HENG ZHANG^1,#, XIAO YANG^2,#, YUJIN GUO³, HAIBO ZHAO⁴, PEI JIANG^5,*, QING-QING YU^3,*

Oncology Research, Vol.33, No.4, pp. 837-849, 2025, DOI:10.32604/or.2024.053882 - 19 March 2025

Abstract As living conditions improve and diagnostic capabilities advance, the incidence of tumors has increased, with cancer becoming a leading cause of death worldwide. Surgery, chemotherapy, and radiotherapy are the most common treatments. Despite advances in treatment options, chemotherapy remains a routine first-line treatment for most tumors. Due to the continuous and extensive use of chemotherapy drugs, tumor resistance often develops, becoming a significant cause of treatment failure and poor prognosis. Recent research has increasingly focused on how long stranded non-coding RNAs (LncRNAs) influence the development of malignant tumors and drug resistance by regulating gene expression More >

Zhenyang He, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4301-4317, 2025, DOI:10.32604/cmc.2025.060550 - 06 March 2025

Abstract Tomato plant diseases often first manifest on the leaves, making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry. However, conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery. This paper proposes a lightweight model for detecting tomato leaf diseases, named LT-YOLO, based on the YOLOv8n architecture. First, we enhance the C2f module into a RepViT Block (RVB) with decoupled token and channel mixers to reduce the cost of feature extraction. Next, we incorporate a novel Efficient… More >

Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Qiong Yang¹, Li Ma^1,2,*, Sami Ullah³, Shanshan Tu¹, Hisham Alasmary⁴, Muhammad Waqas^5,6

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4821-4839, 2025, DOI:10.32604/cmc.2025.059497 - 06 March 2025

Abstract Border Gateway Protocol (BGP), as the standard inter-domain routing protocol, is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems (AS). BGP nodes, communicating in a distributed dynamic environment, face several security challenges, with trust being one of the most important issues in inter-domain routing. Existing research, which performs trust evaluation when exchanging routing information to suppress malicious routing behavior, cannot meet the scalability requirements of BGP nodes. In this paper, we propose a blockchain-based trust model for inter-domain routing. Our model achieves scalability by allowing the master node of… More >

Wei Liu¹, Sen Liu^2,3,*, Yinchao He², Jiaojiao Wang¹, Yu Gu¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4863-4880, 2025, DOI:10.32604/cmc.2025.059295 - 06 March 2025

Abstract To address the issues of slow diagnostic speed, low accuracy, and poor generalization performance in traditional rolling bearing fault diagnosis methods, we propose a rolling bearing fault diagnosis method based on Markov Transition Field (MTF) image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module (CBAM-LCNN). Specifically, we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images. Then, we construct a lightweight convolutional neural network incorporating the convolutional attention module (CBAM-LCNN). Finally, the two-dimensional images obtained from MTF mapping… More >

Naikang Zhong¹, Xiao Lin^1,2,3,4,*, Wen Du⁵, Jin Shi⁶

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5285-5306, 2025, DOI:10.32604/cmc.2025.059102 - 06 March 2025

Abstract Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images. Obtaining class-specific precise representations at different scales is a key aspect of feature representation. However, existing methods often rely on the single-scale deep feature, neglecting shallow and deeper layer features, which poses challenges when predicting objects of varying scales within the same image. Although some studies have explored multi-scale features, they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales. To address these issues, we propose… More >

Chengzhang Zhu^1,2, Ahmed Alasri¹, Tao Xu³, Yalong Xiao^1,2,*, Abdulrahman Noman¹, Raeed Alsabri¹, Xuanchu Duan⁴, Monir Abdullah⁵

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5153-5167, 2025, DOI:10.32604/cmc.2024.058647 - 06 March 2025

Abstract Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment. However, traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level. On the other hand, models that focus on global semantic-level information might overlook critical, subtle local pathological features. To address this issue, we propose an… More >

Xin Liu^1,*, Meng Sun¹, Bo Lin², Shibo Gu¹

Energy Engineering, Vol.122, No.3, pp. 1053-1072, 2025, DOI:10.32604/ee.2025.060489 - 07 March 2025

Abstract Long-term petroleum production forecasting is essential for the effective development and management of oilfields. Due to its ability to extract complex patterns, deep learning has gained popularity for production forecasting. However, existing deep learning models frequently overlook the selective utilization of information from other production wells, resulting in suboptimal performance in long-term production forecasting across multiple wells. To achieve accurate long-term petroleum production forecast, we propose a spatial-geological perception graph convolutional neural network (SGP-GCN) that accounts for the temporal, spatial, and geological dependencies inherent in petroleum production. Utilizing the attention mechanism, the SGP-GCN effectively captures… More >

Jalil Ahmad^1,*, Muhammad Munir^2,*, Nashi Alqahtani^2,3, Tahira Alyas⁴, Muhammad Ahmad⁵, Sadia Bashir⁶, Fasiha Qurashi⁷, Abdul Ghafoor⁸, Hassan Ali–Dinar²

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 281-302, 2025, DOI:10.32604/phyton.2025.061534 - 06 March 2025

Abstract A steady rise in the overall population is creating an overburden on crops due to their global demand. On the other hand, given the current climate change and population growth, agricultural practices established during the Green Revolution are no longer viable. Consequently, innovative practices are the prerequisite of the time struggle with the rising global food demand. The potential of nanotechnology to reduce the phytotoxic effects of these ecological restrictions has shown significant promise. Nanoparticles (NPs) typically enhance plant resilience to stressors by fortifying the physical barrier, optimizing photosynthesis, stimulating enzymatic activity for defense, elevating More >

Pengcheng Cai^1,2, Teng Li³, Jianxin Xu^1,2,*, Xiaobo Li³, Zhiqiang Li^1,2, Zhiwen Xu³, Hua Wang^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.2, pp. 325-349, 2025, DOI:10.32604/fdmp.2024.057496 - 06 March 2025

Abstract In modern engineering, enhancing boiling heat transfer efficiency is crucial for optimizing energy use and several industrial processes involving different types of materials. This study explores the enhancement of pool boiling heat transfer potentially induced by combining perforated copper particles on a heated surface with a sodium dodecyl sulfate (SDS) surfactant in saturated deionized water. Experiments were conducted at standard atmospheric pressure, with heat flux ranging from 20 to 100 kW/m². The heating surface, positioned below the layer of freely moving copper beads, allowed the particle layer to shift due to liquid convection and steam More > Graphic Abstract