Diffusion Strategy Research Articles

Organizational Diffusion: What India Tells Us about How the Far Right Wins

Presenting a world-first data set of 2,831 constitutive organizations of history's largest far-right interorganizational network, this article presents a new explanation for far-right normalization: organizational diffusion. Providing, for the first time, empirical evidence of the large network characteristics of the Indian far right, this article paints a picture of what this network actually looks like, how far it has spread, and what explains its success. In doing so, this explanation unearths a far-right strategy of covert civil society expansion that has largely evaded the party-focused extant study of global far-right electoral mobilization. Identifying this strategy of organizational diffusion, this article argues that it produces three effects that produce broad, flexible, and durable mobilizations: segmented representation, reputational control, and leadership accommodation. Organizational diffusion, present in far-right mobilizations as diverse as Ma Ba Tha, Nippon Kaigi, and the Thai military, presents an important far-right mobilizing tool that exhorts scholars to refocus on covert civil society expansion as a key mechanism of far-right normalization.

Oxygen Vacancy-Mediated Synthesis of Inter-Atomically Ordered Ultrafine Pt-Alloy Nanoparticles for Enhanced Fuel Cell Performance.

Pt-based intermetallics are expected to be the highly active catalysts for oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells but still face great challenges in controllable synthesis of interatomically ordered and ultrafine intermetallic nanoparticles. Here, we propose an oxygen vacancy-mediated atomic diffusion strategy by mechanical alloying to reduce the energy barrier of the transition from interatomic disordering to ordering, and to resist interparticulate sintering via strong M-O-C bonding. This synthesis results in a nanosized core/shell structure featuring an interatomically ordered PtM core and a Pt shell of two to three atomic layers in thickness and can be extended to the multicomponent PtM (M = Co, FeCo, FeCoNi, FeCoNiGa) systems. The electron enrichment in the Pt outer shell induced by the compressive strain leads to the enhanced antibonding orbital occupation below the Fermi level and accelerated OH* desorption kinetics. The optimized PtCo-O/C-6 catalyst presents excellent ORR activity (mass activity = 1.28 A mgPt-1 at 0.9 ViR-free, peak power densities = 2.38/1.25 W cm-2 in H2-O2/-air) and durability (∼1% activity loss in over 50 h in air condition) in fuel cells at a total Pt loading of 0.1 mgPt cm-2. Furthermore, we establish a systematic correlation to elucidate the formation mechanisms of highly ordered intermetallic catalysts underlying oxygen vacancies. This study provides a general approach for the large-scale production of highly ordered and nanosized Pt-dispersed intermetallic catalysts.

An Oriented Diffusion Strategy to Configure All-Region Ultrahigh-Density Metal Single Atoms for High-Capacity Sodium Storage.

Single-atom metals (SAMs), despite being promising for high-utilization catalysis, biomedicine, and energy storage, usually suffer from limited catalytic performance caused by low metal loading. Herein, via an oriented diffusion strategy, all-region ultrahigh-loading (18.9wt.%) Sn-SAMs over carbon nanorings matrix (Sn-SAMs@CNR) are initially achieved based on the transformation of a g-C3N4@SnO2@polydopamine ring-like nested structure. The formation process of Sn-SAMs involves a critical conversion from oxygen-coordination (SnO2) to nitrogen-coordination (Sn-N4) and simultaneous anti-Osterwalder ripening promoted under spatial confinement. Notably, the g-C3N4-derived N-containing gaseous intermediates dynamically drive the oriented diffusion (inside-out diffusion) of Sn-SAMs across the carbon nanorings, realizing an all-region ultrahigh loading of SAMs throughout the carbon matrix. This strategy is also applied to other metal materials (Fe, Co, Ni, Cu, and Sb), and features excellent universality. When applied as the anode for sodium-ion batteries, experimental analyses and theoretical calculations demonstrate that high-loading Sn-N4 active sites significantly optimize electron density distribution and improve reaction kinetics. Consequently, Sn-SAMs@CNR exhibits outstanding durability of 364mAhg-1 even after 5000 cycles with an impressively low (0.00068%) capacity decay per cycle. This work opens up a universally new avenue for all-region ultrahigh loading of SAMs to carbon matrix for high-performance energy storage.

A New Diffusion Strategy Using an Epidemic Spreading Model for Encryption.

