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  • Open Access

    ARTICLE

    The Association between Illness-Related Stigma and Mental Well-Being among Cancer Survivors in Yunnan, China

    Yueting Zhang1,2, Sawitri Assanangkornchai2, Wit Wichaidit2,*

    International Journal of Mental Health Promotion, Vol.28, No.6, 2026, DOI:10.32604/ijmhp.2026.079559 - 23 June 2026

    Abstract Background: Stigma affects the mental well-being of cancer survivors. However, data are scarce regarding the extent to which specific types of stigmas, enacted stigma (stigma from others), and self-stigma (internalized stigma), affect mental well-being. The objective of this study is to describe the extent to which overall illness-related stigma, self-stigma, and enacted stigma are associated with mental well-being among cancer survivors. Methods: A cross-sectional study in Kunming, Yunnan, China, was conducted. Eligible participants were adult cancer survivors who completed a phone-to-WeChat, self-administered survey. Illness-related stigma was measured with the Stigma Scale for Chronic Illnesses, 8-item version… More >

  • Open Access

    ARTICLE

    Wind Power Forecasting Utilizing Bidirectional Gated Recurrent Units in Conjunction with Empirical Mode Decomposition and Bayesian Neural Networks

    Xiaolan Li1,2, Yanting Wang1,2,*

    Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.076417 - 18 June 2026

    Abstract To address the operational challenges of power systems with high renewable penetration, this research targets the non-stationarity and stochasticity of wind power. A novel hybrid framework for probabilistic forecasting and risk assessment is proposed. Initially, Empirical Mode Decomposition (EMD) adaptively decomposes the raw power signal into multi-scale Intrinsic Mode Functions (IMFs) and a residual trend, effectively segregating temporal features and reducing complexity. These components are then fused with historical data to form a comprehensive input. The core predictor is a Bidirectional Gated Recurrent Unit (BiGRU) network enhanced with a Temporal Attention (TA) mechanism. The BiGRU… More >

  • Open Access

    ARTICLE

    Research on MPPT Control and Grid-Connected and Off-Grid Operation Control Strategy of Photovoltaic-Storage Microgrid Based on PSO Algorithm

    Tao Wang1, Ze Feng1,*, Jinghao Ma2, Shenhui Chen2, Jihui Zhang2, Tong Wang2

    Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2025.074054 - 18 June 2026

    Abstract This paper develops an MPPT control strategy utilizing the particle swarm optimization (PSO) algorithm to enhance the tracking accuracy of photovoltaic arrays under complex operating conditions and to mitigate the transient effects on energy storage batteries during grid-connected and off-grid transitions. Initially, the operational principle of the three-phase voltage source PWM converter and the bidirectional DC/DC converter within solar power generation and energy storage systems is carefully examined, leading to the establishment of the appropriate mathematical model. Secondly, a voltage and current double closed-loop control structure utilizing feedforward decoupling is devised to meet the cooperative… More >

  • Open Access

    RETRACTION

    Retraction: CXCL5 Plays a Promoting Role in Osteosarcoma Cell Migration and Invasion in Autocrine- and Paracrine-Dependent Manners

    Oncology Research Editorial Office

    Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.086719 - 16 June 2026

    Abstract This article has no abstract. More >

  • Open Access

    REVIEW

    Monoclonal Antibodies and Derivatives: Therapeutic Tools for Cancer

    Alessandro Poggi*

    Oncology Research, Vol.34, No.7, 2026, DOI:10.32604/or.2026.078483 - 16 June 2026

    Abstract The production of murine monoclonal antibodies (mAbs) with defined specificity in 1975 marked the subsequent revolution of cancer therapy. mAbs have been essential to characterize the functional features of molecules involved in cancer cell growth and dissemination. The murine mAbs have been modified to create humanized antibodies and, subsequently, fully human antibodies for cancer therapy, thereby avoiding the side effects of xenogenic protein. The antibody-drug conjugates (ADCs) increased the antitumor effect of mAbs. We will analyze the functional features of ADCs that recognize the cluster differentiation (CD)30 receptor present on some lymphomas and the human… More >

