diffusion models

Formulation and Evaluation of Biodegradable Microsphere Hydrogel for Ocular Delivery of Metronidazole

Background: Ocular drug delivery is hindered by physiological barriers such as tear turnover and nasolacrimal drainage, leading to poor bioavailability of conventional formulations. Biodegradable hydrogel microspheres offer a promising approach for sustained ocular release of therapeutic agents. Objective: To formulate and evaluate sodium alginate Pluronic F-68 hydrogel microspheres for ocular delivery of metronidazole using a factorial design approach. Methods: Hydrogel microspheres were prepared by ionic crosslinking of sodium alginate and Pluronic F-68, with metronidazole incorporated into the polymeric matrix. Nine formulations (MH1 MH9) were developed and evaluated for entrapment efficiency, particle size, zeta potential, swelling behaviour, and in vitro drug release in simulated tear fluid. Results: Entrapment efficiency ranged from 33.62 percent to 67.37 percent, with MH7 showing the highest drug loading (33.6 percent) and encapsulation efficiency (67.37 percent). Particle size varied between 40.44 µm and 148.28 µm, with MH7 exhibiting a zeta potential of –22.1 mV, indicating good stability. Swelling studies revealed that higher sodium alginate concentrations increased water uptake, whereas higher Pluronic F-68 concentrations reduced it. In vitro release demonstrated that MH7 achieved sustained release, with 49.4 percent drug released over 24 h. Kinetic modelling indicated that drug release followed the Higuchi model (R² = 0.9925), suggesting a diffusion-controlled mechanism. Conclusion: The optimised formulation MH7 demonstrated high encapsulation efficiency, stable particle characteristics, and prolonged drug release, making it a promising candidate for sustained ocular delivery of metronidazole.

The Current's Language: A Narrative Review of Electroanalytical Chemistry, from Faraday's Decomposition to Impedance Spectroscopy

Electroanalytical chemistry reads chemistry through the electrode: the family of methods—polarography, cyclic voltammetry, and impedance spectroscopy—that make the current's response to a programmed potential a quantitative and mechanistic language. This article presents a narrative review of the primary literature that built that language, from Faraday's 1834 electrical decomposition and Nernst's 1889 electromotive activity, through Heyrovský and Shikata's 1925 polarograph and Ilkovič's 1934 diffusion-current equation, Randles's 1948 cathode-ray polarography and Ševčík's 1948 triangular-wave analysis, Nicholson and Shain's 1964 theory of stationary electrode polarography, whose cyclic voltammetry's peaks became the field's fingerprints, Kolthoff and Lingane's 1952 Polarography and Delahay's 1954 instrumental methods, Bard and Faulkner's 2001 Electrochemical Methods, the codifying textbook, and the impedance line of Epelboin, Keddam, and Takenouchi's 1972 reaction models and Orazem and Tribollet's 2008 Electrochemical Impedance Spectroscopy. The synthesis is organized around three themes: the thermodynamic and polarographic foundation, in which the electrode's equilibrium and diffusion currents were formalized; the voltammetric settlement, in which the sweep's peaks acquired their theory and their diagnostic power; and the impedance extension, in which the frequency domain separated the interface's processes. It is concluded that electroanalysis's century is the electrode's conversion into an instrument—its current a language whose grammar the corpus wrote.

The Current's Language: A Narrative Review of Electroanalytical Chemistry, from Faraday's Decomposition to Impedance Spectroscopy

