Search

Weather radar calibration is a crucial factor to be considered for quantitative applications, such as QPE (Quantitative Precipitation Estimation), which is used as input for weather risks management. The present work proposes a novel approach to the end-to-end radar calibration method through the characterization of the radar weighting functions. These are Gaussian functions that model an additional attenuation factor to the radar received power. This approach, based on the inclusion these parameters, allow the obtainment of a calibrated equivalent reflectivity factor expression for a Doppler dual-polarization weather radar that operates in the X band. To calculate these parameters, a UAS (Unmanned Aircraft System) was implemented for suspending the calibration target with a well-defined cross-section and for measuring its inclination due to wind using an IMU (Inertial Measurement Unit). From its measurements, the position of the target can be estimated, which is essential to the characterization of the weighting functions. Their inclusion within the radar equation, alongside the implementation of the angular measurement system highlight the innovation to the traditional radar calibration methodology that does not contemplate them from the explored state-of-the-art. The reflectivity was compared with the measurements from a disdrometer for a moderate rain event. An average reflectivity difference of 0.75 dBZ and a percent bias of 3.3 % were obtained between the expected and estimated measurements when including these functions compared to the 1.51 dBZ and –62.7 % obtained when disregarding them. These experimental results point out that the proposed method can deliver superior accuracy in the reflectivity estimation.

Sesame, a crucial oilseed crop in Ethiopia, ranks second only to coffee in its importance as an exported agricultural commodity. However, inadequate soil fertility management has hampered its productivity despite its substantial international market demand. Hence, this study was conducted to model the response of sesame to different nitrogen fertilizer levels using the AquaCrop model, and to assess the capability of the model as a decision-support tool for optimizing soil fertility management strategies in the study area. The experiment was laid out in a randomized complete block design, consisting of four nitrogen fertilizer rates (0, 23, 46, and 69 kg/ha nitrogen) and three distinct sesame varieties (Setit-1, Setit-2, and Humera-1). Over the course of the three cropping seasons, data on soil physical and chemical properties, crop growth, yield and yield components were collected for each treatment. Evaluation of model performance relied upon established metrics of coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (N-RMSE), model efficiency (E), and degree of agreement (D). Analysis of results revealed the AquaCrop model appropriately calibrated for simulation of soil water content, showing R2 values ranging from 0.92 to 0.98, RMSE values varying from 6.5 to 13.9 mm, E values from 0.78 to 0.94, and D values from 0.95 to 0.99. Similarly, simulation outputs for aboveground biomass (AB) demonstrated good accuracy of the model, with R2 values varying from 0.92 to 0.98, RMSE values ranging from 0.33 to 0.54 tons/ha, and D values from 0.9 to 0.98. Notable accuracy was also observed in the simulation of canopy cover (CC), revealing R2 values between 0.95 and 0.99, and RMSE values ranging from 5.3 to 8.6 %. In conclusion, this study substantiates the successful calibration and validation of the AquaCrop model for predicting sesame response to diverse nitrogen fertilizer levels. The performance of the model in predicting soil water content, CC, AB, and yield highlights its potential as a valuable tool for optimizing soil fertility management and enhancing sesame cultivation practices in Ethiopia.

In a low-cost system with limited hardware capacity, due to the fast speed of the conveyor and the delay in shooting and processing time, a part of the workpieces may be out of view of the camera. As a result, the information received is incomplete. To solve this challenge, this paper proposed a method to accurately estimate the position of workpieces with incomplete information. First, a complete image of the workpiece in the horizontal position is captured and stored. In the case that a part of the workpiece lies outside the image, the contour matching based on the Hu moments between the contour of the incomplete part and different parts of the complete workpiece is conducted to determine which part of the workpiece has been taken by the camera (called the matching part). Then, the distance between the centroid of the matching part and the entire workpiece is calculated. The difference between the orientation angle of the current incomplete part and the matching part is the orientation angle of the entire current workpiece (with a part out of the image boundary). From the centroid distance and orientation angle, the actual centroid of the entire current workpiece can be calculated. Finally, the calibration method is used to transform the pose of the workpiece into the robot base frame. The experimental results demonstrate that the accuracy of the centroid estimation is 2.2 pixels and 1.8 mm in the image and the 3D space, respectively.

The paper deals with the topic of resistance of bonded headless post-installed anchors to uncracked concrete, in particular with the calibration of selected models predicting their ultimate tensile load. In particular, the optimization of two models for predicting the tensile capacity of an anchor is investigated in this paper. First model is based on the determination of the ultimate capacity based on separated failure modes (failure of the extraction of concrete cone and the bond failure). The second optimised model, proposed in this paper, summarizes the effect of both of these parameters into a single exponential function. A model combining the effect of concrete strength and adhesive strength has the potential to better capture the true nature of failure in which both materials are involved. In order to compare these models, an extensive database of experimental results was compiled (including own experiments and also results from various authors). The calibration consisted in finding the most appropriate values of the individual input parameters of the models to fit the experimental results as closely as possible. The models for predicting the tensile capacity of anchors are multiparametric. Therefore, a method using elements of genetic algorithms was used for optimization, suitable for this purpose. Several possible statistical evaluation criteria were used for the evaluation of the fit of the models. The optimization of the models showed that the proposed model combining the effect of concrete strength and bond strength can be optimized to better fit the experimental results.

Although the available traffic data from navigation systems have increased steadily in recent years, it only reflects average travel time and possibly Origin-Destination information as samples, exclusively. However, the number of vehicles participating in the traffic – in other words, the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management – is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level. To tackle this gap, an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure. By optimally scaling the Origin-Destination matrices of the sample fleet, an appropriate model can be approximated to provide traffic flow data beside average speeds. The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model. The method has been tested through two different real-world traffic networks, justifying the viability of the proposed method. Overall, the contribution of the study is a practical solution based on commonly available fleet traffic data, suggested for practitioners in traffic planning and management.

The proposed research is an attempt to advance the state-of-the-art of the numerical modelling of RPB by combining Computational Fluid Dynamics and Machine Learning approaches. The latter creates an accurate framework that should help quantify the potential of Rotating Packed Beds (RPB) technology to intensify conventional CO2 capture processes. To this end, a direct sensitivity analysis is detailed to supplement a machine-learning (ML) algorithm built for calibrating resistance coefficients needed for porous media modelling. The algorithm is used to improve CFD predictions of dry pressure drop in rotating packed beds (RPBs) for a wide range of operating conditions. The sensitivity derivatives with respect to the packing resistance coefficients are demonstrated for the first time in RPBs, which is not available in the current CFD open source and commercial codes. In this regard, sensitivity differential equations are derived from three-dimensional Navier-Stokes equations for porous media in a rotating reference frame. These sensitivity equations are discretized using a finite volume scheme and solved to obtain the sensitivity pressure drop differences at the packing edges. The results are validated against the predictions of the analytical sensitivity analysis and the finite difference approximation. After, the Newton – Gauss method that employs the sensitivity pressure drop derivatives, is used to minimize the error (cost function) between the pressure drop obtained from CFD simulations and the available experimental data. This is achieved by tuning the packing resistance coefficients to the RBPs' operating conditions (gas flowrate and rotating speed) and correlate them using an artificial neural network (ANN). The results of the proposed approach show a significant improvement in porous media-based CFD predictions of RPBs' pressure drop across a wide range of operating conditions and this over conventional porous media-based CFD models. This is necessary for CFD models to be reliably used as a tool that can efficiently improve existing RPBs' designs and/or participate in RPBs' design innovation.

