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  • Open Access

    ARTICLE

    Optimizing Biodiesel Production from Karanja and Algae Oil with Nano Catalyst: RSM and ANN Approach

    Sujeet Kesharvani1, Sakhi Katre1, Suyasha Pandey1, Gaurav Dwivedi1,*, Tikendra Nath Verma2, Prashant Baredar1

    Energy Engineering, Vol.121, No.9, pp. 2363-2388, 2024, DOI:10.32604/ee.2024.052523

    Abstract This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) model to optimize biodiesel yield. Blend of C. vulgaris and Karanja oils is utilized, aiming to reduce free fatty acid content to 1% through single-step transesterification. Optimization reveals peak biodiesel yield conditions: 1% catalyst quantity, 91.47 min reaction time, 56.86°C reaction temperature, and 8.46:1 methanol to oil molar ratio. The ANN model outperforms RSM in yield prediction accuracy. Environmental impact assessment yields an E-factor of 0.0251 at maximum yield, indicating responsible production… More >

  • Open Access

    REVIEW

    Organ Abscission in Plants: With Special Emphasis on Bell Pepper

    Ramiro Reyes-Castro1, Héctor G. Núñez-Palenius1,*, Juan I. Valiente-Banuet2, María E. Sosa-Morales1, Blanca E. Orosco-Alcalá1, Rafael Guzmán-Mendoza1, Graciela M. L. Ruiz-Aguilar1, Rogelio Costilla-Salazar1

    Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1467-1484, 2024, DOI:10.32604/phyton.2024.051644

    Abstract Bell pepper (Capsicum annuum L.), along with potato and tomato, is one of the three most cultivated vegetables in the world. Bell pepper is worldwide accepted due to its characteristics of color, smell, flavor, and texture. Bell pepper is also considered a nutritious food due to its vitamin and antioxidant contents. In Mexico, bell pepper production has a high value because it is grown for the international markets, particularly the United States of America. Nevertheless, the abscission of flowers and fruits is a factor that limits the yield, hinders the planning of activities, and causes a More >

  • Open Access

    ARTICLE

    Variation Characteristics of Root Traits of Different Alfalfa Cultivars under Saline-Alkaline Stress and their Relationship with Soil Environmental Factors

    Tian-Jiao Wei1, Guang Li1, Yan-Ru Cui1, Jiao Xie1, Xing-Ai Gao1, Xing Teng1, Xin-Ying Zhao1, Fa-Chun Guan1,*, Zheng-Wei Liang2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 29-43, 2024, DOI:10.32604/phyton.2023.046078

    Abstract Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield. It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments. In this study, the root biomass, root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline… More >

  • Open Access

    ARTICLE

    Predicting COVID-19 Based on Environmental Factors With Machine Learning

    Amjed Basil Abdulkareem1, Nor Samsiah Sani1,*, Shahnorbanun Sahran1, Zaid Abdi Alkareem Alyessari1, Afzan Adam1, Abdul Hadi Abd Rahman1, Abdulkarem Basil Abdulkarem2

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 305-320, 2021, DOI:10.32604/iasc.2021.015413

    Abstract The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet)… More >

  • Open Access

    ARTICLE

    Composition and abundance of phytoplankton ın relation to physical and chemical variables in The Kars River, Turkey

    Özbay H

    Phyton-International Journal of Experimental Botany, Vol.80, pp. 85-92, 2011, DOI:10.32604/phyton.2011.80.085

    Abstract The phytoplankton of the Kars River was studied from May to October 2005 at five sampling stations. Sixty-six phytoplankton taxa were determined, consisting of Cyanophyta (9), Chlorophyta (25), Euglenophyta (18), Bacillariophyta (7), Cryptophyta (3), Dinophyta (1) and Chrysophyta (3). Total phytoplankton density increased from May to July and then decreased until October. The dominant phytoplankton group was Cyanophyta (36.5 - 64.4%) for most of the study period, followed by Bacillariophyta (20.4 – 38.7%) and Chlorophyta (20.9 – 28.9%). Temperature, pH and dissolved oxygen ranged from 9.6 °C to 21.6 °C; 7.6 to 8.0, and 5.9 More >

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