Map Environment model map. by Yun Qi (560401)

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Code and raw data used in this study. by Zhuolin Yang (14658411)

Performance of machine learning algorithms. by Wenxue Liu (6877754)

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. by Yuxi Long (11024307)

“…<p>^<b>a</b>PATH⁺ shows comparable or better performance with less overfitting, as evidenced by a smaller slope, with much less increase in RMSEs beyond the training dataset, compared to established binding affinity prediction algorithms spanning a variety of methods. …”

A meta-path trace for “APTPVPA” with author Travis as the initial node. by Lang Chai (20521817)

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Reported MATLAB data code. by Jotham Josephat Kimondo (19139409)

Spatial spectrum of the 100th frame under array distortion conditions (20 elements). by Gaoxiang Xing (21755689)

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Source code. by Jesse Wheeler (18462457)

S1 Code - by Jialiang Zhang (1574704)

RC signal variation of CMA algorithm with different modulation orders. by Yucai Wang (513301)

Subjects: “…adaptive equalization algorithm…”

GA pseudo-code. by Jianpeng Zhang (528185)

Comparison of PSNR values of different algorithms. by Xiaojun Wang (50332)

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Changes in FID values of different algorithms. by Xiaojun Wang (50332)

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Comparison of SSIM values of different algorithms. by Xiaojun Wang (50332)

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Changes in loss values for different algorithms. by Xiaojun Wang (50332)

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The relevant code in the manuscript can be found in the supporting information data file. by Xini Fang (20861990)

Path optimization results under different conditions: GL-SSL model’s dynamic path planning in smart port multimodal transportation, showcasing path optimization at port nodes under... by Jiangjiang He (9676377)

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Overview of the C&CG method. by Tianhe Sun (21145341)

“…The model is solved iteratively using the column generation algorithm and strong duality theory. Case studies on a Northeast China power grid demonstrate that, by optimally configuring generation and storage capacity guided by flexibility and other indicators, the proposed method reduces curtailment/load shedding costs and system flexibility insufficiency probability by 45% and 4.3% respectively. …”