Maximum Clustering Research Articles

Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification

In this study, we propose a novel method for identifying lithology using an attention mechanism-enhanced graph convolutional neural network (AGCN). The aim of this method is to address the limitations of traditional approaches that evaluate unbalanced lithology by improving the identification of thin layers and small samples, while providing reliable data support for reservoir evaluation. To achieve this goal, we begin by using Principal Component Analysis (PCA) with maximum and minimum distance clustering (Max-min-distance) to correct the logging curves, which compensates for the low resolution of thin layers and enhances the accuracy of stratigraphic representation. Subsequently, we transform the logging data into graph-structured data by connecting distance similarity points and feature similarity points of the logging samples. We then use the graph convolutional network (GCN) to identify lithology, leveraging both labeled and unlabeled data to enhance the ability to identify lithology in small sample datasets. Additionally, our model incorporates a channel and spatial attention mechanism that assigns weights to the graph structure during lithology identification, improving the model’s capability to discern differences across samples. To evaluate the performance of our model, we constructed a lithology dataset comprising five wells and conducted experiments. The results indicate that our approach achieves a maximum accuracy of 97.67%, surpassing the performance of a singlestructure model in lithology identification. In conclusion, our proposed method provides a promising and effective approach for unbalanced lithology identification, significantly improving accuracy levels.

An image-based spatiotemporal approach for detecting coherence resonance in annular model gas-turbine combustor

When exposed to an intermediate level of noise, dynamical systems near a Hopf bifurcation can reveal deterministic information about the impending oscillatory mode. Known as the coherence resonance, this phenomenon can be used as a precursor to thermoacoustic instability, which is detrimental to practical combustors. In this study, we apply a spatiotemporal dimensionality reduction method, namely the sparsity-promoting dynamic mode decomposition, to detect coherence resonance in a combustor that exhibits a transition toward thermoacoustic instability. We stochastically excite the annular model gas-turbine combustor and acquire its high-speed image for spatiotemporal analysis. As a result, we find that the impending mode of instability is best identified with a maximum clustering level at the intermediate noise amplitude, implying the existence of coherence resonance. To the best of our knowledge, this is the first time that an image-based method has been used for the detection of coherence resonance, opening new possibilities for the prediction of combustion instability without requiring embedded sensors.

Abstract 4343: Revealing ovarian cancer copy number variation in single cells

Abstract The mortality rate associated with ovarian cancer (OvCa) is disproportionately high in comparison to its incidence rate. This is partly due to the heterogeneous nature of the disease, which reduces treatment efficacy and contributes to high rates of relapse and chemotherapy resistance. Most OvCa are epithelial in origin and can be classified into four main subtypes: serous, mucinous, endometrioid, and clear cell. Of these, high grade serous ovarian cancer (HGSOC) is the deadliest. Epithelial ovarian carcinomas (EOC) typically exhibit widespread chromosomal and arm-level copy number abnormalities across most of the genome; in HGSOC, focal amplifications and microdeletions are especially prevalent and indicative of high genomic instability. To understand the heterogeneity of aneuploidy in EOC and HGSOC, we performed shallow single-cell whole genome sequencing on four EOC samples: two HGSOC, one clear cell, and one mixed clear cell and endometrioid. All samples were late stage and treatment naïve, and one sample had a known BRCA2 mutation. Sequencing data was processed by two complementary methods to call copy number alterations. First, we used the Cell Ranger DNA pipeline (10x Genomics) to align cell-identified sequencing reads to human reference genome GRCh38 for coverage-based copy number estimation. Resulting copy number calls were cleaned up for mappability, quality, and noisiness. Each sample was then subject to clustering and subclustering analysis using maximum likelihood genetic clustering algorithms. All samples exhibited a high level of aneuploidy, including characteristic alterations known to be associated with EOC. Two tumors contained readily distinguishable clonal populations, and all samples contained main tumor clones that could be further divided by unique subclonal characteristics. Evidence of polyploidy was also seen in all four specimens, with some tumor clusters exhibiting triploid and tetraploid baselines. In parallel, sequencing data was analyzed by the Copy-number Haplotype Inference in Single-cell by Evolutionary Links (CHISEL) algorithm. CHISEL utilizes both binned read depth ratio and B-allele frequency data to determine allele- and haplotype-specific copy numbers in single cells. Results from CHISEL confirmed the copy number calls from Cell Ranger DNA, and revealed widespread loss of heterozygosity in all samples. These findings were corroborated with allele-specific copy number data derived from matched tumor-normal whole exome sequencing. Furthermore, CHISEL detected polyploidy in one-third of the tumor cells with no preference for the A or B alleles. Overall, our findings highlight that the known heterogeneity of ovarian cancer extends to the level of aneuploidy and CNAs, shedding light on factors which pose significant barriers to effective personalized medicine implementation. Citation Format: Yuxin Jin, Rania Bassiouni, Lee D. Gibbs, Jing Qian, Solomon Rotimi, Michelle G. Webb, Seeta Rajpara, David W. Craig, Lynda D. Roman, John D. Carpten. Revealing ovarian cancer copy number variation in single cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4343.

