Peak Density Research Articles

K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters.

Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed to accelerate it and improve the quality of initialize cluster centers, but few researchers pay attention to the shortcoming of K-means in discovering arbitrary-shaped clusters. Using graph distance (GD) to measure the dissimilarity between objects is a good way to solve this problem, but computing the GD is time-consuming. Inspired by the idea that granular ball uses a ball to represent the local data, we select representatives from a local neighborhood, called natural density peaks (NDPs). On the basis of NDPs, we propose a novel K-means algorithm for identifying arbitrary-shaped clusters, called NDP-Kmeans. It defines neighbor-based distance between NDPs and takes advantage of the neighbor-based distance to compute the GD between NDPs. Afterward, an improved K-means with high-quality initial centers and GD is used to cluster NDPs. Finally, each remaining object is assigned according to its representative. The experimental results show that our algorithms can not only recognize spherical clusters but also manifold clusters. Therefore, NDP-Kmeans has more advantages in detecting arbitrary-shaped clusters than other excellent algorithms.

Cold ion effects in density-asymmetric collisionless magnetic reconnection

The coexistence of low-energy (cold) ions and thermal (warm) ions is commonly observed in space and laboratory plasmas, such as those in magnetopause and fusion fueling processes. In certain events, the cold ion proportion may play a crucial role in plasma processes, especially magnetic reconnection. In this paper, magnetic reconnection with density-asymmetric cold ions is investigated in implicit particle-in-cell (iPIC) simulations. It is found that in such events the reconnection rate decreases as the cold ion distribution depth into the current sheet increases, mainly due to the mass-loading effect. Particularly, a density-peak structure of cold ions is developed in the reconnection region owing to the bounce motion of cold ions entering from the opposite inflow region. In the y–vy phase space where the y-direction is normal to the current sheet, a cold ion ring structure related to the bounce motion is formed and amplified by the Hall electric field. Furthermore, the cold ions become a notable current carrier due to its shorter inertial scale than the warm ions. Consequently, the asymmetry of the cold ion distribution significantly breaks the symmetry in the Hall magnetic field, eventually leading to asymmetric cold ion density peak structure. Such structures can be taken as significant signals of cold ion existence in in situ spacecraft observations.

Distribution and seasonal fluctuation of visceral leishmaniasis vectors sandflie in Lüliang City of Shanxi Province in 2023

To investigate the distribution and seasonal fluctuations of visceral leishmaniasis vectors sandflies in Lüliang City, Shanxi Province, so as to provide insights into assessment of the visceral leishmaniasis transmission risk and formulation of visceral leishmaniasis control measures. A total of 12 natural villages were sampled from Shilou County, Lishi District, Lanxian County, Linxian County and Wenshui County in Lüliang City, Shanxi Province from June to September, 2023, and sandflies were captured using light traps from 7 breeding habitats, including farmers' houses, sheep pens, cattle pens, chicken coops, pig pens, mule and horse pens, and loess-cave dwellings. Following morphological identification of the sandfly species, the distribution of sandflies and the seasonal fluctuations of the sandfly density were analyzed. In addition, the Leishmania was detected in sandflies using a real-time fluorescence quantitative PCR assay. A total of 2 831 sandflies were captured with 156 light traps in Lüliang City from June to September, 2023, including 2 638 female sandflies (93.18%) and 193 male sandflies (6.82%), and the average density was 16.91 sandflies/(light-night). The seasonal fluctuations of the sandfly density all appeared a unimodal distribution in all survey sites, and the sandfly density peaked in July and then declined rapidly. Among all types of breeding habitats, the greatest sandfly density was found in sheep pens [39.04 sandflies/(light-night)]. In addition, 4.08% (2/49) of the sandfly samples were tested positive for Leishmania nucleic acid as revealed by the real-time fluorescence quantitative PCR assay. Sandflies were widely distributed in Lüliang City, Shanxi Province in 2023, and the peak of the sandfly density was observed in July, which had a visceral leishmaniasis transmission risk. Intensified surveillance of visceral leishmaniasis and sandfly vectors is required and targeted vector control is recommended.

