Initial Cluster Research Articles

Energy efficient secured-quality of service routing protocol for mobile ad hoc network using multi-objective optimization

This article proposed a hybrid energy-efficient secured quality of service (QoS) based multipath routing protocol. A modified crow search combined with the tunicate swarm butterfly optimisation algorithm (TSBO) with a density-based clustering technique strategy is proposed for the selection of cluster heads after the initial cluster formations. Among all the nodes, the cluster head is selected, and employing the collaborative trust-based approach (CTBA), which employs the trust factor for mobile ad hoc network (MANET) data transmission, a node's authentication is supplied. Finally, to implement the safe routing technique, this article suggested a hybrid multipath routing protocol combining multi-objective grey wolf optimisation (MO-GWO) with a fruit fly algorithm. The NS3 simulator is used to assess the proposed work. The packet delivery ratio metric performs 4% better than the current models. As a result, the suggested approach performs better for the end-to-end delay, energy consumption, packet delivery ratio (PDR), and throughput; it also uses less energy and has a shorter delay. Additionally, single-path nodes with the same energy value have lower throughput than multi-path nodes.

Research on Daily Load Curve Classification Based on Improved Fuzzy C-means Clustering Algorithm

For the problem of severe unpredictability and three-phase unbalance of user demand in low-voltage distribution networks, a daily load curve clustering technique integrating the sparrow search algorithm (SSA) and the fuzzy C-mean clustering algorithm (FCM) is presented. The initial load data set is pre-processed to lessen the interference of classification results. The load characteristic index of the daily load curve is removed to produce a reduced dimensional data set. The clustering validity index is introduced to solve the optimal number of clusters, and the early warning mechanism of the sparrow search algorithm is adopted to improve the global search capability. These improvements are made to improve the sensitivity of the initial clustering center of the FCM algorithm and the problem of local optimum in the process of finding the optimum. The simulation used to validate the revised clustering algorithm’s accuracy and efficacy for daily load categorization is a reference for resolving the three-phase load unbalance issue.

An improved K-Means clustering based on differential evolution

Randomly selecting the initial cluster center point in the K-means clustering algorithm can lead to the issue of local optima. The paper introduces an enhanced differential evolution algorithm for optimizing K-means clustering. Experimental results demonstrate the superiority of the improved K-means clustering, based on the proposed differential evolution, over other comparison algorithms. Notably, the optimized version exhibits exceptional performance in clustering Iris, Wine, and Glass datasets. The initial clustering center can improve the clustering accuracy in the later stage. Therefore, the proposed algorithm is more superior.

有關《廣雅疏證》中 C-l-/C-r-式聯綿詞的考察

Binomes(聯綿詞) have been extensively discussed in various studies as evidence of the existence of initial clusters(複輔音) in Old Chinese. Notably, binomes with the second syllable being consonant “r-” or “l-” have been particularly emphasized and utilized as evidence for the study of initial clusters. As a consequence, the prevailing consensus among scholars posits that binomes emerged through the division of initial clusters. However, the specific phonetic processes that led to the evolution of binomes from initial clusters remain a relatively understudied aspect. This paper aims to meticulously analyze the C-l-/C-r- binomes found within the compilation “Guangyashuzheng(廣雅疏證)”, a repository containing a plethora of binomes from the Old Chinese period. Guided by the theoretical framework of Zheng-Zhang Shangfang's Old Chinese phonology, this study undertakes the task of categorizing the binomes into discrete groups: K-l-/K-r- binomes, Q-l-/Q-r- binomes, P-l-/P-r- binomes, and S-l-/S-r- binomes. Through a comprehensive analysis, the paper delves into the intricate phonetic relationships that exist between binomes and initial clusters. As a result of this extensive examination, it becomes evident that certain binomes indeed emerged through the fragmentation of initial clusters. This substantiates the necessity for further refinement of our comprehension of the initial cluster system within Old Chinese linguistic structures.