The diffusion phenomenon that exhibits intrinsic similarities is pervasive in cryptography and natural systems, evident in liquid diffusion, epidemic spread, animal migration, and encryption techniques. In cryptography, bytes are systematically diffused in a sequential manner to encrypt the value of each byte in the plaintext in a linear fashion. In contrast, within an epidemic spreading model, the diffusion process can be represented within a complex, multilayered network, encompassing layers such as familial and social transmission dynamics. Transmission links establish connections both within and between individual layers. It has had a more rapid spread than linear approaches due to the particularization of non-linear transmission. In this study, the novelty of a cryptography diffusion strategy based on an epidemic model is first proposed, in which pixels and their dynamic adjacency are considered as vertices and edges, respectively, within a complex network framework. Subsequently, the encryption process is governed by the Susceptible-Vaccinated-Infected-Recovered (SVIR) model integrated with chaotic dynamics. Simulation results demonstrate that the proposed algorithm exhibits faster encryption speed while effectively resisting brute force, statistical, and differential attacks. Furthermore, it demonstrates strong robustness against noise interference and data loss.

MISAO: Ultra-Short-Term Photovoltaic Power Forecasting with Multi-Strategy Improved Snow Ablation Optimizer

The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration–exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with Friedman average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system.

Frequency-domain diffusion adaptation over networks with missing input data

Recently, a modified adapt-then-combine diffusion (mATC) strategy has been developed to handle distributed estimation problem with missing regressions (inputs). However, the mATC algorithm only considers the white input scenario and suffers from high complexity for long model filter lengths. To overcome these shortcomings, this paper proposes novel regularization-based frequency-domain diffusion algorithms for networks with missing input data. First, bias-eliminating cost function based on regularization is established by using the frequency-domain diagonal approximation. Then, with stochastic gradient descent, periodic update, and power normalization schemes, we design the regularization-based frequency-domain least mean square (R-FDLMS) algorithm as well as its normalized variant (R-FDNLMS). The latter converges faster than the former under colored inputs. The stability and steady-state behavior of the R-FDNLMS algorithm are also analyzed. Moreover, two effective power estimation methods are presented for both situations without and with the power ratio between the input signal and perturbation noise, along with a reset mechanism in the first case to enhance tracking performance. Finally, simulations are conducted to illustrate the superiority of the proposed algorithms and the validity of theoretical findings.

LFDT-Fusion: A latent feature-guided diffusion Transformer model for general image fusion

For image fusion tasks, it is inefficient for the diffusion model to iterate multiple times on the original resolution image for feature mapping. To address this issue, this paper proposes an efficient latent feature-guided diffusion model for general image fusion. The model consists of a pixel space autoencoder and a compact Transformer-based diffusion network. Specifically, the pixel space autoencoder is a novel UNet-based latent diffusion strategy that compresses inputs into a low-resolution latent space through downsampling. Simultaneously, skip connections transfer multiscale intermediate features from the encoder to the decoder for decoding, preserving the high-resolution information of the original input. Compared to the existing VAE-GAN-based latent diffusion strategy, the proposed UNet-based strategy is significantly more stable and generates highly detailed images without adversarial optimization. The Transformer-based diffusion network consists of a denoising network and a fusion head. The former captures long-range diffusion dependencies and learns hierarchical diffusion representations, while the latter facilitates diffusion feature interactions to comprehend complex cross-domain information. Moreover, improvements to the diffusion model in noise level, denoising steps, and sampler selection have yielded superior fusion performance across six image fusion tasks. The proposed method illustrates qualitative and quantitative advantages, as evidenced by experimental results in both public datasets and industrial environments. The code is available at: https://github.com/BOYang-pro/LFDT-Fusion.

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

Winding Number Features for Vector Sketch Colorization

AbstractVector sketch software (e.g. Adobe Illustrator, Inkscape) and touch‐interactive technologies have long aided artists in the creation of resolution‐independent digital drawings that mimic the unconstrained nature of freehand sketches. However, artist intent behind stroke topology is often ambiguous, complicating traditional segmentation tasks such as coloring. For inspiration, we turn to the winding number, a classic geometric property of interest for binary segmentation in the presence of boundary data. Its direct application for multi‐region segmentation poses two main challenges: (1) strokes may not be consistently oriented to best identify perceptually salient regions; (2) for interior strokes there is no “correct” orientation, as either choice better distinguishes one of two neighboring regions. Thus, we form a harmonic feature space from multiple winding number fields and perform segmentation via Voronoi/power diagrams in this domain. Our perspective allows both for automatic fill region detection and for a semi‐automatic framework that naturally incorporates user hints and interactive sculpting of results, unlike competing automatic methods. Our method is agnostic to curve orientation and gracefully handles varying gap sizes in the sketch boundary, outperforming state‐of‐the‐art colorization methods on these “gappy” inputs. Moreover, it inherits the ability of winding numbers to specify “fuzzy” boundaries, leading to simple strategies for color diffusion and single‐parameter‐driven growing and shrinking of regions.

