Modeling and Simulation of Concentric and Eccentric Tube Continuum Robots

Unlike conventional manipulators where the robot is actuated at discrete joints, continuum robots are actuated continuously in smooth curves. These robots are often dexterous and compact, allowing them to operate in constrained environments during minimally invasive medical interventions. Since the unconventional robot structure often consists of elastic or flexible materials, the corresponding kinematics formulation is significantly more challenging to derive and simulate. This thesis introduces two different but related continuum robot designs: the concentric tube robot (CTR) and the eccentric tube robot (ETR). These designs utilize multiple pre-curved and superelastic nitinol tubes to actuate the robot. This mechanism also leads to an undesirable behavior called snapping . Based on Cosserat Rod theory, two separate kinematics models are derived, solved, and simulated for CTR and ETR. Additionally, an ETR prototype is designed and constructed for experimental validation. Compared to the simulation, the measured average tip error is about 3.8% of the robot length

Long-Term Air Quality Study in Fairbanks, Alaska: Air Pollutant Temporal Variations, Correlations, and PM2.5 Source Apportionment

As one of the most polluted U.S. cities, Fairbanks was reclassified as a “serious” nonattainment area by the Environmental Protection Agency (EPA) in 2017 for its fine particulate matter (PM2.5) pollution. In this study, November 2013–May 2019 observations of criteria air pollutants (NO2, SO2, CO, O3, PM2.5, and inhalable particulate matter (PM10)) and meteorological parameters (temperature, wind speed, and relative humidity) in Fairbanks were used for temporal variation and correlation analysis, with positive matrix factorization (EPA PMF 5.0) adopted for further PM2.5 source identification. All pollutants exhibited obvious seasonal trends under the influence of climatology, topography, and human activity, while abnormal patterns likely resulted from occasional emission events such as wildfires. Primary and secondary pollutants performed distinctively under similar meteorological conditions due to different decisive factors. Identified PM2.5 sources included sulfate (32.7%), wood smoke (19.3%), gasoline (18.3%), nitrate (15.7%), diesel (9.2%), soil (3.8%), and road salt (1.0%). Compared with the 2005–2012 result, sulfate and nitrate contributions had increased, while wood smoke and diesel contributions had decreased, in which emission control measures as well as a change of sampling sites could play an important role. This systematic analysis offers reference for mitigation measures and pollution prediction. Meanwhile, further field investigation is required for conclusion validation and model improvement

Multi-Resolution Supervision Network with an Adaptive Weighted Loss for Desert Segmentation

Desert segmentation of remote sensing images is the basis of analysis of desert area. Desert images are usually characterized by large image size, large-scale change, and irregular location distribution of surface objects. The multi-scale fusion method is widely used in the existing deep learning segmentation models to solve the above problems. Based on the idea of multi-scale feature extraction, this paper took the segmentation results of each scale as an independent optimization task and proposed a multi-resolution supervision network (MrsSeg) to further improve the desert segmentation result. Due to the different optimization difficulty of each branch task, we also proposed an auxiliary adaptive weighted loss function (AWL) to automatically optimize the training process. MrsSeg first used a lightweight backbone to extract different-resolution features, then adopted a multi-resolution fusion module to fuse the local information and global information, and finally, a multi-level fusion decoder was used to aggregate and merge the features at different levels to get the desert segmentation result. In this method, each branch loss was treated as an independent task, AWL was proposed to calculate and adjust the weight of each branch. By giving priority to the easy tasks, the improved loss function could effectively improve the convergence speed of the model and the desert segmentation result. The experimental results showed that MrsSeg-AWL effectively improved the learning ability of the model and has faster convergence speed, lower parameter complexity, and more accurate segmentation results