Analysis of media content recommendation in the new media era considering scene clustering algorithm

Abstract With the advent of the new media era, we face the problem of information overload every day, based on which this paper proposes a scenario-conscious clustering algorithm for recommending media content. The improved K-means algorithm is used to cluster the media content, an initial cluster center is randomly selected, and the remaining initial cluster centers are obtained by executing the improved algorithm to reduce the number of iterations and avoid neighboring situations. The clustering algorithm is then compared with content-based recommendation techniques, KNN recommendation algorithm, and collaborative filtering recommendation algorithm in terms of accuracy, recall, MAE value, and execution time. The clustering algorithm is significantly better than the other algorithms regarding accuracy and recall. The accuracy of the clustering algorithm is 0.4 when the recommendation sequence is 30, which is 0.4 higher than the collaborative filtering technique, 0.1 higher than the KNN, and 0.12 higher than the content recommendation method. In terms of MAE value, the clustering algorithm outperforms the other algorithms when the number of nearest neighbors is selected to be above 20. In terms of execution time, the longer the amount of data, the more obvious the advantage of the clustering algorithm. Therefore, the applicability and reliability of the model proposed in this paper for media content recommendation are verified in terms of accuracy, recall and execution time, which meet the design requirements.

Consumer behavior analysis based on Internet of Things platform and the development of precision marketing strategy for fresh food e-commerce.

The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript aims to address these limitations by proposing the establishment of a consumer behavior analysis system based on an Internet of Things (IoT) platform. The system harnesses the potential of radio frequency identification devices (RFID) technology for product identification encoding, thus facilitating the monitoring of product sales processes. To categorize consumers, the system incorporates a k-means algorithm within its architectural framework. Furthermore, a similarity metric is employed to evaluate the gathered consumption information and refine the selection strategy for initial clustering centers. The proposed methodology is subjected to rigorous testing, revealing its effectiveness in resolving the issue of insufficient differentiation between customer categories after clustering. Across varying values of k, the average false recognition rate experiences a notable reduction of 20.6%. The system consistently demonstrates rapid throughput and minimal overall latency, boasting an impressive processing time of merely 2ms, thereby signifying its exceptional concurrent processing capability. Through the implementation of the proposed system, the opportunity for further target market segmentation arises, enabling the establishment of core market positioning and the formulation of distinct and precise marketing strategies tailored to diverse consumer cohorts. This pioneering approach introduces an innovative and efficient methodology that e-commerce enterprises can embrace to amplify their marketing endeavors.

Wheat Lodging Direction Detection for Combine Harvesters Based on Improved K-Means and Bag of Visual Words

For the inconsistent lodging of wheat with dense growth and overlapped organs, it is difficult to detect lodging direction accurately and quickly using vehicle vision for harvesters. Therefore, in this paper, the k-means algorithm is improved by designing a validity evaluation function, selecting initial clustering centers by distance, constructing a multidimensional feature vector, and simplifying calculations using triangle inequality. An adaptive image grid division method based on perspective mapping and inverse perspective mapping with a corrected basic equation is proposed for constructing a dataset of wheat lodging directions. The improved k-means algorithm and direction dataset are used to construct a bag of visual words. Based on scale-invariant feature transform, pyramid word frequency, histogram intersection kernel, and support vector machine, the wheat lodging directions were detected in the grid. The proposed method was verified through experiments with images acquired on an intelligent combine harvester. Compared with single-level word frequencies with existing and improved k-means, the mean accuracy of wheat lodging direction detection by pyramid word frequencies with improved k-means increased by 6.71% and 1.11%, respectively. The average time of detection using the proposed method was 1.16 s. The proposed method can accurately and rapidly detect wheat lodging direction for combine harvesters and further realize closed-loop control of intelligent harvesting operations.

Experimental study on equilibrium initiation and extension of multiple clusters of fractures based on true triaxial physical simulation

