Nisa Nacar Çıkan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3279-3327, 2025, DOI:10.32604/cmes.2025.065442 - 30 June 2025

Abstract This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks (UPDNs), focusing on the complex 123-Bus test system. Three scenarios are investigated: (1) simultaneous power loss reduction and voltage profile improvement, (2) minimization of voltage and current unbalance indices under various operational cases, and (3) multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index, active power loss, and current unbalance index. Unlike previous research that oftensimplified system components, this work maintains all equipment, including capacitor banks, transformers, and voltage regulators, to ensure realistic results. The study evaluates twelve metaheuristic More >

RAQUEL GÓMEZ-MOLINA^1,*, RAQUEL MARTÍNEZ^2,3,4, MIGUEL SUÁREZ^2,3,4,*, ANA PEÑA-CABIA¹, MARíA CONCEPCIóN CALDERÓN¹, JORGE MATEO^3,4

Oncology Research, Vol.33, No.7, pp. 1531-1545, 2025, DOI:10.32604/or.2025.063951 - 26 June 2025

Abstract Lynch syndrome (LS), also known as hereditary non-polyposis colorectal cancer (HNPCC), is an inherited condition associated with a higher risk of colorectal cancer (CRC) and other cancers. It is caused by germline mutations in DNA mismatch repair (MMR) genes, including MLH1, MSH2, MSH6 and PMS2. These mutations lead to microsatellite instability (MSI) and defective DNA repair mechanisms, resulting in increased cancer risk. Early detection of LS is crucial for effective management and cancer prevention. Endoscopic surveillance, particularly regular colonoscopy, is recommended for individuals with LS to detect CRC at early stages. Additionally, universal screening of CRC for More > Graphic Abstract

ALEX A. FRANCOEUR^*, NATALIE AYOUB, DANIELLE GREENBERG, KRISHNANSU S. TEWARI

Oncology Research, Vol.33, No.7, pp. 1511-1530, 2025, DOI:10.32604/or.2025.061120 - 26 June 2025

Abstract Endometrial cancer is the most common gynecologic cancer diagnosed in the United States and mortality is on the rise. Advanced and recurrent endometrial cancer represents a treatment challenge as historically there have been limited therapeutic options for patients. In the last several years, multiple practice-changing clinical trials have led to significant improvements in the treatment landscape. This review will cover updates in the treatment and management of advanced and recurrent endometrial cancer with a focus on novel therapeutics, such as anti-PD-L1 and PD-1 inhibitors, poly ADP-ribose polymerase (PARP) inhibitors, antibody-drug conjugates, and hormonal therapy. More >

Tehreem Fatima¹, Kewen Xia^1,*, Wenbiao Yang², Qurat Ul Ain¹, Poornima Lankani Perera¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 811-826, 2025, DOI:10.32604/cmc.2025.063686 - 09 June 2025

Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >

Sahbi Boubaker^1,*, Adel Mellit^2,3,*, Nejib Ghazouani⁴, Walid Meskine⁵, Mohamed Benghanem⁶, Habib Kraiem^7,8

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2237-2259, 2025, DOI:10.32604/cmes.2025.064530 - 30 May 2025

Abstract Electric vehicles (EVs) are gradually being deployed in the transportation sector. Although they have a high impact on reducing greenhouse gas emissions, their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging. To cope with these problems, this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting. The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’ charging scheduling task. By using predictive algorithms for solar generation and load demand… More >

Yiming Zhou¹, Jinhua Zhang^2,3, Kai Long^2,*, Ayesha Saeed², Yutang Chen², Rongrong Geng², Tao Tao⁴, Xiaohui Guo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1743-1761, 2025, DOI:10.32604/cmes.2025.063034 - 30 May 2025

Abstract The jacket structure and transition piece comprise the supporting structure of a bottom-fixed offshore wind turbine (OWT) connected to the steel tower, which determines the overall structural dynamic performance of the entire OWT. Ideally, optimal performance can be realized by effectively coordinating two components, notwithstanding their separate design processes. In pursuit of this objective, this paper proposes a concurrent design methodology for the jacket structure and transition piece by exploiting topology optimization (TO). The TO for a three-legged jacket foundation is formulated by minimizing static compliance. In contrast to conventional TO, two separated volume fractions… More >

CHUCHU ZHANG^1,#, YING LIU^2,#, ZEHUI CHEN¹, YI LIU³, QIYUAN MAO⁴, GE ZHANG⁵, HONGSHENG LIN⁴, JIABIN ZHENG^6,*, HAIYAN LI^1,*

Oncology Research, Vol.33, No.6, pp. 1437-1458, 2025, DOI:10.32604/or.2025.059290 - 29 May 2025

Abstract Objectives: Decades of clinical and fundamental research advancements in oncology have led to significant breakthroughs such as early screening, targeted therapies, and immunotherapy, contributing to reduced mortality rates in cancer patients. Despite these achievements, cancer continues to be a major public health challenge. This study employs bibliometric techniques to visually analyze the English literature on cancer prevention, screening, diagnosis, treatment, and rehabilitation. Methods: We systematically reviewed publications from 01 March 2014, to 01 March 2024, indexed in the Web of Science core collection. Tools such as VOSviewer Version 1.6.20 is characterized by its core idea… More > Graphic Abstract

Wonbyung Lee, Jang Hyun Kim^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5393-5412, 2025, DOI:10.32604/cmc.2025.065413 - 19 May 2025

Abstract Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key More >

Arshiya Sajid Ansari^1,*, Ghadir Altuwaijri², Fahad Alodhyani¹, Moulay Ibrahim El-Khalil Ghembaza³, Shahabas Manakunnath Devasam Paramb³, Mohammad Sajid Mohammadi³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3713-3752, 2025, DOI:10.32604/cmc.2025.064027 - 19 May 2025

Abstract IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication, processing, and real-time monitoring across diverse applications. Due to their heterogeneous nature and constrained resources, as well as the growing trend of using smart gadgets, there are privacy and security issues that are not adequately managed by conventional security measures. This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems. The intersection of AI technologies, including ML, and blockchain, with IoT privacy and security is systematically examined, focusing on their efficacy in addressing… More >

Ali Mohammad Alqudah¹, Zahra Moussavi^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3753-3841, 2025, DOI:10.32604/cmc.2025.063643 - 19 May 2025

Abstract This review presents a comprehensive technical analysis of deep learning (DL) methodologies in biomedical signal processing, focusing on architectural innovations, experimental validation, and evaluation frameworks. We systematically evaluate key deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models, and hybrid systems across critical tasks such as arrhythmia classification, seizure detection, and anomaly segmentation. The study dissects preprocessing techniques (e.g., wavelet denoising, spectral normalization) and feature extraction strategies (time-frequency analysis, attention mechanisms), demonstrating their impact on model accuracy, noise robustness, and computational efficiency. Experimental results underscore the superiority of deep learning… More >