Dynamics of a spatiotemporal SIS epidemic model with distinct mobility range

In this study, we consider an epidemic model for susceptible-infected-susceptible populations with nonlocal dffusion, described by convolution operators. Our main objective is to model different diffusion strategies for the susceptible and infected populations by using distinct kernel functions in the convolution operator to model the different movements of individuals for each class. This approach eliminates the simplification of reducing the stationary problem to a single equation, which makes the analysis more challenging. However, we have shown the existence of at least one positive endemic steady state when the basic reproduction number is greater than one, using the index degree theory of Leray-Schauder. Additionally, we investigated the asymptotic profile of the endemic equilibrium state for both, large and small diffusion rates, to demonstrate the persistence or extinction of the disease. Overall, our study indicates that restricting individual movements only partially cannot eliminate the disease unless the total population size is small.

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

Multi-channel active noise control (ANC) systems can achieve effective attenuation of low-frequency noise in spatial areas and have been extensively studied. Recent works have focused on the application of multitask diffusion strategies in ANC systems based on wireless acoustic sensor and actuator networks (WASAN), which show the advantages of saving computational burden and enhancing flexibility. However, these works all adopt the mean-square error minimization as the optimization criterion, which leads to severe performance degradation in the presence of impulsive noise interference. Therefore, in this paper, we exploit the M-estimate function and the augmented weight vector of the control filter to formulate a robust minimization problem and propose a distributed augmented diffusion filtered-x least mean M-estimate (DADFxLMM) algorithm for multi-channel ANC systems. The proposed algorithm leverages the M-estimate function to overcome the influence of outliers in the error signal and achieves robustness against impulsive noise, and also eliminates the dependence on the symmetry of the acoustic paths by integrating the augmented weight vectors. Moreover, we replace the score function with the update probability of the weight vector to avoid the need for integration and Price’s theorem, and analyze the mean and mean-square performance of the proposed DADFxLMM algorithm under the contaminated Gaussian (CG) noise model. Simulation results under different system configurations demonstrate that the proposed algorithm outperforms the existing multi-channel ANC algorithms in the presence of impulsive noise interference.

Impeding polysulfide diffusion strategies in lithium-sulfur batteries using 3D porous carbon nanosheets integrated by cathode and functional separator

Lithium-sulfur(Li-S) batteries show high theoretical capacity and energy density compared to lithium-ion batteries(LIBs), which are one of the candidates for next-generation energy storage systems. However, rapid capacity fading caused by polysulfide shuttling effects remains critical issue for the commercial development in the Li-S batteries. Therefore, we aimed to derive 3D porous carbon nanosheets(C900) through the simple carbonization of organic salts as base materials for application as both sulfur host and functional separator. C900 had 3D vertically aligned and hierarchical carbon nanosheet structure with a high specific surface area and large pore volume, which was benefits for sulfur hosts because of the efficient electrolyte diffusion pathways and high mass loading of sulfur. Furthermore, nitrogen doped C900(NC900) can be improved chemical reactions with lithium polysulfides. Subsequently, an integrated electrode composed of a cathode with C900 and functional separator with NC900, respectively, was found to be highly efficient in restraining the shuttle effect via multimodal capturing effects. Consequently, all-integrated electrode composed of S-C900 and NC900@PP delivered a high initial specific capacity of 1453mAh·g-1 at 0.1C and achieved cycle stability with 64% capacity retention after 300cycles at 0.5C. This work suggests a potential carbon material design for bi-functional materials in a high-performance Li-S batteries.

