Feature Clustering Research Articles

Investigating the spatial distribution mechanisms of traditional villages from the human geography region: A case study of Jiangnan, China

Investigating the spatial distribution and adaptation mechanisms of traditional villages is crucial for their development and human well-being. However, relatively few studies have focused on the interaction and comprehensive influence of historical-cultural, natural-geographic and socio-economic factors on traditional villages in Jiangnan region. Employing geographically weighted regression (GWR) and Geodetector methods, we developed an integrated framework. We found that (1) Traditional villages exhibit a “one-core, multiple-periphery” clustering pattern (nearest neighbor index <1), with significant high-high and low-low local spatial clustering characteristics and preferring gentle and relatively Complex terrains to adapt to the need to avoid warfare. (2) Urbanization negatively affects traditional village density patterns, while natural-geographic complexity has a positive impact. Their interaction nonlinearly amplifies this effect, with the urbanization-GDP interplay being particularly significant (q = 0.765). (3) Factors exhibit significant spatial heterogeneity in their impact on traditional village density, with the explanatory power increasing by 20% (GWR/R2 = 0.65). The urbanization rate and slope are the most critical socio-economic and natural-geographic factors, respectively (local regression coefficient: 23.31 and 11.58). Our study transcends administrative boundaries, revealing how historical-cultural, natural-geographical, and socio-economic factors collectively influence the distribution of traditional villages. This not only provides a new theoretical foundation for the conservation and development of traditional villages but also provides a new methodology basis for research on traditional villages.

Feature fusion-based food protein subcellular prediction for drug composition

The structure and function of dietary proteins, as well as their subcellular prediction, are critical for designing and developing new drug compositions and understanding the pathophysiology of certain diseases. As a remedy, we provide a subcellular localization method based on feature fusion and clustering for dietary proteins. Additionally, an enhanced PseAAC (Pseudo-amino acid composition) method is suggested, which builds upon the conventional PseAAC. The study initially builds a novel model of representing the food protein sequence by integrating autocorrelation, chi density, and improved PseAAC to better convey information about the food protein sequence. After that, the dimensionality of the fused feature vectors is reduced by using principal component analysis. With prediction accuracies of 99.24% in the Gram-positive dataset and 95.33% in the Gram-negative dataset, respectively, the experimental findings demonstrate the practicability and efficacy of the proposed approach. This paper is basically exploring pseudo-amino acid composition of not any clinical aspect but exploring a pharmaceutical aspect for drug repositioning.

Cluster Analysis and Ablation Success Rate in Atrial Fibrillation Patients Undergoing Catheter Ablation

Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity. It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information. In our cohort of AF inpatients undergoing radiofrequency catheter ablation, we used unsupervised cluster analysis to identify patient subgroups, to compare them with previous studies, and to evaluate their association with different suitable ablation patterns and outcomes. The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017. All participants were aged 18 years or older. They underwent radiofrequency catheter ablation during their hospitalization. They completed the follow-up process under explicit informed consent. Patients with AF of a reversible cause, severe mitral stenosis or prosthetic heart valve, congenital heart disease, new-onset acute coronary syndrome within three months prior to the surgery, or a life expectancy less than 12 months were excluded according to the exclusion criteria. The cohort consisted of 1102 participants with paroxysmal or persistent/long-standing persistent AF. Data on 59 variables representing demographics, AF type, comorbidities, therapeutic history, vital signs, electrocardiographic and echocardiographic findings, and laboratory findings were collected. Overall, data for the variables were rarely missing (<5%), and multiple imputation was used for correction of missing data. Follow-up surveys were conducted through outpatient clinic visits or by telephone. Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure. Early ablation success was defined as the absence of documented AF, atrial flutter, or atrial tachycardia >30 seconds at 6-month follow-up. Hierarchical clustering was performed on the 59 baseline variables. All characteristic variables were standardized to have a mean of zero and a standard deviation of one. Initially, each patient was regarded as a separate cluster, and the distance between these clusters was calculated. Then, the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance. This process continued until all patients formed one whole cluster. The "NbClust" package in R software, capable of calculating various statistical indices, including pseudo t2 index, cubic clustering criterion, silhouette index etc, was applied to determine the optimal number of clusters. The most frequently chosen number of clusters by these indices was selected. A heatmap was generated to illustrate the clinical features of clusters, while a tree diagram was used to depict the clustering process and the heterogeneity among clusters. Ablation strategies were compared within each cluster regarding ablation efficacy. Five statistically driven clusters were identified: 1) the younger age cluster (n=404), characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome (14.4%); 2) a cluster of elderly adults with chronic diseases (n=438), the largest cluster, showing relatively higher rates of hypertension, diabetes, stroke, and chronic obstructive pulmonary disease; 3) a cluster with high prevalence of sinus node dysfunction (n=160), with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation; 4) the heart failure cluster (n=80), with the highest prevalence of heart failure (58.8%) and persistent/long-standing persistent AF (73.7%); 5) prior coronary artery revascularization cluster (n=20), with patients of the most advanced age (median: 69.0 years old) and predominantly male patients, all of whom had prior myocardial infarction and coronary artery revascularization. Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies (79.6% vs. 66.5%; odds ratio [OR]=1.97, 95% confidence interval [CI]: 1.28-3.03). Although extensive ablation strategies had a slightly higher success rate in the heart failure group, the difference was not statistically significant. This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis. Age, chronic disease, sinus node dysfunction, heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes. These subtypes showed differences in ablation success rates, highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients.

Numerical simulation of drag on local particle cluster within a high void fraction environment in supercritical water

The drag of particle cluster within a high void fraction environment in supercritical water (SCW) is investigated by numerical simulation. The results show that the description pattern of particle drag in SCW is the same as that in constant property flow (CPF). The diffusion of pressure in the phenomenon of fluid flow around particle cluster caused that the variation pattern of drag coefficient is contrary to the classical conclusion. The normalized drag coefficient is mainly depending on the thermal properties. The internal flow characteristics of the particle cluster further confirm the effects of fluid flow around it. After separating the effects of thermal properties, the exponent -β representing the effects of void fraction in SCW is similar to that in CPF. A correlation for calculating the particle drag in SCW is developed based on this result.

Metrics for Domain Shift Characterization: Comparisons and New Directions

Domain adaptation is an important area of research, as it aims to remedy the effects of the domain shift due to differences in the distribution between the source domain used for training and the target domain where prediction takes place. However, methods for characterizing the domain shift across datasets are lacking. In this work, we propose a domain shift metric called SpOT, which stands for spherical optimal transport, by operating on the spherical manifold. We realize our approach with a spherical network, used to obtain features, and an orthogonal projection loss, used to impose orthogonality in the feature space. The resulting spherical features have better inter-class separation and lower intra-class variation compared to features in Euclidean space. This type of feature clustering makes each domain representation more compact and more suitable for further analysis. The domain shift between the datasets is calculated using the optimal transport on the spherical features, which has a sound theoretical basis. Our results are further supported by experiments that show the correlation of SpOT with a new gain of transfer measure across domain adaptation datasets.

Volatility forecasts by clustering: Applications for VaR estimation

It is well known that volatility has time-varying and clustering characteristics. The information content of volatility clustering is particularly important in turbulent periods, such as the stage of financial crisis. How to fully mine the implicit information within clusters to predict the volatility in the future is a rarely discussed issue. In this paper, we put forward a partition model to segment volatility into non-overlapping clusters by Fisher’s optimal dissection methodology. Using this model, we can quickly identify the points of structural changes in volatility. By utilizing the information of the nearest cluster, we can perform point estimation and interval estimation on future volatility. In the end, we conduct some empirical examples based on the returns of S&P 500, DAX 30 and FTSE 100 index. We find that our method can improve the volatility forecast and VaR estimations.