Robust and compact maximum margin clustering for high-dimensional data

In the field of machine learning, clustering has become an increasingly popular research topic due to its critical importance. Many clustering algorithms have been proposed utilizing a variety of approaches. This study focuses on clustering of high-dimensional data using the maximum margin clustering approach. In this paper, two methods are introduced: The first method employs the classical maximum margin clustering approach, which separates data into two clusters with the greatest margin between them. The second method takes cluster compactness into account and searches for two parallel hyperplanes that best fit to the cluster samples while also being as far apart from each other as possible. Additionally, robust variants of these clustering methods are introduced to handle outliers and noise within the data samples. The stochastic gradient algorithm is used to solve the resulting optimization problems, enabling all proposed clustering methods to scale well with large-scale data. Experimental results demonstrate that the proposed methods are more effective than existing maximum margin clustering methods, particularly in high-dimensional clustering problems, highlighting the efficacy of the proposed methods.

Enzymatically-induced dynamic assemblies from surface functional stomatocyte nanoreactors.

Collective behavior has become a recent topic of investigation in systems chemistry. In pursuing this phenomenon, we present polymersome stomatocytes loaded with the enzyme urease, which show basic stigmergy-based communication and are capable of signal production, reception, and response by clustering with surface complementary binding partners. The collective behavior is transient and based on the widely known pH-sensitive non-covalent interactions between nitrilotriacetic acid (NTA) and histidine (His) moieties attached to the surface of urease-loaded and empty stomacytes, respectively. Upon the addition of the substrate urea, the urease stomatocytes are able to increase the environmental pH, allowing the NTA units to interact with the surface histidines on the complementary species, triggering the formation of transient clusters. The stomatocytes display a maximum clustering interaction at a pH between 6.3 and 7.3, and interparticle repulsive behavior outside this range. This leads to oscillating behavior, as the aggregates disassemble when the pH increases due to high local urease activity. After bulk pH conditions are restored, clustering can take place again. Within the detectable region of dynamic light scattering, individual stomatocytes can aggregate to agglomerates with 10 times their volume. Understanding and designing population behavior of active colloids can facilitate the execution of cooperative tasks, which are not feasible for individual colloids.

Diversity of niger (Guizotia abyssinica L.F. Cass.) landraces for major agronomic traits

A diverse set of 870 Indian niger accessions were evaluated for various quantitative traits. Principle component analysis revealed the contribution of first three PCs to maximum variability and clustering analysis separated the accessions into eight different clusters. A significant diversity was revealed among these accessions and they can be further used as trait-specific donors in hybridization program for improvement of seed yield related traits in niger.

Integrated Spatio-Temporal Deep Clustering (ISTDC) for cognitive workload assessment

Traditional high-dimensional electroencephalography (EEG) features (spectral or temporal) may not always attain satisfactory results in cognitive workload estimation. In contrast, deep representation learning (DRL) transforms high-dimensional data into cluster-friendly low-dimensional feature space. Therefore, this paper proposes an Integrated Spatio-Temporal Deep Clustering (ISTDC) model that uses DRL followed by a clustering method to achieve better clustering performance. The proposed model is illustrated using four Algorithms and Variational Bayesian Gaussian Mixture Model (VBGMM) clustering method. Temporal and spatial Variational Auto Encoder (VAE) models (mentioned in Algorithm 2 and Algorithm 3) learn temporal and spatial latent features from sequence-wise EEG signals and scalp topographical maps using the Long short-term memory and Convolutional Neural Network models. The concatenated spatio-temporal latent feature (mentioned in Algorithm 4) is passed to the VBGMM clustering method to efficiently estimate workload levels of n-back task. For the 0-back vs. 2-back task, the proposed model achieves the maximum mean clustering accuracy of 98.0%, and it improves by 11.0% over the state-of-the-art method. The results also indicate that the proposed multimodal approach outperforms temporal and spatial latent feature-based unimodal models in workload assessment.

