Ovidijus Grigas^*, Rytis Maskeliūnas

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077324 - 27 April 2026

Abstract Early detection of Mild Cognitive Impairment (MCI) with FDG-PET is essential for timely Alzheimer’s disease intervention. However, PET image quality is limited by low spatial resolution, partial volume effects, and Poisson noise. Standard enhancement methods, such as Bilateral filtering or Contrast Limited Adaptive Histogram Equalization (CLAHE), can increase contrast but often introduce heavy noise or distort image texture, while deep learning methods may produce hallucinated structures. We propose a fully data-adaptive, non-learned 3D enhancement framework whose output is deterministic for a given input volume, that combines Laplacian-based local contrast modulation with a gradient-gated Difference-of-Gaussians (DoG)… More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076888 - 27 April 2026

Abstract The engine serves as the primary component that generates power and drives vehicle movement. Given its critical role, accurately diagnosing engine faults is essential for ensuring vehicle safety and reliability. Recent advances in machine learning (ML) have enabled the development of artificial intelligence (AI)-based diagnostic models with strong predictive performance. However, the lack of transparency in these models constrains user confidence in their diagnostic outcomes. While explainable AI (XAI) methods such as local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) have been introduced to improve interpretability, their reliance on visual outputs requires manual… More >

Saeideh Memarian¹, Andreea M. Oprescu^2,3, Natalia Moreno-Naranjo², Gloria Miró-Amarante², M. Carmen Romero-Ternero^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.073647 - 27 April 2026

Abstract In recent years, the proliferation of social networks has been remarkable, providing a rich source for data mining endeavors. However, a significant challenge lies in safeguarding the privacy of individuals while sharing these databases publicly. Current approaches, such as K-anonymity, L-diversity, and T-closeness, are commonly employed for data anonymization in social networks. However, these techniques entail considerable information loss due to random alterations in the graph-based datasets. To address these limitations, this paper introduces a new anonymization technique called KMFC-GWO, which combines K-Member Fuzzy Clustering with Grey Wolf Optimizer. This integrated method is designed to… More >

Thatsamaphon Boonchuntuk¹, Thanyapisit Buaprakhong¹, Varintorn Sithisint¹, Awirut Phusaensaart¹, Sinthon Wilke¹, Thittaporn Ganokratanaa^1,*, Mahasak Ketcham²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.069005 - 27 April 2026

Abstract Urban traffic congestion directly contributes to excessive energy consumption and urban air pollution, requiring adaptive traffic signal control strategies that incorporate sustainability objectives alongside mobility performance. This study proposes a Large Language Model (LLM) driven traffic signal optimization framework that transforms detailed intersection-level traffic states into structured natural-language prompts, enabling the LLM to reason over congestion patterns, queue asymmetry, phase history, and estimated energy emission impacts. Unlike reinforcement learning (RL) based controllers, the LLM requires no task-specific training and operates in a zero-shot manner through carefully designed structured prompts that encode traffic states, phase history,… More >

Edward Sutanto¹, Rinni Sutanto², Sara Velichkovikj³, Nikola Hadzi-Petrushev⁴, Mitko Mladenov⁴, Dimiter Avtanski^5,6,7, Radoslav Stojchevski^5,6,8,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.076157 - 22 April 2026

Abstract The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research, particularly in oncogenesis. Oncogenesis is a hallmark process in the development of cancer, involving the amplification of proto-oncogenes and the subsequent dysregulation of molecular signaling networks. These pathways—including the RAS/RAF/MEK/ERK, PI3K-AKT, JAK-STAT, TGF-β/Smad, Wnt/β-Catenin, and Notch cascades—have been studied extensively in isolation, with major strides achieved in understanding how they drive cancer. However, there are still many considerations regarding how these networks interact. Ongoing studies show that crosstalk among these pathways occurs through feedback… More >

Kassiani Lalechou, Despoina Pantazi^*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.074452 - 22 April 2026