Fast solution of incompressible flow problems with two-level pressure approximation

This paper develops efficient preconditioned iterative solvers for incompressible flow problems discretised by an enriched Taylor–Hood mixed approximation, in which the usual pressure space is augmented by a piecewise constant pressure to ensure local mass conservation. This enrichment process causes over-specification of the pressure when the pressure space is defined by the union of standard Taylor–Hood basis functions and piecewise constant pressure basis functions, which complicates the design and implementation of efficient solvers for the resulting linear systems. We first describe the impact of this choice of pressure space specification on the matrices involved. Next, we show how to recover effective solvers for Stokes problems, with preconditioners based on the singular pressure mass matrix, and for Oseen systems arising from linearised Navier–Stokes equations, by using a two-stage pressure convection–diffusion strategy. The codes used to generate the numerical results are available online.

Induced Diffusion Effect Realizes Heterogeneous Separation/Aggregation for In Situ Fabrication of Heterostructure with Enhanced Catalytic Activity

AbstractSpace charge transfer, which can be provided by heterogeneous structure, is the key to efficient electrocatalytic processes. However, difficulty in controlling the relative atomic diffusion rates due to different atomic radii and affinity makes heterogeneous nanostructures difficult to be accurately designed. Herein, an induced diffusion strategy based on affinity differences between elements is proposed to direct the separation/aggregation of different components in the precursor. The key to strategy in this study is the different affinity of Cu and Co to S/P in the precursor of CuCo Prussian blue analogue (PBA). By controlling the molar ratio of S and P during the phosphorization/vulcanization process, induced diffusion can be achieved and leads to directional separation/aggregation of Cu and Co species. As a result, a heterogeneous yolk‐shell Cu2S@CoP2‐C‐N‐S structure is successfully synthesized with a reticular CoP2‐C‐N‐Cu‐S as the shell and a Cu2S nanocrystal as the core. The space charge effect of the semiconductor heterojunction resulting from component separation/aggregation significantly promotes oxygen evolution reaction, resulting in an ultralow overpotential of 180 mV at 10 mA·cm−2. This work not only provides a simple method for efficient separation of key components in gas‐solid systems but also provides a method for fine‐tuning the active site in an active‐site‐engineering approach.

Evolution of dispersal and the analysis of a resource flourished population model with harvesting

This study explores a spatially distributed harvesting model that signifies the outcome of the competition of two species in a heterogeneous environment. The model is controlled by reaction-diffusion equations with resource-based diffusion strategies. Two different situations are maintained by the harvesting effects: when the harvesting rates are independent in space and do not exceed the intrinsic growth rate; and when they are proportional to the time-independent intrinsic growth rate. In particular, the competition between both species differs only by their corresponding migration strategy and harvesting intensity. We have computed the main results for the global existence of solutions that represent either coexistence or competitive exclusion of two competing species depending on the harvesting levels and different imposed diffusion strategies. We also established some estimates on harvesting efforts for which coexistence is apparent. Also, some numerical results are exhibited in one and two spatial dimensions, which shed some light on the ecological implementation of the model.

Investigation on synthesis and mechanism of a Sudan Ⅰ copper electrodeposition leveler based on influencing ion diffusion strategy

A series of quaternary ammonium salt derivatives based on 1-phenylazo-2-naphthol (Sudan I) have been synthesized and showed novel ability in inhibiting copper electrodeposition as efficient levelers. The electrochemical test, theoretical calculation and molecular dynamics simulation were presented, and Sudan-QNL exhibited the best ability to adsorb electrode and promote polarization. The microscopy and X-ray diffractions showed that Sudan-QNL can significantly improve the flatness and filling of copper electrodeposition in the printed circuit board (PCB) through hole. Through the observation of copper layer by electron backscatter diffractometer (EBSD) and X-ray diffractometer (XRD), it was found that the addition of additives can significantly refine the grain size and obtain a smooth deposited copper layer with high preferred orientation of the crystal plane. In addition, the high electrostatic adsorption of levelers and its strong binding ability to copper ions and thus affecting the ion diffusion rate may be the fundamental reasons for its role.