Electronic coupling effect of multi-metallic heterostructure to enhance oxygen evolution reaction for quasi-solid-state Zn-air batteries

It remains an urgent task to design electrocatalysts with fast oxygen evolution reaction (OER) kinetics and enriched active sites for flexible Zn-air batteries. In this paper, a structure engineering strategy is proposed to design an electrocatalyst with a heterostructure comprising Co@NC and NiMo-LDH on carbon cloth, which serves as an air electrode for a quasi-solid-state Zn-air battery. This unique heterostructure significantly enhances electrocatalytic activity, producing low OER overpotentials of 198 mV at 10 mA cm-2. Moreover, the assembled rechargeable Zn-air battery exhibits a high peak density and long cycle stability. Density functional theory calculations indicate that the multimetallic heterostructure induced synergistic and electronic coupling effects among the metal active sites, facilitating rapid ion and electron transport during the OER process. In addition, the migration of oxygen species at the Ni and Mo sites reduces the reaction potential barrier, resulting in a low OER overpotential of 160 mV.

ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering

Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, ϵ and μ, making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.

Insights into One-Dimensional Thermoelectric Materials: A Concise Review of Nanowires and Nanotubes.

This brief review covers the thermoelectric properties of one-dimensional materials, such as nanowires and nanotubes. The highly localised peaks of the electronic density of states near the Fermi levels of these nanostructured materials improve the Seebeck coefficient. Moreover, quantum confinement leads to discrete energy levels and a modified density of states, potentially enhancing electrical conductivity. These electronic effects, coupled with the dominance of Umklapp phonon scattering, which reduces thermal conductivity in one-dimensional materials, can achieve unprecedented thermoelectric efficiency not seen in two-dimensional or bulk materials. Notable advancements include carbon and silicon nanotubes and Bi3Te2, Bi, ZnO, SiC, and Si1-xGex nanowires with significantly reduced thermal conductivity and increased ZT. In all these nanowires and nanotubes, efficiency is explored as a function of the diameter. Among these nanomaterials, carbon nanotubes offer mechanical flexibility and improved thermoelectric performance. Although carbon nanotubes theoretically have high thermal conductivity, the improvement of their Seebeck coefficient due to their low-dimensional structure can compensate for it. Regarding flexibility, economic criteria, ease of fabrication, and weight, carbon nanotubes could be a promising candidate for thermoelectric power generation.

Direct and indirect costs of parasitism preceding a population decline of an Arctic ungulate

Parasites negatively affect the fitness of ungulate hosts directly, and in wild ungulates, these effects may be synzootic with other stressors, such as limited nutritional resources. In the Arctic, muskoxen (Ovibos moschatus) occur in a highly seasonal environment and must rely on finite energetic resources for survival and productivity. We investigated the costs of gastrointestinal nematodes on the body condition and reproductive status of 141 muskoxen, on Banks Island, Canada, when the population was at a peak in numbers and density. Using a Partial Least Squares Path Modelling approach, we found that high adult nematode abundance was associated with lower body condition, and high parasite abundance was associated with female reproduction including the indirect effect through on body condition (n = 87). These findings suggest that individuals prioritize energetic reserves for reproduction over parasite defence. In fall 2003, a severe icing event that restricted access to forage was associated with high overwinter mortality of muskoxen and a population crash. Through direct and indirect costs of parasite infection on body condition and reproduction, the high abundance of parasites may have contributed to the effects of this extreme weather event. Understanding the mechanisms in which parasites impact fitness can help explain the ecological drivers of ungulate populations and predict the interactions between the environment and populations.

Diversity and Distribution of Monocot Understory Herbs during Tropical Forest Succession in Northeastern Costa Rica

