Awake craniotomy (AC) is a critical neurosurgical technique for maximizing tumor resection in eloquent brain regions while preserving essential neurological functions like speech and motor control. Despite its widespread adoption, no prior bibliometric analysis has evaluated the most influential research in this field. This study analyzed the top 100 most-cited articles on AC to identify key trends, influential works, and authorship demographics. A systematic search of the Web of Science Core Collection on September 17, 2024, yielded 718 publications, with the top 100 ranked by citation count. Analysis revealed a surge in AC research after 2013, peaking in 2021, with the Journal of Neurosurgery contributing significantly (49 articles; 2,611 citations). Themes included functional mapping, anesthetic techniques, and patient outcomes, with technological advancements such as intraoperative MRI and virtual reality enhancing surgical precision. Authorship analysis highlighted a gender disparity, with male authors occupying 77% of first authorship and 88% of senior roles. These findings underscore AC's evolution, foundational studies, and ongoing advancements while emphasizing the need for greater diversity and inclusion in the field.
This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.