Jiamei Wu^1,*, Yuechuan Zhang², Junxue Wu², Chengxin Hao¹

BIOCELL, Vol.50, No.7, 2026, DOI:10.32604/biocell.2026.071704 - 29 June 2026

Abstract Enterovirus 71 (EV71), a member of the family Picornaviridae, genus Enterovirus, is an agent of hand, foot, and mouth disease (HFMD) and remains a persistent global health concern, particularly among children under five years of age. Although most infections are self-limiting, a significant proportion can progress to severe neurological manifestations such as aseptic meningitis, encephalitis, and fatal pulmonary oedema. Despite substantial advances in research, no universally effective antiviral therapy or broadly protective vaccine has yet been developed. Drawing upon both foundational and recent studies, we evaluate the strength of existing evidence and delineate how these viral… More >

Martin Bombardelli^1,2,3, Gérard Fleury¹, Philippe Lacomme^1,*, Bogdan Vulpescu²

Journal of Quantum Computing, Vol.8, pp. 13-54, 2026, DOI:10.32604/jqc.2026.075438 - 26 June 2026

Abstract This work aims to present the fundamental concepts necessary for performing computations on photonic quantum computers by detailing the gates specific to this architecture and introducing the relationships between standard Pauli gates and their counterparts in photonic systems. Physical considerations related to the optical components are addressed. Theoretical aspects concerning quantum operators are presented, and applied sections demonstrating implementations using the Perceval library developed by Quandela are given. This paper provides a comprehensive, state-of-the-art description of photonic quantum gates in a formal way. The main contribution of this paper is to allow researchers with prior… More >

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 >