Jianhua Zhang^1,2, Wen Sun³, Zhijun Li^1,*

Energy Engineering, Vol.121, No.10, pp. 2893-2906, 2024, DOI:10.32604/ee.2024.051093

Abstract Two types of tightly coupled Selective Catalytic Reduction (SCR) mixers were designed in this study, namely Mixer 1 integrated with an SCR catalyst and Mixer 2 arranged separately. Computational Fluid Dynamics (CFD) software was utilized to model the gas flow, spraying, and pyrolysis reaction of the aqueous urea solution in the tightly coupled SCR system. The parameters of gas flow velocity uniformity and ammonia distribution uniformity were simulated and calculated for both Mixer 1 and Mixer 2 in the tightly coupled SCR system to compare their advantages and disadvantages. The simulation results indicated that Mixer… More >

Yi Pan¹, Lei Xie^2,*, Hongye Su²

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 683-696, 2024, DOI:10.32604/iasc.2023.038543

Abstract In the process of fault detection and classification, the operation mode usually drifts over time, which brings great challenges to the algorithms. Because traditional machine learning based fault classification cannot dynamically update the trained model according to the probability distribution of the testing dataset, the accuracy of these traditional methods usually drops significantly in the case of covariate shift. In this paper, an importance-weighted transfer learning method is proposed for fault classification in the nonlinear multi-mode industrial process. It effectively alters the drift between the training and testing dataset. Firstly, the mutual information method is… More >

Saffari Mohsen^1,2,#, Yi-Ching Lin^3,#, Po-Ching Huang⁴, Yu-Ting Huang⁵, Ru-Yi Huang^6,7, Chi Hsien Huang^8,9,*, Jung-Sheng Chen¹⁰, Wai Chuen Poon¹¹, Mark D. Griffiths¹², Chung-Ying Lin^5,13,14,15,*

International Journal of Mental Health Promotion, Vol.26, No.8, pp. 633-641, 2024, DOI:10.32604/ijmhp.2024.053432

Abstract Background: Physical inactivity is a major public health problem worldwide that results in physical and mental health problems. One major issue for physical inactivity is weight stigma (WS), especially perceived WS, which could lead to a tendency to avoid physical activity (PA). To better understand the association between perceived WS and PA, knowledge of the likely mediators in this association such as weight bias internalization (WBI) and psychological distress were investigated in the present study. Methods: Using a two-wave longitudinal study and convenient sampling, 388 Taiwanese participants (55% females; mean age = 29.7 years [SD… More >

Fuxin Li¹, Enyan Chen¹, Xinxin Chen¹, Jingyu Jia¹, Hemin Wang¹, Jie Zhang¹, Jianrui Sun¹, Xin Li^1,2,3,*

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 2095-2113, 2024, DOI:10.32604/phyton.2024.054231

Abstract Salt stress inhibits plant growth and affects the biosynthesis of its secondary metabolites. Flavonoids are natural compounds that possess many important biological activities, playing a significant role in the medicinal activity of Eucommia ulmoides (E. ulmoides). To investigate the mechanism by which salt stress affects the biosynthesis of flavonoids in E. ulmoides, a comprehensive analysis of metabolomics and transcriptomics was conducted. The results indicated that salt stress led to the wilting and darkening of E. ulmoides leaves, accompanied by a decrease in chlorophyll levels, and significantly induced malondialdehyde (MDA) and relative electrical conductivity. During salt stress, most metabolites in… More >

Carla Etel Suárez^1,*, María Sol Rossini^1,3, Ernesto Francisco Atilio Morici¹, Héctor Daniel Esterlich^1,2

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 2063-2074, 2024, DOI:10.32604/phyton.2024.053193

Abstract The semiarid grasslands of Argentina’s central region have been modified by domestic livestock grazing, both in their composition and structure. The increase in the proportion of woody and non-forage species and the decrease in forage species are some of the most evident results of this process. There is limited available information about the effect of differential grazing pressures on morphometric attributes of native species, and it also depends on the life histories of the species in this grassland. The objective of this work was to evaluate some morphometric aspects in the grasses Poa ligularis Nees ex… More >

Claudia A. Porcelli¹, Gerardo Rubio², Flavio H. Gutiérrez Boem², Raul S. Lavado^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 2009-2018, 2024, DOI:10.32604/phyton.2024.052874

Abstract The effects of the salt stress on plant growth are usually increased by the water stress. We studied the impact of both stresses in simultaneous pulses of drought and salinity on Paspalum dilatatum. This forage species is native to South America, spread in grasslands in many tropical, subtropical, and temperate areas of the world, and very common in grasslands of the Flooding Pampas of Argentina. Mimicking what happens in nature. We compared a pot experiment, a non-stressed control against water stress for a month (midpoint between field capacity and wilting point), and two saline stresses (moderate,… More >

Yuan Huang¹, Jia Wei², Peigang Liu², Yan Zhu², Tianbao Lin², Zhiqiang Lv², Yijun Li³, Mei Zong³, Yun Zhou³, Junshan Gao^1,*, Zilong Xu^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.8, pp. 1995-2008, 2024, DOI:10.32604/phyton.2024.052353

Abstract To provide a scientific basis for controlling mulberry bacterial blight in Bazhong, Sichuan, China (BSC), this study aimed to isolate and purify pathogenic bacteria from diseased branches of mulberry trees in the region and to clarify their taxonomic status using morphological observation, physiological and biochemical detection, molecular-level identification, and the construction of a phylogenetic tree. A total of 218 bacterial strains were isolated from samples of diseased mulberry branches. Of these, 7 strains were identified as pathogenic bacteria based on pathogenicity tests conducted in accordance with Koch’s postulates. Preliminary findings from the analysis of the More >

Lirong Yin¹, Lei Wang¹, Zhuohang Cai², Siyu Lu^2,*, Ruiyang Wang², Ahmed AlSanad³, Salman A. AlQahtani³, Xiaobing Chen⁴, Zhengtong Yin⁵, Xiaolu Li⁶, Wenfeng Zheng^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 771-786, 2024, DOI:10.32604/cmes.2024.052622

Abstract Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset More >

Chun-Ming Wu¹, Mei-Ling Ren^2,*, Jin Lei², Zi-Mu Jiang³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2857-2872, 2024, DOI:10.32604/cmc.2024.053708

Abstract To address the issues of incomplete information, blurred details, loss of details, and insufficient contrast in infrared and visible image fusion, an image fusion algorithm based on a convolutional autoencoder is proposed. The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map. A multi-scale convolution attention module is suggested to enhance the communication of feature information. At the same time, the feature transformation module is introduced to learn more robust feature representations, aiming to preserve the integrity of… More >

Abdallah Ghourabi^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2743-2760, 2024, DOI:10.32604/cmc.2024.053163

Abstract The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The… More >