Ziying Huang¹, Ruikang Ma¹, Anning Li², Yufei Cheng¹, Xiaolin Lin², Mengzhi Li³, Yu Zhang², Liping Shi¹, Linlin Dong^1,*

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.074419

Abstract Epimedium is commonly used to treat bone injury and kidney disease, with prenylated flavonol glycosides (PFGs) as its active ingredients. It has attracted much attention due to prominent healthcare and therapeutic effects, but faces problems of adulteration with closely related species and confusion about geographical origins. In this study, multiple technical approaches were employed to identify its genetic characteristics and metabolic differences. Based on DNA barcoding, 20 batches of samples were analyzed. The genetic distances of matK, ITS and psbA-trnH within species were all smaller than those between species, and psbA-trnH along with ITS + psbA-trnH proved most effective… More >

Elizabeth Martinez¹, Ana K. Escalera-Ordaz¹, Francisco J. Espinosa-García², Yolanda M. García-Rodríguez², Rafael Ariza-Flores³, Javier Ponce-Saavedra⁴, Patricio Apáez-Barrios⁵, Héctor Guillén-Andrade^1,*

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.073438

Abstract Volatile organic compounds (VOCs) play an important role in plant survival and adaptation. They contribute to defense against pests and pathogens, tolerance to abiotic stress, and the mediation of essential ecological interactions such as pollination and attraction of dispersal agents. The complex mixture of VOCs produced by an organism, known as volatilome, varies across species, populations, and individuals, making VOCs a major factor in crop diversification and adaptation. In this context, characterizing the volatilome of crop genotypes can provide insight into their ecological associations and potential relationships with agronomic traits. In this study, the volatilome… More >

Xiaoyu Zhang^1,#, RenFei Zong^1,#, Yan Sun¹, Nan Chen², Kunyao Zhu¹, Hang Tong¹, Tinghao Li¹, Junlong Zhu¹, Zijia Qin¹, Linfeng Wu¹, Aimin Wang¹, Weiyang He^1,*

Oncology Research, DOI:10.32604/or.2025.072084

Abstract Objective: While cisplatin-based chemotherapy is pivotal for advanced bladder cancer, acquired resistance remains a major obstacle. This study investigates key molecular drivers of this resistance and potential reversal strategies. Methods: We established GC (Gemcitabine and Cisplatin)-resistant T24-R and UC3-R cell lines from T24 and UM-UC-3 (UC3) cells. Transcriptomic and proteomic analyses identified differentially expressed molecules. Apoptosis and cell viability were assessed by flow cytometry and CCK-8 (Cell Counting Kit-8) assays, while RT-qPCR (Reverse Transcription Quantitative Polymerase Chain Reaction) and Western blot analyzed gene and protein expression. Immunofluorescence evaluated FAK (Focal Adhesion Kinase) phosphorylation, and a… More >

Ahmed K. Ali¹, Jungpil Shin^2,*, Yujin Lim^3,*, Da-Hun Seong³

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073871

Abstract Single-signal detection in orthogonal frequency-division multiplexing (OFDM) systems presents a challenge due to the time-varying nature of wireless channels. Although conventional methods have limitations, particularly in multi-input multioutput orthogonal frequency division multiplexing (MIMO-OFDM) systems, this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection. Specifically, we propose two hybrid architectures that integrate a convolutional neural network (CNN) with a recurrent neural network (RNN), namely, CNN-long short-term memory (CNN-LSTM) and CNN-bidirectional-LSTM (CNN-Bi-LSTM), designed to enhance signal detection performance in MIMO-OFDM systems. The proposed CNN-LSTM and CNN-Bi-LSTM architectures are… More >

Shuai Zhao, Zhiwei Zhang^*, Junkai Wang, Ye Yuan, Guoren Wang

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073315

Abstract With the development of sharded blockchains, high cross-shard rates and load imbalance have emerged as major challenges. Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates. Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads. Meanwhile, because of the coupling between consensus and execution, a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution. However, we observe that transaction partitioning and subsequent consensus do not require… More >

José Arturo Ramírez-Fernández¹, Henevith G. Méndez-Figueroa¹, Sebastián Ossandón^2,*, Ricardo Galván-Martínez³, Miguel Ángel Hernández-Pérez³, Ricardo Orozco-Cruz³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072707

Abstract In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical More >

Gowrishankar Jayaraman¹, Ashok Kumar Munnangi², Ramesh Sekaran³, Arunkumar Gopu³, Manikandan Ramachandran^4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071124

Abstract Industrial Cyber-Physical Systems (ICPSs) play a vital role in modern industries by providing an intellectual foundation for automated operations. With the increasing integration of information-driven processes, ensuring the security of Industrial Control Production Systems (ICPSs) has become a critical challenge. These systems are highly vulnerable to attacks such as denial-of-service (DoS), eclipse, and Sybil attacks, which can significantly disrupt industrial operations. This work proposes an effective protection strategy using an Artificial Intelligence (AI)-enabled Smart Contract (SC) framework combined with the Heterogeneous Barzilai–Borwein Support Vector (HBBSV) method for industrial-based CPS environments. The approach reduces run time… More >

Faisal Mahmud¹, Fahim Mahmud², Rashedur M. Rahman^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073519

Abstract Federated Learning (FL) has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and smart systems. As the field continues to evolve, the research field has become more complex and scattered, covering different system designs, training methods, and privacy techniques. This survey is organized around the three core challenges: how the data is distributed, how models are synchronized, and how to defend against attacks. It provides a structured and up-to-date review of… More >

Tahir Mahmood^1,, Muhammad Waseem Ashraf^1,, Shahzadi Tayyaba², Muhammad Munir³, Babiker M. A. Abdel-Banat³ and Hassan Ali Dinar³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072436

Abstract Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In… More >

Shuigen Hu^1,2, Hao Wang³, Qingyang Wei^4,*, Maosen Cao^2,4, Drahomír Novák⁵

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.073665

Abstract As vital hydraulic infrastructures, concrete dams demand uncompromising safety assurance. Seismic effect commonly serves as a potential factor contributing to the damage of concrete dams, making seismic performance analysis crucial for structural integrity. Numerical simulation based on damage mechanics is usually considered as the approach for investigating the seismic damage behavior of concrete dams. To address the limitations of existing studies and extract the key dynamic characteristics of concrete arch dams, a concrete elastoplastic damage mechanics model is adopted, a seismic load input technique involving the viscoelastic boundary along with equivalent nodal forces is generated,… More >