Huaruo Chen^1,2, Gefan Wang¹, Hancai Qiu¹, Hui Ma¹, Zhentao Peng¹, Ruihan Liu³, Feng Xu^4,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073911 - 28 January 2026

Abstract Background: As an important indicator of subjective well-being (SWB), decent work is a key guarantee for the sustainable development of teachers and their psychological health and work quality. Faced with the rapid development of artificial intelligence and the global labor market, vocational college teachers are facing challenges such as workload pressure and limited career development, which may harm their well-being. This study aims to localize the measurement method of decent work in Chinese vocational education based on the theory of the Psychology of Working Theory, and explore the relationship mechanism between organizational support, career adaptability, decent… More >

Kai Xie¹, Shaoqing Yuan², Dayun Zou¹, Jinran Wang^1,*, Genjun Chen¹, Ciwei Gao³, Yinghao Cao¹

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070733 - 27 January 2026

Abstract The construction of spot electricity markets plays a pivotal role in power system reforms, where market clearing systems profoundly influence market efficiency and security. Current clearing systems predominantly adopt a single-system architecture, with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models. Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems in contingency scenarios—a critical gap given redundant systems’ expanding applications in operational environments. This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability, demonstrated through an in-depth case… More >

Hyung Seok Kim^1,#, Ju A Son^2,#, Minji Kang^3,4,#, Soon Sun Kim³, Geum Ok Baek³, Moon Gyeong Yoon³, Se Ha Jang^3,4, Dokyung Jung⁵, Ji Eun Han³, Jae Youn Cheong^3,*, Jung Woo Eun^3,*

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.070392 - 19 January 2026

Abstract Objectives: Early detection of hepatocellular carcinoma (HCC) is a significant challenge due to the limited sensitivity of alpha-fetoprotein (AFP). This study aimed to assess serum-derived extracellular vesicle-encapsulated GULP PTB domain-containing engulfment adaptor 1 (EV-GULP1) as a novel, noninvasive biomarker for HCC detection and prognosis, leveraging the potential of tumor-specific molecules carried by small extracellular vesicles (EVs). Methods: The study utilized both internal and external cohorts of HCC patients and controls. Small EVs were isolated from serum samples, then characterized and validated to confirm their identity. The expression levels of EV-GULP1 were quantified using quantitative reverse transcription… More >

Jixin Wu^1,2, Mingtao Zhou^2,3, Di Wu^2,3, Wenqi Ren⁴, Jiatian Mei^2,3, Shu Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072964 - 12 January 2026

Abstract End-to-end Temporal Action Detection (TAD) has achieved remarkable progress in recent years, driven by innovations in model architectures and the emergence of Video Foundation Models (VFMs). However, existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs, which become particularly pronounced when processing long video sequences. Moreover, the need for precise temporal boundary annotations makes data labeling extremely expensive. In low-resource settings where annotated samples are scarce, direct fine-tuning tends to cause overfitting. To address these challenges, we introduce Dynamic Low-Rank Adapter (DyLoRA), a lightweight fine-tuning framework tailored specifically… More >

Hailong Wang¹, Minglei Duan², Lu Yao³, Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072942 - 12 January 2026

Abstract In image analysis, high-precision semantic segmentation predominantly relies on supervised learning. Despite significant advancements driven by deep learning techniques, challenges such as class imbalance and dynamic performance evaluation persist. Traditional weighting methods, often based on pre-statistical class counting, tend to overemphasize certain classes while neglecting others, particularly rare sample categories. Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning, leading to increased experimental costs due to their instability. This paper proposes a novel CAWASeg framework to address these limitations. Our approach leverages Grad-CAM technology to generate class activation… More >

Yuewei Tian¹, Yang Su², Yujia Wang¹, Lisa Guo¹, Xuyang Wu^3,*, Lei Cao⁴, Fang Ren³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072081 - 12 January 2026

Abstract This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning. The framework integrates blockchain technology, the InterPlanetary File System (IPFS) for distributed storage, and a dynamic differential privacy mechanism to achieve collaborative security across the storage, service, and federated coordination layers. It accommodates both multimodal data classification and object detection tasks, enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection. This effectively mitigates the single-point… More >

Jun Li¹, Chunyan Liang^1,*, Zhiguo Liu¹, Fengpei Ge²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071011 - 12 January 2026

Abstract To enhance speech emotion recognition capability, this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup (AAM) and improved coordinate and shuffle attention (ICASA) methods. The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients, thus enabling information fusion of speech data with different emotions at the acoustic level. The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention (ICA) and shuffle attention (SA) techniques. The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and More >

Hongwei Wu^*, Guojian Li, Hanyun Zhang, Zi Ye, Chao Ma

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071008 - 12 January 2026

Abstract Federated Learning (FL) protects data privacy through a distributed training mechanism, yet its decentralized nature also introduces new security vulnerabilities. Backdoor attacks inject malicious triggers into the global model through compromised updates, posing significant threats to model integrity and becoming a key focus in FL security. Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity, resulting in limited stealth and adaptability. To address the heterogeneity of malicious client devices, this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack (CASBA). By incorporating measurements of clients’… More >

Dao Phuc Minh Huy¹, Gia Nhu Nguyen¹, Dac-Nhuong Le^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070948 - 12 January 2026

Abstract Online examinations have become a dominant assessment mode, increasing concerns over academic integrity. To address the critical challenge of detecting cheating behaviours, this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification. The methodology utilises object detection models—You Only Look Once (YOLOv12), Faster Region-based Convolutional Neural Network (RCNN), and Single Shot Detector (SSD) MobileNet—integrated with classification models such as Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Unit (Bi-GRU), and CNN-LSTM (Long Short-Term Memory). Two distinct datasets were used: the Online Exam Proctoring (EOP) dataset from Michigan State University and… More >

Weichen Wang¹, Shaofeng Wang¹, Wenjing Liu^1,*, Luncai Zhou², Erqing Zhang¹, Ting Gao³, Grigory Petrishin⁴

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071278 - 08 January 2026

Abstract In dry-coupled ultrasonic thickness measurement, thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy. Existing methods struggle to resolve overlap-ping echoes under variable coupling conditions and non-stationary noise. This study proposes a novel dual-criterion framework integrating energy contribution and statistical impulsivity metrics to isolate specimen re-flections from coupling-layer interference. By decomposing A-scan signals into Intrinsic Mode Functions (IMFs), the framework employs energy contribution thresholds (>85%) and kurtosis indices (>3) to autonomously select IMFs containing valid specimen echoes. Hybrid time-frequency thresholding further suppresses interference through amplitude filtering and spectral focusing. More >