A prototype, on-line Dual Energy X-ray Absorptiometer (DXA) has shown high precision of the prediction of carcass composition for the purpose of improved sheep meat grading in the Australian lamb supply chain, albeit with small inaccuracies over time. These inaccuracies were present across hours, and more significantly across days, which were unacceptable for any accreditation of this device as an objective carcass measurement tool in Australia. This inaccuracy demanded the creation of a novel image−processing algorithm for the prototype DXA. This DXA was tested for repeatability of predictions of lamb carcass composition over minutes, hours, and days, using two developed image processing algorithms. There was high immediate repeatability for both algorithms when predicting lean muscle % in 40 lamb carcasses, with a maximum CV of 0.65% over five repeated scans. There was a decrease in the CV of the prediction of lean muscle % of 30 lambs scanned three times over a 48-h period from 5.93 to 1.19% when the superior algorithm was used. The inaccuracies of lean muscle % predictions were associated with increases in the unattenuated space pixel values in DXA images. Improvements of the current algorithm are required to demonstrate repeatability over time for the purpose of accreditation within the Australian sheep meat industry, and for possible expansion of this technology into international supply chains.

Experimental test campaigns have begun to demonstrate the potential of methanol as an alternative fuel for heavy-duty spark-ignited engines. However, there is no consensus yet on the scope of this solution in terms of maximum power and engine size. A zero-dimensional combustion model is therefore being developed outside the scope of this work. Its main objective will be to predict key performance parameters such as power and efficiency as function of engine size. Due to the high loads typically encountered in heavy-duty engines, knock will be the main constraint to maximize the engine's potential. This work therefore aims to find an accurate knock model that can be implemented in the modelling framework. The Livengood–Wu knock integral model is being considered as a good candidate, as it is computationally inexpensive and thus allows for a large number of engine configurations to be modelled within a reasonable time. Due to a lack of autoignition delay times of methanol at conditions relevant to heavy-duty engines, a large database was created using chemical kinetics calculations. A neural network model was trained with the tabulated data for fast data retrieval. To validate whether the knock integral approach is robust enough to be applied to a wide range of engine sizes, a calibration constant was added to match the knock predictions to experimental data. Its value was calculated for three different engines, a light and heavy-duty SI engine and a large-bore dual-fuel engine. They highlight a remarkable difference in calibration constant across the different engines investigated.

This article proposes that the Pitot Cole tube used for measuring water flow in the distribution does not require calibration, as it is an intrinsically self-calibrated equipment. Its function is to transform flow speed (kinetic energy) into manometric pressure, where the measurement is made by manometers or Pitometry Cases. The article also cites a third-degree polynomial equation that automatically corrects the Pitot Cole tube discharge factor rangeability from 3:1 up to 35:1. As a fundamental highlight, added to other article contributions, the credibility achieved today during a flow measurement is cited, reaching an accuracy from 3:1 or better, in the entire measurement range, of 35:1. In the water industry, the Pitot Cole tube interconnected in the Pitometry Case measure and record electronically the flow of treated and distributed water in the consumer sectors and, through specific software, it executes and issues a report comparing the Pitometric measurements against installed and fixed measurement equipment on the same network.

Sports betting’s recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker’s odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that using calibration, rather than accuracy, as the basis for model selection leads to greater returns, on average (return on investment of +34.69% versus -35.17%) and in the best case (+36.93% versus +5.56%). These findings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase profits should therefore select their predictive model based on calibration, rather than accuracy.

Background: D-dimer at a low level is important evidence for excluding the onset and progression of thrombosis. It is readily detectable and yields rapid results, although significant variability exists among different detection systems. Our study aims to enhance the consistency across various detection systems. Methods: Twelve detection systems were included in our study. We sought to address this inconsistency by using various calibrators (two supplied by manufacturers and two comprising pooled human plasma diluted with different diluents) to standardize D-dimer measurements. We categorized the data into three groups according to D-dimer concentration levels: low (≤0.5 mg/L), medium (>0.5 mg/L -

Recently, several memory-type mean estimators (including ratio, product, and logarithmic) have been developed. These estimators rely on exponentially weighted moving average (EWMA), which incorporate both historical and present sample data. In this article, we propose EWMA type calibrated estimators under single and double stratified random sampling (StRS). Because calibration method enhances the estimates by modifying the stratification weight, taking advantage of supplementary information. To evaluate the performance of estimators, various real-world time-scaled data sets pertaining to stock market and weather are taken into account. Additionally, we also conduct a simulation study using a bivariate symmetric data set. The numerical results show the superiority of proposed estimators (y¯TM,y¯TaM) over the adapted ones (y¯PM,y¯PaM).

Precise precipitable water vapor (PWV) observations play a crucially important role in weather and climate research. The Atmospheric Infrared Sounder (AIRS) on-board the Aqua spacecraft is a hyperspectral instrument that offers operational PWV products using infrared (IR) channels. However, the observational accuracy of AIRS-sensed cloudy-sky PWV products is much poorer than that of PWV retrievals under clear sky conditions. We present a new machine learning-based calibration model to improve the observational performance of operational AIRS-derived IR PWV satellite products under all sky conditions, which considers using several dependence elements correlated with remotely sensed IR PWV observations. The first-guess PWV estimates, obtained from the ERA5 reanalysis, are also utilized in the newly developed calibration approach. The ground-based GNSS-sensed PWV observations, collected in 2017 across the world, are used as the expected PWV output for the training of the newly proposed calibration model. The newly calibrated PWV data records considerably outperform operational AIRS-sensed PWV products, compared with independent PWV estimates from radiosonde observations during 2017–2020. The root-mean-square error of operational PWV satellite products decreases 19.05 % from 2.31 mm to 1.87 mm under cloud fraction (CF) = 0 condition (clear sky), 30.71 % from 3.68 mm to 2.55 mm under CF = (0,1] condition (cloudy sky), and 30.14 % from 3.55 mm to 2.48 mm under CF = [0,1] condition (all sky). The calibration method exhibits much higher RMSE reductions than previous approaches that do not utilize the first-guess PWV estimates, illustrating the effectiveness of our calibration approach. This research provides insights into calibrating satellite-sensed PWV estimates based on machine learning by jointly using ground-based and reanalysis-based PWV observations. The newly proposed calibration method has significant potential to calibrate operational PWV products from other satellite-borne sensors, in addition to the AIRS instrument.

