Chenxuan Zhang, Yongqing Qi^*, Ximin Cao, Yanchi Zhang

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069878 - 27 January 2026

Abstract To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems (IES), this paper proposes a low-carbon dispatch model integrating power-to-gas (P2G), carbon capture and storage (CCS), hydrogen fuel cell (HFC), and combined heat and power (CHP). The P2G process is refined into a two-stage structure, and HFC is introduced to enhance hydrogen utilization. Together with CCS and CHP, these devices form a multi-energy conversion system coupling electricity, heat, cooling, and gas. A ladder-type carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.… More >

Suman Kunwar^*

Journal on Artificial Intelligence, Vol.8, pp. 39-49, 2026, DOI:10.32604/jai.2026.076674 - 22 January 2026

Abstract The rise in convenience packaging has led to generation of enormous waste, making efficient waste sorting crucial for sustainable waste management. To address this, we developed DWaste, a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices, including offline functionality. We benchmarked various image classification models (EfficientNetV2S/M, ResNet50/101, MobileNet) and object detection (YOLOv8n, YOLOv11n) including our purposed YOLOv8n-CBAM model using our annotated dataset designed for recycling. We found a clear trade-off between accuracy and resource consumption: the best classifier, EfficientNetV2S, achieved high accuracy (≈96%) but suffered from high latency More >

Afnan Alhindi^*, Saad Al-Ahmadi, Mohamed Maher Ben Ismail

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072625 - 12 January 2026

Abstract Split Learning (SL) has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency. Specifically, neural networks are divided into client and server sub-networks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices, thereby making SL particularly suitable for resource-constrained devices. Although SL prevents the direct transmission of raw data, it does not alleviate entirely the risk of privacy breaches. In fact, the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data. Moreover,… More >

Mohammed Alnusayri¹, Ghulam Mujtaba², Nouf Abdullah Almujally³, Shuoa S. Aitarbi⁴, Asaad Algarni⁵, Ahmad Jalal^2,6, Jeongmin Park^7,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071804 - 12 January 2026

Abstract This paper presents a unified Unmanned Aerial Vehicle-based (UAV-based) traffic monitoring framework that integrates vehicle detection, tracking, counting, motion prediction, and classification in a modular and co-optimized pipeline. Unlike prior works that address these tasks in isolation, our approach combines You Only Look Once (YOLO) v10 detection, ByteTrack tracking, optical-flow density estimation, Long Short-Term Memory-based (LSTM-based) trajectory forecasting, and hybrid Speeded-Up Robust Feature (SURF) + Gray-Level Co-occurrence Matrix (GLCM) feature engineering with VGG16 classification. Upon the validation across datasets (UAVDT and UAVID) our framework achieved a detection accuracy of 94.2%, and 92.3% detection accuracy when More >

Bohui Li¹, Bin Wang¹, Linjie Wu¹, Xingjuan Cai^1,*, Maoqing Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-28, 2026, DOI:10.32604/cmc.2025.070592 - 09 December 2025

Abstract Federated Learning (FL) provides an effective framework for efficient processing in vehicular edge computing. However, the dynamic and uncertain communication environment, along with the performance variations of vehicular devices, affect the distribution and uploading processes of model parameters. In FL-assisted Internet of Vehicles (IoV) scenarios, challenges such as data heterogeneity, limited device resources, and unstable communication environments become increasingly prominent. These issues necessitate intelligent vehicle selection schemes to enhance training efficiency. Given this context, we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions, and develop a dynamic interval multi-objective More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069976 - 09 December 2025

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

D. Bernal^*, O. Ledesma, P. Lamo, J. Bermejo

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-39, 2026, DOI:10.32604/cmc.2025.069746 - 10 November 2025

Abstract This work evaluates an architecture for decentralized authentication of Internet of Things (IoT) devices in Low Earth Orbit (LEO) satellite networks using IOTA Identity technology. To the best of our knowledge, it is the first proposal to integrate IOTA’s Directed Acyclic Graph (DAG)-based identity framework into satellite IoT environments, enabling lightweight and distributed authentication under intermittent connectivity. The system leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) over the Tangle, eliminating the need for mining and sequential blocks. An identity management workflow is implemented that supports the creation, validation, deactivation, and reactivation of IoT devices,… More >

Lincong Zhang¹, Yuqing Liu¹, Kefeng Wei², Weinan Zhao¹, Bo Qian^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069108 - 10 November 2025

Abstract In the field of edge computing, achieving low-latency computational task offloading with limited resources is a critical research challenge, particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications. In scenarios where edge servers are sparsely deployed, the lack of coordination and information sharing often leads to load imbalance, thereby increasing system latency. Furthermore, in regions without edge server coverage, tasks must be processed locally, which further exacerbates latency issues. To address these challenges, we propose a novel and efficient Deep Reinforcement Learning (DRL)-based approach aimed at minimizing average… More >

Lijuan Huang¹, Xianyi Liu², Jinping Liu^2,*, Pengfei Xu^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.068818 - 10 November 2025

Abstract The ubiquity of mobile devices has driven advancements in mobile object detection. However, challenges in multi-scale object detection in open, complex environments persist due to limited computational resources. Traditional approaches like network compression, quantization, and lightweight design often sacrifice accuracy or feature representation robustness. This article introduces the Fast Multi-scale Channel Shuffling Network (FMCSNet), a novel lightweight detection model optimized for mobile devices. FMCSNet integrates a fully convolutional Multilayer Perceptron (MLP) module, offering global perception without significantly increasing parameters, effectively bridging the gap between CNNs and Vision Transformers. FMCSNet achieves a delicate balance between computation… More >

A. M. Abdel-Daiem^a, M. Ahmed^a,, E. R. Shaaban^b,

Chalcogenide Letters, Vol.22, No.2, pp. 131-142, 2025, DOI:10.15251/CL.2025.222.131

Abstract This paper investigates the impact of reduced graphene oxide (rGO) addition on the structural and optical properties of ZnS nanocomposites. The study began with the synthesis of graphene oxide (GO) through the oxidation of natural graphite powder. This process involved using potassium permanganate in a mixture of sulfuric and phosphoric acids, maintained at 50°C for 48 hours. The reaction was terminated using hydrogen peroxide, followed by purification and drying, yielding 1.5 grams of GO. The preparation of ZnS/GO nanocomposites involved dissolving zinc acetate and varying quantities of GO in water, adjusting the pH, and incorporating… More >