Environmental assessment of urban mobility: combining life cycle assessment with land-use and transport interaction modelling – application to Lyon (France)

In France, greenhouse gas (GHG) emissions from transport have grown steadily since 1950 and transport is now the main source of emissions. Despite technological improvements, urban sprawl increases the environmental stress due to car use. This study evaluates urban mobility through assessments of the transport system and travel habits, by applying life cycle assessment methods to the results of mobility simulations that were produced by a Land Use and Transport Interactions (LUTI) model. The environmental impacts of four life cycle phases of urban mobility in the Lyon area (exhausts, fuel processing, infrastructure and vehicle life cycle) were estimated through nine indicators (global warming potential, particulate matter emissions, photochemical oxidant emissions, terrestrial acidification, fossil resource depletion, metal depletion, non-renewable energy use, renewable energy use and land occupancy). GHG emissions were estimated to be 3.02 kg CO2-eq inhabitant−1 day−1 , strongly linked to car use, and indirect impacts represented 21% of GHG emissions, which is consistent with previous studies. Combining life cycle assessment (LCA) with a LUTI model allows changes in the vehicle mix and fuel sources combined with demographic shifts to be assessed, and provides environmental perspectives for transport policy makers and urban planners. It can also provide detailed analysis, by allowing levels of emissions that are generated by different categories of households to be differentiated, according to their revenue and location. Public policies can then focus more accurately on the emitters and be assessed from both an environmental and social point of view

Regional variations in the link between drought indices and reported agricultural impacts of drought

Drought has wide ranging impacts on all sectors. Despite much effort to identify the best drought indicator to represents the occurrence of drought impacts in a particular sector, there is still no consensus among the scientific community on this. Using a more detailed and extensive impact dataset than in previous studies, this paper assesses the regional relationship between drought impacts occurrence in British agriculture and two of the most commonly used drought indices (SPI and SPEI). The largest qualitative dataset on reported drought impacts on British agriculture for the period 1975–2012 spanning all major recent droughts was collated. Logistic regression using generalised additive models was applied to investigate the association between drought indices and reported impacts at the regional level. Results show that SPEI calculated for the preceding six months is the best indicator to predict the probability of drought impacts on agriculture in the UK, although the variation in the response to SPEI6 differed between regions. However, this variation appears to result both from the method by which SPEI is derived, which means that similar values of the index equate to different soil moisture conditions in wet and dry regions, and from the variation in agriculture between regions. The study shows that SPEI alone has limited value as an indicator of agricultural droughts in heterogeneous areas and that such results cannot be usefully extrapolated between regions. However, given the drought sensitivity of agriculture, the integration of regional predictions within drought monitoring and forecasting would help to reduce the large on-farm economic damage of drought and increase the sector's resilience to future drought

Implementing a hybrid cognitive-behavioural therapy for pain-related insomnia in primary care : lessons learnt from a mixed-methods feasibility study

Objectives: To test the feasibility of implementing a brief but intensive hybrid cognitive behavioural therapy (Hybrid CBT) for pain-related insomnia. Design: Mixed-methods, with qualitative process evaluation on a two-arm randomised controlled feasibility trial. Setting: Primary care. Participants: Twenty-five adult patients with chronic pain and insomnia. Intervention: Hybrid CBT or self-help control intervention. Primary and secondary outcome measures: Primary outcomes measures were the Insomnia Severity Index and interference scale of the Brief Pain Inventory (BPI). Secondary outcomes measures were the present pain intensity rating from the BPI, Multidimensional Fatigue Inventory, Hospital Anxiety and Depression Scale and EQ-5D-5L. Results: Fourteen participants were randomised to receive Hybrid CBT, 11 to receive the self-help control treatment. Of the 14 in the Hybrid CBT group, 9 (64%) completed all four treatment sessions (4 discontinued due to poor health; 1 due to time constraints). Adherence to the self-help control treatment was not monitored. The total number of participants completing the 12-week and 24-week follow-ups were 12 (6 in each group; Hybrid CBT: 43%; self-help: 55%) and 10 (5 in each group; Hybrid CBT: 36%; self-help: 45%). Based on the data available, candidate outcome measures appeared to be sensitive to changes associated with interventions. Thematic analysis of pre-postintervention interview data revealed satisfaction with treatment content among those who completed the Hybrid CBT, whereas those in the self-help control treatment wanted more contact hours and therapist guidance. Other practical suggestions for improvement included shortening the duration of each treatment session, reducing the amount of assessment paperwork, and minimising the burden of sleep and pain monitoring. Conclusion: Important lessons were learnt with regard to the infrastructure required to achieve better patient adherence and retention. Based on the qualitative feedback provided by a subset of treatment completers, future trials should also consider lowering the intensity of treatment and streamlining the data collection procedure. Trial registration number: ISRCTN17294365

A Scalable Correlator Architecture Based on Modular FPGA Hardware, Reuseable Gateware, and Data Packetization

A new generation of radio telescopes is achieving unprecedented levels of sensitivity and resolution, as well as increased agility and field-of-view, by employing high-performance digital signal processing hardware to phase and correlate large numbers of antennas. The computational demands of these imaging systems scale in proportion to BMN^2, where B is the signal bandwidth, M is the number of independent beams, and N is the number of antennas. The specifications of many new arrays lead to demands in excess of tens of PetaOps per second. To meet this challenge, we have developed a general purpose correlator architecture using standard 10-Gbit Ethernet switches to pass data between flexible hardware modules containing Field Programmable Gate Array (FPGA) chips. These chips are programmed using open-source signal processing libraries we have developed to be flexible, scalable, and chip-independent. This work reduces the time and cost of implementing a wide range of signal processing systems, with correlators foremost among them,and facilitates upgrading to new generations of processing technology. We present several correlator deployments, including a 16-antenna, 200-MHz bandwidth, 4-bit, full Stokes parameter application deployed on the Precision Array for Probing the Epoch of Reionization.Comment: Accepted to Publications of the Astronomy Society of the Pacific. 31 pages. v2: corrected typo, v3: corrected Fig. 1

Characterizing Signal Loss in the 21 cm Reionization Power Spectrum: A Revised Study of PAPER-64

The Epoch of Reionization (EoR) is an uncharted era in our Universe's history during which the birth of the first stars and galaxies led to the ionization of neutral hydrogen in the intergalactic medium. There are many experiments investigating the EoR by tracing the 21cm line of neutral hydrogen. Because this signal is very faint and difficult to isolate, it is crucial to develop analysis techniques that maximize sensitivity and suppress contaminants in data. It is also imperative to understand the trade-offs between different analysis methods and their effects on power spectrum estimates. Specifically, with a statistical power spectrum detection in HERA's foreseeable future, it has become increasingly important to understand how certain analysis choices can lead to the loss of the EoR signal. In this paper, we focus on signal loss associated with power spectrum estimation. We describe the origin of this loss using both toy models and data taken by the 64-element configuration of the Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER). In particular, we highlight how detailed investigations of signal loss have led to a revised, higher 21cm power spectrum upper limit from PAPER-64. Additionally, we summarize errors associated with power spectrum error estimation that were previously unaccounted for. We focus on a subset of PAPER-64 data in this paper; revised power spectrum limits from the PAPER experiment are presented in a forthcoming paper by Kolopanis et al. (in prep.) and supersede results from previously published PAPER analyses.Comment: 25 pages, 18 figures, Accepted by Ap