Diffusion Model Research Articles

Impact of resin molecular weight on drying kinetics and sag of coatings

Coating performance is influenced by factors inherent to formulation design and processing conditions. Understanding the complex interplay of these attributes enables for mitigating defects, such as sag, which is a gravity-driven phenomenon that impacts the aesthetic and functionality of a coating. The work herein investigates the impact of resin molecular weight and solvent choice on the drying kinetics and sag velocity in polymer films. These films, ranging in thickness from ~60 μm to ~120 μm were formulated with 45 % by weight polymer resin in one of two solvent packages with different relative evaporation rates (RER). Gravimetry was initially used to track drying rate and a one-dimensional diffusion model was utilized to compute the apparent solvent diffusivity. In addition, the film thickness was tracked with optical profilometry. Results from these measurements showed that for fixed molecular weight the drying rate increased by approximately two-fold for the high RER solvent, whereas the apparent diffusivity tended to increase with increasing polymer molecular weight. Films formulated from higher molecular weight resins had greater initial viscosities and thicknesses for identical draw down blade clearance. By extension, the higher apparent diffusivities at greater molecular weights were attributed to effects of prolonged evaporation times for the thicker films. The sag velocity was measured through the thickness of the film for these systems at a 5° incline using the Variable Angle Inspection Microscope (VAIM). Measurements showed an increase in sag velocity for thinner and less viscous films, which was somewhat surprising both because a thinner film will experience lower gravitational stress and quicker drying times as compared to a thicker film. From these data we conclude that formulating a coating with higher molecular weight resin, although likely to increase drying time, will tend to deter sag because of the large impact of viscosity on these phenomena.

Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model

Objective. Brain decoding is a field of computational neuroscience that aims to infer mental states or internal representations of perceptual inputs from measurable brain activity. This study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. Approach. We use several functional magnetic resonance imaging (fMRI) datasets of natural images as stimuli and create a deep learning decoding pipeline inspired by the bottom-up and top-down processes in human vision. Our pipeline includes a linear brain-to-feature model that maps fMRI activity to semantic visual stimuli features. We assume that the brain projects visual information onto a space that is homeomorphic to the latent space of last layer of a pretrained neural network, which summarizes and highlights similarities and differences between concepts. These features are categorized in the latent space using a nearest-neighbor strategy, and the results are used to retrieve images or condition a generative latent diffusion model to create novel images. Main results. We demonstrate semantic classification and image retrieval on three different fMRI datasets: Generic Object Decoding (vision perception and imagination), BOLD5000, and NSD. In all cases, a simple mapping between fMRI and a deep semantic representation of the visual stimulus resulted in meaningful classification and retrieved or generated images. We assessed quality using quantitative metrics and a human evaluation experiment that reproduces the multiplicity of conscious and unconscious criteria that humans use to evaluate image similarity. Our method achieved correct evaluation in over 80% of the test set. Significance. Our study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. The results demonstrate that measurable neural correlates can be linearly mapped onto the latent space of a neural network to synthesize images that match the original content. These findings have implications for both cognitive neuroscience and artificial intelligence.

Preparation and application of cattail residue-based magnetic cellulose composites for tetracycline antibiotics adsorption

Tetracycline antibiotics (TCs) are widely present in wastewater and surface water due to their extensive use. Removal of TCs from water is urgent. In this study, magnetic cellulose (MC) composites were prepared from cattail residues to adsorb oxytetracycline hydrochloride (OTC·HCl), chlortetracycline hydrochloride (CTC·HCl), and tetracycline hydrochloride (TC·HCl) from water. The influence of Fe3O4 content on the antibiotics removal was studied. The synthesized MC composites were characterized using FTIR, XPS, zeta potential, SEM, and XRD. Adsorption mechanisms and characteristics were explored using classic isotherm models (Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich models) and common kinetic models (pseudo-first-order, pseudo-second-order, Elovich and Intraparticle diffusion models). Optimum adsorption capacity was achieved by MC2–1 (mass ratio of cellulose to Fe3O4 was 2:1), with a larger specific surface area (18.24 m2 g−1) and mesoporous structure. The adsorption processes were well described by the pseudo-second-order and Langmuir models. MC2–1 demonstrated maximum adsorption capacities of CTC·HCl (194.29 mg g−1), OTC·HCl (168.25 mg g−1), and TC·HCl (41.10 mg g−1). The results of models fitting and characterizations of the MC composites before and after the adsorption processes revealed that the main adsorption mechanisms were pore filling, hydrogen bonding, electrostatic interactions, and metal complexation. This study proposes a practical approach for utilizing wetland plant residues as an effective adsorbent for tetracycline antibiotics removal.

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models

A drift diffusion model analysis of age-related impact on multisensory decision-making processes

Older adults (OAs) are typically slower and/or less accurate in forming perceptual choices relative to younger adults. Despite perceptual deficits, OAs gain from integrating information across senses, yielding multisensory benefits. However, the cognitive processes underlying these seemingly discrepant ageing effects remain unclear. To address this knowledge gap, 212 participants (18–90 years old) performed an online object categorisation paradigm, whereby age-related differences in Reaction Times (RTs) and choice accuracy between audiovisual (AV), visual (V), and auditory (A) conditions could be assessed. Whereas OAs were slower and less accurate across sensory conditions, they exhibited greater RT decreases between AV and V conditions, showing a larger multisensory benefit towards decisional speed. Hierarchical Drift Diffusion Modelling (HDDM) was fitted to participants’ behaviour to probe age-related impacts on the latent multisensory decision formation processes. For OAs, HDDM demonstrated slower evidence accumulation rates across sensory conditions coupled with increased response caution for AV trials of higher difficulty. Notably, for trials of lower difficulty we found multisensory benefits in evidence accumulation that increased with age, but not for trials of higher difficulty, in which increased response caution was instead evident. Together, our findings reconcile age-related impacts on multisensory decision-making, indicating greater multisensory evidence accumulation benefits with age underlying enhanced decisional speed.

Green technology diffusion model and simulation considering enterprise technology absorption capacity

ABSTRACT The diffusion and wide application of green technologies are important means to achieve climate change mitigation. Concerning the green technology absorption capacity and technology diffusion willingness of enterprises, this paper divided enterprises into four categories, constructed a green technology diffusion model and analysed the equilibrium point and stability of the model. Then, we studied the diffusion process and laws of green technology through simulation experiments. The results show that in the green technology diffusion system, the basic regeneration number is the key factor in determining the equilibrium state of green technology diffusion. The disease-free equilibrium point of the green technology diffusion system is globally asymptotically stable, and the endemic equilibrium point is locally asymptotically stable. In order to improve the speed and scale of green technology diffusion, it is necessary to create green technology diffusion sources through multiple channels, improve enterprise technology absorption capacity through multiple methods, and enhance enterprise green technology diffusion willingness at multiple levels.

The process of sucrose crystallization in the supersaturated solution

Crystallization processes of the solid phase in supersaturated solutions of products of plant origin are used in processing technologies. The theory of diffusion (molecular) mass transfer of the target substance has become widespread when solving problems of technology improvement. The driving force of this process is the difference in the concentration of the solid phase in the solution towards the crystallization center, where the concentration of the solid phase is reduced. During the analytical and quantitative study of the process of solid phase deposition in the crystallization center, Fick's diffusion laws have been used. The influence of solution temperature, concentration of the solid phase, its geometry and dispersed composition on the course of this process have been considered. Based on a diffusion model of the process of crystal formation of sucrose in a vacuum apparatus, a numerical analysis of factors in sugar production technology not covered in the literature has been presented. The problem of quantitative analysis of the process of sucrose crystallization in the working volume of a vacuum apparatus for the production of sugar and other products of plant origin of high commercial standards requires further in-depth study.

