Real-time performance monitoring of tuned mass damper system for a 183 m reinforced concrete chimney

A 183 m reinforced concrete chimney for a coal-fired power station was instrumented in the latter part of its life during the construction of a replacement chimney. Because of concerns about large-amplitude response induced by interference effects from the new chimney in the prevailing upwind direction, a response monitoring system was installed, quickly followed by a tuned mass damper (TMD) system. As well as providing live display of the chimney response, the monitoring system was also used to check the functioning of the TMD. The monitoring system featured a direct implementation of the stochastic subspace identification procedure in the 'virtual instrument' controlling the system, so that modal damping values for the system were displayed automatically, in real-time. The system thus provided an immediate visual indication of increased damping levels during strong winds, showing the correct functioning of the TMD. The paper describes the chimney, the monitoring system and its installation, the data processing and system identification procedure, together with performance data before, during and after installation of the TMD. (C) 2009 Elsevier Ltd. All rights reserved

OFLOPS-SUME and the art of switch characterization

© 2018 IEEE. The philosophy of software-defined networking (SDN) has introduced new challenges in network system management. In contrast to traditional network devices that contained both the control and the data plane functionality in a tightly coupled manner, SDN technologies separate the two network planes and define a remote API for low-level device configuration. Nonetheless, the enhanced flexibility of the SDN paradigm is prone to create novel performance and scalability bottlenecks in the network. To help network managers and application developers better understand the actual behavior of SDN implementations, we present a hardware/software co-design that enables switch characterization at 40 Gbps and beyond. We conduct an evaluation of both software and hardware switches. We expose the unwanted effects of the OpenFlow barrier primitive, potential misbehaviors when adding or modifying a batch of rules, and how simple operations, such as packet modification, can impact the switch forwarding performance. We release the code publicly as open source to promote experiments reproducibility as well as encourage the network community to evolve our solution.This work was supported by the U.K.’s Engineering and Physical Sciences Research Council through the EARL and TOUCAN projects under Grants EP/P025374/1 and EP/L02009/1

Cost-effectiveness of Pharmaceutical-based Pandemic Influenza Mitigation Strategies1

Population prepandemic vaccine and antiviral treatment strategies may be cost-effective

New Insights into the mineralogy of the Atlantis II deep metalliferous sediments, Red Sea

The Atlantis II Deep of the Red Sea hosts the largest known hydrothermal ore deposit on the ocean floor and the only modern analog of brine pool-type metal deposition. The deposit consists mainly of chemical-clastic sediments with input from basin-scale hydrothermal and detrital sources. A characteristic feature is the millimeter-scale layering of the sediments, which bears a strong resemblance to banded iron formation (BIF). Quantitative assessment of the mineralogy based on relogging of archived cores, detailed petrography, and sequential leaching experiments shows that Fe-(oxy)hydroxides, hydrothermal carbonates, sulfides, and authigenic clays are the main “ore” minerals. Mn-oxides were mainly deposited when the brine pool was more oxidized than it is today, but detailed logging shows that Fe-deposition and Mn-deposition also alternated at the scale of individual laminae, reflecting short-term fluctuations in the Lower Brine. Previous studies underestimated the importance of nonsulfide metal-bearing components, which formed by metal adsorption onto poorly crystalline Si-Fe-OOH particles. During diagenesis, the crystallinity of all phases increased, and the fine layering of the sediment was enhanced. Within a few meters of burial (corresponding to a few thousand years of deposition), biogenic (Ca)-carbonate was dissolved, manganosiderite formed, and metals originally in poorly crystalline phases or in pore water were incorporated into diagenetic sulfides, clays, and Fe-oxides. Permeable layers with abundant radiolarian tests were the focus for late-stage hydrothermal alteration and replacement, including deposition of amorphous silica and enrichment in elements such as Ba and Au

Efficiency of two-phase methods with focus on a planned population-based case-control study on air pollution and stroke