Electroanalytical chemistry reads chemistry through the electrode: the family of methods—polarography, cyclic voltammetry, and impedance spectroscopy—that make the current's response to a programmed potential a quantitative and mechanistic language. This article presents a narrative review of the primary literature that built that language, from Faraday's 1834 electrical decomposition and Nernst's 1889 electromotive activity, through Heyrovský and Shikata's 1925 polarograph and Ilkovič's 1934 diffusion-current equation, Randles's 1948 cathode-ray polarography and Ševčík's 1948 triangular-wave analysis, Nicholson and Shain's 1964 theory of stationary electrode polarography, whose cyclic voltammetry's peaks became the field's fingerprints, Kolthoff and Lingane's 1952 Polarography and Delahay's 1954 instrumental methods, Bard and Faulkner's 2001 Electrochemical Methods, the codifying textbook, and the impedance line of Epelboin, Keddam, and Takenouchi's 1972 reaction models and Orazem and Tribollet's 2008 Electrochemical Impedance Spectroscopy. The synthesis is organized around three themes: the thermodynamic and polarographic foundation, in which the electrode's equilibrium and diffusion currents were formalized; the voltammetric settlement, in which the sweep's peaks acquired their theory and their diagnostic power; and the impedance extension, in which the frequency domain separated the interface's processes. It is concluded that electroanalysis's century is the electrode's conversion into an instrument—its current a language whose grammar the corpus wrote.

Congestion Propagation Identification and Prediction Using Self Attention–Based Diffusion Convolutional Approach

Abstract Traffic congestion frequently propagates among the interconnected road networks over time, driven by spatial and temporal factors. Identifying and predicting these patterns is crucial for effective public transport management and urban planning. Existing systems often fail to accurately capture the complex diffusion properties of traffic flow and spatial dependencies, resulting in less reliable predictions. This study utilizes a propagation probability matrix to identify congestion propagation patterns and finds traffic behavior over 24 h, revealing critical insights into congestion trends in a selected road network. In addition, to overcome the remaining limitations, we propose a novel self attention–based diffusion convolutional network (SADCN) that effectively predicts traffic congestion propagation. The proposed model incorporates key spatial relations, including adjacency, diffusion, and propagation probability matrices, to improve the understanding of congestion dynamics. To demonstrate the significance of SADCN, we compare its performance with several recent graph-based models, including fully connected long short-term memory (FC-LSTM), diffusion convolutional recurrent neural network (DCRNN), and attention-based spatial-temporal graph convolutional network (ASTGCN). Compared with existing models, the proposed method achieved superior results, with an accuracy of 0.976, a precision of 0.950, a recall of 0.942, and F 1 -score of 0.946 for 50 epochs. Furthermore, the model outperformed state-of-the-art methods at shorter intervals, such as 5, 10, and 20 epochs, highlighting its faster convergence and efficiency in predicting traffic congestion propagation patterns to improve public transport systems.

Cognitive UAV-driven agro-surveillance framework for predicting crop stress–induced yield loss using spatio-temporal learning and adaptive irrigation control

Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.

DMR-HDM: Data augmentation method based on Dual-Modal Representation and Hierarchical Diffusion Model for network intrusion detection

ChatEvac: End-to-End automation assessment for building evacuation safety based on large language and diffusion models

Adaptive versus non-adaptive sampling for Gaussian-RBF surrogates: a replicated benchmark across analytical and differential-equation models