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model

A new model for chloride ion diffusion in cracked concrete with consideration of damage and strain fields

In this paper, a new model for chloride diffusion in cracked concrete is presented. The model was developed within a standard continuum damage mechanics framework but newly considers both an irreversible damage variable and a strain field, gaps identified through a literature review, offering a more comprehensive description of the diffusion-like processes in concrete with small and moderate cracks than current models. The model utilizes a realistic dependence for the diffusion coefficient on the damage state and employs a bi-logistic mapping function or an S-curve mapping function to effectively describe the non-linear effects of cracks on diffusion in concrete. The dependence on the strain field introduced by this model not only allows examination of diffusion in opening cracks, it also enables the process of crack closing to be simulated, whatever the irreversible damage variable may be. This paper also proposes a novel calibration procedure for experimental testing involving the use of the Brazilian splitting test on samples enclosed in a steel ring. This innovative approach simplifies the process of creating a portfolio of cracks with varying widths. In the calibration tests described here, samples were subjected to natural diffusion while chloride content was monitored in inlet and outlet chambers attached to the samples, eliminating the need for laborious chloride profiling. Our in-depth examination of chloride diffusion in cracked concrete is applicable to analyses of corrosion in steel-reinforced concrete structures worldwide, with the new calibration procedure offering a straightforward method for populating the model with realistic material parameters, thus providing a valuable tool for accurately predicting chloride penetration in real-world scenarios.

NetDiffus: Network traffic generation by diffusion models through time-series imaging

While Machine-Learning based network data analytics are now commonplace for many networking solutions, nonetheless, limited access to appropriate networking data has been an enduring challenge for many networking problems. Causes for lack of such data include complexity of data gathering, commercial sensitivity, as well as privacy and regulatory constraints. To overcome these challenges, we present a Diffusion-Model (DM) based end-to-end framework, NetDiffus, for synthetic network traffic generation which is one of the emerging topics in networking and computing system. NetDiffus first converts one-dimensional time-series network traffic into two-dimensional images, and then synthesizes representative images for the original data. We demonstrate that NetDiffus outperforms the state-of-the-art traffic generation methods based on Generative Adversarial Networks (GANs) by providing 66.4% increase in the fidelity of the generated data and an 18.1% increase in downstream machine learning tasks. We evaluate NetDiffus on seven diverse traffic traces and show that utilizing synthetic data significantly improves several downstream ML tasks including traffic fingerprinting, anomaly detection and traffic classification. The code has been included at https://github.com/Nirhoshan/NetDiffus.

Sensory choices as logistic classification.

Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.

Inversion of fluid-release rates from episodic tremor and slip signals in subduction zones via a coarse-grained reaction diffusion model

Episodic Tremor and Slip (ETS) events showcase dynamic interactions of oscillatory slow slips and tremors deep within subduction zones and offer a window into Earth's internal dynamics. However, the exact mechanisms driving these events remain unresolved. This study proposes a novel approach that goes beyond traditional explanations focused on fluid pressure from mineral dehydration. Existing models often neglect the intricate interplay between fluid and rock pressures across various depths and potential fluid sources. This calls for a more comprehensive understanding of how fluid release from reactions interacts with rock deformation. The present formulation captures the interplay between fluid and solid pressures providing a more rigorous picture of ETS events. It employs a minimalistic and efficient approach based on integrating dehydration reactions. The model thereby develops a generic framework for mineral dehydration, offering an enhanced perspective of the underlying processes without the need to trace down to specific minerals. It allows a refined fit to GPS data by including high-frequency components from linear and nonlinear stability analyses, giving rise to improved correlation coefficients. Through the inclusion of the dynamic interplay between fluid and rock pressure diffusion within subduction zones, we propose a unified model of ETS events.

Service Performance in Banks: The Effects of Customer Relationship Management Systems Extended Usage

With the rapid development of information technology, customer relationship management systems (CRMS) have been widely adopted by banks and play a crucial role in customer development and retention. However, there are also numerous cases of CRMS implementation failures. Further, employees are enabled by CRMS to make service decisions with the support of leaders. The role of leaders in employees’ CRMS usage is understudied. Thus, based on the information systems diffusion model and the theory of leader substitutes, a research model is proposed and empirically tested. The results show that employee CRMS explorative usage has a positive impact on their service performance, while employee CRMS exploitative usage has a significant negative impact on service performance. Besides, significant leadership substitution effects are discussed in CRMS usage.

