Advanced Engine Flows and Combustion

The transport sector accounts for a significant part of carbon emissions worldwide, and so the need to mitigate the greenhouse effect of CO2 from fossil fuel combustion, and to reduce vehicle exhaust emissions has been the primary driver for developing cleaner and more efficient vehicle powertrains, and environmentally friendly fuels.  As alternatives to combustion engines have yet to overcome technical challenges to attain significant utilisation in the transport sector, piston-driven internal combustion engines and gas turbine aero-engines remain very attractive powertrain options due to their high thermal efficiency. Meanwhile, since the introduction of various emissions standards, that have forced the employment of various aftertreatment systems, the evolution of combustion process has been significant. Advanced combustion strategies have attempted to find in-chamber approaches to either meet these emission standards fully and thus avoid the need to use aftertreatment, or at the very least, to lower the performance demands required from aftertreatment systems and thus reducing their cost and complexity. While the main focus of combustion system development has been recently to lower emissions of CO2, there is also significant interest to lower nitric oxides (NOx) and particulate matter (PM) emissions and other harmful emissions

Contributions of local and regional sources to fine PM in the megacity of Paris

The particulate matter source apportionment technology (PSAT) is used together with PMCAMx, a regional chemical transport model, to estimate how local emissions and pollutant transport affect primary and secondary particulate matter mass concentration levels in Paris. During the summer and the winter periods examined, only 13% of the PM2.5 is predicted to be due to local Paris emissions, with 36% coming from mid-range (50–500 km from the center of the Paris) sources and 51% from long range transport (more than 500 km from Paris). The local emissions contribution to simulated elemental carbon (EC) is significant, with almost 60% of the EC originating from local sources during both summer and winter. Approximately 50% of the simulated fresh primary organic aerosol (POA) originated from local sources and another 45% from areas 100–500 km from the receptor region during summer. Regional sources dominated the secondary PM components. During summer more than 70% of the simulated sulfate originated from SO2 emitted more than 500 km away from the center of the Paris. Also more than 45% of secondary organic aerosol (SOA) was due to the oxidation of VOC precursors that were emitted 100–500 km from the center of the Paris. The model simulates more contribution from long range secondary PM sources during winter because the timescale for its production is longer due to the slower photochemical activity. PSAT results for contributions of local and regional sources were compared with observation-based estimates from field campaigns that took place during the MEGAPOLI project. PSAT simulations are in general consistent (within 20%) with these estimates for OA and sulfate. The only exception is that PSAT simulates higher local EC contribution during the summer compared to that estimated from observations

In situ, satellite measurement and model evidence on the dominant regional contribution to fine particulate matter levels in the Paris megacity

International audiencePublished by Copernicus Publications on behalf of the European Geosciences Union. 9578 M. Beekmann et al.: Evidence for a dominant regional contribution to fine particulate matter levels Abstract. A detailed characterization of air quality in the megacity of Paris (France) during two 1-month intensive campaigns and from additional 1-year observations revealed that about 70 % of the urban background fine particulate matter (PM) is transported on average into the megacity from upwind regions. This dominant influence of regional sources was confirmed by in situ measurements during short intensive and longer-term campaigns, aerosol optical depth (AOD) measurements from ENVISAT, and modeling results from PMCAMx and CHIMERE chemistry transport models. While advection of sulfate is well documented for other megacities, there was surprisingly high contribution from long-range transport for both nitrate and organic aerosol. The origin of organic PM was investigated by comprehensive analysis of aerosol mass spectrometer (AMS), radio-carbon and tracer measurements during two intensive campaigns. Primary fossil fuel combustion emissions constituted less than 20 % in winter and 40 % in summer of carbonaceous fine PM, unexpectedly small for a megacity. Cooking activities and, during winter, residential wood burning are the major primary organic PM sources. This analysis suggests that the major part of secondary organic aerosol is of modern origin , i.e., from biogenic precursors and from wood burning. Black carbon concentrations are on the lower end of values encountered in megacities worldwide, but still represent an issue for air quality. These comparatively low air pollution levels are due to a combination of low emissions per inhabitant , flat terrain, and a meteorology that is in general not conducive to local pollution build-up. This revised picture of a megacity only being partially responsible for its own average and peak PM levels has important implications for air pollution regulation policies

Expression and Membrane Topology of Anopheles gambiae Odorant Receptors in Lepidopteran Insect Cells

A lepidopteran insect cell-based expression system has been employed to express three Anopheles gambiae odorant receptors (ORs), OR1 and OR2, which respond to components of human sweat, and OR7, the ortholog of Drosophila's OR83b, the heteromerization partner of all functional ORs in that system. With the aid of epitope tagging and specific antibodies, efficient expression of all ORs was demonstrated and intrinsic properties of the proteins were revealed. Moreover, analysis of the orientation of OR1 and OR2 on the cellular plasma membrane through the use of a novel ‘topology screen’ assay and FACS analysis demonstrates that, as was recently reported for the ORs in Drosophila melanogaster, mosquito ORs also have a topology different than their mammalian counterparts with their N-terminal ends located in the cytoplasm and their C-terminal ends facing outside the cell. These results set the stage for the production of mosquito ORs in quantities that should permit their detailed biochemical and structural characterization and the exploration of their functional properties

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement–the Mobilise-D study protocol

Background The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987

A notion of convergence in fuzzy partially ordered sets

The notion of sequential convergence in fuzzy partially ordered sets, under the name oF-convergence, is well known. Our aim in this paper is to introduce and study a notion of net convergence, with respect to the fuzzy order relation, named o-convergence, which generalizes the former notion and is also closer to our sense of the classic concept of "convergence". The main result of this article is that the two notions of convergence are identical in the area of complete F-lattices. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

On statistical convergence of sequences of closed sets in metric spaces

In this paper, we do further investigations on the statistical inner and outer limits of sequences of closed sets in metric spaces, which were introduced by Nuray, Rhoades, and Talo, Sever, Başar, and generalize the conventional Painleve-Kuratowski inner and outer limits. Also, we provide criteria for checking statistical Wijsman and Hausdorff set convergences and we examine the relationship between Kuratowski and Wijsman statistical convergence. A closer look on the concept of statistical Cauchyness, with respect to the Hausdorff "extended"metric h, completes this research. © 2021 Mathematical Institute Slovak Academy of Sciences 2021