To ensure the measuring accuracy of the wall temperature, the outer wall temperature measurement values by using three kinds of thermocouple welding methods were analyzed and evaluated in the paper, including single-point flush-mounted in the wall groove method, single-point insert-mounted in the wall groove, and outer surface direct welding method, based on the application of a tube-in-tube condensing heat exchanger. And the impacts of silver, tin, and thermal resistance adhesive as filling materials on wall temperature measurement were also investigated, and the results were compared to that obtained without filling materials. The results showed that the wall temperatures measured by the three welding methods were lower than the theoretically calculated value. And the wall temperature measured by the outer surface direct welding method was lowest under the same experimental conditions. The wall temperatures measured by single-point flush-mounted and insert-mounted in the wall groove methods were also affected by different welding filling materials. It was found that the greater the thermal resistance of filling materials, the smaller the heat loss. By analyzing the reasons for the low measured value of wall temperature, a new wall temperature measurement method was developed to improve the accuracy of the current measurement method. Meanwhile, the outer wall temperature measurement experiments of vertical and horizontal heat transfer tubes were carried out to validate and calibrate the improved outer wall temperature measurement method. The results showed that the average outer wall temperature deviation measured by the improved wall temperature measurement method ranged from −0.82% to +2.29% for vertical tubes and −4.75% to −1.44% for horizontal tubes, and the improved measurement method had good measurement accuracy.

Miniaturized tissue equivalent proportional counters (mini-TEPCs) are proper for radiation dosimetry in medical application because the small size of the dosimeter could prevent pile-up effect under the high intensity of therapeutic beam. However, traditional methods of calibrating mini-TEPCs using internal alpha sources are not feasible due to their small size. In this study, we investigated the use of electron and proton edges on Monte Carlo-generated lineal energy spectra as markers for calibrating a 0.9 mm diameter and length mini-TEPC. Three possible markers for each spectrum were calculated and compared using different simulation tools. Our simulations showed that the electron edge markers were more consistent across different simulation tools than the proton edge markers, which showed greater variation due to differences in the microdosimetric spectra. In most cases, the second marker, yδδ, had the smallest uncertainty. Our findings suggest that the lineal energy spectra from mini-TEPCs can be calibrated using Monte Carlo simulations that closely resemble real-world detector and source geometries.

High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial–temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of −0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of −0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K.The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm.Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about −0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.

Study Region: Europe Study Focus: The semi-distributed Hydrological Predictions for the Environment (HYPE) model for the European domain, E-HYPE4, was calibrated for discharge and sediments within a framework allowing for evaluation of the factors limiting model performance. Calibration was conducted using a multi-phase approach during which an initial multi-objective discharge/sediment calibration was followed by an exhaustive sediment calibration in which combinations of sediment routines were assessed. During each calibration phase, ensembles of parameter sets and model routines were simultaneously evaluated against discharge, evapotranspiration, and sediment observations. In total, 20,000 parameter sets were evaluated during the discharge calibration, and a further 20,000 model setups were assessed during the sediment calibration. New Hydrological Insights for the Region: Model performance was best with a highly regionalized model, and the largest drop in achievable performance occurred when transitioning from an ensemble of candidates to a single model setup. Much of the performance gains from a highly regionalized model could, however, be achieved with a much less regionalized model. Inclusion of sediments as a calibration objective provided more value than that of evapotranspiration in regards to reducing equifinality in the calibration for discharge. Evaluation of the erosion routines indicated that an index-based routine performed equally well as a more complex, process-based routine. Finally, analysis of model performance by subbasin attributes revealed the dominant factors — such as landuse, glaciers, abstractions/regulations, groundwater, and lakes/wetlands — affecting model biases for various regions.

The present study aims to find out the noise and vibration & compatibility of OPO (orange peel oil) in the common rail direct injection engine. Various injection strategies like FIP (fuel injection pressure), pilot mass, pilot injection timing, and engine speed are examined at idling mode using the design of experiments software. The obtained results are post-processed and computed in the form of perturbation and surface plots based on RSM (response surface methodology) and analysis of variance. Considering the trade-off with combustion parameters, the engine speed is found to be more responsible for crank level vibrations while FIP created more influence in head level vibrations. OPO B20 (20% OPO + 80% diesel by volume) injected 28° CA bTDC (before top dead centre) is predicted to be optimum by using RSM. In the case of diesel fuel, vibrations at the head level show a noticeable increase in all three directions as the FIP is raised. However, when using a OPO B20, the vibrations primarily in the z-direction are significantly affected by the FIP changes. Additionally, for OPO B20 fuel at idling operation, the pilot mass of 20%, engine speed at 979 rpm, and a fuel injection pressure of 300 bar are the optimal setting condition to achieve favourable characteristics of combustion, vibration and noise.

Background and purpose: Dual-energy computed tomography (DECT) is an emerging technology in radiotherapy (RT). Here, we investigate split-filter DECT throughout the RT treatment chain as compared to single-energy CT (SECT). Materials and methods: DECT scans were acquired with a tin-gold split-filter at 140 kV resulting in a low- and high-energy CT reconstruction (recon). Ten cancer patients (four head-and-neck (HN)​, three rectum​, two anal/pelvis and one abdomen) were DECT scanned without and with iodine administered. A cylindrical and an anthropomorphic HN phantom were scanned with DECT and 120 kV SECT. The DECT images generated were: 120 kV SECT-equivalent (CTmix), virtual monoenergetic images (VMIs), iodine map, virtual non-contrast (VNC), effective atomic number (Zeff), and relative electron density (ρe,w). The clinical utility of these recons was investigated for calibration, delineation, dose calculation and image-guided RT (IGRT). Results: A calibration curve for 75 keV VMI had a root-mean-square-error (RMSE) of 34 HU in closest agreement with the RSME of SECT calibration. This correlated with a phantom-based dosimetric agreement to SECT of γ1%1mm > 98%. A 40 keV VMI recon was most promising to improve tumor delineation accuracy with an average evaluation score of 1.6 corresponding to “partial improvement”. The dosimetric impact of iodine was in general

Cuff-based home blood pressure (BP) devices, which have been the standard for BP monitoring for decades, are limited by physical discomfort, convenience, and their ability to capture BP variability and patterns between intermittent readings. In recent years, cuffless BP devices, which do not require cuff inflation around a limb, have entered the market, offering the promise of continuous beat-to-beat measurement of BP. These devices take advantage of a variety of principles to determine BP, including (1) pulse arrival time, (2) pulse transit time, (3) pulse wave analysis, (4) volume clamping, and (5) applanation tonometry. Because BP is calculated indirectly, these devices require calibration with cuff-based devices at regular intervals. Unfortunately, the pace of regulation of these devices has failed to match the speed of innovation and direct availability to patient consumers. There is an urgent need to develop a consensus on standards by which cuffless BP devices can be tested for accuracy. In this narrative review, we describe the landscape of cuffless BP devices, summarize the current status of validation protocols, and provide recommendations for an ideal validation process for these devices.

Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors.

Three-dimensional (3D) microdisplacement monitoring plays a crucial role in the assembly of large aircraft. This paper presents a broadly applicable high-precision online 3D microdisplacement monitoring method and system based on proximity sensors as well as a corresponding in situ calibration method, which can be applied under various extreme working conditions encountered in the aircraft assembly process, such as compact and obstructed spaces. A 3D monitoring model is first established to achieve 3D microdisplacement monitoring based only on the one-dimensional distances measured by proximity sensors, which concerns the extrinsic sensor parameters, such as the probe base point (PBP) and the unit displacement vector (UDV). Then, a calibration method is employed to obtain these extrinsic parameters with high precision by combining spatial transformation principles and weighted optimization. Finally, calibration and monitoring experiments performed for a tailplane assembly process are reported. The calibration precision for the PBP is better than ±10 μm in the X and Y directions and ±2 μm in the Z direction, and the calibration precision for the UDV is better than 0.07°. Moreover, the accuracy of the 3D microdisplacement monitoring system can reach ±15 μm. In general, this paper provides new insights into the modeling and calibration of 3D microdisplacement monitoring based on proximity sensors and a precise, efficient, and low-cost technical means for performing related measurements in compact spaces during the aircraft assembly process.

Background: Reliability of prostate cancer (PCa) genetic risk score (GRS), that is, the concordance between its estimated risk and observed risk, is required for genetic testing at the individual level. Reliability data are lacking for non-European racial/ethnic populations, which hinders its clinical use and exacerbates racial disparity. Objective: To calibrate PCa ancestry-specific GRS in four racial/ethnic populations. Design, setting, and participants: PCa ancestry-specific GRSs, calculated from published risk-associated single-nucleotide polymorphisms in corresponding racial/ethnic populations, were evaluated in men who participated in 23andMe, Inc. genetic testing and consented for research, including 888 086 of European (EUR), 81 109 of Hispanic (HIS), 30 472 of African (AFR), and 13 985 of East Asian (EAS) ancestry, as classified by 23andMe's ancestry composition algorithm. Outcome measurements and statistical analysis: The concordance between the observed and estimated PCa risks at ten ancestry-specific GRS deciles was measured primarily by using the calibration slope (β), where 1 represents a perfect calibration. Platt scaling was used to correct the systematic bias of GRS. Results and limitations: A linear trend of an increased observed PCa prevalence in men with higher ancestry-specific GRS deciles was found in each racial population (all p-trend

Increasing exhaust gas temperature (EGT) and providing hydrocarbon (HC) reactants by in-cylinder post injection are effective measures to assist diesel engine exhaust aftertreatment operation for efficient pollution control. The aim of this study is to explore calibration guidance of post injection for base engine performance and aftertreatment requirements. Therefore, the effect of start of post injection (SOPI) timing on engine performance and emissions, especially the relationship between the variations of the EGT and HC emissions caused by different post injection timings were experimentally studied with a 2.0 L high-pressure common-rail diesel engine at different loads. The results showed that the maximum torque contribution was produced by post injection at the earliest SOPI timing at all load levels. When the post injection timing was retarded, the engine brake torque decreased gradually, and more energy of the post injected fuel raised the EGT. The EGT decreased and the HC emissions increased significantly as the SOPI timing was further retarded. At the 20%, 50%, and 80% loads, the maximum EGTs were 357.7, 465.4, and 615.8 °C, increased by 11.6%, 8.6%, and 7%, respectively. The maximum HC emissions were 4932, 3929, and 2746 ppm, increased by 12.44, 1.8, and 5.18 times, respectively. Moreover, a tradeoff relationship between the EGT and the HC emissions was observed at different SOPI timings by a normalized analysis. However, no clear tradeoff relationship between the soot and the NOxemissions was found in the SOPI timing sweep. Consequently, the appropriate SOPI timing range should be determined based on the needs of the aftertreatment system, and multiple post injections may be required to simultaneously increase the EGT and provide the HC reactants for aftertreatment. The optimal SOPI timing also needs to be selected based on impacts on soot and NOxat different loads.

With the ability of monitoring both temperature and photothermal agents, the photoacoustic (PA) imaging is a promising guiding tool for the photothermal therapy (PTT). The calibration line which depicts the relative variation of PA amplitude with the temperature should be obtained before using PA thermometer. In existing study, a calibration line was generated based on the data from one spatial position, and used in the whole region of interesting (ROI). However, the generalization of this calibration line in ROI was not verified, especially for ROI with heterogeneous tissues. Moreover, the relationship between the distributions of photothermal agents and effective treatment area is not clear, hindering using photothermal agents’ distribution to optimize the administration-therapy interval. In this study, the distribution of effective photothermal agents and temperature in subcutaneously transplanted tumor mouse models were continuously monitored by 3D photoacoustic/ ultrasonic dual-modality imaging in 8 h after administration. With multiple micro-temperature probes in tumor and surrounding normal tissue, the PA thermometer was calibrated and evaluated at multiple spatial positions for the first time. The generalization in homologous tissue and tissue specificity in heterogeneous tissues of the PA thermometer calibration line were verified. Our study not only validated the effectivity of PA thermometer by proving the generalization of calibration line, but also removes a major obstacle that prevents applying the PA thermometer to a heterogeneous tissues ROI. The positive correlation between the proportion of effective treatment area and the proportion of effective photothermal agent area in the tumor was observed. Since the latter can be monitored with fast PA imaging, PA imaging can be employed as a convenient tool for seeking optimal administration-treatment interval.

The COVID-19 epidemic highlighted the necessity to integrate dynamic human behaviour change into infectious disease transmission models. The adoption of health protective behaviour, such as handwashing or staying at home, depends on both epidemiological and personal variables. However, only a few models have been proposed in the recent literature to account for behavioural change in response to the health threat over time. This study aims to estimate the relevance of TELL ME, a simple and frugal agent-based model developed following the 2009 H1N1 outbreak to explain individual engagement in health protective behaviours in epidemic times and how communication can influence this. Basically, TELL ME includes a behavioural rule to simulate individual decisions to adopt health protective behaviours. To test this rule, we used behavioural data from a series of 12 cross-sectional surveys in France over a 6-month period (May to November 2020). Samples were representative of the French population (N = 24,003). We found the TELL ME behavioural rule to be associated with a moderate to high error rate in representing the adoption of behaviours, indicating that parameter values are not constant over time and that other key variables influence individual decisions. These results highlight the crucial need for longitudinal behavioural data to better calibrate epidemiological models accounting for public responses to infectious disease threats.

