Kiran Saleem¹, Upinder Kaur^2,*, Abdulrahman Mohammed Alamoudi³, Mai Alduailij⁴, Ahmad Subhi Salem Mufleh⁵, Ateeq Ur Rehman^6,*, Salil Bharany⁷

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083670 - 13 August 2026

Abstract Underwater Wireless Sensor Networks (UWSNs) are exceedingly critical for large-scale underwater applications, such as environmental monitoring, infrastructure inspection, target tracking, and marine surveillance. Nevertheless, network lifetime and communication reliability are severely constrained by harsh underwater acoustic conditions, limited battery power, large propagation delays, node mobility, and uneven energy consumption. In response to these issues, this study proposes a Climate-Aware Hybrid Clustering and Routing (CA-HCR-UWSN) framework to enable sustainable, long-term underwater monitoring. This work proposes a hybrid framework that combines Elephant Herding Optimization (EHO) with the Gravitational Search Algorithm (GSA) to provide an effective solution to… More >

Abdulhamid Victor Ibrahim, Haoyuan Li, Bingyang Guo, Ruiyun Yu^*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083186 - 13 August 2026

Abstract The detection of small objects in low-resolution aerial imagery presents a persistent challenge in computer vision, where hardware constraints, imaging altitude, and scene complexity collectively degrade spatial detail to the point where standard detection frameworks fail. Existing super-resolution methods offer partial remedies but are limited by substantial computational costs and by feature discrepancies between Generative Adversarial Network-enhanced and real high-resolution images that degrade downstream detection accuracy. This paper presents YOLO-Flex, a unified framework that addresses these challenges through the co-design of a super-resolution module and a task-adapted object detection network, jointly optimized through a shared… More > Graphic Abstract

Zebiao Liu^1,#, Xuebing Zhan^1,#, Mingquan Chen¹, Junxi Lai², Wenli Zhao^1,*

BIOCELL, Vol.50, No.8, 2026, DOI:10.32604/biocell.2026.081173 - 27 July 2026

Abstract Background: Immunosuppression contributes to breast cancer treatment failure, yet Cluster of Differentiation 74 (CD74) function in macrophages remains unclear. This study investigated how CD74 influences M1 macrophage polarization and its functional and expression profiles in breast cancer. Methods: We used bioinformatics analysis combined with in vitro cell experiments. The expression of CD74 in THP-1-derived M1 macrophages induced by Lipopolysaccharide/Interferon-gamma (LPS/IFN-γ) was knocked down by shRNA. Polarization markers were detected by WB, qPCR, and flow cytometry. Cytokines were detected by Enzyme-Linked Immunosorbent Assay (ELISA). The phagocytosis and killing effect of macrophages on MCF-7 cells were evaluated by a… More >

Mingxuan Ji¹, Jing Gao^1,*, Dantian Zhong¹, Yingqi Xu¹, Shuxiang Yang¹, Zhongxiao Du¹, Yingming Liu²

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.074643 - 12 July 2026

Abstract The intermittency of wind power poses severe challenges to the safe and stable operation of power grids, while conventional forecasting models are deficient in prediction accuracy and adaptability to variable weather conditions. To address these issues, this study proposes an adaptive short-term wind power forecasting model integrating affinity propagation (AP) clustering and a black-winged kite algorithm (BKA)-optimized temporal convolutional network-bidirectional long short-term memory (TCN-BiLSTM) hybrid architecture. First, mutual information was employed to screen key meteorological features, and AP clustering categorized historical data into six distinct weather scenarios. A scenario-specific TCN-BiLSTM model was then constructed for… More >

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 >