Arely A. Vargas-Díaz¹, Daisy Pérez-Brito², Beatriz Hernández-Carlos³, Jairo Cristóbal-Alejo^4,*, Silvia Andrade-Canto², Marcela Gamboa-Angulo^2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2839-2853, 2025, DOI:10.32604/phyton.2025.066682 - 30 September 2025

Abstract In the search for new alternatives to control tropical fungal pathogens, the ethanol extracts (EEs) from Acalypha gaumeri (Euphorbiaceae) roots showed antifungal properties against several tropical fungal phytopathogens. A. gaumeri is classified as endemic to the Yucatan Peninsula, Mexico, an area with distinct rainy, drought and northern seasons. The present study evaluated the antifungal activity of three wild populations of A. gaumeri collected quarterly in different seasons during one year against Alternaria chrysanthemi, Colletotrichum gloeosporioides, and Pseudocercospora fijiensis and explored their ex-situ propagation. The highest activity was shown by the EE from the Tinum wild population during the rainy season against A. chrysanthemi,… More >

Manoharan Madhiarasan^*

Energy Engineering, Vol.122, No.8, pp. 2993-3011, 2025, DOI:10.32604/ee.2025.068358 - 24 July 2025

Abstract Accurate Global Horizontal Irradiance (GHI) forecasting has become vital for successfully integrating solar energy into the electrical grid because of the expanding demand for green power and the worldwide shift favouring green energy resources. Particularly considering the implications of the aggressive GHG emission targets, accurate GHI forecasting has become vital for developing, designing, and operational managing solar energy systems. This research presented the core concepts of modelling and performance analysis of the application of various forecasting models such as ARIMA (Autoregressive Integrated Moving Average), Elaman NN (Elman Neural Network), RBFN (Radial Basis Function Neural Network),… 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 >

Sanghati Banerjee¹, Om Pal², Tauseef Ahmad³, Shruti Kanga⁴, Suraj Kumar Singh^1,*, Bhartendu Sajan¹

Revue Internationale de Géomatique, Vol.34, pp. 71-88, 2025, DOI:10.32604/rig.2025.057963 - 24 February 2025

Abstract Agriculture is a primary activity in many countries, with wheat being a major cereal crop in India. Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dynamics, pricing, and trade. This study focuses on estimating wheat acreage and yield in Barwala block, Hisar district, Haryana, for the 2019–2020 Rabi season using remote sensing techniques. Multi-temporal satellite data capturing phenological stages of wheat (Seedling to Ripening) were processed using supervised classification with a maximum likelihood classifier in ERDAS Imagine. Wheat crop acreage was determined by overlaying ground truth points on… More >

Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

Jiana Chen^1,2, Fangbo Cao^1,2, Min Huang^1,2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 807-817, 2024, DOI:10.32604/phyton.2024.048299 - 29 April 2024

Abstract Transplanting rice varieties grown in different seasons can lead to different yields due to different dry matter production. Early-season rice varieties transplanted in the late season can obtain high yields with short-growth duration and higher yields driven by higher dry matter production. To make clear the variations in dry matter production across seasons, four early-season rice varieties were chosen for late-season transplantation. The grain yield, dry matter accumulation, leaf photosynthetic, and leaf stomatal properties were studied. It was observed that the average yields of these four varieties in the late season were 33% greater, despite… More >

Yang Li^1,2, Jianjun Zhao², Xiaolong Yang², He Wang^1,*, Yuyan Wang¹

Energy Engineering, Vol.121, No.5, pp. 1263-1289, 2024, DOI:10.32604/ee.2024.046784 - 30 April 2024

Abstract Distributed photovoltaic (PV) is one of the important power sources for building a new power system with new energy as the main body. The rapid development of distributed PV has brought new challenges to the operation of distribution networks. In order to improve the absorption ability of large-scale distributed PV access to the distribution network, the AC/DC hybrid distribution network is constructed based on flexible interconnection technology, and a coordinated scheduling strategy model of hydrogen energy storage (HS) and distributed PV is established. Firstly, the mathematical model of distributed PV and HS system is established,… More >

Lipei Chen, Rilie Deng, Xuewen Li, Min Yu, Hongdong Xiao^*

Phyton-International Journal of Experimental Botany, Vol.92, No.12, pp. 3359-3370, 2023, DOI:10.32604/phyton.2023.046877 - 28 December 2023

Abstract

In the rice-wheat rotation system, biochar (BC) can be applied at the initiation of the rice or wheat season. Here, we compared the effects of BC that were applied at two different crop seasons on wheat production, quality, and soil profile properties in a rice-wheat rotation system with nitrogen (N) fertilizer applied at 280 kg/ha rate. Results showed that both wheat grain production and N recovery use efficiency were influenced by BC applied at two crop seasons. Biochar application did not affect the total non-essential amino-acid, but when applied during wheat season, BC significantly (p

… More >

Yaping Luo¹, Na Guo^1,*, Dong Liu², Shuming Peng³, Xinchen Wang⁴, Jie Wu³

Revue Internationale de Géomatique, Vol.32, pp. 39-52, 2023, DOI:10.32604/RIG.2023.046014 - 20 December 2023

Abstract Using hyperspectral data collected in January and June 2022 from the Sha River, the concentrations of total nitrogen (TN) and total phosphorus (TP) were estimated using the differential method. The results indicate that the optimal bands for estimation vary monthly due to temperature fluctuations. In the TN model, the power function model at 586 nm in January exhibited the strongest fit, yielding a fit coefficient (R²) of 0.95 and F-value of 164.57 at a significance level (p) of less than 0.01. Conversely, the exponential model at 477 nm in June provided the best fit, with R² = More > Graphic Abstract

Shumin Sun¹, Peng Yu¹, Jiawei Xing¹, Yan Cheng¹, Song Yang¹, Qian Ai^2,*

Energy Engineering, Vol.120, No.12, pp. 2761-2782, 2023, DOI:10.32604/ee.2023.042635 - 29 November 2023

Abstract Wind power prediction is very important for the economic dispatching of power systems containing wind power. In this work, a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and (long short-term memory) LSTM neural network is proposed and studied. First, the original data is prepossessed including removing outliers and filling in the gaps. Then, the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model. In addition, this study conducts seasonal classification… More >