Sample of Groups: A New Strategy to Find a Representative Point for Each Undisclosed Cluster

Some problems involving the selection of samples from undisclosed groups are relevant in various areas such as health, statistics, economics, and computer science. For instance, when selecting a sample from a population, well-known strategies include simple random and stratified random selection. Another related problem is selecting the initial points corresponding to samples for the K-means clustering algorithm. In this regard, many studies propose different strategies for choosing these samples. However, there is no consensus on the best or most effective approaches, even when considering specific datasets or domains. In this work, we present a new strategy called the Sample of Groups (SOG) Algorithm, which combines concepts from grid, density, and maximum distance clustering algorithms to identify representative points or samples located near the center of the cluster mass. To achieve this, we create boxes with the right size to partition the data and select the representatives of the most relevant boxes. Thus, the main goal of this work is to find quality samples or seeds of data that represent different clusters. To compare our approach with other algorithms, we not only utilize indirect measures related to K-means but also employ two direct measures that facilitate a fairer comparison among these strategies. The results indicate that our proposal outperforms the most commonly used algorithms.

First report of black rot disease in Eruca vesicaria subsp. sativa caused by Xanthomonas campestris pv. campestris in Belgium.

Eruca vesicaria subsp. sativa (Mill.) Thell. (arugula or rocket) is a leafy vegetable originating from the Mediterranean region primarily being sold in bagged salads. From 2014 to 2017, plants (cv. Montana) exhibiting blackened leaf veins and irregular V-shaped chlorotic to necroic lesions at the leaf margins were observed in commercial greenhouses in Flanders, Belgium (Figure S1A). Symptoms started after harvest of the first cut, indicating that leaf injury favours disease development. By the last cut, infections had spread uniformly across the plots, with symptoms advanced to the point where harvesting was no longer profitable. Excised surface-sterilized necrotic leaf tissue and seeds were homogenized in phosphate buffer (PB), followed by dilution plating on Pseudomonas Agar F containing sucrose. After four days at 28°C, bright yellow round, mucoid, convex Xanthomonas-like colonies were obtained, both from leaves and seeds. For confirmation, DNA was extracted from pure cultures after which a partial fragment of gyrB was amplified and sequenced (Holtappels et al. 2022). Amplicons were trimmed to 530 nucleotides (Genbank ON815895-ON815900) according to Parkinson et al. (2007) and compared with the NCBI database. Strain GBBC 3139 shares 100% sequence identity with Xanthomonas campestris pv. campestris (Xcc) type strain LMG 568 and with RKFB 1361-1364, isolated from arugula in Serbia (Prokić et al. 2022). The other isolates from Belgian rocket - GBBC 3036, 3058, 3077, 3217 and 3236 - all have a gyrB sequence 100% identical to that of Xcc strain ICMP 4013, among others. To determine the genetic relatedness to other pathogenic Xc strains, the genomes of GBBC 3077, 3217, 3236 and 3139 were sequenced using a MinION (Nanopore) and non-clonal sequences were submitted to NCBI (BioProject PRJNA967242). Genomes were compared by calculating Average Nucleotide Identity (ANI). This revealed that the Belgian strains cluster together with Xc isolates originating from Brassica crops and separate from strains identified as Xc pv. barbareae, pv. incanae and pv. raphani (Figure S2A). Their designation as pv. campestris is supported by maximum likelihood clustering of concatenated gyrB-avrBs2 sequences (EPPO, 2021; Figure S2B,C). Finally, pathogenicity was verified on five-week-old rocket 'Pronto' plants grown in a commercial potting mix by cutting the leaves along the midrib with scissors dipped into a suspension of 108 cfu/ml of each strain or PB as control (4 plants/strain). Plants were kept in closed polypropylene boxes for 48 hr to support high humidity and facilitate infection. They were then maintained at 25 ± 2 °C. Lesions like those observed on commercial plants developed on the inoculated leaves within one week (Figure S1B). Bacterial colonies reisolated from symptomatic tissue were identified based on gyrB as the strains used for inoculation, thereby fulfilling Koch's postulates. To the best of our knowledge, this is the first report of black rot disease in arugula caused by Xcc in Belgium. Previously, Xcc on arugula has been reported in Argentina, California and Serbia as well (Romero et al. 2008; Rosenthal et al. 2017; Prokić et al. 2022). Arugula being a minor crop in Belgium, challenged by Xcc infections and strong import competition, many growers have abandoned the sector in recent years. Therefore, this study makes a strong case for early detection of disease symptoms and timely application of relevant management strategies in vulnerable crop settings.

