Structural Performance Monitoring Using a Dynamic Data-Driven BIM Environment

Structural health monitoring data has not been fully leveraged to support asset management due to a lack of effective integration with other datasets. A Building Information Modelling (BIM) approach is presented to leverage structural monitoring data in a dynamic manner. The approach allows for the automatic generation of parametric BIM models of structural monitoring systems that include time-series sensor data; and it enables data-driven and dynamic visualisation in an interactive 3D environment. The approach supports dynamic visualisation of key structural performance parameters, allows for the seamless updating and long-term management of data, and facilitates data exchange by generating Industry Foundation Classes (IFC) compliant models. A newly-constructed bridge near Stafford, UK, with an integrated fibre-optic sensor based monitoring system was used to test the capabilities of the developed approach. The case study demonstrated how the developed approach facilitates more intuitive data interpretation, provides a user-friendly interface to communicate with various stakeholders, allows for the identification of malfunctioning sensors thus contributing to the assessment of monitoring system durability, and forms the basis for a powerful data-driven asset management tool. In addition, this project highlights the potential benefits of investing in the development of data-driven and dynamic BIM environments

Real-time statistical modelling of data generated from self-sensing bridges

Instrumentation of infrastructure is changing the way engineers design, construct, monitor and maintain structures such as roads, bridges and underground structures. Data gathered from these instruments have changed the hands-on assessment of infrastructure behaviour to include data processing and statistical analysis procedures. Engineers wish to understand the behaviour of the infrastructure and detect changes – for example, degradation – but are now using high-frequency data acquired from a sensor network. Presented in this paper is a case study that models and analyses in real time the dynamic strain data gathered from a railway bridge which has been instrumented with fibre-optic sensor networks. The high frequency of the data combined with the large number of sensors requires methods that efficiently analyse the data. First, automated methods are developed to extract train passage events from the background signal and underlying trends due to environmental effects. Second, a streaming statistical model which can be updated efficiently is introduced that predicts strain measurements forward in time. This tool is enhanced to provide anomaly detection capabilities in individual sensors and the entire sensor network. These methods allow for the practical processing and analysis of large data sets. The implementation of these contributions will be essential for demonstrating the value of self-sensing structures. </jats:p

The Impact of COVID-19 on the Timing of Rotator Cuff Repair and Method of Postoperative Follow-up

Abstract Objective Rotator cuff repair (RCR) is one of the most common arthroscopic procedures. Our investigation aims to quantify the impact that the COVID-19 pandemic had on RCR, specifically on patients with acute, traumatic injuries. Methods Institutional records were queried to identify patients who underwent arthroscopic RCR between March 1st to October 31st of both 2019 and 2020. Patient demographic, preoperative, perioperative, and postoperative data were collected from electronic medical records. Inferential statistics were used to analyze data. Results Totals of 72 and of 60 patients were identified in 2019 and in 2020, respectively. Patients in 2019 experienced shorter lengths of time from MRI to surgery (62.7 ± 70.5 days versus 115.7 ± 151.0 days; p = 0.01). Magnetic resonance imaging (MRI) scans showed a smaller average degree of retraction in 2019 (2.1 ± 1.3 cm versus 2.6 ± 1.2 cm; p = 0.05) butnodifference in anterior toposterior tear size between years (1.6 ± 1.0 cm versus 1.8 ± 1.0 cm; p = 0.17). Less patients in 2019 had a tele-health postoperative consultation with their operating surgeon compared with 2020 (0.0% versus 10.0%; p = 0.009). No significant changes in complications (0.0% versus 0.0%; p > 0.999), readmission (0.0% versus 0.0%; p > 0.999), or revision rates (5.6% versus 0.0%; p = 0.13) were observed. Conclusion From 2019 to 2020, there were no significant differences in patient demographics or major comorbidities. Our data suggests that even though the time from MRI to surgery was delayed in 2020 and telemedicine appointments were necessary, RCR was still performed in a time in early complications. Level of Evidence III

In patients with severe uncontrolled asthma, does knowledge of adherence and inhaler technique using electronic monitoring improve clinical decision making? A protocol for a randomised controlled trial

