TAYYABA SATTAR¹, IQRA NAZIR¹, MEHREEN JABBAR¹, JAVARIA MALIK¹, SABA AFZAL¹, SANA HANIF², SEYED ALI MOSADDAD³, AHMED HUSSAIN^4,*, HAMID TEBYANIYAN^2,*

Oncology Research, Vol.33, No.5, pp. 1019-1032, 2025, DOI:10.32604/or.2025.060601 - 18 April 2025

Abstract Background: Head and neck cancers (HNC) account for a significant global health burden, with increasing incidence rates and complex treatment requirements. Traditional diagnostic and therapeutic approaches, while effective, often result in substantial morbidity and limitations in personalized care. This review provides a comprehensive overview of the latest innovations in diagnostics and therapeutic strategies for HNC from 2015 to 2024. Methods: A review of literature focused on pe-reviewed journals, clinical trial databases, and oncology conference proceedings. Key areas include molecular diagnostics, imaging technologies, minimally invasive surgeries, and innovative therapeutic strategies. Results: Technologies like liquid biopsy next-generation sequencing… More >

Qian Zhu¹, Qian Kang¹, Tao Xu², Dengxiu Yu^3,*, Zhen Wang¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1855-1879, 2025, DOI:10.32604/cmc.2025.061913 - 16 April 2025

Abstract In this study, we present a deterministic convergence analysis of Gated Recurrent Unit (GRU) networks enhanced by a smoothing regularization technique. While GRU architectures effectively mitigate gradient vanishing/exploding issues in sequential modeling, they remain prone to overfitting, particularly under noisy or limited training data. Traditional regularization, despite enforcing sparsity and accelerating optimization, introduces non-differentiable points in the error function, leading to oscillations during training. To address this, we propose a novel smoothing regularization framework that replaces the non-differentiable absolute function with a quadratic approximation, ensuring gradient continuity and stabilizing the optimization landscape. Theoretically, we rigorously… More >

Murat Cikan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 673-721, 2025, DOI:10.32604/cmes.2025.061699 - 11 April 2025

Abstract This study examines various issues arising in three-phase unbalanced power distribution networks (PDNs) using a comprehensive optimization approach. With the integration of renewable energy sources, increasing energy demands, and the adoption of smart grid technologies, power systems are undergoing a rapid transformation, making the need for efficient, reliable, and sustainable distribution networks increasingly critical. In this paper, the reconfiguration problem in a 37-bus unbalanced PDN test system is solved using five different popular metaheuristic algorithms. Among these advanced search algorithms, the Bonobo Optimizer (BO) has demonstrated superior performance in handling the complexities of unbalanced power… More >

Ziwen Cai^1,2, Yun Zhao^1,2, Zongyi Wang^1,2, Tonghe Wang^3,*, Yunfeng Li^1,2, Hao Wang³

Energy Engineering, Vol.122, No.4, pp. 1221-1248, 2025, DOI:10.32604/ee.2025.061452 - 31 March 2025

Abstract With the severe challenges brought by global climate change, exploring and developing clean and renewable energy systems to upgrade the energy structure has become an inevitable trend in related research. The comprehensive park systems integrated with photovoltaic, energy storage, direct current, and flexible loads (PEDF) is able to play an important role in promoting energy transformation and achieving sustainable development. In order to fully understand the advantages of PEDF parks in energy conservation and carbon reduction, this paper summarizes existing studies and prospects future research directions on the low-carbon operation of the PEDF park. This… More >

DEMEI FENG^1,#, SHENRUI BAI^1,#, GUANJUN CHEN¹, BIBO FU¹, CAILU SONG¹, HAILIN TANG¹, LIANG WANG^2,*, HUA WANG^1,*

Oncology Research, Vol.33, No.4, pp. 965-974, 2025, DOI:10.32604/or.2024.057065 - 19 March 2025

Abstract Objectives: The optimal treatment strategy for early-stage natural killer/T-cell lymphoma (NKTCL) remains unclear. This study aimed to evaluate and compare the clinical outcomes and adverse events (AEs) associated with two treatment regimens for early-stage NKTCL: pegaspargase with concurrent radiation therapy (P+CCRT) and pegaspargase, gemcitabine, and oxaliplatin (P-GEMOX) with sequential radiation therapy (SERT). Propensity score matching (PSM) was employed to ensure balanced comparison between these regimens. Methods: We assessed the efficacy of P+CCRT from a phase II trial and P-GEMOX combined with SERT using real-world data. PSM was conducted at a 1:1 ratio with a caliper… More >

Md Naeem Hossain¹, Md. Abdur Rahim², Md Mustafizur Rahman^1,3,*, Devarajan Ramasamy¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3643-3692, 2025, DOI:10.32604/cmc.2025.061749 - 06 March 2025

Abstract The automotive sector is crucial in modern society, facilitating essential transportation needs across personal, commercial, and logistical domains while significantly contributing to national economic development and employment generation. The transformative impact of Artificial Intelligence (AI) has revolutionised multiple facets of the automotive industry, encompassing intelligent manufacturing processes, diagnostic systems, control mechanisms, supply chain operations, customer service platforms, and traffic management solutions. While extensive research exists on the above aspects of AI applications in automotive contexts, there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research. This review introduces a… More >

Nanjiang Zhong^*, Xinchen Jiang, Yuan Yao

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4741-4757, 2025, DOI:10.32604/cmc.2025.059536 - 06 March 2025

Abstract The proliferation of rumors on social media has caused serious harm to society. Although previous research has attempted to use deep learning methods for rumor detection, they did not simultaneously consider the two key features of temporal and spatial domains. More importantly, these methods struggle to automatically generate convincing explanations for the detection results, which is crucial for preventing the further spread of rumors. To address these limitations, this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models (LLMs) to automatically generate explanations for the detection results.… More >

Ruoxin Li^1,*, Shaoxiong Wu¹, Fengping Deng¹, Zhongli Tian¹, Hua Cai¹, Xiang Li¹, Xu Xu¹, Qi Liu^2,3

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2969-2984, 2025, DOI:10.32604/cmc.2025.060230 - 17 February 2025

Abstract Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and… More >

Dalal AL-Alimi¹, Mohammed A. A. Al-qaness^2,3,*, Robertas Damaševičius^4,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3539-3561, 2025, DOI:10.32604/cmc.2025.059869 - 17 February 2025

Abstract Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions… More >

Amjad Rehman¹, Muhammad Mujahid¹, Alex Elyassih¹, Bayan AlGhofaily¹, Saeed Ali Omer Bahaj^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 41-72, 2025, DOI:10.32604/cmc.2024.058036 - 03 January 2025

Abstract In computer vision and artificial intelligence, automatic facial expression-based emotion identification of humans has become a popular research and industry problem. Recent demonstrations and applications in several fields, including computer games, smart homes, expression analysis, gesture recognition, surveillance films, depression therapy, patient monitoring, anxiety, and others, have brought attention to its significant academic and commercial importance. This study emphasizes research that has only employed facial images for face expression recognition (FER), because facial expressions are a basic way that people communicate meaning to each other. The immense achievement of deep learning has resulted in a… More >