Laboratory estimation of black carbon emissions from cookstoves

Recent estimations show that residential solid fuel combustion accounts for 25% of global black carbon (BC) emissions (Lamarque et al., 2010). Thus, the control of these emissions through the implementation of cleaner cooking technologies could be crucial for climate change mitigation (Venkataraman et al., 2005). However, BC emission factors for biofuel cooking stoves have been poorly estimated due to the wide distribution and remote location of the stoves and the relatively complex existing assessment methods. This work presents results on BC emission factors (EF) estimation from combustion of biomass cooking systems in Western Africa (in Senegal). Three stones fire (traditional stove), Noflaye Jegg (rocket stove), Jambaar bois (ceramic improved stove) and a gasifier were analysed under laboratory conditions at the Centre de Recherche sur les Energies Renouvelables (CERER) in Dakar. Two types of fuels (wood species) were tested: Casuarina Equisetifolia (Filao) and Cordyla Pinnata (Dimb). Three replicates of the standardized Water Boiling Test with two phases (cold start and simmer) were conducted at the laboratory to test each cooking system. PM2.5 emissions were collected on quartz fibre filters, and BC content was subsequently analysed using three analytical methods: i) Nexleaf system, in which a photograph of the filter is compared with a calibrated reference scale; ii) the EEL43 Smoke Stain Reflectometer; and iii) the Sunset Laboratory OCEC Analyzer. The two first were compared with the third one, considered the internal reference

National identity predicts public health support during a global pandemic

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.publishedVersio

Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

Illinois-Specific LRFR Live-Load Factors Based on Truck Data

This research project has a focus on the load and resistance factored rating (LRFR) live-load factors for load rating bridges in Illinois. The study’s objectives were to examine the adequacy of available Illinois weigh-in-motion (WIM) data and to develop refined live-load factors for Illinois LRFR practice, based on recorded truck loads in Illinois. There are currently 20 operating WIM sites in Illinois, each next to a weigh station. Initially, only one WIM site was providing two lanes of truck-weight data simultaneously recorded, while the remaining 19 were collecting data for the driving lane only. Twolane WIM data are important for live-load factor refinement because it is the cluster events involving trucks in different lanes that induce maximum load effects in primary bridge components such as girders. Thus, such data are critical to live-load factors. Upon recommendation from this project, the capability of passing-lane recording was promptly added to two more of the 20 sites. An additional effort was made in this study to simulate the passing lane’s data for the remaining 17 sites, to maximize the use of Illinois-relevant WIM data for covering the entire state. This simulation used the probability of multiple trucks in a cluster, based on WIM data from eight states including Illinois. It also used truck-weight-demography information and headway distances of trucks in cluster from all available Illinois sites. This simulation method was tested and proven in the present project to be reliable for calibration here for Illinois. The resulting truck records of these 17 sites and those recorded at the other 3 sites capable of providing two lanes of truckweight data from 2013 to 2017 were then used to develop refined live-load factors for LRFR in Illinois. Illinois trucks are seen in these WIM data to be less severe than those weighed in Canada, which were used in calibrating the current AASHTO LRFD Bridge Design Specifications (BDS) (2017). Illinois trucks recorded in the WIM data were also found to have behaved with little or no influence from the nearby weigh station. Four load-rating cases are addressed in this project in calibrating LRFR live-load factors for Illinois: design load, legal load, routine-permit load, and special-permit load. Based on calibration using Illinois truckweight records, no change for the design load rating is recommended. Lower live-load factors are recommended for the other three cases for Illinois than those prescribed in the current MBE, by about 8% to 14%, depending on average daily truck traffic (ADTT). Illustrative examples using the recommended live-load factors have been prepared and presented in this report. It is also recommended that Illinois Department of Transportation (IDOT) continue to keep the WIM stations well-maintained, including periodical calibration of the weight sensors and systems; gather more truck-weight-data; review them at least biennially; and focus on possible growth of truck load in both magnitude and volume. When funding becomes available, passinglane recording is recommended to be added to those WIM sites that currently do not have this capability. Truck-data gathering is also recommended for sites where congested truck traffic is often observed, given adequate funding for such facilities.IDOT-R27-171Ope