Oncology Research Editorial Office

Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.086727 - 16 June 2026

Abstract This article has no abstract. More >

Li Chen¹, Fan Zhang^2,*, Guangwei Xie³, Yanzhao Gao¹, Xiaofeng Qi¹, Mingqian Sun²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078314 - 15 June 2026

Abstract Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery. Spiking Neural Networks (SNNs), benefiting from their implicit feedback-based and event-driven brain-inspired dynamics, offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models. However, existing SNN-based approaches for remote sensing object detection—particularly for small, arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts. In this work, we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into… More >

Trong-Thua Huynh^1,*, De-Thu Huynh², Cong-Sang Duong¹, Hong-Son Nguyen¹, Quoc H. Nguyen³, Lam-Thanh Tu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080616 - 27 May 2026

Abstract Deep iris recognition models are often trained on Cartesian grids, whereas iris texture follows a concentric structure with angular periodicity. This representational mismatch can weaken rotation robustness and limit pupil-to-limbus context modeling, while many pipelines still rely on accurate segmentation masks. We propose RadialFormer, an efficient mask-free iris recognition framework that performs representation learning directly in the polar domain. The pipeline first estimates pupil/iris parameters (cx,cy,rin,rout) using a percentile radial-gradient operator with anatomical constraints, and then applies a crop-based polar transform to obtain a compact 64×512 unwrapped iris map. To better match polar… More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.6, 2026, DOI:10.32604/or.2026.085502 - 21 May 2026

Abstract This article has no abstract. More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.6, 2026, DOI:10.32604/or.2026.085235 - 21 May 2026

Abstract This article has no abstract. More >

Thanh Hoa Vo^1,2, Edel McNeela^1,2, Orla O’Donnovan^1,2, Jai Prakash Mehta³, Van Hoa Nguyen⁴, Sweta Rani^1,2,*

Oncology Research, Vol.34, No.6, 2026, DOI:10.32604/or.2026.075346 - 21 May 2026

Abstract Long non-coding RNAs (lncRNAs) have emerged as key regulators of drug resistance in human epidermal growth factor receptor 2 (HER2)-positive breast cancer, a subtype in which both intrinsic and acquired resistance to HER2-targeted therapies remain major clinical challenges. Although mechanistic studies have begun to reveal how lncRNAs modulate signaling pathways, interact with microRNAs, and influence the tumor microenvironment, dedicated investigations in HER2-positive disease are still limited. This review synthesizes current evidence across epigenetic, transcriptional, and post-transcriptional mechanisms of resistance, including competing endogenous RNA (ceRNA) networks, RNA-binding protein interactions, and exosome-mediated intercellular communication. Particular emphasis is… More >

Jia-Rou Lin¹, Chun-Hsiung Tseng^1,*, Hao-Chiang Koong Lin², Andrew Chih-Wei Huang³

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079379 - 08 May 2026

Abstract Effective pair programming relies heavily on optimal partner compatibility, a requirement that is often difficult to scale manually in software engineering education. This study presents the empirical validation of Codenote, an AI-driven Integrated Development Environment (IDE) designed to automate personality-aware group formation. By integrating a behavioral analysis mechanism, Codenote infers student personality traits from coding patterns to construct complementary pairs, thereby facilitating intelligent collaborative learning. To validate the system’s effectiveness, a controlled experiment was conducted to assess the impact of this AI-mediated pairing strategy on students’ self-efficacy across adaptive, innovative, and persuasive domains. Results indicate… More >

Sam Nguyen-Xuan^1,*, Han Nguyen²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078727 - 08 May 2026

Abstract We propose a Gloss-Internal Graph Construction and Encoding framework that represents compound glosses as directed, labeled graphs and integrates them into a Transformer via a graph-aware encoder. We evaluate our approach against Rule-Based Gloss Decomposition (RBGD) and Linear Gloss Sequence Encoding (LGSE) baselines on ASLG-PC12 and PHOENIX-2014T. Results show consistent improvements over both baselines, achieving gains of up to +3.2 BLEU-4 over LGSE and +7.0 BLEU-4 over RBGD on ASLG-PC12. On PHOENIX-2014T, our method yields gains of up to 1.9 BLEU-4 on the development set and 2.4 BLEU-4 on the test set. Ablation studies further More >

Manh-Tuan Ha¹, Nhu-Nghia Bui², Dinh-Quy Vu^1,*, Thai-Viet Dang^2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078280 - 08 May 2026

Abstract The development of Unmanned Surface Vehicles (USVs) has become a key focus in marine robotics, fueling the need for navigation systems capable of performing complex and delicate tasks with speed and precision. However, the end-to-end path tracking process often encounters challenges in learning efficiency, and generalization, and varying environmental conditions. To achieve sample-efficient and robust USV navigation in dynamic maritime environments, the paper proposes a novel hierarchical multi-view latent imitation learning (IL) architecture. By formulating a latent IL objective, the framework disentangles diverse navigation modalities through continuous variables, preventing mode collapse and enhancing behavioral adaptability… More >

Yan Zhu^1,#, Bin Guan^1,#, Wencai Guan¹, Jihong Zhang¹, Shiyu Wang¹, Jimin Shi¹, Wei Fan¹, Qi Lu^2,3, Lingyun Zhang^4,5,*, Guoxiong Xu^1,2,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.075241 - 22 April 2026

Abstract Background: Ovarian cancer poses the greatest threat to survival among gynecologic cancers in women. Long non-coding RNAs (lncRNAs) have emerged as critical regulators in oncogenesis. The current study aimed to elucidate the function and regulatory mechanism of lncRNA KRT7-AS in ovarian cancer. Methods: The clinical significance of KRT7-AS was evaluated through bioinformatics analysis of data from public repositories. KRT7-AS expression was examined by RT-qPCR and fluorescence in situ hybridization. The function analyses were conducted using assays for cell proliferation, migration, invasion, wound healing, and colony formation. Assessment of cell cycle and apoptosis was performed using flow… More >