Xu Chen^1,*, Shuai Wang¹, Kaixun He²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1779-1806, 2025, DOI:10.32604/cmc.2025.066543 - 29 August 2025

Abstract Accurate and reliable photovoltaic (PV) modeling is crucial for the performance evaluation, control, and optimization of PV systems. However, existing methods for PV parameter identification often suffer from limitations in accuracy and efficiency. To address these challenges, we propose an adaptive multi-learning cooperation search algorithm (AMLCSA) for efficient identification of unknown parameters in PV models. AMLCSA is a novel algorithm inspired by teamwork behaviors in modern enterprises. It enhances the original cooperation search algorithm in two key aspects: (i) an adaptive multi-learning strategy that dynamically adjusts search ranges using adaptive weights, allowing better individuals to More >

Jialong Zhang¹, Meijuan Yin^2,*, Yang Pei², Fenlin Liu², Chenyu Wang²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2095-2115, 2025, DOI:10.32604/cmc.2025.065679 - 29 August 2025

Abstract Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion. Existing identification methods based on Graph Neural Networks (GNNs) often lead to yield inaccurate features of influential users due to neighborhood aggregation, and require a large substantial amount of labeled data for training, making them difficult and challenging to apply in practice. To address this issue, we propose a semi-supervised contrastive learning method for identifying influential users. First, the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics… More >

Ziyuan Meng, Yongjun Chen, Hui Wang^*

Journal of Psychology in Africa, Vol.35, No.4, pp. 489-496, 2025, DOI:10.32604/jpa.2025.070068 - 17 August 2025

Abstract This study tested a multilevel model of the workplace territorial behaviors and employees’ knowledge sharing relationship, with team identification serving as a mediator and task interdependence as a moderator. Data were collected from 253 employees (females = 128, mean age = 28.626, SD = 6.470) from 40 work teams from different industries in China. Path analysis results indicated that workplace territorial behaviors were associated with lower employee knowledge sharing. Team identification enhanced employee knowledge sharing and partially mediated the relationship between workplace territorial behaviors and employee knowledge sharing. Task interdependence enhanced knowledge sharing and strengthened More >

Judy P. Yang^*, Yuan-Jun Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 669-691, 2025, DOI:10.32604/cmes.2025.068945 - 31 July 2025

Abstract Much of the research has focused on identifying bridge frequencies for health monitoring, while the bridge damping ratio also serves as an important factor in damage detection. This study presents an enhanced method for identifying bridge damping ratios using a two-axle, three-mass test vehicle, relying on wheel responses captured by only two mounted sensors. Damping ratio estimation formulas are derived using both the Hilbert Transform (HT) and Wavelet Transform (WT), with a consistent formulation that confirms accurate estimation is achievable with minimal instrumentation, particularly when addressing the support effect. A comparative analysis of the two More >

Zhun Yu^1,2, Jie Wang^1,2, Guoping Xu^1,2,*

Oncology Research, Vol.33, No.8, pp. 2013-2035, 2025, DOI:10.32604/or.2025.061757 - 18 July 2025

Abstract Objectives: Triple-negative breast cancer (TNBC) presents a major treatment challenge due to its aggressive behavior. The dysfunction of the Golgi apparatus (GA) contributes to the development of various cancers. This study aimed to utilize GA-related genes (GARGs) to forecast the prognosis and immune profile of TNBC. Methods: The data were downloaded from The Cancer Genome Atlas (TCGA) database, including 175 TNBC and 99 healthy samples. The differentially expressed GARGs (DEGARGs) were analyzed using the TCGA biolinks package. The patients with TNBC were classified into two clusters utilizing the ConsensusClusterPlus package according to prognosis-related DEGARGs, followed by… More >

Fuheng Qu¹, Hailong Li^1,*, Ping Wang², Sike Guo², Lu Wang², Xiaofeng Li^3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3911-3925, 2025, DOI:10.32604/cmc.2025.063820 - 03 July 2025

Abstract Rice spike detection and counting play a crucial role in rice yield research. Automatic detection technology based on Unmanned Aerial Vehicle (UAV) imagery has the advantages of flexibility, efficiency, low cost, safety, and reliability. However, due to the complex field environment and the small target morphology of some rice spikes, the accuracy of detection and counting is relatively low, and the differences in phenotypic characteristics of rice spikes at different growth stages have a significant impact on detection results. To solve the above problems, this paper improves the You Only Look Once v8 (YOLOv8) model,… More >

Harsh Kumar¹, Shilpa Suman², Abhishek Rawat^2,*, Rajat Subhra Chatterjee³, Dheeraj Kumar⁴, B. S. Chaudhary⁵

Revue Internationale de Géomatique, Vol.34, pp. 351-362, 2025, DOI:10.32604/rig.2025.064031 - 30 June 2025

Abstract The unconsolidated soils of the Indo-Gangetic Plains (IGP) contribute significantly to the amplification of seismic damage during earthquakes. Site-specific effects play a critical role in intensifying ground motion and shaping the spatial distribution of seismic hazards. This study aims to investigate the spatial variability of seismic hazards using geophysical and geological parameters such as lithology, shear wave velocity, soil texture, basement depth, and proximity to fault lines. Training data were derived from common hazard points identified in earthquake catalogues. Several machine learning (ML) models, including Logistic Regression (LR), K-Nearest Neighbors, Random Forest, and Decision Tree, More >

Hao Jiao¹, Xinyu Liu², Chen Wu¹, Chunlei Xu¹, Zhijun Zhou³, Ye Chen³, Guoqiang Sun^2,*

Energy Engineering, Vol.122, No.7, pp. 2977-2992, 2025, DOI:10.32604/ee.2025.064206 - 27 June 2025

Abstract With the continuous expansion of the power system scale and the increasing complexity of operational mode, the interaction between transmission and distribution systems is becoming more and more significant, placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods, this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification. To resolve the scalability issue of coupled transmission and distribution power systems, clustering… More >

Amine Assouguem^1,*, Saoussan Annemer^2,3, Mohammed Kara⁴, Abderrahim Lazraq⁵

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1631-1668, 2025, DOI:10.32604/phyton.2025.065750 - 27 June 2025

Abstract Unlike primary metabolites, secondary metabolites serve critical ecological functions, including plant protection, stress tolerance, and symbiosis. This review focuses on extracting, separating, and identifying the major classes of secondary metabolites, including alkaloids, terpenoids, phenolics, glycosides, saponins, and coumarins. It describes optimized methods regarding plant selection, extraction by solvents, and purification of the metabolites, highlighting the latest advancements in chromatographic and spectroscopic techniques. The review also describes some of the most important problems, such as the instability of the compounds or diversity of the structures, and discusses emerging technologies that solve these issues. Moreover, it examines More >

Yuanyuan Ma¹, Cunzhi Hou¹, Ang Chen¹, Jinghui Zhang¹, Ruixia Jin², Ruixiang Li^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1437-1456, 2025, DOI:10.32604/cmc.2025.064523 - 09 June 2025

Abstract Identifying cloud IP usage scenarios is critical for cybersecurity applications, yet existing machine learning methods rely heavily on numerous features, resulting in high complexity and low interpretability. To address these issues, this paper proposes an approach to identify cloud IPs from the perspective of network attributes. We employ data mining and crowdsourced collection strategies to gather IP addresses from various usage scenarios, which including cloud IPs and non-cloud IPs. On this basis, we establish a cloud IP identification feature set that includes attributes such as Autonomous System Number (ASN) and organization information. By analyzing the… More >