Weijia Tang, Qiang Li^*, Ningyu Zhang

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.074698 - 18 June 2026

Abstract Large-scale offshore wind farm clusters (OWFCs) have been increasingly connected to the power grid, and requires advanced forecasting models to enhance the prediction accuracy of OWFC’s power output. This paper proposes a multi-source fusion with patch-guided multi-task learning for power prediction of offshore wind farm clusters. Unlike traditional graph-based approaches that rely on predefined topological relationships, which are limited in capturing the highly similar but rapidly changing meteorological conditions among closely spaced offshore farms, the proposed model employs a parameter-sharing multi-task learning network to achieves both independence and correlation among offshore wind farm clusters, followed More >

Zoe Bell¹, Corey D. Chan^1,2, Rachel Howarth³, Andrea Atkinson¹, Zakareya Gamie^1,4, Daniel Frankel⁵, Oana Bretcanu⁵, Kenneth S. Rankin^1,2,*

Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.075617 - 16 June 2026

Abstract Objectives: Chondrosarcoma is the most common type of primary bone sarcoma in adults with a high risk of local recurrence and metastasis. Chondrosarcomas are largely resistant to chemotherapy and radiotherapy, meaning that surgery is the mainstay of treatment for most patients. Therefore, new therapeutic targets are required. Cluster of differentiation 44 (CD44) is a transmembrane protein that has roles in cell proliferation, adhesion and migration and is shown to be overexpressed in several cancer types. Consequently, this work was undertaken to understand whether CD44 could be a potential therapeutic target in chondrosarcoma. Methods: In this study,… More > Graphic Abstract

Anh Thi Diem Nguyen^1,2,#, Tham Vo¹, Vinh Truong Hoang^3,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082845 - 15 June 2026

Abstract Keeping customers engaged remains a major challenge in appointment-based services, where user behavior continuously shifts due to seasonal, market, and social factors. These dynamic changes often cause concept drift, rendering traditional deep clustering models unreliable because they assume stable data distributions. Most existing approaches handle representation learning, parameter optimization, and model updating as separate components, limiting their adaptability in real-world streaming environments. This study proposes Hybrid-RL, a novel adaptive clustering framework that unifies incremental deep representation learning, multi-head reinforcement learning for joint hyperparameter optimization (number of clusters, latent dimension, and clustering method), incremental model updating,… More >

Zhihao Zhang^1,#, Zhuodong Liu^1,#, Xiangyu Li², Lei Zhang^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081922 - 15 June 2026

Abstract Financial fraud detection across institutions faces a fundamental tension between the need for diverse training data and regulatory prohibitions on sharing sensitive records. Existing federated learning approaches suffer from performance degradation under non-IID distributions and substantial utility losses when uniform differential privacy is applied to inherently sparse fraud signals. To this end, this paper proposes HiFraud, a hierarchical federated framework featuring three key components: fraud-aware dynamic clustering with complementarity regularization to group institutions by fraud pattern similarity while preserving rare-type representation; star-chain knowledge transfer augmented by not-true-class distillation to propagate novel fraud patterns rapidly within… More >

Bhanu Talwar^1,*, Puneet Thapar¹, Tahani Alsubait², Mai Alduailij³, Ateeq Ur Rehman^4,*, Salil Bharany⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080977 - 15 June 2026

Abstract Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates… More >

Poluru Eswaraiah¹, Uddagiri Sirisha^2,*, Shaik Abdul Nabi³, Revathi Durgam⁴, Pallavi Malavath⁵, Gilakara Muni Nagamani⁶

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078511 - 15 June 2026

Abstract The growing volume of digital text complicates the extraction of relevant information from unstructured data. Transformer models such as BERT, ALBERT, and RoBERTa are powerful, but they may face challenges in hyperparameter optimization and adaptation to new domains. To address this issue, a hybrid ensemble BERT model is suggested, optimized using the Walrus Optimization Algorithm (WaOA). The framework applies PCA to reduce dimensionality, ontology normalization, and K-means clustering to improve semantic comprehension. Experimental results on the SQuAD 2.0 and MS MARCO datasets show that the proposed model outperforms the baseline models. WaOA (Weighted Average of More >

Amjad Rehman¹, Tariq Mahmood^1,2, Faten S. Alamri^3,*, Muhammad I. Khan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.076683 - 27 May 2026

Abstract The rapid deployment of Wireless Sensor Networks (WSNs) faces critical challenges due to sensor nodes’ limited energy and communication capabilities, which restrict network lifetime and data transmission efficiency. Traditional clustering and routing protocols often lead to unbalanced energy consumption and uneven load distribution, whereas intelligent optimization approaches are hindered by high computational costs and slow convergence. This research formulates the clustering and routing problems in WSNs as an optimization challenge under resource and energy constraints, aiming to improve stability, energy efficiency, and throughput. This research proposed three quantum optimization-based solutions to address complex issues. First,… More >

Zujun Ding, Zhi Liu, Peng Huang, Yuhan Qian, Chengyi Li, Zizhuo Yu, Hui Huang, Baolian Liu, Wan Chen, Jie Ji^*

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.074440 - 27 May 2026

Abstract Under extreme weather conditions (such as hurricanes and heatwaves causing sudden drops in renewable energy output and surges in load), microgrid operations face severe challenges due to the uncertainty of renewable energy and load fluctuations. Although existing research has focused on microgrid optimal scheduling or electric vehicle integration, there has not yet been a systematic approach to multi-timescale scheduling that combines electric vehicle fleets under extreme weather scenarios, and particularly, explicit modeling of weather events and their impact on component failure rates and transmission lines is lacking. This paper proposes, for the first time, a… More >

Mengqi Liu, Haiqing Yao^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078181 - 08 May 2026

Abstract For the wide-coverage application scenarios, wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring, and use the mobile charger (MC) to support the sustainable charging of the network. Many efforts focus on optimizing the cluster head selection and mobile charger scheduling to improve the network energy efficiency and reliability. However, the existing work tends to use fixed triggering mechanism for cluster head (CH) rotation, and may trigger the rotation either too early or too late. Besides, the existing charging triggering mechanisms cannot track the changes in… More >

Jianing Zhang¹, Zhikui Chen^1,*, Jing Gao¹, Peng Li²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076717 - 08 May 2026

Abstract With the increasing development of ocean information technology, the multi-view fuzzy clustering is attracting increasing attention in pattern mining for massive multi-view ocean data of heterogeneous distributions, owing to its superior performance. However, the previous multi-view fuzzy clustering methods cannot fully consider informative topologies hidden in data distributions, which are crucial to recognize partitions of data. Moreover, they fail to capture invariant structures of multi-view ocean data in learning clustering-specific fusion representation. In addition, they do not take into consideration consistencies contained in the manifolds of data generation in mining soft patterns. To address those… More >