Open-Set Domain Adaptation via Feature Clustering and Separation for Fault Diagnosis

Open-Set Domain Adaptation via Feature Clustering and Separation for Fault Diagnosis

Tuning-free sparse clustering via alternating hard-thresholding

Model-based clustering is a commonly-used technique to partition heterogeneous data into homogeneous groups. When the analysis is to be conducted with a large number of features, analysts face simultaneous challenges in model interpretability, clustering accuracy, and computational efficiency. Several Bayesian and penalization methods have been proposed to select important features for model-based clustering. However, the performance of those methods relies on a careful algorithmic tuning, which can be time-consuming for high-dimensional cases. In this paper, we propose a new sparse clustering method based on alternating hard-thresholding. The new method is conceptually simple and tuning-free. With a user-specified sparsity level, it efficiently detects a set of key features by eliminating a large number of features that are less useful for clustering. Based on the selected key features, one can readily obtain an effective clustering of the original high-dimensional data under a general sparse covariance structure. Under mild conditions, we show that the new method leads to clusters with a misclassification rate consistent to the optimal rate as if the underlying true model were used. The promising performance of the new method is supported by both simulated and real data examples.

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Influence maximization (IM) has drawn significant attention in recent years. Most existing IM methods primarily focus on homogeneous networks, and do not take into account the heterogeneity and the attributes of different types of nodes in heterogeneous networks. However, heterogeneous networks are ubiquitous in real world, encompassing rich semantics and complex structural information. Additionally, the clustering characteristics inherent in a network have a critical and substantial impact on the process of information diffusion, which is often overlooked in IM models designed for heterogeneous networks. To address the challenges posed by the heterogeneity and clustering structure in heterogeneous networks, we propose a novel deep learning framework based on a self-supervised clustered heterogeneous graph transformer for IM in heterogeneous networks, which we have named SCHGT-IM. SCHGT-IM aggregates the heterogeneity and clustering information in heterogeneous networks and incorporates a clustered cascade (CC) model as an information diffusion model to enhance the realism of simulations. We evaluate the performance of SCHGT-IM in comparison with that of state-of-the-art IM models using three academic heterogeneous networks extracted from the DBLP dataset. The experimental results on influence spread demonstrate that SCHGT-IM is superior to fourteen state-of-the-art algorithms and is highly effective in selecting influential seed nodes of different types from heterogeneous networks.

Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China

Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China

Chitosan catechol-tannic acid composite hydrogel and cryogel with antimicrobial and hemostatic properties

Hydrogels containing catechol group have received attention in the biomedical field due to their robust adhesive/cohesive capabilities, biocompatibility, and hemostatic abilities. Catechol-functionalized chitosan holds promise for preparing self-assembly hydrogels. However, issues of inefficient gelation and instability still persist in these hydrogels. In the current study, we synthesized chitosan catechol (CC) of high catechol substitution (∼28 %) and combined CC with tannic acid (TA, which also contains catechol) to form self-healing CC-TA hydrogels. The catechol-enriched CC-TA composite hydrogels showed rapid gelation and mechanical reinforcement (shear modulus ∼110 Pa). In situ coherent small-angle X-ray scattering (SAXS) coupled with rheometry revealed a morphological feature of mesoscale clusters (∼20 nm) within CC-TA hydrogel. The clusters underwent dynamic destruction under large-amplitude oscillatory shear, corresponding with the strain-dependent and self-healing behavior of the CC-TA hydrogel. The composite hydrogel had osmotic-responsive and notable adhesive properties. Meanwhile, CC-TA composite cryogel prepared simply through freeze-thawing procedures exhibited distinctive macroporous structure (∼200 μm), high water swelling ratio (∼7000 %), and favorable compressive modulus (∼8 kPa). The sponge-like cryogel was fabricated into swabs, demonstrating hemostatic capacity. The CC-TA composites, in both hydrogel and cryogel forms, possessed ROS scavenging ability, antimicrobial activity, and cell compatibility with potentials in biological applications.

A retrospective prognostic evaluation using unsupervised learning in the treatment of COVID-19 patients with hypertension treated with ACEI/ARB drugs.