<p>Abstract</p> <p>Background</p> <p>We plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO₂) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO₂exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on our planned study. We develop a two-phase method for this study and compare the performance of our method with the performance of other two-phase methods.</p> <p>Methods</p> <p>A two-phase case-control study was simulated with a varying number of first- and second-phase subjects. Estimation methods: <it>Method 1</it>: Effect estimation with second-phase data only. <it>Method 2</it>: Effect estimation by adjusting the first-phase estimate with the difference between the adjusted and unadjusted second-phase estimate. The first-phase estimate is based on individual disease status and residential address for all study subjects that are linked to register data on NO₂-exposure for each geographical area. <it>Method 3</it>: Effect estimation by using the expectation-maximization (EM) algorithm without taking area-level register data on exposure into account. <it>Method 4</it>: Effect estimation by using the EM algorithm and incorporating group-level register data on NO₂-exposure.</p> <p>Results</p> <p>The simulated scenarios were such that, unbiased or marginally biased (< 7%) odds ratio (OR) estimates were obtained with all methods. The efficiencies of method 4, are generally higher than those of methods 1 and 2. The standard errors in method 4 decreased further when the case/control ratio is above one in the second phase. For all methods, the standard errors do not become substantially reduced when the number of first-phase controls is increased.</p> <p>Conclusion</p> <p>In the setting described here, method 4 had the best performance in order to improve efficiency, while adjusting for varying participation rates across areas.</p

Influence of soil and climate on root zone storage capacity

Root zone storage capacity (Sr) is an important variable for hydrology and climate studies, as it strongly influences the hydrological functioning of a catchment and, via evaporation, the local climate. Despite its importance, it remains difficult to obtain a wellâ founded catchment representative estimate. This study tests the hypothesis that vegetation adapts its Sr to create a buffer large enough to sustain the plant during drought conditions of a certain critical strength (with a certain probability of exceedance). Following this method, Sr can be estimated from precipitation and evaporative demand data. The results of this â climateâ based methodâ are compared with traditional estimates from soil data for 32 catchments in New Zealand. The results show that the differences between catchments in climateâ derived catchment representative Sr values are larger than for soilâ derived Sr values. Using a model experiment, we show that the climateâ derived Sr can better reproduce hydrological regime signatures for humid catchments; for more arid catchments, the soil and climate methods perform similarly. This makes the climateâ based Sr a valuable addition for increasing hydrological understanding and reducing hydrological model uncertainty.Key Points:Plants develop their root systems to survive droughtsModel root zone storage capacity (Sr) can be inferred from climate recordsModel experiment shows that Sr is stronger influenced by climate than by soilPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137190/1/wrcr21890.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137190/2/wrcr21890_am.pd

App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74–0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model

Long-term exposure to air pollution and hospital admissions for ischemic stroke. A register-based case-control study using modelled NOx as exposure proxy

Background: Long-term exposure to air pollution is a hypothesized risk factor for ischemic stroke. In a large case-control study with a complete study base, we investigated whether hospital admissions for ischemic stroke were associated with residential concentrations of outdoor NOx, as a proxy for exposure to air pollution, in the region of Scania, Southern Sweden. Methods: We used a two-phase case-control study design, including as first-phase controls all individuals born between 1923 and 1965 and residing in Scania in 2002 (N=556 912). We defined first-phase cases as first-time ischemic stroke patients residing in Scania and registered in the Swedish stroke register between 2001 and 2005 (N=4 904) and second-phase cases as cases for whom we had information on smoking status, diabetes, and medication for hypertension (N=4 375). For the controls, information on these covariables was collected from a public health survey, resulting in 4 716 second-phase controls. With a geographical information system and an emission database, individual residential outdoor annual mean NOx concentration was modelled. The data were analyzed with logistic regression. Results: We found no evident association between NOx and ischemic stroke. For example, the odds ratio for ischemic stroke associated with the NOx category 20-30 mu g/m(3) compared to the reference category of <10 mu g/m(3) was 0.95 (95% CI 0.86-1.06). Conclusion: In this study area, with generally low levels of air pollution, using a complete study base, high-quality ascertainment of cases, and individually modelled exposure, we did not observe any clear association between NOx and ischemic stroke hospital admissions