A cost-effective dual-layer grain boundary diffusion strategy to significantly enhance coercivity of sintered Nd-Ce-Fe-B magnets

In an attempt to obtain cost-effective sintered Nd-Ce-Fe-B magnet and overcome the coercivity-remanence trade-off challenge caused by heavy rare earth (HRE) diffusion, a novel dual-layer coating method has been proposed for the grain boundary diffusion process (GBDP). A high coercivity increment of 8.56 kOe is achieved by dual-layer coated DyHx/Pr80Al20 (DH/PA) diffusion with 0.8 wt% DyHx and a slight remanence reduction from 12.85 to 12.71 kG. Although the DyHx diffused magnet has a coercivity increment of 9.24 kOe, it consumes twice as much DyHx as the DH/PA diffused magnet at the great sacrifice of remanence. This can be attributed to the formation of continuous grain boundary phase and thin Dy-rich shell via microstructural observations. Further TEM characterization indicates that the amorphous triple junction grain boundary phase (TJP) gradually transforms into the crystalline Nd2O3 structure after GBDP. The coexistence of cubic-Nd2O3 phase and hexagonal-Nd2O3 phase found in the dual-layer coated Pr80Al20/DyHx (PA/DH) diffused magnet creates a more intricate grain boundary phase structure, hindering the diffusion of Dy. Comparably, the formation of single cubic-Nd2O3 (Ia3̅ structure) grain boundary phase in the DH/PA and DyHx diffused magnets is an important reason for the significant increase in coercivity. Based on the evaluation of magnetic performance and cost, the DH/PA diffused magnet achieves a higher price-performance ratio (PPR). This cost-effective bilayer grain boundary diffusion strategy provides a valuable reference for high PPR Nd-Ce-Fe-B magnet fabrication.

Notes on the tourist exploitation of archaeological sites: A case of study of best practices applied to rural heritage in Spain

Beyond the mere promotion and diffusion of information, the dissemination of heritage sites also requires heightening social awareness of their historical and cultural value, which in turn intrinsically implies a greater appreciation and protection of this heritage by population groups close to it. Effective dissemination involves considering a number of values, actions and criteria. Heritage sites have an intrinsic value as an expression of the history and culture of a community, and it is therefore essential to respect and conserve their authenticity and originality. Dissemination may take the form of a number of actions, such as the creation of educational materials, the organisation of cultural events, guided tours and the promotion of research and conservation as part of the creation of new diffusion strategies. It is therefore of vital importance to identify clearly defined criteria for the selection and dissemination of such spaces, taking into consideration their historical and cultural significance, their state of conservation and their potential for driving awareness and promoting education. In this sense, the dissemination of heritage sites requires an integrated approach that combines research, conservation and promotion that involves both the local community and visitors in their preservation and enjoyment.

Nonequal-length image encryption based on bitplane chaotic mapping

In recent years, extensive research has focused on encryption algorithms for square images, with relatively little attention given to nonsquare images. This paper introduces a novel encryption algorithm tailored for nonequal length images, integrating bit-plane chaotic mapping and Arnold transformation. To effectively implement the algorithm, the plain image is initially transformed into two equal-sized binary sequences. A new diffusion strategy is then introduced to mutually diffuse these sequences, followed by the use of a chaotic map to control the swapping of binary elements between them, enabling permutation of bits across different bitplanes. Finally, the positional information of the image is scrambled using the Arnold transform, resulting in the generation of the encrypted image. By utilizing nonequal Arnold transformation parameters and the initial value of the Lorenz chaotic map as keys, the transmission of keys is simplified, and the cryptosystem gains infinite key space to resist brute force attacks. Experimental results and security analysis confirm the effectiveness of the proposed quantum image encryption algorithm in encrypting nonsquare images, demonstrating good performance in terms of nonstatistical properties, key sensitivity, and robustness. Furthermore, simulation experiments based on Qiskit successfully validate the correctness and feasibility of the quantum image encryption algorithm.

Cross‐layer all‐interface defect passivation with pre‐buried additive toward efficient all‐inorganic perovskite solar cells

AbstractThe buried interface in the perovskite solar cell (PSC) has been regarded as a breakthrough to boost the power conversion efficiency and stability. However, a comprehensive manipulation of the buried interface in terms of the transport layer, buried interlayer, and perovskite layer has been largely overlooked. Herein, we propose the use of a volatile heterocyclic compound called 2‐thiopheneacetic acid (TPA) as a pre‐buried additive in the buried interface to achieve cross‐layer all‐interface defect passivation through an in situ bottom‐up infiltration diffusion strategy. TPA not only suppresses the serious interfacial nonradiative recombination losses by precisely healing the interfacial and underlying defects but also effectively enhances the quality of perovskite film and releases the residual strain of perovskite film. Owing to this versatility, TPA‐tailored CsPbBr3 PSCs deliver a record efficiency of 11.23% with enhanced long‐term stability. This breakthrough in manipulating the buried interface using TPA opens new avenues for further improving the performance and reliability of PSC.