Shaojie Min¹, Yaxiao Luo¹, Kebing Liu¹, Qingyuan Gong², Yang Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.073175 - 09 December 2025

Abstract User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them… More >

Binglei Yue, Aili Jiang, Chun Yang, Junwei Lei, Heng Liu, Yin Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.071047 - 10 November 2025

Abstract With the growing advancement of wireless communication technologies, WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution. Among the available signal types, Channel State Information (CSI) offers fine-grained temporal, frequency, and spatial insights into multipath propagation, making it a crucial data source for human-centric sensing. Recently, the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments. This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI. We first outline mainstream CSI acquisition tools and their hardware specifications, More >

Xingkang Jiang^*, Jing Tian, Yong Xu

Canadian Journal of Urology, Vol.32, No.6, pp. 539-540, 2025, DOI:10.32604/cju.2025.073363 - 30 December 2025

Abstract This article has no abstract. More >

Victor De Araujo^1,2,3,*, Pedro Jardim^3,4, Poliana Pessôa³, Juliano Vasconcelos^2,5, Matheus Souza⁶, José Garcia⁷, Jozef Švajlenka⁸, André Christoforo^3,1

Journal of Renewable Materials, Vol.13, No.12, pp. 2479-2524, 2025, DOI:10.32604/jrm.2025.02025-0127 - 23 December 2025

Abstract Finger-joint lumber is a sustainable building product commercialized as a structural solution for beams, pillars and other thin flat load-bearing elements. This study aims to study finger-joint lumber and its industry to promote this engineered wood product. The first research stage assessed the collection of publications on finger-joint lumber available globally, in which a structured protocol was developed to prospect studies based on two complementary methodologies: PRISMA 2020 using Scopus and Web of Science databases, and Snowball using both forward and backward models to complete with additional literature. The second research stage assessed finger-joint lumber… More >

Daskshnamoorthy Manivannan^*

Journal on Internet of Things, Vol.7, pp. 71-97, 2025, DOI:10.32604/jiot.2025.072809 - 24 December 2025

Abstract Attribute-Based Encryption (ABE) secures data by tying decryption rights to user attributes instead of identities, enabling fine-grained access control. However, many ABE schemes are unsuitable for Internet of Things (IoT) due to limited device resources. This paper critically surveys ABE schemes developed specifically for IoT over the past decade, examining their evolution, strengths, limitations, and access control capabilities. It provides insights into their security, effectiveness, and real-world applicability, highlights the current state of ABE in securing IoT data and access, and discusses remaining challenges and open issues. More >

Shahin Imran^1,2, Maki Katsuhara^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3653-3665, 2025, DOI:10.32604/phyton.2025.073959 - 01 December 2025

Abstract Barley (Hordeum vulgare L.) employs the Na⁺ transporter HvHKT1;1, which is an N⁺-selective transporter. This study characterized the full-length HvHKT1;1 (HvHKT1;1-FL) and three mRNA variants (HvHKT1;1-V1, -V2, and -V3), which encode polypeptides of 64.7, 54.0, 40.5, and 32.9 kDa, respectively. Tissue-specific expression profiling revealed that HvHKT1;1-FL is the most abundant transcript across leaf, sheath, and root tissues under normal conditions, with the highest expression in leaves. Under 150 mM NaCl stress, HvHKT1;1-FL and its variants showed a dynamic, time-dependent expression pattern, with peak leaf expression at 2 h, sheath expression at 12 h, and root expression at 2 h, suggesting their… More >

Kinzah Noor¹, Agbotiname Lucky Imoize^2,*, Michael Adedosu Adelabu³, Cheng-Chi Lee^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1575-1664, 2025, DOI:10.32604/cmes.2025.073200 - 26 November 2025

Abstract The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern… More > Graphic Abstract

Saeed Asadi¹, Hajar Kazemi Naeini¹, Delaram Hassanlou², Abolhassan Pishahang³, Saeid Aghasoleymani Najafabadi⁴, Abbas Sharifi⁵, Mohsen Ahmadi^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1259-1301, 2025, DOI:10.32604/cmes.2025.070528 - 26 November 2025

Abstract The growing energy demand of buildings, driven by rapid urbanization, poses significant challenges for sustainable urban development. As buildings account for over 40% of global energy consumption, innovative solutions are needed to improve efficiency, resilience, and environmental performance. This paper reviews the integration of Digital Twin (DT) technologies and Machine Learning (ML) for optimizing energy management in smart buildings connected to smart grids. A key enabler of this integration is the Internet of Things (IoT), which provides the sensor networks and real-time data streams that fee/d DT–ML frameworks, enabling accurate monitoring, forecasting, and adaptive control.… More >

Shohreh Moradpour^1,#, Shuai Zhao^2,#, Mojgan Entezari¹, Shamsollah Ayoubi^3,*, Seyed Roohollah Mousavi⁴

Revue Internationale de Géomatique, Vol.34, pp. 809-829, 2025, DOI:10.32604/rig.2025.069538 - 06 November 2025

Abstract Accurate spatial prediction of soil organic carbon (SOC) and soil inorganic carbon (SIC) is vital for land management decisions. This study targets SOC/SIC mapping challenges at the watershed scale in central Iran by addressing environmental heterogeneity through a random forest (RF) model combined with bootstrapping to assess prediction uncertainty. Thirty-eight environmental variables—categorized into climatic, soil physicochemical, topographic, geomorphic, and remote sensing (RS)-based factors—were considered. Variable importance analysis (via) and partial dependence plots (PDP) identified land use, RS indices, and topography as key predictors of SOC. For SIC, soil reflectance (Bands 5 and 7, ETM+), topography, More > Graphic Abstract

Canlin Cui¹, Junyu Yao^1,*, Heng Xia^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4275-4306, 2025, DOI:10.32604/cmc.2025.069097 - 23 October 2025

Abstract High-quality data is essential for the success of data-driven learning tasks. The characteristics, precision, and completeness of the datasets critically determine the reliability, interpretability, and effectiveness of subsequent analyzes and applications, such as fault detection, predictive maintenance, and process optimization. However, for many industrial processes, obtaining sufficient high-quality data remains a significant challenge due to high costs, safety concerns, and practical constraints. To overcome these challenges, data augmentation has emerged as a rapidly growing research area, attracting considerable attention across both academia and industry. By expanding datasets, data augmentation techniques improve greater generalization and more… More >