Prior frequency guided diffusion model for limited angle (LA)-CBCT reconstruction

Objective. Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy. Reconstructing CBCTs from limited-angle acquisitions (LA-CBCT) is highly desired for improved imaging efficiency, dose reduction, and better mechanical clearance. LA-CBCT reconstruction, however, suffers from severe under-sampling artifacts, making it a highly ill-posed inverse problem. Diffusion models can generate data/images by reversing a data-noising process through learned data distributions; and can be incorporated as a denoiser/regularizer in LA-CBCT reconstruction. In this study, we developed a diffusion model-based framework, prior frequency-guided diffusion model (PFGDM), for robust and structure-preserving LA-CBCT reconstruction. Approach. PFGDM uses a conditioned diffusion model as a regularizer for LA-CBCT reconstruction, and the condition is based on high-frequency information extracted from patient-specific prior CT scans which provides a strong anatomical prior for LA-CBCT reconstruction. Specifically, we developed two variants of PFGDM (PFGDM-A and PFGDM-B) with different conditioning schemes. PFGDM-A applies the high-frequency CT information condition until a pre-optimized iteration step, and drops it afterwards to enable both similar and differing CT/CBCT anatomies to be reconstructed. PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans. The two variants of PFGDM were tested and compared with current available LA-CBCT reconstruction solutions, via metrics including peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Main results. PFGDM outperformed all traditional and diffusion model-based methods. The mean(s.d.) PSNR/SSIM were 27.97(3.10)/0.949(0.027), 26.63(2.79)/0.937(0.029), and 23.81(2.25)/0.896(0.036) for PFGDM-A, and 28.20(1.28)/0.954(0.011), 26.68(1.04)/0.941(0.014), and 23.72(1.19)/0.894(0.034) for PFGDM-B, based on 120°, 90°, and 30° orthogonal-view scan angles respectively. In contrast, the PSNR/SSIM was 19.61(2.47)/0.807(0.048) for 30° for DiffusionMBIR, a diffusion-based method without prior CT conditioning. Significance. PFGDM reconstructs high-quality LA-CBCTs under very-limited gantry angles, allowing faster and more flexible CBCT scans with dose reductions.

DSR-Diff: Depth map super-resolution with diffusion model

Color-guided depth map super-resolution (CDSR) improves the spatial resolution of a low-quality depth map with the corresponding high-quality color map, benefiting various applications such as 3D reconstruction, virtual reality, and augmented reality. While conventional CDSR methods typically rely on convolutional neural networks or transformers, diffusion models (DMs) have demonstrated notable effectiveness in high-level vision tasks. In this work, we present a novel CDSR paradigm that utilizes a diffusion model within the latent space to generate guidance for depth map super-resolution. The proposed method comprises a guidance generation network (GGN), a depth map super-resolution network (DSRN), and a guidance recovery network (GRN). The GGN is specifically designed to generate the guidance while managing its compactness. Additionally, we integrate a simple but effective feature fusion module and a transformer-style feature extraction module into the DSRN, enabling it to leverage the guiding prior in the extraction, fusion, and reconstruction of multi-model images. Taking into account both accuracy and efficiency, our proposed method has shown superior performance in extensive experiments compared to state-of-the-art methods. Specifically, our proposed DSR-Diff demonstrates remarkable improvements of 18.7% and 15.9% in terms of RMSE at scales 8 and 16 on the NYU v2 dataset while saving nearly 30% of the runtime, compared to existing SOTA method. Our codes will be made available at https://github.com/shiyuan7/DSR-Diff.

An Underwater Image Enhancement Method Based on Diffusion Model Using Dual-Layer Attention Mechanism

Diffusion models have been increasingly utilized in various image-processing tasks, such as segmentation, denoising, and enhancement. These models also show exceptional performance in enhancing underwater images. However, conventional models for underwater image enhancement often face the challenge of simultaneously improving color restoration and super-resolution. This paper introduces a dual-layer attention mechanism that integrates spatial and channel attention to enhance color restoration, while preserving critical image features. Additionally, specific scale factors and interpolation methods are employed during the upsampling process to increase resolution. The proposed DL-UW method achieves significant enhancements in color, illumination, and resolution for low-quality underwater images, resulting in high PSNR, SSIM, and UIQM values. The model demonstrates a robust performance on different datasets, confirming its broad applicability and effectiveness.

