The objective of this study was to develop a method to assess and analyze the total allelopathic potential of crop germplasm and to test this method on four winter wheat accessions commonly planted in the Loess Platea...The objective of this study was to develop a method to assess and analyze the total allelopathic potential of crop germplasm and to test this method on four winter wheat accessions commonly planted in the Loess Plateau. A systems engineering model was developed and used to evaluate the total allelopathic potential of crop cultivars. In addition, a method for quantifying the total allelopathic potential in crop accessions was presented. Total allelopathic potential of four winter wheat accessions from the Loess Plateau was estimated and compared using a systems theory approach. The model assessed allelopathic potential in different parts of the plants from the time wheat turned green in spring until maturity. Results from these models indicated that the four wheat accessions had very weak allelopathic potential. Allelopathic potential declined in the order Xiaoyan 22 〉 Ningdong 1 〉 Fengchan 3 〉 Bima 1. This system engineering evaluation method allows for the assessment of allelopathic potential among crop varieties. It will help plant breeders to select and develop allelopathic crop accessions that combine weed suppression properties with agronomic traits related to yield and quality.展开更多
This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core obj...This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for researchers and practitioners in selecting tools for their unique challenges.展开更多
文摘The objective of this study was to develop a method to assess and analyze the total allelopathic potential of crop germplasm and to test this method on four winter wheat accessions commonly planted in the Loess Plateau. A systems engineering model was developed and used to evaluate the total allelopathic potential of crop cultivars. In addition, a method for quantifying the total allelopathic potential in crop accessions was presented. Total allelopathic potential of four winter wheat accessions from the Loess Plateau was estimated and compared using a systems theory approach. The model assessed allelopathic potential in different parts of the plants from the time wheat turned green in spring until maturity. Results from these models indicated that the four wheat accessions had very weak allelopathic potential. Allelopathic potential declined in the order Xiaoyan 22 〉 Ningdong 1 〉 Fengchan 3 〉 Bima 1. This system engineering evaluation method allows for the assessment of allelopathic potential among crop varieties. It will help plant breeders to select and develop allelopathic crop accessions that combine weed suppression properties with agronomic traits related to yield and quality.
文摘This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for researchers and practitioners in selecting tools for their unique challenges.