In the context of tight sandstone reservoirs in the Ordos Basin characterized by compact and heterogeneous rock formations, conventional fracturing techniques yield monolithic fracture shapes, rendering 3D reservoir reconstruction unattainable. This study investigates the principles governing balanced initiation and propagation of fractures in multi-cluster fracturing within unconventional fracturing technology. Employing a large-scale true triaxial simulation experiment system, we utilize the dimensional analysis method (π theorem) to design a physical simulation experiment similarity criterion. Through various experimental adjustments involving proportioning, curing, and mechanical testing, we generate an artificially cured rock mass with mechanical parameters akin to the target layer. The rock mass is maintained at a size of 30 cm × 30 cm × 30 cm. Systematic physical simulation experiments on unconventional volume fracturing are carried out using the 30 cm-sized dense sandstone outcrop rock mass. Taking the conventional fracturing technology as a reference and manipulating experimental conditions and design parameters, we simulate the non-equilibrium initiation and extension behaviors of fracturing fractures under five unconventional volume fracturing technologies, namely hydraulic pulse pretreatment, temporary plugging between clusters, flow-limiting method, cyclic loading and unloading, and pulse intermittent fracturing. Through this, we elucidate the non-equilibrium initiation and extension laws governing multi-cluster fractures. Comparative analysis with conventional fracturing, known for inducing stress interference on fractures and inhibiting their expansion, revels that the five unconventional volume fracturing techniques mitigate stress interference in multi-cluster fracturing. This promotes uniform fracture initiation and expansion, facilitating the creation of complex fractures and larger reconstructed volumes. Among these techniques, inter-cluster block fracturing stands out for its exceptional ability to generate complex fracture networks. The research culminates in the development and refinement of a balanced fracture and extension control technique tailored for multiple cluster fractures in bulk fracturing. This technique significantly contributes to enhancing the degree of 3D reconstruction achievable in unconventional tight oil and gas reservoirs.

On the approximation of Euclidean SL via geometric method

Single-linkage with distance-r stopping condition (SL) is a classical clustering technique, which can discover arbitrary shaped clusters. However, its O(n2) worst-case time complexity is still a challenge, where n is the number of points in the dataset. In this paper, we present a relaxed version of the problem in Euclidean space, called the ρ-approximate SL problem. We propose a grid-based algorithm, called ρ-SL, to solve this problem in O(n+ν) expected time, where ν is an integer associated with the maximum and minimum coordinates of each dimension. ρ-SL first partitions the dataset into non-empty grids and constructs a grid tree to organize them for efficient non-empty neighboring grid queries. Then, an initial cluster structure is established to keep track of all pairs of grids that may be mergeable. ρ-SL proceeds to identify the pairs of grids that are mergeable by a fast merging algorithm which takes into account the spatial relationships among points to reduce unnecessary distance calculations. Experiments were conducted on synthetic and real-world datasets to evaluate the performance of ρ-SL. The results show that the proposed algorithm is more efficient than SL and its fastest variants.

Two improved N-two-stage K-means clustering aggregation algorithmic paradigms for HFLTS possibility distributions

The available method based on statistical principles for aggregating hesitant fuzzy linguistic term set (HFLTS) possibility distribution is the N-two-stage algorithmic aggregation paradigm driven by the K-means clustering (N2S-KMC). Nonetheless, the N2S-KMC method is subject to two significant limitations. (i) The grouping technique is capable of effectively partitioning decision-making information into N groups. However, it does not determine the appropriate placement of members within each group, as the number of computations is dependent on the number of elements present in each group, rather than the elements themselves. (ii) The initial clustering centers of K-means clustering are chosen without adhering to the distribution law within the aggregated hesitant 2-tuple linguistic terms set (H2TLTS) possibility distribution. This may result in a reduction in the clustering performance. In order to address the aforementioned limitations, we suggest two enhancement techniques for the former. Firstly, we propose the utilization of the minimum average difference (MAD) method to ascertain the number of groups. This approach aims to reduce the time required for the initial stage of aggregation following grouping. Secondly, we recommend the implementation of the maximize compactness degree of inter-group grouping (MCDIGG) method. This method enables the identification of group members, resulting in a more concentrated distribution of data subsequent to grouping. The present study suggests the utilization of MAD and MCDIGG techniques as a substitute for the grouping approach in the N2S-KMC model. This leads to the development of a new algorithm, IN2S-DO-KMC, wherein the data is partitioned into K subsets in a descending order to determine the initial center for KMC. Furthermore, with respect to the issue present in the subsequent phase, we propose the utilization of the density canopy (DC) algorithm to perform pre-clustering of the data and produce the initial clustering center and the quantity of clusters for the K-means algorithm. Subsequently, a refined version of the N2S-KMC model, denoted as IN2S-DC-KMC, has been suggested. Ultimately, an empirical study is conducted to assess the validity and practicability of the proposed framework for evaluating failure modes in medical devices. The outcomes are evaluated with regards to the efficacy of the algorithm, the numerical dispersion, and the pragmatic ramifications.

TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning

Multivariate data analysis, as an important research topic in the field of machine learning, focuses on how to utilize the intrinsic connection between feature variables and target variables. However, in the face of complex multivariate prediction environments, existing single prediction models often fail to obtain ideal results. Meanwhile, existing ensemble prediction models are not always adapted to certain complex data. Moreover, the randomness in the clustering process cannot guarantee the clustering accuracy. Therefore, to improve the model’s prediction accuracy and ability to adapt to complex data and reduce the impact of randomness on clustering accuracy, this paper designs a multivariate prediction model utilizing three-way clustering (TWC) and ensemble learning, which is named the TWC-EL model. First, the initial division of the sample set is realized by k-means clustering algorithm, and further the sample set is divided again via the k-means clustering algorithm to solve the problem of clustering accuracy. Then, the results of clustering twice are combined according to the difference in the number of intersection points and the distance from the samples to the center point of each cluster, and the core and fringe regions of each cluster in the initial clustering results are obtained, forming a new TWC method. Next, based on the correlation between the regions, the obtained core and fringe regions are classified into low-correlation, medium-correlation and high-correlation regions, and an ensemble prediction model is designed by combining the advantages of the Elman neural network model, the Extreme Learning Machine (ELM) model and the back propagation neural network (BPNN) model. Finally, the experimental analysis results exhibit that the constructed TWC-EL model is efficient and feasible, and points out the excellent performance compared with the existing prediction models. The validity of the TWC method and the ensemble prediction model in the proposed TWC-EL model are verified by experiments, respectively.

Robust and fast algorithm design for efficient Wi-Fi fingerprinting based indoor positioning systems

Indoor positioning systems (IPS) based on Wi-Fi fingerprinting have gained significant attention due to their potential for providing location-based services. Large scale IPS deployments require implementation of robust, accurate, and fast algorithms. Data analytics assisted algorithms provide positioning accuracy improvements, however their integration within real-time and scalable solutions significantly depends on the computational complexity. Therefore, we propose robust and computationally efficient algorithms for performance enhancements in Wi-Fi fingerprinting-based IPS. A robust radio map algorithm based on enhanced statistical cluster initialization was designed for efficient indoor environment characterization. The proposed data filtering algorithm leveraged smart clustering to mitigate real-time data variations. The designed area classification algorithm was based on smart dual-band data aggregation. We evaluated the performance of proposed algorithms based on accuracy and computation time. The performance evaluations signified accuracy and computational enhancement, in comparison to related benchmark techniques. The data filtering and area classification algorithms required 40% less computation time. Simultaneously, 14.5% and 36% accuracy improvements were recorded for the area classification and radio map algorithms respectively. The proposed algorithms have the potential to significantly enhance IPS performance in a variety of real-time applications, including indoor navigation and asset tracking.

Cluster formation and relaxation in particle rafts under uniform radial expansion.

Particle rafts floating at an air-liquid interface exhibit a variety of behaviors when interfacial flow is introduced. Motivated by previous experimental observations, the failure pattern of particle rafts under uniform radial expansion is reported in this paper. The expansion process is specifically designed to expand the system affinely in the radial direction and to keep the velocity gradient constant throughout. A strong resemblance to the results of particle rafts under uniform uniaxial expansion is found. The size of the cluster emerging as the rafts are pulled apart scales inversely with the pulling velocity. This is a result of two competing velocities: the interparticle separation speed provided by the flow and a size-dependent relaxation speed for clustering. A model, generalized from a one-dimensional linear (in)stability calculation, is in agreement with the failure morphology found for this radially expanded system. Nonlinear relaxation and particle rearrangement are observed after the initial clustering occurs. This is a feature unique to a two-dimensional system. With its easily accessible particle dynamics at the microscopic level, this system provides insights into the morphology controlled by two competing mechanisms in two or higher dimensions and across different scales.

Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification

This study addresses the importance of focal nodes in understanding the structural composition of networks. To identify these crucial nodes, a novel technique based on parallel Fuzzy Cognitive Maps (FCMs) is proposed. By utilising the focal nodes produced by the parallel FCMs, the algorithm efficiently creates initial clusters within the population. The community discovery process is accelerated through a distributed genetic algorithm that leverages the focal nodes obtained from the parallel FCM. This approach mitigates the randomness of the algorithm, addressing the limitations of the random population selection commonly found in genetic algorithms. The proposed algorithm improves the performance of the genetic algorithm by enabling informed decision making and forming a better initial population. This enhancement leads to improved convergence and overall algorithm performance. Furthermore, as graph sizes grow, traditional algorithms struggle to handle the increased complexity. To address this challenge, distributed algorithms are necessary for effectively managing larger data sizes and complexity. The proposed method is evaluated on diverse benchmark networks, encompassing both weighted and unweighted networks. The results demonstrate the superior scalability and performance of the proposed approach compared to the existing state-of-the-art methods.

