Grambank reveals the importance of genealogical constraints on linguistic diversity and highlights the impact of language loss

While global patterns of human genetic diversity are increasingly well characterized, the diversity of human languages remains less systematically described. Here we outline the Grambank database. With over 400,000 data points and 2,400 languages, Grambank is the largest comparative grammatical database available. The comprehensiveness of Grambank allows us to quantify the relative effects of genealogical inheritance and geographic proximity on the structural diversity of the world's languages, evaluate constraints on linguistic diversity, and identify the world's most unusual languages. An analysis of the consequences of language loss reveals that the reduction in diversity will be strikingly uneven across the major linguistic regions of the world. Without sustained efforts to document and revitalize endangered languages, our linguistic window into human history, cognition and culture will be seriously fragmented.Genealogy versus geography Constraints on grammar Unusual languages Language loss Conclusio

Dynamic multi-sensor operation and read-out for highly selective gas sensor systems

We describe hardware and algorithms which enable highly selective and sensitive operation of the two gas sensor types used in the SENSIndoor project. The resistance of a metal-oxide semiconductor (MOS) type can rise above 1 G Omega in temperature cycled operation (TCO), which is measured using a logarithmic amplifier. A silicon-carbide based, gas-sensitive field-effect transistor (SiC-FET) driven with a combination of TCO and gate-bias cycled operation (GBCO) is used as second, complimentary sensor. The cyclic sensor signals exhibit distinct shape changes depending on the gas present which is captured by pattern recognition. In this study we use Linear Discriminant Analysis (LDA) for discrimination and Partial Least Squares Regression (PLSR) for quantification of ppb concentrations of target VOCs in changing ppm concentrations of interfering gases for indoor air quality assessment. (C) 2016 The Authors. Published by Elsevier Ltd

Selective detection of hazardous VOCs for indoor air quality applications using a virtual gas sensor array

An approach for detecting hazardous volatile organic compounds (VOCs) in ppb and sub-ppb concentrations is presented. Using three types of metal oxide semiconductor (MOS) gas sensors in temperature cycled operation, formaldehyde, benzene and naphthalene in trace concentrations, reflecting threshold limit values as proposed by the WHO and European national health institutions, are successfully identified against a varying ethanol background of up to 2 ppm. For signal processing, linear discriminant analysis is applied to single sensor data and sensor fusion data. <br><br> Integrated field test sensor systems for monitoring of indoor air quality (IAQ) using the same types of gas sensors were characterized using the same gas measurement setup and data processing. Performance of the systems is reduced due to gas emissions from the hardware components. These contaminations have been investigated using analytical methods. Despite the reduced sensitivity, concentrations of the target VOCs in the ppb range (100 ppb of formaldehyde; 5 ppb of benzene; 20 ppb of naphthalene) are still clearly detectable with the systems, especially when using the sensor fusion method for combining data of the different MOS sensor types

Highly sensitive benzene detection with metal oxide semiconductor gas sensors &ndash; an inter-laboratory comparison

For detection of benzene, a gas sensor system with metal oxide semiconductor (MOS) gas sensors using temperature-cycled operation (TCO) is presented. The system has been tested in two different laboratories at the concentration range from 0.5 up to 10 ppb. The system is equipped with three gas sensors and advanced temperature control and read-out electronics for the extraction of features from the TCO signals. A sensor model is used to describe the sensor response in dependence on the gas concentration. It is based on a linear differential surface reduction (DSR) at a low temperature phase, which is linked to an exponential growth of the sensor conductance. To compensate for cross interference to other gases, the DSR is measured at three different temperatures (200, 250, 300 °C) and the calculated features are put into a multilinear regression (partial least square regression – PLSR) for the quantification of benzene at both laboratories. In the tests with the first set-up, benzene was supplied in defined gas profiles in a continuous gas flow with variation of humidity and various interferents, e.g. toluene and carbon monoxide (CO). Depending on the gas background and interferents, the quantification accuracy is between ±0.2 and ±2 ppb. The second gas mixing system is based on a circulation of the carrier gas stream in a closed-loop control for the benzene concentration and other test gases based on continuously available reference measurements for benzene and other organic and inorganic compounds. In this system, a similar accuracy was achieved for low background contaminations and constant humidity; the benzene level could be quantified with an error of less than 0.5 ppb. The transfer of regression models for one laboratory to the other has been tested successfully

Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise - Part II

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r2=0.13-0.83), NO2 (r2=0.24-0.93), O3 (r2=0.22-0.84), PM10 (r2=0.54-0.83), PM2.5 (r2=0.33-0.40) and SO2 (r2=0.49-0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements

Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r = 0.13–0.83), NO (r = 0.24–0.93), O (r = 0.22–0.84), PM10 (r = 0.54–0.83), PM2.5 (r = 0.33–0.40) and SO (r = 0.49–0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements.Peer Reviewe