Broad-leaved monocot herbs form one of the most common and diverse growth forms of Neotropical plants. Their significance and frequency of occurrence is particularly notable in the understories of tropical rainforests, where they form a dominant element. We assessed and quantified changes in the cover and diversity of understory herb communities in a chronosequence of 1 ha permanent plots established as part of a multidisciplinary study on tropical forest regeneration in the Atlantic lowlands of northeastern Costa Rica. Sampled were two young stands cleared 12 years ago, two secondary forests with 21 and 39 of years of recovery since clearance, and two stands in old-growth primary forest. Changes in species composition during succession were assessed using Chao’s Jaccard similarity index. Observed species richness ranged from 15 to 26 species in individual plots, with the greatest number of species in the 21-year intermediate-age and fewest in the young 12-year plots. Herb species sampled represented 6 families, 15 genera, and 39 species, with the Araceae contributing the largest number of species. Ten species were sampled in all six stands, while fourteen species were found exclusively in one plot. Herb density (ramets m−2) showed a hump-shade trend, with peak density in the intermediate stands and a lower level in mature and young secondary forests. Mean herb cover in 25 m2 quadrats ranged from 2.0% (young stand) to 22.7% (intermediate-age stand) and differed significantly both among stand types and among sites. Both observed and estimated species richness increased along the chronosequence as a whole, with the highest number of species in primary forest, although only slightly higher than in intermediate-age stands. Over half of the species exhibited some degree of clonal growth, with the extent of clonal spread varying among species and forest stands. Although we did not find a clear pattern between clonality and forest age, we observed a greater number of clonal patches in secondary over primary forest stands.

A mixer architecture using GaN-based split-gate nanowire transistor

Frequency mixer is an essential block in radio-frequency signal processing for frequency translation and phase comparison. The most common mixers are fabricated using passive elements which suffer from significant conversion loss and low isolation. Mixers using active devices are used less frequently and rather less matured on GaN technology. Here, we demonstrate a mixer based on GaN split-gate nanowire transistor, allowing low conversion loss and high isolation. A constriction is formed by electrostatic modulation of the effective gate width. The threshold voltage of the transistor is modified by one of the gate voltages through the width variation, while the other gate voltage biases the transistor in the saturation region. The nonlinear dependency of the transistor characteristics on the two gate voltages facilitates frequency translation. The mixing characteristics of this architecture are verified both experimentally and theoretically. The output power spectral density peaks at the difference frequency with a minimal conversion loss. Extremely high isolation is measured using three-port S-parameter measurements. The proposed architecture shows multiple benefits, additionally facilitating monolithic mixers on the GaN platform.

Nonbreeding distributions of four declining Nearctic–Neotropical migrants are predicted to contract under future climate and socioeconomic scenarios

ABSTRACT Climate and land use/land cover change are expected to influence the stationary nonbreeding distributions of 4 Nearctic–Neotropical migrant bird species experiencing population declines: Cardellina canadensis (Canada Warbler), Setophaga cerulea (Cerulean Warbler), Vermivora chrysoptera (Golden-winged Warbler), and Hylocichla mustelina (Wood Thrush). Understanding how and where these species’ distributions shift in response to environmental drivers is critical to inform conservation planning in the Neotropics. For each species, we quantified current (2012 to 2021) and projected future (2050) suitable climatic and land use/land cover conditions as components of stationary nonbreeding distributions. Multi-source occurrence data were used in an ensemble modeling approach with covariates from 3 global coupled climate models (CMCC-ESM2, FIO-ESM-2-0, MIROC-ES2L) and 2 shared socioeconomic pathways (SSP2-RCP4.5, SSP5-RCP8.5) to predict distributions in response to varying climatic and land use/land cover conditions. Our findings suggest that distribution contraction, upslope elevational shifts in suitable conditions, and limited shifts in latitude and longitude will occur in 3 of 4 species. Cardellina canadensis and S. cerulea are expected to experience a moderate distribution contraction (7% to 29% and 19% to 43%, respectively), primarily in response to expected temperature changes. The V. chrysoptera distribution was modeled by sex, and females and males were projected to experience a major distribution contraction (56% to 79% loss in suitable conditions for females, 46% to 65% for males), accompanied by shifts in peak densities to higher elevations with minimal changes in the upper elevation limit. Expected changes in precipitation had the greatest effect on V. chrysoptera. Hylocichla mustelina experienced the smallest distribution change, consistent with the species’ flexibility in habitat selection and broader elevational range. We recommend defining priority areas for conservation as those where suitable conditions are expected to remain or arise in the next 25 years. For V. chrysoptera in particular, it is urgent to ensure that mid-elevation forests in Costa Rica and Honduras are adequately managed and protected.