Cas phosphorylation regulates focal adhesion assembly.

Integrin-mediated cell attachment rapidly induces tyrosine kinase signaling. Despite years of research, the role of this signaling in integrin activation and focal adhesion assembly is unclear. We provide evidence that the Src-family kinase (SFK) substrate Cas (Crk-associated substrate, p130Cas, BCAR1) is phosphorylated, and associated with its effectors, Crk/CrkL, in clusters that are precursors of focal adhesions. The initial phospho-Cas clusters contain integrin b1 in its inactive, bent closed, conformation. Later, phospho-Cas and total Cas levels decrease as integrin b1 is activated and core focal adhesion proteins including vinculin, talin, kindlin and paxillin are recruited. Cas is required for cell spreading and focal adhesion assembly in epithelial and fibroblast cells on collagen and fibronectin. Cas cluster formation requires Cas, Crk/CrkL, SFKs and Rac1 but not vinculin. Rac1 provides positive feedback onto Cas through reactive oxygen, opposed by negative feedback from the ubiquitin proteasome system. The results suggest a two-step model for focal adhesion assembly in which clusters of phospho-Cas, effectors and inactive integrin b1 grow through positive feedback prior to integrin activation and recruitment of core focal adhesion proteins.

Water-Body Detection in Sentinel-1 SAR Images with DK-CO Network

Synthetic Aperture Radar (SAR) is an active microwave sensor with all-day/night and all-weather detection capability, which is crucial for detecting surface water resources. Surface water-body such as rivers, lakes, reservoirs, and ponds usually appear as dark areas in SAR images. Accurate and automated extraction of these water bodies can provide valuable data for the management and strategic planning of surface water resources and effectively help prevent and control drought and flood disasters. However, most deep learning-based methods rely on manually labeled samples for model training and testing, which is inefficient and may introduce errors. To address this problem, this paper proposes a novel water-body detection method that combines optimization algorithms and deep learning techniques to automate water-body label extraction and improve the accuracy of water-body detection. First, this paper uses a swarm intelligence optimization algorithm, Dung Beetle Optimizer (DBO), to optimize the initial cluster center of the K-means clustering algorithm, which is called the DBO-K-means (DK) method. The DK method divides the training images into four categories and extracts the water bodies in them to generate the water-body labels required for deep learning model training and testing, and the whole process does not require human intervention. Then, the labels generated by DK and training data set images are fed into the Classifier–Optimizer (CO) for training. The classifier performs a dense classification task at the pixel level, resulting in an initial result image with blurred boundaries of the water body. Then, the optimizer takes this preliminary result image and the original SAR image as input, performs fine-grained optimization on the preliminary result, and finally generates a result image with a clear water-body boundary. Finally, we evaluated the accuracy of water-body detection using multiple performance indicators including ACC, precision, F1-Score, recall, and Kappa coefficient. The results show that the values of all indicators exceed 93%, which demonstrates the high accuracy and reliability of our proposed water-body detection method. Overall, this paper presents a novel DK-based approach that improves the automation and accuracy of deep learning methods for detecting water bodies in SAR images by enabling automatic sample extraction and optimization of deep learning models.

A new Chinese text clustering algorithm based on WRD and improved K-means

Text clustering has been widely used in data mining, document management, search engines, and other fields. The K-means algorithm is a representative algorithm of text clustering. However, traditional K-means algorithm often uses Euclidean distance or cosine distance to measure the similarity between texts, which is not effective in face of high-dimensional data and cannot retain enough semantic information. In response to the above problems, we combine word rotator’s distance with the K-means algorithm, and propose the WRDK-means algorithm, which use word rotator’s distance to calculate the similarity between texts and preserve more text features. Furthermore, we define a new cluster center initialization method that improves cluster instability during random initial cluster center selection. And, to solve the problem of inconsistent length between texts, we propose a new iterative approximation method of cluster centers. We selected three suitable datasets and five evaluation indicators to verify the feasibility of the proposed algorithm. Among them, the RI value of our algorithm exceeds 90%. And for Marco_F1, our scheme was about 37.77%, 23.2%, 13.06% and 20.12% better than other four methods, respectively.