Introduction: Many patients with asthma remain poorly controlled despite the use of inhaled corticosteroids and long-acting beta agonists. Poor control may arise from inadequate adherence, incorrect inhaler technique or because the condition is refractory. Without having an objective assessment of adherence, clinicians may inadvertently add extra medication instead of addressing adherence. This study aims to assess if incorporating objectively recorded adherence from the Inhaler Compliance Assessment (INCA) device and lung function into clinical decision making provides more cost-effective prescribing and improves outcomes. Methods and analysis: This prospective, randomised, multicentre study will compare the impact of using information on adherence to influence asthma treatment. Patients with severe uncontrolled asthma will be included. Data on adherence, inhaler technique and electronically recorded peak expiratory flow rate will be used to promote adherence and guide a clinical decision protocol to guide management in the active group. The control group will receive standard inhaler and adherence education. Medications will be adjusted using a protocol based on Global Initiativefor Asthma (GINA) recommendations. The primary outcome is the between-group difference in the proportion of patients who have refractory disease and are prescribed appropriate medications at the end of 32 weeks. A co-primary outcome is the difference between groups in the rate of adherence to salmeterol/fluticasone inhaler over the last 12 weeks. Secondary outcomes include changes in symptoms, lung function, type-2 cytokine biomarkers and clinical outcomes between both groups. Cost-effectiveness and cost-utility analyses of the INCA device intervention will be performed. The economic impact of a national implementation of the INCA-SUN programme will be evaluated. Ethics and dissemination:The results of the study will be published as a manuscript in peer-reviewed journals. The study has been approved by the ethics committees in the five participating hospitals. Trial registration NCT02307669; Pre-results

Use of digital measurement of medication adherence and lung function to guide the management of uncontrolled asthma (INCA Sun):a multicentre, single-blinded, randomised clinical trial

BACKGROUND: The clinical value of using digital tools to assess adherence and lung function in uncontrolled asthma is not known. We aimed to compare treatment decisions guided by digitally acquired data on adherence, inhaler technique, and peak flow with existing methods.METHODS: A 32-week prospective, multicentre, single-blinded, parallel, randomly controlled trial was done in ten severe asthma clinics across Ireland, Northern Ireland, and England. Participants were 18 years or older, had uncontrolled asthma, asthma control test (ACT) score of 19 or less, despite treatment with high-dose inhaled corticosteroids, and had at least one severe exacerbation in the past year despite high-dose inhaled corticosteroids. Patients were randomly assigned in a 1:1 ratio to the active group or the control group, by means of a computer-generated randomisation sequence of permuted blocks of varying sizes (2, 4, and 6) stratified by fractional exhaled nitric oxide (FeNO) concentration and recruitment site. In the control group, participants were masked to their adherence and errors in inhaler technique data. A statistician masked to study allocation did the statistical analysis. After a 1-week run-in period, both groups attended three nurse-led education visits over 8 weeks (day 7, week 4, and week 8) and three physician-led treatment adjustment visits at weeks 8, 20, and 32. In the active group, treatment adjustments during the physician visits were informed by digital data on inhaler adherence, twice daily digital peak expiratory flow (ePEF), patient-reported asthma control, and exacerbation history. Treatment was adjusted in the control group on the basis of pharmacy refill rates (a measure of adherence), asthma control by ACT questionnaire, and history of exacerbations and visual management of inhaler technique. Both groups used a digitally enabled Inhaler Compliance Assessment (INCA) and PEF. The primary outcomes were asthma medication burden measured as proportion of patients who required a net increase in treatment at the end of 32 weeks and adherence rate measured in the last 12 weeks by area under the curve in the intention-to-treat population. The safety analyses included all patients who consented for the trial. The trial is registered with ClinicalTrials.gov, NCT02307669 and is complete.FINDINGS: Between Oct 25, 2015, and Jan 26, 2020, of 425 patients assessed for eligibility, 220 consented to participate in the study, 213 were randomly assigned (n=108 in the active group; n=105 in the control group) and 200 completed the study (n=102 in the active group; n=98 in the control group). In the intention-to-treat analysis at week 32, 14 (14%) active and 31 (32%) control patients had a net increase in treatment compared with baseline (odds ratio [OR] 0·31 [95% CI 0·15-0·64], p=0·0015) and 11 (11%) active and 21 (21%) controls required add-on biological therapy (0·42 [0·19-0·95], p=0·038) adjusted for study site, age, sex, and baseline FeNO. Three (16%) of 19 active and 11 (44%) of 25 control patients increased their medication from fluticasone propionate 500 μg daily to 1000 μg daily (500 μg twice a day; adjusted OR 0·23 [0·06-0·87], p=0·026). 26 (31%) of 83 active and 13 (18%) of 73 controls reduced their medication from fluticasone propionate 1000 μg once daily to 500 μg once daily (adjusted OR 2·43 [1·13-5·20], p=0·022. Week 20-32 actual mean adherence was 64·9% (SD 23·5) in the active group and 55·5% (26·8) in the control group (between-group difference 11·1% [95% CI 4·4-17·9], p=0·0012). A total of 29 serious adverse events were recorded (16 [55%] in the active group, and 13 [45%] in the control group), 11 of which were confirmed as respiratory. None of the adverse events reported were causally linked to the study intervention, to the use of salmeterol-fluticasone inhalers, or the use of the digital PEF or INCA.INTERPRETATION: Evidence-based care informed by digital data led to a modest improvement in medication adherence and a significantly lower treatment burden.FUNDING: Health Research Board of Ireland, Medical Research Council, INTEREG Europe, and an investigator-initiated project grant from GlaxoSmithKline.</p

