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 >

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 >