Multi-Level and Multiple Aspect Semantic Trajectory Model: Application to the Tourism Domain

Here we design a semantic trajectory model responding to specific needs expressed by tourism analyst experts. Thus, this model takes into account: (i) the description of sequences of imbricated semantic segments, (ii) the definition of enrichment data integrating spatial, temporal and thematic dimensions and (iii) the association of such data with positions or with trajectory segments. Each of these features is necessary for the processing and analysis of tourist mobility data, which we will detail. For validation purposes, we experiment our model on two outdoor mobility track scenarios computed in a processing chain. We also show that our model is generic and extensible thanks to two other scenarios on different datasets.

Neyo: A katabatic dry wind

The objective of this study is to explain how the Neyo wind stream develops and how it can damage crops and endanger people’s lives in the Darfur Region of Sudan. Due to lack of official meteorological observations, the data was retrieved from NOAA Air Resources Laboratory (ARL) used by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT). Three scenarios for katabatic flow were formulated. Forces acting on air parcels were calculated for different slope angles. HYSPLIT vertical soundings revealed a reservoir of dry air over the area. Air parcels moving down slope allow dry air to flow across the thermal inversion layer. Due to the moisture deficit between the down flowing air and the humid grass surface, the leaves get dehydrated.

Implementation of a Lagrangian Stochastic Particle Trajectory Model (LaStTraM) to Simulate Concentration and Flux Footprints Using the Microclimate Model ENVI-Met

The number of studies evaluating flux or concentration footprints has grown considerably in recent years. These footprints are vital to understand surface–atmosphere flux measurements, for example by eddy covariance. The newly developed backwards trajectory model LaStTraM (Lagrangian Stochastic Trajectory Model) is a post-processing tool, which uses simulation results of the holistic 3D microclimate model ENVI-met as input. The probability distribution of the particles is calculated using the Lagrangian Stochastic method. Combining LaStTraM with ENVI-met should allow us to simulate flux and concentration footprints in complex urban environments. Applications and evaluations were conducted through a comparison with the commonly used 2D models Kormann Meixner and Flux Footprint Predictions in two different meteorological cases (stable, unstable) and in three different detector heights. LaStTraM is capable of reproducing the results of the commonly used 2D models with high accuracy. In addition to the comparison with common footprint models, studies with a simple heterogeneous and a realistic, more complex model domain are presented. All examples show plausible results, thus demonstrating LaStTraM’s potential for the reliable calculation of footprints in homogeneous and heterogenous areas.

Trajectories of Pain After Cardiac Surgery: Implications for Measurement, Reporting, and Individualized Treatment

Background: Postoperative pain after cardiac surgery is a significant problem, but studies often report pain value as an average of the study cohort, obscuring clinically meaningful differences in pain trajectories. We sought to characterize heterogeneity in postoperative pain experiences. Methods: We enrolled patients undergoing a cardiac surgery at a tertiary care center between January 2019 and February 2020. Participants received an electronically-delivered questionnaire every 3 days for 30 days to assess incision site pain level. We evaluated the variability in pain trajectories over 30 days by the cohort-level mean with confidence band and latent classes identified by group-based trajectory model. Group-based trajectory model estimated the probability of belonging to a specific trajectory of pain. Results: Of 92 patients enrolled, 75 provided ≥3 questionnaire responses. The cohort-level mean showed a gradual and consistent decline in the mean pain level, but the confidence bands covered most of the pain score range. The individual-level trajectories varied substantially across patients. Group-based trajectory model identified 4 pain trajectories: persistently low (n=9, 12%), moderate declining (initially mid-level, followed by decline; n=26, 35%), high declining (initially high-level, followed by decline; n=33, 44%), and persistently high pain (n=7, 9%). Persistently high pain and high declining groups did not seem to be clearly distinguishable until approximately postoperative day 10. Patients in persistently low pain trajectory class had a numerically lower median age than the other 3 classes and were below the lower confidence band of the cohort-level approach. Patients in the persistently high pain trajectory class had a longer median length of hospital stay than the other 3 classes and were often higher than the upper confidence band of the cohort-level approach. Conclusions: We identified 4 trajectories of postoperative pain that were not evident from a cohort-level mean, which has been a common way of reporting pain level. This study provides key information about the patient experience and indicates the need to understand variation among sites and surgeons and to investigate determinants of different experience and interventions to mitigate persistently high pain.