Mechanical analysis of radial performance in biodegradable polymeric vascular stents manufactured using micro-injection molding.

Micro-injection molding (MiM) is a promising technique for manufacturing biodegradable polymeric vascular stents (BPVSs) at scale, in which a trapezoidal strut cross section is needed to ensure high-quality de-molding. However, there is a lack of research on the influence of the strut cross-sectional shape on its mechanical properties, posing a challenge in determining the key geometries of the strut when using MiM to produce BPVSs. Hence, this work has investigated the relationships between the geometry parameters, including the de-molding angle, and the radial support property of BPVSs using the finite element method. The results reveal that the radial stiffness of BPVSs is significantly affected by the de-molding angle, which can be counteracted by adjusting strut height, bending radius, and strut thickness. Stress distribution analysis underscores the crucial role of the curved portion of the support ring during compression, with the inner side of the curved region experiencing stress concentration. A mathematical model has been established to describe the relationships between the geometry parameters and the radial support property of the BPVSs. Notably, the radius of the neutral layer emerges as a key determinant of radial stiffness. This study is expected to serve as a guideline for the development of BPVSs that can be manufactured using MiM.

eFarm: A Tool for Better Observing Agricultural Land Systems.

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human-land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.