Self-monitoring of freeze–thaw damage using triphasic electric conductive concrete

The effect of freeze–thaw cycles on concrete is of great importance for durability evaluation of concrete structures in cold regions. In this paper, damage accumulation was studied by following the fractional change of impedance (FCI) with number of freeze–thaw cycles (N). The nano-carbon black (NCB), carbon fiber (CF) and steel fiber (SF) were added to plain concrete to produce the triphasic electrical conductive (TEC) and ductile concrete. The effects of NCB, CF and SF on the compressive strength, flexural properties, electrical impedance were investigated. The concrete beams with different dosages of conductive materials were studied for FCI, N and mass loss (ML), the relationship between FCI and N of conductive concrete can be well defined by a first order exponential decay curve. It is noted that this nondestructive and sensitive real-time testing method is meaningful for evaluating of freeze–thaw damage in concrete.

Concrete with triphasic conductive materials for self-monitoring of cracking development subjected to flexure

In this study, the macro steel fiber (SF), carbon fiber (CF) and nano carbon black (NCB) as triphasic conductive materials were added into concrete, in order to improve the conductivity and ductility of concrete. The influence of NCB, SF and CF on the post crack behavior and conductivity of concrete was explored. The effect of the triphasic conductive materials on the self-diagnosing ability to the load–deflection property and crack widening of conductive concrete member subjected to bending were investigated. The relationship between the fractional change in surface impedance (FCR) and the crack opening displacement (COD) of concrete beams with conductive materials has been established. The results illustrated that there is a linear relationship between COD and FCR.

The quantified-Self and wearable technologies in the workplace: implications and challenges for their implementations

Introduction of technologies in the workplace have led to a dramatic change. These changes have come with an increased capacity to gather data about one’s working performance (i.e. productivity), as well as the capacity to track one’s personal responses (i.e. emotional, physiological, etc.) to this changing workplace environment. This movement of self-monitoring or self-sensing using diverse types of wearable sensors combined with the use of computing has been identified as the Quantified-Self. Miniaturization of sensors, reduction in cost and a non-stop increase in the computer power capacity has led to a panacea of wearables and sensors to track and analyze all types of information. Utilized in the personal sphere to track information, a looming question remains, should employers use the information from the Quantified-Self to track their employees’ performance or well-being in the workplace and will this benefit employees? The aim of the present work is to layout the implications and challenges associated with the use of Quantified-Self information in the workplace. The Quantified-Self movement has enabled people to understand their personal life better by tracking multiple information and signals; such an approach could allow companies to gather knowledge on what drives productivity for their business and/or well-being of their employees. A discussion about the implications of this approach will cover 1) Monitoring health and well-being, 2) Oversight and safety, and 3) Mentoring and training. Challenges will address the question of 1) Privacy and Acceptability, 2) Scalability and 3) Creativity. Even though many questions remain regarding their use in the workplace, wearable technologies and Quantified-Self data in the workplace represent an exciting opportunity for the industry and health and safety practitioners who will be using them.

Real-time monitoring of progression towards renal failure in primary care patients

First published online: December 16, 2014.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.