Stroke risk is currently estimated as part of the composite risk of cardiovascular disease (CVD). We investigated if composite-CVD risk prediction tools QRISK3 and Pooled Cohort Equations-PCE, derived from middle-aged adults, are as good as stroke-specific Framingham Stroke Risk Profile-FSRP and QStroke for capturing the true risk of stroke in older adults. External validation for 10y stroke outcomes was performed in men (60-79y) of the British Regional Heart Study. Discrimination and calibration were assessed in separate validation samples (FSRP n = 3762, QStroke n = 3376, QRISK3 n = 2669 and PCE n = 3047) with/without adjustment for competing risks. Sensitivity/specificity were examined using observed and clinically recommended thresholds. Performance of FSRP, QStroke and QRISK3 was further compared head-to-head in 2441 men free of a range of CVD, including across age-groups. Observed 10y risk (/1000PY) ranged from 6.8 (hard strokes) to 11 (strokes/transient ischemic attacks). All tools discriminated weakly, C-indices 0.63–0.66. FSRP and QStroke overestimated risk at higher predicted probabilities. QRISK3 and PCE showed reasonable calibration overall with minor mis-estimations across the risk range. Performance worsened on adjusting for competing non-stroke deaths. However, in men without CVD, QRISK3 displayed relatively better calibration for stroke events, even after adjustment for competing deaths, including in oldest men. All tools displayed similar sensitivity (63–73 %) and specificity (52–54 %) using observed risks as cut-offs. When QRISK3 and PCE were evaluated using thresholds for CVD prevention, sensitivity for stroke events was 99 %, with false positive rate 97 % suggesting existing intervention thresholds may need to be re-examined to reflect age-related stroke burden.

The world's underdeveloped countries, who have been affected the worst, are experiencing an increase in climatic issues. Today more than ever, the requirement for precise measurements and predictions of pollutants with low-cost installation is essential. Low-cost air quality sensors are susceptible to inaccurate readings, frequent outages, and erratic operating circumstances. To provide an efficient and adaptable calibration method in such a setting, a cautious approach is required. There are several commercially available monitoring options; a typical system uses particle sensor and gas to keep track of the air quality. These sensor values are compared to established criteria, and alerts are then generated when those thresholds are exceeded. An IoT node with many sensors for a few pollutants, including NH3, NO2, CO, and PM 2.5 as well as air humidity & the ambient temperature, is designed for this purpose. The Internet of Things node is installed inside a research facility to gather indoor air data for research purposes and proof of concept. The suggested system may use GSM/WiFi technology to transmit real-time air quality reports to a web site and web/mobile app, and it can also produce alarms when it notices abnormalities in the air quality. The platform is appropriate for Big Information analysis applications including weather forecasts, and traffic predictions since it can capture sensor readings as fast as one sample per second. Normalized to the average CO, O3, and NO2 sensor readings and association of sensor information are optimal in range. The goal of the study is to advance the field of distributed, low-cost sensing devices for environment intelligence applications.

Study region: Three watersheds in the Lake Champlain Basin of Vermont, USA. Study focus: Watershed models are essential for evaluating the impact of watershed management; however, they contain many parameters that are not directly measurable. These parameters are commonly estimated by calibration against observed data, often streamflow. Unfortunately, many areas lack long-term streamflow records, making parameter estimation in low data environments (LDE) challenging. A new calibration technique, simultaneous multi-basin calibration (MBC), was developed to estimate model parameters in LDE. Three Soil and Water Assessment Tool (SWAT) model initializations for USGS gages with ∼ 2-year records in the Lake Champlain Basin of Vermont, USA, were evaluated by comparing MBC and the commonly used similarity-based regionalization (SBR) approach, where calibrated parameters from a watershed with an extended data record are transferred to the LDE receptor watersheds. In MBC, each watershed is initialized, and observed flows from each initialization are aggregated to generate a combined streamflow record of sufficient length to calibrate using a differential evolution algorithm. New hydrological insights for the region: Using this new MBC method, we demonstrate improved model performance and more realistic model parameter values. This study demonstrates that short periods of hydrological measurement from multiple locations in a basin can represent a system similarly to long term measurements and that even short records taken at multiple locations significantly improve our hydrologic knowledge of a system as compared to relying on the similarity of a basin with a long record of flow. In addition, this study revealed that the hydrologic response is mediated by the interplay of very low soil-saturated hydraulic conductivity (Ksat) and cracking soils. As a result, even if Ksat is very low, cracking clays have a large impact on runoff production Garna et al. (2022).

We compared the internal standard calibration and standard additions calibration methods in the determination of Cd, Cr, Fe, Mn, Pb and Zn in municipal effluent using microwave-induced plasma atomic emission spectrometer. Comparison was based on validation parameters such as linearities, linear ranges, limits of detection (LOD), precisions and recoveries obtained using both calibration methods. Both methods gave good linearities applicable to a simple linear regression data fitting. Severe loss of linearity was observed at levels beyond 3 mg/L particularly for Zn. Considering this, the compromise linear range for all target analytes was established at LOD–3 mg/L. While the LODs for the target analytes were sufficiently low for both methods, the internal standard method was found to be more sensitive compared to standard additions method. Thus, the optimal linear ranges were accurately established at 0.24–0.96 mg/L for the internal standard and 1.10–1.96 mg/L for the standard additions method. Analyte recoveries including for the certified reference material averaged ∼ 100%. Therefore, all target analytes with the exception of Cd, which was possibly below the method detection limit were confidently detected and accurately quantified in municipal effluent samples. Overall, the excellent quantitative data obtained in this study inferred a high level of confidence in the developed methods in that they differed by less than 10%. Indisputably, the performance of the MP-AES operated in internal standard- and standard additions modes have proved to be a sufficiently sensitive technique especially for heavy metals occurring at sub-ppm levels.

Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods (SCE-UA, GA and PEST) and two Bayesian-based methods (GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA (v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method (MCMC_Pmax) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.

Agricultural emissions of ammonia (NH3) reduce air quality and biodiversity. Measuring the effectiveness of mitigations measures requires rapid monitoring tools, however, conventional methods are labour intensive and costly. This study evaluated the performance of a prototype metal oxide semiconductor (MOS) gas sensor for monitoring NH3. Conventional methods were used to calibrate sensor conductance. The metal oxide semiconductor (MOS) gas sensor was calibrated against NH3 released from a 0.1 M phosphate buffer spiked with ammonium chloride and NH3 released from recently spread cattle slurry. Field measurements using the MOS sensor were compared with values measuring a Bruker Open Path Air Monitoring System. Sensor conductance and NH3 concentration were described using single site Langmuir adsorption model. Field calibrations suggest a higher detection limit above 0.1 ppm and coefficients of determination were 0.93 and 0.89 for sensors 1 and 2, respectively. For prototypes deployed under field conditions, sensitivities of 2.2 and 2.4 with nonlinearity constants of 0.53 and 0.51, were found for sensor 1 and 3 respectively. Average R2 values were 0.88 for sensor 1 and 0.92 for sensor 3. The calibrations were used to calculate NH3 concentrations from slurry emissions using MOS sensor conductance. NH3 concentrations between 0.2 and 1 ppm, were measured with standard deviation of 20% of verified concentrations. The MOS sensor is sensitive enough to detect NH3 emission from agricultural sources with concentrations above 0.2 ppm. Low power and cost of MOS sensors are an advantage over existing techniques.

Ride-sourcing services have become increasingly important in meeting people's mobility needs since their emergence. Compared to traditional street-hailing taxi services, ride-sourcing services significantly reduce the matching frictions in the markets by matching drivers and passengers with relatively distant distances through an online platform. Motivated by this new feature as well as the need for designing operating and regulating strategies, researchers have attempted to describe these innovative ride-sourcing markets through mathematical models, the core of which is the matching functions for characterizing matching frictions. Previous studies have developed a variety of matching functions for ride-sourcing markets, including perfect matching function, Cobb-Douglas type matching function, queuing models, and some physical models. However, less is known about the applicability and performance of these matching functions, that is, under what situations each of these matching functions well characterizes the real market. To address this issue, this paper makes one of the first attempts to calibrate, validate, and compare the prevailing matching functions in the literature, and ascertain the conditions of their applicability. In particular, we establish a simulator to simulate a total of 420 scenarios of the ride-sourcing market under different combinations of supply and demand. The key performance metrics, including the matching rate in the market, passengers' average matching time, passengers' average pick-up time, and passengers' average total waiting time, are utilized to test and compare seven widely used matching functions under various market scenarios.

This work has the main objective to develop a calibration method for micro flow measuring devices based on the front tracking principle, in the Volume and Flow Laboratory (LVC) of the Portuguese Institute of Quality (IPQ), in a partnership with and the Department of Mechanical and Industrial Engineering (DEMI) of The New University of Lisbon under the project MeDD II – Metrology for Drug Delivery.The experimental setup consists of using a camera and Python software to track the distance traveled by the meniscus of a liquid inside a capillary tube and calculate the flow rate according to appropriated equations. Experimental tests using a flow generator and a flow meter were carried out for different flow rates. Two different setups with cameras of different specifications were tested.To validate the developed front tracking method, internal comparisons using a Nexus pump and a Sensirion meter were performed with the gravimetric and interferometric methods already implemented at LVC. The results obtained by these two comparisons were consistent.This new developed method can therefore be used to calibrate flow devices from 1000 μL/h down to 1 μL/h with an uncertainty value from 2% to 7% (k = 2). The calibration procedure is easy to apply and can be used for both syringe and flow meters. This method is simple to implement in a laboratory and the setup is relatively cheap compared to the gravimetric or interferometric method.Finally, the front tracking method was applied to the calibration of an implantable insulin pump used by diabetic patients.

Purpose: This study aimed to identify the risk factors of carbapenem-resistant Enterobacteriaceae (CRE) acquisition to build a nomogram for CRE acquisition risk prediction and evaluate its performance. Methods: This unmatched case-control study included 352 adult patients (55 patients and 297 controls) admitted to the intensive care unit (ICU) of a 453-bed secondary referral hospital between January 1, 2018, and September 31, 2019, in Busan, South Korea. The nomogram was built with the identified risk factors using multiple logistic regression analysis. Its performance was analyzed using calibration-in-the-large, the slope of the calibration plot, concordance statistic (c-statistic), and the sensitivity and specificity of the training set, subsets, and a new test set. Results: The risk factors of CRE acquisition among ICU patients at a secondary referral hospital were Acute Physiology and Chronic Health Evaluation II score at the time of admission, use of a central venous catheter and a nasogastric tube, as well as use of cephalosporin antibiotics. At 20.0% of the predicted CRE acquisition risk in the training set, the calibration-in-the-large was 0, slope of the calibration plot was 1, c-statistic was .93, sensitivity was 85.5%, and specificity was 84.8%. The performance was relatively good in the subsets and new test set. Conclusion: The nomogram can be used to monitor the CRE acquisition risk for ICU patients who have a similar case mix to patients in the study hospitals. Future studies need to involve more rigorous methodology and larger samples.

Background and purpose: Brachytherapy treatment outcomes depend on the accuracy of the delivered dose distribution, which is proportional to the reference air-kerma rate (RAKR). Current societal recommendations require the medical physicist to compare the measured RAKR values to the manufacturer source calibration certificate. The purpose of this work was to report agreement observed in current clinical practice in the European Union. Materials and methods: A European survey was performed for high- and pulsed-dose-rate (HDR and PDR) high-energy sources (192Ir and 60Co), to quantify observed RAKR differences. Medical physicists at eighteen hospitals from eight European countries were contacted, providing 1,032 data points from 2001 to 2020. Results: Over the survey period, 77% of the 192Ir measurements used a well chamber instead of the older Krieger phantom method. Mean differences with the manufacturer calibration certificate were 0.01% ± 1.15% for 192Ir and –0.1% ± 1.3% for 60Co. Over 95% of RAKR measurements in the clinic were within 3% of the manufacturer calibration certificate. Conclusions: This study showed that the agreement level was generally better than that reflected in prior societal recommendations positing 5%. Future recommendations on high-energy HDR and PDR source calibrations in the clinic may consider tightened agreements levels.

Optical remote sensing (RS) with robust algorithms is needed for accurate assessment of crop canopy features. Despite intensive studies on algorithms, their performance using RS needs to be improved. We evaluated five different algorithms (partial-least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), locally-weighted-PLSR (PLSRLW) and PLSR with feature selection (PLSRFS)) for rapid assessment of leaf area index (LAI) and canopy water content (CWC) for rice canopies using canopy reflectance spectra over visible to short-wave infrared region. Two pooled datasets of LAI (600) and CWC (480) were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season. The performance of each algorithm was evaluated using coefficient of determination (R2). Results showed that PLSRLW performed more accurately than other algorithms with R2 values 0.77 and 0.66 for LAI and CWC, respectively. We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model. The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples, which has similar canopy structure as the test sample. Subsetting of the canopy spectral data results large error values in test dataset, therefore the use of entire season canopy spectral data should be used for model calibration.

The WEPPcloud interface is a new online decision-support tool for the Water Erosion Prediction Project (WEPP) model that facilitates data preparation and model runs, and summarizes model outputs into tables and maps that are easily interpretable by users. The interface can be used by land and water managers in the United States, Europe, and Australia interested in simulating streamflow, sediment and pollutant loads from both undisturbed and disturbed (e.g. post-wildfire or post-treatment such as thinning or prescribed fires) forested watersheds. This article contains full hydrologic model runs for 28 forested watersheds in the U.S. Pacific Northwest with the WEPPcloud online interface. It also includes links to repositories with the individual model runs, a table containing default model parameters for disturbed conditions, and figures with model outputs as compared to observed data. The data in the repositories include all the raw data input and output from the model as well as the processed data, which can be accessed through tables and shapefiles to provide additional insights into the model outputs. Lastly, the article describes how the data are organized and the content of each folder containing the data. These model runs are useful for anyone interested in modeling forested watersheds with the WEPPcloud interface.

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions.

