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Background: Lockdown measures, including school closures, due to the COVID-19 pandemic have caused widespread disruption to children’s lives. The aim of this study was to explore the impact of a national lockdown on children’s physical activity using seasonally matched accelerometry data. Methods: Using a pre/post observational design, 179 children aged 8 to 11 years provided physical activity data measured using hip-worn triaxial accelerometers worn for 5 consecutive days prepandemic and during the January to March 2021 lockdown. Multilevel regression analyses adjusted for covariates were used to assess the impact of lockdown on time spent in sedentary and moderate to vigorous physical activity. Results: A 10.8-minute reduction in daily time spent in moderate to vigorous physical activity (standard error: 2.3 min/d, P P P P Conclusion: These findings suggest that the loss of in-person schooling was the single largest impact on physical activity in this cohort of primary school children in London, Luton, and Dunstable, United Kingdom.

In this study, we present a cohort study involving 106 COPD patients using portable environmental sensor nodes with attached air pollution sensors and activity-related sensors, as well as daily symptom records and peak flow measurements to monitor patients’ activity and personal exposure to air pollution. This is the first study which attempts to predict COPD symptoms based on personal air pollution exposure. We developed a system that can detect COPD patients’ symptoms one day in advance of symptoms appearing. We proposed using the Probabilistic Latent Component Analysis (PLCA) model based on 3-dimensional and 4-dimensional spectral dictionary tensors for personalised and population monitoring, respectively. The model is combined with Linear Dynamic Systems (LDS) to track the patients’ symptoms. We compared the performance of PLCA and PLCA-LDS models against Random Forest models in the identification of COPD patients’ symptoms, since tree-based classifiers were used for remote monitoring of COPD patients in the literature. We found that there was a significant difference between the classifiers, symptoms and the personalised versus population factors. Our results show that the proposed PLCA-LDS-3D model outperformed the PLCA and the RF models between 4 and 20% on average. When we used only air pollutants as input, the PLCA-LDS-3D forecasting results in personalised and population models were 48.67 and 36.33% accuracy for worsening of lung capacity and 38.67 and 19% accuracy for exacerbation of COPD patients’ symptoms, respectively. We have shown that indicators of the quality of an individual’s environment, specifically air pollutants, are as good predictors of the worsening of respiratory symptoms in COPD patients as a direct measurement.

Statement from the Committee on the Medical Effects of Air Pollutants (COMEAP) on the differential toxicity of particulate matter according to source or constituents Particulate matter in outdoor air comes from a wide range of different sources and contains many different chemical components. Understanding which of these sources and components are particularly harmful would help to focus policies and interventions to reduce impacts on health. COMEAP recently (July 2022) published an updated statement on this topic, drawing on recent reviews of the available evidence. It noted that particulate matter, and constituents of particulate matter, associated with combustion and road traffic have received the most research attention. However, there was insufficient coherence in the epidemiological evidence to conclude that these sources of particulate matter play a significantly greater role in the health effects of particulate air pollution than other sources or constituents. A comprehensive review of the evidence reveals that toxicity has been attributed to a wide variety of constituents and sources, possibly acting by a number of different mechanisms. However, the complexity of sources, sizes and chemical composition of particulate matter makes it extremely difficult to identify which characteristics of particles might make them less harmful or more toxic than others. Future research assessing the influence of chemical composition of particulate matter should offer greater insights into toxicity. But, at present, PM2.5 (the mass concentration of fine particulate matter in air) remains the most suitable metric for evaluating health impacts and regulating particulate air pollution.

Unlimited access to a motorway network can, in overloaded conditions, cause a loss of throughput. Ramp metering, by controlling access to the motorway at onramps, can help avoid this loss of throughput. The queues that form at onramps are dependent on the metering rates chosen at the onramps, and these choices affect how the capacities of different motorway sections are shared amongst competing flows. In this paper we perform an analytical study of a fluid, or differential equation, model of a linear network topology with onramp queues. The model allows for adaptive arrivals, in the sense that the rate at which external traffic enters the queue at an onramp can depend on the current perceived delay in that queue. The model also includes a ramp metering policy which uses global onramp queue length information to determine the rate at which traffic enters the motorway from each onramp. This ramp metering policy minimizes the maximum delay over all onramps and produces equal delay times over many onramps. The paper characterizes both the dynamics and the equilibrium behavior of the system under this policy. While we consider an idealized model that leaves out many practical details, an aim of the paper is to develop analytical methods that yield interesting qualitative insights and might be adapted to more general contexts. The paper can be considered as a step in developing an analytical approach towards studying more complex network topologies and incorporating other model features.

The Environment Act (2021) requires the government to set at least one long-term air-quality target, as well as a target for fine particulate matter (PM2.5) air pollution. COMEAP provided independent expert advice to the Department for Environment, Food and Rural Affairs (Defra) to inform the development of targets to reduce both the annual average (mean) concentration of PM2.5 in ambient air and long term population exposures. COMEAP’s advice includes that: a focus on reducing long-term average concentrations of PM2.5 is appropriate evidence indicates that PM2.5 pollution can have harmful effects on people’s health at low concentrations reducing concentrations to, or below, the World Health Organization’s new Air Quality Guideline (5 µg/m3) would benefit public health available studies have not indicated a threshold of effect below which there is no harm reducing exposure of the whole population would achieve the greatest overall public health benefit some individuals or groups are more at risk, but it might be difficult to reflect this in a national targets framework the health benefits of reducing other pollutants, such as nitrogen dioxide and ozone, should not be overlooked. In response to Defra’s evidence report explaining the rationale for its proposed targets, COMEAP: recommended that Defra should consider roles in continuing to improve air quality, even where legally binding targets are met suggested that interim targets would be important to ensure early progress and maximise health benefits stressed the importance of considering inequalities noted that international engagement will be required

