The time that water takes to travel through the terrestrial hydrological cycle and the critical zone is of great interest in Earth system sciences with broad implications for water quality and quantity. Most water age studies to date have focused on individual compartments (or subdisciplines) of the hydrological cycle such as the unsaturated or saturated zone, vegetation, atmosphere, or rivers. However, recent studies have shown that processes at the interfaces between the hydrological compartments (e.g., soil‐atmosphere or soil‐groundwater) govern the age distribution of the water fluxes between these compartments and thus can greatly affect water travel times. The broad variation from complete to nearly absent mixing of water at these interfaces affects the water ages in the compartments. This is especially the case for the highly heterogeneous critical zone between the top of the vegetation and the bottom of the groundwater storage. Here, we review a wide variety of studies about water ages in the critical zone and provide (1) an overview of new prospects and challenges in the use of hydrological tracers to study water ages, (2) a discussion of the limiting assumptions linked to our lack of process understanding and methodological transfer of water age estimations to individual disciplines or compartments, and (3) a vision for how to improve future interdisciplinary efforts to better understand the feedbacks between the atmosphere, vegetation, soil, groundwater, and surface water that control water ages in the critical zone.

Objective: To provide an overview of ML models and data streams utilized for automated surgical phase recognition. Background: Phase recognition identifies different steps and phases of an operation. ML is an evolving technology that allows analysis and interpretation of huge data sets. Automation of phase recognition based on data inputs is essential for optimization of workflow, surgical training, intraoperative assistance, patient safety, and efficiency. Methods: A systematic review was performed according to the Cochrane recommendations and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. PubMed, Web of Science, IEEExplore, GoogleScholar, and CiteSeerX were searched. Literature describing phase recognition based on ML models and the capture of intraoperative signals during general surgery procedures was included. Results: A total of 2254 titles/abstracts were screened, and 35 full-texts were included. Most commonly used ML models were Hidden Markov Models and Artificial Neural Networks with a trend towards higher complexity over time. Most frequently used data types were feature learning from surgical videos and manual annotation of instrument use. Laparoscopic cholecystectomy was used most commonly, often achieving accuracy rates over 90%, though there was no consistent standardization of defined phases. Conclusions: ML for surgical phase recognition can be performed with high accuracy, depending on the model, data type, and complexity of surgery. Different intraoperative data inputs such as video and instrument type can successfully be used. Most ML models still require significant amounts of manual expert annotations for training. The ML models may drive surgical workflow towards standardization, efficiency, and objectiveness to improve patient outcome in the future. Registration PROSPERO: CRD42018108907

No abstract

TS is an accepted serious gaming application for learning cognitive aspects of LC with established construct, face, and content validity. There appeared to be a synergy between TS and the VR trainer. Therefore, the two training modalities should accompany one another in a multimodal training approach to laparoscopy.