Details of 23 basic benchmark functions. by Ying Li (38224)

The losses of the training process before and after the optimization of loss function. by Peihan Wen (6324315)

mAP@0.5 line charts for different models. by Meiling Shi (13148944)

Supplementary file 1_Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field.pdf by Shuangshuang Zhang (701020)

“…Aiming at this problem, this paper establishes a set of dynamic and static integrated classification model of tight sandstone gas wells in Sulige based on XGBoost algorithm. After comparison and verification, it is proved to be accurate and reliable. …”

Improved LMedS-based for sag measurement accuracy of transmission lines via PSO method by Xingzhi Ren (21400870)

“…Furthermore, a transmission line sag measurement method based on an improved Least Median of Squares (LMedS) algorithm is introduced. …”

Table 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx by Hongwei Li (17402)

“…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”

Table 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx by Hongwei Li (17402)

“…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”

Image 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

“…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”

Image 3_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

“…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”

Image 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

“…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”

Fig 11 - by Masashi Yagi (3186909)

Fig 14 - by Masashi Yagi (3186909)

Fig 9 - by Masashi Yagi (3186909)

Fig 6 - by Masashi Yagi (3186909)

Graphical implementation of the links. by Thibault Rolland (14833626)

Description of the real-world dataset. by Fadi K. Dib (5204807)

“…Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. …”

Screenshot of our visualization tool MGDrawVis. by Fadi K. Dib (5204807)

“…Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. …”

Comparison of differences in TCSE and VLS. by Shun Li (330132)

“…The proposed dynamic tuning and multi-objective optimization platform can enhance the comprehensive performance boundary of lasers in complex environments, providing a highly stable light source design solution for precision spectral measurement, high-resolution sensing, and quantum communication fields.…”

Physical model architecture of resonant cavity. by Shun Li (330132)

“…The proposed dynamic tuning and multi-objective optimization platform can enhance the comprehensive performance boundary of lasers in complex environments, providing a highly stable light source design solution for precision spectral measurement, high-resolution sensing, and quantum communication fields.…”

Comparison of differences in ELL and MTR. by Shun Li (330132)

“…The proposed dynamic tuning and multi-objective optimization platform can enhance the comprehensive performance boundary of lasers in complex environments, providing a highly stable light source design solution for precision spectral measurement, high-resolution sensing, and quantum communication fields.…”