BACKGROUND: Adjacent segment pathology (ASP) remains a concern following treatment with cervical disc arthroplasty (CDA) and anterior cervical discectomy and fusion (ACDF). Radiographic ASP (RASP) is ASP identified on imaging, which may or may not include clinical symptoms. The risk factors for development of RASP and its clinical effects remain controversial. In part 1 of a 2-part publication we evaluate the incidence and predictors of RASP as well as determine whether any association exists between RASP and patient-reported outcomes (PROs). METHODS: Data were prospectively collected during a US Food and Drug Administration randomized, multicenter, investigational device exemption trial comparing CDA (Mobi-C; Zimmer Biomet, Westminster, CO) with ACDF. Multiple post hoc analyses were conducted on RASP as it related to demographics and patient outcomes. Kaplan-Meier estimates of time to Kellgren-Lawrence (K-L) grade 3/4 were calculated separately for all groups. Multivariate Cox proportional hazard models were used analyze whether RASP was associated with patient preoperative demographic characteristics and preoperative and postoperative radiographic characteristics. The association of RASP with PROs was analyzed using generalized estimating equations and matched, retrospective cohort analysis. RESULTS: The incidence of grade 3/4 RASP was lower for patients treated with CDA when initial treatment was at 1 level (27% vs 47%, P < .0001) and at 2 levels (14% vs 49%, P < .0001). Kaplan-Meier estimates indicated significantly lower probability of grade 3/4 RASP over time for patients receiving CDA (P < .001). Treatment with ACDF, treatment of 1 level, higher age, body mass index, higher preoperative physical components score, and a lower Cobb angle were associated with elevated risk of grade 3/4 RASP. CDA was shown to be more effective than ACDF (64.4%; 95% CI = 50.9, 74.2; P < .0001) at preventing RASP. CONCLUSIONS: The incidence and risk of RASP is decreased when patients are treated with CDA compared with ACDF. Although the mechanism of CDA that generates this protective effect is not understood, PROs remain unaffected through 7 years despite changes in RASP.

Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NOx levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NOx (n=428) and NO2 (n=472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO2 concentrations. NO and NO2 concentrations were highest in commercial/business/industrial (66 and 76 μg/m3, respectively) and high-density residential areas (47 and 59 μg/m3, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 μg/m3 for NO and NO2, and a clear seasonal variation, with the mean NO2 of 63 μg/m3 (non-Harmattan) increased by 25-56% to 87 μg/m3 (Harmattan) across different site types. The NO2/NOx ratio was also elevated by 19-28%. Both NO and NO2 levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO2 and NO2/NOx and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25 – 180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.

In emergency situations like the coronavirus pandemic, decisions must be made quickly, with only partial information. But good decisions are still possible using risk–benefit analysis. By Peter J. Diggle, Tim Gowers, Frank Kelly and Neil Lawrence, In emergency situations like the coronavirus pandemic, decisions must be made quickly, with only partial information. But good decisions are still possible using risk–benefit analysis. By Peter J. Diggle, Tim Gowers, Frank Kelly and Neil Lawrence.

International audience

International audience

We analyze a tractable model of a limit order book on short time scales, where the dynamics are driven by stochastic fluctuations between supply and demand. We establish the existence of a limiting distribution for the highest bid, and for the lowest ask, where the limiting distributions are confined between two thresholds. We make extensive use of fluid limits in order to establish recurrence properties of the model. We use the model to analyze various high-frequency trading strategies, and comment on the Nash equilibria that emerge between high-frequency traders when a market in continuous time is replaced by frequent batch auctions., Revision of the submitted version: correcting an error (caught by Jan Swart) in the statement of Proposition 4.1 (1), propagating to Theorem 2.1 (1)

© 2019 Association for Computing Machinery. Congestion control (CC) is the key to achieving ultra-low latency, high bandwidth and network stability in high-speed networks. From years of experience operating large-scale and high-speed RDMA networks, we find the existing high-speed CC schemes have inherent limitations for reaching these goals. In this paper, we present HPCC (High Precision Congestion Control), a new high-speed CC mechanism which achieves the three goals simultaneously. HPCC leverages in-network telemetry (INT) to obtain precise link load information and controls traffic precisely. By addressing challenges such as delayed INT information during congestion and overreaction to INT information, HPCC can quickly converge to utilize free bandwidth while avoiding congestion, and can maintain near-zero in-network queues for ultra-low latency. HPCC is also fair and easy to deploy in hardware. We implement HPCC with commodity programmable NICs and switches. In our evaluation, compared to DCQCN and TIMELY, HPCC shortens flow completion times by up to 95%, causing little congestion even under large-scale incasts.

BackgroundThere is evidence that ambient air pollution is associated with increased risk of infant mortality. Road traffic is a major contributor to ambient air pollution, but it also generates noi...

We combine the power flow model with the proportionally fair optimization criterion to study the control of congestion within a distribution electric grid network. The form of the mathematical optimization problem is a convex second order cone that can be solved by modern non-linear interior point methods and constitutes the core of a dynamic simulation of electric vehicles (EV) joining and leaving the charging network. The preferences of EV drivers, represented by simple algorithmic strategies, are conveyed to the optimizing component by real-time adjustments to user-specific weighting parameters that are then directly incorporated into the objective function. The algorithmic strategies utilize a small number of parameters that characterize the user's budgets, expectations on the availability of vehicles and the charging process. We investigate the collective behaviour emerging from individual strategies and evaluate their performance by means of computer simulation., Comment: 7 pages, 4 figures, COMPENG conference