This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor B (ARB) drugs and to identify key features affecting patient prognosis using an unsupervised learning method. A large-scale clinical dataset, including patient information, medical history, and laboratory test results, was collected. Two hundred patients with COVID-19 and hypertension were included. After cluster analysis, patients were divided into good and poor prognosis groups. The unsupervised learning method was used to evaluate clinical characteristics and prognosis, and patients were divided into different prognosis groups. The improved wild dog optimization algorithm (IDOA) was used for feature selection and cluster analysis, followed by the IDOA-k-means algorithm. The impact of ACEI/ARB drugs on patient prognosis and key characteristics affecting patient prognosis were also analysed. Key features related to prognosis included baseline information and laboratory test results, while clinical symptoms and imaging results had low predictive power. The top six important features were age, hypertension grade, MuLBSTA, ACEI/ARB, NT-proBNP, and high-sensitivity troponin I. These features were consistent with the results of the unsupervised prediction model. A visualization system was developed based on these key features. Using unsupervised learning and the improved k-means algorithm, this study accurately analysed the prognosis of patients with COVID-19 and hypertension. The use of ACEI/ARB drugs was found to be a protective factor for poor clinical prognosis. Unsupervised learning methods can be used to differentiate patient populations and assess treatment effects. This study identified important features affecting patient prognosis and developed a visualization system with clinical significance for prognosis assessment and treatment decision-making.

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Aiming at the problem that the search efficiency of key vulnerable nodes in large-scale networks is not high and the consideration factors are not comprehensive enough, in order to improve the time and space efficiency of search and the accuracy of results, a key vulnerable node discovery method based on Bayesian attack subgraphs and improved fuzzy C-means clustering is proposed. Firstly, the attack graph is divided into Bayesian attack subgraphs, and the analysis results of the complete attack graph are quickly obtained by aggregating the information of the attack path analysis in the subgraph to improve the time and space efficiency. Then, the actual threat features of the vulnerability nodes are extracted from the analysis results, and the threat features of the vulnerability itself in the common vulnerability scoring standard are considered to form the clustering features together. Next, the optimal number of clusters is adaptively adjusted according to the variance idea, and fuzzy clustering is performed based on the extracted clustering features. Finally, the key vulnerable nodes are determined by setting the feature priority. Experiments show that the proposed method can optimize the time and space efficiency of analysis, and the fuzzy clustering considering multiple features can improve the accuracy of analysis results.

Spatiotemporal Pattern and Spatial Convergence of Land Use Carbon Emission Efficiency in the Pan-Pearl River Delta: Based on the Difference in Land Use Carbon Budget

Research on land use carbon emission efficiency (LUCEE) in the Pan-Pearl River Delta (PPRD) can aid in formulating regional differentiated carbon reduction strategies. In this work, the inversion of carbon emissions using night-time light (NTL) data and the modified Carnegie Ames Stanford Approach (CASA) model were used to measure the net carbon emissions from land use (NCELU). On this basis, the SBM-undesirable model was used to assess the LUCEE. Additionally, the exploratory spatial data analysis (ESDA), Dagum Gini coefficient, and spatial convergence model were further introduced to analyze the spatial correlation, regional differences, and convergence trend of the LUCEE. Findings indicate that: (1) The NCELU showed an increasing fluctuation. During the period of 2006–2020, the NCELU increased from −168.58 million tons to −724.65 million tons. (2) The LUCEE exhibited a three-phase fluctuating downward trend of “decrease–rise–decrease”. The LUCEE first decreased from 0.612 in 2006 to 0.544 in 2008, then gradually increased to 0.632 in 2016, and finally decreased to 0.488 in 2020. Spatially, the LUCEE manifested a distribution characteristic of “high in the north and south, low in the middle”, with distinct spatial clustering features. (3) The overall Gini coefficient in the study period increased from 0.1819 to 0.2461. The primary contributor to the overall difference over the entire sample period was hypervariable density. (4) The PPRD and its various subregions displayed significant features of absolute and conditional β convergence. The speed of regional convergence from fastest to slowest was central &gt; west &gt; east, with the absolute convergence speeds of 0.0505, 0.0360, and 0.0212, respectively. Finally, policy recommendations are proposed to achieve regional carbon neutrality for the PPRD.

Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering

Semi-supervised clustering aims to guide the clustering by utilizing auxiliary information about the class labels. Among the semi-supervised clustering categories, the constraint-based approach uses the available pairwise constraints in some steps of the clustering procedure, usually by adding new terms to the objective function. Considering this category, Semi-supervised FCM (SSFCM) is a semi-supervised version of the fuzzy c-means algorithm, which takes advantage of fuzzy logic and auxiliary class distribution knowledge. Despite the performance enhancement caused by incorporating this extra knowledge in the clustering process, semi-supervised fuzzy approaches still suffer from some problems. All the data attributes in the feature space are assumed to have equal importance in the cluster formation, while some features may be more informative than others. Thus the feature importance issue is not addressed in the semi-supervised category. This paper proposes a novel Semi-Supervised Fuzzy c-means approach, which is designed based on Feature-Weight, and Cluster-Weight learning, named SSFCM-FWCW. Inspired by the SSFCM, a fuzzy objective function is presented, which is composed of (1) a semi-supervised term representing the external class knowledge; (2) a feature weighting; and (3) a cluster weighting. Both feature weights and cluster weights are determined adaptively during the clustering. Considering these two techniques leads to insensitivity to the initial center selection, insensitivity to noise, and consequently helps to form an optimal clustering structure. Experimental comparisons are carried out on several benchmark datasets to evaluate the proposed approach's performance, and promising results are achieved. The Matlab implementation source code of the proposed method is publicly accessible at https://github.com/Amin-Golzari-Oskouei/SSFCM-FWCW.

The coupling coordination between health service supply and regional economy in China: spatio-temporal evolution and convergence.

The coordination of health service supply and regional economy is an integral path to promote China's prosperity. Based on the coupling mechanism of health service supply and regional economy, we sampled the data from 30 provinces in China from 2009 to 2021 in this study and constructed the evaluation index system. Additionally, we calculated the coupling coordination degree (HED) of the two through the coupling coordination degree model. We further used the kernel density estimation, Moran's I index, and spatial β convergence model to assess the dynamic evolution trends, spatial aggregation effect, and spatial convergence characteristics of coupling coordination. (1) HED in China showed a rising trend during the study period but with large regional differences, forming a gradient distribution pattern of "high in the east and low in the west." (2) The results of Kernel density estimation show that HED has formed a gradient differentiation phenomenon within each region in China. (3) HED has modeled spatial clustering characteristics during the study period, with high-value clusters mainly appearing in the eastern region and low-value clusters appearing in the northwestern region. (4) There are absolute β-convergence and conditional β-convergence trends in HED in China and the three major regions during the study period, but there is an obvious regional heterogeneity in the control factors. The research provides a reference for accurately implementing policies according to different levels of health service supply and economic development, in addition to narrowing the regional differences of the coupling coordination between the regional economy and health service supply.

Single-Cell Transcriptome Analysis Reveals Dynamic Populations of Vascular Cells in Neointimal Hyperplasia.