Abstract Cancer-associated thrombosis (CAT) is a leading cause of morbidity and mortality among cancer patients. While venous thromboembolic events have been extensively studied due to their higher incidence, arterial thrombosis in cancer patients—referred to as cancer-associated arterial thromboembolism (CA-ATE)—is less well understood but may pose a greater danger. The pathophysiology of CA-ATE involves complex interactions between the tumor microenvironment, cancer cells, patient-related factors, and cancer therapies. Some chemotherapeutic agents, particularly platinum-based compounds (cisplatin, oxaliplatin), gemcitabine, taxanes, and targeted therapies such as tyrosine kinase inhibitors (TKIs), have been associated with an increased risk of arterial thrombosis. In… More > Graphic Abstract

Chia-Chia Chao¹, Cheng-Yao Lin^2,3,4, Po-Chun Chen^5,6,7, Wen-Tsung Huang², Teng-Song Weng⁸, Sheng-Yen Hsiao^2,9,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.074383 - 22 April 2026

Abstract Background: Partial epithelial–mesenchymal transition (p-EMT) is a dynamic cellular state associated with metastasis and adverse outcomes in multiple cancers, but its prognostic significance in ovarian cancer remains unclear. This study aimed to develop and validate an ovarian cancer–specific transcriptomic signature based on p-EMT–related genes, and to determine whether this signature can improve prognostic stratification and overall survival prediction across independent cohorts. Methods: A pan-cancer p-EMT gene set was curated from ten published studies. Using transcriptomic and clinical data from TCGA-OV (n = 488), a six-gene p-EMT signature was developed via LASSO regression to generate a… More >

Abdul Manan^1,2, Sidra Ilyas^2,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.074185 - 22 April 2026

Abstract Hepatocellular carcinoma (HCC) remains a significant global health challenge, with therapeutic efficacy in advanced stages often limited by underlying liver dysfunction and adaptive resistance. In this review, the evolving landscape of molecular targets and combinatorial strategies is critically examined, with a particular focus on the transition from preclinical discovery to clinical application. While traditional molecular heterogeneity is acknowledged, the aim is to elucidate how emerging computational paradigms are redefining target discovery and therapeutic stratification in HCC. The primary purpose is to evaluate the role of Artificial Intelligence (AI) and Machine Learning (ML) as integrative tools… More > Graphic Abstract

Jun-Hui Chen¹, Si-Run Du¹, Chang Liu¹, Bei-Bei Liu¹, Hai-Ying Xu², Xin-Ying Ji², Bo Feng³, Chun-Zheng Ma³, Jun-Hui Guo^3,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.073484 - 22 April 2026

Abstract Esophageal cancer (EC) ranks among the most lethal gastrointestinal malignancies. Due to challenges in early diagnosis, molecular heterogeneity, and therapeutic resistance, patient prognosis remains extremely poor, necessitating the development of novel biomarkers and therapeutic targets. As a core effector of the Hippo signaling pathway, the potential significance of Yes-associated protein 1 (YAP1) has garnered increasing attention. This paper aims to systematically summarize the multi-omics research, molecular mechanisms, and preclinical/translational evidence for YAP1, covering its activation pathways, biological functions, clinical significance, and therapeutic strategies. We elucidated YAP1’s multidimensional regulatory network in EC, including Hippo-dependent and -independent mechanisms, cross-regulation… More >

Yahya Ghazwani^1,2,3, Mohammad Alghafees^1,2,3,*, Mishari Alshasha^1,2,3, Fahad Brayan^1,2,3, Abdulrahman Alsayyari^1,2,3, Ali Alyami^1,2,3

Canadian Journal of Urology, Vol.33, No.2, pp. 291-308, 2026, DOI:10.32604/cju.2026.077411 - 20 April 2026

Abstract Objectives: The emergence of artificial intelligence (AI) and predictive modeling offers prospects for clinical, anatomical, and imaging factor combination, like radiomics, to help with stone-free status (SFS) estimation and peroperative decision-making. The goal of this study was, therefore, to define the present performance range, determine sources of heterogeneity, and determine methodological practices permitting reliable implementation by varied circumstances. Methods: We searched six bibliographic databases through 19 September 2025. Studies deriving or validating AI/predictive models for SFS after ureteroscopy were eligible. Independent dual screening, duplicate data extraction, and risk-of-bias consideration using QUADAS-AI were conducted. Results: Five retrospective… More >