Novel amino-ethyl carboxymethyl cellulose crosslinked ampholyte hydrogel development for Methyl orange removal from waste water

In the modern era, with the rapid growth of various industries, the issues of energy crisis and environmental pollution have garnered increasing attention. One significant source of industrial pollution is printing and dyeing wastewater. This wastewater often contains dyes that have aromatic structures and azo groups, such as Methyl orange (MO), which are both toxic and difficult to degrade. If these dyes are released into the wastewater stream without any treatment, they can have adverse effects on ecological balance and human health. Therefore, it is crucial to identify suitable treatment strategies to efficiently remove dyes from wastewater systems before discharge. In this study, the Methyl orange (MO) azo dye has been removed from dyes-contaminated wastewater, for the first time, using a novel amino-ethyl carboxymethyl cellulose crosslinked ampholyte hydrogel (AECMC). Different characterization methods, including FTIR, TGA, and DSC were used to characterize the generated AECMC compounds. The water absorption and cationic exchange capacities were assessed. Factors affecting the MO anions adsorption including MO concentration, adsorption pH, temperature, time, adsorbent dose, and agitation speed have been investigated. Moreover, the kinetics of the adsorption process was assessed by the use of three models: pseudo-first-order, Pseudo-second-order, and Elovich. Moreover, the mechanism of the adsorption process was monitored using the Intraparticle diffusion and Boyd models. Additionally, the adsorption isotherm was examined using established models such as Langmuir, Freundlich, and Temkin isotherms. The thermodynamic characteristics of the MO adsorption process have been investigated at various adsorption temperatures using the Van't Hoff model. The results obtained from the study indicate that the process of MO adsorption adhered to the Pseudo-second-order kinetic model, the Langmuir isotherm model was found to be applicable, and spontaneous and exhibited an endothermic character. In conclusion, the developed novel amino-ethyl carboxymethyl cellulose crosslinked ampholyte hydrogels (AECMC) have successive in the removal of the MO anionic dye from contaminated wastewater.

Parameter identification method of information propagation models based on different network structures

In this paper, we create the rumor propagation model with diffusion behavior by considering the state of the rumor in both the time dimension and the space dimension comprehensively. Meanwhile, we demonstrate the reaction–diffusion model using Turing patterns after determining the prerequisites for their occurrence. In order to achieve the purpose of predicting and controlling rumors in time, we choose to utilize the parameter identification technique based on the Barzilai–Borwein (BB) algorithm and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In the numerical simulation section, we first investigate how the rumor avoidance rate and cross-diffusion coefficients affect the propagation of rumors. Then, based on a continuous spatio-temporal system and complex network system, respectively, we perform parameter identification for the propagation model. We thoroughly examine how the type of algorithm, the quantity of unknown parameters, and the network structure affect the identification outcomes in terms of the cost function, error curve, and program function time. When the model constructed in this paper is used for parameter identification on different network structures, the error gap between the final value and the target value is not significant. However, the cost function and time consumption for parameter identification on complex networks are much smaller than on the continuous medium.

A Numerical simulation method for analyzing 1H spin diffusion NMR for Multicomponent and multiphase polymer systems

A numerical simulation method, namely, SDNMR-WEBFIT, is reported for simulating proton spin diffusion NMR based on the Levenberg-Marquardt algorithm and a pseudo-2D diffusion model. This method is used for the precise quantification of dynamics heterogeneity of the interphase within multiphase polymer systems. The numerical simulation method provides measurements of spin-lattice relaxation time (T1), proton density (ρH), lamellar thickness (d), and spin diffusion coefficient (D) for each component. The pseudo-2D diffusion model is employed to simulate the proton spin diffusion build-up/decay curves, simultaneously calculating the lateral fraction of island-like structures (x-ratio). Such approach was successfully applied to various polymer systems, such as semi-crystalline polymer (Poly(ε-caprolactone), PCL), block copolymers (Styrene-butadiene-styrene triblock copolymer, SBS), and plasticized semi-polymers (Polvinyl alcohol, PVA).

Superresolution emulation of large cosmological fields with a 3D conditional diffusion model

Superresolution emulation of large cosmological fields with a 3D conditional diffusion model