Adaptive sampling extends a nested design point by point, but repeated surrogate fitting and acquisition search are justified only when they improve prediction or reduce expensive model evaluations. This paper presents a controlled, replicated comparison of nine sampling strategies within a fixed Gaussian radial-basis-function (RBF) pipeline and examines when sequential acquisition is justified. Nine strategies—Random, Latin-hypercube sampling (LHS), scrambled Halton, sequential maximin, P-greedy, a nearest-neighbour leave-one-out (NN-LOO) proxy, a β-NN-LOO/P proxy, and RBF criteria in the spirit of MEPE and EIGF—are compared on seven analytical and differential-equation problem/QoI combinations: the smooth analytic Branin function; a steady heat problem with a discontinuous conductivity (Heat); an advection-dominated Graetz problem with a boundary layer (Graetz); and a two-species reaction–diffusion system with two (2S-RD-2P) or four (2S-RD-4P) active parameters, each with a reaction-ODE functional ψ1 and a reaction–diffusion state functional ψ2. Thirty design replications use a common 2000-point validation design. Supported endpoint improvements in the original adaptive-versus-static comparison require agreement between Holm-adjusted permutation and paired Wilcoxon analyses; tolerance performance combines attainment probability with the conditional first evaluated budget. No strategy dominates within the tested suite and budgets. Geometry-retaining adaptive criteria improve Heat, Graetz and both four-parameter 2S-RD QoIs, whereas LHS remains effective on both two-parameter QoIs. Sequential maximin is best by endpoint median on Branin and 2S-RD-4P/ψ2, second on 2S-RD-4P/ψ1, but eighth on Graetz, so response-informed sampling does not uniformly dominate strong nested geometry. Heat accuracy changes materially over εscore ∈ {0.5, 1, 2}, and fitting the physical rather than logarithmic Heat target worsens every compared method’s physicalscale endpoint median. At Nc = 4000 the four-dimensional candidate pool is much coarser than its two-dimensional counterpart; quadrupling the 2S-RD-4P pool gives small, non-systematic endpoint shifts but less stable detailed rankings. The resulting decision map is a scoped benchmark-based guide; its sensitivity to the scoring parameter, fitted-target scale and finite candidate pool is stated explicitly.

A chemo-mechanical coupled time-dependent numerical model for concrete under sulfate attack: From ion diffusion to non-uniform plastic damage

IrAADM: An attention-augmented diffusion model for infrared image super-resolution

Denim: Heterogeneous Compute-in-Memory Accelerator Exploiting Denoising--Similarity for Diffusion Models

Lesion-Level Subtypes of White Matter Hyperintensity Evolution Beyond Spatial Location.

BACKGROUND AND OBJECTIVES White matter hyperintensities (WMHs) are common neuroimaging markers of cerebrovascular pathology in aging and neurodegeneration. Despite their clinical relevance, WMH are typically quantified using global burden measures that assume a relatively homogeneous pathologic process. However, growing evidence suggests substantial biological heterogeneity across lesions. We aimed to identify lesion-level WMH subtypes beyond anatomic location and evaluate their associations with neurodegeneration and vascular risk. METHODS We conducted a longitudinal observational study analyzing 3224 MRI scans from 403 participants spanning cognitively normal aging, mild cognitive impairment, Alzheimer, and Parkinson disease. Imaging at baseline and 2-year follow-up included structural, diffusion, and resting-state MRI. A total of 2107 WMH lesions were identified, and lesion-wise longitudinal changes were used to derive subtypes using unsupervised clustering. Associations with neurodegeneration and vascular risk factors were assessed using multivariable models with false discovery rate correction. RESULTS Three lesion subtypes (L1-L3) were identified, frequently coexisting within the same individual. L1 lesions were the most prevalent (48.1%), predominated in cognitively normal individuals, and exhibited relatively stable trajectories without association with brain atrophy. L2 lesions represented a less frequent (11.3%) unstable subtype associated with weight gain (odds ratio [OR] 1.33, 95% CI 1.22-1.45; pFDR ≤ 0.001), suggesting metabolic vulnerability. L3 lesions (40.6%) represented an unstable subtype associated with brain atrophy (β = -0.11, 95% CI -0.16 to -0.05; pFDR < 0.001), older age (OR 1.15, 95% CI 1.07-1.23; pFDR < 0.001), and vascular risk reflected by pulse pressure changes (OR 1.09, 95% CI 1.03-1.15; pFDR = 0.006). Global WMH burden was no longer associated with brain atrophy after accounting for L3 lesion burden. Clustering robustness was supported by sensitivity analyses excluding anatomical location and by external validation in an independent cohort reproducing the main atrophy-related findings. DISCUSSION WMH are not a homogeneous entity but comprise biologically distinct lesion subtypes with differential neurobiological and clinical significance. Lesion composition may therefore offer a more informative framework than global WMH burden for understanding cerebrovascular contributions to aging and neurodegeneration, with potential implications for risk stratification, clinical interpretation, and targeted interventions.