Gülay Zulkadir^*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1879-1896, 2025, DOI:10.32604/phyton.2025.064144 - 27 June 2025

Abstract Soil salinization is a prominent global environmental issue that considerably affects the sustainable development of agriculture worldwide. Maize, a key crop integral to the global agricultural economy, is especially susceptible to the detrimental impacts of salt stress, which can impede its growth and development from the germination phase through to the seedling stage. Soil salinity tends to escalate due to improper irrigation methods, particularly in arid and semi-arid environments. Consequently, it is essential to evaluate potential genotypes and select those with high salt tolerance. In this study, 39 popcorn kernel genotypes were examined under varying… More >

Yuhe Wang^1,2, Haijun Sun³, Yaqiong Hao^2,4, Xiancan Zhu¹, Ju Min^2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.6, pp. 1735-1750, 2025, DOI:10.32604/phyton.2025.063868 - 27 June 2025

Abstract Over the past three decades, the expansion of intensive vegetable farming in southern China has led to excessive nitrogen (N) and phosphorus (P) fertilizer application, causing substantial N and P runoff losses. This study investigated four major vegetable production regions in southern China—the upper reaches of the Yangtze River (U-YR), the middle lower reaches of the Yangtze River (ML-YR), the Southeast Coast (SC), and the Pearl River basin (PR)—analyzing 175 published articles to characterize spatiotemporal patterns of N and P fertilizer applications and associated runoff losses from 1992 to 2021. The result showed that the… More >

Lanying Chen^1,2, Yifu Cai³, Qiumei Quan^3,*, Yunxiang Li³

Phyton-International Journal of Experimental Botany, Vol.94, No.5, pp. 1519-1532, 2025, DOI:10.32604/phyton.2025.064112 - 29 May 2025

Abstract This study investigates the diurnal patterns of nectar secretion, sugar content, and caloric value in Epimedium wushanense, and their interaction mechanisms with pollinator behavior under varying environmental conditions. Nectar secretion exhibited a diurnal pattern, peaking between 11:00 and 13:00, with progressive increases in both volumes (19.07 ± 1.66 μL/day) and caloric value (6.03 ± 0.55 cal/day) over four consecutive days, culminating in maximal production on Day 4 (p < 0.05). Floral bagging significantly altered nectar traits (Mann-Whitney U test, p < 0.05), with bagged inflorescences demonstrating 61.82% higher nectar volume productivity relative to unbagged controls. Pollinator visitation,… More >

Zhebin Sun¹, Wei Wang¹, Mingxuan Du², Tao Liang¹, Yang Liu¹, Hailong Fan³, Cuiping Li², Xingxu Zhu², Junhui Li^2,*

Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

Yingyong Zou^*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625 - 19 May 2025

Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

Tatyana Lyubimova^1,*, Anatoly Lepikhin², Yanina Parshakova¹, Andrey Bogomolov², Alibek Issakhov³

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.4, pp. 741-756, 2025, DOI:10.32604/fdmp.2025.061649 - 06 May 2025

Abstract The article presents the results of in-kind measurements and numerical modeling of the formation of water characteristics in the Kama River, which is used for technical water supply in the production of potash fertilizers. In the warm season, risks arise that threaten the sustainability of the water supply. It was found that in the summer, when the studied section of the Kama River is backed up by the Kama Hydroelectric Power Station, there is a significant decrease in flow rates, which leads to vertical stratification of water properties. This, in turn, significantly limits the possibilities… More >

Jianxin Feng^1,2,*, Xiaoyao Liu^1,2

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2087-2107, 2025, DOI:10.32604/cmc.2025.060252 - 16 April 2025

Abstract As a form of discrete representation learning, Vector Quantized Variational Autoencoders (VQ-VAE) have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capacity. However, existing VQ-VAEs often perform quantization in the spatial domain, ignoring global structural information and potentially suffering from codebook collapse and information coupling issues. This paper proposes a frequency quantized variational autoencoder (FQ-VAE) to address these issues. The proposed method transforms image features into linear combinations in the frequency domain using a 2D fast Fourier transform (2D-FFT) and performs adaptive quantization on these frequency components… More >

Saad Alahmari^1,*, Abdulwhab Alkharashi²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

Dah-Jing Jwo^1,2,*, Yi Chang², Ta-Shun Cho³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2771-2789, 2025, DOI:10.32604/cmes.2025.057825 - 03 March 2025

Abstract In this paper, an advanced satellite navigation filter design, referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter (VBMCEKF), is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers. The proposed design modifies the extended Kalman filter (EKF) for the global navigation satellite system (GNSS), integrating the maximum correntropy criterion (MCC) and the variational Bayesian (VB) method. This adaptive algorithm effectively reduces non-line-of-sight (NLOS) reception contamination and improves estimation accuracy, particularly in time-varying GNSS measurements. Experimental results show that the proposed method significantly outperforms conventional approaches in estimation More >

Liyao Yang¹, Hongyan Ma^1,2,3,*, Yingda Zhang¹, Wei He¹

Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >