Background: Chronic obstructive pulmonary disease (COPD) has a high incidence rate in China, but the diagnosis rate remains insufficient. This study aimed to explore and compare COPD screening tools ...for primary healthcare institutions in China. Purpose: Exploring COPD Screening Tools and Their Combined Use for Primary Healthcare Institutions in China. Patients and Methods: From September 2022 to March 2023, a screening for COPD was conducted among residents aged 35 years and above in primary healthcare institutions in Beijing, China. The screening involved the use of the CAPTURE scale, COPD-SQ scale, and peak expiratory flow rate test. Any positive results from these screening tests were followed by further pulmonary function testing to confirm the diagnosis. Sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic (ROC) curves were calculated for each screening tool alone and in combination. Results: A total of 986 individuals completed the screening tests. The positive rates for the CAPTURE scale, COPD- SQ scale, and peak flow meter screening were 41.78%, 29.11%, and 52.03%, respectively. Of the participants, 166 (24.09%) underwent pulmonary function tests, with an average age of 61.69 + or - 13.68 years. The peak flow meter screening showed the highest sensitivity (83.78%) when used alone, while the COPD-SQ scale exhibited the best specificity (59.69%), positive predictive value (31.58%), and negative predictive value (58.56%). Significant differences (P<0.05) were observed between any two of the three screening tools. Among the combinations, the peak flow meter screening + COPD-SQ scale showed the highest accuracy, with a Youden index of 0.277 and an AUC of 0.638. Conclusion: There is variation in the accuracy of existing screening tools for COPD when used alone. For primary healthcare institutions, the optimal COPD screening tool is the combination of peak flow meter screening and the COPD-SQ questionnaire. If limited by screening equipment conditions, the COPD-SQ questionnaire can be used alone for screening. Keywords: primary health care institutions, chronic obstructive pulmonary disease, COPD, screening questionnaire, peak flow rate test, pulmonary function tests
Green roof, as a popular low impact development practice, has become important to mitigate adverse impacts of future climate change on urban stormwater. However, there is limited information ...regarding assessment of the effectiveness of green roofs in response to uncertain future climate change challenges. In this study, the validated model was used to simulate the reduction performance of green roofs on urban catchment outflow and assess their cost-effectiveness in response to design storms under climate change scenarios. Results showed that the median runoff volume of urban catchments increased by 12.5 %–14.6 % and 15.5 %–18.1 % and the median peak flow rate increased by 14.4 %–17.8 % and 17.9 %–22.1 % under SSP2-4.5 and SSP5-8.5 scenarios, respectively. This indicated the variability of runoff volume and peak flow changes for short return storm events caused by climate change was relatively high. Green roof implementation had reasonable mitigation effects on runoff volume and peak flow amplification in urban catchments caused by climate change. The median runoff volume reduction of green roofs for the 1-year storm was 15.2 % under SSP2-4.5 scenario. As rainfall intensity increased, the median runoff volume reduction of green roofs significantly declined to 5.6 % for the 100-year storm. However, the variations of runoff volume and peak flow reduction of green roofs were relatively smaller for longer return periods under climate change scenarios. Runoff reduction percentages of green roofs increased linearly with their implementation cost. The average value of the cost-effectiveness (C/E) index for green roofs was 91.2 %/million $ under base climate condition, and it decreased to 88.9 %/million $ and 88.4 %/million $ for SSP2-4.5 and SSP5-8.5 scenarios, respectively. The C/E values decreased with increasing storm return period, and the values were relatively lower in SSP5-8.5 scenarios. These results could help to understand the potential role of green roofs to mitigate the impacts of future climate change.
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•Green roofs had reasonable mitigation on runoff amplification caused by climate change.•Variations of runoff reduction were smaller for longer return periods under climate change.•Runoff reduction of green roofs increased linearly with their implementation costs.•Cost-effectiveness of green roofs was 88.9–88.4 %/million $ for climate change scenarios.•Cost-effectiveness values were lower in SSP5-8.5 and decreased with increasing return periods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Storm direction and storm velocity play a critical role in streamflow response; and despite evidence of ongoing changes in storm tracks around the world, there is no practical approach to efficiently ...assess and quantify the role of these storm properties in streamflow magnitude. We address this technical gap by introducing the Directional Unit Hydrograph (Directional-UH) model to systematically evaluate the storm hydrograph as a function of storm direction and storm velocity. The Directional-UH is based on the well-known theory of the unit hydrograph, which has served as a foundational block of multiple rainfall-runoff models for streamflow prediction. The Directional-UH relaxes the assumption of spatial uniform rainfall prescribed in the original concept of the unit hydrograph, by incorporating storm direction and storm velocity into the unit hydrograph function. The storm structure within the Directional-UH is represented by rectangular storms moving with constant velocity over a linear trajectory. We demonstrated, based on observations of extreme rainfall events, that rectangular storm representations can reproduce streamflow responses similar to those expected from actual radar rainfall observations. The Turkey River basin located in Iowa, USA, is used as a testbed to illustrate three practical applications of the Directional-UH model. First, the storm trajectory that produces the highest peak flow response is identified. Second, the conditions that lead to the rainfall-runoff resonance are determined, which occurs when the storm motion and the flood wave are in sync to maximize the peak flow response. Third, streamflow responses from consecutive storm events are quantified, allowing exploration and identification of critical combinations of storm events that exacerbate the magnitude of flood events. Overall, the simple hydrological inference offered by the Directional-UH makes this model a unique and essential hydrological tool that provides new perspectives to expand our understanding of rainfall-runoff dynamics through the lenses of storm direction and storm velocity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A correct estimation of the instantaneous peak flow (IPF) is crucial to reducing the consequences of flash floods. An approach to estimate the IPF, obtained by combining Soil and Water Assessment ...Tool (SWAT) simulation and machine-learning models, was proposed and then verified by comparison with observation-based results in the Ladra river basin, northwest Spain. The SWAT model has been used to estimate the maximum mean daily flow (MMDF), and machine-learning models have been used to estimate the IPF based on MMDF. Four nonlinear time-series intelligence models, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and extreme learning machine (ELM) were applied, and their results were compared. The Modified Nash-Sutcliffe efficiency coefficient (MNSE) and the index of agreement (d) were used to evaluate SWAT performance while simulating MMDF, and the coefficient of determination (R2) and the root mean square error (RMSE) were employed to evaluate the performance of these intelligent systems. According to the results, the SWAT hydrological model is a useful tool to simulate MMDF. Validation analyses resulted in values of statistical indexes (MNSE = 0.64 and d = 0.95). Regarding intelligent systems, the results show that they can be successfully used in predicting IPF, but ELM has demonstrated a superior ability to estimate IPF from the MMDF (R2 = 0.86 and RMSE = 48.59). The results of this study can contribute to predicting IPF in areas where sub-daily observational data are scarce, thereby reducing uncertainties associated with IPF estimations.
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•SWAT model has been used to estimate maximum mean daily flows.•Intelligent Systems estimate instantaneous peak flow based on the daily flows.•Extreme Learning and Support Vector Machine were optimal to estimate peak flow.•Predictions correlated with experimental data with coefficients of 0.86–0.88.•Results assist predicting peak flow with limited sub-daily observational data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Flood‐related fatalities and impacts on society surpass those from all other natural disasters globally. While the inclusion of large‐scale climate drivers in streamflow (or high‐flow) prediction has ...been widely studied, an explicit link to global‐scale long‐lead prediction is lacking, which can lead to an improved understanding of potential flood propensity. Here we attribute seasonal peak‐flow to large‐scale climate patterns, including the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO), using streamflow station observations and simulations from PCR‐GLOBWB, a global‐scale hydrologic model. Statistically significantly correlated climate patterns and streamflow autocorrelation are subsequently applied as predictors to build a global‐scale season‐ahead prediction model, with prediction performance evaluated by the mean squared error skill score (MSESS) and the categorical Gerrity skill score (GSS). Globally, fair‐to‐good prediction skill (20% ≤ MSESS and 0.2 ≤ GSS) is evident for a number of locations (28% of stations and 29% of land area), most notably in data‐poor regions (e.g., West and Central Africa). The persistence of such relevant climate patterns can improve understanding of the propensity for floods at the seasonal scale. The prediction approach developed here lays the groundwork for further improving local‐scale seasonal peak‐flow prediction by identifying relevant global‐scale climate patterns. This is especially attractive for regions with limited observations and or little capacity to develop flood early warning systems.
Key Points
Attribution of long‐lead large‐scale climate signals to seasonal peak‐flow is identified globally
Season‐ahead seasonal peak‐flow prediction models are constructed globally
Fair‐to‐good prediction skill is evident for many locations, most notably data‐poor regions (e.g., West and Central Africa)
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
•Robustness-based weighting techniques on changing climate conditions are studied.•Weighting hydrological models improved performances on contrasting conditions.•The Granger-Ramanathan type A showed ...generally more robust performances.•More complex hydrological models can add value in contrasting climate conditions.
Reliable hydrological projections are of great importance for climate change impact studies, especially knowing that these analyses can allow identifying regional adaptation and mitigation strategies for the future. However, the literature has highlighted that hydrological models used under climate conditions that are contrasting to those used during their calibrations can lower their performance and reliability, an issue that lowers the confidence in hydrological projections and adds uncertainty to their analyses. More studies are needed to explore this issue and evaluate potential strategies that might improve hydrological models’ reliability in climate change impact studies. Thus, the present study evaluates the robustness of hydrological models under contrasting climatic conditions and investigates the use of weighting techniques to improve their combined performance and reliability. The robustness of five lumped hydrological models is analysed using a Differential Split-Sample Testing (DSST) that evaluates their performance under cold, warm, humid and dry historical contrasting conditions over 77 basins covering different hydroclimatic conditions (two domains, one located in Quebec, Canada, and one in Mexico). Additionally, four basins were selected from the study area to evaluate the robustness of a more complex semi-distributed and more physically-based hydrological model and compare its simulations against the simpler lumped hydrological models. Based on the resulting performance of each hydrological model, five different weighting methods were applied to evaluate the potential improvements in the multi-model ensemble performance and quantify their effects on hydrological projections, particularly on future peak flows. For each basin, these streamflow projections were produced using two regional climate simulations (one per studied domain) issued from the Canadian Regional Climate Model version 5 (CRCM5) under the Representative Concentration Pathway (RCP) 8.5 for the 1976–2005, 2041–2070 and 2070–2099 periods. The results showed that weighting hydrological models, even with the most simplistic methods, showed better performances over historical contrasting conditions than the best-performing lumped hydrological model. Between the different weighting methods, the Granger-Ramanathan type A showed the overall best performance among the different basins and climate conditions, particularly in peak streamflows. Over the CRCM5-driven peak flow projections, the weighting methods Granger-Ramanathan types A and B produced the largest impacts on the projected floods magnitudes and climate change signals. On the other hand, the additional tests using the semi-distributed and more physically-based hydrological model revealed that this model showed more robust simulations than the weighted lumped hydrological models on low flows over the four selected basins. Additionally, more robust high-flow simulations were observed over a small snow-dominated basin, suggesting a potential added value in adding more complex hydrological models to simulate conditions under a changed climate.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Electrical impedance tomography (EIT) is a non-invasive diagnostic tool for evaluating lung function. The objective of this study was to compare respiratory flow variables calculated from thoracic ...EIT measurements with corresponding spirometry variables. Ten healthy research horses were sedated and instrumented with spirometry via facemask and a single-plane EIT electrode belt around the thorax. Horses were exposed to sequentially increasing volumes of apparatus dead space between 1,000 and 8,500 mL, in 5-7 steps, to induce carbon dioxide rebreathing, until clinical hyperpnea or a tidal volume of 150% baseline was reached. A 2-min stabilization period followed by 2 minutes of data collection occurred at each timepoint. Peak inspiratory and expiratory flow, inspiratory and expiratory time, and expiratory nadir flow, defined as the lowest expiratory flow between the deceleration of flow of the first passive phase of expiration and the acceleration of flow of the second active phase of expiration were evaluated with EIT and spirometry. Breathing pattern was assessed based on the total impedance curve. Bland-Altman analysis was used to evaluate the agreement where perfect agreement was indicated by a ratio of EIT:spirometry of 1.0. The mean ratio (bias; expressed as a percentage difference from perfect agreement) and the 95% confidence interval of the bias are reported. There was good agreement between EIT-derived and spirometry-derived peak inspiratory -15% (-46-32) and expiratory 10% (-32-20) flows and inspiratory -6% (-25-18) and expiratory 5% (-9-20) times. Agreement for nadir flows was poor -22% (-87-369). Sedated horses intermittently exhibited Cheyne-Stokes variant respiration, and a breath pattern with incomplete expiration in between breaths (
breaths). Electrical impedance tomography can quantify airflow changes over increasing tidal volumes and changing breathing pattern when compared with spirometry in standing sedated horses.
•The impervious surface has the most contribution to storm runoff of the community.•The reduction capacity of single GI was limited, especially in bigger storms.•The integrated GIs has significantly ...reduction effects and can optimize the benefits.
The risk of urban flooding is increasing as a result of rapid urbanization. Green infrastructure (GI) is an emerging planning and design concept to mitigate urban flooding. A community scale simulation model was developed to quantify the effectiveness of GI on reducing the volume and peak flow of urban flooding. Five scenarios, namely expanding green space, converting to concave green space, constructing a runoff retention structure, converting to porous brick pavement, and combining previous four measures were considered for an urban community in Beijing. The outcomes showed that the model performed responsively to simulate the storm runoffs at varying recurrence intervals under these scenarios. Simulation results showed that, the impervious surfaces have the most contribution to the storm runoffs of the community. The reduction capacity for single GI facility was limited, especially in bigger storm events. The integrated GI configuration has effective reduction percentage, such as the total runoff reduction was ranged from 100% to 85.0% and the peak flow reduced 100–92.8%. This work can guide local planners and decision makers in their actions on green infrastructures in community scale.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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•Monitored rainfall-runoff responses from 13 urban and two forested watersheds.•Statistically compared measurements to current and novel hydrologic design models.•Greater uncertainty ...in peak flow predictions compared to runoff volume.•Different design models necessary for specific watershed / rainfall characteristics.
The need for resilient stormwater infrastructure is increasingly critical as urbanization and climate change continue to threaten water resources. Engineers and practitioners require reliable methodologies to estimate rainfall-runoff responses to adequately size and design sewer pipes and inlets, flood controls, and stormwater control measures (SCMs). The National Resource Conservation Service Technical Release 55 (often referred to as curve number CN method), Simple, and Rational methods are methodologies commonly implemented for such designs by regulatory agencies due to the limited inputs needed to estimate runoff; however, uncertainty is present in each model since they simplify actual hydrological processes. In this study, 13 urban and two forested watersheds were monitored, and their observed hydrologic responses were compared to modeled hydrologic responses utilizing the aforementioned methods. Significant differences in observed normalized runoff volumes (i.e., runoff coefficients) and normalized peak flow rates were found between watersheds with similar watershed characteristics and rainfall patterns, demonstrating the meticulous model inputs required to differentiate hydrologic responses between similar watersheds. A suite of alternative predictive models, informed by feature selection algorithms, were formulated and compared to the performance of standard methods. Results suggested that composite CN methods were the best predictors of event runoff volume across all watersheds (Nash Sutcliffe NSE and Kling Gupta Efficiencies KGE of 0.74 and 0.52, respectively), but were outperformed by the Simple method for watersheds with more than 45% impervious cover (NSE and KGE scores of 0.85 and 0.76, respectively). However, composite CN methods underestimated runoff volume from every watershed, a limitation that was intended to be addressed through the creation of the distributed CN method. In the distributed approach, runoff volume estimations were improved compared to the composite CN approach only when directly connected impervious area in the watershed was extremely high or extremely low. The multi-linear regression runoff volume model created herein did not outperform traditional runoff models except when rainfall depth was less than 12.5 mm (i.e., the storms for which traditional runoff volume estimation methods performed the worst). Uncertainty in modeled peak flow rate was substantially greater than for runoff volume (NSE and KGE scores between 0.48 – 0.55 and 0.39 – 0.67, respectively) across all methodologies. There is a continued need to develop more dependable estimates of peak flow which are critical to the design of pipes, flood routing, and hydrograph prediction. Overall, these results suggest one model is not optimal in all scenarios. Municipalities, regulatory agencies, and stormwater engineers should consider the adoption of multiple methodologies and use guidance from the results herein to provide recommendations as to when each model is most applicable.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
20.
Peak Flow Meter Equipped with Inspection Results Indicator Nadiya Garnis Sallyfan; Endro Yulianto; Torib Hamzah
Journal of electronics, electromedical engineering, and medical informatics,
01/2020, Volume:
2, Issue:
1
Journal Article
Peer reviewed
Open access
Peak Flow Meter (PFM) is a tool to measure the Peak Flow of Air Expiration in the road (PFR) or commonly referred to as Peak Expiration Flow (PEF) and to connect asthma. The value of PEF can help a ...number of factors in age, respiratory muscle strength, height and gender. Airway measurements are used to measure patients suffering from asthma. This peak flow meter tool works based on the air pressure produced from the patient's puff using the MPX5100GP pressure sensor in the range of 0 to 100 kPa and the voltage output is 0.2 to 4.7 VDC to increase wind pressure in the patient. From the pressure converted to voltage and enter the 0 from the Arduino nano minimum system circuit to be processed into analog data and then put into units of liters / second, the value of the flow meter is sent and replaced to a PC with the Delphi7 application. The measurement results of PEF values at peak flow meters have an error value of less than 5% This peak flow meter tool also has a consideration value of 0.095475 so that this tool can be said to be very certain to be used as asthma. Then it can be concluded that the peak flow meter is feasible and meets the specified requirements
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