LIWEN FENG^1,2,#,*, YUTING CHEN^3,#, WENYI JIN⁴

Oncology Research, Vol.33, No.5, pp. 1091-1103, 2025, DOI:10.32604/or.2024.054207 - 18 April 2025

Abstract Osteosarcoma (OS) is a prevalent primary bone malignancy with limited treatment options. Therefore, it is imperative to investigate and understand the mechanisms underlying OS pathogenesis. Cancer-associated fibroblasts (CAFs) are markedly abundant in tumor stromal cells and are essentially involved in the modulation of tumor occurrence and development. In recent years, CAFs have become a hotspot as researchers aim to elucidate CAF mechanisms that regulate tumor progression. However, most studies on CAFs are limited to a few common cancers, and their association with OS remains elusive. This review describes the role and current knowledge of CAFs More >

Tareq Hamadneh¹, Belal Batiha², Omar Alsayyed³, Widi Aribowo⁴, Zeinab Montazeri⁵, Mohammad Dehghani^5,*, Frank Werner^6,*, Haider Ali⁷, Riyadh Kareem Jawad⁸, Ibraheem Kasim Ibraheem⁹, Kei Eguchi¹⁰

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2677-2718, 2025, DOI:10.32604/cmc.2025.064087 - 16 April 2025

Abstract In this study, a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm (BaOA). Inspired by the human interactions between barbers and customers, BaOA captures two key processes: the customer’s selection of a hairstyle and the detailed refinement during the haircut. These processes are translated into a mathematical framework that forms the foundation of BaOA, consisting of two critical phases: exploration, representing the creative selection process, and exploitation, which focuses on refining details for optimization. The performance of BaOA is evaluated using 52 standard… More >

Yaping Qi^1,*, Weijie Yang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1555-1559, 2025, DOI:10.32604/cmc.2025.064061 - 16 April 2025

Abstract AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms such as Digital Catalysis Platform (DigCat) and Dynamic Database of Solid-State Electrolyte (DDSE) demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development. These databases facilitate data standardization, high-throughput screening, and cross-disciplinary collaboration, addressing key challenges in materials informatics. As AI techniques advance, materials databases are expected to play an increasingly vital role in accelerating research and innovation. More >

Mehran Tarif¹, Mohammadhossein Homaei^2,*, Amir Mosavi^3,4,5

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1791-1820, 2025, DOI:10.32604/cmc.2025.063962 - 16 April 2025

Abstract Underwater Wireless Sensor Networks (UWSNs) are gaining popularity because of their potential uses in oceanography, seismic activity monitoring, environmental preservation, and underwater mapping. Yet, these networks are faced with challenges such as self-interference, long propagation delays, limited bandwidth, and changing network topologies. These challenges are coped with by designing advanced routing protocols. In this work, we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks (UWF-RPL), an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network nodes. Our method extends RPL with the aid of fuzzy logic More >

Lianqiang Wu, Deming Lei^*, Yutong Cai

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1771-1789, 2025, DOI:10.32604/cmc.2025.063944 - 16 April 2025

Abstract Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines (BPM). In this study, the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered, and an adaptive cooperated shuffled frog-leaping algorithm (ACSFLA) is proposed to minimize makespan and total tardiness simultaneously. ACSFLA determines the search times for each memeplex based on its quality, with more searches in high-quality memeplexes. An adaptive cooperated and diversified search mechanism is applied, dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality. During the… More >

Sheik Mohideen Shah¹, Meganathan Selvamani¹, Mahesh Thyluru Ramakrishna^2,*, Surbhi Bhatia Khan^3,4,5, Shakila Basheer⁶, Wajdan Al Malwi⁷, Mohammad Tabrez Quasim⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2907-2926, 2025, DOI:10.32604/cmc.2025.063809 - 16 April 2025

Abstract Efficient energy management is a cornerstone of advancing cognitive cities, where AI, IoT, and cloud computing seamlessly integrate to meet escalating global energy demands. Within this context, the ability to forecast electricity consumption with precision is vital, particularly in residential settings where usage patterns are highly variable and complex. This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory (LSTM) network. Leveraging a dataset containing over two million multivariate, time-series observations collected from a single household over nearly four years, our model addresses the limitations of traditional time-series forecasting… More >

De Rosal Ignatius Moses Setiadi^1,*, T. Sutojo¹, Supriadi Rustad¹, Muhamad Akrom¹, Sudipta Kr Ghosal², Minh T. Nguyen³, Arnold Adimabua Ojugo⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2161-2188, 2025, DOI:10.32604/cmc.2025.063729 - 16 April 2025

Abstract Data security has become a growing priority due to the increasing frequency of cyber-attacks, necessitating the development of more advanced encryption algorithms. This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps (SQQLSR), a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range. The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X, Y, and Z axes to enhance randomness. Extensive numerical experiments validate the effectiveness of SQQLSR. The proposed method achieves a maximum Lyapunov exponent (LE) of ≈55.265, surpassing traditional chaotic maps in unpredictability. The bifurcation analysis… More >

Ting-Yu Lin¹, Kuo-Chen Hung^2,*, Josef Jablonsky³, Kuo-Ping Lin¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1901-1927, 2025, DOI:10.32604/cmc.2025.063543 - 16 April 2025

Abstract VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) has been developed and applied for over twenty-five years, gaining recognition as a prominent multi-criteria decision-making (MCDM) method. Over this period, numerous studies have explored its applications, conducted comparative analyses, integrated it with other methods, and proposed various modifications to enhance its performance. This paper aims to delve into the fundamental principles and objectives of VIKOR, which aim to maximize group utility and minimize individual regret simultaneously. However, this study identifies a significant limitation in the VIKOR methodology: its process amplifies the weight of individual regret, and the calculated… More >

Suhaila Abuowaida^1,*, Hamza Abu Owida², Deema Mohammed Alsekait^3,*, Nawaf Alshdaifat⁴, Diaa Salama AbdElminaam^5,6, Mohammad Alshinwan⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3303-3333, 2025, DOI:10.32604/cmc.2025.063470 - 16 April 2025

Abstract Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise, dependency on the operator, and the variation of image quality. This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations: This work adds three things: (1) a changed ResNet-50 backbone with sequential 3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries; (2) a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory; and (3) an adaptive feature fusion strategy that changes local and… More >

Mozhgan Gholami¹, Ali Ghaffari^1,2,3,*, Nahideh Derakhshanfard¹, Nadir iBRAHIMOĞLU⁴, Ali Asghar Pourhaji Kazem²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1561-1605, 2025, DOI:10.32604/cmc.2025.063304 - 16 April 2025

Abstract The Internet has been enhanced recently by blockchain and Internet of Things (IoT) networks. The Internet of Things is a network of various sensor-equipped devices. It gradually integrates the Internet, sensors, and cloud computing. Blockchain is based on encryption algorithms, which are shared database technologies on the Internet. Blockchain technology has grown significantly because of its features, such as flexibility, support for integration, anonymity, decentralization, and independent control. Computational nodes in the blockchain network are used to verify online transactions. However, this integration creates scalability, interoperability, and security challenges. Over the last decade, several advancements… More >