Fast registration method for sequential star images.

Registration of sequential star images is an important component of space observation. Especially for onboard devices, the efficiency and robustness of registration algorithms are particularly important. The typical star image registration approach based on star matching has a low tolerance for incorrect matching, and the time cost will increase rapidly as the number of stars increases. Due to the high overlap and rigid transformation of adjacent star images, a proposed method based on star angular distance (SAD) is presented. Stars are easy to locate and extract as natural feature points, and there are a large number of identical stars in adjacent star images. The rotation and translation of the SAD, composed of identical stars in adjacent star images, are the same. Therefore, maximum intersection clustering (MIC) was proposed to cluster rotation and translation, and Gaussian weight iteration (GWI) was proposed to estimate rigid transformation parameters. The use of SAD as a star image feature reduces the complexity of star image features, which can improve the efficiency of the algorithm. MIC can tolerate errors within a certain range, and GWI can lessen their impact on the results, increasing the algorithm's robustness. Experimental results show that the proposed method can improve the trend of rapidly increasing computation as the number of stars increases and avoid the restriction that transformation parameters must be obtained with correctly matching stars. Compared to the typical triangle method and SAD similarity method, the proposed method has higher efficiency under different numbers of stars, and translation, rotation, and location errors.

Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting

Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days; (ii) the same kind of data are used as the training data of long short-term memory network; (iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day.

A tenuis relationship: traditional taxonomy obscures systematics and biogeography of the ‘Acropora tenuis’ (Scleractinia: Acroporidae) species complex

Abstract Molecular phylogenetics has fundamentally altered our understanding of the taxonomy, systematics and biogeography of corals. Recently developed phylogenomic techniques have started to resolve species-level relationships in the diverse and ecologically important genus Acropora, providing a path to resolve the taxonomy of this notoriously problematic group. We used a targeted capture dataset (2032 loci) to investigate systematic relationships within an Acropora clade containing the putatively widespread species Acropora tenuis and its relatives. Using maximum likelihood phylogenies and genetic clustering of single nucleotide polymorphisms from specimens, including topotypes, collected across the Indo-Pacific, we show ≥ 11 distinct lineages in the clade, only four of which correspond to currently accepted species. Based on molecular, morphological and geographical evidence, we describe two new species; Acropora rongoi n. sp. and Acropora tenuissima n. sp. and remove five additional nominal species from synonymy. Systematic relationships revealed by our molecular phylogeny are incongruent with traditional morphological taxonomy and demonstrate that characters traditionally used to delineate species boundaries and infer evolutionary history are homoplasies. Furthermore, we show that species within this clade have much smaller geographical ranges and, consequently, population sizes than currently thought, a finding with profound implications for conservation and management of reef corals.

Movement and Genomic Methods Reveal Mechanisms Promoting Connectivity in a Declining Shorebird: The Lesser Yellowlegs

Integrating tracking technology and molecular approaches provides a comprehensive picture of contemporary and evolutionary mechanisms promoting connectivity. We used mitochondrial DNA and double digest restriction-site associated DNA (ddRAD) sequencing combined with satellite telemetry to investigate the connectivity of geographically disparate breeding populations of a declining boreal shorebird, the lesser yellowlegs (Tringa flavipes). We were able to track 33 individuals on their round-trip migrations to Central and South America and back to the boreal wetlands of North America. Nearly all (93%) adults captured on the breeding grounds returned to within 5 km of the original capture site, with a median dispersal distance of 629 m. While our telemetry data revealed limited breeding dispersal in adults, genetic data uncovered significant interconnectedness across the species’ range. Very little genetic structure was estimated at ddRAD autosomal (ΦST = 0.001), Z-linked (ΦST = 0.001), and mtDNA loci (ΦST = 0.020), and maximum likelihood-based clustering methods placed all individuals in a single cluster regardless of capture location, indicating the species is panmictic. Our data indicate that large-scale juvenile dispersal is the main mechanism maintaining connectivity in this species, resulting in the absence of genomic structure.