Neointimal hyperplasia (NIH) is the pathological basis of vascular injury disease. Vascular cells are the dominant cells in the process of NIH, but the extent of heterogeneity amongst them is still unclear. A mouse model of NIH was constructed by inducing carotid artery ligation. Single-cell sequencing was then used to analyze the transcriptional profile of vascular cells. Cluster features were determined by functional enrichment analysis, gene set scoring, pseudo-time analysis, and cell-cell communication analysis. Additionally, immunofluorescence staining was conducted on vascular tissues from fibroblast lineage-traced (PdgfraDreER-tdTomato) mice to validate the presence of Pecam1+Pdgfra+tdTomato+ cells. The left carotid arteries (ligation) were compared to right carotid arteries (sham) from ligation-induced NIH C57BL/6 mice. Integrative analyses revealed a high level of heterogeneity amongst vascular cells, including fourteen clusters and seven cell types. We focused on three dominant cell types: endothelial cells (ECs), vascular smooth muscle cells (vSMCs), and fibroblasts. The major findings were: (1) four subpopulations of ECs, including ECs4, mesenchymal-like ECs (ECs1 and ECs2), and fibro-like ECs (ECs3); (2) four subpopulations of fibroblasts, including pro-inflammatory Fibs-1, Sca1+ Fibs-2, collagen-producing Fibs-3, and mesenchymal-like Fibs-4; (3) four subpopulations of vSMCs, including vSMCs-1, vSMCs-2, vSMCs-3, and vSMCs-3-derived vSMCs; (4) ECs3 express genes related to extracellular matrix (ECM) remodeling and cell migration, and fibro-like vSMCs showed strong chemokine secretion and relatively high levels of proteases; (5) fibro-like vSMCs that secrete Vegfa interact with ECs mainly through vascular endothelial growth factor receptor 2 (Vegfr2). This study presents the dynamic cellular landscape within NIH arteries and reveals potential relationships between several clusters, with a specific focus on ECs3 and fibro-like vSMCs. These two subpopulations may represent potential target cells for the treatment of NIH.

Video anomaly detection guided by clustering learning

With the fuzzy boundary between normal and abnormal video data, which cannot be well distinguished by most methods, anomaly detection in video requires better characterization of the data. First we give a convolution-enhanced self-attentive video auto-encoder based on the U-Net architecture, which can extract richer image features. Secondly we design a dual-scale feature clustering structure for this encoder, which simultaneously compresses the channel and spatial structure features of the image to represent the features to obtain good coding characteristics and expand the boundary between normal and abnormal data. We also verify that our approach is equivalent to a class of auto-encoders for memory-guided learning. Finally, in the reconstruction task, since video auto-encoders are capable of triggering temporal time leakage phenomena that can lead to network performance degradation, we propose an anomaly score computation paradigm for video auto-encoders that utilizes the average frame anomaly score of a video clip to compute the first frame anomaly score in that video clip.Extensive experiments on three benchmark datasets show that our method outperforms most existing methods on large datasets with complex patterns. The code will be published at the following link: Anomaly-detection-guided-by-clustering-learning

Multicolour photometry of LEO mega-constellations Starlink and OneWeb

ABSTRACT The development of low earth orbit (LEO) mega-constellation fundamentally threatens ground-based optical astronomical observations. To study the photometric properties of the LEO mega-constellations, we used the Xinglong 50 cm telescope to conduct a large-sample, high-precision, and multicolour target-tracking photometry of two typical LEO mega-constellations: Starlink and OneWeb. Over a three-month observation period starting on 2022 January 1, we collected 1447 light curves of 404 satellites in four typical versions: Starlink v1.0, DarkSat, VisorSat, Starlink v1.5, and OneWeb. According to data statistics, Starlink v1.0 has the smallest median magnitude at clear and Sloan Digital Sky Survey gri band, and OneWeb is the dimmest bus. The brightness of Starlink v1.5 is slightly brighter than VisorSat. We construct a detailed photometric model with solar phase angle variations by calculating the illumination-visibility geometry based on the orbital parameters. Our data analysis shows that the solar phase angle is the significant characteristic which influencing Starlink satellites’ brightness, but it is not sensitive to OneWeb satellites. VisorSat and Starlink v1.5 versions, which are equipped with deployable visors, have significantly reduced scattered light compared to the previous Starlink v1.0 version. The multiband LOWESS and colour index are analysed in characterizing the energy and colour features of LEO mega-constellation satellites. This work found that the proportion of scattered sunlight mitigation achieved with VisorSat and Starlink v1.5 was 55.1 and 40.4 per cent, respectively. The colour index of different buses shows an evident clustering feature. Our observation and analysis could provide valuable quantitative data and photometric models, which can contribute to assessing the impact of LEO mega-constellations on astronomical observations.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.