Shih-Lin Lin^*, Yi-Hsuan Chen

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078862 - 27 April 2026

Abstract Urban intersections contain severe blind zones where buildings and roadside obstacles block line-of-sight sensing, limiting the ability of autonomous vehicles to anticipate hidden hazards. This paper presents an urban-intersection-oriented non-line-of-sight (NLOS) perception framework that exploits specular reflections from building surfaces using 77 GHz frequency-modulated continuous-wave (FMCW) automotive radar. All evaluations are conducted in a MATLAB-based simulation environment that models intersection geometry, building-induced occlusions, and specular reflection-assisted propagation, and generates 77-GHz FMCW radar echoes under controllable interference; real-world validation with measured radar data and richer multipath/material modeling is planned as future work. To improve robustness under… More >

Ryul Kim, Young Hwan Choi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077480 - 30 March 2026

Abstract Leakage events occurring at multiple locations simultaneously generate overlapping and topology-dependent pressure signatures, making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems (WDSs). While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior, most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified. To bridge this gap, this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization. First, a multi-task learning framework based on Graph Attention Networks (GAT) is… More >

Oncology Research Editorial Office

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.078461 - 19 January 2026

Abstract This article has no abstract. More >

Changcheng Li^1,2, Weimeng Chang^1,2, Dahai Zhang^1,*, Jinghan He¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069389 - 27 December 2025

Abstract Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts. This paper proposes a novel partitioning method based on deep reinforcement learning. First, the partitioning decision process is formulated as a Markov decision process (MDP) model to maximize the modularity. Corresponding key partitioning constraints on parallel restoration are considered. Second, based on the partitioning objective and constraints, the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function. The soft bonus scaling mechanism… More >

Hanyong Lee^#, Ye-Chan Park^#, Jaesung Lee^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4977-4993, 2025, DOI:10.32604/cmc.2025.068156 - 23 October 2025

Abstract Restoring texts corrupted by visually perturbed homoglyph characters presents significant challenges to conventional Natural Language Processing (NLP) systems, primarily due to ambiguities arising from characters that appear visually similar yet differ semantically. Traditional text restoration methods struggle with these homoglyph perturbations due to limitations such as a lack of contextual understanding and difficulty in handling cases where one character maps to multiple candidates. To address these issues, we propose an Optical Character Recognition (OCR)-assisted masked Bidirectional Encoder Representations from Transformers (BERT) model specifically designed for homoglyph-perturbed text restoration. Our method integrates OCR preprocessing with a… More >

Qun Ma¹, Zhimin Liu¹, Quanlai Zhou¹, Wei Liang^1,*, Jing Wu^2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.7, pp. 2183-2194, 2025, DOI:10.32604/phyton.2025.067807 - 31 July 2025

Abstract The Leymus chinensis grassland is one of the most widely distributed associations in the warm temperate grassland and due to overgrazing in recent years, it has experienced varying degrees of degradation. Vegetative regeneration via bud banks serves as the primary way of vegetation reproduction in the L. chinensis grassland ecosystem. However, the role of the bud bank in the vegetation regeneration of grazing grassland remains unclear. Based on the relationship between the under-ground bud bank and above-ground vegetation of L. chinensis grassland under different grazing stages, this study aimed to explore whether the grazing grassland could self-recover through… More >

Wuyi Zhu¹, Meng Zou¹, Wenji Liu¹, Linlin Cui^2,*

Revue Internationale de Géomatique, Vol.34, pp. 363-377, 2025, DOI:10.32604/rig.2025.065708 - 14 July 2025

Abstract Exploring the spatiotemporal changes in Fractional Vegetation Coverage (FVC) helps to more accurately understand the quality of the ecological environment, which is of great significance for regional ecological protection and sustainable economic development. The study takes the Chengdu-Chongqing urban agglomeration as the research area, analyzes the characteristics and trends of vegetation cover changes from 2000 to 2020 using the Google Earth Engine cloud platform, and explores its driving factors based on the enhanced regression tree model. The results show that: (1) From 2000 to 2020, the annual FVC of the Chengdu-Chongqing urban agglomeration showed a… More >

Tarek Muallim¹, Haluk Kucuk^2,*, Muhammet Bareket¹, Metin Kahraman¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3557-3581, 2025, DOI:10.32604/cmes.2025.064733 - 30 June 2025

Abstract This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional (1D) barcodes and Quick Response (QR) codes, addressing critical challenges in logistics operations. The proposed solution leverages an efficient Pix2Pix-based framework, a type of conditional Generative Adversarial Network (GAN) optimized for image-to-image translation tasks, enabling the recovery of degraded barcodes and QR codes with minimal computational overhead. A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments, such as low contrast, misalignment, physical wear,… More >

Xincong Shi^1,2, Yuze Ji^3,*, Xinrui Wang³, Ruimin Tian³, Chao Zhang²

Energy Engineering, Vol.122, No.3, pp. 1199-1220, 2025, DOI:10.32604/ee.2025.061586 - 07 March 2025

Abstract With the development of integrated power and gas distribution systems (IPGS) incorporating renewable energy sources (RESs), coordinating the restoration processes of the power distribution system (PS) and the gas distribution system (GS) by utilizing the benefits of RESs enhances service restoration. In this context, this paper proposes a coordinated service restoration framework that considers the uncertainty in RESs and the bi-directional restoration interactions between the PS and GS. Additionally, a coordinated service restoration model is developed considering the two systems’ interdependency and the GS’s dynamic characteristics. The objective is to maximize the system resilience index… More >

Yitong Zhou, Tianliang Lu^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3591-3608, 2025, DOI:10.32604/cmc.2024.057554 - 17 February 2025

Abstract Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in practical applications, around 30% to 50% of images are obstructed to varied extents, for as by the presence of masks, glasses, or hats. Repairing the masked facial images will enhance face recognition accuracy by 10% to 20%. Simultaneously, market research indicates a rising demand for efficient facial recognition technology within the security and surveillance sectors, with projections suggesting that the global facial recognition market would exceed US$3… More >