Active and passive exploration for spatial knowledge acquisition: A meta-analysis.

Literature reported mixed evidence on whether active exploration benefits spatial knowledge acquisition over passive exploration. Active spatial learning typically involves at least physical control of one's movement or navigation decision-making, while passive participants merely observe during exploration. To quantify the effects of active exploration in learning large-scale, unfamiliar environments, we analysed previous findings with the multi-level meta-analytical model.

Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic.

The global outbreak of COVID-19 has fundamentally reshaped human mobility. Compared to other modes of transportation, how spatiotemporal patterns of urban bike-sharing have evolved since the outbreak is yet to be fully understood, especially for bike-sharing systems operating on a smaller scale. Taking Pittsburgh as a case study, we examined the changes in spatiotemporal dynamics of shared bike usage from 2019 to 2021.

Geographically-explicit Ecological Momentary Assessment (GEMA) Architecture and Components: Lessons Learned from PMOMS.

Geographically explicit Ecological Momentary Assessment (GEMA), an extension of Ecological Momentary Assessment (EMA), allows to record time-stamped geographic location information for behavioral data in the every-day environments of study participants. Considering that GEMA studies are continually gaining the attention of researchers and currently there is no single approach in collecting GEMA data, in this paper, we propose and present a GEMA architecture that can be used to conduct any GEMA study based on our experience developing and maintaining the Postpartum Mothers Mobile Study (PMOMS). Our GEMA client-server architecture can be customized to meet the specific requirements of each GEMA study.

A Global Path Planner for Safe Navigation of Autonomous Vehicles in Uncertain Environments.

Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments.

Understanding Pregnancy and Postpartum Health Using Ecological Momentary Assessment and Mobile Technology: Protocol for the Postpartum Mothers Mobile Study.

Background: There are significant racial disparities in pregnancy and postpartum health outcomes, including postpartum weight retention and cardiometabolic risk. These racial disparities are a result of a complex interplay between contextual, environmental, behavioral, and psychosocial factors.

Objective: This protocol provides a description of the development and infrastructure for the Postpartum Mothers Mobile Study (PMOMS), designed to better capture women's daily experiences and exposures from late pregnancy through 1 year postpartum.

An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level.

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of "just-in-time" injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app.

Understanding route choices for wheelchair navigation.

Purpose: To validate a personalized routing technique with wheelchair users, understand their route choices and acquire their feedback on the necessity of wheelchair navigation and the importance of personalized routes.

Method: A routing technique using a weighting method, called Absolute Restriction Method (ARM), was employed to compute personalized routes based on users' routing preferences. The evaluation involves five manual wheelchair users.

Design considerations for a personalized wheelchair navigation system.

Individuals with mobility impairments such as wheelchair users are often at a disadvantage when traveling to a new place, as their mobility can be easily affected by environmental barriers, and as such, even short trips can be difficult and perhaps impossible. We envision a personalized wheelchair navigation system based on a PDA equipped with wireless Internet access and GPS that can provide adaptive navigation support to wheelchair users in any geographic environment. Requirements, architectures and components of such a system are described in this paper.

TMpro web server and web service: transmembrane helix prediction through amino acid property analysis.

Unlabelled: TMpro is a transmembrane (TM) helix prediction algorithm that uses language processing methodology for TM segment identification. It is primarily based on the analysis of statistical distributions of properties of amino acids in transmembrane segments. This article describes the availability of TMpro on the internet via a web interface.

High-throughput modeling and analysis of protein structural dynamics.

Protein function is a dynamic property closely related to the conformational mechanisms of protein structure in its physiological environment. To understand and control the function of target proteins, it becomes increasingly important to develop methods and tools for predicting collective motions at the molecular level. In this article, we review computational methods for predicting conformational dynamics and discuss software tools for data analysis.

oGNM: online computation of structural dynamics using the Gaussian Network Model.

An assessment of the equilibrium dynamics of biomolecular systems, and in particular their most cooperative fluctuations accessible under native state conditions, is a first step towards understanding molecular mechanisms relevant to biological function. We present a web-based system, oGNM that enables users to calculate online the shape and dispersion of normal modes of motion for proteins, oligonucleotides and their complexes, or associated biological units, using the Gaussian Network Model (GNM). Computations with the new engine are 5-6 orders of magnitude faster than those using conventional normal mode analyses.

iGNM: a database of protein functional motions based on Gaussian Network Model.

Motivation: The knowledge of protein structure is not sufficient for understanding and controlling its function. Function is a dynamic property. Although protein structural information has been rapidly accumulating in databases, little effort has been invested to date toward systematically characterizing protein dynamics.