Objectives: Procalcitonin (PCT) is an important biomarker of sepsis and respiratory infections. Various automated immunoassays for measuring PCT in patient plasma are available in medical laboratories. However, due to a lack of international reference material for PCT, the assays are not always comparable. Design and methods: In this study, we compared a new turbidimetric immunoassay from DiaSys, measured on the Abbott Architect c16000 and Alinity c, with four BRAHMS-associated chemiluminescence immunoassays (Abbott Architect i2000SR, Alinity i, Roche Cobas e411 and DiaSorin Liaison XL) using 120 random patient plasma samples from the clinical laboratory routine at the University Medical Center Goettingen. Results: The DiaSys assay showed clear differences as compared to the BRAHMS-associated assays when measured on Architect c: i.e. 58% positive mean bias vs. Architect i, 67% vs. Cobas and 23% vs. Liaison. As a result, additional 19% our patients would have a suspected bacterial infection, when using PCT values from the DiaSys assay and commonly accepted decision limits. A crosscheck of the DiaSys calibrator on the BRAHMS-associated systems showed a low recovery of the calibrator material (approx. 50%). Conclusions: Overall, this study shows significant differences between the DiaSys and BRAHMS-associated assays. This could be attributed to a potential DiaSys calibrator problem. This highlights the need for an international reference material for harmonization of the PCT assays.

Study region: Toowoomba, Queensland, Australia Study focus: In this study we derive loss model parameters suitable for use in the dynamic loss Australian Representative Basin Model (ARBM) through the calibration of a continuous simulation hydrologic model. We compare the derived parameters to those published in the literature, and our results highlight the need to develop a database of calibrated loss parameters for urban catchments. New hydrological insights: The development of design storms for flood modelling commonly uses the initial loss/continuous loss model to estimate the conversion of rainfall to runoff. This loss model, when applied to pervious areas, uses parameters that have been calibrated for gauged rural catchments. These same parameters are often applied to the pervious component of ungauged urban catchments with minimal understanding of the resulting impact on runoff. This research uses a continuous simulation modelling approach to calibrate parameters suitable for use in the ARBM loss model built into the hydrological modelling software XPRAFTS. Through a two-stage calibration approach, the model offered a satisfactory fit (Nash Sutcliffe Efficiency > 0.5) for 9 of the 11 selected storm events, with seven events exceeding a Nash Sutcliffe Efficiency of 0.75. Events used in the calibration/validation included peak flows as low as 9 m3/s and as high as 600 m3/s. Developing these loss model parameters offers new insights into the suitability of a dynamic loss model approach in an urban catchment in regional Australia and provides an alternative to the parameters already available in the literature which were found to overestimate the peak flow in frequent events.

Study region: West Africa. Study focus: The objective of this study was to quantitively evaluate streamflow previously simulated by the Soil and Water Assessment Tool (SWAT) model in an ungauged basin (Ogun River) that was calibrated/validated using GLEAM (v3.0a) AET data (Odusanya et al., 2019). To evaluate this simulated streamflow in the ungauged Ogun River Basin, a gauged basin (Ouémé River) was selected based on proximity and on similar catchment attributes. Firstly, the non-unique parameter sets from the SWAT set-up in the ungauged Ogun River Basin were transferred to SWAT that was setup in the neighbouring gauged Ouémé River Basin (referred to as the regionalisation method). Secondly, the SWAT model in the Ouémé River Basin was setup by calibrating the model using the observed streamflow data (referred to as the conventional method). New hydrological insights for the region: In the gauged Ouémé River Basin, using the conventional method, a higher streamflow performance was obtained for all non-unique SWAT simulations, compared with the regionalisation method. However, a satisfactory streamflow performance was obtained with the regionalisation method in the south of Ouémé River Basin (Save gauge: highest NSE = 0.79, R2 = 0.81, PBIAS = 2.1, KGE = 0.85) and at the Ouémé River Basin outlet (Bonou gauge: highest NSE = 0.79, R2 = 0.84, PBIAS = −4.5, KGE = 0.84) that had the most similar attributes to the Ogun River Basin. The study findings show that using satellite-based AET data to calibrate a hydrological model can lead to an acceptable model performance in ungauged basins. The regionalisation method was useful for assessing the simulated streamflow in the ungauged basin, especially when catchment similarity is ensured, and parameter non-uniqueness is applied. This research contributes to advancement in alternative means of modelling and evaluation of hydrologic model simulations in an ungauged basin.

The PurpleAir PA-II-SD is a low-cost particulate matter (PM2.5 and PM10) sensor that is currently available on the market. It is one of many such low-cost and commercially available particulate matter sensors which are being adopted by individuals and researchers worldwide. With growing use of these sensors, there is an interest in better understanding the performance and characteristics of these devices.Data was collected from twelve of these low-cost PurpleAir PA-II-SD sensors and two high fidelity Met One E-BAM PLUS instruments installed at a single location, on the campus of the American University of Beirut, in Beirut, Lebanon over a period of time from June 28, 2020 to September 30, 2020. The data was collected with the aim of assessing inter-sensor variability for the PurpleAir sensors and the sensor accuracy of the PurpleAir when compared to a high fidelity Met One E-BAM PLUS instrument.

Abstract Introduction The thrombin generation (TG) test is a global hemostasis assay sensitive to procoagulant conditions. However, some TG assays may underestimate elevated TG when the thrombin fluorogenic substrate is depleted or fluorescence is attenuated by the inner filter effect (IFE). Objectives We sought to elucidate the extent to which procoagulant conditions require correcting for fluorogenic substrate depletion and/or IFE. Methods We analyzed corrections for substrate depletion and IFE and their effect on TG parameters in plasma samples with elevated blood coagulation factors in the presence or absence of thrombomodulin via commercial calibrated automated thrombogram (CAT) platform and in‐house software capable of internal thrombin calibration with or without CAT‐like artifact correction. Results Elevated thrombin peak height (TPH) and endogenous thrombin potential (ETP) were detected with 2× and 4× increases in blood coagulation factors I, V, VIII, IX, X, and XI, or prothrombin in the presence or absence of artifact correction. The effect of the CAT algorithm was evident in TG curves from both low procoagulant (thrombomodulin‐supplemented) and procoagulant (factor‐supplemented) plasma samples. However, in all samples, with the exception of elevated prothrombin, CAT’s correction was small (

Abstract Background The thrombin generation (TG) assay is a feasible but labor‐intensive method for detecting global coagulation. It enables comprehensive assessment of anticoagulation, while drug‐specific assays assess only exposure. Traditionally, the Calibrated Automated Thrombogram (CAT) has been used, however the ST Genesia (Diagnostica Stago) allows automated evaluation. Objective We aimed to observe coagulation using the ST Genesia and compare the data with those of CAT in anticoagulated patients. Patients and methods In total, 43 frozen‐thawed samples were studied using DrugScreen to assess direct oral anticoagulants (DOACs), warfarin, and low‐molecular‐weight heparin. Twenty samples (nine rivaroxaban, five apixaban, three warfarin, and three heparin) were also compared using CAT (5 pM tissue factor). Results TG reduction in DrugScreen depended on the specific drug and modestly correlated with DOAC levels (lag time R2 = 0.36; peak R2 = 0.50). The best correlation was observed with peak thrombin and rivaroxaban‐specified anti–activated factor X (anti‐Xa) activity (R2 = 0.60). When comparing ST Genesia with CAT, only the results for apixaban concorded (R2 = 0.97). Unlike CAT, ST Genesia yielded a normal endogenous thrombin potential (ETP) in 77% (24/31) activated factor X inhibitor cases, and it failed to give readouts at international normalized ratio (INR) ≥4.5 and at anti‐Xa ≥1.0 IU/mL. Conclusion The ST Genesia data did not correlate with CAT, but it was independently associated with INR, anti‐Xa, and DOAC concentrations. The lag time and peak responses were similar; the major differences were that ST Genesia showed no ETP effect of DOACs and failed to give readout at high INR or anti‐Xa activity.