  • Open Access

    ARTICLE

    A Hybrid Learning Framework for Underwater Image Enhancement

    Sami Ullah1,2, Najmul Hassan2, Naeem Bhatti2, Asad Saleem1,*

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082467 - 15 June 2026

    Abstract Underwater imaging facilitates the exploration of the underwater environment. However, irregular optical absorption and light scattering in water, ranging from clear to highly turbid conditions, often result in low visibility, color distortion, and blurriness in underwater images (UWIs). Conventional UWI enhancement methods are limited by inefficient physical modeling, while deep learning-based approaches are constrained by the scarcity of paired training datasets. In this work, we propose a hybrid learning framework for UWI enhancement that leverages the usefulness of both conventional and deep learning-based techniques. At first, we preprocess the UWIs using a revised underwater physical… More >

  • Open Access

    ARTICLE

    Scale-Robust Cross-Scale Representation Learning for Aerial Crop Pest Recognition

    Kemeng Zhu1, Dingju Zhu1,2,*, Shihua Mao1, Jinchen Wu3, Depeng Kong4, Kaileung Yung5, Andrew W. H. Ip6

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082431 - 15 June 2026

    Abstract Unmanned aerial vehicles (UAVs) have become an increasingly important platform for agricultural remote sensing, yet the accurate recognition of pests and diseases is frequently compromised by drastic scale variability and complex environmental backgrounds. To address these challenges, this study introduces a novel attention-driven approach centered on a Multi-Scale Grouped Channel–Spatial Dual Attention (MS-GCDA) mechanism. The MS-GCDA module achieves robust feature calibration by decoupling and jointly modeling multi-scale spatial contexts and grouped channel dependencies, which significantly enhances the model’s sensitivity to fine-grained disease symptoms while suppressing background clutter. This core mechanism is integrated into Augmented EfficientNet… More >

  • Open Access

    ARTICLE

    Location Privacy Protection of Data Elements in ICVs: A Key Update Mechanism for Defending Against Chosen-Ciphertext Attacks

    Lei Wang1, Hongji Luo2, Yong Heng2, Jingnan Tang2, Xiaochuan Ju2, Jianwei An1,*, Haitao Xu1, Xianwei Zhou1

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082418 - 15 June 2026

    Abstract In intelligent connected vehicles (ICVs) system, driving users connect to service providers (SPs) to obtain location-based services (LBS). Users transmit large volumes of encrypted sensitive information related to their itineraries to SPs to access value-added services. Attackers may launch chosen-ciphertext attacks (CCA) against SPs by exploiting the malleability of homomorphic encryption. This enables adversaries to infer or steal private key information, thereby threatening the long-term privacy of user data. Furthermore, existing key management technologies in ICVs system predominantly rely on passive defense strategies and suffer from limitations such as single protection mechanisms, delayed updates, and More >

  • Open Access

    ARTICLE

    An Enhanced Genetic Algorithm via an Innovative Elite Retention Strategy for Task Offloading in MEC Scenarios

    Chengyu Hou1,2, Wenzao Li2, Hanyun Li3, Kui Liu1, Zhuoning Zhao1, Hongping Shu1,*

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081575 - 15 June 2026

    Abstract The rapid growth of Internet of Things (IoT) and 5G technologies has led to a sharp increase in computing demands from wireless devices, making efficient task offloading a critical challenge. Key issues include reducing application latency, lowering the energy consumption of terminal devices, and improving overall system performance, all of which directly affect user experience. Traditional genetic algorithms (GA), inspired by biological evolution, have been widely used in task offloading, but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios, limiting their effectiveness. To address these drawbacks,… More >

  • Open Access

    ARTICLE

    An Adaptive Multi-Scale Dilated Convolution Network for Real-Time Road Black Ice Detection

    Sun-Kyoung Kang1, Yeonwoo Lee2,*

    CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081553 - 15 June 2026

    Abstract Black ice formation on road surfaces presents a serious hazard due to its low visibility and high slipperiness, underscoring the critical need for timely and accurate detection in intelligent transportation systems. In this paper, we propose AdaMsDCNet, an adaptive multi-scale dilated convolution network designed for real-time black-ice semantic segmentation on resource-constrained edge platforms, applying a Convolutional Neural Network (CNN) with an adaptive Multi-Scale Dilated Convolution (MsDC) feature fusion encoder-decoder architecture. The key concept of AdaMsDCNet is to employ an encoder-decoder architecture with parallel multi-scale dilated convolutional paths that adjust dilation rates at different encoder depths… More >

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