OpenSAFELY: a platform for analysing electronic health records designed for reproducible research

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY’s reproducibility-by-design approach in detail

Simplified Models for LHC New Physics Searches

This document proposes a collection of simplified models relevant to the design of new-physics searches at the LHC and the characterization of their results. Both ATLAS and CMS have already presented some results in terms of simplified models, and we encourage them to continue and expand this effort, which supplements both signature-based results and benchmark model interpretations. A simplified model is defined by an effective Lagrangian describing the interactions of a small number of new particles. Simplified models can equally well be described by a small number of masses and cross-sections. These parameters are directly related to collider physics observables, making simplified models a particularly effective framework for evaluating searches and a useful starting point for characterizing positive signals of new physics. This document serves as an official summary of the results from the "Topologies for Early LHC Searches" workshop, held at SLAC in September of 2010, the purpose of which was to develop a set of representative models that can be used to cover all relevant phase space in experimental searches. Particular emphasis is placed on searches relevant for the first ~50-500 pb-1 of data and those motivated by supersymmetric models. This note largely summarizes material posted at http://lhcnewphysics.org/, which includes simplified model definitions, Monte Carlo material, and supporting contacts within the theory community. We also comment on future developments that may be useful as more data is gathered and analyzed by the experiments.Comment: 40 pages, 2 figures. This document is the official summary of results from "Topologies for Early LHC Searches" workshop (SLAC, September 2010). Supplementary material can be found at http://lhcnewphysics.or

Landscape Movements of Migratory Birds and Bats Reveal an Expanded Scale of Stopover

Many species of birds and bats undertake seasonal migrations between breeding and over-wintering sites. En-route, migrants alternate periods of flight with time spent at stopover – the time and space where individuals rest and refuel for subsequent flights. We assessed the spatial scale of movements made by migrants during stopover by using an array of automated telemetry receivers with multiple antennae to track the daily location of individuals over a geographic area ∼20×40 km. We tracked the movements of 322 individuals of seven migratory vertebrate species (5 passerines, 1 owl and 1 bat) during spring and fall migratory stopover on and adjacent to a large lake peninsula. Our results show that many individuals leaving their capture site relocate within the same landscape at some point during stopover, moving as much as 30 km distant from their site of initial capture. We show that many apparent nocturnal departures from stopover sites are not a resumption of migration in the strictest sense, but are instead relocations that represent continued stopover at a broader spatial scale

OpenSAFELY: A platform for analysing electronic health records designed for reproducible research

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID‐19 pandemic, we developed the secure, transparent, analytic open‐source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code‐sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY's reproducibility‐by‐design approach in detail

Data on vegetation across forest edges from the FERN(Forest Edge Research Network)

Published versionMany studies have focused on vegetation across forest edges to study impacts of edges created by human activities on forest structure and composition, or patterns of vegetation at inherent natural edges. Our objective was to create a database of plant-related variables across different types of edges from various studies (mainly from across Canada, but also in Brazil and Belize) to facilitate edge research. We compiled data on vegetation along more than 300 transects perpendicular to forest edges adjacent to clear-cuts, burned areas, bogs, lakes, barrens, insect disturbances, and riparian areas from 24 studies conducted over the past three decades. Data were compiled for more than 400 plant species and forest structure variables (e.g., trees, logs, canopy cover). All data were collected with a similar sampling design of quadrats along transects perpendicular to forest edges, but with varying numbers of transects and quadrats, and distances from the edge. The purpose for most of the studies was either to determine the distance of edge influence (edge width) or to explore the pattern of vegetation along the edge to interior gradient. We provide data tables for the cover of plant species and functional groups, the species and size of live and dead trees, the density of saplings, maximum height of functional groups and shrub species, and the cover of functional groups at different heights (vertical distribution of vegetation). The Forest Edge Research Network (FERN) database provides extensive data on many variables that can be used for further study including meta-analyses and can assist in answering questions important to conservation efforts (e.g., how is distance of edge influence from created edges affected by different factors?). We plan to expand this database with subsequent studies from the authors and we invite others to contribute to make this a more global database. The data are released under a CC0 license. When using these data, we ask that you cite this data paper and any relevant publications listed in our metadata file. We also encourage you to contact the first author if you are planning to use or contribute to this database