Si Qin, Shu Tang, Lijiao Xie, Jianbo Zhu, Jianguo Sun^*

Oncology Research, Vol.33, No.12, pp. 4145-4154, 2025, DOI:10.32604/or.2025.068300 - 27 November 2025

Abstract Background: Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare subtype of primary non-small cell lung cancer (NSCLC), with no established treatment guidelines. We present a case of a young female with PPLELC who achieved a pathological complete response (pCR) in both primary and metastatic lesions after receiving combined immunotherapy and radiotherapy. Case description: We present a 33-year-old female patient with stage IVa (cT2bN0M1b) PPLELC. As a first-line treatment, the patient received seven cycles of nab-paclitaxel combined with toripalimab (a PD-1 inhibitor) and achieved stable disease. This was followed by toripalimab maintenance therapy for nearly 30 months.… More >

Mekhled Alharbi^1,*, Khalid Haseeb², Mamoona Humayun^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2601-2616, 2025, DOI:10.32604/cmes.2025.073039 - 26 November 2025

Abstract Emerging technologies and the Internet of Things (IoT) are integrating for the growth and development of heterogeneous networks. These systems are providing real-time devices to end users to deliver dynamic services and improve human lives. Most existing approaches have been proposed to improve energy efficiency and ensure reliable routing; however, trustworthiness and network scalability remain significant research challenges. In this research work, we introduce an AI-enabled Software-Defined Network (SDN)- driven framework to provide secure communication, trusted behavior, and effective route maintenance. By considering multiple parameters in the forwarder selection process, the proposed framework enhances network More >

Tae-hyeon Yun¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2707-2731, 2025, DOI:10.32604/cmes.2025.072357 - 26 November 2025

Abstract The dynamic, heterogeneous nature of Edge computing in the Internet of Things (Edge-IoT) and Industrial IoT (IIoT) networks brings unique and evolving cybersecurity challenges. This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework by MITRE and introduces a lightweight, data-driven scoring model that enables rapid identification and prioritization of attacks. Inspired by the Factor Analysis of Information Risk model, our proposed scoring model integrates four key metrics: Common Vulnerability Scoring System (CVSS)-based severity scoring, Cyber Kill Chain–based difficulty estimation, Deep Neural Networks-driven detection scoring, and frequency… More >

Ehan Sohn¹, Sangmyung Lee¹, Sunggon Kim¹, Kiwook Sohn¹, Manish Kumar², Yongseok Son^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2761-2785, 2025, DOI:10.32604/cmes.2025.070888 - 26 November 2025

Abstract With the accelerated growth of the Internet of Things (IoT), real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local. Since these devices often handle personal information under limited resources, cryptographic algorithms must be executed efficiently. Their computational characteristics strongly affect system performance, making it necessary to analyze resource impact and predict usage under diverse configurations. In this paper, we analyze the phase-level resource usage of AES variants, ChaCha20, ECC, and RSA on an edge device and develop a prediction model. We apply these algorithms… More >

Saeed Asadi¹, Hajar Kazemi Naeini¹, Delaram Hassanlou², Abolhassan Pishahang³, Saeid Aghasoleymani Najafabadi⁴, Abbas Sharifi⁵, Mohsen Ahmadi^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528 - 26 November 2025

Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >

Tamara Zhukabayeva^1,2, Zulfiqar Ahmad^1,3,*, Nurbolat Tasbolatuly⁴, Makpal Zhartybayeva¹, Yerik Mardenov^1,4, Nurdaulet Karabayev^1,*, Dilaram Baumuratova^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2573-2599, 2025, DOI:10.32604/cmes.2025.070426 - 26 November 2025

Abstract The Industrial Internet of Things (IIoT), combined with the Cyber-Physical Systems (CPS), is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems. There is a lack of explainability, challenges with imbalanced attack classes, and limited consideration of practical edge–cloud deployment strategies in prior works. In the proposed study, we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations (SHAP)-based Explainable AI (XAI) to attack detection and classification in IIoT-CPS settings. It includes not only unsupervised clustering (K-Means and DBSCAN) to extract… More >

Kunjabihari Swain¹, Murthy Cherukuri^1,*, Indu Sekhar Samanta², Bhargav Appasani^3,*, Nicu Bizon^4,5, Mihai Oproescu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1993-2015, 2025, DOI:10.32604/cmes.2025.067121 - 26 November 2025

Abstract Transmission line faults pose a significant threat to power system resilience, underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring, economic loss prevention, and blackout avoidance. Extreme learning machine (ELM) offers a compelling solution for rapid classification, achieving network training in a single epoch. Leveraging the Internet of Things (IoT) and the virtual instrumentation capabilities of LabVIEW, ELM can enable the swift and precise identification of transmission line faults. This paper presents a regularized radial basis function (RBF) ELM-based fault detection and classification system for transmission lines, utilizing a LabVIEW More >

Xiaobo Jiang, Hongchao Zheng^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1695-1716, 2025, DOI:10.32604/sdhm.2025.063890 - 17 November 2025

Abstract Construction failures caused by unforeseen circumstances, such as natural disasters, environmental degradation, and structural weaknesses, present significant challenges in achieving durability, safety, and sustainability. This research addresses these challenges through the development of advanced emergency rescue systems incorporating wood-derived nanomaterials and IoT-enabled Structural Health Monitoring (SHM) technologies. The use of nanocellulose which demonstrates outstanding mechanical capabilities and biodegradability alongside high resilience allowed developers to design modular rescue systems that function effectively even under challenging conditions while providing real-time failure protection. Experimental data from testing showed that the replacement system strengthened load-bearing limits by 20% while… More >

Seungyeon Baek^1,#, Jueun Jeon^2,#, Byeonghui Jeong¹, Young-Sik Jeong^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1213-1236, 2025, DOI:10.32604/cmes.2025.070679 - 30 October 2025

Abstract With the advent of the sixth-generation wireless technology, the importance of using artificial intelligence of things (AIoT) devices is increasing to enhance efficiency. As massive volumes of data are collected and stored in these AIoT environments, each device becomes a potential attack target, leading to increased security vulnerabilities. Therefore, intrusion detection studies have been conducted to detect malicious network traffic. However, existing studies have been biased toward conducting in-depth analyses of individual packets to improve accuracy or applying flow-based statistical information to ensure real-time performance. Effectively responding to complex and multifaceted threats in large-scale AIoT… More >

Mohammad A Al Khaldy¹, Ahmad Nabot², Ahmad Al-Qerem^3,*, Mohammad Alauthman⁴, Amina Salhi^5,*, Suhaila Abuowaida⁶, Naceur Chihaoui⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 673-697, 2025, DOI:10.32604/cmes.2025.070004 - 30 October 2025

Abstract The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in data processing and resource management for time-critical applications. Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems, while pure edge computing faces resource constraints that limit processing capabilities. This paper addresses these challenges by proposing a novel Deep Reinforcement Learning (DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments. Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency. The framework introduces three key innovations: (1)… More >