Hanifah Rahmi Fajrin^1,2, Se Dong Min^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072609 - 12 January 2026

Abstract Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning. As research in this domain continues to expand, various segmentation techniques have been proposed across classical image processing, machine learning (ML), deep learning (DL), and hybrid/ensemble models. This study conducts a systematic literature review using the PRISMA methodology, analyzing 57 selected articles to explore how these methods have evolved and been applied. The review highlights the strengths and limitations of each approach, identifies commonly used public datasets, and observes emerging trends in model integration and clinical relevance. More >

Tomoki Sekimori^1,*, Ichiro Kawahata^2,*

BIOCELL, Vol.49, No.11, pp. 2125-2136, 2025, DOI:10.32604/biocell.2025.070394 - 24 November 2025

Abstract Lewy body diseases (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB), are neurodegenerative disorders characterized by the intracellular aggregation and accumulation of α-Synuclein (αSyn), leading to neuronal death. Although these diseases primarily present with symptoms affecting the central nervous system (CNS), such as motor and cognitive impairment, increasing research suggests that their roots may be found in the gut. This review summarizes recent findings and key historical insights into the involvement of the gut in αSyn pathology. The topics covered include pathological observations in patients with LBD, animal models investigating the propagation More >

Issah Zabsonre Alhassan^1,2,*, Gaddafi Abdul-Salaam¹, Michael Asante¹, Yaw Marfo Missah¹, Alimatu Sadia Shirazu¹

Journal of Cyber Security, Vol.7, pp. 165-196, 2025, DOI:10.32604/jcs.2025.063529 - 07 July 2025

Abstract Pseudorandom number generators (PRNGs) are foundational to modern cryptography, yet existing approaches face critical trade-offs between cryptographic security, computational efficiency, and adaptability to emerging threats. Traditional PRNGs (e.g., Mersenne Twister, LCG) remain widely used in low-security applications despite vulnerabilities to predictability attacks, while machine learning (ML)-driven and chaos-based alternatives struggle to balance statistical robustness with practical deployability. This study systematically evaluates traditional, chaos-based, and ML-driven PRNGs to identify design principles for next-generation systems capable of meeting the demands of high-security environment like blockchain and IoT. Using a framework that quantifies cryptographic robustness (via NIST SP… More >

Ayda Baghery Saghchy Khorasani¹, Mahda Delshad², Mohammad-Javad Sanaei^2,3, Atieh Pourbagheri-Sigaroodi², Ali Pirsalehi⁴, Davood Bashash^2,*

BIOCELL, Vol.49, No.4, pp. 539-562, 2025, DOI:10.32604/biocell.2025.059970 - 30 April 2025

Abstract Hepatocellular carcinoma (HCC) is the most common primary liver tumor and the third leading cause of cancer-related mortality globally. The phosphatidylinositol-3 kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway is critically involved in HCC pathogenesis, stimulating uncontrolled cell proliferation, survival, and tumor progression. The overactivation of this pathway is strongly linked to poor prognosis, making it a crucial target for therapeutic intervention. The oncogenic roles of PI3K/AKT/mTOR components in HCC have been highlighted, noting that class I PI3K deregulation, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) upregulation, and mTOR overexpression could be associated… More >

Leonardo Di G. Sigalotti^*, Carlos A. Vargas

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2281-2337, 2025, DOI:10.32604/cmes.2025.060497 - 03 March 2025

Abstract The evaporation of micrometer and millimeter liquid drops, involving a liquid-to-vapor phase transition accompanied by mass and energy transfer through the liquid-vapor interface, is encountered in many natural and industrial processes as well as in numerous engineering applications. Therefore, understanding and predicting the dynamics of evaporating flows have become of primary importance. Recent efforts have been addressed using the method of Smoothed Particle Hydrodynamics (SPH), which has proven to be very efficient in correctly handling the intrinsic complexity introduced by the multiscale nature of the evaporation process. This paper aims to provide an overview of… More > Graphic Abstract

Huajiang Ruan^1,2, Panjun Sun^1,2, Yuanyuan Dong^1,2, Hamid Tahaei¹, Zhaoxi Fang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1645-1680, 2025, DOI:10.32604/cmc.2024.059746 - 17 February 2025

Abstract Traditional Global Positioning System (GPS) technology, with its high power consumption and limited performance in obstructed environments, is unsuitable for many Internet of Things (IoT) applications. This paper explores LoRa as an alternative localization technology, leveraging its low power consumption, robust indoor penetration, and extensive coverage area, which render it highly suitable for diverse IoT settings. We comprehensively review several LoRa-based localization techniques, including time of arrival (ToA), time difference of arrival (TDoA), round trip time (RTT), received signal strength indicator (RSSI), and fingerprinting methods. Through this review, we evaluate the strengths and limitations of More >

Jiajia Liu^*, Mingxing Tian, Xusheng Mao

Energy Engineering, Vol.122, No.1, pp. 379-404, 2025, DOI:10.32604/ee.2024.058137 - 27 December 2024

Abstract After a century of relative stability in the electricity sector, the widespread adoption of distributed energy resources, along with recent advancements in computing and communication technologies, has fundamentally altered how energy is consumed, traded, and utilized. This change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized approach. At the heart of this transformation are innovative energy distribution models, like peer-to-peer (P2P) sharing, which enable communities to collaboratively manage their energy resources. The effectiveness of P2P sharing not only improves the economic prospects for prosumers, who… More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Sumika Chauhan, Govind Vashishtha^*, Radoslaw Zimroz

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1983-2020, 2024, DOI:10.32604/cmes.2024.055633 - 31 October 2024

Abstract Sensors, vital elements in data acquisition systems, play a crucial role in various industries. However, their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance. Early fault detection is therefore critical for minimizing downtime and ensuring system reliability. This paper delves into the contemporary landscape of fault diagnosis techniques for sensors, offering valuable insights for researchers and academicians. The papers begin by exploring the different types and causes of sensor faults, followed by a discussion of the various fault diagnosis methods employed in industrial sectors. The advantages and limitations More >

Artur V. Dmitrenko^1,2,*, Vladislav M. Ovsyannikov²

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 1925-1939, 2024, DOI:10.32604/fdmp.2024.048389 - 23 August 2024

Abstract The differential equations of continuum mechanics are the basis of an uncountable variety of phenomena and technological processes in fluid-dynamics and related fields. These equations contain derivatives of the first order with respect to time. The derivation of the equations of continuum mechanics uses the limit transitions of the tendency of the volume increment and the time increment to zero. Derivatives are used to derive the wave equation. The differential wave equation is second order in time. Therefore, increments of volume and increments of time in continuum mechanics should be considered as small but finite More >