Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends.

Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i.

Advancing the network theory of mental disorders: A computational model of panic disorder.

The network theory of psychopathology posits that mental disorders are systems of mutually reinforcing symptoms. This framework has proven highly generative but does not specify precisely how any specific mental disorder operates as such a system. Cognitive behavioral theories of mental disorders provide considerable insight into how these systems may operate.

Comparing Open, Laparoscopic and Robotic Liver Resection for Metastatic Colorectal Cancer-A Systematic Review and Network Meta-Analysis.

Colorectal liver metastases (CRLM) can be surgically managed through open resections (OLR), laparoscopic resections (LLR), or robotic liver resections (RLR). However, there is ongoing uncertainty regarding the safety and effectiveness of minimally invasive approaches like LLR and RLR. This study aims to clarify these issues by conducting a network meta-analysis (NMA) to compare outcomes across OLR, LLR and RLR for patients with CRLM.