Abstract Background Activated coagulation factor XIa (FXIa) is an impurity and primary source of procoagulant activity in thrombosis‐implicated immune globulin (IG) products. Several assays, of varying quality and precision are used to assess FXIa‐like procoagulant activity in units relevant to their respective principles. Objectives To advance unified reporting, we sought to employ the World Health Organization reference reagents (RRs) to present the results of differing methodologies in units of FXIa activity and rank the sensitivity and robustness of these methodologies. Methods RR 11/236 served as a calibrator in several FXIa‐sensitive blood coagulation tests: two commercial chromogenic FXIa assays (CAs); a nonactivated partial thromboplastin time (NaPTT); an in‐house fibrin generation (FG) assay; an in‐house thrombin generation (TG) assay; and an assay for FXIa‐ and kallikrein‐like proteolytic activities based on cleavage of substrate SN13a. Some assays were tested in either normal or FXI‐deficient plasma. Results Each method demonstrated a sigmoidal dose‐response to RRs. NaPTT was the least sensitive to FXIa and the least precise; our in‐house TG was the most sensitive; and the two CAs were the most precise. All methods, except for SN13a, which is less specific for thrombotic impurities, gave comparable (within 20% difference) FXIa activity assignments for IG lots. Conclusions Purified FXIa reference standards support quantitation of FXIa levels in IG products in all tested assay methodologies. This should help to standardize the measurement of thrombotic potentials in IG products and prevent products exhibiting high procoagulant activity from distribution for patient use. Further research is needed to address the effect of IG product‐specific matrixes on assay performance.

A new design of soil erosion and rainfall simulator is presented at this study as an attempt to deduce a mathematical formula of soil surface erosion phenomenon to describe the behavior of the sandy soil under the rainfall simulation, soil deformation such gullies and surface eroding rills are monitored by the Digital Close Range Photogrammetry (DCRP) technique that includes capturing digital images with a smart cellphone camera, and a Terrestrial Laser Scanner (TLS) to digitalize the soil surface as a smart point cloud data to produce Digital Elevation Models (DEM) with accuracy reaches 0.10 mm 3D surface model. The results show that the mentioned methods give typical digital surface feature express especially of using geometrical adjustment that controls the orientation of the digital surface, the digital imaging feature detection technology achieves accurate results despite its small cost compared to the laser scanner that the cost of imaging equipment and software are used did not exceed 12% of TLS cost. Results of the statistical observations, a 3 order polynomial mathematical formula was generated through the digital photogrammetry method that describes the sandy soil behavior under the rainfall simulation as a relation between the eroding rate and the duration through the different slopes.

Introduction: Two prediction models for IgA nephropathy (IgAN) using clinical variables and the Oxford MEST scores were developed and validated in 2 multiethnic cohorts. Additional external validation is required. Methods: Biopsy-proven Chinese and Argentinian patients with IgAN were included. The primary outcome was defined as a 50% decline in estimated glomerular filtration rate (eGFR) or end-stage renal disease. C-statistics and stratified analyses were used for model discrimination, coefficient of determination (R2D) for model fit, and calibration plots for model calibration. Baseline survival function was also evaluated. Results: A total of 1275 patients were enrolled, with a mean age of 34 (interquartile range: 27–42) years, 50% of whom (638 of 1275) were men. Use of renin-angiotensin system blockers was higher than in previously reported cohorts, whereas other variables were comparable. The C-statistic of the models was 0.81, and R2D was higher than reported. Survival curves in the subgroups (

This study develops a pedestrian microsimulation model for an international airport that implements a social force model to simulate business-as-usual as well as pandemic pedestrian movement scenarios. The study follows an extensive calibration procedure to formulate pedestrian behavior within the microsimulation environment to reflect COVID-19 restrictions. The calibration process includes three components, which are visual observations, sensitivity analysis, and parameter combinations. The calibration process identifies the combination of the parameters that yields a pedestrian behavior for maximizing the distances among pedestrians in the airport. The study tests three scenarios: (1) a base case scenario with no social distancing parameters in effect, (2) a social distancing scenario that implements calibrated social force model parameters, and (3) a social distancing scenario through a combined implementation of the calibrated parameters and an operational improvement strategy. The simulation results for calibration reveal that the identified combination of the parameters yields 3.8% of instances of pedestrian movements that demonstrate a proximity of less than 2 m between pedestrians. The social distancing scenario demonstrates a significant reduction of such instances by 93–94% in comparison to a scenario with no social distancing strategy implemented. Moreover, the results show that social distancing is likely to increase pedestrian’s activity and queue time in the airport. According to the simulation results, it appears that the airport authorities may require queue management processes, infrastructure development, and additional resource deployment to alleviate the negative impacts of social distancing policies on pedestrian movements and operations within the airport.

Study Region: Alaska and hydrologically connected portions of Canada Study Focus: Rugged terrain and harsh conditions make observational hydroclimatic data, especially runoff data, extremely limited across most of Alaska. Consequently, many previous hydrologic studies in Alaska have focused on smaller-domain trends, where pockets of hydroclimatic observations are clustered. Using a combination of reanalysis and remotely sensed gridded products, we develop a calibration framework for a monthly resolution water budget model, over the entire Alaskan domain, that does not rely on runoff. The domain is comprised of distributed model response units (MRUs), each representing a Hydrologic Unit Code 12 basin. We use a Fourier Amplitude Sensitivity Test (FAST) to inform a two-step calibration process; first calibrating to actual evapotranspiration and then to fractional snow-covered area (fSCA), resulting in a range of parameter solutions for each MRU. New Hydrological Insights for the Region: Using this framework, the model performs best in the moderate to dry Alaskan interior; in the absence of glaciation and where permafrost is generally not continuous. Modelled runoff for recent decades shows increased runoff trends at lower latitudes and slightly decreased runoff trends at higher latitudes within the Alaskan domain. These findings reveal that reliance upon non-runoff fluxes for model calibration is viable in this cold region and that better characterization of hydroclimatic data is necessary to improve confidence in cold region modeling studies.