Big data: an optimized approach for cluster initialization

The k-means, one of the most widely used clustering algorithm, is not only faster in computation but also produces comparatively better clusters. However, it has two major downsides, first it is sensitive to initialize k value and secondly, especially for larger datasets, the number of iterations could be very large, making it computationally hard. In order to address these issues, we proposed a scalable and cost-effective algorithm, called R-k-means, which provides an optimized solution for better clustering large scale high-dimensional datasets. The algorithm first selects O(R) initial points then reselect O(l) better initial points, using distance probability from dataset. These points are then again clustered into k initial points. An empirical study in a controlled environment was conducted using both simulated and real datasets. Experimental results showed that the proposed approach outperformed as compared to the previous approaches when the size of data increases with increasing number of dimensions.

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

The classification of marine sediment based on acoustic data is crucial for various applications such as marine resource exploitation, marine engineering construction, and marine ecological environment maintenance. It serves as a valuable alternative to limited geological sampling. However, the accuracy of sediment classification is limited due to constraints in acoustic data detection methods, data quality, and classification techniques. To address this issue, this study proposes an automatic classification method for marine sediment using an improved U-convolutional neural network and K-means clustering algorithm. In the coding part, a spatial pyramid pool layer is introduced to fuse low-dimensional feature data of different scales with the features of each level of the corresponding coding layer. This fusion method enhances the accuracy of the constructed relationship between the physical property parameters of the seabed bottom. The K-means clustering algorithm is optimized through selecting the point at the density center as the initial clustering center during the initial clustering center selection stage. This approach solves the sensitivity problem of the initial clustering center of K-means, improves the edge extraction effect of sediment types, and enhances the classification accuracy of sediment types. To validate the proposed method, an application test is conducted in the Northern Slope area of the South China Sea. The mean grain size of sediments in the study area is predicted using the improved U-Net neural network and the seafloor reflection intensity of the sub-bottom profile. Compared to the standard U-Net network results, the mean grain size prediction results show an increase of 4.9% and 2.8%, respectively. The sediment with the predicted mean grain size is then classified using the K-means clustering algorithm, resulting in the division of five sediment types: gravelly sand, sand, silty sand, sandy silt, and clayey silt. These classifications align well with the South China Sea sediment type map. The findings of this study not only provide an important supplement to existing marine sediment classification methods but also contribute significantly to understanding the sedimentary environment and processes in the Northern Slope of the South China Sea.

Impact of Fe-doped H2/O2 flame equivalence ratio on the fate and temperature history of early particles

The temperature and species concentration history experienced by the gas-borne nanoparticles during their evolution in the flame has a major impact on their size, morphology, composition, and crystallinity. In our recent work (Combust. Flame, 244 (2022) 112251), we have reported optical emission measurements of a Fe(CO)5-doped H2/O2/Ar fuel-lean (ɸ = 0.5) flame, revealing that the temperature of the early-formed nanoparticles exceeds the gas temperature by several hundred degrees, while the particle volume fraction increases sharply, followed by rapid disintegration in the reaction zone. This behavior, modeled by single particle Monte-Carlo simulations indicates involvement of heterogeneous reactive processes at the particle surface, such as particle reduction and oxidation, growth and etching. Within the refined approach of the current study, reactive and non-reactive collisions were treated consistently, assuming rapid thermalization between the impinging molecule and the particle, with subsequent random energy sampling to determine reactivity. In the present work, we test the limits and validity of the heterogeneous flame-particle interaction model by manipulating the oxidation–reduction and growth-etching balance by varying the equivalence ratio (0.25<ɸ<1.5). For the entire range of equivalence ratios studied in experiments and simulations, we find a deviation between the particle and gas phase temperatures with significantly higher particle temperature, which is continued until a full degree of iron oxidation within the particle (O/Fe ratio=3/2) is reached. Validating the simulations against the measurements of particle temperature and volume fraction over a wide range of equivalence ratios, emphasized the necessity to account for gas-phase Fe-atom concentration depletion. We incorporated nucleation theory to estimate initial cluster population, linking Fe-concentration variation in the gas phase and the stochastic particle evolution model. The surface reaction parameters in our current work were updated using density functional theory literature data, and validation of the model predictions against experimental data, across the entire range of equivalence ratios.