Tailoring the surface topography and crystallinity of EuMnO3 films: A study on sintering effects

We obtained EuMnO3 films through a sol-gel process at varying temperatures, aiming to investigate spatial layout variations influenced by different sintering temperatures. Our results indicate that films adopt an orthorhombic structure when sintered between 800 °C and 850 °C, accompanied by a reduction in nanocrystal size during annealing. Surface analysis reveals that films processed at higher temperatures exhibit increased roughness, negative asymmetry, heightened kurtosis, elevated peak densities, and more distinct peak forms. Furthermore, these films display surfaces with greater anisotropy and lower spatial frequencies compared to those sintered at lower temperatures. Nevertheless, fractal analysis uncovers that both the EuMnO800 and EuMnO850 samples showcase heightened spatial complexity, a more uniform nanotexture, optimal surface porosity, and improved topographic consistency. The findings suggest that films sintered at 800 °C display a spatial arrangement with superior topographic qualities, rendering them potentially valuable for the advancement of semiconductor films derived from perovskite rare-earth oxides for various technological applications.

Representative co-location pattern post-mining based on maximal row instances representation model

The mining result set of spatial prevalent co-location patterns(SPCPs) is often large and redundant, especially when the prevalence threshold is set to low or long SPCPs are present. Meanwhile, the distribution of SPCPs in continuous space and complex spatial relationships with each other of spatial data make the compression and reorganization of SPCPs a challenging problem. To solve this problem, in this paper, a representative co-location pattern mining framework based on the maximal row instance(MRI) representation model is proposed. First, the MRI representation model is proposed to effectively preserve the pattern distribution information of prevalent co-location patterns. To establish the MRI representation model, the basic algorithm and geometric algorithm are proposed in this paper. Two materialization methods based on the MRI representation model, 0-1 vector and key–value vector, are presented. Secondly, the similarity measure of SPCPs under the context of the MRI representation model is proposed, which calculates the similarity between any two co-location patterns without adding additional information such as domain background. Furthermore, the mining framework based on the k nearest neighbors density peak clustering algorithm is presented to extract representative co-location patterns. Finally, the efficiency and scalability of the proposed method are verified on the synthetic data and real data. Compared with the existing methods, the representative co-location patterns of the proposed method have well compression performances.

Evaluation Method of Magnetic Field Stability for Robotic Arc Welding Based on Sample Entropy and Probability Distribution

In this study, we analyzed the arc magnetic field to assess the stability of the arc welding process, particularly in robotic welding where direct measurement of welding current is challenging, such as under water. The characteristics of the magnetic field were evaluated based on low-frequency fluctuations and the symmetry of the signals. We used double-wire pulsed MIG welding for our experiments, employing Q235 steel with an 8.0 mm thickness as the material. Key parameters included an average voltage of 19.8 V, current of 120 A, and a wire feeding speed of 3.3 m/min. Our spectral analysis revealed significant correlations between welding stability and factors such as the direct current (DC) component and the peak power spectral density (PSD) frequency. To quantify this relationship, we introduced a novel approach using sample entropy and mix sample entropy (MSE) as new evaluation metrics. This method achieved a notable accuracy of 88%, demonstrating its effectiveness in assessing the stability of the robotic welding process.

Carotid web catheter angiography hemodynamic parameters

BackgroundCarotid web (CaW)-related contrast stagnation on digital subtraction angiography (DSA) may be a marker supporting the pathophysiological theory of stasis and thrombosis/embolization. We aim to assess the correlation between DSA...

