Xiaozhe Guo¹, Suyan Long², Ziyu Yue², Yifan Wang², Guanting Yin¹, Yuyang Wang¹, Zhaoyuan Wu^1,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069820 - 27 December 2025

Abstract The increasing complexity of China’s electricity market creates substantial challenges for settlement automation, data consistency, and operational scalability. Existing provincial settlement systems are fragmented, lack a unified data structure, and depend heavily on manual intervention to process high-frequency and retroactive transactions. To address these limitations, a graph-based unified settlement framework is proposed to enhance automation, flexibility, and adaptability in electricity market settlements. A flexible attribute-graph model is employed to represent heterogeneous multi-market data, enabling standardized integration, rapid querying, and seamless adaptation to evolving business requirements. An extensible operator library is designed to support configurable settlement… More >

Kelvin Kang Wee Tang^1,*, Muhammad Hafiz Mohd Yatim¹, Norhadija Darwin², Wan Anom Wan Aris¹, Sim Ching Yen³, Nurfazira Mohamed Fadil³

Revue Internationale de Géomatique, Vol.34, pp. 587-602, 2025, DOI:10.32604/rig.2025.065583 - 06 August 2025

Abstract The integration of Unmanned Aerial Vehicles (UAVs) and Uncrewed Surface Vehicles (USVs) has revolutionized topographic and bathymetric mapping, significantly enhancing the accuracy and efficiency of geospatial data acquisition processes. This innovative approach synergistically combines terrestrial data collected by UAVs with underwater data obtained through USVs, culminating in the creation of unified high-resolution Digital Elevation Models (DEMs) of the river basin region represents a vital step toward understanding the dynamic interactions between land and water bodies. Hence, the seamless Topo-Bathymetric Elevation Model offers a detailed perspective of the river system, supporting informed decision-making in addressing sediment… More >

Aya M. Al-Zoghby^1,2, Ahmed Ismail Ebada^1,*, Aya S. Saleh¹, Mohammed Abdelhay³, Wael A. Awad¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4155-4193, 2025, DOI:10.32604/cmc.2025.065571 - 30 July 2025

Abstract Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics, advancing precision medicine by enabling integration and learning from diverse data sources. The exponential growth of high-dimensional healthcare data, encompassing genomic, transcriptomic, and other omics profiles, as well as radiological imaging and histopathological slides, makes this approach increasingly important because, when examined separately, these data sources only offer a fragmented picture of intricate disease processes. Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling, more robust disease characterization, and improved treatment decision-making. This review… More >

Mesut Ulu^1,*, Yusuf Sait Türkan², Kenan Mengüç³, Ersin Namlı², Tarık Küçükdeniz²

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2259-2281, 2024, DOI:10.32604/cmc.2024.052323 - 15 August 2024

Abstract Today, urban traffic, growing populations, and dense transportation networks are contributing to an increase in traffic incidents. These incidents include traffic accidents, vehicle breakdowns, fires, and traffic disputes, resulting in long waiting times, high carbon emissions, and other undesirable situations. It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities. This study presents a model for forecasting the traffic incident duration of traffic events with high precision. The proposed model goes through a 4-stage process using various features to predict the… More >

Yolotzin Apatzingán Palomino-Hermosillo¹, Ángel Elpidio Díaz-Jasso², Rosendo Balois-Morales¹, Verónica Alhelí Ochoa-Jiménez^1,3, Pedro Ulises Bautista-Rosales¹, Guillermo Berumen-Varela^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1717-1732, 2024, DOI:10.32604/phyton.2024.052216 - 30 July 2024

Abstract Soursop (Annona muricata L.) is a tropical fruit highly valued for its unique flavor, nutritional value, and health-promoting properties. The ripening process of soursop involves complex changes in gene expression and metabolite accumulation, which have been studied using various omics technologies. Transcriptome analysis has provided insights into the regulation of key genes involved in ripening, while metabolic compound analysis has revealed the presence of numerous bioactive compounds with potential health benefits. However, the integration of transcriptome and metabolite compound data has not been extensively explored in soursop. Therefore, in this paper, we present a comprehensive analysis… More >