Robust stabilization for a class of complex production process systems based on multi-dimensional moving pattern

This paper studies robust stabilization problem and state feedback controller design problem for a class of complex production process systems by pattern class variable instead of state variable or output variable. Firstly, the maximum entropy clustering method is introduced and modeling method based on multi-dimensional moving pattern is given. Nonlinear state space models of systems with input-delay are established by using the trajectories of pattern class variable. Then sufficient conditions for robust stabilization of the system are obtained by Linear Matrix Inequality. In addition, a state feedback controller is designed based on this condition. Finally, the effectiveness of the methods is illustrated by taking the actual working conditions data as an example.

Improving the accuracy of the Water Detect algorithm using Sentinel-2, Planetscope and sharpened imagery: a case study in an intermittent river

ABSTRACT Mapping surface water using remotely sensed optical imagery is a particular challenge in intermittent rivers because water contracts down to narrow linear features and isolated pools, which require accurate water detection methods and reliable image datasets. Of the many methods that use optical sensors to identify water, the Water Detect algorithm stands out as one of the best options due to its classification accuracy, open-source code, and because it does not require ancillary data. However, in the original study, the Water Detect algorithm was only tested with Sentinel-2 imagery. High-resolution and high-frequency imagery, such as Planetscope, combined with sharpening and band synthesizing techniques have the potential to improve the accuracy of surface water mapping, but their benefit to the Water Detect algorithm remains unknown. Uncertainty also exists about the extent to which different input parameters (i.e. maximum clustering and regularization) influence the accuracy of Water Detect. Practitioners seeking to map surface water in intermittent rivers need guidance on a best-practice approach to improve the accuracy of Water Detect. To meet this need, we automated an existing method for sharpening and synthesizing bands and applied it to a series of multispectral Sentinel-2 and Planetscope images. We then developed a sensitivity analysis algorithm that compared the accuracy for all possible combinations of input parameters in a given range for the water detection process – enabling optimal parameters to be identified. We applied this workflow to an 81 km stretch of the lower Fitzroy River (Western Australia) to periods when spatial water extent varied markedly, i.e. mid-wet (February), early-dry (June), and late-dry season (October), across three years with variable wet season flow. We found that the ability to accurately detect surface water using multispectral imagery was increased by using input parameters identified by the sensitivity analysis and using Visible + Near-infrared (VNIR) bands, with relatively little gained by image sharpening unless the area of interest was burnt or experienced considerable shading. Also, the regularization parameter exerted less influence on results than maximum clustering. Importantly, the accuracy of the Water Detect algorithm can vary drastically if input parameters are not calibrated to local conditions. Results also revealed that our approach was adept at detecting linear features in intermittent rivers. We recommend that practitioners using Water Detect to identify surface water undertake a workflow similar to that described here to improve the accuracy of the Water Detect algorithm. The automated routines provided by this study will significantly assist practitioners in doing so. Increasing the accuracy with which we detect and map water in intermittent rivers will improve our understanding and management of these important systems which are under increasing threat.

Design of an Intelligent Processing System for Business Data Analysis Based on Improved Clustering Algorithm

In order to further optimize the processing of business data and increase the effectiveness and accuracy of business data analysis, this research investigated the design of an intelligent processing system for business data analysis based on improved clustering algorithms. Some issues with business data analysis can be efficiently resolved by enhancing clustering algorithms, resulting in the provision of more intelligent, thorough, and precise business data analysis services for businesses and organizations. The improved clustering algorithm is compared with the traditional clustering algorithm to analyze the experimental data, the results showed that the maximum clustering effect of the improved clustering algorithm was above 80%, the highest robustness was 91.65%, and the highest point in algorithm performance was 90.97%. From these three aspects, it can be seen that improved clustering algorithms have more advantages than traditional clustering algorithms. Therefore, improving clustering algorithms can make business data processing more efficient, accurate, and reliable, providing enterprises and organizations with higher quality business data analysis services.

Tobacco Use and Cessation among a Nationally Representative Sample of Men in India, 2019-2021.

