Bin Zhang¹, En-Cheng Liou^2,*, Yi-Chih Tung³, Muhammad Usman^2,4, Chiung-An Chen^2,4, Chao-Shun Yang^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2551-2571, 2025, DOI:10.32604/cmes.2025.072471 - 26 November 2025

Abstract Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication (ISAC) systems, enabling precise navigation in environments where Global Positioning System (GPS) signals are unavailable. Existing methods, such as map-based navigation or site-specific fingerprinting, often require intensive data collection and lack generalization capability across different buildings, thereby limiting scalability. This study proposes a cross-site, map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge. The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes, facilitating accurate… More >

Bo Yang^1,2, Yu Zhou³, Jiawei Tian^4,*, Xiang Zhang², Fupei Guo², Shan Liu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2253-2276, 2025, DOI:10.32604/cmes.2025.072031 - 26 November 2025

Abstract Accurate calibration of surgical instruments and ultrasound probes is essential for achieving high precision in image guided minimally invasive procedures. However, existing methods typically treat the calibration of the needle tip and the ultrasound probe as two independent processes, lacking an integrated calibration mechanism, which often leads to cumulative errors and reduced spatial consistency. To address this challenge, we propose a joint calibration model that unifies the calibration of the surgical needle tip and the ultrasound probe within a single coordinate system. The method formulates the calibration process through a series of mathematical models and… More >

Zehui Qi, Sixing Wu^*, Jianbin Li

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3739-3766, 2025, DOI:10.32604/cmc.2025.067180 - 23 September 2025

Abstract False Data Injection Attacks (FDIAs) pose a critical security threat to modern power grids, corrupting state estimation and enabling malicious control actions that can lead to severe consequences, including cascading failures, large-scale blackouts, and significant economic losses. While detecting attacks is important, accurately localizing compromised nodes or measurements is even more critical, as it enables timely mitigation, targeted response, and enhanced system resilience beyond what detection alone can offer. Existing research typically models topological features using fixed structures, which can introduce irrelevant information and affect the effectiveness of feature extraction. To address this limitation, this… More >

Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2683-2683, 2025, DOI:10.32604/cmes.2025.069871 - 31 August 2025

Abstract This article has no abstract. More >

Qiang Gao¹, Lei Shen^1,*, Jiaming Shi², Xinfa Gu², Shanyun Gu¹, Yuwei Ge¹, Yang Xie¹, Xiaoqiong Zhu¹, Baoguo Zang¹, Ming Zhang¹, Muhammad Shahzad Nazir², Jie Ji²

Energy Engineering, Vol.122, No.7, pp. 2767-2800, 2025, DOI:10.32604/ee.2025.065600 - 27 June 2025

Abstract The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems. Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multi-modal data fusion. This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large language models (LLMs) with adaptive optimization, achieving three key innovations: (1) A hierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction, (2) Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds (Daubechies-4 basis, 6-level decomposition), and… More >

Ye Zeng^1,*, Bingmei M. FU^2,*

BIOCELL, Vol.49, No.6, pp. 925-930, 2025, DOI:10.32604/biocell.2025.065879 - 24 June 2025

Abstract This article has no abstract. More >

Trong Hieu Luu¹, Phan Nguyen Ky Phuc², Quang Hieu Ngo^1,*, Thanh Tam Nguyen³, Huu Cuong Nguyen¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5643-5656, 2025, DOI:10.32604/cmc.2025.064007 - 19 May 2025

Abstract This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields. Utilizing a drone equipped with a high-resolution camera, images are captured 14 days post-sowing at a consistent altitude of six meters, employing autonomous flight for uniform data acquisition. The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage. The methodology follows a two-step process: first, the GoogleNet deep learning network identifies the location and center points of rice plants. Then, the U-Net deep learning network performs classification and… More >

Yuhang Li¹, Li Sun¹, Tao Wang¹, Bingjun Yu², Zhihong Gao³, Xiaochun Shu¹, Tengyue Yan¹, Weibing Zhuang^1,2,*, Zhong Wang^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.4, pp. 1211-1223, 2025, DOI:10.32604/phyton.2025.063647 - 30 April 2025

Abstract bHLH transcription factors, widely exist in various plants, and are vital for the growth and development of these plants. Among them, many have been implicated in anthocyanin biosynthesis across various plants. In the present study, a PdbHLH57 gene, belonging to the bHLH IIIf group, was characterized, which was isolated and cloned from the colored-leaf poplar ‘Zhongshancaiyun’ (ZSCY). The cDNA sequence of PdbHLH57 was 1887 base pairs, and the protein encoded by PdbHLH57 had 628 amino acids, the isoelectric point and molecular weight of which were 6.26 and 69.75 kDa, respectively. Through bioinformatics analysis, PdbHLH57 has been classified… More >

Kassymbek Ozhikenov¹, Zhadyra Alimbayeva^1,*, Chingiz Alimbayev^1,2,*, Aiman Ozhikenova¹, Yeldos Altay¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5257-5284, 2025, DOI:10.32604/cmc.2025.061508 - 06 March 2025

Abstract Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution… More >

Sachin Sharma^1,2,*, Brajesh Kumar Singh³, Hitendra Garg²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4691-4708, 2025, DOI:10.32604/cmc.2025.061252 - 06 March 2025

Abstract Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools. The manual forgery localization is often reliant on forensic expertise. In recent times, machine learning (ML) and deep learning (DL) have shown promising results in automating image forgery localization. However, the ML-based method relies on hand-crafted features. Conversely, the DL method automatically extracts shallow spatial features to enhance the accuracy. However, DL-based methods lack the global co-relation of the features due to this… More >