Akmalbek Abdusalomov¹, Kudratjon Zohirov², Sojida Ochilova², Jakhongir Oramov³, Zafar Ruziyev³, Malika Rustamova⁴, Gulrukh Sherboboyeva⁵, Komil Tashev^6,7, Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081292 - 15 June 2026

Abstract Unmanned aerial vehicles (UAVs) are also increasingly becoming more often in the transportation infrastructure of smart cities, so that they can successfully achieve real-time observation of traffic, emergency coordination, and two-way communication relaying. However, the security and privacy risks arising in open, highly mobile intelligent transportation systems (ITS) enabled by UAVs are critical, as they pose threats of impersonation, replay, Sybil, and tracking attacks. Secondly, standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity. To address these challenges, this study introduces a Generative-AI-assisted… More >

Sheyun Zhang, Ruichun Gu^*, Chaofeng Li, Zhijian Dong, Hefei Wang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081065 - 15 June 2026

Abstract Federated learning (FL) enables collaborative model training without sharing raw data. However, in real-world applications, clients often exhibit statistical heterogeneity, missing classes, and long-tailed distributions, which can substantially degrade the generalization performance of conventional parameter aggregation and some personalization approaches. Moreover, distillation or alignment-based methods may suffer from unstable supervision and difficult optimization under highly heterogeneous settings. To this end, this paper proposes a novel method called FKD-RTM (Heterogeneous Federated Knowledge Distillation Based on Residual-Enhanced Tree-to-MLP Knowledge Transfer). The key idea is to decouple local teaching from globally aggregatable student learning: we introduce a Gradient… More >

Bhanu Talwar^1,*, Puneet Thapar¹, Tahani Alsubait², Mai Alduailij³, Ateeq Ur Rehman^4,*, Salil Bharany⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080977 - 15 June 2026

Abstract Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates… More >

Meihuizi Jia^1,*, Hongyan Ran¹, Shanshan Li²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080333 - 15 June 2026

Abstract Large language models (LLMs) have demonstrated considerable ability in solving various tasks via Chain-of-Thought (CoT) prompting, which has precipitated extensive research into their application for complex mathematical reasoning problems. However, current research on mathematical reasoning with CoT predominantly focuses on textual mathematical tasks, such as math word problems, while paying limited attention to multimodal geometric scenarios. To bridge this gap, we propose KG-HoT, a model that harnesses the generative and comprehension capabilities of Multimodal large language models (MLLMs) to enhance complex geometric problem-solving in multimodal systems. Our knowledge-grounded approach enables MLLMs to generate hybrid chains-of-thought More >

Huaixuan Yan, Yan Gong^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080178 - 15 June 2026

Abstract To correctly diagnose faults in farm machinery, we need to know a lot about the field and have experience with maintenance. However, most of this important information is stored in old, unstructured documents like technical manuals and expert logs. These documents don’t have a standard way to be represented digitally, which makes it very hard to build automated diagnosis systems. There are three main technical problems with getting structured knowledge out of this kind of text: noise from optical character recognition (OCR) during digitization, the extreme lack of labeled samples in specialized fields (low-resource constraints),… More >

Isam Bahaa Aldallal¹, Saadaldeen Rashid Ahmed^2,3, Abdullahi Abdu Ibrahim¹, Oguz Bayat⁴, Abu Saleh Musa Miah⁵, Fahmid Al Farid^6,7,*, Md. Hezerul Abdul Karim^6,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078742 - 15 June 2026

Abstract Urban parking problems worsen traffic jams, gas use, and pollution. Old parking systems often lack up-to-date space information, which annoys drivers and wastes their time. This research presents a smart IoT-enabled real-time space monitoring and booking system applicable to both urban parking management and Smart Manufacturing logistics environments, including loading bay coordination and Automated Guided Vehicle (AGV) docking station management. The system employs ultrasonic and IR sensors, managed by an Arduino UNO, to identify vehicles and track space availability. A servo-motor regulates entry. Slot data is presented on a Liquid Crystal Display screen and accessible More >

Bernard P. Zeigler^*

Digital Engineering and Digital Twin, Vol.4, pp. 1-26, 2026, DOI:10.32604/dedt.2026.082492 - 02 June 2026

Abstract As Digital Twin (DT) applications expand into complex, dynamic environments, a formal methodology is lacking to ensure that the embedded digital models remain adequate for specific stakeholder goals over time. This article introduces the Experimental Frame–System Under Test (EFSUT) methodology, providing a principled foundation for linking high-level stakeholder questions to the specific models capable of answering them. EFSUT organizes the digital engineering process around three core constructs: stakeholder questions, experimental frames that formalize observational requirements, and models related through morphisms. This structure allows developers to reason about model choice, reduction, and adequacy with technical rigor… More >

Xiang Li^1,2, Jie Zhang^1,2,*, Yuxin Cheng^1,2, Jiaohao Xie^1,2, Zhaoqi Xiong^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.5, 2026, DOI:10.32604/fdmp.2026.079253 - 27 May 2026

Abstract Current prediction methods for wellbore temperature and pressure in gas storage injection–production wells are commonly based on the simplifying assumption of pure methane, thereby neglecting the multi-component nature of real natural gas and limiting predictive accuracy. To overcome this shortcoming, this study develops a comprehensive model for the coupled temperature and pressure fields in wellbores transporting multi-component natural gas mixtures. The proposed framework explicitly accounts for compositional effects by integrating key thermophysical properties, including density, viscosity, compressibility factor, and Joule–Thomson coefficient, into the governing flow equations, thereby enhancing the fidelity of the ensuing injection and More >

Chaoshuai Duan, Yin Wang, Guohua Zhu^*, Xiaotian Zhang, Jiale Wang, Zhen Wang

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081676 - 27 May 2026

Abstract Hybrid fiber reinforced plastic (HFRP) composites, especially intra-layer carbon/glass hybrids, offer a promising balance of specific strength, impact resistance, and cost efficiency for thin-walled energy-absorbing structures. This study investigates the low-velocity impact response and energy absorption of intra-layer carbon/glass hybrid hat-shaped beams. Tensile and impact tests evaluated the effects of hybrid ratio and fiber orientation. A multiscale damage model based on micromechanical damage and failure criteria was established via Abaqus/VUMAT, integrating stress amplification factors to bridge micro-meso-macro scales. Experimental results show that carbon fibers aligned with the loading direction yield hybrid composites with superior tensile… More >

Yenjou Wang¹, Chihtan Cheng², Jia-Wei Chang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080888 - 27 May 2026

Abstract As large language models (LLMs) become increasingly integrated into enterprise decision-making processes, structural pressures such as version drift, cross-source evidence integration, and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency, traceability, and governability. This paper systematically reviews key technological developments relevant to enterprise requirements, including document perception, retrieval-augmented generation (RAG), hybrid RAG-KG architectures, fine-grained attribution evaluation, and multi-agent coordination. The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability, but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making. More >