Tobacco users are exposed to a higher risk of noncommunicable diseases, leading to premature mortality and disability-adjusted life years (DALYs). The future prediction indicates that tobacco-related mortality and morbidity rates will substantially increase in coming years. The study is aimed at assessing the prevalence of tobacco consumption and cessation attempts for different tobacco products among adult men in India. The study utilized information from India's latest National Family Health Survey-5 (NFHS-5) data which was conducted during 2019-21, including 988,713 adult men aged 15 years and above and 93,144 men aged 15-49. Results suggest that 38 percent of men consume tobacco, including 29% in urban and 43% in rural areas. Among the men aged 35-49 years, the odds were significantly higher for consuming any form of tobacco (AOR: 7.36, CI: 6.72-8.05), smoking cigarettes (AOR: 2.56, CI: 2.23-2.94), and smoking bidi (AOR: 7.12, CI: 4.75-8.82) as compared to those aged 15-19. The application of multilevel model indicates that tobacco usages are not evenly distributed. In addition, there is maximum clustering of tobacco usages found around household level factors. Further, 30% of men aged 35-49 years attempted to stop consuming tobacco. Though 27% of men tried to quit tobacco in the last 12 months and 69% of men are exposed to secondhand smoke, 51% of men who received advice for quitting tobacco and visited the hospital in the last 12 months belong to the lowest wealth quintile. These findings prioritize promoting awareness about adverse effects of tobacco use, especially in rural areas, and capacitate them to adopt cessation efforts so that those who want to quit may be successful in their efforts. In addition, the health system's response to the tobacco epidemic in the country should be strengthened by training of service providers to promote cessation efforts through appropriate counselling of all the patients visiting them in the context of tobacco use in any form as key drivers of the increasing burden of noncommunicable diseases (NCDs) in the country.

System asymptotic stability analysis of a kind of complex production processes based on multi-dimensional moving pattern

This paper presents a new method to study the system stability of a kind of complex production processes. The system dynamics is described by multi-dimensional pattern class variables instead of state variables or output variables. The system asymptotic stability is defined by system matrix and output class partition. Firstly, “pattern moving space” is constructed by a new maximum entropy clustering method. Multi-dimensional pattern class variables measured by pattern class centers are defined in this new space. Then the nonlinear state space model of the relevant control system is derived and established through two-step prediction model. On the basis of the original stability in Euclidean space, the multi-dimensional pattern moving state space is defined and Lyapunov stability is given in this space. Finally, experimental results show the effectiveness of the proposed conditions.

Evolutionary modeling and robustness analysis of multi-domain cooperative communication network under the environment of Internet of things

In the future, the Internet of things will reduce the cell radius and increase the number of low-power nodes to support thousands of times of traffic growth under 5G. As a virtual multiple input multiple output technology, cooperative communication technology can solve these problems effectively. According to the evolution characteristics of cooperative communication networks, a multi-domain cooperative communication network evolution model with preferential attachment and random attachment is constructed in this article. And then, the network properties and robustness are analyzed using the mean-field method and different attacks. Aiming at the resource constraints and resource allocation problems of communication nodes, a relay selection strategy based on the combination of maximum degree and minimum clustering coefficient is proposed. The simulation results show that the relay node selection strategy based on the combination of maximum degree and minimum clustering coefficient has significant advantages in selection steps and selection time, which greatly enhanced the performance of relay selection in multi-domain cooperative communication networks. Through real-time monitoring and updating of the performance and security indicators of the multi-domain cooperative communication networks, it provides a strong guarantee for the node deployment and security management of the Internet of things cooperative communication system.

Human-computer interaction emotional design and innovative cultural and creative product design

To make the interface design of computer application system better, meet the psychological and emotional needs of users, and be more humanized, the emotional factor is increasingly valued by interface designers. In the design of human-computer interaction graphical interfaces, the designer attaches great importance to the emotional design of the interface, and enhances the humanized design of the interface, which cannot only improve the comfort of the interface, but also improve the fun of the interface, to ensure the psychological and emotional needs of users can be better satisfied. It may acquire information that is favorable to innovative design by utilizing cluster analysis algorithm to tackle the problem of complicated cultural information, and then utilize cellular genetic algorithm to carry out creative design of cultural items. It increases the availability of cultural and creative goods. The classic cluster analysis technique offers the maximum data clustering effect of 53.3%, according to the findings of this paper’s experiments. While the improved cluster analysis algorithm has the highest data clustering effect of 90%. It can be seen that the improved cluster analysis algorithm can effectively perform cluster analysis on a large amount of data in cultural and creative products. It thus finds out the most suitable designer’s creative information, which helps designers create better products.