A Six-Axis MEMS Force–Torque Sensor With Micro-Newton and Nano-Newtonmeter Resolution

This paper describes the design of a six-axis microelectromechanical systems (MEMS) force-torque sensor. A movable body is suspended by flexures that allow deflections and rotations along the x-, y-, and z-axes. The orientation of this movable body is sensed by seven capacitors. Transverse sensing is used for all capacitors, resulting in a high sensitivity. A batch fabrication process is described as capable of fabricating these multiaxis sensors with a high yield. The force sensor is experimentally investigated, and a multiaxis calibration method is described. Measurements show that the resolution is on the order of a micro-Newton and nano-Newtonmeter. This is the first six-axis MEMS force sensor that has been successfully developed.     

Multisensory VR interaction for protein-docking in the CoRSAIRe project

Proteins take on their function in the cell by interacting with other proteins or biomolecular complexes. To study this process, computational methods, collectively named protein docking, are used to predict the position and orientation of a protein ligand when it is bound to a protein receptor or enzyme, taking into account chemical or physical criteria. This process is intensively studied to discover new biological functions for proteins and to better understand how these macromolecules take on these functions at the molecular scale. Pharmaceutical research also employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals to select likely molecular candidates for drug design. The basic hypothesis of our work is that Virtual Reality (VR) and multimodal interaction can increase efficiency in reaching and analysing docking solutions, in addition to fully a computational docking approach. To this end, we conducted an ergonomic analysis of the protein–protein current docking task as it is carried out today. Using these results, we designed an immersive and multimodal application where VR devices, such as the three-dimensional mouse and haptic devices, are used to interactively manipulate two proteins to explore possible docking solutions. During this exploration, visual, audio, and haptic feedbacks are combined to render and evaluate chemical or physical properties of the current docking configuration.     

Conditions and constraints of food processing in space

Requirements and constraints of food processing in space include a balanced diet, food variety, stability for storage, hardware weight and volume, plant performance, build-up of microorganisms, and waste processing. Lunar, Martian, and space station environmental conditions include variations in atmosphere, day length, temperature, gravity, magnetic field, and radiation environment. Weightlessness affects fluid behavior, heat transfer, and mass transfer. Concerns about microbial behavior include survival on Martian and lunar surfaces and in enclosed environments. Many present technologies can be adapted to meet space conditions.     

Performance analysis of available bandwidth estimation tools for grid networks

Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Differently than previous investigations (Jain et al., ; Shriram et al., in Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin, 2005), our experiments consider a series of relevant metrics such as accuracy of the estimation, convergence time, degree of intrusion in the grid links, and ability to handle multiple simultaneous estimations. No previous work has analyzed the use of available bandwidth tools for the derivation of efficient grid scheduling.     

Reducing the Health and Digital Divides: A Model for Using Community‐Based Participatory Research Approach to E‐Health Interventions in Low‐Income Hispanic Communities

Low‐income Hispanics are the most digitally underserved population in the U.S. This article examines the potential of community‐based participatory research approach to e‐health to decrease the disparities in access to technology and health information in low‐income Hispanic communities. To demonstrate this framework, we describe the process of designing a community‐based e‐health intervention to increase knowledge and parental self‐efficacy in coping with young children's mental health problems including mental health service utilization. Our model incorporates utilizing promotoras de salud (lay community health educators) and community media principles to create the content of e‐health interventions and train community members in using the technology. This case study illustrates the processes involved in using this approach, barriers for participatory e‐health interventions in bridging the Digital Divide, and lessons learned.     

Outliers detection in environmental monitoring databases

Environmental monitoring is nowadays an important task in many industrial operations. In order to comply with strong environmental laws, they have implemented monitoring systems based on a network of air quality and meteorological stations providing real-time measurements of key variables associated to the distribution of pollutants in surrounding areas. These measurements can be contaminated by outliers, which must be discarded in order to have a consistent set of data. This work presents a nonlinear procedure for outliers detection based on residual analysis of regression with Partial Least Squares and Artificial Neural Networks. In order to minimize the negative effect of outliers in the training dataset a learning algorithm with regularization is proposed. This algorithm is based on a Quasi-Newton optimization method and it was tested on a simulated nonlinear process, on real data from environmental monitoring contaminated with synthetic outliers, and finally applied to a real environmental monitoring data obtained from a monitoring station and having natural outliers. The results are encouraging and further developments are foreseen for including information from neighboring stations and emission source operation.