Dual Key-Parameter Sensing System Development for Urolithiasis Recurrence Prevention

Author(s):  
Wen-Yaw Chung ◽  
Roozbeh F. Ramezani ◽  
Chean-Yeh Cheng ◽  
Chien-Hua Wu ◽  
Tzong Rong Ger ◽  
...  
2016 ◽  
Vol 49 (12) ◽  
pp. 1412-1417 ◽  
Author(s):  
A. Piccinini ◽  
V. Pesenti Campagnoni ◽  
S. Ierace ◽  
F. Floreani

Author(s):  
Mitja Trkov ◽  
Jingang Yi ◽  
Tao Liu ◽  
Kang Li

Shoe-floor interactions such as friction force and deformation/local slip distributions are among the critical factors to determine the risk for potential slip and fall. In this paper, we present modeling, analysis, and experiments to understand the slip and force distributions between the shoe sole and floor surface during the normal gait and the slip and fall gait. The computational results for the slip and friction force distribution are based on the spring-beam networks model. The experiments are conducted with several new sensing techniques. The in-situ contour footprint is accurately measured by a set of laser line generators and image processing algorithms. The force distributions are obtained by combining two types of force sensor measurements: implanted conductive rubber-based force sensor arrays in the shoe sole and six degree-of-freedom (6-DOF) insole force/torque sensors. We demonstrate the sensing system development through extensive experiments. Finally, the new sensing system and modeling framework confirm that the use of required coefficient of friction and the deformation measurements can real-time predict the slip occurrence.


Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 41
Author(s):  
Jad Sabek ◽  
Paula Martínez-Pérez ◽  
Jaime García-Rupérez

A computational study of the interaction between cardiac troponin I and its specific antibody is carried out. The aim of this study is to characterize the binding process by determining the binding sites, number of interactions and energies. Furthermore, a selectivity study of the binding efficiency of the cardiac troponin I antibody with the cardiac troponin I and with its principal interferon, the skeletal troponin I, is also performed to demonstrate that selectivity assays for sensing studies can be carried out computationally. Computational and simulation tools such as FTSite, FTMap, FTDock and pyDock were used to determine the binding sites and molecular docking performance, allowing us to obtain relevant information for a subsequent sensing system development.


1997 ◽  
Vol 272 (1) ◽  
pp. C1-C26 ◽  
Author(s):  
R. E. Stewart ◽  
J. A. DeSimone ◽  
D. L. Hill

Major advances in the understanding of mammalian gustatory transduction mechanisms have occurred in the past decade. Recent research has revealed that a remarkable diversity of cellular mechanisms are involved in taste stimulus reception. These mechanisms range from G protein-and second messenger-linked receptor systems to stimulus-gated and stimulus-admitting ion channels. Contrary to widely held ideas, new data show that some taste stimuli interact with receptive sites that are localized on both the apical and basolateral membranes of taste cells. Studies of taste system development in several species indicate that the transduction pathways for some stimuli are modulated significantly during the early postnatal period. In addition, recent investigations of adult peripheral gustatory system plasticity strongly suggest that the function of the Na+ sensing system can be modulated by circulating hormones, growth factors, or cytokines.


2012 ◽  
Vol 466-467 ◽  
pp. 1310-1314
Author(s):  
Jie Wu ◽  
Yi He Sun

Emerging green-energy cyber-physical systems (CPS), in particular electric-drive vehicles (PHEV, HEV, and EV), have demonstrated great potentials to significantly reduce greenhouse gas emissions and the ever-growing dependence on foreign oil. Few studies have focused on the user-specific driving behavior and its significant impact on electric-drive vehicles fuel efficiency, battery system life-cycle and the environment. This paper presents a personalized mobile sensing system development for the emerging green-energy CPS, which captures user’s run-time driving behavior and characterizes its impact on (P)HEV operations. The proposed sensing computing system has been deployed in a number of PHEVs and HEVs, with user studies of four different drivers and over 150 driving trips under various road and traffic conditions. Using the extracted real-world hybrid vehicle and user driving data, we have conducted detailed analytical studies of users’ specific driving patterns and their impacts on hybrid vehicle electric energy and fuel efficiency.


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