Laboratory Life:Twenty Years of Experimental Research with Locals and New Comers in Remote Rural Areas

Through the Latour concept of philosophy of science of laboratory life,the life course of a(geo)ethnographer researcher is analyzed,in order to establish the emotional or more personal dimensions that do not usually appear in research papers,but that have notable relevance in qualitative research.The text is structured in three large parts:A conceptual vision of the new experimental geography;the experimental geo-ethnography in old social,new social and place scenarios;and finally,the most personal and emotional dimension of the research.The first part establishes the geo-ethnographic experimental context and the assumptions in fieldwork,in addition to the geo-ethnographic reconciliation process.In the second part,the main qualitative research problems related to mobility,resistance and dynamics in place are established.Finally,in the third part the research perceptions,the personal experiences,and the emotional dimension of the research in remote rural areas are established.The laboratory life of a researcher,allows one to establish scalar conclusions and rigor in qualitative research.

BLE beacons for sample position estimation in a life science automation laboratory

Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation method.In this paper,BLE sensing devices for sample position estimation are proposed.The BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data deviation.The experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing samples.The Kalman filter brings benefits to stable distance predictions.Apart from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.