of swelling can be either diffusion or osmosis, initiating the imbibition of fluid inside the elastomer and progressively swelling it. Work presented here investigates diffusion as the swelling mechanism. Swelling experiments are conducted at two temperatures (room and 50°C) using water of different salinities (0.6% and 12%) as the swelling medium. Changes in volume, thickness, mass, and hardness are recorded. Measurements are taken before swelling and after 1, 2, 4, 7, 10, 16, 23, and 30 days of swelling. As expected, volume, thickness, and mass of the elastomer increase with increase in the number of swelling days, while hardness shows a decreasing trend. More variation is observed for all quantities in low-salinity brine as compared to high salinity, at both temperatures. However, density values are larger for high-salinity brine at both temperatures. Stokes–Einstein formula is used to determine the diffusion coefficients. Viscosity is measured using a Cannon–Fenske apparatus of size 50. Larger values of diffusion coefficient are found in low-salinity water at both temperatures, consistent with the higher amount of swelling and the faster swelling rate. These results and the diffusion-based approach will help in understanding the mechanics of swelling phenomenon. This work can aid in the development of new analytical and semi-analytical models that can predict seal pressure and other performance factors more accurately for applications in oil and gas wells.

A diffusion-based approach for modelling crack tip behaviour under fatigue-oxidation conditions

Modelling of crack tip behaviour was carried out for a nickel-based superalloy subjected to high temperature fatigue in a vacuum and air. In a vacuum, crack growth was entirely due to mechanical deformation and thus it was sufficient to use accumulated plastic strain as a criterion. To study the strong effect of oxidation in air, a diffusion-based approach was applied to investigate the full interaction between fatigue and oxygen penetration at a crack tip. Penetration of oxygen into the crack tip induced a local compressive stress due to dilatation effect. An increase in stress intensity factor range or dwell times imposed at peak loads resulted in enhanced accumulation of oxygen at the crack tip. A crack growth criterion based on accumulated levels of oxygen and plastic strain at the crack tip was subsequently developed to predict the crack growth rate under fatigue-oxidation conditions. The predicted crack-growth behaviour compared well with experimental results.