Several outstanding issues concerning the ionosphere of Mars

Several outstanding issues concerning the ionosphere of Mars can be addressed with the support of radio occultation observations acquired near the solar zenith angle limit of 45∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}. First, two fundamentally different types of instruments (topside radar sounding, radio occultations) have been used to characterize how the subsolar peak density changes over the solar cycle. Here we find that their results for solar minimum and solar maximum values of peak density are consistent. This verifies that systematic errors do not affect peak density measurements from either type of instrument. Second, reported descriptions of how the peak altitude changes with solar zenith angle are inconsistent. Specifically, values reported by radio occultation and radar sounder instruments for the characteristic lengthscale used to describe changes in peak altitude with solar zenith angle vary by a factor of two. We find that the change in peak altitude with solar zenith angle is governed by a lengthscale that is close to the thermospheric scale height, as predicted theoretically. However, this behavior is only apparent when the Mars–Sun distance is held fixed. Reported smaller values of this lengthscale, which are not consistent with theoretical expectations, were adversely affected by using near-terminator peak altitude values only, which display marked variability. Third, Viking Lander entry science data have been interpreted to suggest that the M1 layer is not present in the ionosphere at solar zenith angles of 45∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} or smaller. We show radio occultation observations at 45∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} in which the M1 layer is present. This suggests that the basic structure of the dayside ionosphere remains the same at all solar zenith angles.Graphical

Flexible density peak clustering for real-world data

In density based clustering, the density peak algorithm has attracted much attention due to its effectiveness and simplicity, and a vast amount of clustering approaches have been proposed based on this algorithm. Some of these works require manual selection of cluster centers with a decision graph, where human involvement leads to uncertainty in clustering results. In order to avoid human involvement, some other algorithms depend on user-specified number of clusters to determine cluster centers automatically. However, it is well known that accurate estimation of number of clusters is a long-standing difficulty in data clustering. In this paper we present a sequential density peak clustering algorithm to extract clusters one by one, thereby determining the number of clusters automatically and avoiding manual selection of cluster centers in the meanwhile. Starting from a density peak, our algorithm generates an initial cluster surrounding the density peak in the first step, and then obtains the final cluster by expanding the initial cluster based on the relative density relationship among neighboring data points. With a peeling-off strategy, we obtain all the clusters sequentially. Our algorithm works well with clusters of Gaussian distribution and is therefore potential for clustering of real-world data. Experiments with a large number of synthetic and real datasets and comparisons with existing algorithms demonstrate the effectiveness of the proposed algorithm.

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies

The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model’s performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.

Learning neural implicit surfaces with local probability standard variance

AbstractReconstructing geometric shapes from sparse multiview has always been a challenging task. With the development of neural implicit surfaces, geometry‐based volume rendering surface reconstruction methods have been proven to be able to reconstruct high‐quality surfaces. However, existing geometry‐based reconstruction methods completely associate volume density with signed distance function or unsigned distance function, resulting in the same volume density peak that can only be reconstructed near the object surface. When there are transparent surfaces in the scene, existing methods prioritize the reconstruction of opaque surfaces, neglecting the reconstruction of transparent surfaces, which is disadvantageous when reconstructing real scenes. To solve this problem, we introduce local probability standard variance, which calculates volume density together with signed distance function. In this way, it can reconstruct the volume density that matches the transparency characteristics of the object surface. The method can reconstruct the surface of transparent objects, and experiments on two transparent surface datasets show that the method performs better.

Confined magnon dispersion in ferromagnetic and antiferromagnetic thin films in a second quantization approach: The case of Fe and NiO

We present a methodology based on the calculation of the inelastic scattering from magnons via the spin-scattering function in confined geometries such as thin films using a second quantization formalism, for both ferromagnetic and antiferromagnetic materials. The case studies are chosen with an aim to demonstrate the effects of film thickness and crystal orientation on magnon modes, using bcc Fe(100) and NiO with (100) and (111) crystallographic orientations as prototypical systems. Due to the quantization of the quasimomentum, we observe a granularity in the inelastic spectra in the reciprocal space path reflecting the orientation of the thin film. This approach also allows for the capture of softer modes that appear due to the partial interaction of magnetic moments close to the surface in a thin film geometry, in addition to bulk modes. The softer modes are also affected by crystallographic orientation, as illustrated by the different surface-related peaks of the NiO magnon density of states at approximately ∼65meV for (100) and ∼42meV for a (111)-oriented film. Additionally, we explore the role of anisotropy, revealing that anisotropy increases the overall hardness of the magnon modes. The introduction of a surface anisotropy produces a shift of the surface-related magnon DOS peak to higher energies with increased surface anisotropy, and in some cases leads to a surface-confined mode. Published by the American Physical Society 2024

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.